diff --git a/.gitignore b/.gitignore
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index 0000000..052c273
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.idea/*
+05_coeqtl_mapping/launch_sbatch_files.sh
diff --git a/01_association_metrics/.ipynb_checkpoints/GRNBoost2-checkpoint.ipynb b/01_association_metrics/.ipynb_checkpoints/GRNBoost2-checkpoint.ipynb
new file mode 100644
index 0000000..b1b9528
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+++ b/01_association_metrics/.ipynb_checkpoints/GRNBoost2-checkpoint.ipynb
@@ -0,0 +1,349 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['pdf.fonttype'] = 42\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "%run dataset.ipynb\n",
+    "\n",
+    "def select_gene_nonzeroratio(df, ratio):\n",
+    "    nonzerocounts = np.count_nonzero(df.values, axis=0) / df.shape[0]\n",
+    "    selected_genes = df.columns[nonzerocounts > ratio]\n",
+    "    return selected_genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasetname = 'onemillionv2'\n",
+    "dataset = DATASET(datasetname)\n",
+    "dataset.load_dataset()\n",
+    "data_sc = dataset.data_sc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "395\n"
+     ]
+    }
+   ],
+   "source": [
+    "monocyte_ut = data_sc[(data_sc.obs['time']=='UT') & (data_sc.obs['cell_type_lowerres']=='monocyte')]\n",
+    "monocyte_ut_df = pd.DataFrame(data=monocyte_ut.X.toarray(),\n",
+    "                              index=monocyte_ut.obs.index,\n",
+    "                              columns=monocyte_ut.var.index)\n",
+    "mono_genes = select_gene_nonzeroratio(df=monocyte_ut_df, ratio=0.50)\n",
+    "print(len(mono_genes))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(11482, 379) (194, 379)\n"
+     ]
+    }
+   ],
+   "source": [
+    "bp_df = pd.read_csv('mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.1PCAsOverSamplesRemoved.txt.gz',\n",
+    "                   compression='gzip',\n",
+    "                   sep='\\t', index_col=0)\n",
+    "name_mapping_dic = pd.read_csv('features_v3_reformated_names.tsv',\n",
+    "                          sep ='\\t',\n",
+    "                          names=['geneid', 'genename']).set_index(['geneid'])['genename'].T.to_dict()\n",
+    "\n",
+    "bp_df['geneid'] = [item.split('.')[0] for item in bp_df.index]\n",
+    "bp_df['genename'] = [name_mapping_dic.get(geneid) for geneid in bp_df['geneid']]\n",
+    "bp_df = bp_df.dropna(subset=['genename'])\n",
+    "bp_df = bp_df.drop('geneid', axis=1)\n",
+    "bp_df = bp_df.set_index('genename')\n",
+    "print(bp_df.shape)\n",
+    "\n",
+    "bp_trans_df = bp_df.T\n",
+    "common_genes = list(set(mono_genes) & set(bp_trans_df.columns))\n",
+    "selected_mono_df = monocyte_ut_df[common_genes]\n",
+    "selected_bp_df = bp_trans_df[common_genes]\n",
+    "print(selected_mono_df.shape, selected_bp_df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected_mono_df.T.to_csv('sc_Expression.csv', sep=',')\n",
+    "selected_bp_df.T.to_csv('bp_Expression.csv', sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create this fake pseudo time ordering because it's required to run the Beeline tool, but not used by GRNBoost2\n",
+    "fake_timepoint_bp = pd.DataFrame(index=selected_bp_df.index)\n",
+    "fake_timepoint_bp['time'] = np.arange(selected_bp_df.shape[0])\n",
+    "fake_timepoint_bp.to_csv('bp_timepoint.fake.csv',\n",
+    "                        sep=',')\n",
+    "fake_timepoint_sc = pd.DataFrame(index=selected_mono_df.index)\n",
+    "fake_timepoint_sc['time'] = np.arange(selected_mono_df.shape[0])\n",
+    "fake_timepoint_sc.to_csv('sc_timepoint.fake.csv',\n",
+    "                        sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# perform GRNBoost2 with BEELINE, see the yaml files in the same directory\n",
+    "# python BLRunner.py --config config-files/config_bp_mono.yaml\n",
+    "# python BLRunner.py --config config-files/config_sc_mono.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene1_sc</th>\n",
+       "      <th>Gene2_sc</th>\n",
+       "      <th>EdgeWeight_sc</th>\n",
+       "      <th>Gene1_bp</th>\n",
+       "      <th>Gene2_bp</th>\n",
+       "      <th>EdgeWeight_bp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sorted_genepairs</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CCL3;CCL4</th>\n",
+       "      <td>CCL3</td>\n",
+       "      <td>CCL4</td>\n",
+       "      <td>554.503642</td>\n",
+       "      <td>CCL3</td>\n",
+       "      <td>CCL4</td>\n",
+       "      <td>55.157748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CCL4;CCL3</th>\n",
+       "      <td>CCL4</td>\n",
+       "      <td>CCL3</td>\n",
+       "      <td>480.484753</td>\n",
+       "      <td>CCL4</td>\n",
+       "      <td>CCL3</td>\n",
+       "      <td>77.414467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S100A9;S100A8</th>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>341.726427</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>104.542395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S100A8;S100A9</th>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>284.321568</td>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>65.915233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S100A9;LYZ</th>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>LYZ</td>\n",
+       "      <td>221.872616</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>LYZ</td>\n",
+       "      <td>0.149064</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Gene1_sc Gene2_sc  EdgeWeight_sc Gene1_bp Gene2_bp  \\\n",
+       "sorted_genepairs                                                      \n",
+       "CCL3;CCL4            CCL3     CCL4     554.503642     CCL3     CCL4   \n",
+       "CCL4;CCL3            CCL4     CCL3     480.484753     CCL4     CCL3   \n",
+       "S100A9;S100A8      S100A9   S100A8     341.726427   S100A9   S100A8   \n",
+       "S100A8;S100A9      S100A8   S100A9     284.321568   S100A8   S100A9   \n",
+       "S100A9;LYZ         S100A9      LYZ     221.872616   S100A9      LYZ   \n",
+       "\n",
+       "                  EdgeWeight_bp  \n",
+       "sorted_genepairs                 \n",
+       "CCL3;CCL4             55.157748  \n",
+       "CCL4;CCL3             77.414467  \n",
+       "S100A9;S100A8        104.542395  \n",
+       "S100A8;S100A9         65.915233  \n",
+       "S100A9;LYZ             0.149064  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sc_edges = pd.read_csv('sc_edges.csv', sep='\\t')\n",
+    "sc_edges['sorted_genepairs'] = [';'.join(item) for item in sc_edges[['Gene1', 'Gene2']].values]\n",
+    "bp_edges = pd.read_csv('bp_edges.csv', sep='\\t')\n",
+    "bp_edges['sorted_genepairs'] = [';'.join(item) for item in bp_edges[['Gene1', 'Gene2']].values]\n",
+    "\n",
+    "sc_edges = sc_edges.set_index('sorted_genepairs')\n",
+    "bp_edges = bp_edges.set_index('sorted_genepairs')\n",
+    "concated_edges = pd.concat([sc_edges.add_suffix('_sc'), bp_edges.add_suffix('_bp')], axis=1)\n",
+    "\n",
+    "concated_edges.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SpearmanrResult(correlation=0.16937964029402044, pvalue=0.0)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "concated_edges = concated_edges.dropna()\n",
+    "spearmanr(concated_edges['EdgeWeight_sc'], concated_edges['EdgeWeight_bp'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Spearman r = 0.17')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coef, p = spearmanr(concated_edges['EdgeWeight_sc'], concated_edges['EdgeWeight_bp'])\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.scatter(concated_edges['EdgeWeight_sc'], concated_edges['EdgeWeight_bp'], s=1, alpha=0.5)\n",
+    "plt.xlabel('Edge weight from scRNAseq')\n",
+    "plt.ylabel('Edge weight from BLUEPRINT')\n",
+    "plt.title(f'Spearman r = {coef:.2f}')\n",
+    "# plt.savefig('grnboost2_sc_bp_comparison.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/01_association_metrics/.ipynb_checkpoints/compare_cell_classification-checkpoint.ipynb b/01_association_metrics/.ipynb_checkpoints/compare_cell_classification-checkpoint.ipynb
new file mode 100644
index 0000000..75b0224
--- /dev/null
+++ b/01_association_metrics/.ipynb_checkpoints/compare_cell_classification-checkpoint.ipynb
@@ -0,0 +1,1164 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['pdf.fonttype'] = 42\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run dataset.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onemillionv2 = DATASET('onemillionv2')\n",
+    "onemillionv2.load_dataset()\n",
+    "\n",
+    "onemillionv3 = DATASET('onemillionv3')\n",
+    "onemillionv3.load_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_sc = onemillionv2.data_sc[onemillionv2.data_sc.obs['time']=='UT']\n",
+    "data_obs = data_sc.obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>orig.ident</th>\n",
+       "      <th>nCount_RNA</th>\n",
+       "      <th>nFeature_RNA</th>\n",
+       "      <th>batch</th>\n",
+       "      <th>lane</th>\n",
+       "      <th>chem</th>\n",
+       "      <th>exp.id</th>\n",
+       "      <th>timepoint</th>\n",
+       "      <th>percent.mt</th>\n",
+       "      <th>nCount_SCT</th>\n",
+       "      <th>nFeature_SCT</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>cell_type_lowerres</th>\n",
+       "      <th>assignment</th>\n",
+       "      <th>bare_barcode_lane</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGAGTACAT_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>1518</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>1.560694</td>\n",
+       "      <td>3297.0</td>\n",
+       "      <td>1438</td>\n",
+       "      <td>mono 2</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>LLDeep_1370</td>\n",
+       "      <td>AAACCTGAGAGTACAT_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGTGTCTCA_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>5597.0</td>\n",
+       "      <td>1652</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>3.394676</td>\n",
+       "      <td>3353.0</td>\n",
+       "      <td>1507</td>\n",
+       "      <td>th1 CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>LLDeep_0434</td>\n",
+       "      <td>AAACCTGAGTGTCTCA_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAGTCGATT_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>3039.0</td>\n",
+       "      <td>849</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>3.685423</td>\n",
+       "      <td>2786.0</td>\n",
+       "      <td>849</td>\n",
+       "      <td>naive CD8T</td>\n",
+       "      <td>CD8T</td>\n",
+       "      <td>LLDeep_1319</td>\n",
+       "      <td>AAACCTGCAGTCGATT_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCATTCGACA_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>3876.0</td>\n",
+       "      <td>1048</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>3.766770</td>\n",
+       "      <td>2996.0</td>\n",
+       "      <td>1047</td>\n",
+       "      <td>mono 1</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>LLDeep_1370</td>\n",
+       "      <td>AAACCTGCATTCGACA_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGGTAATAGCA_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>4272.0</td>\n",
+       "      <td>1141</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>4.564607</td>\n",
+       "      <td>3076.0</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>mono 1</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>LLDeep_0434</td>\n",
+       "      <td>AAACCTGGTAATAGCA_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              orig.ident  nCount_RNA  nFeature_RNA  \\\n",
+       "index                                                                \n",
+       "AAACCTGAGAGTACAT_180925_lane1   1M_cells      5190.0          1518   \n",
+       "AAACCTGAGTGTCTCA_180925_lane1   1M_cells      5597.0          1652   \n",
+       "AAACCTGCAGTCGATT_180925_lane1   1M_cells      3039.0           849   \n",
+       "AAACCTGCATTCGACA_180925_lane1   1M_cells      3876.0          1048   \n",
+       "AAACCTGGTAATAGCA_180925_lane1   1M_cells      4272.0          1141   \n",
+       "\n",
+       "                                      batch          lane chem  exp.id  \\\n",
+       "index                                                                    \n",
+       "AAACCTGAGAGTACAT_180925_lane1  180925_lane1  180925_lane1   V2       2   \n",
+       "AAACCTGAGTGTCTCA_180925_lane1  180925_lane1  180925_lane1   V2      12   \n",
+       "AAACCTGCAGTCGATT_180925_lane1  180925_lane1  180925_lane1   V2      11   \n",
+       "AAACCTGCATTCGACA_180925_lane1  180925_lane1  180925_lane1   V2       2   \n",
+       "AAACCTGGTAATAGCA_180925_lane1  180925_lane1  180925_lane1   V2      12   \n",
+       "\n",
+       "                              timepoint  percent.mt  nCount_SCT  nFeature_SCT  \\\n",
+       "index                                                                           \n",
+       "AAACCTGAGAGTACAT_180925_lane1        UT    1.560694      3297.0          1438   \n",
+       "AAACCTGAGTGTCTCA_180925_lane1        UT    3.394676      3353.0          1507   \n",
+       "AAACCTGCAGTCGATT_180925_lane1        UT    3.685423      2786.0           849   \n",
+       "AAACCTGCATTCGACA_180925_lane1        UT    3.766770      2996.0          1047   \n",
+       "AAACCTGGTAATAGCA_180925_lane1        UT    4.564607      3076.0          1131   \n",
+       "\n",
+       "                                cell_type cell_type_lowerres   assignment  \\\n",
+       "index                                                                       \n",
+       "AAACCTGAGAGTACAT_180925_lane1      mono 2           monocyte  LLDeep_1370   \n",
+       "AAACCTGAGTGTCTCA_180925_lane1    th1 CD4T               CD4T  LLDeep_0434   \n",
+       "AAACCTGCAGTCGATT_180925_lane1  naive CD8T               CD8T  LLDeep_1319   \n",
+       "AAACCTGCATTCGACA_180925_lane1      mono 1           monocyte  LLDeep_1370   \n",
+       "AAACCTGGTAATAGCA_180925_lane1      mono 1           monocyte  LLDeep_0434   \n",
+       "\n",
+       "                                           bare_barcode_lane time  \n",
+       "index                                                              \n",
+       "AAACCTGAGAGTACAT_180925_lane1  AAACCTGAGAGTACAT_180925_lane1   UT  \n",
+       "AAACCTGAGTGTCTCA_180925_lane1  AAACCTGAGTGTCTCA_180925_lane1   UT  \n",
+       "AAACCTGCAGTCGATT_180925_lane1  AAACCTGCAGTCGATT_180925_lane1   UT  \n",
+       "AAACCTGCATTCGACA_180925_lane1  AAACCTGCATTCGACA_180925_lane1   UT  \n",
+       "AAACCTGGTAATAGCA_180925_lane1  AAACCTGGTAATAGCA_180925_lane1   UT  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_obs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'DC', 'B']\n",
+    "plot_df = data_obs['cell_type_lowerres'].value_counts(normalize=True).loc[celltypes]\n",
+    "plt.bar(x=plot_df.index, height=plot_df.values)\n",
+    "plt.ylim([0, 0.40])\n",
+    "plt.savefig('cell_abundance_oelenv2.pdf')\n",
+    "plt.savefig('cell_abundance_oelenv2.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predicted.celltype.l1</th>\n",
+       "      <th>predicted.celltype.l1.score</th>\n",
+       "      <th>predicted.celltype.l2</th>\n",
+       "      <th>predicted.celltype.l2.score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>barcode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGAAACCAT_180920_lane1</th>\n",
+       "      <td>CD8 T</td>\n",
+       "      <td>0.755924</td>\n",
+       "      <td>CD8 TEM</td>\n",
+       "      <td>0.755924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGTAGCCGA_180920_lane1</th>\n",
+       "      <td>Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>CD16 Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAATCTACG_180920_lane1</th>\n",
+       "      <td>NK</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NK</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCACATCCAA_180920_lane1</th>\n",
+       "      <td>other</td>\n",
+       "      <td>0.546320</td>\n",
+       "      <td>ILC</td>\n",
+       "      <td>0.546320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAGCTCGAC_180920_lane1</th>\n",
+       "      <td>NK</td>\n",
+       "      <td>0.733094</td>\n",
+       "      <td>NK</td>\n",
+       "      <td>0.733094</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              predicted.celltype.l1  \\\n",
+       "barcode                                               \n",
+       "AAACCTGAGAAACCAT_180920_lane1                 CD8 T   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                  Mono   \n",
+       "AAACCTGCAATCTACG_180920_lane1                    NK   \n",
+       "AAACCTGCACATCCAA_180920_lane1                 other   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                    NK   \n",
+       "\n",
+       "                               predicted.celltype.l1.score  \\\n",
+       "barcode                                                      \n",
+       "AAACCTGAGAAACCAT_180920_lane1                     0.755924   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                     1.000000   \n",
+       "AAACCTGCAATCTACG_180920_lane1                     1.000000   \n",
+       "AAACCTGCACATCCAA_180920_lane1                     0.546320   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                     0.733094   \n",
+       "\n",
+       "                              predicted.celltype.l2  \\\n",
+       "barcode                                               \n",
+       "AAACCTGAGAAACCAT_180920_lane1               CD8 TEM   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1             CD16 Mono   \n",
+       "AAACCTGCAATCTACG_180920_lane1                    NK   \n",
+       "AAACCTGCACATCCAA_180920_lane1                   ILC   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                    NK   \n",
+       "\n",
+       "                               predicted.celltype.l2.score  \n",
+       "barcode                                                     \n",
+       "AAACCTGAGAAACCAT_180920_lane1                     0.755924  \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                     1.000000  \n",
+       "AAACCTGCAATCTACG_180920_lane1                     1.000000  \n",
+       "AAACCTGCACATCCAA_180920_lane1                     0.546320  \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                     0.733094  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "azimuith_df = pd.read_csv(\n",
+    "    '1M_v2_20201029_azimuth.tsv',\n",
+    "    sep='\\t', index_col=0\n",
+    ")\n",
+    "azimuith_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['monocyte', 'CD4T', 'CD8T', 'NK', 'megakaryocyte', 'B', 'DC', 'plasma B', 'unknown', 'hemapoietic stem']\n",
+       "Categories (10, object): ['B', 'hemapoietic stem', 'megakaryocyte', 'NK', ..., 'CD4T', 'CD8T', 'monocyte', 'DC']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_obs['cell_type_lowerres'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['CD8 T', 'Mono', 'NK', 'other', 'CD4 T', 'DC', 'B', 'other T'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "azimuith_df['predicted.celltype.l1'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predicted.celltype.l1</th>\n",
+       "      <th>predicted.celltype.l1.score</th>\n",
+       "      <th>predicted.celltype.l2</th>\n",
+       "      <th>predicted.celltype.l2.score</th>\n",
+       "      <th>cell_type_mapped</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>barcode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGAAACCAT_180920_lane1</th>\n",
+       "      <td>CD8 T</td>\n",
+       "      <td>0.755924</td>\n",
+       "      <td>CD8 TEM</td>\n",
+       "      <td>0.755924</td>\n",
+       "      <td>CD8T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGTAGCCGA_180920_lane1</th>\n",
+       "      <td>Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>CD16 Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAATCTACG_180920_lane1</th>\n",
+       "      <td>NK</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NK</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCACATCCAA_180920_lane1</th>\n",
+       "      <td>other</td>\n",
+       "      <td>0.546320</td>\n",
+       "      <td>ILC</td>\n",
+       "      <td>0.546320</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAGCTCGAC_180920_lane1</th>\n",
+       "      <td>NK</td>\n",
+       "      <td>0.733094</td>\n",
+       "      <td>NK</td>\n",
+       "      <td>0.733094</td>\n",
+       "      <td>NK</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              predicted.celltype.l1  \\\n",
+       "barcode                                               \n",
+       "AAACCTGAGAAACCAT_180920_lane1                 CD8 T   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                  Mono   \n",
+       "AAACCTGCAATCTACG_180920_lane1                    NK   \n",
+       "AAACCTGCACATCCAA_180920_lane1                 other   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                    NK   \n",
+       "\n",
+       "                               predicted.celltype.l1.score  \\\n",
+       "barcode                                                      \n",
+       "AAACCTGAGAAACCAT_180920_lane1                     0.755924   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                     1.000000   \n",
+       "AAACCTGCAATCTACG_180920_lane1                     1.000000   \n",
+       "AAACCTGCACATCCAA_180920_lane1                     0.546320   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                     0.733094   \n",
+       "\n",
+       "                              predicted.celltype.l2  \\\n",
+       "barcode                                               \n",
+       "AAACCTGAGAAACCAT_180920_lane1               CD8 TEM   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1             CD16 Mono   \n",
+       "AAACCTGCAATCTACG_180920_lane1                    NK   \n",
+       "AAACCTGCACATCCAA_180920_lane1                   ILC   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                    NK   \n",
+       "\n",
+       "                               predicted.celltype.l2.score cell_type_mapped  \n",
+       "barcode                                                                      \n",
+       "AAACCTGAGAAACCAT_180920_lane1                     0.755924             CD8T  \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                     1.000000         monocyte  \n",
+       "AAACCTGCAATCTACG_180920_lane1                     1.000000               NK  \n",
+       "AAACCTGCACATCCAA_180920_lane1                     0.546320             None  \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                     0.733094               NK  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mapping_names = {'CD8 T': 'CD8T', \n",
+    "                 'CD4 T': 'CD4T',\n",
+    "                 'Mono': 'monocyte',\n",
+    "                 'NK': 'NK',\n",
+    "                 'B': 'B',\n",
+    "                 'DC': 'DC'}\n",
+    "azimuith_df['cell_type_mapped'] = [mapping_names.get(name) for name in \n",
+    "                                   azimuith_df['predicted.celltype.l1']]\n",
+    "azimuith_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type_lowerres</th>\n",
+       "      <th>cell_type_mapped</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGAGTACAT_180925_lane1</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGTGTCTCA_180925_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAGTCGATT_180925_lane1</th>\n",
+       "      <td>CD8T</td>\n",
+       "      <td>CD8T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCATTCGACA_180925_lane1</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGGTAATAGCA_180925_lane1</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              cell_type_lowerres cell_type_mapped\n",
+       "AAACCTGAGAGTACAT_180925_lane1           monocyte         monocyte\n",
+       "AAACCTGAGTGTCTCA_180925_lane1               CD4T             CD4T\n",
+       "AAACCTGCAGTCGATT_180925_lane1               CD8T             CD8T\n",
+       "AAACCTGCATTCGACA_180925_lane1           monocyte         monocyte\n",
+       "AAACCTGGTAATAGCA_180925_lane1           monocyte         monocyte"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_classification_df = pd.concat([data_obs[['cell_type_lowerres']],\n",
+    "                                      azimuith_df[['cell_type_mapped']]],\n",
+    "                                     axis=1).dropna()\n",
+    "merged_classification_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
+    "accuracy_df = pd.DataFrame(\n",
+    "    data=np.zeros((6, 6)),\n",
+    "    index=celltypes,\n",
+    "    columns=celltypes\n",
+    ")\n",
+    "for celltype_onemillionv2 in celltypes:\n",
+    "    for celltype_azimuth in celltypes:\n",
+    "        common_classification_num = merged_classification_df[\n",
+    "            (merged_classification_df['cell_type_lowerres']==celltype_onemillionv2) & \n",
+    "            (merged_classification_df['cell_type_mapped']==celltype_azimuth)\n",
+    "        ].shape[0]\n",
+    "        onemillion_classification_num = merged_classification_df[\n",
+    "            (merged_classification_df['cell_type_lowerres']==celltype_onemillionv2)\n",
+    "        ].shape[0]\n",
+    "        azimuth_classification_num = merged_classification_df[\n",
+    "            (merged_classification_df['cell_type_mapped']==celltype_azimuth)\n",
+    "        ].shape[0]\n",
+    "        accuracy_df[celltype_onemillionv2].loc[celltype_azimuth] = common_classification_num/onemillion_classification_num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6, 5))\n",
+    "sns.heatmap(accuracy_df, vmin=0, vmax=1,\n",
+    "            cmap=\"YlGnBu\", \n",
+    "            annot=True, annot_kws={\"size\": 10},\n",
+    "            fmt='.2f')\n",
+    "plt.xlabel('Cell type in Oelen v2')\n",
+    "plt.ylabel('Cell type by Azimuth Algorithm')\n",
+    "plt.savefig('marker_gene_azimuth_classification.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predicted.celltype.l1</th>\n",
+       "      <th>predicted.celltype.l1.score</th>\n",
+       "      <th>predicted.celltype.l2</th>\n",
+       "      <th>predicted.celltype.l2.score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>barcode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCCAAGATACCAA_190109_lane1</th>\n",
+       "      <td>CD8 T</td>\n",
+       "      <td>0.981818</td>\n",
+       "      <td>CD8 Naive</td>\n",
+       "      <td>0.955707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCAAGTCCCTAA_190109_lane1</th>\n",
+       "      <td>CD4 T</td>\n",
+       "      <td>0.379819</td>\n",
+       "      <td>CD4 CTL</td>\n",
+       "      <td>0.327444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAAGAGTGC_190109_lane1</th>\n",
+       "      <td>CD4 T</td>\n",
+       "      <td>0.740111</td>\n",
+       "      <td>CD4 Naive</td>\n",
+       "      <td>0.723822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAATCCAGT_190109_lane1</th>\n",
+       "      <td>CD4 T</td>\n",
+       "      <td>0.916869</td>\n",
+       "      <td>CD4 TCM</td>\n",
+       "      <td>0.468549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACACTATCCC_190109_lane1</th>\n",
+       "      <td>Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>CD14 Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              predicted.celltype.l1  \\\n",
+       "barcode                                               \n",
+       "AAACCCAAGATACCAA_190109_lane1                 CD8 T   \n",
+       "AAACCCAAGTCCCTAA_190109_lane1                 CD4 T   \n",
+       "AAACCCACAAGAGTGC_190109_lane1                 CD4 T   \n",
+       "AAACCCACAATCCAGT_190109_lane1                 CD4 T   \n",
+       "AAACCCACACTATCCC_190109_lane1                  Mono   \n",
+       "\n",
+       "                               predicted.celltype.l1.score  \\\n",
+       "barcode                                                      \n",
+       "AAACCCAAGATACCAA_190109_lane1                     0.981818   \n",
+       "AAACCCAAGTCCCTAA_190109_lane1                     0.379819   \n",
+       "AAACCCACAAGAGTGC_190109_lane1                     0.740111   \n",
+       "AAACCCACAATCCAGT_190109_lane1                     0.916869   \n",
+       "AAACCCACACTATCCC_190109_lane1                     1.000000   \n",
+       "\n",
+       "                              predicted.celltype.l2  \\\n",
+       "barcode                                               \n",
+       "AAACCCAAGATACCAA_190109_lane1             CD8 Naive   \n",
+       "AAACCCAAGTCCCTAA_190109_lane1               CD4 CTL   \n",
+       "AAACCCACAAGAGTGC_190109_lane1             CD4 Naive   \n",
+       "AAACCCACAATCCAGT_190109_lane1               CD4 TCM   \n",
+       "AAACCCACACTATCCC_190109_lane1             CD14 Mono   \n",
+       "\n",
+       "                               predicted.celltype.l2.score  \n",
+       "barcode                                                     \n",
+       "AAACCCAAGATACCAA_190109_lane1                     0.955707  \n",
+       "AAACCCAAGTCCCTAA_190109_lane1                     0.327444  \n",
+       "AAACCCACAAGAGTGC_190109_lane1                     0.723822  \n",
+       "AAACCCACAATCCAGT_190109_lane1                     0.468549  \n",
+       "AAACCCACACTATCCC_190109_lane1                     1.000000  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "azimuith_df_v3 = pd.read_csv(\n",
+    "    '1M_v3_20201106_azimuth.tsv',\n",
+    "    sep='\\t', index_col=0\n",
+    ")\n",
+    "azimuith_df_v3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping_names = {'CD8 T': 'CD8T', \n",
+    "                 'CD4 T': 'CD4T',\n",
+    "                 'Mono': 'monocyte',\n",
+    "                 'NK': 'NK',\n",
+    "                 'B': 'B',\n",
+    "                 'DC': 'DC'}\n",
+    "azimuith_df_v3['cell_type_mapped'] = [mapping_names.get(name) for name in \n",
+    "                                      azimuith_df_v3['predicted.celltype.l1']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_scv3 = onemillionv3.data_sc[onemillionv3.data_sc.obs['time']=='UT']\n",
+    "data_obsv3 = data_scv3.obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type_lowerres</th>\n",
+       "      <th>cell_type_mapped</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCCAAGATACCAA_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD8T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAAGAGTGC_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAATCCAGT_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAGGTACGA_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCAGTCTACGTA_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              cell_type_lowerres cell_type_mapped\n",
+       "AAACCCAAGATACCAA_190109_lane1               CD4T             CD8T\n",
+       "AAACCCACAAGAGTGC_190109_lane1               CD4T             CD4T\n",
+       "AAACCCACAATCCAGT_190109_lane1               CD4T             CD4T\n",
+       "AAACCCACAGGTACGA_190109_lane1               CD4T             CD4T\n",
+       "AAACCCAGTCTACGTA_190109_lane1               CD4T             CD4T"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_classification_df_v3 = pd.concat([data_obsv3[['cell_type_lowerres']],\n",
+    "                                       azimuith_df_v3[['cell_type_mapped']]],\n",
+    "                                       axis=1).dropna()\n",
+    "merged_classification_df_v3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
+    "accuracy_df_v3 = pd.DataFrame(\n",
+    "    data=np.zeros((6, 6)),\n",
+    "    index=celltypes,\n",
+    "    columns=celltypes\n",
+    ")\n",
+    "for celltype_onemillionv2 in celltypes:\n",
+    "    for celltype_azimuth in celltypes:\n",
+    "        common_classification_num = merged_classification_df_v3[\n",
+    "            (merged_classification_df_v3['cell_type_lowerres']==celltype_onemillionv2) & \n",
+    "            (merged_classification_df_v3['cell_type_mapped']==celltype_azimuth)\n",
+    "        ].shape[0]\n",
+    "        onemillion_classification_num = merged_classification_df_v3[\n",
+    "            (merged_classification_df_v3['cell_type_lowerres']==celltype_onemillionv2)\n",
+    "        ].shape[0]\n",
+    "        azimuth_classification_num = merged_classification_df_v3[\n",
+    "            (merged_classification_df_v3['cell_type_mapped']==celltype_azimuth)\n",
+    "        ].shape[0]\n",
+    "        accuracy_df_v3[celltype_onemillionv2].loc[celltype_azimuth] = common_classification_num/onemillion_classification_num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>monocyte</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>NK</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CD4T</th>\n",
+       "      <td>0.952222</td>\n",
+       "      <td>0.157213</td>\n",
+       "      <td>0.003671</td>\n",
+       "      <td>0.001845</td>\n",
+       "      <td>0.016403</td>\n",
+       "      <td>0.001099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD8T</th>\n",
+       "      <td>0.046923</td>\n",
+       "      <td>0.787428</td>\n",
+       "      <td>0.001606</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.014666</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>0.000513</td>\n",
+       "      <td>0.000409</td>\n",
+       "      <td>0.993346</td>\n",
+       "      <td>0.177122</td>\n",
+       "      <td>0.000386</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>0.000085</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000574</td>\n",
+       "      <td>0.821033</td>\n",
+       "      <td>0.000193</td>\n",
+       "      <td>0.001099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NK</th>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>0.054950</td>\n",
+       "      <td>0.000688</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.968352</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000115</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.997802</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              CD4T      CD8T  monocyte        DC        NK         B\n",
+       "CD4T      0.952222  0.157213  0.003671  0.001845  0.016403  0.001099\n",
+       "CD8T      0.046923  0.787428  0.001606  0.000000  0.014666  0.000000\n",
+       "monocyte  0.000513  0.000409  0.993346  0.177122  0.000386  0.000000\n",
+       "DC        0.000085  0.000000  0.000574  0.821033  0.000193  0.001099\n",
+       "NK        0.000256  0.054950  0.000688  0.000000  0.968352  0.000000\n",
+       "B         0.000000  0.000000  0.000115  0.000000  0.000000  0.997802"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_df_v3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6, 5))\n",
+    "sns.heatmap(accuracy_df_v3, vmin=0, vmax=1,\n",
+    "            cmap=\"YlGnBu\", \n",
+    "            annot=True, annot_kws={\"size\": 10},\n",
+    "            fmt='.2f')\n",
+    "plt.xlabel('Cell type in Oelen v3')\n",
+    "plt.ylabel('Cell type by Azimuth Algorithm')\n",
+    "plt.savefig('marker_gene_azimuth_classification_oelenv3.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/01_association_metrics/GRNBoost2.ipynb b/01_association_metrics/GRNBoost2.ipynb
new file mode 100644
index 0000000..b1b9528
--- /dev/null
+++ b/01_association_metrics/GRNBoost2.ipynb
@@ -0,0 +1,349 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['pdf.fonttype'] = 42\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "%run dataset.ipynb\n",
+    "\n",
+    "def select_gene_nonzeroratio(df, ratio):\n",
+    "    nonzerocounts = np.count_nonzero(df.values, axis=0) / df.shape[0]\n",
+    "    selected_genes = df.columns[nonzerocounts > ratio]\n",
+    "    return selected_genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasetname = 'onemillionv2'\n",
+    "dataset = DATASET(datasetname)\n",
+    "dataset.load_dataset()\n",
+    "data_sc = dataset.data_sc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "395\n"
+     ]
+    }
+   ],
+   "source": [
+    "monocyte_ut = data_sc[(data_sc.obs['time']=='UT') & (data_sc.obs['cell_type_lowerres']=='monocyte')]\n",
+    "monocyte_ut_df = pd.DataFrame(data=monocyte_ut.X.toarray(),\n",
+    "                              index=monocyte_ut.obs.index,\n",
+    "                              columns=monocyte_ut.var.index)\n",
+    "mono_genes = select_gene_nonzeroratio(df=monocyte_ut_df, ratio=0.50)\n",
+    "print(len(mono_genes))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(11482, 379) (194, 379)\n"
+     ]
+    }
+   ],
+   "source": [
+    "bp_df = pd.read_csv('mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.1PCAsOverSamplesRemoved.txt.gz',\n",
+    "                   compression='gzip',\n",
+    "                   sep='\\t', index_col=0)\n",
+    "name_mapping_dic = pd.read_csv('features_v3_reformated_names.tsv',\n",
+    "                          sep ='\\t',\n",
+    "                          names=['geneid', 'genename']).set_index(['geneid'])['genename'].T.to_dict()\n",
+    "\n",
+    "bp_df['geneid'] = [item.split('.')[0] for item in bp_df.index]\n",
+    "bp_df['genename'] = [name_mapping_dic.get(geneid) for geneid in bp_df['geneid']]\n",
+    "bp_df = bp_df.dropna(subset=['genename'])\n",
+    "bp_df = bp_df.drop('geneid', axis=1)\n",
+    "bp_df = bp_df.set_index('genename')\n",
+    "print(bp_df.shape)\n",
+    "\n",
+    "bp_trans_df = bp_df.T\n",
+    "common_genes = list(set(mono_genes) & set(bp_trans_df.columns))\n",
+    "selected_mono_df = monocyte_ut_df[common_genes]\n",
+    "selected_bp_df = bp_trans_df[common_genes]\n",
+    "print(selected_mono_df.shape, selected_bp_df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected_mono_df.T.to_csv('sc_Expression.csv', sep=',')\n",
+    "selected_bp_df.T.to_csv('bp_Expression.csv', sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create this fake pseudo time ordering because it's required to run the Beeline tool, but not used by GRNBoost2\n",
+    "fake_timepoint_bp = pd.DataFrame(index=selected_bp_df.index)\n",
+    "fake_timepoint_bp['time'] = np.arange(selected_bp_df.shape[0])\n",
+    "fake_timepoint_bp.to_csv('bp_timepoint.fake.csv',\n",
+    "                        sep=',')\n",
+    "fake_timepoint_sc = pd.DataFrame(index=selected_mono_df.index)\n",
+    "fake_timepoint_sc['time'] = np.arange(selected_mono_df.shape[0])\n",
+    "fake_timepoint_sc.to_csv('sc_timepoint.fake.csv',\n",
+    "                        sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# perform GRNBoost2 with BEELINE, see the yaml files in the same directory\n",
+    "# python BLRunner.py --config config-files/config_bp_mono.yaml\n",
+    "# python BLRunner.py --config config-files/config_sc_mono.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene1_sc</th>\n",
+       "      <th>Gene2_sc</th>\n",
+       "      <th>EdgeWeight_sc</th>\n",
+       "      <th>Gene1_bp</th>\n",
+       "      <th>Gene2_bp</th>\n",
+       "      <th>EdgeWeight_bp</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sorted_genepairs</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CCL3;CCL4</th>\n",
+       "      <td>CCL3</td>\n",
+       "      <td>CCL4</td>\n",
+       "      <td>554.503642</td>\n",
+       "      <td>CCL3</td>\n",
+       "      <td>CCL4</td>\n",
+       "      <td>55.157748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CCL4;CCL3</th>\n",
+       "      <td>CCL4</td>\n",
+       "      <td>CCL3</td>\n",
+       "      <td>480.484753</td>\n",
+       "      <td>CCL4</td>\n",
+       "      <td>CCL3</td>\n",
+       "      <td>77.414467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S100A9;S100A8</th>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>341.726427</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>104.542395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S100A8;S100A9</th>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>284.321568</td>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>65.915233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S100A9;LYZ</th>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>LYZ</td>\n",
+       "      <td>221.872616</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>LYZ</td>\n",
+       "      <td>0.149064</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Gene1_sc Gene2_sc  EdgeWeight_sc Gene1_bp Gene2_bp  \\\n",
+       "sorted_genepairs                                                      \n",
+       "CCL3;CCL4            CCL3     CCL4     554.503642     CCL3     CCL4   \n",
+       "CCL4;CCL3            CCL4     CCL3     480.484753     CCL4     CCL3   \n",
+       "S100A9;S100A8      S100A9   S100A8     341.726427   S100A9   S100A8   \n",
+       "S100A8;S100A9      S100A8   S100A9     284.321568   S100A8   S100A9   \n",
+       "S100A9;LYZ         S100A9      LYZ     221.872616   S100A9      LYZ   \n",
+       "\n",
+       "                  EdgeWeight_bp  \n",
+       "sorted_genepairs                 \n",
+       "CCL3;CCL4             55.157748  \n",
+       "CCL4;CCL3             77.414467  \n",
+       "S100A9;S100A8        104.542395  \n",
+       "S100A8;S100A9         65.915233  \n",
+       "S100A9;LYZ             0.149064  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sc_edges = pd.read_csv('sc_edges.csv', sep='\\t')\n",
+    "sc_edges['sorted_genepairs'] = [';'.join(item) for item in sc_edges[['Gene1', 'Gene2']].values]\n",
+    "bp_edges = pd.read_csv('bp_edges.csv', sep='\\t')\n",
+    "bp_edges['sorted_genepairs'] = [';'.join(item) for item in bp_edges[['Gene1', 'Gene2']].values]\n",
+    "\n",
+    "sc_edges = sc_edges.set_index('sorted_genepairs')\n",
+    "bp_edges = bp_edges.set_index('sorted_genepairs')\n",
+    "concated_edges = pd.concat([sc_edges.add_suffix('_sc'), bp_edges.add_suffix('_bp')], axis=1)\n",
+    "\n",
+    "concated_edges.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SpearmanrResult(correlation=0.16937964029402044, pvalue=0.0)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "concated_edges = concated_edges.dropna()\n",
+    "spearmanr(concated_edges['EdgeWeight_sc'], concated_edges['EdgeWeight_bp'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Spearman r = 0.17')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coef, p = spearmanr(concated_edges['EdgeWeight_sc'], concated_edges['EdgeWeight_bp'])\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.scatter(concated_edges['EdgeWeight_sc'], concated_edges['EdgeWeight_bp'], s=1, alpha=0.5)\n",
+    "plt.xlabel('Edge weight from scRNAseq')\n",
+    "plt.ylabel('Edge weight from BLUEPRINT')\n",
+    "plt.title(f'Spearman r = {coef:.2f}')\n",
+    "# plt.savefig('grnboost2_sc_bp_comparison.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/01_association_metrics/README.md b/01_association_metrics/README.md
new file mode 100644
index 0000000..cf61992
--- /dev/null
+++ b/01_association_metrics/README.md
@@ -0,0 +1,31 @@
+# 01_association_metrics
+
+
+In *setting_files_for_grnboost2* there are setting files for running GRNBoost2 on scRNAseq and BIOS data
+
+*GRNBoost2.ipynb*: Prepare the input files for BEELINE, and examine the GRNBoost2 results for scRNAseq and for BIOS data
+
+*rho_comparison_lowlyexpressed.R*:  explores the differences between Spearman correlation and Rho propensity specially for very lowly expressed genes
+
+*scorpius_and_slingshot_clean.R*: calculates the pseudotime ordering for Oelen v2 classical monocytes, using SCORPIUS and Slingshot algorithms
+
+*scvelo_analysis_dm.py*: runs RNA velocity analysis on Oelen v3 dataset classical monocytes after creating loom files using [velocyto](http://velocyto.org/velocyto.py/tutorial/cli.html) to get both spliced and unspliced gene count matrices
+
+*compare_cell_classification.ipynb*: compares the aximuth cell type classification with the marker gene cell type classification in Oelen v2 and v3 dataset for untreated cells
+
+Metacell calculation and evaluation files are all in the directory *metacell*:
+
+*metacell_per_sample_original_algorithm.R*: calculates metacells based on original algorithm (implemented in the metacell R package)
+
+*metacells_from_leiden.R*: calculates metacells based on grouping from leiden clustering
+
+*create_genesets.R*: split all genes expressed in Oelen v3 dataset, Monocytes, into different expression bins for treshold-dependent evaluation with BLUEPRINT
+
+*metacell_general_correlation_tp.R*: calculates correlation from metacells (original or leiden) for different expression tresholds from *create_genesets.R* for comparison with BLUEPRINT
+
+*single_cell_correlation_tp.R*: calculates correlation from single cell dataset for different expression tresholds from *create_genesets.R* for comparison with BLUEPRINT
+
+*eval_blueprint_genesets.R*: compares correlation from BLUEPRINT with correlation from metacells/single cell for different expression tresholds, using correlation vlaues from *metacell_general_correlation_tp.R* and *single_cell_correlation_tp.R*
+
+*plot_overview_metacell.R*: visualize outputs from metacell evaluation in one plot
+
diff --git a/01_association_metrics/compare_cell_classification.ipynb b/01_association_metrics/compare_cell_classification.ipynb
new file mode 100644
index 0000000..75b0224
--- /dev/null
+++ b/01_association_metrics/compare_cell_classification.ipynb
@@ -0,0 +1,1164 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['pdf.fonttype'] = 42\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run dataset.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onemillionv2 = DATASET('onemillionv2')\n",
+    "onemillionv2.load_dataset()\n",
+    "\n",
+    "onemillionv3 = DATASET('onemillionv3')\n",
+    "onemillionv3.load_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_sc = onemillionv2.data_sc[onemillionv2.data_sc.obs['time']=='UT']\n",
+    "data_obs = data_sc.obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>orig.ident</th>\n",
+       "      <th>nCount_RNA</th>\n",
+       "      <th>nFeature_RNA</th>\n",
+       "      <th>batch</th>\n",
+       "      <th>lane</th>\n",
+       "      <th>chem</th>\n",
+       "      <th>exp.id</th>\n",
+       "      <th>timepoint</th>\n",
+       "      <th>percent.mt</th>\n",
+       "      <th>nCount_SCT</th>\n",
+       "      <th>nFeature_SCT</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>cell_type_lowerres</th>\n",
+       "      <th>assignment</th>\n",
+       "      <th>bare_barcode_lane</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGAGTACAT_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>5190.0</td>\n",
+       "      <td>1518</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>1.560694</td>\n",
+       "      <td>3297.0</td>\n",
+       "      <td>1438</td>\n",
+       "      <td>mono 2</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>LLDeep_1370</td>\n",
+       "      <td>AAACCTGAGAGTACAT_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGTGTCTCA_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>5597.0</td>\n",
+       "      <td>1652</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>3.394676</td>\n",
+       "      <td>3353.0</td>\n",
+       "      <td>1507</td>\n",
+       "      <td>th1 CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>LLDeep_0434</td>\n",
+       "      <td>AAACCTGAGTGTCTCA_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAGTCGATT_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>3039.0</td>\n",
+       "      <td>849</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>3.685423</td>\n",
+       "      <td>2786.0</td>\n",
+       "      <td>849</td>\n",
+       "      <td>naive CD8T</td>\n",
+       "      <td>CD8T</td>\n",
+       "      <td>LLDeep_1319</td>\n",
+       "      <td>AAACCTGCAGTCGATT_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCATTCGACA_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>3876.0</td>\n",
+       "      <td>1048</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>3.766770</td>\n",
+       "      <td>2996.0</td>\n",
+       "      <td>1047</td>\n",
+       "      <td>mono 1</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>LLDeep_1370</td>\n",
+       "      <td>AAACCTGCATTCGACA_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGGTAATAGCA_180925_lane1</th>\n",
+       "      <td>1M_cells</td>\n",
+       "      <td>4272.0</td>\n",
+       "      <td>1141</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>180925_lane1</td>\n",
+       "      <td>V2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>4.564607</td>\n",
+       "      <td>3076.0</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>mono 1</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>LLDeep_0434</td>\n",
+       "      <td>AAACCTGGTAATAGCA_180925_lane1</td>\n",
+       "      <td>UT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              orig.ident  nCount_RNA  nFeature_RNA  \\\n",
+       "index                                                                \n",
+       "AAACCTGAGAGTACAT_180925_lane1   1M_cells      5190.0          1518   \n",
+       "AAACCTGAGTGTCTCA_180925_lane1   1M_cells      5597.0          1652   \n",
+       "AAACCTGCAGTCGATT_180925_lane1   1M_cells      3039.0           849   \n",
+       "AAACCTGCATTCGACA_180925_lane1   1M_cells      3876.0          1048   \n",
+       "AAACCTGGTAATAGCA_180925_lane1   1M_cells      4272.0          1141   \n",
+       "\n",
+       "                                      batch          lane chem  exp.id  \\\n",
+       "index                                                                    \n",
+       "AAACCTGAGAGTACAT_180925_lane1  180925_lane1  180925_lane1   V2       2   \n",
+       "AAACCTGAGTGTCTCA_180925_lane1  180925_lane1  180925_lane1   V2      12   \n",
+       "AAACCTGCAGTCGATT_180925_lane1  180925_lane1  180925_lane1   V2      11   \n",
+       "AAACCTGCATTCGACA_180925_lane1  180925_lane1  180925_lane1   V2       2   \n",
+       "AAACCTGGTAATAGCA_180925_lane1  180925_lane1  180925_lane1   V2      12   \n",
+       "\n",
+       "                              timepoint  percent.mt  nCount_SCT  nFeature_SCT  \\\n",
+       "index                                                                           \n",
+       "AAACCTGAGAGTACAT_180925_lane1        UT    1.560694      3297.0          1438   \n",
+       "AAACCTGAGTGTCTCA_180925_lane1        UT    3.394676      3353.0          1507   \n",
+       "AAACCTGCAGTCGATT_180925_lane1        UT    3.685423      2786.0           849   \n",
+       "AAACCTGCATTCGACA_180925_lane1        UT    3.766770      2996.0          1047   \n",
+       "AAACCTGGTAATAGCA_180925_lane1        UT    4.564607      3076.0          1131   \n",
+       "\n",
+       "                                cell_type cell_type_lowerres   assignment  \\\n",
+       "index                                                                       \n",
+       "AAACCTGAGAGTACAT_180925_lane1      mono 2           monocyte  LLDeep_1370   \n",
+       "AAACCTGAGTGTCTCA_180925_lane1    th1 CD4T               CD4T  LLDeep_0434   \n",
+       "AAACCTGCAGTCGATT_180925_lane1  naive CD8T               CD8T  LLDeep_1319   \n",
+       "AAACCTGCATTCGACA_180925_lane1      mono 1           monocyte  LLDeep_1370   \n",
+       "AAACCTGGTAATAGCA_180925_lane1      mono 1           monocyte  LLDeep_0434   \n",
+       "\n",
+       "                                           bare_barcode_lane time  \n",
+       "index                                                              \n",
+       "AAACCTGAGAGTACAT_180925_lane1  AAACCTGAGAGTACAT_180925_lane1   UT  \n",
+       "AAACCTGAGTGTCTCA_180925_lane1  AAACCTGAGTGTCTCA_180925_lane1   UT  \n",
+       "AAACCTGCAGTCGATT_180925_lane1  AAACCTGCAGTCGATT_180925_lane1   UT  \n",
+       "AAACCTGCATTCGACA_180925_lane1  AAACCTGCATTCGACA_180925_lane1   UT  \n",
+       "AAACCTGGTAATAGCA_180925_lane1  AAACCTGGTAATAGCA_180925_lane1   UT  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_obs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'DC', 'B']\n",
+    "plot_df = data_obs['cell_type_lowerres'].value_counts(normalize=True).loc[celltypes]\n",
+    "plt.bar(x=plot_df.index, height=plot_df.values)\n",
+    "plt.ylim([0, 0.40])\n",
+    "plt.savefig('cell_abundance_oelenv2.pdf')\n",
+    "plt.savefig('cell_abundance_oelenv2.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predicted.celltype.l1</th>\n",
+       "      <th>predicted.celltype.l1.score</th>\n",
+       "      <th>predicted.celltype.l2</th>\n",
+       "      <th>predicted.celltype.l2.score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>barcode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGAAACCAT_180920_lane1</th>\n",
+       "      <td>CD8 T</td>\n",
+       "      <td>0.755924</td>\n",
+       "      <td>CD8 TEM</td>\n",
+       "      <td>0.755924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGTAGCCGA_180920_lane1</th>\n",
+       "      <td>Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>CD16 Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAATCTACG_180920_lane1</th>\n",
+       "      <td>NK</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NK</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCACATCCAA_180920_lane1</th>\n",
+       "      <td>other</td>\n",
+       "      <td>0.546320</td>\n",
+       "      <td>ILC</td>\n",
+       "      <td>0.546320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAGCTCGAC_180920_lane1</th>\n",
+       "      <td>NK</td>\n",
+       "      <td>0.733094</td>\n",
+       "      <td>NK</td>\n",
+       "      <td>0.733094</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              predicted.celltype.l1  \\\n",
+       "barcode                                               \n",
+       "AAACCTGAGAAACCAT_180920_lane1                 CD8 T   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                  Mono   \n",
+       "AAACCTGCAATCTACG_180920_lane1                    NK   \n",
+       "AAACCTGCACATCCAA_180920_lane1                 other   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                    NK   \n",
+       "\n",
+       "                               predicted.celltype.l1.score  \\\n",
+       "barcode                                                      \n",
+       "AAACCTGAGAAACCAT_180920_lane1                     0.755924   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                     1.000000   \n",
+       "AAACCTGCAATCTACG_180920_lane1                     1.000000   \n",
+       "AAACCTGCACATCCAA_180920_lane1                     0.546320   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                     0.733094   \n",
+       "\n",
+       "                              predicted.celltype.l2  \\\n",
+       "barcode                                               \n",
+       "AAACCTGAGAAACCAT_180920_lane1               CD8 TEM   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1             CD16 Mono   \n",
+       "AAACCTGCAATCTACG_180920_lane1                    NK   \n",
+       "AAACCTGCACATCCAA_180920_lane1                   ILC   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                    NK   \n",
+       "\n",
+       "                               predicted.celltype.l2.score  \n",
+       "barcode                                                     \n",
+       "AAACCTGAGAAACCAT_180920_lane1                     0.755924  \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                     1.000000  \n",
+       "AAACCTGCAATCTACG_180920_lane1                     1.000000  \n",
+       "AAACCTGCACATCCAA_180920_lane1                     0.546320  \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                     0.733094  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "azimuith_df = pd.read_csv(\n",
+    "    '1M_v2_20201029_azimuth.tsv',\n",
+    "    sep='\\t', index_col=0\n",
+    ")\n",
+    "azimuith_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['monocyte', 'CD4T', 'CD8T', 'NK', 'megakaryocyte', 'B', 'DC', 'plasma B', 'unknown', 'hemapoietic stem']\n",
+       "Categories (10, object): ['B', 'hemapoietic stem', 'megakaryocyte', 'NK', ..., 'CD4T', 'CD8T', 'monocyte', 'DC']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_obs['cell_type_lowerres'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['CD8 T', 'Mono', 'NK', 'other', 'CD4 T', 'DC', 'B', 'other T'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "azimuith_df['predicted.celltype.l1'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predicted.celltype.l1</th>\n",
+       "      <th>predicted.celltype.l1.score</th>\n",
+       "      <th>predicted.celltype.l2</th>\n",
+       "      <th>predicted.celltype.l2.score</th>\n",
+       "      <th>cell_type_mapped</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>barcode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGAAACCAT_180920_lane1</th>\n",
+       "      <td>CD8 T</td>\n",
+       "      <td>0.755924</td>\n",
+       "      <td>CD8 TEM</td>\n",
+       "      <td>0.755924</td>\n",
+       "      <td>CD8T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGTAGCCGA_180920_lane1</th>\n",
+       "      <td>Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>CD16 Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAATCTACG_180920_lane1</th>\n",
+       "      <td>NK</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NK</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCACATCCAA_180920_lane1</th>\n",
+       "      <td>other</td>\n",
+       "      <td>0.546320</td>\n",
+       "      <td>ILC</td>\n",
+       "      <td>0.546320</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAGCTCGAC_180920_lane1</th>\n",
+       "      <td>NK</td>\n",
+       "      <td>0.733094</td>\n",
+       "      <td>NK</td>\n",
+       "      <td>0.733094</td>\n",
+       "      <td>NK</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              predicted.celltype.l1  \\\n",
+       "barcode                                               \n",
+       "AAACCTGAGAAACCAT_180920_lane1                 CD8 T   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                  Mono   \n",
+       "AAACCTGCAATCTACG_180920_lane1                    NK   \n",
+       "AAACCTGCACATCCAA_180920_lane1                 other   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                    NK   \n",
+       "\n",
+       "                               predicted.celltype.l1.score  \\\n",
+       "barcode                                                      \n",
+       "AAACCTGAGAAACCAT_180920_lane1                     0.755924   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                     1.000000   \n",
+       "AAACCTGCAATCTACG_180920_lane1                     1.000000   \n",
+       "AAACCTGCACATCCAA_180920_lane1                     0.546320   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                     0.733094   \n",
+       "\n",
+       "                              predicted.celltype.l2  \\\n",
+       "barcode                                               \n",
+       "AAACCTGAGAAACCAT_180920_lane1               CD8 TEM   \n",
+       "AAACCTGAGTAGCCGA_180920_lane1             CD16 Mono   \n",
+       "AAACCTGCAATCTACG_180920_lane1                    NK   \n",
+       "AAACCTGCACATCCAA_180920_lane1                   ILC   \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                    NK   \n",
+       "\n",
+       "                               predicted.celltype.l2.score cell_type_mapped  \n",
+       "barcode                                                                      \n",
+       "AAACCTGAGAAACCAT_180920_lane1                     0.755924             CD8T  \n",
+       "AAACCTGAGTAGCCGA_180920_lane1                     1.000000         monocyte  \n",
+       "AAACCTGCAATCTACG_180920_lane1                     1.000000               NK  \n",
+       "AAACCTGCACATCCAA_180920_lane1                     0.546320             None  \n",
+       "AAACCTGCAGCTCGAC_180920_lane1                     0.733094               NK  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mapping_names = {'CD8 T': 'CD8T', \n",
+    "                 'CD4 T': 'CD4T',\n",
+    "                 'Mono': 'monocyte',\n",
+    "                 'NK': 'NK',\n",
+    "                 'B': 'B',\n",
+    "                 'DC': 'DC'}\n",
+    "azimuith_df['cell_type_mapped'] = [mapping_names.get(name) for name in \n",
+    "                                   azimuith_df['predicted.celltype.l1']]\n",
+    "azimuith_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type_lowerres</th>\n",
+       "      <th>cell_type_mapped</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGAGTACAT_180925_lane1</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGAGTGTCTCA_180925_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCAGTCGATT_180925_lane1</th>\n",
+       "      <td>CD8T</td>\n",
+       "      <td>CD8T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGCATTCGACA_180925_lane1</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCTGGTAATAGCA_180925_lane1</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              cell_type_lowerres cell_type_mapped\n",
+       "AAACCTGAGAGTACAT_180925_lane1           monocyte         monocyte\n",
+       "AAACCTGAGTGTCTCA_180925_lane1               CD4T             CD4T\n",
+       "AAACCTGCAGTCGATT_180925_lane1               CD8T             CD8T\n",
+       "AAACCTGCATTCGACA_180925_lane1           monocyte         monocyte\n",
+       "AAACCTGGTAATAGCA_180925_lane1           monocyte         monocyte"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_classification_df = pd.concat([data_obs[['cell_type_lowerres']],\n",
+    "                                      azimuith_df[['cell_type_mapped']]],\n",
+    "                                     axis=1).dropna()\n",
+    "merged_classification_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
+    "accuracy_df = pd.DataFrame(\n",
+    "    data=np.zeros((6, 6)),\n",
+    "    index=celltypes,\n",
+    "    columns=celltypes\n",
+    ")\n",
+    "for celltype_onemillionv2 in celltypes:\n",
+    "    for celltype_azimuth in celltypes:\n",
+    "        common_classification_num = merged_classification_df[\n",
+    "            (merged_classification_df['cell_type_lowerres']==celltype_onemillionv2) & \n",
+    "            (merged_classification_df['cell_type_mapped']==celltype_azimuth)\n",
+    "        ].shape[0]\n",
+    "        onemillion_classification_num = merged_classification_df[\n",
+    "            (merged_classification_df['cell_type_lowerres']==celltype_onemillionv2)\n",
+    "        ].shape[0]\n",
+    "        azimuth_classification_num = merged_classification_df[\n",
+    "            (merged_classification_df['cell_type_mapped']==celltype_azimuth)\n",
+    "        ].shape[0]\n",
+    "        accuracy_df[celltype_onemillionv2].loc[celltype_azimuth] = common_classification_num/onemillion_classification_num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6, 5))\n",
+    "sns.heatmap(accuracy_df, vmin=0, vmax=1,\n",
+    "            cmap=\"YlGnBu\", \n",
+    "            annot=True, annot_kws={\"size\": 10},\n",
+    "            fmt='.2f')\n",
+    "plt.xlabel('Cell type in Oelen v2')\n",
+    "plt.ylabel('Cell type by Azimuth Algorithm')\n",
+    "plt.savefig('marker_gene_azimuth_classification.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>predicted.celltype.l1</th>\n",
+       "      <th>predicted.celltype.l1.score</th>\n",
+       "      <th>predicted.celltype.l2</th>\n",
+       "      <th>predicted.celltype.l2.score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>barcode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCCAAGATACCAA_190109_lane1</th>\n",
+       "      <td>CD8 T</td>\n",
+       "      <td>0.981818</td>\n",
+       "      <td>CD8 Naive</td>\n",
+       "      <td>0.955707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCAAGTCCCTAA_190109_lane1</th>\n",
+       "      <td>CD4 T</td>\n",
+       "      <td>0.379819</td>\n",
+       "      <td>CD4 CTL</td>\n",
+       "      <td>0.327444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAAGAGTGC_190109_lane1</th>\n",
+       "      <td>CD4 T</td>\n",
+       "      <td>0.740111</td>\n",
+       "      <td>CD4 Naive</td>\n",
+       "      <td>0.723822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAATCCAGT_190109_lane1</th>\n",
+       "      <td>CD4 T</td>\n",
+       "      <td>0.916869</td>\n",
+       "      <td>CD4 TCM</td>\n",
+       "      <td>0.468549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACACTATCCC_190109_lane1</th>\n",
+       "      <td>Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>CD14 Mono</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              predicted.celltype.l1  \\\n",
+       "barcode                                               \n",
+       "AAACCCAAGATACCAA_190109_lane1                 CD8 T   \n",
+       "AAACCCAAGTCCCTAA_190109_lane1                 CD4 T   \n",
+       "AAACCCACAAGAGTGC_190109_lane1                 CD4 T   \n",
+       "AAACCCACAATCCAGT_190109_lane1                 CD4 T   \n",
+       "AAACCCACACTATCCC_190109_lane1                  Mono   \n",
+       "\n",
+       "                               predicted.celltype.l1.score  \\\n",
+       "barcode                                                      \n",
+       "AAACCCAAGATACCAA_190109_lane1                     0.981818   \n",
+       "AAACCCAAGTCCCTAA_190109_lane1                     0.379819   \n",
+       "AAACCCACAAGAGTGC_190109_lane1                     0.740111   \n",
+       "AAACCCACAATCCAGT_190109_lane1                     0.916869   \n",
+       "AAACCCACACTATCCC_190109_lane1                     1.000000   \n",
+       "\n",
+       "                              predicted.celltype.l2  \\\n",
+       "barcode                                               \n",
+       "AAACCCAAGATACCAA_190109_lane1             CD8 Naive   \n",
+       "AAACCCAAGTCCCTAA_190109_lane1               CD4 CTL   \n",
+       "AAACCCACAAGAGTGC_190109_lane1             CD4 Naive   \n",
+       "AAACCCACAATCCAGT_190109_lane1               CD4 TCM   \n",
+       "AAACCCACACTATCCC_190109_lane1             CD14 Mono   \n",
+       "\n",
+       "                               predicted.celltype.l2.score  \n",
+       "barcode                                                     \n",
+       "AAACCCAAGATACCAA_190109_lane1                     0.955707  \n",
+       "AAACCCAAGTCCCTAA_190109_lane1                     0.327444  \n",
+       "AAACCCACAAGAGTGC_190109_lane1                     0.723822  \n",
+       "AAACCCACAATCCAGT_190109_lane1                     0.468549  \n",
+       "AAACCCACACTATCCC_190109_lane1                     1.000000  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "azimuith_df_v3 = pd.read_csv(\n",
+    "    '1M_v3_20201106_azimuth.tsv',\n",
+    "    sep='\\t', index_col=0\n",
+    ")\n",
+    "azimuith_df_v3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping_names = {'CD8 T': 'CD8T', \n",
+    "                 'CD4 T': 'CD4T',\n",
+    "                 'Mono': 'monocyte',\n",
+    "                 'NK': 'NK',\n",
+    "                 'B': 'B',\n",
+    "                 'DC': 'DC'}\n",
+    "azimuith_df_v3['cell_type_mapped'] = [mapping_names.get(name) for name in \n",
+    "                                      azimuith_df_v3['predicted.celltype.l1']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_scv3 = onemillionv3.data_sc[onemillionv3.data_sc.obs['time']=='UT']\n",
+    "data_obsv3 = data_scv3.obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type_lowerres</th>\n",
+       "      <th>cell_type_mapped</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AAACCCAAGATACCAA_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD8T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAAGAGTGC_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAATCCAGT_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCACAGGTACGA_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAACCCAGTCTACGTA_190109_lane1</th>\n",
+       "      <td>CD4T</td>\n",
+       "      <td>CD4T</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              cell_type_lowerres cell_type_mapped\n",
+       "AAACCCAAGATACCAA_190109_lane1               CD4T             CD8T\n",
+       "AAACCCACAAGAGTGC_190109_lane1               CD4T             CD4T\n",
+       "AAACCCACAATCCAGT_190109_lane1               CD4T             CD4T\n",
+       "AAACCCACAGGTACGA_190109_lane1               CD4T             CD4T\n",
+       "AAACCCAGTCTACGTA_190109_lane1               CD4T             CD4T"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_classification_df_v3 = pd.concat([data_obsv3[['cell_type_lowerres']],\n",
+    "                                       azimuith_df_v3[['cell_type_mapped']]],\n",
+    "                                       axis=1).dropna()\n",
+    "merged_classification_df_v3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
+    "accuracy_df_v3 = pd.DataFrame(\n",
+    "    data=np.zeros((6, 6)),\n",
+    "    index=celltypes,\n",
+    "    columns=celltypes\n",
+    ")\n",
+    "for celltype_onemillionv2 in celltypes:\n",
+    "    for celltype_azimuth in celltypes:\n",
+    "        common_classification_num = merged_classification_df_v3[\n",
+    "            (merged_classification_df_v3['cell_type_lowerres']==celltype_onemillionv2) & \n",
+    "            (merged_classification_df_v3['cell_type_mapped']==celltype_azimuth)\n",
+    "        ].shape[0]\n",
+    "        onemillion_classification_num = merged_classification_df_v3[\n",
+    "            (merged_classification_df_v3['cell_type_lowerres']==celltype_onemillionv2)\n",
+    "        ].shape[0]\n",
+    "        azimuth_classification_num = merged_classification_df_v3[\n",
+    "            (merged_classification_df_v3['cell_type_mapped']==celltype_azimuth)\n",
+    "        ].shape[0]\n",
+    "        accuracy_df_v3[celltype_onemillionv2].loc[celltype_azimuth] = common_classification_num/onemillion_classification_num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>monocyte</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>NK</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CD4T</th>\n",
+       "      <td>0.952222</td>\n",
+       "      <td>0.157213</td>\n",
+       "      <td>0.003671</td>\n",
+       "      <td>0.001845</td>\n",
+       "      <td>0.016403</td>\n",
+       "      <td>0.001099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD8T</th>\n",
+       "      <td>0.046923</td>\n",
+       "      <td>0.787428</td>\n",
+       "      <td>0.001606</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.014666</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>0.000513</td>\n",
+       "      <td>0.000409</td>\n",
+       "      <td>0.993346</td>\n",
+       "      <td>0.177122</td>\n",
+       "      <td>0.000386</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>0.000085</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000574</td>\n",
+       "      <td>0.821033</td>\n",
+       "      <td>0.000193</td>\n",
+       "      <td>0.001099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NK</th>\n",
+       "      <td>0.000256</td>\n",
+       "      <td>0.054950</td>\n",
+       "      <td>0.000688</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.968352</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000115</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.997802</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              CD4T      CD8T  monocyte        DC        NK         B\n",
+       "CD4T      0.952222  0.157213  0.003671  0.001845  0.016403  0.001099\n",
+       "CD8T      0.046923  0.787428  0.001606  0.000000  0.014666  0.000000\n",
+       "monocyte  0.000513  0.000409  0.993346  0.177122  0.000386  0.000000\n",
+       "DC        0.000085  0.000000  0.000574  0.821033  0.000193  0.001099\n",
+       "NK        0.000256  0.054950  0.000688  0.000000  0.968352  0.000000\n",
+       "B         0.000000  0.000000  0.000115  0.000000  0.000000  0.997802"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_df_v3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6, 5))\n",
+    "sns.heatmap(accuracy_df_v3, vmin=0, vmax=1,\n",
+    "            cmap=\"YlGnBu\", \n",
+    "            annot=True, annot_kws={\"size\": 10},\n",
+    "            fmt='.2f')\n",
+    "plt.xlabel('Cell type in Oelen v3')\n",
+    "plt.ylabel('Cell type by Azimuth Algorithm')\n",
+    "plt.savefig('marker_gene_azimuth_classification_oelenv3.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/01_association_metrics/metacell/create_genesets.R b/01_association_metrics/metacell/create_genesets.R
new file mode 100644
index 0000000..d0fe688
--- /dev/null
+++ b/01_association_metrics/metacell/create_genesets.R
@@ -0,0 +1,39 @@
+# ------------------------------------------------------------------------------
+# Gene selections for metacell evaluation: generate files for different
+# gene subsets (expressed in x% until (x+20)% of the cells with x between 20-80)
+# for Oelen v3 dataset, Monocytes
+# This allows threshold dependent evaluation for BLUEPRINT comparison. 
+# See details downstream scripts metacell_general_correlation_tp.R, 
+# single_cell_correlation_tp.R, eval_blueprint_genesets.R
+# ------------------------------------------------------------------------------
+
+library(Seurat)
+
+#Load complete seurat object
+seurat<-readRDS("seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.rds")
+DefaultAssay(seurat)<-"RNA"
+
+#Filter for monocytes
+seurat<-seurat[,seurat$cell_type_lowerres=="monocyte"]
+
+#Selected cutoffs
+cutoffs<-c(1,0.8,0.6,0.4,0.2)
+
+#Split into lists dependent on expression cutoff
+exprGenes.singleCell<-rowSums(as.matrix(seurat@assays$RNA@counts)>0)/ncol(seurat)
+
+print(paste("Number of genes expressed in at least 50% of cells:",
+            sum(exprGenes.singleCell>=0.5)))
+
+for(i in 1:(length(cutoffs)-1)){
+  gene.subset<-rownames(seurat)[exprGenes.singleCell<=cutoffs[i] &
+                                  exprGenes.singleCell>cutoffs[i+1]]
+  print(paste("Number of genes with expression cutoff",
+              cutoffs[i+1],":",length(gene.subset)))
+  
+  write.table(gene.subset,
+              file=paste0("metacell_general/eval_allmethods/gene_lists/",
+                          "mono_expr_genes_cut_",cutoffs[i+1],".txt"),
+              row.names = FALSE,col.names = FALSE,quote=FALSE)
+}
+
diff --git a/01_association_metrics/metacell/eval_blueprint_genesets.R b/01_association_metrics/metacell/eval_blueprint_genesets.R
new file mode 100644
index 0000000..2320fb4
--- /dev/null
+++ b/01_association_metrics/metacell/eval_blueprint_genesets.R
@@ -0,0 +1,127 @@
+# ------------------------------------------------------------------------------
+# Compare correlation from BLUEPRINT with correlation from metacells/single cell
+# for different expression tresholds
+# Here: shown for leiden metacells, calculation done the same way for original
+#       metacells and single cell
+# ------------------------------------------------------------------------------
+
+library(reticulate) # to read the single cell data (numpy)
+library(data.table)
+library(ggplot2) # only required if plotting=TRUE
+
+np <- import("numpy")
+
+#Iterate over the list of different gene sets
+corr_files<-c("metacell_general/leiden_metacells/correlation_r_leiden_SCT_cutoff08.tsv",
+              "metacell_general/leiden_metacells/correlation_r_leiden_SCT_cutoff06.tsv",
+              "metacell_general/leiden_metacells/correlation_r_leiden_SCT_cutoff04.tsv",
+              "metacell_general/leiden_metacells/correlation_r_leiden_SCT_cutoff02.tsv")
+
+res_file<-"metacell_general/eval_allmethods/sc_leiden_SCT_eval.tsv"
+
+plotting<-TRUE
+
+#Write column headers
+write.table(data.frame("Condition","Num_pairs","Corr_corr","Test","File"),
+            file=res_file,quote=FALSE,sep="\t", 
+            row.names=FALSE, col.names = FALSE)
+
+#Load the large blueprint data set
+path<-"blueprint/allGenePairs_BlueprintScMonocytes_GeneGeneCorrelationComparison.pairwiseSpearman."
+corr.blue.vals <- np$load(paste0(path,"npy"))
+corr.blue.vals<-corr.blue.vals[,1]
+
+corr.blue<-fread(paste0(path,"genePairs.txt"),header=FALSE)
+
+#Split into gene1 and gene2
+corr.blue$gene1<-sapply(corr.blue$V1,function(s) strsplit(s,"/")[[1]][1])
+corr.blue$gene2<-sapply(corr.blue$V1,function(s) strsplit(s,"/")[[1]][2])
+corr.blue$V1<-NULL
+corr.blue$corr.blue<-corr.blue.vals
+rm(corr.blue.vals)
+
+for(cfile in corr_files){
+  
+  print(paste("Processing:",cfile))
+  
+  #Load corr.mc.r
+  corr.mc.r<-read.table(cfile)
+  
+  #Filter Blueprint matrix
+  corr.genes<-unique(c(corr.mc.r$Gene1,corr.mc.r$Gene2))
+  corr.blue.subset<-corr.blue[gene1 %in% corr.genes & 
+                         gene2 %in% corr.genes]
+  
+  #Order correctly so that gene1 smaller than gene2
+  corr.blue.subset$swap<-ifelse(corr.blue.subset$gene1 < corr.blue.subset$gene2, 
+                                corr.blue.subset$gene1,corr.blue.subset$gene2)
+  corr.blue.subset$gene2<-ifelse(corr.blue.subset$gene1 < corr.blue.subset$gene2, 
+                                 corr.blue.subset$gene2,corr.blue.subset$gene1)
+  corr.blue.subset$gene1<-corr.blue.subset$swap
+  corr.blue.subset$swap<-NULL
+  colnames(corr.blue.subset)<-c("Gene1","Gene2","Correlation.blue")
+  
+  #Merge everything
+  corr.mc.r<-merge(corr.mc.r,corr.blue.subset,by=c("Gene1","Gene2"))
+  corr.mc.r<-reshape2::melt(corr.mc.r,id.vars=c("Gene1","Gene2","Correlation.blue"))
+  colnames(corr.mc.r)[4:5]<-c("Condition","Correlation")
+  
+  #Correlation for all genes
+  corr.corr<-sapply(unique(corr.mc.r$Condition), function(tp)  
+    cor(corr.mc.r$Correlation.blue[corr.mc.r$Condition==tp],
+        corr.mc.r$Correlation[corr.mc.r$Condition==tp],
+        method="pearson",use = "pairwise.complete.obs"))
+  
+  res<-data.frame(condition=unique(corr.mc.r$Condition),
+                  num.pairs=as.vector(table(corr.mc.r$Condition)),
+                  corr.corr,
+                  test="allGenes",
+                  file=cfile)
+  
+  write.table(res, file=res_file,quote=FALSE,sep="\t",
+              append=TRUE,row.names=FALSE, col.names = FALSE)
+
+  corr.mc.r<-corr.mc.r[! is.na(corr.mc.r$Correlation.blue),]
+  if(plotting){
+    corr.mc.r$class<-ifelse(corr.mc.r$Correlation.blue>0,
+                            "positive","negative")
+    
+    g<-ggplot(corr.mc.r,aes(x=Correlation.blue,y=Correlation,color=class))+
+      geom_point()+facet_wrap(~Condition,ncol=3)+
+      xlab("Correlation Blueprint")+ylab("Correlation MC")
+    ggsave(g,file=paste0("metacell_general/eval_allmethods/plots/",
+                         "comp_corr_blue_",strsplit(cfile,"/")[[1]][2],".png"))
+  }
+  
+  #Correlation for genes with positive correlation
+  corr.mc.r.pos<-corr.mc.r[corr.mc.r$Correlation.blue>0,]
+  corr.corr<-sapply(unique(corr.mc.r.pos$Condition), function(tp)  
+    cor(corr.mc.r.pos$Correlation.blue[corr.mc.r.pos$Condition==tp],
+        corr.mc.r.pos$Correlation[corr.mc.r.pos$Condition==tp],
+        method="pearson",use = "pairwise.complete.obs"))
+  
+  res<-data.frame(condition=unique(corr.mc.r.pos$Condition),
+                  num.pairs=as.vector(table(corr.mc.r.pos$Condition)),
+                  corr.corr,
+                  test="posGenes",
+                  file=cfile)
+  
+  write.table(res, file=res_file,quote=FALSE,sep="\t",
+              append=TRUE,row.names=FALSE, col.names = FALSE)
+  
+  #Correlation for genes with negative correlation
+  corr.mc.r.neg<-corr.mc.r[corr.mc.r$Correlation.blue<0,]
+  corr.corr<-sapply(unique(corr.mc.r.neg$Condition), function(tp)  
+    cor(corr.mc.r.neg$Correlation.blue[corr.mc.r.neg$Condition==tp],
+        corr.mc.r.neg$Correlation[corr.mc.r.neg$Condition==tp],
+        method="pearson",use = "pairwise.complete.obs"))
+  
+  res<-data.frame(condition=unique(corr.mc.r.neg$Condition),
+                  num.pairs=as.vector(table(corr.mc.r.neg$Condition)),
+                  corr.corr,
+                  test="negGenes",
+                  file=cfile)
+  
+  write.table(res, file=res_file,quote=FALSE,sep="\t",
+              append=TRUE,row.names=FALSE, col.names = FALSE)
+}
diff --git a/01_association_metrics/metacell/metacell_general_correlation_tp.R b/01_association_metrics/metacell/metacell_general_correlation_tp.R
new file mode 100644
index 0000000..028644e
--- /dev/null
+++ b/01_association_metrics/metacell/metacell_general_correlation_tp.R
@@ -0,0 +1,188 @@
+# ------------------------------------------------------------------------------
+# Calculate correlation per timepoint (and sample if stated) from the
+# metacells (original or leiden) for different
+# gene sets (split dependent on gene expression cutoff) for comparison with
+# metacells (see corresponding files create_genesets.R,
+# metacell_general_correlation_tp.R and eval_blueprint_genesets.R)
+# Input: Seurat object, file with selected genes
+# Output: files with correlation values (r-values and p-values)
+# ------------------------------------------------------------------------------
+
+library(Hmisc)
+library(optparse)
+
+#Parse arguments
+option_list = list(
+  make_option(c("-g","--selectedGenes"), 
+              default="../../benchmark/celltypes/gene_expressed_over_hald_cells.txt",
+              help="path to list with selected genes"),
+  make_option(c("-m","--method"),
+              default="metacell",
+              help="method for metacell grouping (leiden[_SCT] or metacell)"),
+  make_option(c("-s","--perSample"),action="store_true",
+              default=FALSE,
+              help="Shall the evaluation be done for each sample separatly"),
+  make_option(c("-o","--outputFile"),
+              default="timepoint_monocytes",
+              help="Suffix of the output files")
+)
+
+opt_parser = OptionParser(option_list=option_list)
+opt = parse_args(opt_parser)
+
+pathSelectedGenes<-opt$selectedGenes
+type<-opt$method
+perSample<-opt$perSample
+outputSuffix<-opt$outputFile
+
+print(paste("Evaluating",type,"for gene set:"))
+print(pathSelectedGenes)
+
+print(paste("Evaluating each sample individually:", perSample))
+
+#For leiden clustering
+if(type=="leiden") {
+  setwd("leiden_metacells/")
+  pseudobulkFile<-"metacell_leiden.RDS"
+  annotationFile<-"annotations_mc_leiden_tp.tsv"
+  
+  #Read pseudobulk data frame
+  metacell.allsamples<-readRDS(pseudobulkFile)
+
+  #For leiden clustering based on SCT counts
+} else if(type=="leiden_SCT") {
+  setwd("leiden_metacells/")
+  pseudobulkFile<-"metacell_leiden_SCT.RDS"
+  annotationFile<-"annotations_mc_leiden_SCT_tp.tsv"
+  
+  #Read pseudobulk data frame
+  metacell.allsamples<-readRDS(pseudobulkFile)
+  
+} else if(type=="metacell"){
+  setwd("metacell_general/metacell")
+  
+  metacellDir<-"metacells_K20_minCells10"
+  setwd(metacellDir)
+
+  pseudobulkFile<-"pseudobulk_metacell.RDS"
+  annotationFile<-"annotations_metacell.tsv"
+  
+  metacell.allsamples<-readRDS(pseudobulkFile)
+} else {
+  stop("Metacell method type not known!")
+}
+  
+##########################
+
+#Read annotation data frame
+annotations.allsamples<-read.table(annotationFile)
+colnames(annotations.allsamples)[2]<-"timepoint"
+
+#Select which genes shall be chosen for correlation (same as for single cell)
+selected.genes<-read.table(pathSelectedGenes,
+                           header=FALSE)
+metacell.allsamples<-metacell.allsamples[selected.genes$V1,]
+
+#Result data frame (correlation and pvalues)
+corr.df<-NULL
+pval.df<-NULL
+
+correlationRes<-function(meta_counts,colName){
+  
+  #Be carefull: rcorr does not work with less than 5 samples
+  corr.mc<-rcorr(t(meta_counts), type="spearman")
+  
+  #Create a pairwise data frame for the correlation
+  corr.pairs.mc<-as.data.frame(as.table(corr.mc$r),
+                               stringsAsFactors = FALSE)
+  corr.pairs.mc<-corr.pairs.mc[corr.pairs.mc$Var1<corr.pairs.mc$Var2,]
+  colnames(corr.pairs.mc)<-c("Gene1","Gene2",colName)
+  
+  #Create a pairwise data frame for the pvalue
+  corr.pairs.pval<-as.data.frame(as.table(corr.mc$P),
+                                 stringsAsFactors = FALSE)
+  corr.pairs.pval<-corr.pairs.pval[corr.pairs.pval$Var1<corr.pairs.pval$Var2,]
+  colnames(corr.pairs.pval)<-c("Gene1","Gene2",colName)
+  
+  return(list(corr.pairs.mc,corr.pairs.pval))
+}
+
+if(perSample){
+  
+  #Calculate correlation for each sample
+  samples<-unique(annotations.allsamples$sample)
+  for(sample in samples){
+    print(paste("Processing sample:",sample))
+    
+    annot.sample<-annotations.allsamples[annotations.allsamples$sample==sample,]
+    for(timepoint in unique(annot.sample$timepoint)){
+      
+      #Run the analysis only if at least 5 meta-cells exists
+      #Probably increase the threshold again to more later ...
+      mc.ids.timepoint<-annot.sample$metacell[annot.sample$timepoint==timepoint]
+      if(length(mc.ids.timepoint)>4){
+        
+        print(paste("Calculate correlation for timepoint",timepoint))
+        
+        meta_counts<-metacell.allsamples[,mc.ids.timepoint]
+        tmp<-correlationRes(meta_counts,colName = paste0(timepoint,"-",sample))
+        corr.pairs.mc<-tmp[[1]]
+        corr.pairs.pval<-tmp[[2]]
+        
+        #Concatinate the sample - timepoint pairs
+        if(is.null(corr.df)){
+          corr.df<-corr.pairs.mc
+          pval.df<-corr.pairs.pval
+        } else {
+          corr.df<-merge(corr.df,corr.pairs.mc,by=c("Gene1","Gene2"),
+                         all=TRUE)
+          pval.df<-merge(pval.df,corr.pairs.pval,by=c("Gene1","Gene2"),
+                         all=TRUE)
+        }
+        
+      } else {
+        print(paste("Skip timepoint",timepoint,"(too less metacells)"))
+      }
+    }
+  }
+  
+} else {
+
+  annot.sample<-annotations.allsamples
+  for(timepoint in unique(annot.sample$timepoint)){
+    
+    #Run the analysis only if at least 5 meta-cells exists
+    #Probably increase the threshold again to more later ...
+    mc.ids.timepoint<-annot.sample$metacell[annot.sample$timepoint==timepoint]
+    if(length(mc.ids.timepoint)>4){
+      
+      print(paste("Calculate correlation for timepoint",timepoint))
+      
+      meta_counts<-metacell.allsamples[,mc.ids.timepoint]
+      tmp<-correlationRes(meta_counts,colName = timepoint)
+      corr.pairs.mc<-tmp[[1]]
+      corr.pairs.pval<-tmp[[2]]
+      
+      #Concatinate the sample - timepoint pairs
+      if(is.null(corr.df)){
+        corr.df<-corr.pairs.mc
+        pval.df<-corr.pairs.pval
+      } else {
+        corr.df<-merge(corr.df,corr.pairs.mc,by=c("Gene1","Gene2"),
+                       all=TRUE)
+        pval.df<-merge(pval.df,corr.pairs.pval,by=c("Gene1","Gene2"),
+                       all=TRUE)
+      }
+      
+    } else {
+      print(paste("Skip timepoint",timepoint,"(too less metacells)"))
+    }
+  }
+}
+
+write.table(corr.df,
+            file=paste0("correlation_r_",outputSuffix,".tsv"),
+            sep="\t",quote=FALSE)
+write.table(pval.df,
+            file=paste0("correlation_pval_",outputSuffix,".tsv"),
+            sep="\t",quote=FALSE)
\ No newline at end of file
diff --git a/01_association_metrics/metacell/metacell_per_sample_original_algorithm.R b/01_association_metrics/metacell/metacell_per_sample_original_algorithm.R
new file mode 100644
index 0000000..e3d1b52
--- /dev/null
+++ b/01_association_metrics/metacell/metacell_per_sample_original_algorithm.R
@@ -0,0 +1,274 @@
+# ------------------------------------------------------------------------------
+# Metacell algorithm (original) run for each sample separately
+# with Oelen v3 dataset (Monocytes)
+# Take all 200 variable genes 
+# (but removing for each sample the ones with too low coverage)
+#
+# Remarks: 
+# * metacells uses a "data base", the processed files are not loaded
+#   directly in the workspace
+# * in the function mcell_mc_from_coclust_balanced the parameters
+#   K and min_mc_size can be used to change the "size" of meta cells,
+#   but too small is not recommended
+#
+# ------------------------------------------------------------------------------
+
+library(metacell)
+library(SingleCellExperiment)
+library(ggplot2)
+library(optparse)
+
+#Parse arguments
+option_list = list(
+  make_option(c("-K","--coclustK"), default="20",
+              help="Parameter K of mcell_mc_from_coclust_balanced",
+              type="integer"),
+  make_option(c("-m","--coclustMinMcSize"), default="10",
+              help="Parameter min_mc_size of mcell_mc_from_coclust_balanced",
+              type="integer")
+)
+
+opt_parser = OptionParser(option_list=option_list)
+opt = parse_args(opt_parser)
+
+#Parameters to change the granularity of the samples
+mc_coclustK<-opt$coclustK
+mc_coclustMin_size<-opt$coclustMinMcSize
+
+print(paste("Running meta cells with following parameters (changing granularity):",
+            "K",mc_coclustK,"min_mc_size",mc_coclustMin_size))
+
+#Create directories to save the meta cells and the correlation results
+mainDir<-paste0("metacells_K",mc_coclustK,"_minCells",mc_coclustMin_size)
+if(!dir.exists(mainDir)) 
+  dir.create(mainDir)
+
+setwd(mainDir)
+
+##########################################################################
+# Important note: a lot of the parameter are manged over tgconfig
+# see: https://github.com/tanaylab/tgconfig
+
+# To check all set parameters
+#tgconfig::get_package_params('metacell')
+
+#Set number of cores (otherwise I get issues for very small data sets)
+tgconfig::set_param('mc_cores', 8, 'metacell')
+
+#Issues with downsampling matrix => set parameter for downsampling lower 
+#(probably problem as matrix is too sparse)
+#tgconfig::set_param("scm_n_downsamp_gstat",300,'metacell')
+
+########################################################################
+
+#Option to create additional plots for visualization
+allPlots<-FALSE
+fileType<-"seurat"
+
+#Load the h5ad object and convert it to a single cell object
+if(fileType=="h5ad"){
+  
+  library(reticulate) # to load h5ad object
+  library(zellkonverter) # to convert h5ad object to single cell object
+  
+  sc<-import("scanpy")
+  adata<-sc$read("../../seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad")
+  #Filter for monocytes
+  adata<-adata[adata$obs$cell_type_lowerres=="monocyte"]$copy()
+  sce<-AnnData2SCE(adata)
+  rm(adata)
+  
+  #Convert assay name to counts (before called X)
+  assayNames(sce)<-c("counts")
+
+  #Alternatively read a seurat object  
+} else if (fileType=="seurat"){
+  
+  library(Seurat)
+  
+  seurat<-readRDS("../../seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.rds")
+  
+  #Filter for monocytes
+  seurat<-seurat[,seurat$cell_type_lowerres=="monocyte"]
+  
+  #Convert into a single cell object
+  sce <- as.SingleCellExperiment(seurat,assay="RNA")
+  rm(seurat)
+  
+} else {
+  stop("File type not known!")
+}
+
+#Select all remaining samples
+samples<-as.character(unique(sce@colData$assignment))
+
+#Save the results
+metacell.allsamples<-NULL
+annotations.allsamples<-NULL
+annotations.percell<-NULL
+#Calculate meta-cell algorithm for each sample separatly
+#Also option to calculate correlation, but currently not done
+for(sample in samples){
+  
+  print(paste("Processing sample:",sample))
+  sce.sample.full<-sce[,sce@colData$assignment == sample]
+
+  #Create data base directory
+  if(!dir.exists("database")){
+    dir.create("database/")
+  } else {
+    do.call(file.remove, list(list.files("database/", full.names = TRUE)))
+  }
+  scdb_init("database/", force_reinit=T)
+  
+  #Upload SCE object
+  #Filter for genes with at least 4 counts (preliminary before the real filtering downstream 
+  #to reduce the calculation burden)
+  mat<-scm_import_sce_to_mat(sce.sample.full[rowSums(counts(sce.sample.full))>3,])
+  scdb_add_mat(sample, mat)
+  
+  #Create a directory for figures
+  if(!dir.exists("figs")) dir.create("figs/")
+  scfigs_init("figs/")
+  
+  #Create a gset for generating the knn graph 
+  mcell_add_gene_stat(gstat_id="stat", mat_id=sample)
+  mcell_gset_filter_varmean(gset_id="sample_feat", gstat_id="stat", T_vm=0.08, force_new=T)
+  #Sampled coverage of at least T_tot and threshold for the third highest UMI count > T_top3
+  mcell_gset_filter_cov(gset_id = "sample_feat", gstat_id="stat", T_tot=100, T_top3=2)
+  #Check generated gene set
+  gset<-scdb_gset("sample_feat")
+  print(paste("Number of selected genes:",length(gset@gene_set)))
+  
+  #Create the knn graph based on correlation
+  mcell_add_cgraph_from_mat_bknn(mat_id=sample,
+                                 gset_id = "sample_feat",
+                                 graph_id="sample_graph",
+                                 K=50,
+                                 dsamp=T)
+  
+  #Resample cells from the graph to robustly define groups
+  mcell_coclust_from_graph_resamp(
+    coc_id="sample_coc500",
+    graph_id="sample_graph",
+    min_mc_size=20,
+    p_resamp=0.75,
+    n_resamp=500)
+  
+  #Remark the size of the meta cells can be influenced by the paramters 
+  #K and min_mc_size (for both is true: the smaller, the more cells ...)  
+  mcell_mc_from_coclust_balanced(
+    coc_id="sample_coc500",
+    mat_id= sample,
+    mc_id= paste0(sample,"_mc"),
+    K=mc_coclustK,
+    min_mc_size=mc_coclustMin_size, 
+    alpha=2)
+  
+  #Plotting outlier (only possible for small groups)
+  if(allPlots){
+    mcell_plot_outlier_heatmap(mc_id=paste0(sample,"_mc"), 
+                               mat_id = sample, T_lfc=3) 
+  }
+
+  #Split and filter metacells using dbscan and outlier gene detection
+  mcell_mc_split_filt(new_mc_id=paste0(sample,"_mc_f"),
+                      mc_id=paste0(sample,"_mc"),
+                      mat_id=sample,
+                      T_lfc=3, plot_mats=F)
+  
+  ##Selecting marker genes automatically
+  mcell_gset_from_mc_markers(gset_id="sample_markers", mc_id=paste0(sample,"_mc_f"))
+  mc_colorize_default(paste0(sample,"_mc_f"))
+  
+  #Creating a heatmap of genes and metacells 
+  #(also not really well visible with too many cells)
+  if(allPlots){
+    mcell_mc_plot_marks(mc_id=paste0(sample,"_mc_f"), gset_id="sample_markers", 
+                        mat_id=sample)
+  }
+
+  #Create graph layout
+  mcell_mc2d_force_knn(mc2d_id=paste0(sample,"_2dproj"),
+                       mc_id=paste0(sample,"_mc_f"), graph_id="sample_graph")
+  #Plotting also not really interesting for two large 
+  mcell_mc2d_plot(mc2d_id=paste0(sample,"_2dproj"))
+  
+  #Save it again as a h5ad object to compare the results
+  #So far no direct exporting function found, therefore processing the object myself
+  #See https://tanaylab.github.io/metacell/reference/tgMCCov-class.html
+  sce_meta<-scdb_mc(paste0(sample,"_mc_f"))
+  
+  #Meta cell annotations
+  mc.annot<-data.frame(metaCell=sce_meta@mc)
+  mc.annot$cell<-rownames(mc.annot)
+  rownames(mc.annot)<-NULL
+  mc.annot<-rbind(mc.annot,
+                  data.frame(metaCell=0,
+                             cell=sce_meta@outliers))
+  
+  annotations.percell<-rbind(annotations.percell,
+                             mc.annot)
+    
+  #Check distributions between cell types and stimulation results
+  annotations<-sce.sample.full@colData
+  #annotations$cell<-rownames(annotations)
+  annotations<-merge(mc.annot,annotations,by.x="cell",by.y="bare_barcode_lane")
+    
+  perMetacell<-as.data.frame(table(annotations$metaCell))
+  
+  #Plot only timepoint for now
+  freqs<-as.data.frame(table(annotations$metaCell,
+                             annotations$timepoint))
+  freqs<-merge(freqs,perMetacell,by="Var1",suffixes=c(".spc",".mc"))
+  freqs$Fraction<-freqs$Freq.spc/freqs$Freq.mc
+
+  g<-ggplot(freqs,aes(x=as.factor(Var1),y=Fraction,fill=Var2))+
+      geom_bar(stat="identity")+
+      xlab("Meta cell (0=Outlier)")+
+      scale_fill_discrete(name = "Time point")+
+      ggtitle(paste("Cell number in total:",sum(freqs$Freq.spc)))
+  ggsave(g,filename=paste0("figs/barplot_time_ct_",sample,".png"))
+  
+  #Create a pseudobulk object with the meta-cell annotation (without outliers)
+  mc.annot<-mc.annot[mc.annot$metaCell>0,]
+  sc.counts<-counts(sce.sample.full)[,mc.annot$cell]
+  all(colnames(sc.counts)==mc.annot$cell)
+  
+  mc.annot$metaCell<-as.factor(paste0(sample,"_mc_",mc.annot$metaCell))
+  mc.pseudobulk<- t(apply(sc.counts, 1, tapply, mc.annot$metaCell,
+               sum, na.rm=T))
+  
+  #Normalize to 10,000 per metacell
+  libSize<-colSums(mc.pseudobulk)
+  mc.pseudobulk<-t(t(mc.pseudobulk)/libSize*10000)
+  
+  metacell.allsamples<-cbind(metacell.allsamples,mc.pseudobulk)
+  
+  #Create a majority annotation for each metacell
+  annotations$metaCell<-paste0(sample,"_mc_",annotations$metaCell)
+  timepoint.mc<-sapply(colnames(mc.pseudobulk),
+                       function(id) names(which.max(table(
+    annotations$timepoint[
+      annotations$metaCell==id]))))
+  
+  annot.mc<-data.frame(metacell=names(timepoint.mc),
+                       timepoint=timepoint.mc,
+                       sample=sample)
+  
+  #Add how many cells where part of the meta-cell
+  perMetacell$Var1<-paste0(sample,"_mc_",perMetacell$Var1)
+  colnames(perMetacell)<-c("metacell","cell.count")
+  annot.mc<-merge(annot.mc,perMetacell,by="metacell")
+  annotations.allsamples<-rbind(annotations.allsamples,annot.mc)
+  
+}
+
+#Save results
+write.table(annotations.allsamples,file="annotations_metacell.tsv",sep="\t")
+write.table(annotations.percell,file="annotations_singlecell_metacell.tsv",sep="\t")
+saveRDS(metacell.allsamples,file="pseudobulk_metacell.RDS")
+
+#Delete the database directory
+unlink("database",recursive = TRUE)
+
diff --git a/01_association_metrics/metacell/metacells_from_leiden.R b/01_association_metrics/metacell/metacells_from_leiden.R
new file mode 100644
index 0000000..bd9a811
--- /dev/null
+++ b/01_association_metrics/metacell/metacells_from_leiden.R
@@ -0,0 +1,117 @@
+# ------------------------------------------------------------------------------
+# Implement own method to generate metacells based on leiden clustering
+# Run leiden clustering separatley for each donor (run on Oelen v3, Monocytes)
+# and use group cells that are part of the same cluster
+# ------------------------------------------------------------------------------
+
+library(Seurat)
+
+#Load complete seurat object
+seurat<-readRDS("../../seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.rds")
+DefaultAssay(seurat)<-"SCT" #3000 most variable genes already identified
+
+#Filter for monocytes
+seurat<-seurat[,seurat$cell_type_lowerres=="monocyte"]
+
+#Resolution for leiden clusters
+leidenRes<-100
+print(paste("Leiden resolution:",leidenRes))
+
+type<-"SCT" #choose RNA or SCT
+print(paste("Normalization:",type))
+
+#Files with overall annotation and metacell matrix
+annot_mc_all<-NULL
+annot_mc_major_all<-NULL
+metacellBulk_all<-NULL
+
+#Iterate over all samples
+samples<-levels(seurat$assignment)
+for(donor in samples){
+  
+  print(paste("Processing donor:",donor))
+  
+  #Filter for the donor
+  seurat_donor<-seurat[,seurat$assignment==donor]
+  
+  #Calculate PCA
+  seurat_donor<-RunPCA(seurat_donor, verbose=FALSE)
+  
+  #Generate kNN graph and leidern clustering
+  seurat_donor <- FindNeighbors(seurat_donor, dims = 1:20)
+  seurat_donor <- FindClusters(seurat_donor, resolution = leidenRes, 
+                               algorithm = 4, #4=Leiden
+                               group.singletons=FALSE)
+                          #don't assign all singletons to the nearest cluster 
+  
+  #Save metacell - cell annotation
+  annot_mc<-data.frame(cluster=Idents(seurat_donor),
+                       metacell=paste0("mc_",Idents(seurat_donor),"_",donor),
+                       sample=donor,
+                       cell=names(Idents(seurat_donor)),
+                       row.names=NULL)
+  annot_mc_all<-rbind(annot_mc_all,annot_mc)
+  
+  #Create pseudobulk
+  #all(colnames(seurat_donor)==annot_mc$cell)
+  if(type=="RNA"){
+    metacellBulk <- t(apply(as.matrix(seurat_donor@assays$RNA@counts), 1, tapply, 
+                            as.factor(annot_mc$cluster),
+                            mean, na.rm=T))
+  } else if (type=="SCT"){
+    metacellBulk <- t(apply(as.matrix(seurat_donor@assays$SCT@counts), 1, tapply, 
+                            as.factor(annot_mc$cluster),
+                            mean, na.rm=T))    
+  } else {
+    stop(paste("Matrix type",type,"not known! Only RNA or SCT!"))
+  }
+
+  
+  colnames(metacellBulk)<-paste0("mc_",1:ncol(metacellBulk),"_",donor)
+  metacellBulk_all<-cbind(metacellBulk_all,metacellBulk)
+  
+  #Get majority annotation
+  meta.data<-seurat_donor@meta.data
+  meta.data$cell<-rownames(meta.data)
+  meta.data<-merge(meta.data,annot_mc,
+                   by.x="cell",by.y="cell")
+  
+  # Annotate each meta-cell to the most frequent condition
+  timepoint.mc<-sapply(colnames(metacellBulk),
+                       function(id) names(which.max(table(
+                         meta.data$timepoint[
+                           meta.data$metacell==id]))))
+  
+  #Save majority annotation
+  annot_mc_major<-data.frame(metacell=names(timepoint.mc),
+                             condition=unlist(timepoint.mc),
+                             sample=donor,
+                             row.names=NULL)
+  
+  annot_mc_major_all<-rbind(annot_mc_major_all,annot_mc_major)  
+  
+}
+
+
+
+if(type=="RNA"){
+  #Save per cell annotation
+  write.table(annot_mc_all,file="annotations_metacell_leiden_perCell.tsv",sep="\t")
+  write.table(annot_mc_major_all,file="annotations_mc_leiden_tp.tsv",sep="\t")
+  
+  #Save peudobulk counts
+  saveRDS(metacellBulk_all, file="metacell_leiden.RDS")
+} else if(type=="SCT"){
+  write.table(annot_mc_all,file=paste0("annotations_metacell_leiden_SCT_perCell_",
+                                       leidenRes,".tsv"),
+              sep="\t")
+  write.table(annot_mc_major_all,file=paste0("annotations_mc_leiden_SCT_tp_",
+                                             leidenRes,".tsv"),
+              sep="\t")
+  
+  #Save peudobulk counts
+  saveRDS(metacellBulk_all, file=paste0("metacell_leiden_SCT_",
+                                        leidenRes,".RDS"))
+}
+
+
diff --git a/01_association_metrics/metacell/plot_overview_metacell.R b/01_association_metrics/metacell/plot_overview_metacell.R
new file mode 100644
index 0000000..00c9ea1
--- /dev/null
+++ b/01_association_metrics/metacell/plot_overview_metacell.R
@@ -0,0 +1,180 @@
+# ------------------------------------------------------------------------------
+# Supplementary figure to show MetaCell overview
+# * Expression distribution of genes in a cell
+# * number of (meta)cells per sample
+# * comparison with Blueprint
+# ------------------------------------------------------------------------------
+
+library(Seurat)
+library(dplyr)
+library(ggplot2)
+library(ggpubr)
+
+theme_set(theme_bw())
+
+# ------------------------------------------------------------------------------
+# Expression distribution of genes in a cell
+# ------------------------------------------------------------------------------
+
+#Load the single cell object and get expressed genes 
+seurat<-readRDS("seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.rds")
+
+#Filter for monocytes
+seurat<-seurat[,seurat$cell_type_lowerres=="monocyte" &
+                 seurat$timepoint=="UT"]
+
+exprGenes.singleCell<-rowSums(as.matrix(seurat@assays$SCT@counts)>0)/ncol(seurat)
+
+print(paste("Number of genes expressed in at least 50% of cells:",
+            sum(exprGenes.singleCell>=0.5)))
+
+exprGene.df<-data.frame(expr.perc=sort(exprGenes.singleCell),
+                        position=1:length(exprGenes.singleCell),
+                        Type="SingleCell",
+                        stringsAsFactors = FALSE)
+
+#Metacell
+metacell.allsamples<-readRDS("metacell_general/metacell/metacells_K20_minCells10/pseudobulk_metacell.RDS")
+#Keep only UT metacells
+meta_annot<-read.table("metacell_general/metacell/metacells_K20_minCells10/annotations_metacell.tsv")
+#all(meta_annot$metacell == colnames(metacell.allsamples))
+metacell.allsamples<-metacell.allsamples[,meta_annot$timepoint == "UT"]
+exprGenes.metacell<-rowSums(metacell.allsamples>0)/ncol(metacell.allsamples)
+
+print(paste("Number of genes expressed in at least 50% of cells:",
+            sum(exprGenes.metacell>=0.5)))
+
+exprGene.df<-rbind(exprGene.df,
+                   data.frame(expr.perc=sort(exprGenes.metacell),
+                              position=1:length(exprGenes.metacell),
+                              Type="MetaCell",
+                              stringsAsFactors = FALSE))
+
+#Leiden
+metacell.allsamples<-readRDS("metacell_general/leiden_metacells/metacell_leiden_SCT.RDS")
+#Keep only UT metacells
+meta_annot<-read.table("metacell_general/leiden_metacells/annotations_mc_leiden_SCT_tp.tsv")
+#all(meta_annot$metacell == colnames(metacell.allsamples))
+metacell.allsamples<-metacell.allsamples[,meta_annot$condition == "UT"]
+exprGenes.metacell<-rowSums(metacell.allsamples>0)/ncol(metacell.allsamples)
+
+print(paste("Number of genes expressed in at least 50% of cells:",
+            sum(exprGenes.metacell>=0.5)))
+
+exprGene.df<-rbind(exprGene.df,
+                   data.frame(expr.perc=sort(exprGenes.metacell),
+                              position=1:length(exprGenes.metacell),
+                              Type="Leiden",
+                              stringsAsFactors = FALSE))
+
+
+g.1<-ggplot(exprGene.df,aes(x=position,y=expr.perc,color=Type))+geom_point()+
+  xlab("Gene index")+ylab("Expressed in x% of the cells")+
+  scale_color_discrete("Method")+
+  theme(axis.title = element_text(size=14),
+        axis.text=element_text(size=13),
+        legend.position = "none")
+
+# ------------------------------------------------------------------------------
+# Number of (meta)cells per sample
+# ------------------------------------------------------------------------------
+
+counts_all_mc<-NULL
+
+#Load metacell annotation leiden
+mc_method<-read.table("metacell_general/leiden_metacells/annotations_mc_leiden_SCT_tp.tsv",
+                      stringsAsFactors = FALSE)
+
+mc_method<-mc_method%>%
+  group_by(sample,condition)%>%
+  summarize(counts=n())
+
+mc_method$method<-"Leiden"
+counts_all_mc<-rbind(counts_all_mc,mc_method)
+
+#Load metacell annotation 
+mc_method<-read.table("metacell_general/metacell/metacells_K20_minCells10/annotations_metacell.tsv",
+                      stringsAsFactors=FALSE)
+mc_method<-mc_method%>%
+  group_by(sample,timepoint)%>%
+  summarize(counts=n())
+
+mc_method$method<-"MetaCell"
+colnames(mc_method)<-colnames(counts_all_mc)
+counts_all_mc<-rbind(counts_all_mc,mc_method)
+
+#Get number of single cells per sample and condition
+sc_annot<-seurat@meta.data
+sc_annot<-sc_annot%>%
+  group_by(assignment,timepoint)%>%
+  summarize(counts=n())
+sc_annot$method<-"SingleCell"
+colnames(sc_annot)<-colnames(counts_all_mc)
+counts_all_mc<-rbind(counts_all_mc,sc_annot)
+
+#Filter to show only UT cells
+counts_all_mc<-counts_all_mc[counts_all_mc$condition=="UT",]
+
+#Create plot
+g.2<-ggplot(counts_all_mc,aes(x=method,y=counts,fill=method))+
+  geom_boxplot()+
+  ylab("Number of (meta)cells per sample")+
+  xlab("Method")+
+  scale_y_log10()+
+  scale_fill_discrete("Method")+
+  theme(legend.position = "none",
+        axis.title = element_text(size=14),
+        axis.text = element_text(size=13),
+        legend.title = element_text(size=13),
+        legend.text= element_text(size=13))
+
+# ------------------------------------------------------------------------------
+# BLUEPRINT comparison
+# ------------------------------------------------------------------------------
+
+res<-read.table("metacell_general/eval_allmethods/perCondition_eval.tsv",header=TRUE)
+res2<-read.table("metacell_general/eval_allmethods/singleCell_eval.tsv",header=TRUE)
+res3<-read.table("metacell_general/eval_allmethods/sc_leiden_SCT_eval.tsv",header=TRUE)
+#Parse method
+res$method<-ifelse(grepl("leiden",res$File),"leiden",
+                   ifelse(grepl("MetaCellar",res$File),"MetaCellaR","metacell"))
+res2$method<-"singleCell"
+res3$method<-ifelse(grepl("leiden",res3$File),"leiden_SCT","singleCell_SCT")
+
+res<-rbind(res,res2,res3)
+rm(res2,res3)
+
+#Parse cutoff
+res$cutoff<-as.numeric(stringi::stri_match(res$File,regex="cutoff(.*?)(_|\\.)")[,2])
+res$cutoff<-paste0(as.character(res$cutoff*10),"%")
+
+#Filter it to show only UT results and allGenes
+res<-res[res$Condition=="UT" & res$Test == "allGenes",]
+
+#Show only SCT results (also used later and noMetaCellaR)
+res<-res[res$method %in% c("leiden_SCT","metacell","singleCell_SCT"),]
+rename_methods<-setNames(c("Leiden","MetaCell","SingleCell"),
+                         c("leiden_SCT","metacell","singleCell_SCT"))
+res$method<-rename_methods[res$method]
+
+g.3<-ggplot(res,aes(x=cutoff,y=Corr_corr,fill=method))+
+  geom_bar(stat="identity",position="dodge")+
+  ylab("Correlation with BLUEPRINT")+
+  xlab("Genes stratified by x% expression in single cell")+
+  scale_fill_discrete("Method")+
+  theme(legend.position = "bottom",
+        axis.title = element_text(size=14),
+        axis.text = element_text(size=13),
+        legend.title = element_text(size=13),
+        legend.text= element_text(size=13))
+
+g.bottom<-ggarrange(g.2,g.3,ncol=2,widths=c(0.4,0.6),
+                    common.legend = TRUE,legend="bottom",
+                    labels=c("b)","c)"))
+g<-ggarrange(g.1,g.bottom,ncol=1,
+             labels=c("a)",""))
+ggsave(g,file="metacell_general/plots/metacell_overview_suppfigure.pdf",
+       width=9,height=9)
+ggsave(g,file="metacell_general/plots/metacell_overview_suppfigure.png",
+       width=9,height=9)
+
diff --git a/01_association_metrics/metacell/single_cell_correlation_tp.R b/01_association_metrics/metacell/single_cell_correlation_tp.R
new file mode 100644
index 0000000..6d40e2c
--- /dev/null
+++ b/01_association_metrics/metacell/single_cell_correlation_tp.R
@@ -0,0 +1,157 @@
+# ------------------------------------------------------------------------------
+# Calculate correlation per timepoint (and sample if stated) from the
+# original single cell dataset (Oelen v3 dataset, Monocytes) for different
+# gene sets (split dependent on gene expression cutoff) for comparison with
+# metacells (see corresponding files create_genesets.R,
+# metacell_general_correlation_tp.R and eval_blueprint_genesets.R)
+# Input: Seurat object, file with selected genes
+# Output: files with correlation values (r-values and p-values)
+# ------------------------------------------------------------------------------
+
+library(Seurat)
+library(Hmisc) #for fast calculation of correlation
+library(optparse)
+
+#Parse arguments
+option_list = list(
+  make_option(c("-g","--selectedGenes"), 
+              default="benchmark/celltypes/gene_expressed_over_hald_cells.txt",
+              help="path to list with selected genes"),
+  make_option(c("-s","--perSample"),action="store_true",
+              default=FALSE,
+              help="Shall the evaluation be done for each sample separatly"),
+  make_option(c("-t","--type"),
+              default="RNA",
+              help="Use either RNA count matrix (RNA) or SCT count matrix (SCT)."),
+  make_option(c("-o","--outputFile"),
+              default="timepoint_monocytes",
+              help="Suffix of the output files")
+)
+
+opt_parser = OptionParser(option_list=option_list)
+opt = parse_args(opt_parser)
+
+pathSelectedGenes<-opt$selectedGenes
+perSample<-opt$perSample
+matrixType<-opt$type
+outputSuffix<-opt$outputFile
+
+print(paste("Evaluating single cell data for gene set:"))
+print(pathSelectedGenes)
+
+print(paste("Evaluating each sample individually:", perSample))
+
+#Load complete seurat object
+seurat<-readRDS("seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.rds")
+
+#Filter for monocytes
+seurat<-seurat[,seurat$cell_type_lowerres=="monocyte"]
+
+#Select which genes shall be chosen for correlation (same as for single cell)
+selected.genes<-read.table(pathSelectedGenes,
+                           header=FALSE)
+seurat<-seurat[selected.genes$V1,]
+
+#Result data frame (correlation and pvalues)
+corr.df<-NULL
+pval.df<-NULL
+
+correlationRes<-function(meta_counts,colName){
+  
+  #Be carefull: rcorr does not work with less than 5 samples
+  corr.mc<-rcorr(t(meta_counts), type="spearman")
+  #corr.mc<-cor(t(meta_counts), method="spearman")
+  
+  #Create a pairwise data frame for the correlation
+  corr.pairs.mc<-as.data.frame(as.table(corr.mc$r),
+                               stringsAsFactors = FALSE)
+  corr.pairs.mc<-corr.pairs.mc[corr.pairs.mc$Var1<corr.pairs.mc$Var2,]
+  colnames(corr.pairs.mc)<-c("Gene1","Gene2",colName)
+  
+  #Create a pairwise data frame for the pvalue
+  corr.pairs.pval<-as.data.frame(as.table(corr.mc$P),
+                                 stringsAsFactors = FALSE)
+  corr.pairs.pval<-corr.pairs.pval[corr.pairs.pval$Var1<corr.pairs.pval$Var2,]
+  colnames(corr.pairs.pval)<-c("Gene1","Gene2",colName)
+  
+  return(list(corr.pairs.mc,corr.pairs.pval))
+}
+
+if(perSample){
+  
+  #Calculate correlation for each sample
+  samples<-unique(seurat$assignment)
+  for(sample in samples){
+    print(paste("Processing sample:",sample))
+
+    for(timepoint in unique(seurat$timepoint[seurat$assignment==sample])){
+
+      print(paste("Calculate correlation for timepoint",timepoint))
+
+      meta_counts<-seurat[,seurat$assignment==sample &
+                            seurat$timepoint==timepoint]
+      if(matrixType=="RNA"){
+        meta_counts<-as.matrix(meta_counts@assays$RNA@counts)
+      } else if (matrixType=="SCT"){
+        meta_counts<-as.matrix(meta_counts@assays$SCT@counts)
+      } else {
+        stop(paste("Matrix type",matrixType,"not known! Only RNA or SCT!"))
+      }
+      tmp<-correlationRes(meta_counts,colName = paste0(timepoint,"-",sample))
+      corr.pairs.mc<-tmp[[1]]
+      corr.pairs.pval<-tmp[[2]]
+
+      #Concatinate the sample - timepoint pairs
+      if(is.null(corr.df)){
+        corr.df<-corr.pairs.mc
+        pval.df<-corr.pairs.pval
+      } else {
+        corr.df<-merge(corr.df,corr.pairs.mc,by=c("Gene1","Gene2"),
+                       all=TRUE)
+        pval.df<-merge(pval.df,corr.pairs.pval,by=c("Gene1","Gene2"),
+                       all=TRUE)
+      }
+    }
+  }
+
+} else {
+
+  for(timepoint in unique(seurat$timepoint)){
+    
+    print(paste("Calculate correlation for timepoint",timepoint))
+    
+    meta_counts<-seurat[,seurat$timepoint==timepoint]
+    if(matrixType=="RNA"){
+      meta_counts<-as.matrix(meta_counts@assays$RNA@counts)
+    } else if (matrixType=="SCT"){
+      meta_counts<-as.matrix(meta_counts@assays$SCT@counts)
+    } else {
+      stop(paste("Matrix type",matrixType,"not known! Only RNA or SCT!"))
+    }
+    
+    tmp<-correlationRes(meta_counts,colName = timepoint)
+    corr.pairs.mc<-tmp[[1]]
+    corr.pairs.pval<-tmp[[2]]
+    
+    #Concatinate the sample - timepoint pairs
+    if(is.null(corr.df)){
+      corr.df<-corr.pairs.mc
+      pval.df<-corr.pairs.pval
+    } else {
+      corr.df<-merge(corr.df,corr.pairs.mc,by=c("Gene1","Gene2"),
+                     all=TRUE)
+      pval.df<-merge(pval.df,corr.pairs.pval,by=c("Gene1","Gene2"),
+                     all=TRUE)
+    }
+    
+  }
+}
+
+write.table(corr.df,
+            file=paste0("metacell_general/eval_allmethods/corr_sc/correlation_r_",
+                        outputSuffix,".tsv"),
+            sep="\t",quote=FALSE)
+write.table(pval.df,
+            file=paste0("metacell_general/eval_allmethods/corr_sc/correlation_pval_",
+                        outputSuffix,".tsv"),
+            sep="\t",quote=FALSE)
\ No newline at end of file
diff --git a/01_association_metrics/rho_comparison_lowlyexpressed.R b/01_association_metrics/rho_comparison_lowlyexpressed.R
new file mode 100644
index 0000000..e8577a9
--- /dev/null
+++ b/01_association_metrics/rho_comparison_lowlyexpressed.R
@@ -0,0 +1,68 @@
+# ------------------------------------------------------------------------------
+# Explore the differences between Spearman correlation and Rho propensity
+# for very lowly expressed genes; due to computational very demanding 
+# calculation of Rho values for many genes, the comparison is done on a subset
+# of 50 very lowly expressed genes (expressed in >0% and <5% of cells) 
+# and 50 very highly expressed genes (expressed in >95% of the cells)
+# Input: Seurat object of Oelen v3 dataset (UT monocytes)
+# Output: scatterplot for comparison
+# ------------------------------------------------------------------------------
+
+library(Seurat)
+library(propr)
+library(Matrix)
+library(ggplot2)
+
+theme_set(theme_bw())
+
+#Load complete seurat object
+seurat<-readRDS("seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.rds")
+
+#Filter for monocytes and UT timepoint
+seurat<-seurat[,seurat$cell_type_lowerres == "monocyte"]
+seurat<-seurat[,seurat$timepoint == "UT"]
+
+# Get non-zero-ratio of each gene
+nozeroratio<-rowMeans(seurat$RNA@data>0)
+
+full_count_matrix<-t(as.matrix(seurat$RNA@data))
+
+#Select 50 very lowly expressed genes and 50 very highly expressed genes
+set.seed(1)
+low_genes<-sample(names(nozeroratio)[nozeroratio > 0 & nozeroratio < 0.05],50)
+high_genes<-sample(names(nozeroratio)[nozeroratio > 0.9],50)
+
+#Calculate rho values
+res<-propr::perb(full_count_matrix,
+                 select=c(low_genes,high_genes))@matrix
+
+propr<-reshape2::melt(res)
+propr$Var1<-as.character(propr$Var1)
+propr$Var2<-as.character(propr$Var2)
+propr<-propr[propr$Var1 < propr$Var2,]
+
+#Compare with spearman values
+spearman<-cor(full_count_matrix[,c(low_genes,high_genes)],method="spearman")
+spearman<-reshape2::melt(spearman)
+spearman$Var1<-as.character(spearman$Var1)
+spearman$Var2<-as.character(spearman$Var2)
+spearman<-spearman[spearman$Var1 < spearman$Var2,]
+
+#Combine both into one plot
+all(propr$Var1 == spearman$Var1)
+all(propr$Var2 == spearman$Var2)
+
+propr$corr<-spearman$value
+
+propr$type<-ifelse(propr$Var1 %in% low_genes,
+                   ifelse(propr$Var2 %in% high_genes,"mixed","both_low"),
+                   ifelse(propr$Var2 %in% high_genes,"both_high","mixed"))
+
+g<-ggplot(propr,aes(x=corr,y=value,color=type))+
+  geom_point(alpha=0.5)+
+  xlab("Spearman correlation")+
+  ylab("Rho proportionality")+
+  xlim(-0.2,1)+ylim(-0.2,1)+
+  scale_color_discrete("Expression gene pair")+
+  geom_abline()
+ggsave(g,file="test_rho.pdf")
diff --git a/01_association_metrics/scorpius_and_slingshot_clean.R b/01_association_metrics/scorpius_and_slingshot_clean.R
new file mode 100644
index 0000000..6ca2390
--- /dev/null
+++ b/01_association_metrics/scorpius_and_slingshot_clean.R
@@ -0,0 +1,143 @@
+require(Seurat)
+require(slingshot)
+library(tradeSeq)
+library(RColorBrewer)
+library(SingleCellExperiment)
+
+all <- readRDS('1M_v3_mediumQC_sct_celltyped_minimized_rnascaled.rds')
+degenes <- read.table('degenes_monocyteUTX3hCA.txt')$V1
+mono1 <- subset(x = all, subset = cell_type == 'mono 1')
+mono1Ca <- subset(mono1, subset = (timepoint == 'UT') | (timepoint == 'X3hCA') | (timepoint == 'X24hCA'))
+library(Matrix)
+writeMM(GetAssayData(mono1Ca, assay='SCT', slot='data'), 
+        "mono1Ca_allgenes.mtx")
+write.table(as.matrix(mono1Ca[[]]), 'mono1Ca_allgenes.meta.csv', sep=",")
+write.table(as.matrix(rownames(mono1Ca)), 'mono1Ca_allgenes.genes.txt')
+mono1Ca_de3h <- subset(mono1, subset = (timepoint == 'UT') | (timepoint == 'X3hCA') | (timepoint == 'X24hCA'),
+                  features = degenes)
+# also select DE genes
+
+# plot
+pdf("pca_umap_sling_mono1CA_degenesUTX3h.pdf")
+mono1Ca_de3h <- RunPCA(mono1Ca_de3h, npcs=10)
+mono1Ca_de3h <- FindNeighbors(mono1Ca_de3h, verbose = FALSE, dims = 1:10)
+mono1Ca_de3h <- FindClusters(mono1Ca_de3h, pc=1:10, algorithm = 2, random.seed = 256, resolution = 0.8)
+mono1Ca_de3h <- RunUMAP(mono1Ca_de3h, dims = 1:10, reduction = "pca")
+
+DimPlot(mono1Ca_de3h, reduction = "pca",
+        group.by = "timepoint", pt.size = 0.5, label = TRUE, repel = TRUE)
+ElbowPlot(mono1Ca_de3h, ndims=10)
+DimPlot(mono1Ca_de3h, reduction = 'umap',
+        group.by = "lane", pt.size = 0.5, label = TRUE, repel = TRUE)
+DimPlot(mono1Ca_de3h, pt.size = 0.5, reduction = "umap", 
+        group.by = "timepoint", label = TRUE)
+DimPlot(mono1Ca_de3h, pt.size = 0.5, reduction = "umap", 
+        group.by = "SCT_snn_res.0.8", label = TRUE)
+
+# slingshot
+mono1sling <- slingshot(Embeddings(mono1Ca_de3h, "umap"), clusterLabels = mono1Ca_de3h$SCT_snn_res.0.8, 
+                       start.clus = 0, stretch = 0)
+saveRDS(mono1Ca_de3h, 'mono1Ca_degenes.Rda')
+saveRDS(mono1sling, 'mono1sling_degenes.Rda')
+# load the expression data
+mono1Ca_degenes <- readRDS('mono1Ca_degenes.Rda')
+# load the slingshot
+mono1sling <- readRDS('mono1sling_degenes.Rda')
+pdf("evaluateK_chooseKnots.pdf")
+mono1ca_matrix <- as.matrix(GetAssayData(mono1Ca_de3h, slot='data'))
+icMat <- evaluateK(counts = mono1ca_matrix, 
+                   sds = mono1sling, k = 3:10, 
+                   nGenes = 200, verbose = T)
+pdf("slingshot_pseudotime.pdf")
+pseudotime <- slingPseudotime(mono1sling)
+
+nc <- 2
+nms <- colnames(pseudotime)
+nr <- ceiling(length(nms)/nc)
+par(mfrow = c(nr, nc))
+for (i in nms) {
+  ggplot(data.frame(pseudotime), aes(x=i, color=timepoint)) +
+    geom_histogram(fill="white", alpha=0.5, position="identity")
+}
+
+ggplot_frame = data.frame(pseudotime)
+ggplot_frame$timepoint <- mono1Ca_degenes[[]]$timepoint
+ggplot(ggplot_frame, aes(x=curve1, color=timepoint)) +
+  geom_histogram(fill="white", alpha=0.5, position="identity")
+ggplot(ggplot_frame, aes(x=curve2, color=timepoint)) +
+  geom_histogram(fill="white", alpha=0.5, position="identity")
+ggplot(ggplot_frame, aes(x=curve3, color=timepoint)) +
+  geom_histogram(fill="white", alpha=0.5, position="identity")
+ggplot(ggplot_frame, aes(x=curve4, color=timepoint)) +
+  geom_histogram(fill="white", alpha=0.5, position="identity")
+dev.off()
+
+#cellWeights <- slingCurveWeights(mono1sling)
+#sce <- fitGAM(counts = GetAssayData(mono1Ca, slot='data'), 
+#              pseudotime = pseudotime, cellWeights = cellWeights,
+library(viridis)
+pdf('slingshot_cells_in_different_linearges.pdf')
+nc <- 2
+nms <- colnames(pseudotime)
+nr <- ceiling(length(nms)/nc)
+pal <- viridis(100, end = 0.95)
+par(mfrow = c(nr, nc))
+for (i in nms) {
+  colors <- pal[cut(pseudotime[,i], breaks = 100)]
+  plot(reducedDim(mono1sling), col = colors, pch = 16, cex = 0.5, main = i)
+  lines(mono1sling, lwd = 2, col = 'black', type = 'lineages')
+}
+
+dev.off()
+
+
+library(SCORPIUS)
+mono1Ca_degenes <- readRDS('mono1Ca_degenes.Rda')
+#pdf('SCORPIUS_plots.pdf')
+expression <- t(as.matrix(GetAssayData(mono1Ca_degenes, slot='data')))
+group_name <- factor(as.character(mono1Ca_degenes[[]]$timepoint))
+# try with PCA
+#pdf('scorpius_pca.pdf')
+#pearson_space <- reduce_dimensionality(expression, "pearson")
+#pearson_traj <- infer_trajectory(pearson_space)
+#draw_trajectory_plot(pearson_space, group_name, pearson_traj$path, contour = TRUE)
+#dev.off()
+
+space <- reduce_dimensionality(expression, "spearman")
+traj <- infer_trajectory(space)
+saveRDS(space, 'scorpius_space.rds')
+saveRDS(traj, 'scorpius_traj.rds')
+# save traj#time in tsv
+write.table(as.matrix(traj$time), file='scorpius_trajtime.tsv', sep = '\t')
+write.table(as.matrix(traj$path), file='scorpius_trajpath.tsv', sep = '\t')
+
+# load scorpius results
+space <- readRDS('scorpius_space.rds')
+traj <- readRDS('scorpius_traj.rds')
+draw_trajectory_plot(space, group_name, traj$path, contour = TRUE)
+histogram_data <- data.frame("time" = matrix(unlist(traj$time), nrow=length(traj$time), byrow=T),
+                             row.names=names(traj$time))
+histogram_data$timepoint <- mono1Ca_degenes[[]]$timepoint
+
+ggplot(histogram_data, aes(x=time, color=timepoint)) +
+  geom_histogram(fill="white", alpha=0.5, position="identity")
+
+# draw_trajectory_heatmap(space, traj$time, progression_group=group_name)
+pdf('scorpius_heatmap.pdf')
+gimp <- gene_importances(
+  expression, 
+  traj$time, 
+  num_permutations = 10, 
+  num_threads = 8, 
+  ntree = 10000,
+  ntree_perm = 1000
+) 
+saveRDS(gimp, 'scorpius_gimp.rds')
+gimp$qvalue <- p.adjust(gimp$pvalue, "BH", length(gimp$pvalue))
+gene_sel <- gimp$gene[gimp$qvalue < .05]
+expr_sel <- scale_quantile(expression[,gene_sel])
+
+modules <- extract_modules(scale_quantile(expr_sel), traj$time, verbose = T) # needs more RAM than 50G
+draw_trajectory_heatmap(expr_sel, traj$time, group_name, modules)
+dev.off()
+
diff --git a/01_association_metrics/scvelo_analysis_dm.py b/01_association_metrics/scvelo_analysis_dm.py
new file mode 100644
index 0000000..4cddacd
--- /dev/null
+++ b/01_association_metrics/scvelo_analysis_dm.py
@@ -0,0 +1,100 @@
+"""
+RNA velocity analysis using the dynamic model
+run on all samples of Oelen v3 dataset for classical monocytes (mon1, mono2)
+and filtered for the 2000 most variable genes
+
+Input: loom files generated from velocyto
+Output: hd5ad object with RNA velocity estimates
+"""
+
+import scvelo as scv
+import pandas as pd
+import os
+
+scv.logging.print_version()
+scv.settings.verbosity = 3  # show errors(0), warnings(1), info(2), hints(3)
+scv.settings.set_figure_params('scvelo')  # for beautified visualization
+
+#Load annotation file with UMAP coordinates
+fpath="annotations/umap_monocytes.tsv"
+umap_coords=pd.read_csv(fpath, sep='\t')
+
+#Load data for each processed lane
+lanes=os.listdir("velocyto")
+ldata_array=[]
+for lane in lanes:
+    
+    print(lane)
+    
+    #Get loom file for each lane (file name unfortnuately not always the same)
+    files=os.listdir("velocyto/"+lane)
+    file=[f for f in files if f.endswith(".loom")]
+    lfile="velocyto/"+lane+"/"+file[0]
+    
+    #Read file
+    ldata = scv.read(lfile, cache=True)
+    
+    #Filter monocytes from file
+    filteredNames=[barcodeName.split(":")[1] for barcodeName in ldata.obs.index]
+    filteredNames=[barcodeName.replace("x","")+"_"+lane for barcodeName in filteredNames]
+    ldata.obs.index=filteredNames
+    umap_coords_filtered=umap_coords[umap_coords["Unnamed: 0"].isin(filteredNames)]
+    
+    #Filter for monocyotes (barcode in umap file)
+    ldata=ldata[umap_coords_filtered["Unnamed: 0"],:].copy()
+    
+    #Make variable names unique
+    ldata.var_names_make_unique()
+    #Add ldata object
+    ldata_array.append(ldata)
+
+ldata_filtered=ldata_array[0].concatenate(ldata_array[1:], batch_key='lane', 
+                          batch_categories=lanes,index_unique=None)
+
+#Delete variables which are not required anymore
+del ldata
+del ldata_array
+
+#Add information about cell types and time points (more annotations are available)
+annotations=pd.read_csv("seurat_object_meta.tsv", sep='\t')
+annotations=annotations.loc[ldata_filtered.obs.index.tolist(),:]
+ldata_filtered.obs["timepoint"]=annotations["timepoint"]
+ldata_filtered.obs["celltype"]=annotations["cell_type"]
+
+#Filter for only monocytes 1 and 2
+ldata_filtered=ldata_filtered[ldata_filtered.obs.celltype.isin(["mono 1","mono 2"])].copy()
+
+#RNA velocity analysis
+scv.utils.show_proportions(ldata_filtered)
+#Filter genes with less than 20 counts (spliced + unspliced) and 
+#reduce to the top 2000 highly variable genes
+scv.pp.filter_and_normalize(ldata_filtered, min_shared_counts=20, n_top_genes=2000)
+scv.pp.moments(ldata_filtered)
+
+#Run dynamic model
+scv.tl.recover_dynamics(ldata_filtered)
+scv.tl.velocity(ldata_filtered, mode='dynamical')
+scv.tl.velocity_graph(ldata_filtered)
+
+#Add UMAP coordinates
+umap_coords.index=umap_coords["Unnamed: 0"]
+umap_coords=umap_coords.loc[ldata_filtered.obs.index.tolist(),:]
+ldata_filtered.obsm["X_umap"]=umap_coords[["umap_1","umap_2"]].to_numpy()
+
+#Save file
+ldata_filtered.write("h5ad_objects/scveloAnalysis_dynamic_velocity_womono34.h5ad")
+
+#Create plot with embedding
+scv.pl.velocity_embedding_stream(ldata_filtered, basis='umap', color=['timepoint', 'celltype'],
+                                 show=False,save="embedding_dynamic_monocytes_womono34.png")
+
+scv.pl.velocity_graph(ldata_filtered,color="timepoint",
+                       show=False,save="velocityGraph_dynamic_monocytes_womono34.png")
+
+#Calculate pseudotime
+scv.tl.latent_time(ldata_filtered)
+scv.pl.scatter(ldata_filtered, color='latent_time', cmap='gnuplot',
+               show=False,save="latenttime_dynamic_monocytes_womono34.png")
+
+#Save file
+ldata_filtered.write("h5ad_objects/scveloAnalysis_dynamic_velocity_latenttime_womono34.h5ad")
diff --git a/01_association_metrics/setting_files_for_grnboost2/config_bp_mono.yaml b/01_association_metrics/setting_files_for_grnboost2/config_bp_mono.yaml
new file mode 100644
index 0000000..1fcae52
--- /dev/null
+++ b/01_association_metrics/setting_files_for_grnboost2/config_bp_mono.yaml
@@ -0,0 +1,45 @@
+# Input Settings: initialize base input folder names,
+# dataset collections, and algorithms to run over
+input_settings:
+
+    # Base input directory
+    input_dir : "inputs"
+
+    # Subdirectory of inputs that datasets are placed in
+    dataset_dir: "example"
+
+    # Denotes a list of datasets, each with the following parameters:
+    #   name: Name of the dataset. May be used in logging or other
+    #       messages written during execution
+    #
+    #   exprData: scRNA-Seq expression data file. Cells are along the
+    #            columns and genes are along the rows.
+    #   cellData: a file containing pseudotime ordering, or any other 
+    #             information about cells.
+    #   trueEdges: Name of the refrence network file in the
+    #              edge list format. Needed for evaluation.
+    datasets:
+        - name: "compare_grnboost2_bp_mono"
+          exprData: "bp_Expression.csv"
+          cellData: "bp_timepoint.fake.csv"
+          trueEdges: "selected_mono1data.string.csv"
+
+    # Denotes a list of algorithms to run. Each has the following parameters:
+    #   name: Name of the algorithm. Must be recognized by the pipeline, see
+    #       code for acceptable values
+    #
+    #   should_run: whether or not to run the algorithm
+    #
+    #   params: any additional, algorithm-specific parameters
+    #       should be specified in the params map for a given algorithm
+    #
+    algorithms:
+        - name: "GRNBOOST2"
+          params:
+              should_run: [True]
+# Output Settings: initialize base output folder names
+output_settings:
+
+    # Base output directory
+    output_dir: "outputs"
+    output_prefix: "compare_grnboost2_bp_mono"
diff --git a/01_association_metrics/setting_files_for_grnboost2/config_sc_mono.yaml b/01_association_metrics/setting_files_for_grnboost2/config_sc_mono.yaml
new file mode 100644
index 0000000..a3f61cf
--- /dev/null
+++ b/01_association_metrics/setting_files_for_grnboost2/config_sc_mono.yaml
@@ -0,0 +1,45 @@
+# Input Settings: initialize base input folder names,
+# dataset collections, and algorithms to run over
+input_settings:
+
+    # Base input directory
+    input_dir : "inputs"
+
+    # Subdirectory of inputs that datasets are placed in
+    dataset_dir: "example"
+
+    # Denotes a list of datasets, each with the following parameters:
+    #   name: Name of the dataset. May be used in logging or other
+    #       messages written during execution
+    #
+    #   exprData: scRNA-Seq expression data file. Cells are along the
+    #            columns and genes are along the rows.
+    #   cellData: a file containing pseudotime ordering, or any other 
+    #             information about cells.
+    #   trueEdges: Name of the refrence network file in the
+    #              edge list format. Needed for evaluation.
+    datasets:
+        - name: "compare_grnboost2_sc_mono"
+          exprData: "sc_Expression.csv"
+          cellData: "sc_timepoint.fake.csv"
+          trueEdges: "selected_mono1data.string.csv"
+
+    # Denotes a list of algorithms to run. Each has the following parameters:
+    #   name: Name of the algorithm. Must be recognized by the pipeline, see
+    #       code for acceptable values
+    #
+    #   should_run: whether or not to run the algorithm
+    #
+    #   params: any additional, algorithm-specific parameters
+    #       should be specified in the params map for a given algorithm
+    #
+    algorithms:
+        - name: "GRNBOOST2"
+          params:
+              should_run: [True]
+# Output Settings: initialize base output folder names
+output_settings:
+
+    # Base output directory
+    output_dir: "outputs"
+    output_prefix: "compare_grnboost2_sc_mono"
diff --git a/02_correlation_evaluation/README.md b/02_correlation_evaluation/README.md
new file mode 100644
index 0000000..b4706fb
--- /dev/null
+++ b/02_correlation_evaluation/README.md
@@ -0,0 +1,35 @@
+# 02_correlation_evaluation
+
+*blueprint_normalize.sh* normalize BLUEPRINT dataset, as well as regress out the first PC
+
+*blueprint_correlation.py*: calculate the co-expression for gene pairs in BLUEPRINT data
+
+*compare_blueprint_cutoffs_CD4T.py* : Compare correlation between Blueprint and single cell (Oelen v3 dataset) for different expression thresholds (number of cells expressing the gene), implemented for UT and CD4+ T cells here
+
+*compare_immunexut_cutoffs_CD4T.py*: Same approach as in *compare_blueprint_cutoffs_CD4T.py*, but comparing correlation between ImmuNexUT and Oelen v3 dataset instead
+
+*correlation_between_datasets.R*: check Pearson correlation between data sets (for CD4+ T cells) for single cell vs single cell dataset comparison, single cell vs bulk dataset comparison and bulk vs bulk dataset comparison, afterwards combines all results in one large heatmap
+
+*correlation_between_datasets_extended.R*: check if the correlation values between matched cell types for single cell and bulk (ImmuNexUT) are higher than for not-matched cell types
+
+*correlation_between_datasets_othercts.R*: extension of *correlation_between_datasets.R* that includes all cell types (not only CD4+ T cells)
+
+*correlation_timepoint_combined_indivs_1mio.py*: calculate the co-expression for genes that are expressed in more than 50% cells in Oelen v2 and v3 dataset
+
+*correlation_timepoint_combined_indivs_ng.py*: calculate the co-expression for genes that are expressed in more than 50% cells in van der Wijst dataset
+
+*correlation_timepoint_combined_indivs_stemiv2.py*: calculate the co-expression for genes that are expressed in more than 50% cells in van Blokland v2 dataset
+
+*correlation_timepoint_combined_indivs_stemiv3.py*: calculate the co-expression for genes that are expressed in more than 50% cells in van Blokland v3 dataset
+
+*figure2_barplot_cutoffs.R*: create barplots from the results of *compare_blueprint_cutoffs_CD4T.py* and *compare_immunexut_cutoffs_CD4T.py*
+
+*figure2_scatterplots.R*: creates inset plots for Main Figure 2 (a,b,d), showing scatterplots of gene pair-wise Spearman correlation values between two data sets for a) Oelen v3 dataset vs van Blokland v2 dataset (both CD4+ T cells), b) ImmuNexUT - van Blokland v2 (naive CD4+ T cells and CD4+ T cells) and c) Blueprint - ImmuNexUT (both naive CD4+ T cells)
+
+*normalize_ImmuNexUT.R*: preprocessingImmuNexUT data (separately for each cell type with a matching single-cell cell type) following the description in the corresponding publication (filtering lowly expressed genes, TMM normalization and batch correction) followed by correlation calculation for all genes expressed in 50% of the cells of the Oelen v3 dataset (for comparison with single cell data)
+
+*wilcoxon_test_crispr.R*: Benchmark our correlation results from single cell (Oelen v3, CD4+ T cells) and bulk (ImmuNexUT, naive CD4+ T cells) with a public CRISPR perturbation dataset using Wilcoxon Rank Sum Test
+
+*wilcoxon_test_string.R*: Compare if correlated pairs from single cell (Oelen v3, CD4+ T cells) and bulk (ImmuNexUT, naive CD4+ T cells) are enriched in STRING database (Using the same strategy as in CRISPR validation with Wilcoxon Rank Sum Test)
+
+
diff --git a/02_correlation_evaluation/blueprint_correlation.py b/02_correlation_evaluation/blueprint_correlation.py
new file mode 100644
index 0000000..9e8c5e4
--- /dev/null
+++ b/02_correlation_evaluation/blueprint_correlation.py
@@ -0,0 +1,48 @@
+import pandas as pd
+import numpy as np
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+from scipy.stats import spearmanr, pearsonr
+import scanpy as sc
+import seaborn as sns
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+# %matplotlib inline
+
+# %%bash
+# export HDF5_USE_FILE_LOCKING='FALSE'
+
+def read_numpy(fileprefix, rowname='rows', colname='cols'):
+    data = np.load(fileprefix+'.npy')
+    rows = [item.strip() for item in open(fileprefix+f'.{rowname}.txt', 'r').readlines()]
+    cols = [item.strip() for item in open(fileprefix+f'.{colname}.txt', 'r').readlines()]
+    return pd.DataFrame(data=data,
+                        index=rows,
+                        columns=cols)
+
+def get_pairwise_correlations(corr_df):
+    corrmatrix = corr_df.corr()
+    triuindices = np.triu_indices(corrmatrix.shape[0], k=1)
+    return corrmatrix.values[triuindices]
+
+blueprint_mappings = pd.read_csv('../blueprint/blueprint_mappings.txt',
+                                 sep='\t', index_col=0)['Gene name'].T.to_dict()
+data = pd.read_csv('../blueprint/mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.1PCAsOverSamplesRemoved.txt.gz',
+                  sep='\t', index_col=0, compression='gzip')
+
+data.index = [item.split('.')[0] for item in data.index]
+data['genename'] = [blueprint_mappings.get(ids) for ids in data.index]
+print(data.shape)
+data = data.dropna(subset=['genename']).drop_duplicates(subset=['genename'])
+data = data.set_index('genename')
+
+data.head()
+coefs, ps = spearmanr(data, axis=1)
+print(coefs.shape)
+np.save('mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.1PCAsOverSamplesRemoved.spearmanr.npy',
+       coefs)
+np.save('mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.1PCAsOverSamplesRemoved.spearmanrPvalues.npy',
+       ps)
+with open('mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.1PCAsOverSamplesRemoved.spearmanr.genes.txt',
+         'w') as f:
+    f.write('\n'.join(data.index.values))
\ No newline at end of file
diff --git a/02_correlation_evaluation/blueprint_normalize.sh b/02_correlation_evaluation/blueprint_normalize.sh
new file mode 100644
index 0000000..1c295e2
--- /dev/null
+++ b/02_correlation_evaluation/blueprint_normalize.sh
@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+#SBATCH --time=7:00:00
+#SBATCH --cpus-per-task=10
+#SBATCH --mem=30gb
+#SBATCH --nodes=1
+#SBATCH --open-mode=append
+#SBATCH --export=NONE
+#SBATCH --get-user-env=L
+
+module purge
+module load Java
+
+jar_file=eqtl-mapping-pipeline-1.4.9-SNAPSHOT/eqtl-mapping-pipeline.jar
+traitfile=./blueprint/tcel_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.txt.gz
+outdir=./blueprint
+logFile=./blueprint/blueprint_cd4t_adjustPCA.log
+java -Xmx30g -Xms30g -jar ${jar_file} \
+--mode normalize \
+--in ${traitfile} \
+--out ${outdir} \
+--adjustPCA \
+--maxnrpcaremoved 3 \
+--stepsizepcaremoval 1 | tee ${logFile}
+
+jar_file=eqtl-mapping-pipeline-1.4.9-SNAPSHOT/eqtl-mapping-pipeline.jar
+traitfile=./blueprint/mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.txt.gz
+outdir=./blueprint
+logFile=./blueprint/blueprint_normalize.log
+java -Xmx30g -Xms30g -jar ${jar_file} \
+--mode normalize \
+--in ${traitfile} \
+--out ${outdir} \
+--adjustPCA \
+--maxnrpcaremoved 3 \
+--stepsizepcaremoval 1 | tee ${logFile}
\ No newline at end of file
diff --git a/02_correlation_evaluation/compare_blueprint_cutoffs_CD4T.py b/02_correlation_evaluation/compare_blueprint_cutoffs_CD4T.py
new file mode 100644
index 0000000..381d0dd
--- /dev/null
+++ b/02_correlation_evaluation/compare_blueprint_cutoffs_CD4T.py
@@ -0,0 +1,108 @@
+# ---------------------------------------------------------------------------------------
+# Compare correlation between Blueprint and single cell (Oelen v3 dataset)
+# for different thresholds (number of cells expressing the gene),
+# implemented for UT and CD4T cells here
+# Input: seurat objects with Oelen v3 dataset and precalculated Blueprint correlation
+#        for all possible gene pairs
+# Output: csv file with the correlation between Blueprint and Oelen v3 for each threshold
+# ---------------------------------------------------------------------------------------
+
+from scipy.stats import spearmanr, pearsonr
+import scanpy as sc
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from time import time
+import os
+import re
+
+# load scanpy object (Oelen v3 dataset)
+alldata = sc.read_h5ad('seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad')
+
+# filter for CD4+ T cells and UT cells
+alldata = alldata[alldata.obs.cell_type_lowerres=='CD4T']
+alldata = alldata[alldata.obs.timepoint=='UT'].copy() #copy to not create only a view object
+
+celltype_data = pd.DataFrame(data=alldata.X.toarray(),
+                                 index=alldata.obs.index,
+                                 columns=alldata.var.index)
+
+# load Blueprint object
+bp_corr = np.load('blueprint_data/tcel_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.spearmanR.npy',mmap_mode="r")
+
+bp_corr_genes = []
+f= open('blueprint_data/tcel_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.spearmanR.cols.txt','r')
+for line in f.readlines():
+    bp_corr_genes.append(line.rstrip())
+
+# method to select genes above a certain nonzero ratio
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    selected_genes = df.columns[nonzerocounts>ratio]
+    return selected_genes
+
+# generate a set of thresholds that should be tested (start with stricter thresholds)
+thresholds = [i/10 for i in range(1,10)]
+thresholds.reverse()
+
+f_out = open("co-expression_indivs_combined/blueprint_cutoff_eval_CD4T.txt", "w")
+f_out.write("threshold,ngenes,corr_pearson\n")
+
+# iterate over all thresholds
+for th in thresholds:
+    
+    #select all genes within the threshold
+    selected_genes = select_gene_nonzeroratio(celltype_data, th)
+    
+    # filter genes that are not in Blueprint
+    selected_genes = list(set(selected_genes) & set(bp_corr_genes))
+    
+    print(f"Number of selected genes for {th}: {len(selected_genes)}")
+    
+    gene_pairs = []
+    for i,gene1 in enumerate(selected_genes):
+        for j in range(i+1, len(selected_genes)):
+            if gene1 < selected_genes[j]:
+                gene_pairs.append(';'.join([gene1, selected_genes[j]]))
+            else:
+                gene_pairs.append(';'.join([selected_genes[j],gene1]))
+
+    # calculate correlation single cell
+    input_df = celltype_data[selected_genes]
+    input_data = spearmanr(input_df, axis=0)[0]
+    input_data_uppertria = input_data[np.triu_indices_from(input_data, 1)]
+
+    corrs_df = pd.DataFrame({'UT': input_data_uppertria},
+                            index=gene_pairs)
+    
+    # filter blueprint and order it the same way as the single cell object
+    filter_bp_genes = [gene in selected_genes for gene in bp_corr_genes]
+    bp_corr_filtered = bp_corr[filter_bp_genes][:,filter_bp_genes]
+    bp_uppertria = bp_corr_filtered[np.triu_indices_from(bp_corr_filtered, 1)]
+    
+    # get genes from the blueprint object
+    bp_corr_genes_filtered = [gene for gene in bp_corr_genes if gene in selected_genes]
+    gene_pairs_bp=[]
+    for i,gene1 in enumerate(bp_corr_genes_filtered):
+        for j in range(i+1, len(bp_corr_genes_filtered)):
+            if gene1 < bp_corr_genes_filtered[j]:
+                gene_pairs_bp.append(';'.join([gene1, bp_corr_genes_filtered[j]]))
+            else:
+                gene_pairs_bp.append(';'.join([bp_corr_genes_filtered[j],gene1]))
+                
+    corrs_df_bp = pd.DataFrame({'BP': bp_uppertria},
+                            index=gene_pairs_bp)
+    
+    # sort both and combine them
+    corrs_df = corrs_df.sort_index()
+    corrs_df_bp = corrs_df_bp.sort_index()
+    #all(corrs_df.index == corrs_df_bp.index)
+    
+    # calculate correlation between datasets and save results
+    corr_data = pearsonr(corrs_df.UT, corrs_df_bp.BP)[0]
+    
+    # save results
+    f_out.write(f"{th},{len(selected_genes)},{corr_data}\n")
+
+# close file
+f.close()
diff --git a/02_correlation_evaluation/compare_immunexut_cutoffs_CD4T.py b/02_correlation_evaluation/compare_immunexut_cutoffs_CD4T.py
new file mode 100644
index 0000000..6ffde6b
--- /dev/null
+++ b/02_correlation_evaluation/compare_immunexut_cutoffs_CD4T.py
@@ -0,0 +1,93 @@
+# ---------------------------------------------------------------------------------------
+# Compare correlation between ImmuNexUT and single cell (Oelen v3 dataset)
+# for different thresholds (number of cells expressing the gene),
+# implemented for UT and CD4T cells here
+# Input: seurat objects with Oelen v3 dataset and normalized ImmuNexUT counts
+# Output: csv file with the correlation between ImmuNexUT and Oelen v3 for each threshold
+# ---------------------------------------------------------------------------------------
+
+from scipy.stats import spearmanr, pearsonr
+import scanpy as sc
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from time import time
+import os
+import re
+
+# load scanpy object (Oelen v3 dataset)
+alldata = sc.read_h5ad('seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad')
+
+# filter for CD4+ T cells and UT cells
+alldata = alldata[alldata.obs.cell_type_lowerres=='CD4T']
+alldata = alldata[alldata.obs.timepoint=='UT'].copy() #copy to not create only a view object
+
+celltype_data = pd.DataFrame(data=alldata.X.toarray(),
+                                 index=alldata.obs.index,
+                                 columns=alldata.var.index)
+
+# load ImmuNexuT object
+counts = pd.read_csv('imd_paper_rna_data/norm_count/Naive_CD4_norm_count.txt',sep="\t")
+immunexut_genes = counts.index.values
+counts = counts.transpose()
+
+# method to select genes above a certain nonzero ratio
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    selected_genes = df.columns[nonzerocounts>ratio]
+    return selected_genes
+
+# generate a set of thresholds that should be tested (start with stricter thresholds)
+thresholds = [i/10 for i in range(1,10)]
+thresholds.reverse()
+
+f_out = open("co-expression_indivs_combined/immunexut_cutoff_eval_CD4T.txt", "w")
+f_out.write("threshold,ngenes,corr_pearson\n")
+
+# iterate over all thresholds
+for th in thresholds:
+    
+    #select all genes within the threshold
+    selected_genes = select_gene_nonzeroratio(celltype_data, th)
+    
+    # filter genes that are not in ImmuNexUT
+    selected_genes = list(set(selected_genes) & set(immunexut_genes))
+    
+    print(f"Number of selected genes for {th}: {len(selected_genes)}")
+    
+    gene_pairs = []
+    for i,gene1 in enumerate(selected_genes):
+        for j in range(i+1, len(selected_genes)):
+            if gene1 < selected_genes[j]:
+                gene_pairs.append(';'.join([gene1, selected_genes[j]]))
+            else:
+                gene_pairs.append(';'.join([selected_genes[j],gene1]))
+
+    # calculate correlation single cell
+    input_df = celltype_data[selected_genes]
+    input_data = spearmanr(input_df, axis=0)[0]
+    input_data_uppertria = input_data[np.triu_indices_from(input_data, 1)]
+
+    corrs_df = pd.DataFrame({'UT': input_data_uppertria},
+                            index=gene_pairs)
+    
+    #c alculate correlation ImmuNexUT
+    input_df_ImmuNexUT = counts[selected_genes]
+    input_data = spearmanr(input_df_ImmuNexUT, axis=0)[0]
+    input_data_uppertria = input_data[np.triu_indices_from(input_data, 1)]
+
+    corrs_df_ImmuNexUT = pd.DataFrame({'BULK': input_data_uppertria},
+                    index=gene_pairs)
+    
+    # sorting both is not necessary here
+    #all(corrs_df.index == corrs_df_ImmuNexUT.index)
+    
+    # calculate correlation between datasets and save results
+    corr_data = pearsonr(corrs_df.UT, corrs_df_ImmuNexUT.BULK)[0]
+    
+    # save results
+    f_out.write(f"{th},{len(selected_genes)},{corr_data}\n")
+
+# close file
+f_out.close()
+
diff --git a/02_correlation_evaluation/correlation_between_datasets.R b/02_correlation_evaluation/correlation_between_datasets.R
new file mode 100644
index 0000000..72ecdfd
--- /dev/null
+++ b/02_correlation_evaluation/correlation_between_datasets.R
@@ -0,0 +1,341 @@
+# ------------------------------------------------------------------------------
+# Check Pearson correlation between data sets (for CD4+ T cells)
+# * for single cell vs single cell data set
+# * for single cell vs bulk data set
+# * for bulk vs bulk data set
+# Combine all results in one large heatmap
+#  -----------------------------------------------------------------------------
+
+library(data.table)
+library(reticulate) # to read the single cell data (numpy)
+library(ggplot2)
+library(viridis)
+library(ggpubr)
+
+np <- import("numpy")
+
+theme_set(theme_bw())
+
+cell_type<-"CD4T"
+
+#Path to different single cell dataset
+datasets<-c(mio_v3="co-expression_indivs_combined/",
+                 mio_v2="co-expression_indivs_combined/one_million_version2/",
+                 stemi_v2="co-expression_indivs_combined/stemi/version2/",
+                 stemi_v3="co-expression_indivs_combined/stemi/version3/",
+                 pilot="co-expression_indivs_combined/ng_updated_version/")
+
+#File endings for different single cell datasets
+file_suffixes<-c(mio_v3="_UT_correlation.csv",
+                mio_v2="_UT_correlation.csv",
+                stemi_v2="_t8w_correlation.csv",
+                stemi_v3="_t8w_correlation.csv",
+                pilot="_correlation.csv")
+
+#Name on plots for different single cell datasets
+dataset_names<-c(mio_v3="Oelen (v3)",
+                 mio_v2="Oelen (v2)",
+                 stemi_v2="van Blokland (v2)",
+                 stemi_v3="van Blokland (v3)",
+                 pilot="van der Wijst")
+
+bulk_datasets<-c("Blueprint","BIOS","ImmuNexUT")
+
+resort<-function(corr){
+  #Split into two genes
+  corr$gene1<-gsub(";.*","",corr$V1)
+  corr$gene2<-gsub(".*;","",corr$V1)
+  
+  #Order them alphabetically
+  corr$V1<-ifelse(corr$gene1 < corr$gene2,corr$V1,
+                  paste0(corr$gene2,";",corr$gene1))
+  corr$gene1<-NULL
+  corr$gene2<-NULL
+  
+  return(corr)
+}
+
+################################################################################
+# Compare single cell with each other
+################################################################################
+
+corr_comp<-NULL
+for(c1 in 1:(length(datasets)-1)){
+
+  #Read correlation file one
+  dataset_name1<-dataset_names[c1]
+  corr_c1<-fread(paste0(datasets[c1],cell_type,
+                        "/",cell_type,file_suffixes[c1]))
+  corr_c1<-resort(corr_c1)
+
+  #Unique genes
+  num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                          gsub(".*;","",corr_c1$V1)))
+  
+  corr_comp<-rbind(corr_comp,
+                   data.frame(c1=dataset_name1,
+                              c2=dataset_name1,
+                              gene_pairs=nrow(corr_c1),
+                              genes_unique=num_genes,
+                              corr=1))
+
+  for(c2 in (c1+1):length(datasets)){
+
+    #Read correlation file two
+    dataset_name2<-dataset_names[c2]
+    corr_c2<-fread(paste0(datasets[c2],cell_type,"/",
+                          cell_type,file_suffixes[c2]))
+    corr_c2<-resort(corr_c2)
+
+    corr<-merge(corr_c1,corr_c2,by=c("V1"))
+
+    #Unique genes
+    num_genes<-length(union(gsub(";.*","",corr$V1),
+                            gsub(".*;","",corr$V1)))
+    
+    corr_comp<-rbind(corr_comp,
+                     data.frame(c1=dataset_name1,
+                                c2=dataset_name2,
+                                gene_pairs=nrow(corr),
+                                genes_unique=num_genes,
+                                corr=cor(corr[[2]],corr[[3]],method="pearson")))
+  }
+}
+
+#Read correlation file one
+c1<-length(datasets)
+dataset_name1<-dataset_names[c1]
+corr_c1<-fread(paste0(datasets[c1],cell_type,
+                      "/",cell_type,file_suffixes[c1]))
+
+#Unique genes
+num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                        gsub(".*;","",corr_c1$V1)))
+
+corr_comp<-rbind(corr_comp,
+                 data.frame(c1=dataset_name1,
+                            c2=dataset_name1,
+                            genes_unique=num_genes,
+                            gene_pairs=nrow(corr_c1),
+                            corr=1))
+
+corr_comp$c1<-factor(corr_comp$c1,levels=dataset_names)
+corr_comp$c2<-factor(corr_comp$c2,levels=dataset_names)
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_datasets.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+################################################################################
+# Compare single cell with bulk
+################################################################################
+
+
+#Special function to read bulk data as they are not all saved in the same file type
+read_bulk_data<-function(dataset_name){
+  
+  if(dataset_name=="Blueprint"){
+    path<-"blueprint_data/tcel_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.spearmanR."
+    rowname_suffix<-"rows.txt"
+    colname_suffix<-"cols.txt"
+      
+    corr_c1 <- np$load(paste0(path,"npy"), mmap_mode="r")
+    row_names<-fread(paste0(path,rowname_suffix),header=FALSE)
+    rownames(corr_c1)<-row_names$V1
+    col_names<-fread(paste0(path,colname_suffix),header=FALSE)
+    colnames(corr_c1)<-col_names$V1
+    rm(row_names,col_names)
+    
+    #Filter for single cell data
+    ct_single_cell<-"CD4T"
+    corr_sc<-fread(paste0("co-expression_indivs_combined/",ct_single_cell,"/",
+                          ct_single_cell,"_UT_correlation.csv"))
+    corr_sc$gene1<-gsub(";.*","",corr_sc$V1)
+    corr_sc$gene2<-gsub(".*;","",corr_sc$V1)
+    sc_genes<-union(corr_sc$gene1,corr_sc$gene2)
+    sc_genes<-sc_genes[sc_genes %in% colnames(corr_c1)]
+    
+    corr_c1<-corr_c1[sc_genes,sc_genes]
+    corr_c1<-reshape2::melt(corr_c1)
+    corr_c1$Var1<-as.character(corr_c1$Var1)
+    corr_c1$Var2<-as.character(corr_c1$Var2)
+    colnames(corr_c1)[1:3]<-c("gene1","gene2","corr")
+    
+    #Order so that gene1 is always the one first in alphabet
+    corr_c1<-corr_c1[corr_c1$gene1!=corr_c1$gene2,]
+    corr_c1$V1<-paste0(corr_c1$gene1,";",corr_c1$gene2)
+    corr_c1$gene1<-NULL
+    corr_c1$gene2<-NULL
+    
+    corr_c1<-corr_c1[,c("V1","corr")]
+    
+  } else if (dataset_name=="BIOS"){
+    corr_c1<-fread("bios/bios_correlation_tcellfiltered.tsv")
+    #all(corr_c1$gene1 < corr_c1$gene2)
+    corr_c1$V1<-paste0(corr_c1$gene1,";",corr_c1$gene2)
+    corr_c1$gene1<-NULL
+    corr_c1$gene2<-NULL
+  } else { #ImmuNexUT
+    corr_c1<-fread("imd_paper_rna_data/correlation/Naive_CD4_correlation.txt")
+    #all(corr_c1$gene1 < corr_c1$gene2)
+    corr_c1$V1<-paste0(corr_c1$gene1,";",corr_c1$gene2)
+    corr_c1$gene1<-NULL
+    corr_c1$gene2<-NULL
+  }
+  
+  return(corr_c1)
+}
+
+corr_comp<-NULL
+for(c1 in 1:length(bulk_datasets)){
+  
+  dataset_name1<-bulk_datasets[c1]
+  corr_c1<-read_bulk_data(dataset_name1)
+  
+  for(c2 in 1:length(datasets)){
+    
+    #Read correlation file two
+    dataset_name2<-dataset_names[c2]
+    corr_c2<-fread(paste0(datasets[c2],cell_type,"/",
+                          cell_type,file_suffixes[c2]))
+    corr_c2<-resort(corr_c2)
+    
+    corr<-merge(corr_c1,corr_c2,by=c("V1"))
+    
+    #Unique genes
+    num_genes<-length(union(gsub(";.*","",corr$V1),
+                            gsub(".*;","",corr$V1)))
+    
+    corr_comp<-rbind(corr_comp,
+                     data.frame(c1=dataset_name1,
+                                c2=dataset_name2,
+                                gene_pairs=nrow(corr),
+                                genes_unique=num_genes,
+                                corr=cor(corr[[2]],corr[[3]],method="pearson")))
+  }
+}
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_singlevsbulk_datasets.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+################################################################################
+# Compare bulk with bulk
+################################################################################
+
+corr_comp<-NULL
+for(c1 in 1:(length(bulk_datasets)-1)){
+
+  #Read correlation file one
+  dataset_name1<-bulk_datasets[c1]
+  corr_c1<-read_bulk_data(dataset_name1)
+
+  #Unique genes
+  num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                          gsub(".*;","",corr_c1$V1)))
+  
+  corr_comp<-rbind(corr_comp,
+                   data.frame(c1=dataset_name1,
+                              c2=dataset_name1,
+                              gene_pairs=nrow(corr_c1),
+                              genes_unique=num_genes,
+                              corr=1))
+
+  for(c2 in (c1+1):length(bulk_datasets)){
+
+    #Read correlation file two
+    dataset_name2<-bulk_datasets[c2]
+    corr_c2<-read_bulk_data(dataset_name2)
+
+    corr<-merge(corr_c1,corr_c2,by=c("V1"))
+
+    #Unique genes
+    num_genes<-length(union(gsub(";.*","",corr$V1),
+                            gsub(".*;","",corr$V1)))
+    
+    corr_comp<-rbind(corr_comp,
+                     data.frame(c1=dataset_name1,
+                                c2=dataset_name2,
+                                gene_pairs=nrow(corr),
+                                genes_unique=num_genes,
+                                corr=cor(corr[[2]],corr[[3]],method="pearson")))
+  }
+}
+
+#Read correlation file one
+c1<-length(bulk_datasets)
+dataset_name1<-bulk_datasets[c1]
+corr_c1<-read_bulk_data(dataset_name1)
+
+#Unique genes
+num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                        gsub(".*;","",corr_c1$V1)))
+
+corr_comp<-rbind(corr_comp,
+                 data.frame(c1=dataset_name1,
+                            c2=dataset_name1,
+                            gene_pairs=nrow(corr_c1),
+                            genes_unique=num_genes,
+                            corr=1))
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_bulk_datasets.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+
+################################################################################
+# Combine all results in one large plot
+################################################################################
+  
+corr_comp<-fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_datasets.tsv")
+corr_comp$c1<-factor(corr_comp$c1,levels=dataset_names)
+corr_comp$c2<-factor(corr_comp$c2,levels=dataset_names)
+g.1<-ggplot(corr_comp,aes(x=c1,y=c2,fill=corr))+
+  geom_tile()+
+  geom_text(aes(label=paste0(round(corr,3),"\n(",genes_unique,")")),size=3)+
+  xlab("Single cell data set")+
+  ylab("Single cell data set")+
+  scale_fill_viridis("Correlation",limits=c(0,1))+
+  scale_y_discrete(labels=c("Oelen (v3)","Oelen (v2)","van Blokland\n(v2)",
+                            "van Blokland\n(v3)","van der Wijst"))+
+  scale_x_discrete(labels=c("Oelen\n(v3)","Oelen\n(v2)","van\nBlokland\n(v2)",
+                            "van\nBlokland\n(v3)","van der\nWijst"))
+
+corr_comp<-fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlevsbulk_datasets.tsv")
+corr_comp$c1[corr_comp$c1=="Blueprint"]<-"BLUEPRINT"
+corr_comp$c2<-factor(corr_comp$c2,levels=bulk_datasets)
+corr_comp$c1<-factor(corr_comp$c1,levels=dataset_names)
+g.2<-ggplot(corr_comp,aes(x=c2,y=c1,fill=corr))+
+  geom_tile()+
+  geom_text(aes(label=paste0(round(corr,3),"\n(",genes_unique,")")),size=3,
+                color="white")+
+  xlab("Bulk data set")+
+  ylab("Single cell data set")+
+  scale_fill_viridis("Correlation",limits=c(0,1))+
+  scale_y_discrete(labels=c("Oelen (v3)","Oelen (v2)","van Blokland\n(v2)",
+                            "van Blokland\n(v3)","van der Wijst"))
+
+corr_comp<-fread("co-expression_indivs_combined/dataset_comp_summary/correlation_bulk_datasets.tsv")
+corr_comp$c1[corr_comp$c1=="Blueprint"]<-"BLUEPRINT"
+corr_comp$c2[corr_comp$c2=="Blueprint"]<-"BLUEPRINT"
+corr_comp$c1<-factor(corr_comp$c1,levels=bulk_datasets)
+corr_comp$c2<-factor(corr_comp$c2,levels=bulk_datasets)
+g.3<-ggplot(corr_comp,aes(x=c1,y=c2,fill=corr))+
+  geom_tile()+
+  geom_text(aes(label=paste0(round(corr,3),"\n(",genes_unique,")"),
+                color=ifelse(corr<0.6,'white','black')),size=3)+
+  scale_color_manual(values=c("black","white"))+
+  xlab("Bulk data set")+
+  ylab("Bulk data set")+
+  scale_fill_viridis("Correlation",limits=c(0,1))+
+  coord_flip()
+  
+
+g_empty<-ggplot()+theme_void()
+g<-ggarrange(g.1,g.2,g_empty,g.3,ncol=2,nrow=2,widths=c(4,3),heights=c(4,3),
+             common.legend = TRUE,legend="bottom",align="hv")
+ggsave(g,file=paste0("co-expression_indivs_combined/plots/corr_datasets_combined.pdf"),
+       width=6.5,height=6.5)
diff --git a/02_correlation_evaluation/correlation_between_datasets_extended.R b/02_correlation_evaluation/correlation_between_datasets_extended.R
new file mode 100644
index 0000000..fd52dd6
--- /dev/null
+++ b/02_correlation_evaluation/correlation_between_datasets_extended.R
@@ -0,0 +1,188 @@
+###############################################################################
+# In order to better interpret the correlation levels:
+# check if correlation between single cell and bulk (ImmuNexUT) is higher for matched
+# cell types compared to not matched cell types
+###############################################################################
+
+library(data.table)
+library(reticulate) # to read the single cell data (numpy)
+library(ggplot2)
+library(viridis)
+library(dplyr)
+
+theme_set(theme_bw())
+
+#Rename cell types
+ct_fullname<-setNames(c("CD8+ T cells","monocytes","NK cells","B cells","DC"),
+                      c("CD8T","monocyte","NK","B","DC"))
+
+#Path to different single cell dataset
+datasets<-c(mio_v3="co-expression_indivs_combined/",
+            mio_v2="co-expression_indivs_combined/one_million_version2/",
+            stemi_v2="co-expression_indivs_combined/stemi/version2/",
+            stemi_v3="co-expression_indivs_combined/stemi/version3/",
+            pilot="co-expression_indivs_combined/ng_updated_version/")
+
+#File endings for different single cell datasets
+file_suffixes<-c(mio_v3="_UT_correlation.csv",
+                 mio_v2="_UT_correlation.csv",
+                 stemi_v2="_t8w_correlation.csv",
+                 stemi_v3="_t8w_correlation.csv",
+                 pilot="_correlation.csv")
+
+#Name on plots for different single cell datasets
+dataset_names<-c(mio_v3="Oelen (v3)",
+                 mio_v2="Oelen (v2)",
+                 stemi_v2="van Blokland (v2)",
+                 stemi_v3="van Blokland (v3)",
+                 pilot="van der Wijst")
+
+#Different bulk datasets
+bulk_datasets<-c("BLUEPRINT","BIOS","ImmuNexUT")
+
+resort<-function(corr){
+  #Split into two genes
+  corr$gene1<-gsub(";.*","",corr$V1)
+  corr$gene2<-gsub(".*;","",corr$V1)
+  
+  #Order them alphabetically
+  corr$V1<-ifelse(corr$gene1 < corr$gene2,corr$V1,
+                  paste0(corr$gene2,";",corr$gene1))
+  corr$gene1<-NULL
+  corr$gene2<-NULL
+  
+  return(corr)
+}
+
+################################################################################
+# Compare single cell vs ImmuNexUT - all cell types against all cell types
+################################################################################
+
+# Cell type matching
+ct_mapping<-data.frame(sc_ct=c("CD4T","CD8T","B","monocyte","NK","DC"),
+                       imn_ct=c("Naive_CD4","Naive_CD8","Naive_B","CL_Mono","NK","mDC"))
+
+corr_comp<-NULL
+for(i in 1:nrow(ct_mapping)){
+  
+  ct <- ct_mapping$imn_ct[i]
+  #cell_type<- "CD4T"
+  
+  #Load ImmuNexUT data
+  combat_tmm<-fread(paste0("imd_paper_rna_data/norm_count/",ct,"_norm_count.txt"))
+  
+  #Load the different single cell data sets
+  for(c1 in 1:length(datasets)){
+    
+    #Load for each single cell dataset all cell types
+    for(cell_type in ct_mapping$sc_ct){
+      
+      #Load single cell data set
+      corr_c2<-fread(paste0(datasets[c1],cell_type,
+                            "/",cell_type,file_suffixes[c1]))
+      corr_c2<-resort(corr_c2)
+      
+      #Filter the ImmuNexUT data set
+      expressed_genes<-union(gsub(";.*","",corr_c2$V1),
+                             gsub(".*;","",corr_c2$V1))
+      expressed_genes<-intersect(expressed_genes,combat_tmm$V1)
+      
+      combat_tmm_filtered<-combat_tmm[combat_tmm$V1 %in% expressed_genes,]
+      combat_tmm_filtered<-as.data.frame(combat_tmm_filtered)
+      rownames(combat_tmm_filtered)<-combat_tmm_filtered$V1
+      combat_tmm_filtered$V1<-NULL
+      
+      #Calculation correlation
+      cor_matrix<-cor(t(combat_tmm_filtered),method="spearman")
+      cor_matrix<-reshape2::melt(cor_matrix)
+      cor_matrix$Var1<-as.character(cor_matrix$Var1)
+      cor_matrix$Var2<-as.character(cor_matrix$Var2)
+      cor_matrix<-cor_matrix[cor_matrix$Var1 < cor_matrix$Var2,]
+      cor_matrix$V1<-paste0(cor_matrix$Var1,";",cor_matrix$Var2)
+      cor_matrix$Var1<-NULL
+      cor_matrix$Var2<-NULL
+      
+      #Compare BIOS with single cell
+      corr<-merge(corr_c2,cor_matrix,by=c("V1"))
+      
+      #Unique genes
+      num_genes<-length(union(gsub(";.*","",corr$V1),
+                              gsub(".*;","",corr$V1)))
+      
+      corr_comp<-rbind(corr_comp,
+                       data.frame(sc_ct=cell_type,
+                                  bulk_ct=ct,
+                                  c1=dataset_names[c1],
+                                  c2="ImmuNexUT",
+                                  gene_pairs=nrow(corr),
+                                  genes_unique=num_genes,
+                                  corr=cor(corr[[2]],corr[[3]],method="pearson")))
+    }
+  }
+}
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_immunexut_mixedcts.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+
+################################################################################
+# Plot the results
+################################################################################
+
+corr_comp<-fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_immunexut_mixedcts.tsv")
+
+ct_mapping<-data.frame(sc_ct=c("CD4T","CD8T","B","monocyte","NK","DC"),
+                       imn_ct=c("Naive_CD4","Naive_CD8","Naive_B","CL_Mono","NK","mDC"))
+
+#Order single cell and bulk the same way
+corr_comp$sc_ct<-factor(corr_comp$sc_ct,levels=ct_mapping$sc_ct)
+corr_comp$bulk_ct<-factor(corr_comp$bulk_ct,levels=ct_mapping$imn_ct)
+
+g<-ggplot(corr_comp,aes(x=sc_ct,y=bulk_ct,fill=corr))+
+  geom_tile()+
+  geom_text(aes(label=paste0(round(corr,3),"\n(",genes_unique,")"),
+                color=ifelse(corr<0.6,'white','black')),size=3)+
+  scale_color_manual(values=c("black","white"))+
+  facet_wrap(~c1)+
+  xlab("Cell type - single cell")+
+  ylab("Cell type - bulk")+
+  scale_fill_viridis("Correlation",limits=c(0,1))+
+  theme(legend.position = "bottom")+
+  guides(color="none")
+  
+
+print(g)
+
+ggsave(g,file="correlation_mixed_cts.png",height=7,width=9)
+
+################################################################################
+# Normalize the columns to always by the diagonal (matched cell types)
+################################################################################
+
+ct_mapping_list<-setNames(c("CD4T","CD8T","B","monocyte","NK","DC"),
+                          c("Naive_CD4","Naive_CD8","Naive_B","CL_Mono","NK","mDC"))
+corr_comp$bulk_matched_ct<-ct_mapping_list[corr_comp$bulk_ct]
+corr_diagonal<-corr_comp[corr_comp$sc_ct==corr_comp$bulk_matched_ct,c("sc_ct","c1","corr")]
+colnames(corr_diagonal)<-c("sc_ct","c1","diag_corr")
+
+corr_comp<-merge(corr_comp,corr_diagonal,by=c("sc_ct","c1"))
+corr_comp$rel_corr<-corr_comp$corr/corr_comp$diag_corr
+
+g<-ggplot(corr_comp,aes(x=sc_ct,y=bulk_ct,fill=rel_corr))+
+  geom_tile()+
+  geom_text(aes(label=paste0(round(rel_corr,3),"\n(",round(corr,3),")"),
+                color=ifelse(rel_corr<1,'white','black')),size=3)+
+  scale_color_manual(values=c("black","white"))+
+  facet_wrap(~c1)+
+  xlab("Cell type - single cell")+
+  ylab("Cell type - bulk")+
+  scale_fill_viridis("Relative corr")+
+  theme(legend.position = "bottom")+
+  guides(color="none")
+
+
+print(g)
+
+ggsave(g,file="correlation_mixed_cts_normalized.png",height=7,width=9)
diff --git a/02_correlation_evaluation/correlation_between_datasets_othercts.R b/02_correlation_evaluation/correlation_between_datasets_othercts.R
new file mode 100644
index 0000000..35a45b1
--- /dev/null
+++ b/02_correlation_evaluation/correlation_between_datasets_othercts.R
@@ -0,0 +1,616 @@
+# ------------------------------------------------------------------------------
+# Extension of correlation_between_datasets.R (which looks only at CD4+ T cells)
+# for other cell types: get Pearson correlation between data sets
+# * for single cell vs single cell data set
+# * for single cell vs bulk data set
+# * for bulk vs bulk data set (here only monocytes)
+# Plot one heatmap for each comparison
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(reticulate) # to read the single cell data (numpy)
+library(ggplot2)
+library(viridis)
+library(dplyr)
+
+theme_set(theme_bw())
+
+#Rename cell types
+ct_fullname<-setNames(c("CD8+ T cells","monocytes","NK cells","B cells","DC"),
+                      c("CD8T","monocyte","NK","B","DC"))
+
+#Path to different single cell dataset
+datasets<-c(mio_v3="co-expression_indivs_combined/",
+            mio_v2="co-expression_indivs_combined/one_million_version2/",
+            stemi_v2="co-expression_indivs_combined/stemi/version2/",
+            stemi_v3="co-expression_indivs_combined/stemi/version3/",
+            pilot="co-expression_indivs_combined/ng_updated_version/")
+
+#File endings for different single cell datasets
+file_suffixes<-c(mio_v3="_UT_correlation.csv",
+                 mio_v2="_UT_correlation.csv",
+                 stemi_v2="_t8w_correlation.csv",
+                 stemi_v3="_t8w_correlation.csv",
+                 pilot="_correlation.csv")
+
+#Name on plots for different single cell datasets
+dataset_names<-c(mio_v3="Oelen (v3)",
+                 mio_v2="Oelen (v2)",
+                 stemi_v2="van Blokland (v2)",
+                 stemi_v3="van Blokland (v3)",
+                 pilot="van der Wijst")
+
+#Different bulk datasets
+bulk_datasets<-c("BLUEPRINT","BIOS","ImmuNexUT")
+
+resort<-function(corr){
+  #Split into two genes
+  corr$gene1<-gsub(";.*","",corr$V1)
+  corr$gene2<-gsub(".*;","",corr$V1)
+  
+  #Order them alphabetically
+  corr$V1<-ifelse(corr$gene1 < corr$gene2,corr$V1,
+                  paste0(corr$gene2,";",corr$gene1))
+  corr$gene1<-NULL
+  corr$gene2<-NULL
+  
+  return(corr)
+}
+
+
+################################################################################
+# 1) Compare single cell with each other
+################################################################################
+
+corr_comp<-NULL
+for(cell_type in c("CD8T","monocyte","NK","B","DC")){
+  
+  for(c1 in 1:(length(datasets)-1)){
+    
+    #Read correlation file one
+    dataset_name1<-dataset_names[c1]
+    corr_c1<-fread(paste0(datasets[c1],cell_type,
+                          "/",cell_type,file_suffixes[c1]))
+    corr_c1<-resort(corr_c1)
+    
+    #Unique genes
+    num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                            gsub(".*;","",corr_c1$V1)))
+    
+    corr_comp<-rbind(corr_comp,
+                     data.frame(cell_type,
+                                c1=dataset_name1,
+                                c2=dataset_name1,
+                                gene_pairs=nrow(corr_c1),
+                                genes_unique=num_genes,
+                                corr=1))
+    
+    for(c2 in (c1+1):length(datasets)){
+      
+      #Read correlation file two
+      dataset_name2<-dataset_names[c2]
+      corr_c2<-fread(paste0(datasets[c2],cell_type,"/",
+                            cell_type,file_suffixes[c2]))
+      corr_c2<-resort(corr_c2)
+      
+      corr<-merge(corr_c1,corr_c2,by=c("V1"))
+      
+      #Unique genes
+      num_genes<-length(union(gsub(";.*","",corr$V1),
+                              gsub(".*;","",corr$V1)))
+      
+      corr_comp<-rbind(corr_comp,
+                       data.frame(cell_type,
+                                  c1=dataset_name1,
+                                  c2=dataset_name2,
+                                  gene_pairs=nrow(corr),
+                                  genes_unique=num_genes,
+                                  corr=cor(corr[[2]],corr[[3]],method="pearson")))
+    }
+  }
+  
+  
+  #Read correlation file one
+  c1<-length(datasets)
+  dataset_name1<-dataset_names[c1]
+  corr_c1<-fread(paste0(datasets[c1],cell_type,
+                        "/",cell_type,file_suffixes[c1]))
+  
+  #Unique genes
+  num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                          gsub(".*;","",corr_c1$V1)))
+  
+  corr_comp<-rbind(corr_comp,
+                   data.frame(cell_type,
+                              c1=dataset_name1,
+                              c2=dataset_name1,
+                              genes_unique=num_genes,
+                              gene_pairs=nrow(corr_c1),
+                              corr=1))
+}
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_datasets_othercts.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+################################################################################
+# Plot comparison of single cell vs single cell (Supplementary Figure)
+################################################################################
+
+corr_comp<-fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_datasets_othercts.tsv")
+corr_comp$c1<-factor(corr_comp$c1,levels=dataset_names)
+corr_comp$c2<-factor(corr_comp$c2,levels=dataset_names)
+corr_comp$cell_type<-ct_fullname[corr_comp$cell_type]
+
+g<-ggplot(corr_comp,aes(x=c1,y=c2,fill=corr))+
+  geom_tile()+
+  geom_text(aes(label=paste0(round(corr,3),"\n(",genes_unique,")"),
+                color=ifelse(corr<0.6,'white','black')),size=3)+
+  scale_color_manual(values=c("black","white"))+
+  xlab("Single cell data set")+
+  ylab("Single cell data set")+
+  scale_fill_viridis("Correlation",limits=c(0,1))+
+  facet_wrap(~cell_type)+
+  scale_y_discrete(labels=c("Oelen (v3)","Oelen (v2)","van Blokland\n(v2)",
+                            "van Blokland\n(v3)","van der Wijst"))+
+  scale_x_discrete(labels=c("Oelen\n(v3)","Oelen\n(v2)","van\nBlokland\n(v2)",
+                            "van\nBlokland\n(v3)","van der\nWijst"))+
+  theme(legend.position=c(0.9,0.1))+
+  guides(color=FALSE)
+print(g)
+
+ggsave(g,file=paste0("co-expression_indivs_combined/plots/corr_single_cell_othercts.png"),
+       width=8.5,height=6.5)
+
+#Get also CD4 T cell results
+corr_comp_ct<-fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_datasets.tsv")
+corr_comp_ct$cell_type<-"CD4T"
+corr_comp_ct<-corr_comp_ct[,colnames(corr_comp),with=FALSE]
+corr_comp<-rbind(corr_comp_ct,corr_comp)
+
+#Get median correlation
+corr_comp%>%
+  group_by(cell_type)%>%
+  summarise(mean(corr),median(corr),min(corr),max(corr))
+
+#Overall distribution across all cell types
+summary(corr_comp$corr)
+
+################################################################################
+# 2) Compare single cell with bulk
+################################################################################
+
+################################################################################
+# For Blueprint - Monocytes
+################################################################################
+
+#Blueprint Monocyte correlation
+path<-"blueprint_data/mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.1PCAsOverSamplesRemoved.spearmanr."
+rowname_suffix<-"genes.txt"
+
+corr_c1 <- np$load(paste0(path,"npy"), mmap_mode="r")
+row_names<-fread(paste0(path,rowname_suffix),header=FALSE)
+rownames(corr_c1)<-row_names$V1
+colnames(corr_c1)<-row_names$V1
+rm(row_names)
+
+corr_comp<-NULL
+cell_type<-"monocyte"
+for(c1 in 1:length(datasets)){
+  
+  #Load single cell data set
+  corr_c2<-fread(paste0(datasets[c1],cell_type,
+                        "/",cell_type,file_suffixes[c1]))
+  corr_c2<-resort(corr_c2)
+  
+  #Filter the Blueprint data set
+  expressed_genes<-union(gsub(";.*","",corr_c2$V1),
+                         gsub(".*;","",corr_c2$V1))
+  expressed_genes<-intersect(expressed_genes,colnames(corr_c1))
+  
+  corr_c1_filtered<-corr_c1[expressed_genes,expressed_genes]
+  corr_c1_filtered<-reshape2::melt(corr_c1_filtered)
+  corr_c1_filtered$Var1<-as.character(corr_c1_filtered$Var1)
+  corr_c1_filtered$Var2<-as.character(corr_c1_filtered$Var2)
+  corr_c1_filtered<-corr_c1_filtered[corr_c1_filtered$Var1 < corr_c1_filtered$Var2,]
+  corr_c1_filtered$V1<-paste0(corr_c1_filtered$Var1,";",corr_c1_filtered$Var2)
+  corr_c1_filtered$Var1<-NULL
+  corr_c1_filtered$Var2<-NULL
+  
+  corr<-merge(corr_c1_filtered,corr_c2,by=c("V1"))
+  
+  #Unique genes
+  num_genes<-length(union(gsub(";.*","",corr$V1),
+                          gsub(".*;","",corr$V1)))
+  
+  corr_comp<-rbind(corr_comp,
+                   data.frame(cell_type,
+                              c1=dataset_names[c1],
+                              c2="BLUEPRINT",
+                              gene_pairs=nrow(corr),
+                              genes_unique=num_genes,
+                              corr=cor(corr[[2]],corr[[3]],method="pearson")))
+}
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_blueprint_mono.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+################################################################################
+# For Blueprint - CD4T
+################################################################################
+
+#Blueprint CD4T correlation
+path<-"blueprint_data/tcel_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.spearmanR."
+rowname_suffix<-"rows.txt"
+
+corr_c1 <- np$load(paste0(path,"npy"), mmap_mode="r")
+row_names<-fread(paste0(path,rowname_suffix),header=FALSE)
+rownames(corr_c1)<-row_names$V1
+colnames(corr_c1)<-row_names$V1
+rm(row_names)
+
+corr_comp<-NULL
+cell_type<-"CD4T"
+for(c1 in 1:length(datasets)){
+  
+  #Load single cell data set
+  corr_c2<-fread(paste0(datasets[c1],cell_type,
+                        "/",cell_type,file_suffixes[c1]))
+  corr_c2<-resort(corr_c2)
+  
+  #Filter the Blueprint data set
+  expressed_genes<-union(gsub(";.*","",corr_c2$V1),
+                         gsub(".*;","",corr_c2$V1))
+  expressed_genes<-intersect(expressed_genes,colnames(corr_c1))
+  
+  corr_c1_filtered<-corr_c1[expressed_genes,expressed_genes]
+  corr_c1_filtered<-reshape2::melt(corr_c1_filtered)
+  corr_c1_filtered$Var1<-as.character(corr_c1_filtered$Var1)
+  corr_c1_filtered$Var2<-as.character(corr_c1_filtered$Var2)
+  corr_c1_filtered<-corr_c1_filtered[corr_c1_filtered$Var1 < corr_c1_filtered$Var2,]
+  corr_c1_filtered$V1<-paste0(corr_c1_filtered$Var1,";",corr_c1_filtered$Var2)
+  corr_c1_filtered$Var1<-NULL
+  corr_c1_filtered$Var2<-NULL
+  
+  corr<-merge(corr_c1_filtered,corr_c2,by=c("V1"))
+  
+  #Unique genes
+  num_genes<-length(union(gsub(";.*","",corr$V1),
+                          gsub(".*;","",corr$V1)))
+  
+  corr_comp<-rbind(corr_comp,
+                   data.frame(cell_type,
+                              c1=dataset_names[c1],
+                              c2="BLUEPRINT",
+                              gene_pairs=nrow(corr),
+                              genes_unique=num_genes,
+                              corr=cor(corr[[2]],corr[[3]],method="pearson")))
+}
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_blueprint_cd4t.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+################################################################################
+# For ImmuNexUT - all cell types
+################################################################################
+
+# Cell type matching
+ct_mapping<-data.frame(sc_ct=c("CD4T","CD8T","B","monocyte","NK","DC"),
+                       imn_ct=c("Naive_CD4","Naive_CD8","Naive_B","CL_Mono","NK","mDC"))
+
+corr_comp<-NULL
+for(i in 1:nrow(ct_mapping)){
+  
+  ct <- ct_mapping$imn_ct[i]
+  cell_type<- ct_mapping$sc_ct[i]
+  
+  #Load ImmuNexUT data
+  combat_tmm<-fread(paste0("imd_paper_rna_data/norm_count/",ct,"_norm_count.txt"))
+  
+  #Load the different single cell data sets
+  for(c1 in 1:length(datasets)){
+    #Load single cell data set
+    corr_c2<-fread(paste0(datasets[c1],cell_type,
+                          "/",cell_type,file_suffixes[c1]))
+    corr_c2<-resort(corr_c2)
+    
+    #Filter the ImmuNexUT data set
+    expressed_genes<-union(gsub(";.*","",corr_c2$V1),
+                           gsub(".*;","",corr_c2$V1))
+    expressed_genes<-intersect(expressed_genes,combat_tmm$V1)
+    
+    combat_tmm_filtered<-combat_tmm[combat_tmm$V1 %in% expressed_genes,]
+    combat_tmm_filtered<-as.data.frame(combat_tmm_filtered)
+    rownames(combat_tmm_filtered)<-combat_tmm_filtered$V1
+    combat_tmm_filtered$V1<-NULL
+    
+    #Calculation correlation
+    cor_matrix<-cor(t(combat_tmm_filtered),method="spearman")
+    cor_matrix<-reshape2::melt(cor_matrix)
+    cor_matrix$Var1<-as.character(cor_matrix$Var1)
+    cor_matrix$Var2<-as.character(cor_matrix$Var2)
+    cor_matrix<-cor_matrix[cor_matrix$Var1 < cor_matrix$Var2,]
+    cor_matrix$V1<-paste0(cor_matrix$Var1,";",cor_matrix$Var2)
+    cor_matrix$Var1<-NULL
+    cor_matrix$Var2<-NULL
+    
+    #Compare BIOS with single cell
+    corr<-merge(corr_c2,cor_matrix,by=c("V1"))
+    
+    #Unique genes
+    num_genes<-length(union(gsub(";.*","",corr$V1),
+                            gsub(".*;","",corr$V1)))
+    
+    corr_comp<-rbind(corr_comp,
+                     data.frame(cell_type,
+                                c1=dataset_names[c1],
+                                c2="ImmuNexUT",
+                                gene_pairs=nrow(corr),
+                                genes_unique=num_genes,
+                                corr=cor(corr[[2]],corr[[3]],method="pearson")))
+  }
+}
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_immunexut_allcts.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+################################################################################
+# For BIOS - all cell types
+################################################################################
+
+#Load the bios expression matrix
+bios_data<-fread("bios/gene_read_counts_BIOS_and_LLD_passQC.tsv.SampleSelection.ProbesWithZeroVarianceRemoved.TMM.SampleSelection.ProbesWithZeroVarianceRemoved.Log2Transformed.ProbesCentered.SamplesZTransformed.CovariatesRemovedOLS.noLLDeep.scGeneOnly.txt.gz")
+
+corr_comp<-NULL
+for(cell_type in c("CD4T","CD8T","monocyte","NK","B","DC")){
+  for(c1 in 1:length(datasets)){
+    
+    corr_c1<-fread(paste0(datasets[c1],cell_type,
+                          "/",cell_type,file_suffixes[c1]))
+    corr_c1<-resort(corr_c1)
+    expressed_genes<-union(gsub(";.*","",corr_c1$V1),
+                            gsub(".*;","",corr_c1$V1))
+    
+    #Filter BIOS for the genes expressed in the respective data set  
+    bios_data_filtered<-bios_data[bios_data$genename %in% expressed_genes,]
+    bios_data_filtered<-as.data.frame(bios_data_filtered)
+    rownames(bios_data_filtered)<-bios_data_filtered$genename
+    bios_data_filtered$genename<-NULL
+    
+    #Calculate correlation for BIOS
+    cor_matrix<-cor(t(bios_data_filtered),method="spearman")
+    cor_matrix<-reshape2::melt(cor_matrix)
+    cor_matrix$Var1<-as.character(cor_matrix$Var1)
+    cor_matrix$Var2<-as.character(cor_matrix$Var2)
+    cor_matrix<-cor_matrix[cor_matrix$Var1 < cor_matrix$Var2,]
+    cor_matrix$V1<-paste0(cor_matrix$Var1,";",cor_matrix$Var2)
+    cor_matrix$Var1<-NULL
+    cor_matrix$Var2<-NULL
+    
+    #Compare BIOS with single cell
+    corr<-merge(corr_c1,cor_matrix,by=c("V1"))
+    
+    #Unique genes
+    num_genes<-length(union(gsub(";.*","",corr$V1),
+                            gsub(".*;","",corr$V1)))
+    
+    corr_comp<-rbind(corr_comp,
+                     data.frame(cell_type,
+                                c1=dataset_names[c1],
+                                c2="BIOS",
+                                gene_pairs=nrow(corr),
+                                genes_unique=num_genes,
+                                corr=cor(corr[[2]],corr[[3]],method="pearson")))
+
+  }
+}
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_bios_allcts.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+################################################################################
+# Plot comparison of single cell vs bulk (Supplementary Figure)
+################################################################################
+
+#Load the different data sets
+corr_comp<-rbind(fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_bios_allcts.tsv"),
+                 fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_immunexut_allcts.tsv"),
+                 fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_blueprint_mono.tsv"),
+                 fread("co-expression_indivs_combined/dataset_comp_summary/correlation_singlecell_blueprint_cd4t.tsv"))
+
+corr_comp%>%
+  group_by(cell_type,c2)%>%
+  summarise(mean(corr),median(corr),min(corr),max(corr))
+ 
+#Remove CD4T cells (already shown in the main figure)
+corr_comp<-corr_comp[corr_comp$cell_type != "CD4T",]
+
+corr_comp$c1<-factor(corr_comp$c1,levels=dataset_names)
+
+corr_comp$cell_type<-ct_fullname[corr_comp$cell_type]
+
+g<-ggplot(corr_comp,aes(x=c2,y=c1,fill=corr))+
+  geom_tile()+
+  geom_text(aes(label=paste0(round(corr,3),"\n(",genes_unique,")"),
+                color=ifelse(corr<0.6,'white','black')),size=3)+
+  scale_color_manual(values=c("black","white"))+
+  xlab("Bulk cell data set")+
+  ylab("Single cell data set")+
+  scale_fill_viridis("Correlation",limits=c(0,1))+
+  facet_wrap(~cell_type)+
+  scale_y_discrete(labels=c("Oelen (v3)","Oelen (v2)","van Blokland\n(v2)",
+                            "van Blokland\n(v3)","van der Wijst"))+
+  theme(legend.position=c(0.9,0.1))+
+  guides(color=FALSE)
+print(g)
+
+ggsave(g,file=paste0("co-expression_indivs_combined/plots/corr_singlevsbulk_othercts.png"),
+       width=8.5,height=6.5)
+
+################################################################################
+# 3) Compare bulk vs bulk for Monocytes
+################################################################################
+
+#Special function to read bulk data as they are not all saved in the same file type
+read_bulk_data<-function(dataset_name){
+  
+  if(dataset_name=="BLUEPRINT"){
+    #Blueprint Monocyte correlation
+    path<-"blueprint_data/mono_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.1PCAsOverSamplesRemoved.spearmanr."
+    rowname_suffix<-"genes.txt"
+    
+    corr_c1 <- np$load(paste0(path,"npy"), mmap_mode="r")
+    row_names<-fread(paste0(path,rowname_suffix),header=FALSE)
+    rownames(corr_c1)<-row_names$V1
+    colnames(corr_c1)<-row_names$V1
+    rm(row_names)
+    
+    #Filter for single cell data
+    ct_single_cell<-"monocyte"
+    corr_sc<-fread(paste0("co-expression_indivs_combined/",ct_single_cell,"/",
+                          ct_single_cell,"_UT_correlation.csv"))
+    corr_sc$gene1<-gsub(";.*","",corr_sc$V1)
+    corr_sc$gene2<-gsub(".*;","",corr_sc$V1)
+    sc_genes<-union(corr_sc$gene1,corr_sc$gene2)
+    sc_genes<-sc_genes[sc_genes %in% colnames(corr_c1)]
+    
+    corr_c1<-corr_c1[sc_genes,sc_genes]
+    corr_c1<-reshape2::melt(corr_c1)
+    corr_c1$Var1<-as.character(corr_c1$Var1)
+    corr_c1$Var2<-as.character(corr_c1$Var2)
+    colnames(corr_c1)[1:3]<-c("gene1","gene2","corr")
+    
+    #Order so that gene1 is always the one first in alphabet
+    corr_c1<-corr_c1[corr_c1$gene1!=corr_c1$gene2,]
+    corr_c1$V1<-paste0(corr_c1$gene1,";",corr_c1$gene2)
+    corr_c1$gene1<-NULL
+    corr_c1$gene2<-NULL
+    
+    corr_c1<-corr_c1[,c("V1","corr")]
+    
+  } else if (dataset_name=="BIOS"){
+
+    #Load the bios expression matrix
+    bios_data<-fread("bios/gene_read_counts_BIOS_and_LLD_passQC.tsv.SampleSelection.ProbesWithZeroVarianceRemoved.TMM.SampleSelection.ProbesWithZeroVarianceRemoved.Log2Transformed.ProbesCentered.SamplesZTransformed.CovariatesRemovedOLS.noLLDeep.scGeneOnly.txt.gz")
+    
+    # Read single cell data to filter for the expressed genes
+    cell_type <- "monocyte"
+    corr_sc<-fread(paste0("co-expression_indivs_combined/",cell_type,
+                          "/",cell_type,"_UT_correlation.csv"))
+    corr_sc<-resort(corr_sc)
+    expressed_genes<-union(gsub(";.*","",corr_sc$V1),
+                           gsub(".*;","",corr_sc$V1))
+    
+    #Filter BIOS for the genes expressed in the respective data set  
+    bios_data_filtered<-bios_data[bios_data$genename %in% expressed_genes,]
+    bios_data_filtered<-as.data.frame(bios_data_filtered)
+    rownames(bios_data_filtered)<-bios_data_filtered$genename
+    bios_data_filtered$genename<-NULL
+    
+    #Calculate correlation for BIOS
+    corr_c1<-cor(t(bios_data_filtered),method="spearman")
+    corr_c1<-reshape2::melt(corr_c1)
+    corr_c1$Var1<-as.character(corr_c1$Var1)
+    corr_c1$Var2<-as.character(corr_c1$Var2)
+    corr_c1<-corr_c1[corr_c1$Var1 < corr_c1$Var2,]
+    corr_c1$V1<-paste0(corr_c1$Var1,";",corr_c1$Var2)
+    corr_c1$Var1<-NULL
+    corr_c1$Var2<-NULL
+    
+  } else { #ImmuNexUT
+    corr_c1<-fread("imd_paper_rna_data/correlation/CL_Mono_correlation.txt")
+    #all(corr_c1$gene1 < corr_c1$gene2)
+    corr_c1$V1<-paste0(corr_c1$gene1,";",corr_c1$gene2)
+    corr_c1$gene1<-NULL
+    corr_c1$gene2<-NULL
+  }
+  
+  return(corr_c1)
+}
+
+#Compare each bulk dataset against all other
+corr_comp<-NULL
+for(c1 in 1:(length(bulk_datasets)-1)){
+  
+  #Read correlation file one
+  dataset_name1<-bulk_datasets[c1]
+  corr_c1<-read_bulk_data(dataset_name1)
+  
+  #Unique genes
+  num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                          gsub(".*;","",corr_c1$V1)))
+  
+  corr_comp<-rbind(corr_comp,
+                   data.frame(c1=dataset_name1,
+                              c2=dataset_name1,
+                              gene_pairs=nrow(corr_c1),
+                              genes_unique=num_genes,
+                              corr=1))
+  
+  for(c2 in (c1+1):length(bulk_datasets)){
+    
+    #Read correlation file two
+    dataset_name2<-bulk_datasets[c2]
+    corr_c2<-read_bulk_data(dataset_name2)
+    
+    corr<-merge(corr_c1,corr_c2,by=c("V1"))
+    
+    #Unique genes
+    num_genes<-length(union(gsub(";.*","",corr$V1),
+                            gsub(".*;","",corr$V1)))
+    
+    corr_comp<-rbind(corr_comp,
+                     data.frame(c1=dataset_name1,
+                                c2=dataset_name2,
+                                gene_pairs=nrow(corr),
+                                genes_unique=num_genes,
+                                corr=cor(corr[[2]],corr[[3]],method="pearson")))
+  }
+}
+
+#Read correlation file one
+c1<-length(bulk_datasets)
+dataset_name1<-bulk_datasets[c1]
+corr_c1<-read_bulk_data(dataset_name1)
+
+#Unique genes
+num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                        gsub(".*;","",corr_c1$V1)))
+
+corr_comp<-rbind(corr_comp,
+                 data.frame(c1=dataset_name1,
+                            c2=dataset_name1,
+                            gene_pairs=nrow(corr_c1),
+                            genes_unique=num_genes,
+                            corr=1))
+
+# Save correlations
+write.table(corr_comp,
+            file="co-expression_indivs_combined/dataset_comp_summary/correlation_bulk_datasets_monocytes.tsv",
+            sep="\t",row.names = FALSE,quote=FALSE)
+
+# Save plot
+corr_comp$c1<-factor(corr_comp$c1,levels=bulk_datasets)
+corr_comp$c2<-factor(corr_comp$c2,levels=bulk_datasets)
+
+g<-ggplot(corr_comp,aes(x=c1,y=c2,fill=corr))+
+  geom_tile()+
+  geom_text(aes(label=paste0(round(corr,3),"\n(",genes_unique,")"),
+                color=ifelse(corr<0.6,'white','black')),size=3)+
+  scale_color_manual(values=c("black","white"))+
+  xlab("Bulk data set")+
+  ylab("Bulk data set")+
+  scale_fill_viridis("Correlation",limits=c(0,1))+
+  guides(color=FALSE)
+
+ggsave(g,file="co-expression_indivs_combined/plots/corr_bulk_mono.png",
+       width=5,height=3)
\ No newline at end of file
diff --git a/02_correlation_evaluation/correlation_timepoint_combined_indivs_1mio.py b/02_correlation_evaluation/correlation_timepoint_combined_indivs_1mio.py
new file mode 100644
index 0000000..15f0bdb
--- /dev/null
+++ b/02_correlation_evaluation/correlation_timepoint_combined_indivs_1mio.py
@@ -0,0 +1,97 @@
+###########################################################################################
+# Calculate correlation for each cell type, selecting always one timepoint (UT)
+# merging all individuals for Oelen v2 and v3 dataset
+###########################################################################################
+
+#from scipy.stats import t, norm
+from scipy.stats import spearmanr
+import scanpy as sc
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from time import time
+import os
+import re
+
+# specify if Oelen v3 or v2 dataset should be used
+version2 = True
+
+# load scanpy object
+lif version2:
+    prefix_results = Path('co-expression_indivs_combined/one_million_version2/')
+else:
+    prefix_results = Path('co-expression_indivs_combined/')
+    
+if version2:
+    alldata = sc.read_h5ad('seurat_objects/1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad')
+else:
+    alldata = sc.read_h5ad('seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad')
+    
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    selected_genes = df.columns[nonzerocounts>ratio]
+    return selected_genes
+
+# extract timepoint from timepoint - stimulation annotation
+def get_time(x):
+    if x == 'UT':
+        return x
+    else:
+        pattern = re.compile(r'\d+h')
+        return re.findall(pattern, x)[0]
+
+
+# extract timepoint from timepoint - stimulation annotation
+observations = alldata.obs.copy()
+observations['time_merged'] = [get_time(item) for item in observations['timepoint']]
+observations['timepoint_id_celltype'] = [f'{item[0]}_{item[1]}' for item
+                                           in observations[['time_merged', 'cell_type_lowerres']].values]
+
+celltypes = ['B', 'CD4T', 'CD8T', 'monocyte', 'DC', 'NK']
+for celltype in celltypes:
+    if not os.path.isdir(prefix_results/celltype):
+        os.mkdir(prefix_results/celltype)
+    starttime = time()
+    print(celltype)
+    specific = alldata[alldata.obs.cell_type_lowerres==celltype]
+    celltype_data = pd.DataFrame(data=specific.X.toarray(),
+                                 index=specific.obs.index,
+                                 columns=specific.var.index)
+
+    # get the set of gene pairs
+    specific_obs = observations[observations['cell_type_lowerres']==celltype]
+
+    for condition in ['UT', '3h', '24h']:
+
+        # filter for the condition
+        celltype_condition_data = celltype_data[specific_obs.time_merged==condition]
+        
+        # take either tsv file with selected genes or filter genes after a nonzero rate
+        if gene_selection_file:
+            selected_genes = pd.read_csv('co-expression_indivs_combined/coexp_tp_union/genelists_tp_union/expressed_gene_'+celltype+'.tsv')
+            selected_genes =selected_genes["genes"].tolist()
+        else:
+            selected_genes = select_gene_nonzeroratio(celltype_condition_data, 0.5)
+
+        print(f"Number of selected genes for {celltype} {condition}: {len(selected_genes)}")
+
+        gene_pairs = []
+        for i,gene1 in enumerate(selected_genes):
+            for j in range(i+1, len(selected_genes)):
+                gene_pairs.append(';'.join([gene1, selected_genes[j]]))
+
+        input_df = celltype_condition_data[selected_genes]
+        input_data = spearmanr(input_df, axis=0)[0]
+        input_data_uppertria = input_data[np.triu_indices_from(input_data, 1)]
+
+        corrs_df = pd.DataFrame(data=input_data_uppertria,
+                                columns=[f'{condition}'],
+                                index=gene_pairs)
+
+        corrs_df.to_csv(prefix_results/celltype/f'{celltype}_{condition}_correlation.csv')
+
+        #Filter for 0.3 correlation cutoff
+        corrs_df = corrs_df[corrs_df[condition]>0.3]
+        corrs_df.to_csv(prefix_results/celltype/f'{celltype}_{condition}_correlation_03filtered.csv')
+
+        print(f"Finished {celltype} with time {time() - starttime}")
diff --git a/02_correlation_evaluation/correlation_timepoint_combined_indivs_ng.py b/02_correlation_evaluation/correlation_timepoint_combined_indivs_ng.py
new file mode 100644
index 0000000..616a978
--- /dev/null
+++ b/02_correlation_evaluation/correlation_timepoint_combined_indivs_ng.py
@@ -0,0 +1,88 @@
+######################################################################
+# Calculate correlation for each cell type for van der Wijst dataset,
+# merging all individuals
+######################################################################
+
+from scipy.stats import spearmanr
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from time import time
+import scanpy as sc
+import os
+
+
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    selected_genes = df.columns[nonzerocounts>ratio]
+    return selected_genes
+
+
+def select_gene_variances(df, ratio):
+    variances = np.var(df.values, axis=0)/df.shape[0]
+    var_thres = np.percentile(variances, ratio)
+    # var_thres = np.nanmedian(variances)
+    selected_genes = df.columns[variances>var_thres]
+    print(selected_genes[:5])
+    return selected_genes
+
+
+def get_genename(df, mapping):
+    df['genename'] = [mapping.get(geneid) for geneid in df.index]
+    df = df.dropna(subset=['genename']).drop_duplicates(subset=['genename'])
+    df = df.set_index('genename')
+    return df
+
+
+# set working directory (to shorten path length)
+os.chdir('./')
+gene_selection_file = False
+
+# load scanpy object
+prefix_results = Path('co-expression_indivs_combined/ng_updated_version')
+# test stemi v2
+# alldata = sc.read_h5ad('seurat_objects/pilot3_subsetted_celltypes_final_ensemble_converted_samples.h5ad')
+alldata = sc.read_h5ad('seurat_objects/pilot3_seurat3_200420_sct_azimuth.h5ad')
+
+# extract timepoint from timepoint - stimulation annotation
+celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK':'NK', 'other T': 'otherT', 'other': 'other', 'B':'B'}
+alldata.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in alldata.obs['predicted.celltype.l1']]
+observations = alldata.obs.copy()
+celltypes = [item for item in observations['cell_type_mapped_to_onemillion'].unique() if not pd.isnull(item)]
+print(celltypes)
+for celltype in celltypes:
+    if not os.path.isdir(prefix_results / celltype):
+        os.mkdir(prefix_results / celltype)
+    starttime = time()
+    print(celltype)
+    specific = alldata[alldata.obs['cell_type_mapped_to_onemillion'] == celltype]
+    celltype_data = pd.DataFrame(data=specific.X.toarray(),
+                                 index=specific.obs.index,
+                                 columns=specific.var.index)
+    print(celltype_data.shape)
+    # get the set of gene pairs
+    specific_obs = observations[observations['cell_type_mapped_to_onemillion'] == celltype]
+    # filter for the condition
+    celltype_condition_data = celltype_data
+    # take either tsv file with selected genes or filter genes after a nonzero rate
+    if gene_selection_file:
+        selected_genes = pd.read_csv(' /genelists_tp_union/expressed_gene_' + celltype + '.tsv')
+        selected_genes = selected_genes["genes"].tolist()
+    else:
+        selected_genes = select_gene_nonzeroratio(celltype_condition_data, 0.5)
+    print(f"Number of selected genes for {celltype} : {len(selected_genes)}")
+    gene_pairs = []
+    for i, gene1 in enumerate(selected_genes):
+        for j in range(i + 1, len(selected_genes)):
+            gene_pairs.append(';'.join([gene1, selected_genes[j]]))
+    input_df = celltype_condition_data[selected_genes]
+    input_data = spearmanr(input_df, axis=0)[0]
+    input_data_uppertria = input_data[np.triu_indices_from(input_data, 1)]
+    corrs_df = pd.DataFrame(data=input_data_uppertria,
+                            columns=[f'UT'],
+                            index=gene_pairs)
+    corrs_df.to_csv(prefix_results / celltype / f'{celltype}_correlation.csv')
+    # Filter for 0.3 correlation cutoff
+    corrs_df = corrs_df[corrs_df['UT'] > 0.3]
+    corrs_df.to_csv(prefix_results / celltype / f'{celltype}_correlation_03filtered.csv')
+    print(f"Finished {celltype} with time {time() - starttime}")
diff --git a/02_correlation_evaluation/correlation_timepoint_combined_indivs_stemiv2.py b/02_correlation_evaluation/correlation_timepoint_combined_indivs_stemiv2.py
new file mode 100644
index 0000000..8312a5f
--- /dev/null
+++ b/02_correlation_evaluation/correlation_timepoint_combined_indivs_stemiv2.py
@@ -0,0 +1,50 @@
+##############################################################################
+# Calculate correlation for each cell type for the van Blokland v2 dataset,
+# timpeoint 6-8 weeks after admission, merging all individuals
+##############################################################################
+
+from scipy.stats import spearmanr
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from time import time
+import os
+
+
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    selected_genes = df.columns[nonzerocounts>ratio]
+    return selected_genes
+
+# set working directory (to shorten path length)
+os.chdir('./')
+
+# load scanpy object
+prefix_results = Path('co-expression_indivs_combined/stemi/')
+stemi_data = pd.read_csv('seurat_objects/stemi_v2_monocyte.csv.gz', compression='gzip', sep=' ', index_col=0).T
+stemi_meta = pd.read_csv('seurat_objects/stemi_v2_monocyte.meta.csv', sep=' ', index_col=0)
+
+for condition in stemi_meta['timepoint.final'].unique():
+    starttime = time()
+    # filter for the condition
+    celltype_condition_data = stemi_data[stemi_meta['timepoint.final']==condition]
+    # take either tsv file with selected genes or filter genes after a nonzero rate
+    selected_genes = select_gene_nonzeroratio(celltype_condition_data, 0.5)
+    print(f"Number of selected genes for stemi {condition}: {len(selected_genes)}")
+    # get gene pair names
+    gene_pairs = []
+    for i,gene1 in enumerate(selected_genes):
+        for j in range(i+1, len(selected_genes)):
+            gene_pairs.append(';'.join([gene1, selected_genes[j]]))
+    # get gene-gene correlations
+    input_df = celltype_condition_data[selected_genes]
+    input_data = spearmanr(input_df, axis=0)[0]
+    input_data_uppertria = input_data[np.triu_indices_from(input_data, 1)]
+    corrs_df = pd.DataFrame(data=input_data_uppertria,
+                            columns=[f'{condition}'],
+                            index=gene_pairs)
+    corrs_df.to_csv(prefix_results/f'monocyte_{condition}_correlation.csv')
+    #Filter for 0.3 correlation cutoff
+    corrs_df = corrs_df[corrs_df[condition]>0.3]
+    corrs_df.to_csv(prefix_results/f'monocyte_{condition}_correlation_03filtered.csv')
+    print(f"Finished {condition} with time {time() - starttime}")
diff --git a/02_correlation_evaluation/correlation_timepoint_combined_indivs_stemiv3.py b/02_correlation_evaluation/correlation_timepoint_combined_indivs_stemiv3.py
new file mode 100644
index 0000000..6892bc5
--- /dev/null
+++ b/02_correlation_evaluation/correlation_timepoint_combined_indivs_stemiv3.py
@@ -0,0 +1,91 @@
+##############################################################################
+# Calculate correlation for each cell type for the van Blokland v3 dataset,
+# timpeoint 6-8 weeks after admission, merging all individuals
+##############################################################################
+
+#from scipy.stats import t, norm
+from scipy.stats import spearmanr
+import scanpy as sc
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from time import time
+import os
+import re
+
+# specify if the gene selection was done before and is passed in a file
+gene_selection_file = False
+
+# set working directory (to shorten path length)
+os.chdir('./')
+
+# load scanpy object
+prefix_results = Path('co-expression_indivs_combined/stemi/version3')
+# test stemi v2
+alldata = sc.read_h5ad('seurat_objects/cardio.integrated.20210301.stemiv3.h5ad')
+
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    selected_genes = df.columns[nonzerocounts>ratio]
+    return selected_genes
+
+# extract timepoint from timepoint - stimulation annotation
+def get_time(x):
+    if x == 'UT':
+        return x
+    else:
+        pattern = re.compile(r'\d+h')
+        return re.findall(pattern, x)[0]
+
+
+# extract timepoint from timepoint - stimulation annotation
+observations = alldata.obs.copy()
+observations['timepoint_id_celltype'] = [f'{item[0]}_{item[1]}' for item
+                                           in observations[['timepoint.final', 'cell_type_lowerres']].values]
+
+celltypes = ['B', 'CD4T', 'CD8T', 'monocyte', 'DC', 'NK']
+for celltype in celltypes:
+    if not os.path.isdir(prefix_results/celltype):
+        os.mkdir(prefix_results/celltype)
+    starttime = time()
+    print(celltype)
+    specific = alldata[alldata.obs.cell_type_lowerres==celltype]
+    celltype_data = pd.DataFrame(data=specific.X.toarray(),
+                                 index=specific.obs.index,
+                                 columns=specific.var.index)
+
+    # get the set of gene pairs
+    specific_obs = observations[observations['cell_type_lowerres']==celltype]
+    for condition in observations['timepoint.final'].unique():
+        # filter for the condition
+        celltype_condition_data = celltype_data[specific_obs['timepoint.final']==condition]
+        
+        # take either tsv file with selected genes or filter genes after a nonzero rate
+        if gene_selection_file:
+            selected_genes = pd.read_csv(' /genelists_tp_union/expressed_gene_'+celltype+'.tsv')
+            selected_genes =selected_genes["genes"].tolist()
+        else:
+            selected_genes = select_gene_nonzeroratio(celltype_condition_data, 0.5)
+
+        print(f"Number of selected genes for {celltype} {condition}: {len(selected_genes)}")
+
+        gene_pairs = []
+        for i,gene1 in enumerate(selected_genes):
+            for j in range(i+1, len(selected_genes)):
+                gene_pairs.append(';'.join([gene1, selected_genes[j]]))
+
+        input_df = celltype_condition_data[selected_genes]
+        input_data = spearmanr(input_df, axis=0)[0]
+        input_data_uppertria = input_data[np.triu_indices_from(input_data, 1)]
+
+        corrs_df = pd.DataFrame(data=input_data_uppertria,
+                                columns=[f'{condition}'],
+                                index=gene_pairs)
+
+        corrs_df.to_csv(prefix_results/celltype/f'{celltype}_{condition}_correlation.csv')
+
+        #Filter for 0.3 correlation cutoff
+        corrs_df = corrs_df[corrs_df[condition]>0.3]
+        corrs_df.to_csv(prefix_results/celltype/f'{celltype}_{condition}_correlation_03filtered.csv')
+
+        print(f"Finished {celltype} with time {time() - starttime}")
diff --git a/02_correlation_evaluation/figure2_barplot_cutoffs.R b/02_correlation_evaluation/figure2_barplot_cutoffs.R
new file mode 100644
index 0000000..df79e75
--- /dev/null
+++ b/02_correlation_evaluation/figure2_barplot_cutoffs.R
@@ -0,0 +1,72 @@
+# ------------------------------------------------------------------------------
+# Create barplot of correlation dependency on expression cutoff
+# for Oelen v3 and ImmuNexUT / Blueprint
+# (only plotting in R, calculation done with python script)
+# Input: correlation comparison between Blueprint and Oelen v3 dataset 
+#        (precalculated in compare_blueprint_cutoffs_CD4T.py) and between
+#        ImmuNexUT and Oelen v3 dataset (precalculated in 
+#        compare_immunexut_cutoffs_CD4T.py)
+# Output: two barplots, one for Blueprint comparsion and one for 
+#         ImmuNexUT comparison
+# ------------------------------------------------------------------------------
+
+library(ggplot2)
+library(RColorBrewer)
+
+theme_set(theme_bw())
+
+################################################################################
+# Plot for ImmuNexUT (main Figure 2c)
+################################################################################
+
+vals<-read.table("co-expression_indivs_combined/immunexut_cutoff_eval_CD4T.txt",
+                 sep=",",header=TRUE)
+
+vals$threshold<-as.factor(vals$threshold)
+g<-ggplot(vals,aes(x=threshold,y=corr_pearson,fill=ngenes))+
+  geom_bar(stat="identity")+
+  geom_text(aes(x = threshold, y = corr_pearson / 2, label = ngenes,
+            color=ifelse(ngenes<1000,'white','black')),size=5)+
+  scale_color_manual(values=c("black","white"))+
+  xlab("Expression cutoff")+
+  ylab("Correlation between Oelen (v3)\nand ImmuNexUT")+ylim(0,1)+
+  scale_fill_distiller("Number of\ngenes",palette="YlOrBr")+
+  theme(legend.position = "bottom",
+        legend.key.width = unit(1, "cm"),
+        axis.title = element_text(size=16),
+        axis.text = element_text(size=14),
+        legend.title=element_text(size=13),
+        legend.text=element_text(size=12))+
+  guides(color=FALSE)
+print(g)
+ggsave(g,file="co-expression_indivs_combined/plots/eval_immunexut_cutoff.pdf",
+       width=6,height=5)
+
+
+################################################################################
+# Plot for Blueprint (Supplement)
+################################################################################
+
+vals<-read.table("co-expression_indivs_combined/blueprint_cutoff_eval_CD4T.txt",
+                 sep=",",header=TRUE)
+
+vals$threshold<-as.factor(vals$threshold)
+g<-ggplot(vals,aes(x=threshold,y=corr_pearson,fill=ngenes))+
+  geom_bar(stat="identity")+
+  geom_text(aes(x = threshold, y = corr_pearson / 2, label = ngenes,
+                color=ifelse(ngenes<1000,'white','black')),size=5)+
+  scale_color_manual(values=c("black","white"))+
+  xlab("Expression cutoff")+
+  ylab("Correlation between Oelen (v3)\nand BLUEPRINT")+ylim(0,1)+
+  scale_fill_distiller("Number of\ngenes",palette="YlOrBr")+
+  theme(legend.position = "bottom",
+        legend.key.width = unit(1, "cm"),
+        axis.title = element_text(size=16),
+        axis.text = element_text(size=14),
+        legend.title=element_text(size=13),
+        legend.text=element_text(size=12))+
+  guides(color=FALSE)
+print(g)
+ggsave(g,file="co-expression_indivs_combined/plots/eval_blueprint_cutoff.png",
+       width=6,height=5)
+
diff --git a/02_correlation_evaluation/figure2_scatterplots.R b/02_correlation_evaluation/figure2_scatterplots.R
new file mode 100644
index 0000000..15925d6
--- /dev/null
+++ b/02_correlation_evaluation/figure2_scatterplots.R
@@ -0,0 +1,162 @@
+# ------------------------------------------------------------------------------
+# Create inset plots for Main Figure 2 (a,b,d), showing scatterplots of
+# gene pair-wise Spearman correlation values between two data sets for
+# a) Oelen v3 dataset vs van Blokland v2 dataset (both CD4+ T cells)
+# b) ImmuNexUT - van Blokland v2 (naive CD4+ T cells and CD4+ T cells)
+# c) Blueprint - ImmuNexUT (both naive CD4+ T cells)
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(reticulate) # to read the single cell data (numpy)
+library(reshape2)
+library(ggplot2)
+library(viridis)
+library(ggpubr)
+
+np <- import("numpy")
+
+theme_set(theme_bw())
+
+#Load single cell
+load_sc_corr_data<-function(path){
+  
+  # Load single cell data
+  corr_ct<-fread(path)
+  corr_ct$gene1<-sapply(corr_ct$V1,function(s) strsplit(s,";")[[1]][1])
+  corr_ct$gene2<-sapply(corr_ct$V1,function(s) strsplit(s,";")[[1]][2])
+  corr_ct$V1<-NULL
+  
+  #Order so that gene1 is always the one first in alphabet
+  corr_ct$swap<-ifelse(corr_ct$gene1 > corr_ct$gene2,corr_ct$gene1,corr_ct$gene2)
+  corr_ct$gene1<-ifelse(corr_ct$gene1 > corr_ct$gene2,corr_ct$gene2,corr_ct$gene1)
+  corr_ct$gene2<-corr_ct$swap
+  corr_ct$swap<-NULL
+  
+  return(corr_ct)
+}
+
+#Load data saved in numpy format
+load_numpy_data<-function(path,rowname_suffix,colname_suffix,corr_sc){
+  corr_bios <- np$load(paste0(path,"npy"), mmap_mode="r")
+  row_names<-fread(paste0(path,rowname_suffix),header=FALSE)
+  rownames(corr_bios)<-row_names$V1
+  col_names<-fread(paste0(path,colname_suffix),header=FALSE)
+  colnames(corr_bios)<-col_names$V1
+  rm(row_names,col_names)
+  
+  #Filter for single cell data
+  sc_genes<-sort(union(corr_sc$gene1,corr_sc$gene2))
+  sc_genes<-sc_genes[sc_genes %in% colnames(corr_bios)]
+  corr_bios<-corr_bios[sc_genes,sc_genes]
+  corr_bios<-reshape2::melt(corr_bios)
+  corr_bios$Var1<-as.character(corr_bios$Var1)
+  corr_bios$Var2<-as.character(corr_bios$Var2)
+  colnames(corr_bios)[1:2]<-c("gene1","gene2")
+  
+  
+  #Order so that gene1 is always the one first in alphabet
+  corr_bios$swap<-ifelse(corr_bios$gene1 > corr_bios$gene2,corr_bios$gene1,corr_bios$gene2)
+  corr_bios$gene1<-ifelse(corr_bios$gene1 > corr_bios$gene2,corr_bios$gene2,corr_bios$gene1)
+  corr_bios$gene2<-corr_bios$swap
+  corr_bios$swap<-NULL
+  corr_bios<-corr_bios[corr_bios$gene1!=corr_bios$gene2,]
+  
+  return(corr_bios)
+}
+
+#Create ggplot 2d histogram based on two correlation data frames
+create_corr_plot<-function(corr_d1,corr_d2,
+                           xlab_text,ylab_text,
+                           annot_text_size=9,annot_text_digits=3){
+  
+  #Merge both
+  corrs<-merge(corr_d1,corr_d2,by=c("gene1","gene2"))
+  
+  print(paste("Overlapping genes:",length(union(corrs$gene1,
+                                                corrs$gene2))))
+  
+  corr_corr<-cor(corrs$corr1,corrs$corr2,
+                 method="pearson")
+  
+  #Plot
+  g<-ggplot(corrs,aes(corr1,corr2))+
+    geom_bin2d(bins=50)+
+    xlab(xlab_text)+
+    ylab(ylab_text)+
+    xlim(-1,1)+ylim(-1,1)+
+    scale_fill_distiller("Density",palette="BuPu",trans="log10",
+                         breaks = c(2, 600), 
+                         labels = c("Low", "High"))+
+    annotate(geom="text", x=-0.95, y=0.95,size=annot_text_size,
+             hjust = 0,vjust=1,
+             label=paste0("r = ",format(corr_corr,digits=annot_text_digits)))+
+    ggtitle("Pairwise gene correlation")+
+    geom_smooth(method="lm",color="black")+
+    theme(legend.position="none",
+          plot.title=element_text(size=25),
+          axis.title=element_text(size=25),
+          axis.text=element_text(size=20))
+  
+  return(g)
+}
+  
+  
+################################################################################
+# For 2a: Oelen v3 - van Blokland v2
+################################################################################
+
+main_celltype<-"CD4T"
+
+corr_oelen<-load_sc_corr_data(paste0("co-expression_indivs_combined/",
+                                     main_celltype,"/",main_celltype,
+                                     "_UT_correlation.csv"))
+
+corr_stemi<-load_sc_corr_data(paste0("co-expression_indivs_combined/stemi/version2/",
+                                     main_celltype,"/",main_celltype,
+                                     "_t8w_correlation.csv"))
+
+colnames(corr_oelen)<-c("corr1","gene1","gene2")
+colnames(corr_stemi)<-c("corr2","gene1","gene2")
+
+#Create gggplot
+g<-create_corr_plot(corr_oelen,corr_stemi,
+                    xlab_text="Oelen (v3)", ylab_text="van Blokland (v2)")
+
+g_leg<-get_legend(g+theme(legend.position = "bottom"))
+g_leg<-as_ggplot(g_leg)
+ggsave(g_leg,file="bios/plots/figure2_legend_inset.pdf",width=3,height=1)
+
+
+ggsave(g,file="bios/plots/figure2a_exampleplot.pdf",width=5,height=5)
+
+################################################################################
+# For 2b: ImmuNexUT - van Blokland v2
+################################################################################
+
+corr_immu<-fread("imd_paper_rna_data/correlation/Naive_CD4_correlation.txt")
+corr_immu$V1<-NULL
+
+colnames(corr_immu)<-c("gene1","gene2","corr1")
+
+#Create gggplot
+g<-create_corr_plot(corr_immu,corr_stemi,
+                    xlab_text="ImmuNexUT",ylab_text="van Blokland (v2)")
+
+ggsave(g,file="bios/plots/figure2b_exampleplot.pdf",width=5,height=5)
+
+################################################################################
+# For 2c: Blueprint - ImmuNexUT
+################################################################################
+
+#Load Blueprint data
+corr_bp<-load_numpy_data(path="blueprint_data/tcel_gene_nor_combat_20151109.ProbesWithZeroVarianceRemoved.ProbesCentered.SamplesZTransformed.spearmanR.",
+                         rowname_suffix="rows.txt",
+                         colname_suffix="cols.txt",
+                         corr_immu)
+
+colnames(corr_bp)<-c("gene1","gene2","corr2")
+
+#Create gggplot
+g<-create_corr_plot(corr_immu,corr_bp,
+                    xlab_text="ImmuNexUT",ylab_text="BLUEPRINT")
+ggsave(g,file="bios/plots/figure2c_exampleplot.pdf",width=5,height=5)
diff --git a/02_correlation_evaluation/normalize_ImmuNexUT.R b/02_correlation_evaluation/normalize_ImmuNexUT.R
new file mode 100644
index 0000000..80e04ea
--- /dev/null
+++ b/02_correlation_evaluation/normalize_ImmuNexUT.R
@@ -0,0 +1,142 @@
+# ------------------------------------------------------------------------------
+# Normalize ImmuNexUT data (separately for each cell type with a matching
+# single-cell cell type) following the description in the corresponding
+# publication (filtering lowly expressed genes, TMM normalization and 
+# batch correction)
+# followed by correlation calculation for all genes expressed in 50% of the cells
+# of the Oelen v3 dataset (for comparison with single cell data)
+# Input: Count matrices downloaded from 
+#        https://humandbs.biosciencedbc.jp/en/hum0214-v5#E-GEAD-397,
+#        correlation estimates from Oelen v3 to identify the expressed genes
+#        for downstream comparisons
+# Output: normalized count matrices (one per cell type), orrelation matrices
+#         for all genes expressed in 50% of the cells of the Oelen v3 dataset and
+#         plots for comparison between ImmuNexUT and Oelen v3 dataset
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(edgeR) #for normalization
+library(sva) # for batch correction with combat
+library(corrplot) # to plot sample correlations
+library(ggplot2)
+library(viridis)
+
+theme_set(theme_bw())
+
+# Cell type matching
+ct_mapping<-data.frame(sc_ct=c("CD4T","CD8T","B","monocyte","NK","DC"),
+                       imn_ct=c("Naive_CD4","Naive_CD8","Naive_B","CL_Mono","NK","mDC"))
+
+
+for(i in 1:nrow(ct_mapping)){
+  
+  ct <- ct_mapping$imn_ct[i]
+  ct_single_cell<- ct_mapping$sc_ct[i]
+  
+  # Try to prevent redoing the whole normalization when the correlation
+  # is already calculated
+  corr_file_name<-paste0("imd_paper_rna_data/correlation/",ct,"_correlation.txt")
+  if(! file.exists(corr_file_name)){
+    
+    counts<-fread(paste0("imd_paper_rna_data/count/",ct,"_count.txt"))
+    
+    #Format to matrix
+    gene_id<-counts$Gene_id
+    gene_name<-counts$Gene_name
+    counts$Gene_name<-NULL
+    counts$Gene_id<-NULL
+    counts<-as.matrix(counts)
+    rownames(counts)<-gene_name
+      
+    #Filter lowly expressed genes (at least 10 in > 90% of samples)
+    counts<-counts[!(rowSums(counts<10) > 0.9 * ncol(counts)),]
+    
+    #Normalize using edgeR (TMM plus log-transformed CPM)
+    dge <- DGEList(counts=counts)
+    dge <- calcNormFactors(dge, method = "TMM")
+    tmm <- cpm(dge) #in publication it says log-transformed CPM, but log-transformation
+                    #is not working in combination with combat ...
+    
+    #Remove batch data using combat
+    batch_data<-fread("imd_paper_rna_data/clinical_diagnosis_age_sex_v2.txt")
+    
+    #Filter batch data for samples in the matrix
+    batch_data<-batch_data[batch_data$id %in% colnames(tmm),]
+    print(paste("Sorted the batch data correctly:",all(batch_data$id == colnames(tmm))))
+    
+    modcombat = model.matrix(~1, data=batch_data)
+    combat_tmm = ComBat(dat=tmm, batch=batch_data$Phase,mod=modcombat, prior.plots = FALSE)
+    
+    #Check that correlation between samples is high
+    cor_matrix<-cor(combat_tmm)
+    
+    #Filter samples with a correlation coefficient less than 0.9
+    cor_coef_mean<-rowMeans(cor_matrix)
+    combat_tmm<-combat_tmm[,names(cor_coef_mean)[cor_coef_mean>=0.9]]
+    
+    # #Plot remaining samples
+    # cor_matrix<-cor(combat_tmm)
+    # png("imd_paper_rna_data/plots/sample_correlation.png")
+    # corrplot(cor_matrix,method="color",order="hclust",tl.col="black",tl.cex=0.2)
+    # dev.off()
+    
+    #Combine genes that appear multiple times in the matrix 
+    combat_tmm<- apply(combat_tmm, 2, tapply, rownames(combat_tmm),
+                       mean, na.rm=T)
+    
+    #Save normalized matrix
+    write.table(combat_tmm, file=paste0("imd_paper_rna_data/norm_count/",ct,"_norm_count.txt"),
+                quote=FALSE,sep="\t")
+    
+    #Read single cell correlation
+    corr_ct<-fread(paste0("co-expression_indivs_combined/",ct_single_cell,"/",
+                          ct_single_cell,"_UT_correlation.csv"))
+    corr_ct$gene1<-gsub(";.*","",corr_ct$V1)
+    corr_ct$gene2<-gsub(".*;","",corr_ct$V1)
+    
+    #Order so that gene1 is always the one first in alphabet
+    corr_ct$swap<-ifelse(corr_ct$gene1 > corr_ct$gene2,corr_ct$gene1,corr_ct$gene2)
+    corr_ct$gene1<-ifelse(corr_ct$gene1 > corr_ct$gene2,corr_ct$gene2,corr_ct$gene1)
+    corr_ct$gene2<-corr_ct$swap
+    corr_ct$swap<-NULL
+    corr_ct$V1<-NULL
+    
+    #Filter for correlation values in CD4 T cells
+    expressed_genes<-union(corr_ct$gene1,corr_ct$gene2)
+    combat_tmm<-combat_tmm[rownames(combat_tmm) %in% expressed_genes,]
+    
+    #Calculation correlation
+    cor_matrix<-cor(t(combat_tmm),method="spearman")
+    cor_matrix<-reshape2::melt(cor_matrix)
+    cor_matrix$Var1<-as.character(cor_matrix$Var1)
+    cor_matrix$Var2<-as.character(cor_matrix$Var2)
+    cor_matrix<-cor_matrix[cor_matrix$Var1 < cor_matrix$Var2,]
+    colnames(cor_matrix)<-c("gene1","gene2","corr")
+    
+    #Save correlation
+    write.table(cor_matrix, file=corr_file_name,
+                quote=FALSE,sep="\t")
+  }
+    
+  #Compare with single cell correlation
+  cor_matrix<-merge(cor_matrix,corr_ct,by=c("gene1","gene2"))
+  
+  ylab_text<-paste("Correlation ImmuNexUT -",ct)
+  plot_path<-paste0("imd_paper_rna_data/plots/correlation_",ct,".png")
+  corr_corr<-cor(cor_matrix$UT,cor_matrix$corr)
+  
+  g<-ggplot(cor_matrix,aes(UT,corr))+
+    geom_bin2d(bins=50)+
+    xlab("Correlation single cell")+
+    ylab(ylab_text)+
+    xlim(-1,1)+ylim(-1,1)+
+    scale_fill_viridis(trans="log10")+
+    annotate(geom="text", x=-0.95, y=0.95,size=8,
+             hjust = 0,vjust=1,
+             label=paste0("r = ",format(corr_corr,digits=2)))+
+    theme(axis.title=element_text(size=16),
+          axis.text=element_text(size=14),
+          legend.position="none")
+  
+  ggsave(g,file=plot_path)
+}
\ No newline at end of file
diff --git a/02_correlation_evaluation/wilcoxon_test_crispr.R b/02_correlation_evaluation/wilcoxon_test_crispr.R
new file mode 100644
index 0000000..bf85c42
--- /dev/null
+++ b/02_correlation_evaluation/wilcoxon_test_crispr.R
@@ -0,0 +1,212 @@
+# ------------------------------------------------------------------------------
+# Benchmark our correlation results from single cell (Oelen v3, CD4+ T cells)
+# and bulk (ImmuNexUT, naive CD4+ T cells)
+# with a public CRISPR perturbation dataset using Wilcoxon Rank Sum Test
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(ggplot2)
+library(ggpubr)
+library(RColorBrewer)
+library(gtools)
+library(dplyr)
+
+theme_set(theme_bw())
+
+#Get colors
+cols_brewer <- c(brewer.pal(n = 3, "Set2")[2],"grey78")
+
+#Set MT correction for KO gene identification
+MTcorrection<-"FDR" #alternatives: "Bonf","FDR"
+print(paste("MT correction:",MTcorrection))
+
+# Load single cell data
+ct<-"CD4T" #alternative "CD8T"
+cond<-"UT"
+
+corr_sc<-fread(paste0("co-expression_indivs_combined/",ct,"/",ct,"_",cond,
+                      "_correlation.csv"))
+corr_sc$gene1<-gsub(";.*","",corr_sc$V1)
+corr_sc$gene2<-gsub(".*;","",corr_sc$V1)
+
+corr_sc$swap<-ifelse(corr_sc$gene1 > corr_sc$gene2,corr_sc$gene1,corr_sc$gene2)
+corr_sc$gene1<-ifelse(corr_sc$gene1 > corr_sc$gene2,corr_sc$gene2,corr_sc$gene1)
+corr_sc$gene2<-corr_sc$swap
+corr_sc$swap<-NULL
+
+# Load ImmuNexUT data (already preprocessed correctly)
+ct<-"Naive_CD4"
+corr_imn<-fread(paste0("imd_paper_rna_data/correlation/",
+                      ct,"_correlation_extended.txt"))
+corr_imn$V1<-NULL
+colnames(corr_imn)[3]<-"UT"
+
+# Filter for genes that are expressed in both data sets
+expressed_genes_sc<-union(corr_sc$gene1,corr_sc$gene2)
+expressed_genes_bulk<-union(corr_imn$gene1,corr_imn$gene2)
+expressed_genes<-intersect(expressed_genes_sc,expressed_genes_bulk)
+
+print(paste("Number of genes expressed in both data sets:",length(expressed_genes)))
+
+# Load perturbation data
+path<-"perturbation_dataset/perturbation_data/CD4T_GATE2019_MAST_DE/WT_KO/"
+path_negControl <- "perturbation_dataset/perturbation_data/CD4T_GATE2019_MAST_DE/WT_NP/"
+
+# Get a list with all DE genes
+files<-list.files(path)
+
+# Use the setting without artifical cells
+files<-files[!startsWith(files,"artificialCells_")]
+genes<-unique(sapply(files,function(fl) strsplit(fl,"\\.")[[1]][1]))
+print(paste0("Unique genes:",length(genes)))
+
+genes<-genes[genes %in% expressed_genes]
+print(paste0("Unique genes expressed in 50% of cells:",length(genes)))
+
+# Iterate over both data sets and all KO genes to perform Wilcoxon test
+p_vals<-NULL
+all_comps<-NULL
+for(data_type in c("ImmuNexUT","sc")){
+  
+  if(data_type == "sc"){
+    corr_ct<-corr_sc
+    expressed_genes<-expressed_genes_sc
+   
+  } else if (data_type == "ImmuNexUT"){
+    corr_ct<-corr_imn
+    expressed_genes<-expressed_genes_bulk
+  }
+  
+  #Bonferroni cutoff corrected for the number of expressed genes
+  if(MTcorrection == "Bonf"){
+    cutoff<-0.05/length(expressed_genes)
+  } else {
+    cutoff<-0.05
+  }
+  
+  # Go over each gene
+  plot_list<-list()
+  for(gene in genes){
+    
+    corr_ct_ko<-corr_ct[corr_ct$gene1==gene,c("gene2","UT")]
+    colnames(corr_ct_ko)[1]<-"gene1"
+    corr_ct_ko<-rbind(corr_ct_ko,corr_ct[corr_ct$gene2==gene,c("gene1","UT")])
+    
+    #Use absolute correlation
+    corr_ct_ko$UT<-abs(corr_ct_ko$UT)
+    
+    #Get all knock_out genes
+    all_measured_ko_genes<-NULL
+    ko_genes_combined<-NULL
+    for(fl in files[startsWith(files,gene)]){
+      ko_genes<-read.table(paste0(path,fl))
+      all_measured_ko_genes<-union(all_measured_ko_genes,rownames(ko_genes))
+      
+      if(MTcorrection=="FDR"){
+        ko_genes<-ko_genes[rownames(ko_genes) %in% expressed_genes,]
+        ko_genes$p_val<-p.adjust(ko_genes$p_val,method="BH")
+      }
+      
+      #Filter for expressed genes and significant threshold
+      ko_genes<-ko_genes[rownames(ko_genes) %in% expressed_genes &
+                           ko_genes$p_val<cutoff,]
+      ko_genes_combined<-union(ko_genes_combined,rownames(ko_genes))
+    }
+    
+    print(gene)
+    
+    genes_found_in_both<-intersect(all_measured_ko_genes,expressed_genes)
+    print(paste("Number genes measured in both:",length(genes_found_in_both)))
+    
+    #Remove false positive DE genes
+    for(fl in list.files(path_negControl,pattern=gene)){
+      fp_genes<-read.table(paste0(path_negControl,fl))
+      
+      if(MTcorrection=="FDR"){
+        fp_genes<-ko_genes[rownames(fp_genes) %in% expressed_genes,]
+        fp_genes$p_val<-p.adjust(fp_genes$p_val,method="BH")
+      }
+      
+      fp_genes<-fp_genes[fp_genes$p_val<cutoff,]
+      ko_genes_combined<-setdiff(ko_genes_combined,rownames(fp_genes))
+    }
+    
+    corr_ct_ko<-corr_ct_ko[corr_ct_ko$gene1 %in% genes_found_in_both,]
+    corr_ct_ko$is_ko<-corr_ct_ko$gene1 %in% ko_genes_combined
+    
+    corr_ct_ko$ko_gene<-paste0(gene," (",sum(corr_ct_ko$is_ko)," DE genes)")
+    corr_ct_ko$data_type<-data_type
+    
+    all_comps<-rbind(all_comps,corr_ct_ko)
+    
+    wt<-wilcox.test(corr_ct_ko$UT[corr_ct_ko$is_ko],corr_ct_ko$UT[!corr_ct_ko$is_ko],
+                    paired=FALSE,alternative="greater")
+    
+    p_vals<-rbind(p_vals,data.frame(ko_gene=paste0(gene," (",sum(corr_ct_ko$is_ko)," DE genes)"),
+                                    data_type,pval=wt$p.value))
+  }
+}
+
+all_comps$data_type<-ifelse(all_comps$data_type=="sc","single cell",
+                            "ImmuNexUT")
+all_comps$data_type<-factor(all_comps$data_type,
+                            levels=c("single cell","ImmuNexUT"))
+all_comps$is_ko<-ifelse(all_comps$is_ko,"DE genes","Non DE\ngenes")
+all_comps$is_ko<-factor(all_comps$is_ko,levels=c("DE genes","Non DE\ngenes"))
+
+p_vals$data_type<-ifelse(p_vals$data_type=="sc","single cell",
+                            "ImmuNexUT")
+
+p_vals$pvaltext<-paste0("p = ",format(round(p_vals$pval,3),nsmall=3))
+p_vals$pvaltext<-ifelse(p_vals$pvaltext=="p = 0.000","p < 0.001",p_vals$pvaltext)
+
+#Set text always to maximal value
+max_corr<-all_comps%>%
+  group_by(ko_gene,data_type)%>%
+  summarize(max_UT=max(UT))
+
+p_vals<-merge(p_vals,max_corr,by=c("ko_gene","data_type"))
+p_vals$max_UT<-p_vals$max_UT*1.1
+p_vals$is_ko<-1.5
+
+g_sc<-ggplot()+
+  geom_violin(data=all_comps[all_comps$data_type=="single cell",],
+               aes(x=is_ko,y=UT,fill=is_ko))+
+  geom_boxplot(data=all_comps[all_comps$data_type=="single cell",],
+               aes(x=is_ko,y=UT,fill=is_ko),
+               width = 0.15, outlier.shape = NA)+
+  geom_text(data=p_vals[p_vals$data_type=="single cell",],
+            aes(x=is_ko,y=max_UT,label=pvaltext),
+            size=6)+
+  facet_wrap(~ko_gene,scales ="free",nrow=1)+
+  xlab("")+
+  ylab("Absolute correlation (single cell)")+
+  scale_fill_manual("DE gene\nafter KO",values=cols_brewer)+
+  theme(legend.position="none",
+        axis.title=element_text(size=15),
+        axis.text=element_text(size=14),
+        strip.text=element_text(size=15))
+
+g_bulk<-ggplot()+
+  geom_violin(data=all_comps[all_comps$data_type=="ImmuNexUT",],
+               aes(x=is_ko,y=UT,fill=is_ko))+
+  geom_boxplot(data=all_comps[all_comps$data_type=="ImmuNexUT",],
+               aes(x=is_ko,y=UT,fill=is_ko),
+               width = 0.15, outlier.shape = NA)+
+  geom_text(data=p_vals[p_vals$data_type=="ImmuNexUT",],
+            aes(x=is_ko,y=max_UT,label=pvaltext),
+            size=6)+
+  facet_wrap(~ko_gene,scales ="free",nrow=1)+
+  xlab("")+
+  ylab("Absolute correlation (ImmuNexUT)")+
+  scale_fill_manual("DE gene\nafter KO",values=cols_brewer)+
+  theme(legend.position="none",
+        axis.title=element_text(size=15),
+        axis.text=element_text(size=14),
+        strip.text=element_text(size=15))
+
+g<-ggarrange(g_sc,g_bulk,ncol=1,align="hv")
+
+ggsave(g,file="perturbation_dataset/plots/wilcoxon_all_combined.pdf",
+       width=15,height=8)
+                          
\ No newline at end of file
diff --git a/02_correlation_evaluation/wilcoxon_test_string.R b/02_correlation_evaluation/wilcoxon_test_string.R
new file mode 100644
index 0000000..996825d
--- /dev/null
+++ b/02_correlation_evaluation/wilcoxon_test_string.R
@@ -0,0 +1,96 @@
+# ------------------------------------------------------------------------------
+# Compare if correlated pairs from single cell (Oelen v3, CD4+ T cells)
+# and bulk (ImmuNexUT, naive CD4+ T cells) are enriched in STRING database
+# (Using the same strategy as in CRISPR validation with 
+# Wilcoxon Rank Sum Test)
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(ggplot2)
+library(RColorBrewer)
+
+theme_set(theme_bw())
+
+#Get colors
+cols_brewer <- c("grey78",brewer.pal(n = 3, "Set2")[2])
+
+cond<-"UT"
+
+plot_list<-NULL
+for(data_type in c("sc","ImmuNexUT")){
+  
+  print(data_type)
+  
+  # Load single cell correlation (cell type specific)
+  if(data_type == "sc"){
+    
+    ct<-"CD4T" #alternative "CD8T"
+    
+    corr_ct<-fread(paste0("co-expression_indivs_combined/",ct,"/",ct,"_",cond,
+                          "_correlation.csv"))
+    corr_ct$gene1<-gsub(";.*","",corr_ct$V1)
+    corr_ct$gene2<-gsub(".*;","",corr_ct$V1)
+    
+    corr_ct$swap<-ifelse(corr_ct$gene1 > corr_ct$gene2,corr_ct$gene1,corr_ct$gene2)
+    corr_ct$gene1<-ifelse(corr_ct$gene1 > corr_ct$gene2,corr_ct$gene2,corr_ct$gene1)
+    corr_ct$gene2<-corr_ct$swap
+    corr_ct$swap<-NULL
+  } else if (data_type == "ImmuNexUT"){
+    
+    ct<-"Naive_CD4"
+    
+    #Read ImmuNexUT data (already preprocessed correctly)
+    corr_ct<-fread(paste0("imd_paper_rna_data/correlation/",
+                          ct,"_correlation.txt"))
+    corr_ct$V1<-NULL
+    colnames(corr_ct)[3]<-"UT"
+  }
+  
+  expressed_genes<-union(corr_ct$gene1,corr_ct$gene2)
+  
+  corr_ct$UT<-abs(corr_ct$UT)
+  
+  #Read STRING data base
+  string<-fread("additional_files/STRING-network.csv")
+  string<-string[string$Gene1 %in% expressed_genes &
+                   string$Gene2 %in% expressed_genes,]  
+    
+  string$swap<-ifelse(string$Gene1 > string$Gene2,string$Gene1,string$Gene2)
+  string$Gene1<-ifelse(string$Gene1 > string$Gene2,string$Gene2,string$Gene1)
+  string$Gene2<-string$swap
+  string$swap<-NULL
+  
+  #Combine with correlation values
+  string$is_string<-TRUE
+  corr_ct<-merge(corr_ct,string,by.x=c("gene1","gene2"),
+                 by.y=c("Gene1","Gene2"),all.x=TRUE)
+  corr_ct$is_string[is.na(corr_ct$is_string)]<-FALSE
+  
+  wt<-wilcox.test(corr_ct$UT[corr_ct$is_string],corr_ct$UT[!corr_ct$is_string],
+                  paired=FALSE,alternative="greater")
+  print(wt$p.value)
+  
+  if(data_type=="sc"){
+    data_type<-"Oelen (v3)"
+  }
+  
+  g<-ggplot(corr_ct,aes(x=is_string,y=UT, fill=is_string))+
+    geom_violin()+
+    geom_boxplot(width = 0.15, outlier.shape = NA)+
+    xlab("Gene pair in STRING network")+
+    ylab("Absolute correlation")+
+    ylim(c(0,1))+
+    ggtitle(paste(data_type,"dataset"))+
+    annotate("text",x=1.5,y=0.9,label=paste0("p =",
+            format(wt$p.value,digits=2)),size=4.5)+
+    scale_fill_manual(values=cols_brewer)+
+    theme(legend.position = "none",
+          plot.title=element_text(size=15),
+          axis.title=element_text(size=14),
+          axis.text=element_text(size=12))
+  plot_list<-c(plot_list,list(g))
+}
+
+g<-ggarrange(plotlist=plot_list,ncol=2,labels=c("a","b"))
+ggsave(g,file=paste0("compare_with_string/plots/string_wilcoxon_combined.pdf"),
+       width=7,height=4)
\ No newline at end of file
diff --git a/03_celltype_individual_comparison/README.md b/03_celltype_individual_comparison/README.md
new file mode 100644
index 0000000..e9df023
--- /dev/null
+++ b/03_celltype_individual_comparison/README.md
@@ -0,0 +1,15 @@
+# 03_celltype_individual_comparison
+
+*compare_individuals_variance.R* : explores for all genes expressed in at least 50% of the cells the variance across individuals
+
+*correlation_between_celltypes.R* : calculates the Pearson correlation of gene pairwise Spearman correlation for all pairwise combinations of cell types within each dataset (for Oelen v2 and v3 dataset, input from *correlation_celltype.py*), taking only genes expressed in 50% of the cells in both cell types; plots results in heatmap afterwards
+
+*correlation_celltype.py* : calculates Spearman correlation for each genepair expressed in 50% of the cells for Oelen dataset (V2) and (V3), separately per cell type, but combing all individuals; provides so the input csv files for *correlation_between_celltypes.R*
+
+*correlation_correlation_distribution_celltypes_and_individuals.R* : combines two basic overview plots: the correlation distribution in each cell type (input from *correlation_celltype.py*) and the concordance of donor-specific correlation (calculates Pearson correlation of gene pairwise Spearman correlation for each combination of individuals within each cell type)
+
+*correlation_subsampling.py* : calculates per donor correlation for each cell type and different numbers of cells for the sample (randomly subsampling to this number of cells), followed by comparison between donors for within the cell type and the subsampling step, taking genepairs expressed in 50% of the cells, using again Oelen v2 and v3 dataset separately
+
+*fit_logcurve_indiv_subsampling_effect.R* : tkes the results from *correlation_subsampling.py* and fitting logarithmic curves for the  relationship between number of cells and concordance between individuals, one per celltype, to better describe this relationship 
+
+*plot_indiv_subsampling_effect.R* : plots results from *correlation_subsampling.py* to show relationship between number of cells and concordance between individuals
diff --git a/03_celltype_individual_comparison/compare_individuals_variance.R b/03_celltype_individual_comparison/compare_individuals_variance.R
new file mode 100644
index 0000000..ffb661c
--- /dev/null
+++ b/03_celltype_individual_comparison/compare_individuals_variance.R
@@ -0,0 +1,81 @@
+# ------------------------------------------------------------------------------
+# Combine gene pair variance across individuals 
+# for Oelen (v2) and (v3) in one plot (taking Z scores)
+# Input: correlation matrices per individual and cell type 
+#        (for comparison of individuals)
+# Output: plot and summary as output text
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(dplyr)
+library(ggplot2)
+library(ggpubr)
+
+theme_set(theme_bw())
+
+path<-"coeqtl_mapping/input/individual_networks/UT/"
+
+cell_types<-c("B","CD4T","CD8T","DC","monocyte","NK")
+
+#Full cell type names as reported in the paper
+cell_types_corrected<-setNames(c("CD4+ T","CD8+ T","Monocyte","NK","DC","B"),
+                               c("CD4T","CD8T","monocyte","NK","DC","B"))
+
+g_list<-NULL
+#Evaluate both Oelen v2 and v3 dataset
+for(dataset in c("onemillionv2","onemillionv3")){
+  corr_summary<-NULL
+  for(ct in cell_types){
+    
+    #Correlation values
+    if(dataset=="onemillionv2"){
+      corr<-fread(paste0(path,dataset,"/UT_",ct,".genesnonzero0.5.zscores.tsv.gz"))
+    } else {
+      corr<-fread(paste0(path,dataset,"/UT_",ct,".genesnonzero0.5.zscores.gz"))
+    }
+    
+    gene_pairs<-corr$V1
+    corr$V1<-NULL
+    
+    #Set Inf values to NA to remove them afterwards
+    corr<-as.matrix(corr)
+    corr[is.infinite(corr)]<-NA
+    
+    #Get mean and variance for each gene pair (drop NA and Inf values from calculation)
+    corr_summary<-rbind(corr_summary,
+                   data.frame(ct,
+                              gene_pairs,
+                              var=apply(corr,1,var,na.rm=TRUE),
+                              mean=apply(corr,1,mean,na.rm=TRUE)))
+  }
+  
+  #Check frequency of "highly variable" genes
+  print(paste("Frequency of highly variable genes for",dataset))
+  tmp<-corr_summary%>%
+    group_by(ct)%>%
+    summarise(freq_high_var=mean(var>2,na.rm=TRUE))
+  print(tmp)
+  print(median(tmp$freq_high_var))
+  
+  #Replace cell type names
+  corr_summary$ct<-cell_types_corrected[corr_summary$ct]
+  
+  g<-ggplot(corr_summary,aes(x=var,color=ct))+
+    geom_density()+
+    xlab("Correlation variance across individuals")+
+    ylab("Density")+
+    ylim(0,2)+
+    xlim(0,15)+
+    scale_color_discrete("Cell type")+
+    theme(axis.title=element_text(size=10),
+          axis.text=element_text(size=9),
+          legend.title=element_text(size=10),
+          legend.text=element_text(size=10))
+  
+  g_list<-c(g_list,list(g))
+}
+
+g<-ggarrange(plotlist=g_list,ncol=2,common.legend = TRUE,
+             legend="bottom",labels=c("a","b"))
+ggsave(g,file="co-expression_indivs_combined/plots/per_indivual_var_zscores_combined.pdf",
+       width=8,height=4)
\ No newline at end of file
diff --git a/03_celltype_individual_comparison/correlation_between_celltypes.R b/03_celltype_individual_comparison/correlation_between_celltypes.R
new file mode 100644
index 0000000..d61c2c1
--- /dev/null
+++ b/03_celltype_individual_comparison/correlation_between_celltypes.R
@@ -0,0 +1,110 @@
+# ------------------------------------------------------------------------------
+# Check Pearson correlation between cell types for Oelen v2 and v3 dataset
+# Input: correlation matrices generated with correlation_timepoint_combined_indivs.py
+# Output: heatmap plot and summary as output text
+#  -----------------------------------------------------------------------------
+
+library(data.table)
+library(ggplot2)
+library(viridis)
+
+theme_set(theme_bw())
+
+cell_types<-c("CD4T","CD8T","monocyte","NK","DC","B")
+
+#Full cell type names as reported in the paper
+cell_types_corrected<-setNames(c("CD4+ T","CD8+ T","Monocyte","NK","DC","B"),
+                               c("CD4T","CD8T","monocyte","NK","DC","B"))
+
+#Check for both Oelen v3 and v2 datasets (paths set dependent on that)
+path_v3<-"co-expression_indivs_combined/"
+path_v2<-"co-expression_indivs_combined/one_million_version2/"
+
+for(version in c("v2","v3")){
+  
+  if(version=="v2"){
+    path<-path_v2
+  } else {
+    path<-path_v3
+  }
+  
+  #Iterate over each cell type combination to do all pairwise comparisons
+  corr_comp<-NULL
+  for(c1 in 1:c(length(cell_types)-1)){
+    
+    #Read correlation file one
+    cell_type1<-cell_types[c1]
+    corr_c1<-fread(paste0(path,cell_type1,"/",cell_type1,"_UT_correlation.csv"))
+    
+    #Unique genes
+    num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                             gsub(".*;","",corr_c1$V1)))
+    
+    corr_comp<-rbind(corr_comp,
+                     data.frame(c1=cell_type1,
+                                c2=cell_type1,
+                                gene_pairs=nrow(corr_c1),
+                                genes_unique=num_genes,
+                                corr=1))
+    
+    for(c2 in (c1+1):length(cell_types)){
+      
+      #Read correlation file two
+      cell_type2<-cell_types[c2]
+      corr_c2<-fread(paste0(path,cell_type2,"/",cell_type2,"_UT_correlation.csv"))
+      
+      corr<-merge(corr_c1,corr_c2,by=c("V1"))
+      
+      #Unique genes
+      num_genes<-length(union(gsub(";.*","",corr$V1),
+                              gsub(".*;","",corr$V1)))
+      
+      corr_comp<-rbind(corr_comp,
+                       data.frame(c1=cell_type1,
+                                  c2=cell_type2,
+                                  gene_pairs=nrow(corr),
+                                  genes_unique=num_genes,
+                                  corr=cor(corr$UT.x,corr$UT.y,method="pearson")))
+    }
+  }
+  
+  #Add last diagonal entry
+  cell_type1<-cell_types[length(cell_types)]
+  corr_c1<-fread(paste0(path,cell_type1,"/",cell_type1,"_UT_correlation.csv"))
+  
+  #Unique genes
+  num_genes<-length(union(gsub(";.*","",corr_c1$V1),
+                          gsub(".*;","",corr_c1$V1)))
+  
+  corr_comp<-rbind(corr_comp,
+                   data.frame(c1=cell_type1,
+                              c2=cell_type1,
+                              gene_pairs=nrow(corr_c1),
+                              genes_unique=num_genes,
+                              corr=1))
+  
+  #Rename cell types to make it coherent with other part of the manuscript
+  corr_comp$c1<-cell_types_corrected[corr_comp$c1]
+  corr_comp$c2<-cell_types_corrected[corr_comp$c2]
+  corr_comp$c1<-factor(corr_comp$c1,levels=cell_types_corrected)
+  corr_comp$c2<-factor(corr_comp$c2,levels=cell_types_corrected)
+  
+  #Create heatmap
+  g<-ggplot(corr_comp,aes(x=c1,y=c2,fill=corr))+
+    geom_tile()+
+    geom_text(aes(label=paste0(round(corr,3),"\n(",genes_unique,")")),size=4)+
+    xlab("Cell type")+
+    ylab("Cell type")+
+    #scale_fill_gradient2("Correlation",limits = c(-1,1),low="darkblue",mid="white",high="darkred")
+    scale_fill_viridis("Correlation",limits=c(0,1))+
+    theme(axis.title = element_text(size=14),
+          axis.text = element_text(size=12),
+          legend.title = element_text(size=14),
+          legend.text = element_text(size=12))
+  ggsave(g,file=paste0("co-expression_indivs_combined/plots/corr_celltypes_",version,".pdf"),
+         width=7,height=5)
+  
+  #Check correlation distribution across cell types
+  summary(corr_comp$corr[corr_comp$c1 != corr_comp$c2])
+  
+}
\ No newline at end of file
diff --git a/03_celltype_individual_comparison/correlation_celltype.py b/03_celltype_individual_comparison/correlation_celltype.py
new file mode 100644
index 0000000..86b3e78
--- /dev/null
+++ b/03_celltype_individual_comparison/correlation_celltype.py
@@ -0,0 +1,96 @@
+#############################################################################################
+# Calculate correlation for each pairwise gene combiation in each cell type and the UT timepoint,
+# where both genes are expressed in at least 50% of the cells
+# merging all individuals for Oelen v2 and v3 dataset (specified in parameter version2)
+# Input: seurat objects with Oelen v2 and v3 dataset
+# Output: correlation values as csv files (one per cell type)
+############################################################################################
+
+#from scipy.stats import t, norm
+from scipy.stats import spearmanr
+import scanpy as sc
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from time import time
+import os
+import re
+
+# specify if Oelen v2 (version2=True) or v3 (version2=False) dataset is used
+version2 = True
+
+# set result path
+if version2:
+    prefix_results = Path('co-expression_indivs_combined/one_million_version2/')
+else:
+    prefix_results = Path('co-expression_indivs_combined/')
+
+# load scanpy object
+if version2:
+    alldata = sc.read_h5ad('seurat_objects/1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad')
+else:
+    alldata = sc.read_h5ad('seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad')
+    
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    selected_genes = df.columns[nonzerocounts>ratio]
+    return selected_genes
+
+# extract timepoint from timepoint - stimulation annotation
+def get_time(x):
+    if x == 'UT':
+        return x
+    else:
+        pattern = re.compile(r'\d+h')
+        return re.findall(pattern, x)[0]
+
+
+# extract timepoint from timepoint - stimulation annotation
+observations = alldata.obs.copy()
+observations['time_merged'] = [get_time(item) for item in observations['timepoint']]
+observations['timepoint_id_celltype'] = [f'{item[0]}_{item[1]}' for item
+                                           in observations[['time_merged', 'cell_type_lowerres']].values]
+
+# iterate over each cell type
+celltypes = ['B', 'CD4T', 'CD8T', 'monocyte', 'DC', 'NK']
+for celltype in celltypes:
+    if not os.path.isdir(prefix_results/celltype):
+        os.mkdir(prefix_results/celltype)
+    starttime = time()
+    print(celltype)
+    specific = alldata[alldata.obs.cell_type_lowerres==celltype]
+    celltype_data = pd.DataFrame(data=specific.X.toarray(),
+                                 index=specific.obs.index,
+                                 columns=specific.var.index)
+
+    # get the set of gene pairs
+    specific_obs = observations[observations['cell_type_lowerres']==celltype]
+
+    # select only UT cells
+    for condition in ['UT']:
+
+        # filter for the condition
+        celltype_condition_data = celltype_data[specific_obs.time_merged==condition]
+        
+        #filter genes after a nonzero rate of at least 0.5
+        selected_genes = select_gene_nonzeroratio(celltype_condition_data, 0.5)
+
+        print(f"Number of selected genes for {celltype} {condition}: {len(selected_genes)}")
+
+        gene_pairs = []
+        for i,gene1 in enumerate(selected_genes):
+            for j in range(i+1, len(selected_genes)):
+                gene_pairs.append(';'.join([gene1, selected_genes[j]]))
+
+        input_df = celltype_condition_data[selected_genes]
+        input_data = spearmanr(input_df, axis=0)[0]
+        input_data_uppertria = input_data[np.triu_indices_from(input_data, 1)]
+
+        corrs_df = pd.DataFrame(data=input_data_uppertria,
+                                columns=[f'{condition}'],
+                                index=gene_pairs)
+
+        corrs_df.to_csv(prefix_results/celltype/f'{celltype}_{condition}_correlation.csv')
+
+
+        print(f"Finished {celltype} with time {time() - starttime}")
diff --git a/03_celltype_individual_comparison/correlation_distribution_celltypes_and_individuals.R b/03_celltype_individual_comparison/correlation_distribution_celltypes_and_individuals.R
new file mode 100644
index 0000000..1400f3d
--- /dev/null
+++ b/03_celltype_individual_comparison/correlation_distribution_celltypes_and_individuals.R
@@ -0,0 +1,167 @@
+# ------------------------------------------------------------------------------
+# Check Pearson correlation between individuals (per cell type)
+# and combine it with correlation levels in the cell type 
+# (as both are below each other in the final figure)
+# Input: 
+# 1) correlation matrices per cell type generated with 
+#    correlation_timepoint_combined_indivs.py
+#    (for correlation levels in each cell type)
+# 2) correlation matrices per individual and cell type 
+#    (for comparison of individuals)
+# Output: plot and summary as output text
+#  -----------------------------------------------------------------------------
+
+library(data.table)
+library(ggplot2)
+library(ggpubr)
+library(RColorBrewer)
+library(dplyr)
+
+theme_set(theme_bw())
+
+path<-"coeqtl_mapping/input/individual_networks/UT/"
+
+cell_types<-c("B","CD4T","CD8T","DC","monocyte","NK")
+
+#Full cell type names as reported in the paper
+cell_types_corrected<-setNames(c("CD4+ T","CD8+ T","Monocyte","NK","DC","B"),
+                               c("CD4T","CD8T","monocyte","NK","DC","B"))
+
+#Get standard color scale
+gg_color_hue <- function(n) {
+  hues = seq(15, 375, length = n + 1)
+  hcl(h = hues, l = 65, c = 100)[1:n]
+}
+
+#Evaluate both Oelen v2 and v3 dataset
+for(dataset in c("onemillionv2","onemillionv3")){
+  
+  #Evaluate correlation distribution in the cell type
+  all_corrs<-NULL
+  tp<-"UT"
+  for(ct in cell_types){
+    
+    #Load correlation values
+    if(dataset=="onemillionv3"){
+      corr_ct<-fread(paste0("co-expression_indivs_combined/",ct,"/",
+                            ct,"_",tp,"_correlation.csv"))
+    } else {
+      corr_ct<-fread(paste0("co-expression_indivs_combined/one_million_version2/",
+                            ct,"/",ct,"_",tp,"_correlation.csv"))     
+    }
+    colnames(corr_ct)[2]<-"corr"
+    
+    #Get absolute correlation
+    corr_ct$corr<-abs(corr_ct$corr)
+    all_corrs<-rbind(all_corrs,
+                     data.frame(level=c("<0.05",">0.05",">0.1",">0.2",">0.3"),
+                                values=c(sum(abs(corr_ct$corr<0.05)),
+                                         sum(abs(corr_ct$corr)>0.05 &
+                                               abs(corr_ct$corr)<0.1),
+                                         sum(abs(corr_ct$corr)>0.1 &
+                                               abs(corr_ct$corr)<0.2),
+                                         sum(abs(corr_ct$corr)>0.2 & 
+                                               abs(corr_ct$corr)<0.3),
+                                         sum(abs(corr_ct$corr)>0.3)),
+                                freq=c(mean(abs(corr_ct$corr<0.05)),
+                                       mean(abs(corr_ct$corr)>0.05 &
+                                              abs(corr_ct$corr)<0.1),
+                                       mean(abs(corr_ct$corr)>0.1 &
+                                              abs(corr_ct$corr)<0.2),
+                                       mean(abs(corr_ct$corr)>0.2 & 
+                                              abs(corr_ct$corr)<0.3),
+                                       mean(abs(corr_ct$corr)>0.3)),
+                                ct,tp))
+  }
+  
+  #Check general distribution of "highly correlated genes"
+  high_corr<-all_corrs[all_corrs$level %in% c(">0.3",">0.2",">0.1"),]
+  high_corr<-high_corr%>%group_by(ct)%>%
+    summarize(high_freq=sum(freq))%>%
+    as.data.frame()
+  median(high_corr$high_freq)  
+  
+  #Get comparison between cell types
+  summary<-NULL
+  for(ct in cell_types){
+    #Load file with all correlation values per individual and cell type
+    #each individual one column
+    if(dataset=="onemillionv3"){
+      corr<-fread(paste0(path,dataset,"/UT_",ct,".genesnonzero0.5.coefs.gz"))
+    } else {
+      corr<-fread(paste0(path,dataset,"/UT_",ct,".genesnonzero0.5.coefs.tsv.gz"))
+    }
+  
+    #Get Pearson correlation between individual-specific correlations
+    gene_pairs<-corr$V1
+    corr$V1<-NULL
+    indiv_corr<-cor(corr,method="pearson")
+    
+    #Melt the upper triangle
+    tmp<-reshape2::melt(indiv_corr)
+    tmp$Var1<-as.character(tmp$Var1)
+    tmp$Var2<-as.character(tmp$Var2)
+    tmp<-tmp[tmp$Var1 < tmp$Var2,]
+    
+    tmp$ct<-ct
+    
+    summary<-rbind(summary,tmp)
+  }
+  
+  #Replace cell type names
+  all_corrs$ct<-cell_types_corrected[all_corrs$ct]
+  summary$ct<-cell_types_corrected[summary$ct]
+  
+  #Sort cell types according to their highly correlated genes
+  sorting<-all_corrs[all_corrs$level==">0.3",]
+  
+  #Sort cell type colors
+  colors_cts<-gg_color_hue(6)
+  colors_cts<-colors_cts[order(sorting$freq)]
+  
+  sorting<-sorting[order(sorting$freq),]
+  
+  #Barplot showing the general correlation distribution in the cell type
+  all_corrs$level<-factor(all_corrs$level,levels=c("<0.05",">0.05",">0.1",">0.2",">0.3"))
+  all_corrs$ct<-factor(all_corrs$ct,levels=sorting$ct)
+  colors_bars<-brewer.pal(n = 6, "YlGnBu")
+  g.1<-ggplot(all_corrs,aes(x=ct,y=freq,fill=level))+
+    geom_bar(stat="identity")+
+    xlab("Cell type")+ylab("Fraction correlated genes")+
+    scale_fill_manual("Absolute\ncorrelation",values=colors_bars[2:6])+
+    theme(legend.position="bottom",
+          axis.title.y = element_text(size=13.5),
+          axis.title.x = element_blank(),
+          axis.text = element_text(size=12),
+          legend.title=element_text(size=11),
+          legend.text=element_text(size=10.5))
+  
+  #Violin plot showing differences between individuals in the cell type
+  summary$ct<-factor(summary$ct,levels=sorting$ct)
+  g.2<-ggplot(summary,aes(x=ct,fill=ct,y=value))+
+    geom_violin()+
+    geom_boxplot(width = 0.15, outlier.shape = NA)+
+    ylim(0,1)+
+    xlab("Cell type")+
+    ylab("Correlation between individuals")+
+    scale_fill_manual(values=colors_cts)+
+    theme(legend.position = "none",
+          axis.title.y = element_text(size=13.5),
+          axis.title.x = element_blank(),
+          axis.text = element_text(size=12))
+
+    
+  g<-ggarrange(g.1,g.2,ncol=1,align="hv")
+  ggsave(g,file=paste0("co-expression_indivs_combined/plots/corr_ct_indiv_",
+                       dataset,".pdf"),
+         width=5,height=6)
+  
+  #Get median correlation in each cell type
+  med_corr<-summary%>%
+    group_by(ct)%>%
+    summarise(median(value,na.rm=TRUE))
+  
+  print(dataset)
+  print(med_corr)
+  
+}
diff --git a/03_celltype_individual_comparison/correlation_subsampling.py b/03_celltype_individual_comparison/correlation_subsampling.py
new file mode 100644
index 0000000..bfd1845
--- /dev/null
+++ b/03_celltype_individual_comparison/correlation_subsampling.py
@@ -0,0 +1,121 @@
+####################################################################################
+# Calculate per sample correlation with subsampled number of cells per donor
+# to explore the relationship between number of cells and
+# and concordance between donors
+# Individuals with a total number of cells below the respective subsampled value
+# are not tested, sampling range from 25 cells to the 75% quantile for the cell type
+# (so that at least 25% of the individuals can be included each time)
+# Selecting again genes expressed in at least 50% of the cells
+# Input: seurat objects with Oelen v2 and v3 dataset
+# Output: Csv file with Pearson correlation values for all individual comparison
+#         per celltype and subsampled number of cells
+####################################################################################
+
+import scanpy as sc
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from scipy.stats import pearsonr, spearmanr
+
+def select_gene_nonzeroratio(df, ratio):
+    '''
+        Select genes with non-zero ratio across all cells > specified ratio
+    '''
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    selected_genes = df.columns[nonzerocounts>ratio]
+    return selected_genes
+
+def calculate_individual_network(individual_df, n_cells=0, random_state=8):
+    '''
+        Randomly select the n_cells from individual_df to calculate the gene-gene spearman network;
+        if n_cell not set, then use all cells from the individual
+        Return: A list of correlation coefficients
+        '''
+    if n_cells > 0:
+        specific_individual_network = individual_df.sample(n_cells, random_state=random_state).corr(method='spearman')
+    else:
+        specific_individual_network = individual_df.corr(method='spearman')
+    indices = np.tril_indices_from(np.zeros((specific_individual_network.shape[0],
+                                             specific_individual_network.shape[0])),
+                                   k=1)
+    return specific_individual_network.values[indices].flatten()
+
+def calculate_correlation(celltype_df, celltype_obs, selected_genes, select_individuals, n_cells=100):
+    '''
+        Calculate all inidividual networks for certain n_cells, and selected_indidviauls
+        Input: celltype_df: gene index in columns, cell index in rows, cell index will be used to match the index of
+        celltype_df; it needs at least one column named 'assignment', storing the individual index for
+        each cell
+        selected_genes: a list of gene index
+        select_individuals: a list of individual index
+        n_cells: number of cells to select from each individual, it should be smaller than the max number of cells
+        in all individuals
+        Output: all_individuals_correlation: a dataframe, each column of values is all the gene-gene spearman
+        correlation coefficients
+        correlation_of_individual_correlations: spearman correlation between all pairs of individuals' networks
+        '''
+    all_individuals_correlation = pd.DataFrame()
+    if select_individuals is not None:
+        for assignment in select_individuals:
+                allcells_individual = celltype_obs[celltype_obs.assignment==assignment].index.values
+                specific_individual_df = celltype_df[selected_genes].loc[allcells_individual]
+                all_individuals_correlation[assignment] = calculate_individual_network(specific_individual_df, n_cells)
+    correlation_of_individual_correlations = all_individuals_correlation.corr(method='pearson')
+    individual_indices = np.triu_indices_from(correlation_of_individual_correlations.values, k=1)
+    return all_individuals_correlation, correlation_of_individual_correlations.values[individual_indices]
+
+# define path (run for Oelen v2 and v3 dataset separately)
+version2 = False
+if version2:
+    input_path = 'seurat_objects/1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'
+    output_path ='co-expression_indivs_subsampled/correlation_individuals_subsampled_1M_v2.csv'
+else:
+    input_path = 'seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'
+    output_path ='co-expression_indivs_subsampled/correlation_individuals_subsampled_1M_v3.csv'
+
+# load single cell data
+alldata = sc.read_h5ad(input_path)
+
+# filter to look only at UT cells
+alldata = alldata[alldata.obs.timepoint == "UT"].copy()
+
+# select common individual per celltype
+celltypes = ['CD4T','NK','monocyte','CD8T','B','DC']
+selected_individuals = {}
+selected_individuals_cell_number = {}
+for celltype in celltypes:
+    celltype_data = alldata[alldata.obs.cell_type_lowerres == celltype]
+    selected_individuals_cell_number[f'{celltype}'] = celltype_data.obs.assignment.value_counts().values
+    selected_individuals[f'{celltype}'] = celltype_data.obs.assignment.value_counts().index
+    #Check distribution of cells per individual
+    print(celltype_data.obs.assignment.value_counts().describe())
+
+# calculate for each celltype
+all_celltype_res = pd.DataFrame()
+for celltype in tqdm(celltypes):
+    celltype_data = alldata[alldata.obs.cell_type_lowerres == celltype]
+    celltype_df = pd.DataFrame(data=celltype_data.X.toarray(),
+                               columns=celltype_data.var.index, # genes
+                               index=celltype_data.obs.index) # cells
+                               
+    # select only genes expressed in at least 50% of the cells
+    selected_genes = select_gene_nonzeroratio(celltype_df, ratio=0.5)
+    
+    # Run each cell type for different number of cells so that at least 25% of individuals have that many cells
+    for cell_num in range(25, int(np.quantile(selected_individuals_cell_number[f'{celltype}'],0.75)),25):
+        
+        print(cell_num)
+        
+        # Select all individuals that have enough cells
+        indivs = selected_individuals[f'{celltype}'][selected_individuals_cell_number[f'{celltype}']>=cell_num]
+        # Get all pairwise correlation for these pairs
+        celltype_correlations = pd.DataFrame(data=calculate_correlation(celltype_df, celltype_data.obs,
+                                                                       selected_genes=selected_genes,
+                                                                       n_cells=cell_num,
+                                                                       select_individuals=indivs)[1],
+                                            columns=['corr'])
+        celltype_correlations['celltype'] = celltype
+        celltype_correlations['cell_num'] = cell_num
+        all_celltype_res = pd.concat([all_celltype_res, celltype_correlations], axis=0)
+
+all_celltype_res.to_csv(output_path)
diff --git a/03_celltype_individual_comparison/fit_logcurve_indiv_subsampling_effect.R b/03_celltype_individual_comparison/fit_logcurve_indiv_subsampling_effect.R
new file mode 100644
index 0000000..09903ea
--- /dev/null
+++ b/03_celltype_individual_comparison/fit_logcurve_indiv_subsampling_effect.R
@@ -0,0 +1,102 @@
+# ------------------------------------------------------------------------------
+# Fit logarithmic curve describing mean correlation between individuals dependent
+# on the number of cells per individual and cell type, fit down separately
+# for each cell type
+# Input: pairwise comparison of all individuals (Pearson correlation) per cell type
+#        and for different numbers of cells 
+#        (subsampling and calculation done in correlation_subsampling.py)
+# Output: logarithmic fit per cell type and curve visualizing the fit
+# ------------------------------------------------------------------------------
+
+library(ggplot2)
+library(dplyr)
+
+theme_set(theme_bw())
+
+suffix<-"v3"
+#suffix<-"v2"
+
+color_coding <- list()
+color_coding[["CD4+ T"]] <- "#2E9D33"
+color_coding[["CD8+ T"]] <- "#126725"
+color_coding[["Monocyte"]] <- "#EDBA1B"
+color_coding[["NK"]] <- "#E64B50"
+color_coding[["B"]] <- "#009DDB"
+color_coding[["DC"]] <- "#965EC8"
+
+#Full cell type names as reported in the paper
+cell_types_corrected<-setNames(c("CD4+ T","CD8+ T","Monocyte","NK","DC","B"),
+                               c("CD4T","CD8T","monocyte","NK","DC","B"))
+
+#Load results
+res<-read.csv(paste0("co-expression_indivs_subsampled/",
+                     "correlation_individuals_subsampled_1M_",suffix,".csv"),
+              stringsAsFactors = FALSE)
+res$X<-NULL
+
+res$celltype<-cell_types_corrected[res$celltype]
+
+res_summary<-res%>%
+  group_by(celltype,cell_num)%>%
+  summarise(mean_corr=mean(corr),
+            quantile_25=quantile(corr,0.25),
+            quantile_75=quantile(corr,0.75))%>%
+  as.data.frame()
+
+#Filter out B cells and DCs because no line can be drawn for them
+res_summary<-res_summary[! res_summary$celltype %in% c("B","DC"),]
+
+#Fit one log function for each cell type
+log_parameters<-NULL
+for(cell_type in unique(res_summary$celltype)){
+  
+  #Fit the linear model
+  res_ct<-res[res$celltype == cell_type,]
+  model_lm<-lm(corr~log(cell_num),data=res_ct)
+  
+  #Save model summary
+  summary_model<-summary(model_lm)
+  print(summary_model)
+  
+  log_parameters<-rbind(log_parameters,
+                        data.frame(cell_type,
+                                   intercept=summary_model$coefficients[1,1],
+                                   log_beta=summary_model$coefficients[2,1],
+                                   adj_r_squared=summary_model$adj.r.squared,
+                                   stringsAsFactors = FALSE))
+  res_summary<-rbind(res_summary,
+                     data.frame(celltype=cell_type,
+                                cell_num=seq(max(res_ct$cell_num)+25,1500,by=25),
+                                mean_corr=NA,
+                                quantile_25=NA,
+                                quantile_75=NA))
+}
+
+
+
+res_summary$fitted_corr<-sapply(1:nrow(res_summary),function(i)
+  log_parameters$intercept[log_parameters$cell_type == res_summary$celltype[i]] +
+  log_parameters$log_beta[log_parameters$cell_type == res_summary$celltype[i]] *
+  log(res_summary$cell_num[i]))
+
+res_summary_melt<-reshape2::melt(res_summary[,c("celltype","cell_num","mean_corr","fitted_corr")],
+                                 id.vars=c("celltype","cell_num"))
+
+g<-ggplot()+
+  geom_line(data=res_summary_melt,aes(x=cell_num,y=value,color=celltype,
+                                      linetype=variable))+
+  geom_point(data=res_summary,aes(x=cell_num,y=mean_corr,color=celltype))+
+  # annotate("text",x=850,y=0.2,hjust=0,
+  #          label=paste0("y ~ -0.56 + 0.21 * log(x), R^2 = 0.98 (CD4+ T)\n",
+  #                       "y ~ -0.48 + 0.20 * log(x), R^2 = 0.86 (CD8+ T)\n",
+  #                       "y ~ -0.53 + 0.20 * log(x), R^2 = 0.94 (Monocyte)\n",
+  #                       "y ~ -0.41 + 0.15 * log(x), R^2 = 0.93 (NK)\n"))+
+  scale_color_manual("Cell type",values=unlist(color_coding))+
+  scale_linetype_discrete("",labels=c("Observed","Predicted"))+
+  xlab("Subsampled number of cells per individual")+
+  ylab("Correlation between individuals")
+print(g)
+
+ggsave(g,file=paste0("co-expression_indivs_subsampled/plots/subsampling_1M_",
+                     suffix,"_fitted_lines.pdf"),
+       width=10,height=4)
diff --git a/03_celltype_individual_comparison/individual_networks_for_selected_genepairs.py b/03_celltype_individual_comparison/individual_networks_for_selected_genepairs.py
new file mode 100644
index 0000000..3f72781
--- /dev/null
+++ b/03_celltype_individual_comparison/individual_networks_for_selected_genepairs.py
@@ -0,0 +1,268 @@
+import argparse
+import os
+import re
+from collections import namedtuple
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import scanpy as sc
+from scipy.stats import rankdata
+from scipy.stats import t, norm
+from tqdm import tqdm
+
+
+def get_time(x):
+    if x == 'UT':
+        return x
+    else:
+        pattern = re.compile(r'\d+h')
+        return re.findall(pattern, x)[0]
+
+
+class DATASET:
+    def __init__(self, datasetname):
+        self.name = datasetname
+        self.path_prefix = Path("./")
+        self.information = self.get_information()
+
+    def get_information(self):
+        if self.name == 'onemillionv2':
+            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'
+            self.individual_id_col = 'assignment'
+            self.timepoint_id_col = 'time'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 'UT',
+                                     'stimulated': '3h'}
+        elif self.name == 'onemillionv3':
+            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'
+            self.individual_id_col = 'assignment'
+            self.timepoint_id_col = 'time'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 'UT',
+                                     'stimulated': '3h'}
+        elif self.name == 'stemiv2':
+            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'
+            self.individual_id_col = 'assignment.final'
+            self.timepoint_id_col = 'timepoint.final'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 't8w',
+                                     'stimulated': 'Baseline'}
+        elif self.name == 'ng':
+            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'
+            self.individual_id_col = 'snumber'
+            self.celltype_id = 'cell_type_mapped_to_onemillion'
+        else:
+            raise IOError("Dataset name not understood.")
+
+    def load_dataset(self):
+        self.get_information()
+        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')
+        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)
+        if self.name.startswith('onemillion'):
+            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]
+        elif self.name == 'ng':
+            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',
+                               'other T': 'otherT', 'other': 'other', 'B': 'B'}
+            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in
+                                                                  self.data_sc.obs['predicted.celltype.l1']]
+
+
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0) / df.shape[0]
+    selected_genes = df.columns[nonzerocounts > ratio]
+    return selected_genes
+
+
+def corr_to_z(coef, num):
+    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))
+    prob = t.cdf(t_statistic, num - 2)
+    z_score = norm.ppf(prob)
+    positive_coef_probs = 1 - prob
+    positive_coef_probs[coef < 0] = 0
+    negative_coef_probs = prob
+    negative_coef_probs[coef > 0] = 0
+    probs = negative_coef_probs + positive_coef_probs
+    return z_score, probs
+
+
+def get_om_name(filename):
+    pattern = re.compile(r'LLDeep_\d\d\d\d')
+    return re.findall(pattern, filename)[0]
+
+
+def get_stemi_name(filename):
+    pattern = re.compile(r'TEST_\d.')
+    return re.findall(pattern, filename)[0]
+
+
+def save_numpy(data_df, prefix):
+    np.save(f'{prefix}.npy', data_df.values)
+    with open(f'{prefix}.cols.txt', 'w') as f:
+        f.write('\n'.join(data_df.columns))
+    with open(f'{prefix}.rows.txt', 'w') as f:
+        f.write('\n'.join(data_df.index))
+    return None
+
+
+def _contains_nan(a, nan_policy='propagate'):
+    policies = ['propagate', 'raise', 'omit']
+    if nan_policy not in policies:
+        raise ValueError("nan_policy must be one of {%s}" %
+                         ', '.join("'%s'" % s for s in policies))
+    try:
+        with np.errstate(invalid='ignore'):
+            contains_nan = np.isnan(np.sum(a))
+    except TypeError:
+        try:
+            contains_nan = np.nan in set(a.ravel())
+        except TypeError:
+            contains_nan = False
+            nan_policy = 'omit'
+    if contains_nan and nan_policy == 'raise':
+        raise ValueError("The input contains nan values")
+    return contains_nan, nan_policy
+
+
+def _chk_asarray(a, axis):
+    if axis is None:
+        a = np.ravel(a)
+        outaxis = 0
+    else:
+        a = np.asarray(a)
+        outaxis = axis
+    if a.ndim == 0:
+        a = np.atleast_1d(a)
+    return a, outaxis
+
+
+def spearmanr_withnan(a, axis=0, nan_policy='propagate'):
+    SpearmanrResult = namedtuple('SpearmanrResult', ('correlation', 'pvalue'))
+    if axis is not None and axis > 1:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays, supplied axis argument {}, "
+                         "please use only values 0, 1 or None for axis".format(axis))
+    a, axisout = _chk_asarray(a, axis)
+    if a.ndim > 2:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays")
+    n_vars = a.shape[1 - axisout]
+    n_obs = a.shape[axisout]
+    if n_obs <= 1:
+        # Handle empty arrays or single observations.
+        return SpearmanrResult(np.nan, np.nan)
+    a_contains_nan, nan_policy = _contains_nan(a, nan_policy)
+    variable_has_nan = np.zeros(n_vars, dtype=bool)
+    if a_contains_nan:
+        if nan_policy == 'propagate':
+            if a.ndim == 1 or n_vars <= 2:
+                return SpearmanrResult(np.nan, np.nan)
+            else:
+                variable_has_nan = np.isnan(a).sum(axis=axisout)
+    a_ranked = np.apply_along_axis(rankdata, axisout, a)
+    rs = np.corrcoef(a_ranked, rowvar=axisout)
+    dof = n_obs - 2  # degrees of freedom
+    # rs can have elements equal to 1, so avoid zero division warnings
+    with np.errstate(divide='ignore'):
+        t_ = rs * np.sqrt((dof / ((rs + 1.0) * (1.0 - rs))).clip(0))
+    prob = 2 * t.sf(np.abs(t_), dof)
+    # For backwards compatibility, return scalars when comparing 2 columns
+    if rs.shape == (2, 2):
+        return SpearmanrResult(rs[1, 0], prob[1, 0])
+    else:
+        rs[variable_has_nan, :] = np.nan
+        rs[:, variable_has_nan] = np.nan
+        return SpearmanrResult(rs, prob)
+
+
+def get_individual_networks_halfratioGenes(data_sc, individual_colname, selected_genes=None):
+    data_df = pd.DataFrame(data=data_sc.X.toarray(),
+                           index=data_sc.obs.index,
+                           columns=data_sc.var.index)
+    selected_genes = select_gene_nonzeroratio(data_df, ratio=0.5)
+    print(f"Selected {len(selected_genes)} genes.")
+    from itertools import combinations
+    selected_genes_sorted_genepairs = [';'.join(sorted(item)) for item in combinations(selected_genes, 2)]
+    coef_df = pd.DataFrame(index=selected_genes_sorted_genepairs)
+    coef_p_df = pd.DataFrame(index=selected_genes_sorted_genepairs)
+    zscore_df = pd.DataFrame(index=selected_genes_sorted_genepairs)
+    zscore_p_df = pd.DataFrame(index=selected_genes_sorted_genepairs)
+    data_selected_df = data_df[selected_genes]
+    print(f"Begin calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals.")
+    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):
+        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]
+        if cell_num > 10:
+            individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]
+            individual_coefs, individual_coef_ps = spearmanr_withnan(individual_df.values, axis=0)
+            individual_coefs_flatten = pd.DataFrame(data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],
+                                                    index=selected_genes_sorted_genepairs).loc[
+                selected_genes_sorted_genepairs]
+            individual_coef_ps_flatten = \
+                pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],
+                             index=selected_genes_sorted_genepairs).loc[selected_genes_sorted_genepairs]
+            coef_df[ind_id] = individual_coefs_flatten
+            coef_p_df[ind_id] = individual_coef_ps_flatten
+            try:
+                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(individual_coefs_flatten.values,
+                                                                                     cell_num)
+                zscore_df[ind_id] = individual_zscores_flatten
+                zscore_p_df[ind_id] = individual_zscore_ps_flatten
+            except:
+                continue
+        else:
+            print("Deleted this individual because of low cell number", cell_num)
+    return coef_df, coef_p_df, zscore_df, zscore_p_df
+
+
+def get_individual_networks_given_celltype_condition_datasetname_for_6major_celltypes(datasetname, condition='UT',
+                                                                                      genelist=None):
+    # load the data and data information
+    celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC']
+    dataset = DATASET(datasetname)
+    dataset.load_dataset()
+    print(f"{datasetname} loaded.")
+    # calculate the individual network for specific condition and celltype
+    for celltype in celltypes:
+        print(datasetname, celltype, condition)
+        if datasetname == 'ng':
+            data_selected = dataset.data_sc[(dataset.data_sc.obs[dataset.celltype_id] == celltype)]
+        else:
+            data_selected = dataset.data_sc[(dataset.data_sc.obs[dataset.celltype_id] == celltype) &
+                                            (dataset.data_sc.obs[dataset.timepoint_id_col] == dataset.chosen_condition[
+                                                condition])]
+        individual_coefs_df, individual_coefs_p_df, individual_zscores_df, individual_zscores_p_df = \
+            get_individual_networks_halfratioGenes(
+                data_selected,
+                dataset.individual_id_col,
+                genelist
+            )
+        print(individual_coefs_df.head())
+        save_prefix = Path(
+            'coeqtl_mapping/input')
+        if not os.path.exists(save_prefix / 'individual_networks' / condition / datasetname):
+            os.mkdir(save_prefix / 'individual_networks' / condition / datasetname)
+        individual_coefs_df.to_csv(
+            save_prefix / 'individual_networks' / condition / datasetname / f'{condition}_{celltype}.genesnonzero0.5.coefs.gz',
+            sep='\t', compression='gzip')
+        individual_zscores_df.to_csv(
+            save_prefix / 'individual_networks' / condition / datasetname / f'{condition}_{celltype}.genesnonzero0.5.zscores.gz',
+            sep='\t', compression='gzip')
+        print("Saved ")
+    return None
+
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--datasetname', type=str, dest='datasetname')
+    parser.add_argument('--condition', type=str, dest='condition')
+    return parser
+
+
+def run_get_individual_networks_given_celltype_condition_datasetname():
+    args = argumentsparser().parse_args()
+    print(f"Starting to calculate individual network for {args.datasetname}, {args.celltype}, {args.condition}.")
+    get_individual_networks_given_celltype_condition_datasetname_for_6major_celltypes(condition=args.condition,
+                                                                                      datasetname=args.datasetname)
+    return None
+
+
+if __name__ == '__main__':
+    run_get_individual_networks_given_celltype_condition_datasetname()
diff --git a/03_celltype_individual_comparison/plot_indiv_subsampling_effect.R b/03_celltype_individual_comparison/plot_indiv_subsampling_effect.R
new file mode 100644
index 0000000..103007e
--- /dev/null
+++ b/03_celltype_individual_comparison/plot_indiv_subsampling_effect.R
@@ -0,0 +1,55 @@
+# ------------------------------------------------------------------------------
+# Plot effect of number of cells on correlation between individuals
+# Input: pairwise comparison of all individuals (Pearson correlation) per cell type
+#        and for different numbers of cells 
+#        (subsampling and calculation done in correlation_subsampling.py)
+# Output: violin plot showing trend
+# ------------------------------------------------------------------------------
+
+library(ggplot2)
+
+theme_set(theme_bw())
+
+suffix<-"v3"
+#suffix<-"v2"
+
+color_coding <- list()
+color_coding[["CD4+ T"]] <- "#2E9D33"
+color_coding[["CD8+ T"]] <- "#126725"
+color_coding[["Monocyte"]] <- "#EDBA1B"
+color_coding[["NK"]] <- "#E64B50"
+color_coding[["B"]] <- "#009DDB"
+color_coding[["DC"]] <- "#965EC8"
+
+#Full cell type names as reported in the paper
+cell_types_corrected<-setNames(c("CD4+ T","CD8+ T","Monocyte","NK","DC","B"),
+                               c("CD4T","CD8T","monocyte","NK","DC","B"))
+
+#Load results
+res<-read.csv(paste0("co-expression_indivs_subsampled/",
+                     "correlation_individuals_subsampled_1M_",suffix,".csv"))
+res$X<-NULL
+
+res$celltype<-cell_types_corrected[res$celltype]
+
+#Filter out some values to make it more visible
+res<-res[res$cell_num %in% seq(25,500,50),]
+
+res$cell_num<-as.factor(res$cell_num)
+
+g<-ggplot(res,aes(x=cell_num,y=corr,fill=celltype))+
+  geom_violin(position = position_dodge(0.9)) +
+  xlab("Subsampled number of cells per individual")+
+  ylab("Correlation between individuals")+
+  ylim(0,1)+
+  scale_fill_manual("Cell type",values=color_coding)+
+  theme(axis.title = element_text(size=16),
+        axis.text = element_text(size=14),
+        legend.title = element_text(size=13),
+        legend.text = element_text(size=13),
+        legend.position=c(0.9,0.2))+
+  guides(fill=guide_legend(nrow=3,byrow=FALSE))
+print(g)
+ggsave(g,file=paste0("co-expression_indivs_subsampled/plots/subsampling_1M_",
+                     suffix,"_filtered.pdf"),
+       width=14,height=4)
diff --git a/04_coeqtl_mapping/.ipynb_checkpoints/examine_bios_replication-checkpoint.ipynb b/04_coeqtl_mapping/.ipynb_checkpoints/examine_bios_replication-checkpoint.ipynb
new file mode 100644
index 0000000..8c5b906
--- /dev/null
+++ b/04_coeqtl_mapping/.ipynb_checkpoints/examine_bios_replication-checkpoint.ipynb
@@ -0,0 +1,1013 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import spearmanr\n",
+    "from pathlib import Path\n",
+    "from scipy.stats import t, norm\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "def flip_zscore(zscore, coeqtlallele, altaf, altallele):\n",
+    "    if not pd.isnull(zscore):\n",
+    "        if coeqtlallele == altallele:\n",
+    "            coeqtlaf = altaf\n",
+    "        else:\n",
+    "            coeqtlaf = 1 - altaf\n",
+    "        if coeqtlaf > 0.5:\n",
+    "            return -zscore\n",
+    "        else:\n",
+    "            return zscore\n",
+    "    else:\n",
+    "        return np.nan\n",
+    "    \n",
+    "def flip_allele(altaf, altallele, refallele):\n",
+    "    if altaf > 0.5:\n",
+    "        return refallele\n",
+    "    else:\n",
+    "        return altallele"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_withbios_prefix = Path(\n",
+    "    \"./coeqtl_mapping/output\"\n",
+    ")\n",
+    "filter_type = 'filtered_results'\n",
+    "\n",
+    "def flip_direction(allele1, allele2, zscore2):\n",
+    "    if allele1 == allele2:\n",
+    "        return zscore2\n",
+    "    else:\n",
+    "        return -1*zscore2\n",
+    "\n",
+    "\n",
+    "def get_z_score(t_statistic, num):\n",
+    "    prob = t.cdf(t_statistic, num - 2)\n",
+    "    z_score = norm.ppf(prob)\n",
+    "    return z_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "color_dict = {'CD4T': '#2E9D33',\n",
+    "              'CD8T': 'darkgreen',\n",
+    "              'monocyte': '#EDBA1B',\n",
+    "              'NK': '#E64B50',\n",
+    "              'DC': '#965EC8',\n",
+    "              'B': '#009DDB',\n",
+    "              'cMono': 'peru',\n",
+    "              'ncMono': 'y',\n",
+    "              'CD4T_individual_100': '#2E9D33',\n",
+    "              'CD4T_individual_50': '#2E9D33',\n",
+    "              'CD4T_50': '#2E9D33',\n",
+    "              'CD4T_150': '#2E9D33',\n",
+    "              'CD4T_250': '#2E9D33'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "workdir = Path(\"./coeqtl_mapping/\")\n",
+    "bios_replication_filtered_df = pd.read_csv(\n",
+    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/filtered_results/replication_summary.csv', \n",
+    "    index_col=0\n",
+    ").set_index('celltype')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "celltype = 'CD4T'\n",
+    "eqtldf = pd.read_csv(\n",
+    "    workdir/f'input/snp_selection/eqtl/UT_{celltype}_eQTLProbesFDR0.05-ProbeLevel_withAF.tsv',\n",
+    "    sep='\\t'\n",
+    "    )\n",
+    "eqtldf['snp_eqtlgene'] = ['_'.join(item) for item in eqtldf[['SNPName', 'genename']].values]\n",
+    "eqtl_allele_af_df = eqtldf.drop_duplicates(subset=['snp_eqtlgene', 'AlleleAssessed', 'AF'])\n",
+    "eqtl_allele_af_dict = eqtl_allele_af_df.set_index('snp_eqtlgene')[['AlleleAssessed', 'AF', 'alt_allele', 'ref_allele']].T.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "biostype = 'onlyRNAAlignMetrics_rmLLD'\n",
+    "celltype = 'CD4T'\n",
+    "filter_type = 'filtered_results'\n",
+    "\n",
+    "coeqtl_df = pd.read_csv(\n",
+    "    coeqtl_withbios_prefix/filter_type/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbios{biostype}.tsv.gz',\n",
+    "    compression='gzip', \n",
+    "    index_col=0, \n",
+    "    sep='\\t')\n",
+    "coeqtl_df = coeqtl_df.dropna(subset=['t_bios'])\n",
+    "coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
+    "                            coeqtl_df[['t_bios', \n",
+    "                                       'num_individuals_bios']].values]\n",
+    "coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
+    "                                  coeqtl_df[['SNPEffectAllele', \n",
+    "                                             'assessed_allele_bios',\n",
+    "                                             'zscore_bios']].values]\n",
+    "\n",
+    "isConcordant = lambda x:True if x[0]*x[1] > 0 else False\n",
+    "coeqtl_df['is_concordant'] = [isConcordant(item) for item in \n",
+    "                              coeqtl_df[['MetaPZ', 'flipped_zscore_bios']].values]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>snp_genepair</th>\n",
+       "      <th>Gene</th>\n",
+       "      <th>GeneChr</th>\n",
+       "      <th>GenePos</th>\n",
+       "      <th>GeneStrand</th>\n",
+       "      <th>GeneSymbol</th>\n",
+       "      <th>SNP</th>\n",
+       "      <th>SNPChr</th>\n",
+       "      <th>SNPPos</th>\n",
+       "      <th>SNPAlleles</th>\n",
+       "      <th>...</th>\n",
+       "      <th>gene1_bios</th>\n",
+       "      <th>gene2_bios</th>\n",
+       "      <th>assessed_allele_bios</th>\n",
+       "      <th>num_individuals_bios</th>\n",
+       "      <th>isinteractionterm_bios</th>\n",
+       "      <th>snp_genepair_bios</th>\n",
+       "      <th>corrected_p_bios</th>\n",
+       "      <th>zscore_bios</th>\n",
+       "      <th>flipped_zscore_bios</th>\n",
+       "      <th>is_concordant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>snp_gene1_gene2</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_NPM1</th>\n",
+       "      <td>rs7605824_NPM1;SH3YL1</td>\n",
+       "      <td>NPM1;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NPM1;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>NPM1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_NPM1;SH3YL1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.617874</td>\n",
+       "      <td>-3.617874</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_CD48</th>\n",
+       "      <td>rs7605824_CD48;SH3YL1</td>\n",
+       "      <td>CD48;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CD48;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>CD48</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_CD48;SH3YL1</td>\n",
+       "      <td>0.784422</td>\n",
+       "      <td>-0.446946</td>\n",
+       "      <td>-0.446946</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_RPS13</th>\n",
+       "      <td>rs7605824_RPS13;SH3YL1</td>\n",
+       "      <td>RPS13;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPS13;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>RPS13</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_RPS13;SH3YL1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.489377</td>\n",
+       "      <td>-3.489377</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_RPL31</th>\n",
+       "      <td>rs7605824_RPL31;SH3YL1</td>\n",
+       "      <td>RPL31;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPL31;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>RPL31</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_RPL31;SH3YL1</td>\n",
+       "      <td>0.349601</td>\n",
+       "      <td>-1.325633</td>\n",
+       "      <td>-1.325633</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_RPL3</th>\n",
+       "      <td>rs7605824_RPL3;SH3YL1</td>\n",
+       "      <td>RPL3;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPL3;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>RPL3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_RPL3;SH3YL1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.854851</td>\n",
+       "      <td>-3.854851</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_ACTB</th>\n",
+       "      <td>rs4147638_ACTB;SMDT1</td>\n",
+       "      <td>ACTB;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ACTB;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>ACTB</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_ACTB;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.748326</td>\n",
+       "      <td>3.748326</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_RPS25</th>\n",
+       "      <td>rs4147638_RPS25;SMDT1</td>\n",
+       "      <td>RPS25;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPS25;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>RPS25</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_RPS25;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.773036</td>\n",
+       "      <td>-5.773036</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_RPS3A</th>\n",
+       "      <td>rs4147638_RPS3A;SMDT1</td>\n",
+       "      <td>RPS3A;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPS3A;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>RPS3A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_RPS3A;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.434777</td>\n",
+       "      <td>-4.434777</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_RPS18</th>\n",
+       "      <td>rs4147638_RPS18;SMDT1</td>\n",
+       "      <td>RPS18;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPS18;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>RPS18</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_RPS18;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.128733</td>\n",
+       "      <td>-7.128733</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_RPL11</th>\n",
+       "      <td>rs4147638_RPL11;SMDT1</td>\n",
+       "      <td>RPL11;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPL11;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>RPL11</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_RPL11;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.896748</td>\n",
+       "      <td>-5.896748</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>497 rows × 55 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  snp_genepair          Gene  GeneChr  \\\n",
+       "snp_gene1_gene2                                                         \n",
+       "rs7605824_SH3YL1_NPM1    rs7605824_NPM1;SH3YL1   NPM1;SH3YL1        2   \n",
+       "rs7605824_SH3YL1_CD48    rs7605824_CD48;SH3YL1   CD48;SH3YL1        2   \n",
+       "rs7605824_SH3YL1_RPS13  rs7605824_RPS13;SH3YL1  RPS13;SH3YL1        2   \n",
+       "rs7605824_SH3YL1_RPL31  rs7605824_RPL31;SH3YL1  RPL31;SH3YL1        2   \n",
+       "rs7605824_SH3YL1_RPL3    rs7605824_RPL3;SH3YL1   RPL3;SH3YL1        2   \n",
+       "...                                        ...           ...      ...   \n",
+       "rs4147638_SMDT1_ACTB      rs4147638_ACTB;SMDT1    ACTB;SMDT1       22   \n",
+       "rs4147638_SMDT1_RPS25    rs4147638_RPS25;SMDT1   RPS25;SMDT1       22   \n",
+       "rs4147638_SMDT1_RPS3A    rs4147638_RPS3A;SMDT1   RPS3A;SMDT1       22   \n",
+       "rs4147638_SMDT1_RPS18    rs4147638_RPS18;SMDT1   RPS18;SMDT1       22   \n",
+       "rs4147638_SMDT1_RPL11    rs4147638_RPL11;SMDT1   RPL11;SMDT1       22   \n",
+       "\n",
+       "                         GenePos  GeneStrand    GeneSymbol        SNP  SNPChr  \\\n",
+       "snp_gene1_gene2                                                                 \n",
+       "rs7605824_SH3YL1_NPM1     217730         NaN   NPM1;SH3YL1  rs7605824       2   \n",
+       "rs7605824_SH3YL1_CD48     217730         NaN   CD48;SH3YL1  rs7605824       2   \n",
+       "rs7605824_SH3YL1_RPS13    217730         NaN  RPS13;SH3YL1  rs7605824       2   \n",
+       "rs7605824_SH3YL1_RPL31    217730         NaN  RPL31;SH3YL1  rs7605824       2   \n",
+       "rs7605824_SH3YL1_RPL3     217730         NaN   RPL3;SH3YL1  rs7605824       2   \n",
+       "...                          ...         ...           ...        ...     ...   \n",
+       "rs4147638_SMDT1_ACTB    42475695         NaN    ACTB;SMDT1  rs4147638      22   \n",
+       "rs4147638_SMDT1_RPS25   42475695         NaN   RPS25;SMDT1  rs4147638      22   \n",
+       "rs4147638_SMDT1_RPS3A   42475695         NaN   RPS3A;SMDT1  rs4147638      22   \n",
+       "rs4147638_SMDT1_RPS18   42475695         NaN   RPS18;SMDT1  rs4147638      22   \n",
+       "rs4147638_SMDT1_RPL11   42475695         NaN   RPL11;SMDT1  rs4147638      22   \n",
+       "\n",
+       "                          SNPPos SNPAlleles  ... gene1_bios  gene2_bios  \\\n",
+       "snp_gene1_gene2                              ...                          \n",
+       "rs7605824_SH3YL1_NPM1     280819        G/A  ...     SH3YL1        NPM1   \n",
+       "rs7605824_SH3YL1_CD48     280819        G/A  ...     SH3YL1        CD48   \n",
+       "rs7605824_SH3YL1_RPS13    280819        G/A  ...     SH3YL1       RPS13   \n",
+       "rs7605824_SH3YL1_RPL31    280819        G/A  ...     SH3YL1       RPL31   \n",
+       "rs7605824_SH3YL1_RPL3     280819        G/A  ...     SH3YL1        RPL3   \n",
+       "...                          ...        ...  ...        ...         ...   \n",
+       "rs4147638_SMDT1_ACTB    42487900        G/A  ...      SMDT1        ACTB   \n",
+       "rs4147638_SMDT1_RPS25   42487900        G/A  ...      SMDT1       RPS25   \n",
+       "rs4147638_SMDT1_RPS3A   42487900        G/A  ...      SMDT1       RPS3A   \n",
+       "rs4147638_SMDT1_RPS18   42487900        G/A  ...      SMDT1       RPS18   \n",
+       "rs4147638_SMDT1_RPL11   42487900        G/A  ...      SMDT1       RPL11   \n",
+       "\n",
+       "                        assessed_allele_bios  num_individuals_bios  \\\n",
+       "snp_gene1_gene2                                                      \n",
+       "rs7605824_SH3YL1_NPM1                      A                2491.0   \n",
+       "rs7605824_SH3YL1_CD48                      A                2491.0   \n",
+       "rs7605824_SH3YL1_RPS13                     A                2491.0   \n",
+       "rs7605824_SH3YL1_RPL31                     A                2491.0   \n",
+       "rs7605824_SH3YL1_RPL3                      A                2491.0   \n",
+       "...                                      ...                   ...   \n",
+       "rs4147638_SMDT1_ACTB                       G                2491.0   \n",
+       "rs4147638_SMDT1_RPS25                      G                2491.0   \n",
+       "rs4147638_SMDT1_RPS3A                      G                2491.0   \n",
+       "rs4147638_SMDT1_RPS18                      G                2491.0   \n",
+       "rs4147638_SMDT1_RPL11                      G                2491.0   \n",
+       "\n",
+       "                        isinteractionterm_bios       snp_genepair_bios  \\\n",
+       "snp_gene1_gene2                                                          \n",
+       "rs7605824_SH3YL1_NPM1                     True   rs7605824_NPM1;SH3YL1   \n",
+       "rs7605824_SH3YL1_CD48                     True   rs7605824_CD48;SH3YL1   \n",
+       "rs7605824_SH3YL1_RPS13                    True  rs7605824_RPS13;SH3YL1   \n",
+       "rs7605824_SH3YL1_RPL31                    True  rs7605824_RPL31;SH3YL1   \n",
+       "rs7605824_SH3YL1_RPL3                     True   rs7605824_RPL3;SH3YL1   \n",
+       "...                                        ...                     ...   \n",
+       "rs4147638_SMDT1_ACTB                      True    rs4147638_ACTB;SMDT1   \n",
+       "rs4147638_SMDT1_RPS25                     True   rs4147638_RPS25;SMDT1   \n",
+       "rs4147638_SMDT1_RPS3A                     True   rs4147638_RPS3A;SMDT1   \n",
+       "rs4147638_SMDT1_RPS18                     True   rs4147638_RPS18;SMDT1   \n",
+       "rs4147638_SMDT1_RPL11                     True   rs4147638_RPL11;SMDT1   \n",
+       "\n",
+       "                        corrected_p_bios  zscore_bios flipped_zscore_bios  \\\n",
+       "snp_gene1_gene2                                                             \n",
+       "rs7605824_SH3YL1_NPM1           0.000000    -3.617874           -3.617874   \n",
+       "rs7605824_SH3YL1_CD48           0.784422    -0.446946           -0.446946   \n",
+       "rs7605824_SH3YL1_RPS13          0.000000    -3.489377           -3.489377   \n",
+       "rs7605824_SH3YL1_RPL31          0.349601    -1.325633           -1.325633   \n",
+       "rs7605824_SH3YL1_RPL3           0.000000    -3.854851           -3.854851   \n",
+       "...                                  ...          ...                 ...   \n",
+       "rs4147638_SMDT1_ACTB            0.000000    -3.748326            3.748326   \n",
+       "rs4147638_SMDT1_RPS25           0.000000     5.773036           -5.773036   \n",
+       "rs4147638_SMDT1_RPS3A           0.000000     4.434777           -4.434777   \n",
+       "rs4147638_SMDT1_RPS18           0.000000     7.128733           -7.128733   \n",
+       "rs4147638_SMDT1_RPL11           0.000000     5.896748           -5.896748   \n",
+       "\n",
+       "                       is_concordant  \n",
+       "snp_gene1_gene2                       \n",
+       "rs7605824_SH3YL1_NPM1           True  \n",
+       "rs7605824_SH3YL1_CD48           True  \n",
+       "rs7605824_SH3YL1_RPS13          True  \n",
+       "rs7605824_SH3YL1_RPL31          True  \n",
+       "rs7605824_SH3YL1_RPL3           True  \n",
+       "...                              ...  \n",
+       "rs4147638_SMDT1_ACTB            True  \n",
+       "rs4147638_SMDT1_RPS25           True  \n",
+       "rs4147638_SMDT1_RPS3A           True  \n",
+       "rs4147638_SMDT1_RPS18           True  \n",
+       "rs4147638_SMDT1_RPL11           True  \n",
+       "\n",
+       "[497 rows x 55 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coeqtl_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# flip direction according to AF\n",
+    "coeqtl_df['eqtl_effect_allele'] = [eqtl_allele_af_dict.get(eqtl)['AlleleAssessed'] for eqtl in \n",
+    "                                       coeqtl_df['snp_eqtlgene']]\n",
+    "coeqtl_df['eqtl_alt_af'] = [eqtl_allele_af_dict.get(eqtl)['AF'] for eqtl in coeqtl_df['snp_eqtlgene']]\n",
+    "coeqtl_df['eqtl_alt_allele'] = [eqtl_allele_af_dict.get(eqtl)['alt_allele'] for eqtl in \n",
+    "                                    coeqtl_df['snp_eqtlgene']]\n",
+    "coeqtl_df['eqtl_ref_allele'] = [eqtl_allele_af_dict.get(eqtl)['ref_allele'] for eqtl in \n",
+    "                                    coeqtl_df['snp_eqtlgene']]\n",
+    "coeqtl_df[f'MetaPZ_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
+    "                                                   for zscore, coeqtlallele, altaf, altallele in\n",
+    "                                                   coeqtl_df[[f'MetaPZ',\n",
+    "                                                                  f'SNPEffectAllele',\n",
+    "                                                                  'eqtl_alt_af',\n",
+    "                                                                  'eqtl_alt_allele']].values]\n",
+    "coeqtl_df[f'flipped_zscore_bios_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
+    "                                                   for zscore, coeqtlallele, altaf, altallele in\n",
+    "                                                   coeqtl_df[[f'flipped_zscore_bios',\n",
+    "                                                                  f'SNPEffectAllele',\n",
+    "                                                                  'eqtl_alt_af',\n",
+    "                                                                  'eqtl_alt_allele']].values]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9637681159420289\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(3, -5, 'Concordance = 0.96\\nrb = 0.61')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
+    "coeqtl_nonsig = coeqtl_df[coeqtl_df['corrected_p_bios']>0.05]\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.scatter(coeqtl_nonsig['MetaPZ_flippedforAF'], \n",
+    "            coeqtl_nonsig['flipped_zscore_bios_flippedforAF'], \n",
+    "            label='Insignificant',\n",
+    "            edgecolor='gray',\n",
+    "            facecolor='white', alpha=1)\n",
+    "plt.scatter(coeqtl_sig['MetaPZ_flippedforAF'],\n",
+    "            coeqtl_sig['flipped_zscore_bios_flippedforAF'],  \n",
+    "            label='Significant',\n",
+    "            edgecolor=color_dict[celltype],\n",
+    "            facecolor=color_dict[celltype], alpha=1)\n",
+    "plt.plot([-15, 12], [0, 0], linestyle='--', color='lightgray')\n",
+    "plt.plot([0, 0], [-6.5, 4], linestyle='--', color='lightgray')\n",
+    "plt.legend()\n",
+    "\n",
+    "concordance_rate = coeqtl_sig[coeqtl_sig['is_concordant']].shape[0] / coeqtl_sig.shape[0]\n",
+    "print(concordance_rate)\n",
+    "\n",
+    "celltype_rb = bios_replication_filtered_df.loc[celltype]['r']\n",
+    "plt.text(3, -5, f'Concordance = {concordance_rate:.2f}\\nrb = {celltype_rb:.2f}')\n",
+    "\n",
+    "# plt.savefig('bios_replication.cd4t.filtered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_ci_manual(t, s_err, n, x, x2, y2, ax=None):\n",
+    "    \"\"\"Return an axes of confidence bands using a simple approach.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    .. math:: \\left| \\: \\hat{\\mu}_{y|x0} - \\mu_{y|x0} \\: \\right| \\; \\leq \\; T_{n-2}^{.975} \\; \\hat{\\sigma} \\; \\sqrt{\\frac{1}{n}+\\frac{(x_0-\\bar{x})^2}{\\sum_{i=1}^n{(x_i-\\bar{x})^2}}}\n",
+    "    .. math:: \\hat{\\sigma} = \\sqrt{\\sum_{i=1}^n{\\frac{(y_i-\\hat{y})^2}{n-2}}}\n",
+    "    \n",
+    "    References\n",
+    "    ----------\n",
+    "    .. [1] M. Duarte.  \"Curve fitting,\" Jupyter Notebook.\n",
+    "       http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/CurveFitting.ipynb\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    if ax is None:\n",
+    "        ax = plt.gca()\n",
+    "    \n",
+    "    ci = t * s_err * np.sqrt(1/n + (x2 - np.mean(x))**2 / np.sum((x - np.mean(x))**2))\n",
+    "    ax.fill_between(x2, y2 + ci, y2 - ci, alpha=0.1, color='gray')\n",
+    "    return ax\n",
+    "\n",
+    "from scipy import stats\n",
+    "def equation(a, b):\n",
+    "    \"\"\"Return a 1D polynomial.\"\"\"\n",
+    "    return np.polyval(a, b) \n",
+    "\n",
+    "x=coeqtl_df['MetaPZ_flippedforAF']\n",
+    "y=coeqtl_df['flipped_zscore_bios_flippedforAF']\n",
+    "\n",
+    "p, cov = np.polyfit(x, y, 1, cov=True)                     # parameters and covariance from of the fit of 1-D polynom.\n",
+    "y_model = equation(p, x)     \n",
+    "# Statistics\n",
+    "n = y.size                                           # number of observations\n",
+    "m = p.size                                                 # number of parameters\n",
+    "dof = n - m                                                # degrees of freedom\n",
+    "t = stats.t.ppf(0.975, n - m)                              # used for CI and PI bands\n",
+    "# Estimates of Error in Data/Model\n",
+    "resid = y - y_model                           \n",
+    "chi2 = np.sum((resid / y_model)**2)                        # chi-squared; estimates error in data\n",
+    "chi2_red = chi2 / dof                                      # reduced chi-squared; measures goodness of fit\n",
+    "s_err = np.sqrt(np.sum(resid**2) / dof)                    # standard deviation of the error\n",
+    "\n",
+    "# Plotting --------------------------------------------------------------------\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "# Data\n",
+    "ax.scatter(\n",
+    "    x, y\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Fit\n",
+    "ax.plot(x, y_model, \"-\", color=\"0.1\", linewidth=1.5, alpha=0.5, label=\"Fit\")  \n",
+    "\n",
+    "x2 = np.linspace(np.min(x), np.max(x), 100)\n",
+    "y2 = equation(p, x2)\n",
+    "\n",
+    "# Confidence Interval (select one)\n",
+    "plot_ci_manual(t, s_err, n, x, x2, y2, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-46-55674587bbd9>:19: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  coeqtl_sig['celltype'] = celltype\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f6de56225b0>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# withbiostechnicalandcelltypePICs\n",
+    "sig_df = pd.DataFrame()\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharex=True, sharey=True)\n",
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC']\n",
+    "for i in range(2):\n",
+    "    for j in range(3):\n",
+    "        celltype = celltypes[i*3+j]\n",
+    "        coeqtl_df = pd.read_csv(\n",
+    "            coeqtl_withbios_prefix/filter_type/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbiosonlyRNAAlignMetrics_rmLLD.tsv.gz',\n",
+    "            compression='gzip', index_col=0, sep='\\t')\n",
+    "        coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
+    "                                    coeqtl_df[['t_bios', \n",
+    "                                               'num_individuals_bios']].values]\n",
+    "        coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
+    "                                          coeqtl_df[['SNPEffectAllele', \n",
+    "                                                     'assessed_allele_bios',\n",
+    "                                                     'zscore_bios']].values]\n",
+    "        # flip the direction according to AF\n",
+    "        coeqtl_df['eqtl_effect_allele'] = [eqtl_allele_af_dict.get(eqtl)['AlleleAssessed'] for eqtl in \n",
+    "                                       coeqtl_df['snp_eqtlgene']]\n",
+    "        coeqtl_df['eqtl_alt_af'] = [eqtl_allele_af_dict.get(eqtl)['AF'] for eqtl in coeqtl_df['snp_eqtlgene']]\n",
+    "        coeqtl_df['eqtl_alt_allele'] = [eqtl_allele_af_dict.get(eqtl)['alt_allele'] for eqtl in \n",
+    "                                            coeqtl_df['snp_eqtlgene']]\n",
+    "        coeqtl_df['eqtl_ref_allele'] = [eqtl_allele_af_dict.get(eqtl)['ref_allele'] for eqtl in \n",
+    "                                            coeqtl_df['snp_eqtlgene']]\n",
+    "        coeqtl_df[f'MetaPZ_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
+    "                                                           for zscore, coeqtlallele, altaf, altallele in\n",
+    "                                                           coeqtl_df[[f'MetaPZ',\n",
+    "                                                                          f'SNPEffectAllele',\n",
+    "                                                                          'eqtl_alt_af',\n",
+    "                                                                          'eqtl_alt_allele']].values]\n",
+    "        coeqtl_df[f'flipped_zscore_bios_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
+    "                                                           for zscore, coeqtlallele, altaf, altallele in\n",
+    "                                                           coeqtl_df[[f'flipped_zscore_bios',\n",
+    "                                                                          f'SNPEffectAllele',\n",
+    "                                                                          'eqtl_alt_af',\n",
+    "                                                                          'eqtl_alt_allele']].values]\n",
+    "        ## end flip\n",
+    "        coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
+    "        coeqtl_sig['celltype'] = celltype\n",
+    "        sig_df = pd.concat([coeqtl_sig, sig_df], axis=0)\n",
+    "        significant_ratio = coeqtl_sig.shape[0] / coeqtl_df.shape[0]\n",
+    "        coeqtl_sig_samedirection = coeqtl_sig[((coeqtl_sig['MetaPZ']>0) & (coeqtl_sig['flipped_zscore_bios']>0)) | \n",
+    "                                              ((coeqtl_sig['MetaPZ']<0) & (coeqtl_sig['flipped_zscore_bios']<0))]\n",
+    "        consistent_ratio = coeqtl_sig_samedirection.shape[0] / coeqtl_sig.shape[0]\n",
+    "        # draw\n",
+    "        ax = axes[i][j]\n",
+    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']>0.05], \n",
+    "                   coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']>0.05], alpha=0.5,\n",
+    "                   label='Non-sig')\n",
+    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']<=0.05],\n",
+    "                    coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']<=0.05], alpha=0.5,\n",
+    "                    label='Sig')\n",
+    "        ax.set_xlabel('single cell')\n",
+    "        ax.set_ylabel('BIOS')\n",
+    "        ax.set_title(celltype)\n",
+    "        ax.text(-2, -8, \n",
+    "                f'Significant ratio: {significant_ratio:.2f}\\nConcordance ratio: {consistent_ratio:.2f}')\n",
+    "ax.legend(loc='upper left')\n",
+    "        \n",
+    "# plt.savefig('bios_replication.filtered_results.scatterplots.pdf')\n",
+    "# plt.savefig('bios_replication.filtered_results.scatterplots.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-18-0693f2cb76ec>:19: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  coeqtl_sig['celltype'] = celltype\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f6dedb25820>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# unfiltered results \n",
+    "# withbiosonlyRNAAlignMetrics_rmLLD\n",
+    "sig_df = pd.DataFrame()\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharex=True, sharey=True)\n",
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC']\n",
+    "for i in range(2):\n",
+    "    for j in range(3):\n",
+    "        celltype = celltypes[i*3+j]\n",
+    "        coeqtl_df = pd.read_csv(\n",
+    "            coeqtl_withbios_prefix/'unfiltered_results'/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbiosonlyRNAAlignMetrics_rmLLD.tsv.gz',\n",
+    "            compression='gzip', index_col=0, sep='\\t')\n",
+    "        coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
+    "                                    coeqtl_df[['t_bios', \n",
+    "                                               'num_individuals_bios']].values]\n",
+    "        coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
+    "                                          coeqtl_df[['SNPEffectAllele', \n",
+    "                                                     'assessed_allele_bios',\n",
+    "                                                     'zscore_bios']].values]\n",
+    "        coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
+    "        coeqtl_sig['celltype'] = celltype\n",
+    "        sig_df = pd.concat([coeqtl_sig, sig_df], axis=0)\n",
+    "        # draw\n",
+    "        ax = axes[i][j]\n",
+    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']>0.05], \n",
+    "                   coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']>0.05], alpha=0.5,\n",
+    "                   label='Non-sig')\n",
+    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']<=0.05],\n",
+    "                    coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']<=0.05], alpha=0.5,\n",
+    "                    label='Sig')\n",
+    "        ax.set_xlabel('single cell')\n",
+    "        ax.set_ylabel('BIOS')\n",
+    "        ax.set_title(celltype)\n",
+    "ax.legend(loc='upper left')\n",
+    "# plt.savefig('bios_replication.unfiltered_results.scatterplots.pdf')\n",
+    "# plt.savefig('bios_replication.unfiltered_results.scatterplots.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/04_coeqtl_mapping/.ipynb_checkpoints/plot_example_imputed_zero-checkpoint.ipynb b/04_coeqtl_mapping/.ipynb_checkpoints/plot_example_imputed_zero-checkpoint.ipynb
new file mode 100644
index 0000000..fc28e77
--- /dev/null
+++ b/04_coeqtl_mapping/.ipynb_checkpoints/plot_example_imputed_zero-checkpoint.ipynb
@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "import re\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "from scipy.stats import spearmanr, pearsonr\n",
+    "from scipy.stats import t, norm\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "\n",
+    "def get_time(x):\n",
+    "    if x == 'UT':\n",
+    "        return x\n",
+    "    else:\n",
+    "        pattern = re.compile(r'\\d+h')\n",
+    "        return re.findall(pattern, x)[0]\n",
+    "\n",
+    "\n",
+    "class DATASET:\n",
+    "    def __init__(self, datasetname):\n",
+    "        self.name = datasetname\n",
+    "        self.path_prefix = Path(\"./seurat_objects\")\n",
+    "        self.information = self.get_information()\n",
+    "    def get_information(self):\n",
+    "        if self.name == 'onemillionv2':\n",
+    "            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'\n",
+    "            self.individual_id_col = 'assignment'\n",
+    "            self.timepoint_id_col = 'time'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 'UT',\n",
+    "                                     'stimulated': '3h'}\n",
+    "        elif self.name == 'onemillionv3':\n",
+    "            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'\n",
+    "            self.individual_id_col = 'assignment'\n",
+    "            self.timepoint_id_col = 'time'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 'UT',\n",
+    "                                     'stimulated': '3h'}\n",
+    "        elif self.name == 'stemiv2':\n",
+    "            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'\n",
+    "            self.individual_id_col = 'assignment.final'\n",
+    "            self.timepoint_id_col = 'timepoint.final'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 't8w',\n",
+    "                                     'stimulated': 'Baseline'}\n",
+    "        elif self.name == 'ng':\n",
+    "            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'\n",
+    "            self.individual_id_col = 'snumber'\n",
+    "            self.celltype_id = 'cell_type_mapped_to_onemillion'\n",
+    "        else:\n",
+    "            raise IOError(\"Dataset name not understood.\")\n",
+    "    def load_dataset(self):\n",
+    "        self.get_information()\n",
+    "        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')\n",
+    "        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)\n",
+    "        if self.name.startswith('onemillion'):\n",
+    "            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]\n",
+    "        elif self.name == 'ng':\n",
+    "            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',\n",
+    "                               'other T': 'otherT', 'other': 'other', 'B': 'B'}\n",
+    "            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in\n",
+    "                                                          self.data_sc.obs['predicted.celltype.l1']]\n",
+    "    def get_cMono_ncMono(self):\n",
+    "        def tell_cmono_foronemillion(x):\n",
+    "            if x == 'mono 1' or x == 'mono 3' or x == 'mono 4':\n",
+    "                return 'cMono'\n",
+    "            elif x == 'mono 2':\n",
+    "                return 'ncMono'\n",
+    "        if self.name.startswith('onemillion'):\n",
+    "            self.data_sc.obs['sub_monocytes'] = [tell_cmono_foronemillion(x) for x in\n",
+    "                                                 self.data_sc.obs['cell_type']]\n",
+    "            self.cmono = self.data_sc[self.data_sc.obs['sub_monocytes'] == 'cMono']\n",
+    "            self.ncmono = self.data_sc[self.data_sc.obs['sub_monocytes'] == 'ncMono']\n",
+    "        elif self.name.startswith('stemi'):\n",
+    "            self.cmono = self.data_sc[self.data_sc.obs['cell_type'] == 'cMono']\n",
+    "            self.ncmono = self.data_sc[self.data_sc.obs['cell_type'] == 'ncMono']\n",
+    "        elif self.name == 'ng':\n",
+    "            self.cmono = self.data_sc[self.data_sc.obs['predicted.celltype.l2'] == 'CD14 Mono']\n",
+    "            self.ncmono = self.data_sc[self.data_sc.obs['predicted.celltype.l2'] == 'CD16 Mono']\n",
+    "        else:\n",
+    "            raise IOError(\"Dataset name not understood.\")\n",
+    "\n",
+    "example_savedir = Path(\n",
+    "    \"/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/examples\"\n",
+    ")\n",
+    "\n",
+    "import subprocess\n",
+    "bashfile_path = '/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/bios/select_snps_from_vcf.sh'\n",
+    "def get_snps_from_vcffile(bashfile_path, vcf_path, snps_path, savepath):\n",
+    "    response = subprocess.run([bashfile_path, vcf_path, snps_path, savepath])\n",
+    "    print(response)\n",
+    "    return None\n",
+    "\n",
+    "# sample id mapping\n",
+    "gtefile = pd.read_csv(\n",
+    "    '/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/summary/gte-fix.tsv',\n",
+    "    sep='\\t'\n",
+    ")\n",
+    "gte_dict = gtefile.set_index(\"expressionsampleID\")[\"genotypesampleID\"].T.to_dict()\n",
+    "\n",
+    "\n",
+    "def corr_to_z(coef, num):\n",
+    "    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))\n",
+    "    prob = t.cdf(t_statistic, num - 2)\n",
+    "    z_score = norm.ppf(prob)\n",
+    "    positive_coef_probs = 1 - prob\n",
+    "    positive_coef_probs[coef < 0] = 0\n",
+    "    negative_coef_probs = prob\n",
+    "    negative_coef_probs[coef > 0] = 0\n",
+    "    probs = negative_coef_probs + positive_coef_probs\n",
+    "    return z_score, probs\n",
+    "\n",
+    "\n",
+    "def get_individual_networks_selected_genepairs(data_df, data_sc, individual_colname, genepair, fillna=False):\n",
+    "#     data_df = pd.DataFrame(data=data_sc.X.toarray(),\n",
+    "#                            index=data_sc.obs.index,\n",
+    "#                            columns=data_sc.var.index)\n",
+    "    gene1, gene2 = genepair.split(';')\n",
+    "    sorted_genepair = [';'.join(sorted([gene1, gene2]))]\n",
+    "    coef_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    coef_p_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    zscore_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    zscore_p_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    data_selected_df = data_df[[gene1, gene2]]\n",
+    "    print(\n",
+    "        f\"Calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals and;\\n{genepair}\"\n",
+    "    )\n",
+    "    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):\n",
+    "        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]\n",
+    "        if cell_num > 10:\n",
+    "            individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]\n",
+    "            individual_coefs, individual_coef_ps = spearmanr(individual_df.values, axis=0)\n",
+    "            if data_selected_df.shape[1] == 2:\n",
+    "                individual_coefs_flatten = pd.DataFrame(data = [individual_coefs],\n",
+    "                                                        index = sorted_genepair)\n",
+    "                individual_coef_ps_flatten = \\\n",
+    "                pd.DataFrame(data=[individual_coef_ps],\n",
+    "                             index=sorted_genepair)\n",
+    "            else:\n",
+    "                individual_coefs_flatten = pd.DataFrame(\n",
+    "                    data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],\n",
+    "                    index=sorted_genepair).loc[sorted_genepair]\n",
+    "                individual_coef_ps_flatten = \\\n",
+    "                pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],\n",
+    "                             index=sorted_genepair).loc[sorted_genepair]\n",
+    "            coef_df[ind_id] = individual_coefs_flatten\n",
+    "            coef_p_df[ind_id] = individual_coef_ps_flatten\n",
+    "            try:\n",
+    "                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(\n",
+    "                    individual_coefs_flatten.values,\n",
+    "                    cell_num\n",
+    "                )\n",
+    "                zscore_df[ind_id] = individual_zscores_flatten\n",
+    "                zscore_p_df[ind_id] = individual_zscore_ps_flatten\n",
+    "            except:\n",
+    "                continue\n",
+    "        else:\n",
+    "            print(\"Deleted this individual because of low cell number\", cell_num)\n",
+    "    if fillna:\n",
+    "        zscore_df = zscore_df.fillna(0)\n",
+    "    return data_selected_df, zscore_df, zscore_p_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset onemillionv2 from /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/seurat_objects 1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad\n"
+     ]
+    }
+   ],
+   "source": [
+    "datasetname = 'onemillionv2'\n",
+    "dataset = DATASET(datasetname)\n",
+    "dataset.load_dataset()\n",
+    "data_sc = dataset.data_sc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CompletedProcess(args=['/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/bios/select_snps_from_vcf.sh', '/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/genotypevcfs/chr1/GenotypeData.vcf.gz', PosixPath('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/examples/snplist.rs221045'), PosixPath('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/examples/rs221045.vcf')], returncode=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>#CHROM</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>REF</th>\n",
+       "      <th>ALT</th>\n",
+       "      <th>QUAL</th>\n",
+       "      <th>FILTER</th>\n",
+       "      <th>INFO</th>\n",
+       "      <th>FORMAT</th>\n",
+       "      <th>1_LLDeep_1191</th>\n",
+       "      <th>...</th>\n",
+       "      <th>s21</th>\n",
+       "      <th>s43</th>\n",
+       "      <th>s24</th>\n",
+       "      <th>s23</th>\n",
+       "      <th>s45</th>\n",
+       "      <th>s26</th>\n",
+       "      <th>s25</th>\n",
+       "      <th>s28</th>\n",
+       "      <th>s27</th>\n",
+       "      <th>s29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>16530049</td>\n",
+       "      <td>rs221045</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>GT:DS</td>\n",
+       "      <td>0/0:0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>0/0:0.010000000000000009</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 182 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   #CHROM       POS        ID REF ALT QUAL FILTER INFO FORMAT 1_LLDeep_1191  \\\n",
+       "0       1  16530049  rs221045   T   C    .      .    .  GT:DS      0/0:0.03   \n",
+       "\n",
+       "   ...      s21                       s43      s24      s23      s45      s26  \\\n",
+       "0  ...  0/1:1.0  0/0:0.010000000000000009  0/1:1.0  0/0:0.0  0/0:0.0  1/1:2.0   \n",
+       "\n",
+       "       s25      s28      s27      s29  \n",
+       "0  0/0:0.0  0/1:1.0  0/0:0.0  0/1:1.0  \n",
+       "\n",
+       "[1 rows x 182 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "celltype = 'monocyte'\n",
+    "snp_id = 'rs221045'\n",
+    "chromosome = '1'\n",
+    "snp_vcf_path = example_savedir/f'{snp_id}.vcf'\n",
+    "with open(example_savedir/f'snplist.{snp_id}', 'w') as f:\n",
+    "    f.write(f'{snp_id}\\n')\n",
+    "vcf_path = f'/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/genotypevcfs/chr{chromosome}/GenotypeData.vcf.gz'\n",
+    "get_snps_from_vcffile(bashfile_path, vcf_path, example_savedir/f'snplist.{snp_id}', snp_vcf_path)\n",
+    "gt = pd.read_csv(snp_vcf_path, sep='\\t', skiprows=6)\n",
+    "gt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating networks for 72 individuals and;\n",
+      "AC005076.5;ARHGEF19\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/72 [00:00<?, ?it/s]/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/scipy/stats/stats.py:4264: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
+      "  warnings.warn(SpearmanRConstantInputWarning())\n",
+      "100%|██████████| 72/72 [00:00<00:00, 210.51it/s]\n",
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 42.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Not Imputed SpearmanrResult(correlation=-0.028018282506059713, pvalue=0.8942369051146191)\n",
+      "Imputed SpearmanrResult(correlation=-0.24638574744096847, pvalue=0.03833253459364005)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# genepair = 'RP1-29C18.10;ZNF501'\n",
+    "# genepair = 'CCDC15;UNC5B'\n",
+    "# genepair = 'GSTM3;RP1-29C18.10'\n",
+    "# genepair = 'MMEL1;SARS2'\n",
+    "genepair = 'AC005076.5;ARHGEF19'\n",
+    "gene1, gene2 = genepair.split(';')\n",
+    "\n",
+    "if datasetname == 'ng':\n",
+    "    ut_celltype = data_sc[data_sc.obs[dataset.celltype_id]==celltype]\n",
+    "else:\n",
+    "    ut_celltype = data_sc[(data_sc.obs[dataset.celltype_id]==celltype) &\n",
+    "                          (data_sc.obs[dataset.timepoint_id_col]==dataset.chosen_condition['UT'])]\n",
+    "\n",
+    "ut_celltype_df = pd.DataFrame(data=ut_celltype.X.toarray(),\n",
+    "                              columns=ut_celltype.var.index,\n",
+    "                              index=ut_celltype.obs.index)\n",
+    "selected_expression_df, ut_zscore_df, ut_zscore_p_df = get_individual_networks_selected_genepairs(\n",
+    "    data_df = ut_celltype_df,\n",
+    "    data_sc = ut_celltype,\n",
+    "    individual_colname = dataset.individual_id_col,\n",
+    "    genepair = genepair,\n",
+    "    fillna=False\n",
+    ")\n",
+    "\n",
+    "ut_t = ut_zscore_df.T\n",
+    "ut_t['gt_sampleid'] = [gte_dict.get(name) for name in ut_t.index]\n",
+    "ut_t = ut_t.set_index('gt_sampleid')\n",
+    "common_individuals = list(set(gt.columns) & set(ut_t.index))\n",
+    "gt_t = gt[common_individuals].T\n",
+    "gt_t['genotype'] = [item.split(':')[0].count('1') for item in gt_t[0]]\n",
+    "concat_df = pd.concat([gt_t, ut_t], axis=1).replace([np.inf, -np.inf], np.nan).dropna()\n",
+    "print('Not Imputed', spearmanr(concat_df['genotype'], concat_df[genepair]))\n",
+    "\n",
+    "ut_t_imputed = ut_zscore_df.fillna(0).T\n",
+    "ut_t_imputed['gt_sampleid'] = [gte_dict.get(name) for name in ut_t_imputed.index]\n",
+    "ut_t_imputed = ut_t_imputed.set_index('gt_sampleid')\n",
+    "common_individuals_imputed = list(set(gt.columns) & set(ut_t_imputed.index))\n",
+    "gt_t_imputed = gt[common_individuals_imputed].T\n",
+    "gt_t_imputed['genotype'] = [item.split(':')[0].count('1') for item in gt_t_imputed[0]]\n",
+    "concat_imputed_df = pd.concat([gt_t_imputed, ut_t_imputed], axis=1).replace([np.inf, -np.inf], np.nan).dropna()\n",
+    "print('Imputed', spearmanr(concat_imputed_df['genotype'], concat_imputed_df[genepair]))\n",
+    "\n",
+    "# dosage_dict = gt_t['genotype'].T.to_dict()\n",
+    "# selected_expression_df_withsample = pd.concat([selected_expression_df,\n",
+    "#                                                ut_celltype.obs[[dataset.individual_id_col]]],\n",
+    "#                                              axis=1)\n",
+    "# selected_expression_df_withsample['gt_sampleid'] = [gte_dict.get(name) for name in\n",
+    "#                                                     selected_expression_df_withsample[dataset.individual_id_col]]\n",
+    "# selected_expression_df_withsample['genotype'] = [dosage_dict.get(gt_sampleid) for gt_sampleid in\n",
+    "#                                                selected_expression_df_withsample['gt_sampleid']]\n",
+    "\n",
+    "sns.set_style('white')\n",
+    "refallele = gt['REF'].values[0]\n",
+    "altallele = gt['ALT'].values[0]\n",
+    "snp_name = f'{snp_id}_{altallele}'\n",
+    "\n",
+    "_, axes = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
+    "ax1, ax2 = axes\n",
+    "\n",
+    "im_coef, im_p = spearmanr(concat_imputed_df['genotype'], concat_imputed_df[genepair])\n",
+    "sns.violinplot(x=concat_imputed_df['genotype'],  \n",
+    "               y=concat_imputed_df[genepair], \n",
+    "               ax=ax1,\n",
+    "              inner=None)\n",
+    "sns.swarmplot(x=concat_imputed_df['genotype'],  \n",
+    "               y=concat_imputed_df[genepair], \n",
+    "               ax=ax1,\n",
+    "              color='black')\n",
+    "ax1.set_title(f'Imputed r={im_coef:.2f}; pvalue {im_p:.4f}')\n",
+    "# ax1.set_xticklabels([f'{refallele}{refallele}', \n",
+    "#                      f'{refallele}{altallele}',\n",
+    "#                      f'{altallele}{altallele}'])\n",
+    "ax1.set_xlabel(snp_id)\n",
+    "\n",
+    "coef, p = spearmanr(concat_df['genotype'], concat_df[genepair])\n",
+    "sns.violinplot(x=concat_df['genotype'],  \n",
+    "               y=concat_df[genepair], \n",
+    "               ax=ax2,\n",
+    "              inner=None)\n",
+    "sns.swarmplot(x=concat_df['genotype'],  \n",
+    "               y=concat_df[genepair], \n",
+    "               ax=ax2,\n",
+    "              color='black')\n",
+    "ax2.set_xlabel('')\n",
+    "ax2.set_title(f'Not Imputed r={coef:.2f}; pvalue {p:.4f}')\n",
+    "# ax2.set_xticklabels([f'{refallele}{refallele}', \n",
+    "#                      f'{refallele}{altallele}',\n",
+    "#                      f'{altallele}{altallele}'])\n",
+    "ax2.set_xlabel(snp_id)\n",
+    "plt.savefig(example_savedir/f'{snp_name}_ref{refallele}_alt{altallele}_{gene1}_{gene2}.{celltype}_{datasetname}.full.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 42.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/Zfbz88PNzY21a9fa9HPKERgYaFOzd/Xq1TvGdvtjTJ8+Pd+atpxkrWXLlrn6CdSpU4fTp09bty9fvozBYKBGjRp53kuv1zNu3DgqVqx4x8RMpVIVOEIpL7e/DoGBgdbtPn368Pvvv2M2mwkJCbE23a5Zs4YtW7awePFigoODSU1NpVWrVnk+tru7u02ftlt/bnf6Od3ubl67kJAQLl++TFpamvV35vTp0za1iLcymUzEx8eTlpaGr68vZ86c4e2337Z+eh06dGiuGrhbGY1GLl++LAlcKTVhwgT69+/PyJEjrfsCAwNzvfevXbuWq6m8OOX3/ivofQO5y6PKlSsTHBxs0wR4qwoVKnDt2jXq1KmT63Hzc/tj9O7dO9+yMidZGzNmjLVPXo6c923jxo0By/swJ47b1alTx6aPV0ZGBpcuXcq3uc5oNNq04hTGtWvXrIn8ra95uXLl6NChA+vXr+f8+fP07t3b+hp8/PHHqFQqfvvtN/z8/Ni8eXO+5fHtP7u4uDjr93f6Od3ubl67U6dO0bJlSxo1agRA48aNady4MXv27KF+/fr4+vry8ccfW8//5JNPrPfdu3cvx48fp0OHDoBl0IJGo+Hvv//myy+/BCx9Al944QVmzZpFu3btChV/UZAmVAexfft2Dh48iF6v57PPPqNJkyZUrlyZ8uXLU7FiRVavXo3JZGLFihU2Awn8/f2JiYlBr9cDoFarGTRoELNmzbKOwImJibH2M3j00UdZtWoVUVFRZGZm3tWntxwzZsywGf1561dBzSBhYWFs27aNgwcPkpGRwWeffUa3bt1sqslzGAwGJkyYgKurK7Nnz7bWdOXYv38/V69eRVEUrl27xpw5c+jatav1+Lx58+7YVLJkyRKuX79OUlKStUN1jl69erF7925+/vlnm8QnPT0dnU6Hn58fmZmZfPLJJ/neP6fPxqlTp8jOzrbpN3Knn9P9vHY1a9akfv36zJ8/n+zsbDZt2sSZM2fo0aMHABs3buT8+fOYzWYSEhJ4//33efDBB/H19QUs/UmWL19OVlYWWVlZLF261NqX5OjRo9bf06ysLCIiIoiLi7MWhqL0qV69Or169bIZsdilSxcuXrzImjVrMBqNrFu3jqioKB566CHA0lpwPwOWCiO/919B7xuwlHlXrlyxbjdu3BgvLy8iIiLIysrCZDLx999/c+zYMcAyKCkiIoLk5GSuX79+TyM3165dm2+ZV9CHy759+7J48WJiYmKIiYlh8eLF+Y6W79atG2fPnmXDhg1kZ2czf/586tata024li9fbi0roqKiiIiIsEkmnn766Tv2Tfviiy/IzMzk7NmzrFy50qbMCwsLY/Xq1WzYsMGmL3N6ejoeHh6UK1eOmJgYvv7663zvX79+fbZv305SUhI3btzgu+++sx6708/pfl67Ro0acfDgQWsz/cmTJzl06JC13Lp06RKJiYmYTCa2b9/O0qVLeeGFFwB46aWX2LBhA5GRkURGRhIaGsqgQYOsfTb//vtvRo0axZtvvkloaGiBr29RkwTOQfTp04f58+dbRzt+9NFH1mPvvvsu33zzDW3atCEqKopmzZpZj7Vt25aQkBA6duxImzZtAHj11VepXr06gwcPpnnz5gwfPtw6erNLly48++yzPPvss3Tr1o22bduW2HPMqfJ/5ZVXaN++Penp6TbTV0yfPt3aYfrIkSNs27aN3bt306pVK+tcbzkDNU6ePMkTTzxB06ZNGTJkCA888ABvvPGG9V7Xrl2jefPmBcbTp08fRo4cySOPPELVqlWtb2iw1FDkDA65tZDr168fVapUoVOnTvTu3ZumTZvme/+aNWsybtw4hg8fTvfu3XP1FSvo53Q/rx1YPmEeP36cVq1aMWfOHP773/9Svnx5wJIojho1iubNmxMWFoZarbZJ5GfNmkV0dDRdunShc+fOXL582dq/UK/XM2PGDNq0aUPnzp3ZsWMHERERhapFFPYzbtw4myZMPz8/FixYwOLFi2nTpg1ff/01CxYssP6OPPPMM2zYsIFWrVoxc+bMYokpv/ffnd43AwcOJCoqipYtWzJ27Fg0Gg1ffvklp0+fpmvXrrRt29Zmrq4XX3yRKlWq0LVrV0aOHEnfvn2L5fnkZciQITz88MOEhYURFhZGly5dbAYe9O7dm99++w2A8uXLM2/ePObOnUurVq04duyYzQfEw4cPExYWRtOmTQkPD6dz585MnjzZerwwZV7r1q3p1q0bw4cPZ+TIkXTs2NF6LDQ0lIsXLxIQEGBTm/7iiy9y8uRJWrZsSXh4eIEjc/v27Uu9evUIDQ1l5MiRNmXnnX5O9/PatW7dmvHjxzNhwgSaNWvG+PHjGT16tPX5HT9+nLCwMJo3b84nn3zCnDlzrLV5Xl5e1i45FSpUwM3NDXd3d+sH2sWLF5OQkMAbb7xh/TvUu3fvAl/noqJS7rZdSZS4qVOnUrFiRZs5f8T96du3L99++y1+fn55Hg8NDWXmzJm0b9++hCMTQsj7r2hdv36dl156iaVLl+Z5PGdqoBMnTqDVSs8qRyE/KVEmrV692t4hCCFEiahUqVK+yZtwXNKEKoQQQgjhYKQJVQghhBDCwUgNnBBCCCGEg5EETgghhBDCwZSpQQxt2rQp1MoCQgjnER0dzf79++0dxn2T8kuIsqeg8qtMJXBBQUGsXLnS3mEIIUrQrcuROTIpv4Qoewoqv6QJVQghhBDCwUgCJ4QQQgjhYCSBE0IIIYRwMJLACSGEEEI4GEnghBBCCCEcjCRwQgghhBAORhI4IYQQQggHY9cEbseOHfTo0YNu3boRERGR67iiKMycOZNu3boRFhbGiRMnCn3t3frzzz/p1asXmZmZXL58mYyMDJvjN27cID4+3mZfWloaV65csdlnNBq5cOECJpPJZr+j3XPOnDlMnz6d+xEbG0tCQgIZGRlcvnzZ5pjJZOLChQsYjUab/VeuXCEtLc1mX3x8PHFxcTb7HO2eiqJw8eJF9Ho9ji4zM5NLly7ZOwwhhCjbFDsxGo1K165dlUuXLinZ2dlKWFiYcvbsWZtz/vjjD+W5555TzGazcuTIEWXgwIGFvjYv/fv3z3O/l5eXAth8eXt7K4sWLVKMRqMyfPhwRa1WKxqNRhk7dqxiNpuVefPmKR4eHgqgdO7cWYmPj1d27typVKlSRQGUGjVqKAcPHlSuXbumtG7d2qHuefvXhQsX7upnazAYlGHDhikqlUrRaDSKi4uLAiht27ZVYmJilD///FOpVq2aAihBQUHK7t27lfj4eKVTp04KoHh6eirz589XzGazMmbMGEWj0ShqtVoZMWKEYjQalW+++Ubx9vZ2qHu6ubkpgBIYGKhs2LDhrl7P0mTJkiWKj4+PAijNmzdXLl++bO+Q7ii/972jcZbnIYQovILe93ZL4A4fPqyMHDnSur1gwQJlwYIFNue8+eabypo1a6zb3bt3V2JiYgp1bV7yeiGuXLmSZ9KS80d34cKFufZ///33ikajsdk3ZcoUpW7dujb7WrdurYwePdoh73nrl6ur6139bL///vt87zV27FilRYsWNvsefPBB5eWXX7bZp9Vqle+++y7X9d98843i6urqkPfM+QoODlaMRuNdvaalQUpKSq4PO88++6y9w7ojZ0l8nOV5CCEKr6D3vd2W0oqJiaFSpUrW7YoVK3Ls2LECz6lUqRIxMTGFurawvvnmm3yPZWVl5bkG2Z49e3I1PZ44cYK///7bZt/Jkydxc3MrkXseOHCgSO95q+zs7HyP5eXkyZP5Hjt16hSnTp2y2Xf69GmqV69us89oNLJ3795c1+/fvz9XPCV5zxo1atzzPXNcuXKF1NRUfH198zxeWl2+fDlXs3FBP2shhBDFx2594BRFybVPpVIV6pzCXFtYBfXzqlq1KsOHD7e5t0aj4bnnniMgIMDm3LCwMB599FGbfX369KF3794lcs9nn322SO95q9sToTsp6F69e/fOdbxXr1706dPHZl+FChV47rnnUKv//RVVqVSMGDEi14LeJXnP2/fdzT1zdOjQweGSN4B69epRu3Ztm323v8ZCCCFKSElVA96utDShKoqiPPnkkzbNQkFBQcpjjz2mHD9+XFEURVm6dKnSpk0bpUOHDsrq1asVRVGUQ4cOKT179lQaN26szJ49WzGbzcqNGzeU4cOHK/Xr11dGjx6tJCUlKUajUXnnnXeURo0aOcw91Wr1PTef5vjpp5+U1q1bK82bN1dat26tNGrUSHn33XcVk8mkJCYmKuHh4Ur9+vWVESNGKHFxcYrZbFbef/99pXHjxkqvXr2UI0eOKIqiKJGRkUr79u2Vtm3bKsuWLVMURVH++usvJSwszGHu2aBBA6Vt27bKgw8+qDz55JNKdHT0Pb2mpcGZM2eU/v37Kw0aNFCmTZumGAwGe4d0R87S9Ogsz0MIUXilsg+cwWBQQkNDbQYi/P333zbnbNu2zWYQw+OPP17oa/MiBaAQZY+zvO+d5XkIIQqvVPaB02q1TJ8+nVGjRmEymXj88cepU6cOP//8MwBDhw6lS5cubN++nW7duuHu7s6sWbMKvFYIIYQQoiywWwIH0KVLF7p06WKzb+jQodbvVSoVb731VqGvFUIIIYQoC2QlBiGEEEIIByMJnBBCCCGEg5EETgghhBDCwUgCJ4QQQgjhYCSBE0IIIYRwMJLACSGEEEI4GEnghBBCCCEcjCRwQgghhBAORhI4IYQQQggHIwmcEEIIIYSDkQSuFMrSm4hPy8ZkVuwdihBCCCFKIbuuhSrypqCQkmkgPduIj7sL3m4uqNUqe4clhBBCiFJCauBKMaNZIT5dz7XkTDL1RnuHI4QQQohSQhI4B5BtNHM9OYu4tGzM0qwqhBBClHnShOogFCAl00Cm3oinqxYPFy1uOo29wxJCCCGEHUgC52AMJoWkDAPJGHDRqCnnrsXLVfrICSGEEGWJJHAOSgH0JjNxaXqSMgx4umrx1GlxdVGjUkkyJ4QQQjgzSeCcgNGskJxpICXTgEatwtNVi5ebFletNLEKIYQQzkgSOCeicEsyl2WgnJsLfh46aV4VQgghnIwkcE5KUSA500CG3oivhw4vV600rQohhBBOQhI4J2cwKdxIzSYpQ28ZvarT4uYiTatCCCGEI5MEroywjl7NMKDTqnHXaXDTanB10aCRJlYhhBDCoUgCV8YoWCYGzjaaUWFApQIXjRpXFw0eLhrcdRppahVCCCFKOUngyjAFS1+5nIQuNdMyt5yPh4v0mRNCCCFKMUnghFXO3HI3UrNJTNfj4arFVaNGq1Hh5iI1c0IIIURpIQmcyJPRrJCSaQBABWjUKjx0WrQaFTqNGjcXjUxPIoQQQtiJJHClzMrDV9h44jp1K3nTvnYAFbxd7R2SdX65lKx/EzpL3zkNri5qXDVqXDRqdFq1JHVCCCFECZAErpRZtPsCx6NT+N+JGD7bEkW9St50DAmgY0gA1fw97B0ecGvfORPZRhNgm9S5uVhq6Fy1arQatV1jFUIIIZyRJHClzCeDm/L51ii2nYklNcvI6eupnL6eyte7LlDVz52OdSzJXN1K3qhLUZ+025O65EwDahWoVZb+c+6uGty1GknohBBCiCIgCVwp80BFb2Y/3ogrCRn8X3Qyu87GsTsqnhtp2VxOzOTnA5f5+cBl/L10dKgdQMcQf5pU9cWlFCZGZgXMikJatpG0bCPq25tdtWp0Gml2FUIIIe6WJHCllFajpnk1P5pX82N8aAhnYlLZHRXPrqg4/onPID5Nz2//d5Xf/u8qnq4a2tXyp2NIAK1qlMddVzpXWjAX0OzqoVPjerOGzkWjkhGvQgghRAEkgXMAKpWKepXKUa9SOZ7rWJPLCRnsjopjV1QcJ6+lkp5tYvOpWDafisVFo6JFdT86hQTQrrY/vh46e4efr9x96SwTC6uwTC6s1ajRqlWWr5sDJSS5E0IIISSBc0hVy3swpHU1hrSuRlxaNnvPWWrmjlxKwmBS2Hc+gX3nE1CroGGQDx1CAugUEkAlHzd7h35HimK7WsStVDf71OUkdJqcbA/rf6hu2VarVKg1KlxUluZaWTJMCCGEs5AEzsEFeLkS1qQKYU2qkJZtZP/5eHZFxXPgQgKZBhPHriRz7EoyX/5xjpAKXnQI8adjnQBqBXg6XE2WooBJUTCZlVzJXUFymmq1astUJy4aNVqtChWWZBGz5b7KzT57Of9bvkC5+b1apUKtVuGiVqNWY0kgb4/xtnhv3avcdq5apUKjUaG9ed+c829NVO39M1Juvt5Gk4LBbAYFFBTUahVeri52jU0IIcoySeCciJerlq71K9K1fkX0RjOHLyWyKyqOPVHxJGUaiLqRRtSNNL7b+w+VfdzoGBJAhxB/GlTxceraqZymWr3JjN5U+MQv7ztBJqYiiStHnq+86t9aRI3akuBp1ap/t1X/JohqlQqVGtSoLM8VBbPZkniZzJZk1FJZefOmOYkq/yasys2bqVSWe6tUKgxGM0az2fKsFdsE1M1FIwmcEELYkSRwTkqnVdO2lj9ta/ljekTh5NUUdt3sN3ctOYtryVksP3SF5Yeu4OfhQrvalkEQzav5odOWvhGtzuz2mrmcnTkJltGc5xlCCCHKMEngygCNWkWjYB8aBfswpkstzselW6cnibqRRmKGgXV/XWfdX9dxd9HQumZ5Oob406aWP16u8isihBBClDby17mMUalU1K7gRe0KXjzbvgbXkjPZFRXP7qg4jkcnk2kwsf3vG2z/+wZatYqmVX3pWCeA9rX9CfCy/7JeQgghhJAErsyr7OPOoBbBDGoRTFKG/uaI1ngO/pOAwaRw8J9EDv6TyKebz/JgZW863FzWq2r50rGslxBCCFEW2SWBS0pKYtKkSURHRxMUFMSnn36Kj49PrvN27NjBe++9h9lsZtCgQYSHhwMwb948li1bRvny5QGYPHkyXbp0KdHn4Ix8PXT0bFSZno0qk6k3ceBiAruj4th3PoG0bCMnr6Vy8loqC3deoLq/h3WN1gcqetl9tKQQQghRltglgYuIiKBdu3aEh4cTERFBREQEr776qs05JpOJGTNmsHjxYipWrMjAgQMJDQ0lJCQEgOHDh/Pcc8/ZI/wywV2nocsDFejyQAWMJjNHLydZVoI4F0d8mp5/4jP4J/4SP+2/RAUvV+v0JI2DfGS9UyGEEKKY2SWB27JlCz/88AMA/fr14+mnn86VwB07dozq1atTtWpVAHr37s2WLVusCZwoOVqNmpY1ytOyRnnGdw3hzPVUy4jWs3FcTszkRlo2kUevEnn0Kt5uWtrV8qdDSAAta/jh7lI6l/USQgghHJldErj4+HgCAwMBCAwMJCEhIdc5MTExVKpUybpdsWJFjh07Zt3+6aefiIyMpGHDhkydOjXPJlhR9NQqFfUrl6N+5XI836kWl+IzrNOTnL6eSmqWkY0nY9h4MgZXrZqW1f3oWCeAtrX88XGXecOEEEKIolBsCdzw4cOJi4vLtX/ixImFul5Rcs99ldPPaujQoYwdOxaVSsVnn33GBx98wPvvv39f8Yp7U83fgyf9q/Fkm2rcSM1md1Qcu6PiOHolmWyjmd3n4tl9Lh61ChoH+9AxJID2IQFUKlf6l/USQgghSqtiS+C+/fbbfI/5+/sTGxtLYGAgsbGx1sEIt6pUqRLXr1+3bsfExFhr7QICAqz7Bw0axJgxY4oucHHPKni70q9ZEP2aBZGaZWDfecsgiAMXEsgymjl6OZmjl5P5fNs5QgK96BQSQMc6AdTw95BBEEIIIcRdsEsTamhoKJGRkYSHhxMZGUnXrl1zndOoUSMuXrzI5cuXqVixImvXruXjjz8GsCZ/AJs3b6ZOnTolGr+4M283F7o9WJFuD1Yk22Di4D+J7I6KZ8+5OFKyjETFphEVm8biPRcJ8nW3DIIICeDBKuVQSzInhBBCFMguCVx4eDgTJ05kxYoVVK5cmc8++wyw1LJNmzaNhQsXotVqmT59OqNGjcJkMvH4449bE7WPPvqI06dPAxAUFMSMGTPs8TREIbm6aOgQEkCHkABM5gc4Hp1s7TcXk5JNdFImyw5eYdlBy7JeOXPNNa3qK8t6CSGEEHlQKXl1NnNSAwYMYOXKlfYO444y9UauJ2flvUamE1EUhajYtJvJXDwX4tJtjnvoNLSpWZ6OIQG0rlkeT1nWq9Rwc9FQxdfd3mEUiqO87+/EWZ6HEKLwCnrfy19EYTcqlYo6Fb2pU9GbER1qEp2Uye6b05OcuJpCht7EtjM32HbmBi4aFc2q+dExxJ/2tQMo76mzd/hCCCGE3UgCJ0qNIF93BresyuCWVUlI17PnnGWN1sOXEjGYFA5cSODAhQTmbjrLg1XKWVaCqBNAkIPUBAkhhBBFRRK4UkilUuGu02JWFMxmBbOioPDv1CrKzX+cuYm1vKeOPo0r06dxZTL0Rg5cSGDn2Tj2X0ggQ2/ixNUUTlxN4asd56kZ4EnHEMvkwXUCZVkvIYQQzk8SuFLIzUVDJZ/cKxgoioJZAdPNpM5kVjCYzGQbzRhNZoxmBUVRyOnV6CwJnodOy0N1A3mobiB6481lvc7FsTsqnoR0PRfi0rkQl84P+y4R6O1qrZlrFOSDRi3JnBBCCOcjCZwDUalUaFQUmJSYzApGsxmzWcGoKJhMCtlGM9kGk6Umz8GTO51WTeua5WldszwvdVU4dS2FXWfj2H0uniuJmcSmZrPySDQrj0RTzk1Lu9qW6UlaVvfDVZb1EkII4SQkgXMyGrUKjTrvRCUnuTOZFFKyjGTqjQ6byIFlWa8GVXxoUMWH8M61uBifYRkEERXH3zFppGQZ2XAihg0nYnDTqmlVszwdQgJoV6s83m6yrJcQQgjHJQlcGWJN7rTg4aolPdtIapaRLIORWyeTccSkTqVSUTPAk5oBnjzVtjoxKVnsjopnV1Qcx64kkWU0s/NsHDvPxqFRq2hyc1mvDiEBVPB2tXf4QgghxF2RBK4M83TV4umqtfSfM+UMkLjZ5HqzX53BZMbsgBldxXJuDGgexIDmQSRnGth33pLMHbyYSLbRzOFLSRy+lMR/t0ZRt5I3HW+uBFHd39PeoQshhBB3JAmcQKtRo72l1dX9linWjCYzmXoT6XoTWQajQyZzPu4u9GhQiR4NKpFlMHHwYiK7z8Wx51w8qVlGzlxP5cz1VL7ZdZFgP3fLIIiQAOpV9pZlvYQQQpRKksCJAmk1arzd1Xi7u1iSOYOJbIMZvclSQ2e+OTLWUbi5aOhYxzJK1Wgycyw6mV1nLclcbGo2VxIz+eXPy/zy52X8PXW0v1kz17SqLy4aWdZLCCFE6SAJnCg0rUaNt0aNt9u/+0xmhSyDiQy9iQy9EZMDZXNajZrm1fxoXs2P8aEhnI1NY+fZOHZHxXExPoP4dD1r/u8aa/7vGp6uGtrWtMw116Zmedx1MqJVCCGE/UgCJ+6LRq2y9qUzmXWkZxtIyTSiN5ntHdpdUalUPFDRmwcqevNcx5pcScxgV1Q8u87GcfJaCunZJracjmXL6VhcNCpaVPejY0gA7Wv74+shy3oJIYQoWZLAiSKjUaso567D282FNL2RlAwjepPJZoSrowj282BIKw+GtKpKfFo2e85ZBkEcuZSEwaSw73wC+84noFZBwyAfOoQE0DHEn8o+sqyXEEKI4ldgArdv3z42btzItWvX0Gq1VK9enUGDBlG9evWSik84IJVKhberC96uLuhvTiKcefPLkZpYc/h7uRLWpAphTaqQlm1k//kEdkXFceBCApkGE8euJHPsSjJf/nGO2hU86RASQKeQAGpV8JRlvUQuUq4KIYpCvgncnDlziI+Pp23btsTFxREUFES1atV46aWXGD16ND179izJOIWD0mnV6LSWQRAms0JGtpHUbCPZRsesmfNy1dK1fiBd61uW9Tp8KZFdUXHsiYonKdPAuRvpnLuRzvd7/6Gyjxsdbg6CaFBFlvUSUq4KIYpOvgnc9u3bWbNmDQC9e/fmqaee4j//+Q89evRg2LBhUtCIu6ZRq/B2d8Hb3QWDyYzBaCbLaCJTb0JvMt93QmcwGDgfdZbqNWri5l78TZk6rZq2tfxpW8sf0yMKJ6+msOvmShDXkrO4lpzFikPRrDgUja+7C+1rWwZBtKjuh04rI1rLIilXRUHOnz+Pm5sbVapUsXcowgHkm8CpVCqSkpLw9fUlNjYWs9nSKd3HxwfFEatORKniolHjolHj4aoFT9AbzWTojSRnGu6pmfXYkUOMfmYIsTHX8fH145Mvv+ahrt05dGAfa1evpFLlKgx9ZgTe5XyK4dlYktNGwT40CvZhTJdaXIhLv5nMxRMVm0ZSpoF1x6+z7vh13Fws67l2DAmgbU1/vNykK2pZIeWqyEtWVhaPP/4469atQ61WM3r0aL744gtiY2OJiIggKSmJZ555hsaNG9s7VFGK5PuXY8yYMfTv35+aNWty/vx53n77bQASEhKoV69eScUnyghLU6sOL1ctSRkGUrMNd1UjN+ONKcTGXAcgOSmRN1+dyKyP5zFy6ADrH8l1v61i1YY/ir1fmkqlolYFL2pV8OKZdjW4npzFrijL9CR/RSeTZTCz4+84dvxtWdarWVVfOoQE0CHEnwAvWdbLmUm5KvKyePFi1q1bB4DZbObLL7+kf//+jBkzhvPnzwPw+eefs3fvXpo1a2bPUEUpkm8C16tXL9q3b8/ly5epXr065cqVA6B8+fJ8/PHHJRagKFu0GjUB3q54uWlJyTSQrjcWKpH758I5m+1r0Vf46duvrckbwF9HD3PsyCGaNG9Z1GEXqJKPGwNbBDOwRTBJGXr2nk9g19k4Dv6TgMGkcPCfRA7+k8hnW85Sv7K3dY3WauU9SjROUfykXBV5OXv2bK59q1evtiZvANnZ2SxatIh58+aVZGiiFCuwI46vry+NGjWyFjI5zp07l88VQhQNNxcNgeXcqOzjTjl3lzsOAOjWK8xmu3NoN7y8vXOd5+HpVaRx3i1fDx09G1bivf4NiRzbgbfDHuSR+oF4uVo+S526lsrCnRcYvvhPhi/+k693nuf09RRpXnMiUq6K2/Xr18+mZcDd3Z0OHTrkOs/Ly77llyhd7qnzzXPPPccff/xRxKEIkZubiwY3Fw3l3FxIytCTnm0kr1Rm+swP8fX1Y++uHTRq0ozJr71JzPXrbNmwnpTkJAAeGzCYOnVLTzOVu05D5wcq0PmBChhNZv7vimVZr13n4ohP03MpIYMlBzJYcuAyAV66m3PNBdAk2AetLOvldKRcLbs6d+7MsmXLmD9/Ph4eHrz22mu0b9+er7/+mq1btwJQpUoVxo4da+dIRWmSbwI3c+bMPPcrikJKSkqxBSQsr7HMH2ZLp1UTWM6NLL2JlKzcTatu7u5MeXOGzTW+fuXZuv8of2zeSKUqQbTt0KmEoy48rUZNi+p+tKjux/iuIZy5nnqz31w8lxIyiEvTs/roVVYfvYq3m5a2tfzpEOJPqxrlcXeRZb0cRVkoV6X8ujcDBw5k4MCBNvs2btzIxo0bSUpKok+fPnjn0aogyq58E7hff/2VqVOnotPlXibo999/L9agyqpVq1YxceJErl27xtChQ/nqq69wc3O784VliJtOg5tOg8FkJjXLQEqmgYIGrfqV96f/4KElF2ARUKtU1K9cjvqVy/F8p1pcis+wTk9y+noqqVlGNp2MYdPJGHRaNS1vLuvVrpY/Ph4u9g5fFMCZy9WrV6/yzDPPsHXrVho3bszixYulw/190mg0MrWMyFe+CVyjRo2oU6cOzZs3z3VMOlEWvfj4eJ588kmysrIA+P777wkJCeHNN9+0c2Slk4tGTXlPV7xcXUhI15OhN9o7pGJTzd+DJ/2r8WSbatxIzWbPuTh2nY3j6JVk9EYze87Fs+dcPGoVNA72uTmiNYBK5ST5L22cuVwdP348W7ZsAeD//u//GDp0KKdOnZLaOFGqZOqNpGebKOfu4vDzceabwP33v//F1TXvKQ1y2uRF0Tl69Kg1ecuxf/9+O0XjOHRaNRXLuZKSqSY1y4jBZEYBkhIT0Lq44OXlXE0OFbxd6ds0iL5Ng0jLMrLvQrx1Wa8sg5mjl5M5ejmZ+dvOERLoRaeQADrWCaCGv4f8IS0FnLlc3bdvn832mTNnSEpKws/Pz04ROa7s7GwSExOpVKmSvUNxCkaTZZ7R1CwjeqMZVODt7vjzb+abfmZkZOBeArPZC4vmzZvj4WE7bUSnTqW3z1ZpolKp8PHQEVzegwqeLkydMJpW9WvSql4N5sx6x97hFRsvNy2P1K/I22ENWPVCe2b2a8CjDSrh425pRo2KTWPxnos8991Bnl50gC//OMfx6GSHXI/WWThzuXp7edWgQQNJ3u7Bzz//TOXKlalcuTIdO3YkNjbW3iE5rCy9iRup2UQnZRKXpifbaPmAn+dIOAeUbwI3btw46/fjx48vkWDKMj8/P1asWEH9+vUpV64cL7zwApMmTbJ3WA5n+dIlLP9lCWazGb1ez5efzuHQgX13vtDBubpoaF87gCmP1mXFmHbMHdyEgS2CrM2oV5OyWH7oChN+Ocrgr/by8ca/2Xc+3vJpVJQYZy5X582bx2OPPYaHhwft27dn6dKl9g7J4SQnJzNq1CgSExMB2L17N2+99Zado3IsJrNCaqaB6MRMriVnkpp1b6v7OIJ86xBvnXfq8uXLJRJMWdezZ0/psHqf/vrrr1z7zv99ihat29ohGvvQqFU0qepLk6q+vNClNudupFunJzl/I53EDANr/7rG2r+u4aHT0KZmeTqEBNC6ZnnrfHSieDhzuVqhQgVWr15t7zAc2vnz58nIyLDZl1eZJnLLMphIyzKSrjc6bcJ2uwLXQs3reyFKs549ezJ37lzrtlar5fGwnvh76ki6x3VWHZlKpSIk0IuQQC+Gd6jB1aRMdt8c0Xo8OoUMvYltZ26w7cwNtGoVzav50rFOAO1rB1DeM/dISXF/pFwVBWnYsCHBwcFcuXLFuk8+1OfPbFZI1xtJzTSSbTQ5S8tooeWbwJ0+fZrmzZujKArZ2dnWUVM5c/wcPny4xIIsSy5evMjVq1dp06YNGo3M73W3unXrxsKFC/n8889xc3OjatWqhIaGEhwczKz3P6BB8zak3eU6q86kiq87g1pWZVDLqiRm6Nl7zjII4tA/iRhMCgcuJnLgYiJzN53lwSrl6BASQKeQAIL8nLPfVklz9nI1PT2dQ4cOUb9+fSpUqGDvcByOi4sL69atY8qUKZw/f56mTZuyYsUKvvnmG8LDw5k6daq9QywVzGbFMo1UlgGDqYwW5oBKKUNr9AwYMICVK1faO4x8vfbaa8yePRtFUQgJCWHr1q1UrVrV3mE5rE8++YSXX37Zuu3r68vly5dxcXMnLctIalbBc8iVJRl6IwcuJLIrKo795+NJ15tsjtcM8KRDiD8dQwKoE+iFu05LFV/HSOpK+/u+sEr789izZw99+vQhMTERV1dXvv76a5566il7h+WwoqOjqVWrFnq93rrv559/ZsiQIXaMyr6yDCYy9EbSsowY76PwVgFV/Nxx1Zb+SpKC3vf5DmLYu3ev9fvb+2ps3LixiEITOc6dO2dN3gCioqKYPXu2naNybJs3b7bZTkpK4tChQ7hqNfh7uVLF1wNvNxfusMxqmeCh0/JQ3QpM612flWPbM/vxRoQ1rmxtRr0Ql86P+y4x5sfDDF24n/nbosgymO5wV3E7Zy5Xp0yZYu18n52dzcSJEzEYDHaOynHt2LHDJnmD3GVaWZBlMJGQns2VhAyuJWWSlGG4r+TNmeSbwH344YfW7ydMmGBz7Msvvyy+iMqo6OjoXAuW39oPQty922eB1+l0PPjgg/9ua9VU8P43kZM8zsJFo6ZVjfJM6vYAy0a3Zd7QpgxpVZXgm82osanZ/LT/EhtOXLdzpI7HmcvV2xPShIQEMjMz7RSN42vatGmufWVlZYucJtJrSZaRpEkZBvQ35/gU/8o3gbs1mbg9sShDra4lpl27dtSoUcNm35NPPmmfYJzE1KlTCQsLQ6VSERAQwKJFi/Lsl5OTyFX2cYwq9ZKkVqloUMWH8M61+G5EKxYNb8nIDjUY1CKY0HqB9g7P4ThzuXp7edW7d2/KlStnp2gcX/369fn0008pV64cGo2GZ555hvDwcHuHVayyDCbi07K5nJjBjdRsMg2mMttfuTDuaRSqjJ4qei4uLmzbto0PPviA6Ohohg0bxuDBg+0dlkPz9vbmt99+IzU1FXd3d7TagqfIcNNpqOLiRlKGnuQ7rLFaFqlUKmr4e1LD3xM3Fw3ebrLu6t1y5nJ15syZBAQEsHnzZpo2bcprr71m75Ac3ksvvcQLL7yAwWDA09PT3uEUm4xsIylZRjL1Rqlluwv5/kW7fPkyY8aMyfU9SNNecalRowYLFiywdxhO5caNG6xdu5bg4GC6du16xz+SKpUKP09XPFy1JKYbpEARRcqZy1WNRsPLL79sM3BI3B+TycTGjRtJSkoiLCwMHx8fe4dUpPRGM0kZetKzpZy9F/kmcF988YX1+5EjR9ocu31biNLor7/+omPHjqSkpADwxBNP8MsvvxTqWlethko+GtKzjSRlGMg2Sod9cf+kXBWFZTabeeSRR/jjjz8AqFSpEvv376datWr2DawIKIpCSqahTM7NWZTyTeBat25dknEIUeTmzJljTd4Ali5dyvTp020GMtyJp6sWD52G1CyDjH4S903KVVFYW7dutSZvANevX+fLL7/k/ffft19QRSDLYCIhTU+WfCi+b/kOYti8eTM//fSTdXvQoEF07dqVrl278r///a9EghPifqSnpxdq352oVCrKueuo4utOORmtKu6DlKuisIqq/CoNTGaF1GwDMclZXEvOlOStiOSbwH399deEhoZat/V6PStWrOCHH37g559/LpHghLgfY8aMQa3+91e8devWtGzZ8p7vp9WoCfB2pWI5N1w0ksaJuyflqiisHj16EBISYt12c3Pjueees2NEd8dsVkjLNhCbksWVxAxupGSTrjfKqNIilG8TqsFgoHLlytbtFi1a4Ofnh5+f333P7ZOUlMSkSZOIjo4mKCiITz/9NM/Oma+99hp//PEH/v7+/P7773d9vSjbHnnkEfbs2cOyZcsIDg5m1KhRRTLSz8NVi06rJi5NT4beWASRirKiOMtV4Vzc3NzYu3cvCxcuJCkpiaeffpqGDRvaO6w7ytKbSM02klGGFpW3l3xr4G7tOwQwffp06/cJCQn39aARERG0a9eOjRs30q5dOyIiIvI8b8CAAXz99df3fL0Qbdq04eOPP2bSpEl4e3sX2X21GjWVfNyo4OWKVpZyEIVUnOWqcD4BAQHWJRZLe/KWZTBx/WYTaWqWDE4oCfkmcI0bN2bZsmW59v/yyy80btz4vh50y5Yt9OvXD4B+/frluzxIq1at8qxZK+z1jiYjI4OIiAjeeustjh8/bu9wRCF4u7tY+sa5u+Dg03iJElCc5WppsG3bNl5//XWWL1+O2Wy2dziiBJjNCgnp2VxLziRDpl0qUfk2ob7++uuMGzeONWvW0KBBAwBOnDiBXq9n/vz59/Wg8fHxBAZaZnEPDAy860+e93t9adWjRw927doFwPvvv8/WrVvp2LGjnaMSd6LVqAnwcsVLpyU+XS9Tjoh8FWe5am9fffWVzbx2L774IvPmzbNjRKI46Y1mMvVGUrIMGEySttlDvgmcv78/v/zyC3v37iUqKgqALl260K5du0LdePjw4cTFxeXaP3HixHuL1Mn9+eef1uQNLH1l5s+fLwmcA3HTaaisdSMhQ09qpkE+iYpc7rdcLc3mzp1rs/3VV18xe/ZsPDw87BSRKA7ZRhMpmUbSsg0yIMHO8k3gkpKSAMt6bPXr18+139fXt8Abf/vtt/ke8/f3JzY2lsDAQGJjYylfvnyhAy6K60sjF5fcyxLltU+Ubmq1igAvVzx1GhLTDTJcXti433K1NLu9vNJqtTajwIVj0xvNpGQaSJXErdTIN4EbMGAAKpUKRVG4ceOGtclSURRUKhVbtmy55wcNDQ0lMjKS8PBwIiMj6dq1a4leXxo1bdqU3r17s3btWgA8PT2ZNGmSnaMS98pdp8VdpyUt20ByhlGaVQVQvOWqvb3++usMGzYM5eZf95dffhk3Nzc7RyXul95oJjXLQGqWrA9d2uSbwG3dutX6fb9+/YiMjCyyBw0PD2fixImsWLGCypUr89lnnwEQExPDtGnTWLhwIQCTJ0/mwIEDJCYm0rlzZ8aPH8+gQYPyvd7RRUZGsmbNGq5evUrfvn0JDg62d0jiPnm5uuCp05KWbSQpQy99Rcq44ixX7W3o0KE0bNiQLVu20KxZM7p06WLvkMQ9UhSFDL2JjGwTaXqpcSut8k3gblUUc2fdys/Pj++++y7X/ooVK1qTN4BPPvnkrq53dFqtlv79+9s7DFHEVCoV3m6WRC4xQ09KlhSIoujL1dKgUaNGNGrUiNQsA1kGE65atVM+T2djMivojSayjWYyDSb0RjNmsyL9eEu5QiVwQoj7p1ar8PdyxVNGqwonl5RhwGgyo1arcNGo0WnVuGrU1u/VMnei3WUZTGTojWQZzOiNJhQFSdgcTL4J3OLFi63fx8fH22wDjBgxoviiEsKJuek0VHFxIylDT1Km1MaVJWWnXLXU3pjMCiaziSyD5cOKClCpuJnIaXDVWhI8F40arUYGPBQnvdGM3mTCYDSTZTSTpTdJwubg8k3gbl00d/DgwQ67iK4QpZFKpcLP0xV3nZaENL2MVi0jynq5qgCKAtlGM9lGM6lYkjpUoFGp0N5M5tx0alw1GnRaSeruldFkJstoIttgJlNvwmA2y4dFJ5NvAvfiiy/me1FGRkaxBCNEWePmoqGyrxspmQaSMmX5GWcn5Wpuys1/jIqC8WZtXWqWpaZOq1bhqtXg5qKWmrpCMJkVUrMMpGcb0ZskYXN2Bb4TYmJi+Ouvv9Dr9YClyv+TTz6he/fuJRKcEGWBSqXCx0NHZR93vFy1SO8g5yblauEoChhMCmnZRuLS9FxPzuJKYgbRiRnEpmSRkJ5tGSyhN2E0lb1lu8xmxZLsZhtITM8mJsXy+iSk68k2SvJWFuRbA/ftt9+yYMECqlevjl6v55lnnuGDDz6gb9++rFy5siRjFKLQ/u///o+EhAQ6deqEVutYY3R0WjWB5dwsBXKaHqPUxjkdKVfv3e3Nrzly+tWpVSpctGq0ajUataX2Tq1WoVWr0WkcY+DEjRs3OHjwIC1atLDOEZhDbzSTbbI0iWYbTBhu1rBJKVF25fsXbtmyZfzvf//D19eXq1ev0r17d3788UeaNm1aguEJUXhPP/00P/74IwB169Zlx44duQpBR+Dt6oKbVkNiup70bFkc2plIuVr0chI7s6Jg1JsA2/6ktw6ccNVqcNWWztGwv/32G0888QRZWVnodDqWLFlC3379Sc40kKE3YjQrUqsmbOTbhOrq6mpd1qVKlSrUqFFDCpkSsG/fPn799VdSU1PtHYpD2bdvnzV5Azhz5oxDL6TtorHUxgWUc0Vbiv7IiPvj7OXq1atX+eWXX4j6+4y9Q7FSAPPNmruULAM30rK5lpzJpYR0ohMzuJGaTXKGnky9EaPJbF1JolhjUizNnzlfadkGJk2eTFZWFgB6vZ6JkyYTnZRJcqZlsXhJ3sTt8q2Bu379OjNnzrRux8fH22xPmzateCMrg0aMGGFdQ7ZChQrs3LmTunXr2jcoB3H9+vVc+65evcqrr77KkiVLCA4OZs6cOXTq1MkO0d07b1cX3LWWdVXTsg1SG+fgnLlcXb9+Pf369bP27Zs2czYjwsfaOaq85dUcmzMaVoWlOVatUqHTWmrqNCoVKjWoUKFRq9CqLf/faZLinAlyDSYzBpOCyaxgVhT0RrPNgCUFiLkeY3NtbGwMG9evZe4HM0lJSeaJp4YzbtKrRfo6CMeWbwI3ZcoUm+0GDRoUezBl2cmTJ63JG1j6QsyZM8dmZQqRv27dulG5cmWuXbsGgFqtxtXVlTlz5gCWZC4sLIwrV67g5eVlz1DvmlajpkI5V9yz1SSmy3JcjsyZy9U333zTmrwBfDr7PYYNH4VOp7NjVIWXMxrWUmOnAAp6kxmybc+7PdHTqNWo1bccu3kvk0nBaC58P7X+g4fy4+J/y/sevR9j3MinMBgMAHzy/gyqVq/BYwMG3fuTFE4l3wQuvyWdsrOzbdbzE0UjKSkp177ExMSSD8RBeXp6smvXLj755BMSEhIYOXJkrqXYkpOTOXjwIA899JB9grxPXta+cVIb56icuVy9vbzKzEjHoNc7TAJXWLcnekZz0czhOG3mbKrVqMnB/Xtp1rI1AYEVWbNyuc05e3ZskwROWBVqQh2TycT27duZMmUKDz/8MOvXry/uuJzeoUOHaNq0KRqNhkcffZSaNWvSqFEj63GVSsWoUaPsGKHjqVWrFp9//jlLlizhkUceoXnz5jbHXV1dHb7GI6c2LsDLFY30jXNojlyuZmZm8swzz6DT6ahVqxa///47zz//vM05ffoNxNPBarvtycXFhedeGM+X3y4h/MWJNGnWPNc5DRo1LfnARKlV4DwLf/75J2vWrGH79u00btyYw4cPs2XLFtzd3UsqPqekKApPPPEE586dA2DDhg1MnjyZbdu2MW/ePK5cucJTTz3lsDVFpcV//vMfTpw4werVq6lQoQKffvopFSpUsHdYRcLb3QWdi5r4VFnFwdE4Q7n6wQcf8MMPPwBw4cIFhgwZQnR0NNWrV+d///sftes3YvDTI+0cpWOrXacu02d9xKez3yMjPY2+A59gyDPOstSaKAr5JnCdO3emSpUqDBkyhClTpuDl5UVoaKhDFTKlVWxsrDV5y7F37142b97MV199RWxsLKmpqbRu3RoPDw87Ren4vL29WbVqFenp6bi5uaHRaOwdUpFy1VpWcZA1VR2Hs5Sre/futdlOT09n06ZNzJ8/n927dxNS5wEaNWtJk+Yt7RShc3h21BiefPY5TEYjbg72OyKKX75NqN27dycmJob169ezbds2MjIy7jjiRhROYGAgDzzwgM2+li1b8uyzz3L9+nXMZjPLli3jww8/tFOEzsXT09PpkrccOWuqVvZxx03rnM/RmThLuXr7aG5vb28WL17M7t27AYg6+zeTXhhVIlNyODsXFxdJ3kSe8k3gpk2bxtatWxk+fDj79++nR48eJCQksG7dujK3AHNRU6lULFu2jDZt2uDm5ka/fv0YNmwY2dm2w50OHjxopwiFo8lZU9XXwwUHzAfKDGcpV1999VWef/55vLy8qF+/PitWrODo0aM25/xz4RwpyUl2iU+IsqDAPnAqlYp27drRrl07DAYDO3fuZO3atbzzzjvs37+/pGJ0Sk2aNGHfvn3W7eTkZLy8vEhLS7Pue/jhh+0RmnBQKpWK8p6ueOi0JKbryTRI37jSyBnKVTc3NyIiIoiIiLDue/jhh/npp5+s2/UbNMLH188e4QlRJhR6sUgXFxdCQ0MJDQ21zhYtio6Pjw+RkZG8+uqrREdHM2zYMF566SV7hyUckKU2zp3UbANJMm9cqeZM5ep///tfDAYDmzdvpl6DRsz4cK69QxLCqd0xgZs3bx7jx4+3bs+dOxdPT08GDRqEn598uipKXbt25fDhw/YOQzgJb1cXPF20JGfqScky2sz8LuzLGcvV8uXLs3TpUgAuJ6TLBwchitkd54G7fd6sRo0aodVqef/994stKCFE0VCr/x3k4KErdIW7KGZSrgoh7tcdS/TQ0FCb7UceeaTYginrsrKy+Pnnn4mOjmbgwIHUq1fP3iEJJ6HTqqnk40ZqpoHEDD1GqY2zK2ctV3ft2sXmzZsJrl2Ph3v0dsgRtkI4inxr4BISEmy2V69ezcyZM1m6dKkMDS8mjz76KCNHjuTNN9+kadOmueZaEndHURQ+/vhjWrRoQd++ffnrr7/sHZLdebu7UNnXHS9XLfKnteQ5c7n6zTff0KlTJ9555x2ef2Yo701/zd4hObyD+/cy/In+PN7zYZb99J29wxGlTL4J3HPPPWf9/osvvuC3336jQYMG7N69W6r5i8HBgwfZvn27dTs7O5t58+bZMSLHFxERwSuvvMLhw4f57bff6Natm8N3FC8KLho1geXcqFDOFZ2mUKvpiSLizOXqxx9/bLP90+KFZGZk2Ckaxxcfd4MRT/Rn57bNHD10kNcmvciWDevsHZYoRfJtQr310+CmTZv46aef8PDwoE+fPgwYMKBEgitL1Orcf0iddfLZkvLbb7/ZbMfExHDgwAE6d+5sp4hKFy9XFzx1WlKzDCRlGKRZtQQ4c7l6exmmUqulCfU+7Nm5nYwM27kBN29YR9cevewU0f0xKwoGoxmDSUFvMmO4+aW/uc9gMlv3642K9XjOtt7m/H+vydmnv3XfbY9z6zV6kxmAiY/UYexDIXZ+Ve5PvglcVlYWJ0+exGw2YzKZrEs6ubi45JlsiPvTvHlzevTowYYNGwBwd3eXaUTuU7169Vi37t9PrFqtlpAQx37DFjWVSkU5dx1eri4kZepJliW5ipUzl6tTp07lmWeesSapI8LHygoC96F2yAOF2pcfRVEwmnMSmluSmVsSnPySptsTnkIlTQU8jt5kLnWj4E9dS7F3CPct3wSuQoUK1ip9Hx8fYmNjCQwMJDExUWqGismaNWtYuXIlV69epX///tSoUcPeITm01157jT179rBv3z7c3d354IMPqFKlir3DKpXU6n8nAU5I05NllEmAi4Mzl6tPPfUUDz74IJs3byaodn3adS57E5Hn1DLpb0l+DMZbE5pbkybFJlm6PeExmDxp1e85Dq35DrPJSFDDdpwLaMfUX4+hvzVpyus+JkscpStlujMV4KJV46JRodOocdGo0Wkt/7toVDf/V6O7+f2/x/49rtOqb16runmvW665eczdRUO3BhXt/XTvW74J3A8//JDn/nLlytnMti2KjouLC0888YS9w3AaAQEBLFiwgIULF1K7dm1GjRpl75BKvZwluZIy9CRJbVyRc/ZytXnz5jRv3rzE5oG7tZZJf3vz3K2JlLUGyWxJfmy286o5Um47bi5U0lTUtUzm6t3wbq/HnJmM0qw3+y9nAEXbr1CjVv2bMFmTn5v/a1U2CVJ+SZXOui/vxEp3y31vTax0tz2OVq0qkWZ3FZa+wI7urieG0mg0uEu1uHAA27dv55FHHsFoNAKwcuVKdu7caeeoSj+VyjJ3nLvUxpUYZypXz1xP5eTVJDL0tyVNNk1xtonVrbVGt15z+/l5JU2OpjC1TDqtGo1iYtN7L5Jy7SIAmX9tZOjMH6lcIyRXzZJtwpRP0nRLgnZrDZVa+ik6rHua2bN///6sWrWqqGMRokjNnz/fmryBZY6qQ4cO0aJFCztG5Tjyqo1LSU5i0YL5XL54nqGDH2fw4MH2DtNpOEO5uvLwFSYv+z97h5GLVq2yqf35N4n5t/bn1u2Cj+fVhHd7TdRtTX63NOtpClnLtGXDOn69mbwBGLOz0JzdyuhnexTjK+X8fo/8lU3rf6dJw/q8+vLL+Pj42Duke3ZPCZyjFzKibNDpdIXaJ/J3e23c4GGDOHRgHwCRvy4jJSVFmqaLiDOUq5V83PB205KebSygb9K/NUQF1RjdXquk097aB8o2sSooadJqVA5Zy+TikruscnFxsUMkzmPJd4t481XL4MDfV8HO7dttpu9yNIVO4NLS0rh48SJVq1Z16IxVlB2TJ08mMjKS9HTLUPy+ffvSqFEjO0flmNxcNKTHXbEmbzkWL14sCdx9cLZytX3tAI691Z0riRkO2bxZmnTo8jDNWrbmyMEDAJT392fYcHmv3Y+Vv/xos71jxw7Onz9PrVq17BTR/cm3F98rr7xinTV8586d9O7dmzlz5tCvXz/Wr19fYgEKca+aN2/OmTNnmD9/PqtXr+bXX3+1d0gOzc/PD63W9jNfYGCgnaJxTGWhXJW534qGRqNhSeR65n39PTPnfMaGXQepWr2GvcNyaOUDAmy2dTodvr6+9gmmCORbA3fmzBnKly8PWPoS/fTTTwQHB5OQkMDw4cPp2bNniQUpxL0KCgpi7Nix9g7DKQQEBPDGG2/wzjvvAJaEbvr06XaOyrFIuSruhk6no9dj/e0dhtMY/8pr/LlvLynJSQBMmzbN+n50RPkmcGazmbS0NLy8vFCpVNb5s8qXL4/JJKPShCiL3n77bYYMGUJUVBRdunTB29vb3iE5FClXhbCfRk2asePwCf7cu5s2TRvQqMGD9g7pvuSbwI0bN45nnnmGJ598kubNm/PSSy/RtWtX9u3bR6dOnUoyRiFEKVKvXj3q1atn7zAckpSrQtiXt3c5unbvSRU/x5+2J98ErlevXjRo0IBly5Zx8eJFTCYTR44coXfv3lLQCCHEPZByVQhRVAochVq9enVeffXVkopFiCK3atUqlixZQnBwMK+++qospSXsTspVUVgXz59j0YLPSUlJZtCTz9Ch80P2DkmUIvkmcO+//z7du3eXSU+Fw1qxYgWDBg2ybq9bt46TJ086/JqTwnFJuSoKKy0tlcF9uhEfdwOAtZG/8nPkelq2bW/nyERpkW8Ct3r1av78808SExPp2bMnffr04cEHHbvDnyhbbl938u+//2b//v20by8FoLAPKVdFYe3ctsWavIFlAMzqX5dJAies8k3gKlWqxMqVK7l48SJr167l1VdfxWQy0adPH3r37k3NmjVLMk4h7lqlSpVstlUqFRUrVrRTNEJIuSoKr0KF3HMsVgiU8kv8K9+JfHMmY6xRowbjxo1j7dq1fPrpp2RnZxMeHn5fD5qUlMSIESPo3r07I0aMIDk5Oc/zXnvtNdq1a0efPn1s9s+bN49OnTrRt29f+vbt69BLYYjiM3XqVKpWrWrdnjRpErVr17ZjRKKsK85yVTiXlm3bEzbg3y4gderW46mRz9sxIlHa5FsDpyi5l0HJmT7g5Zdfvq8HjYiIoF27doSHhxMREUFERESenXoHDBjAU089xX/+859cx4YPH85zzz13X3EI51azZk2ioqLYuXMnwcHB1K1b194hiTKuOMtV4Xw+XbCI0S9OIiUlmZZt2kn/XWEj3xq4n376qdgedMuWLfTr1w+Afv36sXnz5jzPa9WqlVOsDyjsR6fT0bVrV0neRKlQnOWqcE71GzaiTfuOkryJXPKtgfP09ERRFI4dO0ZMTAwqlYrAwEAaN25832vdxcfHW9dQDAwMtK4NeDd++uknIiMjadiwIVOnTpVETwhR6hVnuSqEKFvyTeB27drFO++8Q/Xq1a0dv69fv86lS5d466236NixY4E3Hj58OHFxcbn2T5w48f4iBoYOHcrYsWNRqVR89tlnfPDBB7z//vv3fV8hhChO91uuCiFEjnwTuPfee4/FixcTHBxss//y5cuEh4ezfv36Am/87bff5nvM39+f2NhYAgMDiY2NvevFZAMCAqzfDxo0iDFjxtzV9UIIYQ/3W64Kx6ECVCpQq1Ro1CrMioKiWPaZlX/7Qyo3z82hKJZ9QtxJvgmcyWTKNQ0DQMWKFTEajff1oKGhoURGRhIeHk5kZCRdu3a9q+tzkj+AzZs3U6dOnfuKRwghSkJxlquieN2akKlVKtRqS2Kmtu4DkwIooNOq0Gk0uGjVaNR5N42bzQoKlkROfbP53KwoGM0KJrOC3mjCYFIwmBTMitma9EmCJ3Lkm8A9/vjjDBw4kF69elG5cmUArl27xrp16xg4cOB9PWh4eDgTJ05kxYoVVK5cmc8++wyAmJgYpk2bxsKFCwGYPHkyBw4cIDExkc6dOzN+/HgGDRrERx99xOnTpwEICgpixowZ9xWPEEKUhOIsV8X9UQEatQo3Fw0aterf2rObyZpWo0KnURdZX0W1NbH7935qVGhvjlXwdM3959loMmM0K5jNCkazGb1RwWA2YzCaMd1M7kTZoVLyGtd+07lz59iyZQsxMTEoikKlSpUIDQ0lJCSkJGMsMgMGDGDlypX2DkMIUYJK2/v+XsvV0vY8CnI5IR2DyTGyCZUKPHVavFy1eOSRNDmKbKMJo8mS3JkVBbMCppuJntFk+V8BkBo8VEAVP3dctaV/ZG9B7/sCf1tr164tE58KIUQRknLV/lQq0Gk0eOjUeLq6oNPmO6OWw3DVaigo/1RymmdNCgbFjN5gJstgRm8ySVLnoPL9rd2xY4f1+9TUVN544w3CwsJ4+eWX8xxdKoQQomBSrtqPRq3C01VLBW9Xgv08CPJzx8/T1SmSt8JQqVS4aNS46TR4u7rg7+VKkJ87VXzdqeTjRsVybgR46Sjn5oJOoyafrnuiFMn3N3fu3LnW7z/44AMCAgJYsGABjRo1Yvr06SUSnBBCOBMpV0uOCtCqVbhq1fh5uBDk607Fcm54u7ngoikbSVthuGo1uOu0eLpqKeeuI8DbleDyHgT7eVCpnBvlPXV46LRob/YLFKVHoRr8jx8/zurVqwHL/G6rVq0q1qCEEMLZSblaPFSAu05LOTfH7tNmb1qNGq1Gjcct+7KNJvQmS/Or3mS2DqqQwRP2ke/HkPj4eBYvXsyiRYtIS0uzWcPPbDaXSHBCFFZsbCyPPfYYHh4edOjQgRMnTliPnTx5UpqnRKkg5WrxUavAy1VLZR9Lk6CjJW+zZs0iMDCQ4OBgIiIirPuvX7/O2bNn7RjZv1y1/za/VvZxp2p5T6rerKnz83DBXadBp1FbR/GK4pVvAjd48GDS09PJyMigf//+JCYmAnDjxg3q169fYgEKURjjx49nzZo1ZGZmsmfPHoYMGUJMTAzNmjWjQYMGVKlShQ8//NDeYYoyTsrVoqXCMoK0gpcrQX4eBJZzw01X+kcW3u7333/njTfe4MaNG0RHRzN69GiOHDnCK6+8QlBQEA888AAPPfQQqamp9g41F61GjYerFj9PS1IXXN6DauU9CPK1JHb+npZ+dbdOzyKKRr4fUV588cU891eoUEH+EIpSZ+fOnTbbx48f5+233+bo0aMAGAwGXn/9dZ588slcs+ALUVKkXC0aKiyjSP08XRyupi0vt5dfAEuWLOHjjz+2bm/fvp0vv/ySKVOmlGRo90SlUlkmM85jgIjemNP0apnHTm8yYzCZMd+coVhaYwuvwN/8nPmKYmNjAcvC8127dpUh8KLUadu2rU0fonr16nH58mWbc0wmExcuXJAETtiVlKt3L2cVBI1ahbuLFg+dxikStxxt27bNtc/Pzy/XvqioqJIIp1jptOo7J3YmheybU5xI/7r85duEGhERweTJkwFo1KgRjRo1AiyrI9zaPi9EafD555/zyCOPoFKpaNq0KT///DMDBgywOScoKIg2bdrYKUIhpFwtjJzRo56uWnw9XKhQzpXKvjf7W5X3JMDb1amSN4D+/fvzxhtv4O3tjb+/P59++imjR4/G29s713nOSqe1NMWWc9cRcHOKkyBfDwK8LKNgpfk1t3zfBb/++iu///47Li4uNvuHDx9Onz59CA8PL/bghCisKlWqsGnTJhRFsS5107RpU7KysliyZAnBwcG8/fbb6HQ6O0cqyjIpV/OWU8Pm5eqCl5sWV23RLVnlKGbOnMnMmTNt9m3dupX33nuPxMREwsPD6dmzp52isw9LbZ2Ocu6W7ZxaOoPJjMGkkG201NiV1ebXfBM4lUpFbGwsQUFBNvtv3LhR5t5YwnHc/rs5duxYxo4da6donE9mZiaLFi3i7Nmz9O3bl4cfftjeITkUKVf/ZVkNQY2HToO7ixadVn3L+qACoGXLljK9zC3yan5VFAWDScFkNmNUFAzGmytMGC3Nr7cndXt2bmfL/9bSrNGDhD8/Cjc3t5J7AkUs3wTu9ddfZ/jw4VSvXt266PLVq1e5dOkSb775ZokFKIQoPQYOHMi6desA+Oyzz1i6dCmDBw+2c1SOo6yWqyosi7dr1Sp0Wg06jQpXFw1uLo43YlSULjkDJqw9wlwt/5nNClkGExkGExnZRkxmhdUrlzNpzEgAvgU2bdzAmjVr7BF2kcg3gevcuTMbNmzg2LFjNosuN2rUCI1G3nRClDUXL160Jm855s+fLwncXShr5WrO8lWeOkuyVtZqGYX9qNUqPFwtkzkrnjqyDCZ++e5rm3N+//13Ll26RLVq1ewU5f0psCeoWq0mODgYFxcXVCoVgYGBTlnICCHuzM3NDbVabTPhrKenpx0jckxloVzVqNV46NR4uznHQvHCsalUKtx1Wny8vWz2azQa52xCPXXqFG+99RapqalUqlQJRVG4fv065cqV46233qJBgwYlGWeZkJKSwtdff010dDRDhgyhVatW9g5JCKtKlSoxduxYPv/8cwDc3d15/fXX7RyVY3H2cnXNmjVs3ryZJk2a8Oyzz6KRNUdFKfLGG2+wY8cOsrKyAMu8jIGBgXaO6j4o+XjssceUo0eP5tp/5MgRJSwsLL/LSrX+/fvbO4R8mc1mpWXLlgqWPpeKRqNRtmzZYu+whMhlx44dyjfffKNcuXLF3qEUSml6399PuVqankdePv30U2v5BSijRo2yd0hC5HL58mXlm2++UXbu3GnvUAqloPd9vh+PMjMzadKkSa79TZs2JTMzs7jyyTJr//79HDx40LptMplYsGCBHSMSIm+dOnVi5MiRuUZSijtz5nJ1/vz5Ntvffvst6enpdopGiLwFBwczcuRIOnbsaO9Q7luBgxjCw8Pp168flSpVAiyL6kZGRtKpU6cSC7CsyKsvkfQvEsK5OHO56uHhYbPt6uqKVutcE+4KUZrk++6aNm0a27dvty75oigKFStWZNiwYXTp0qUkYywTGjVqxMCBA1mxYgUAPj4+vPzyy3aOSghRlJy5XH3rrbcYNGgQJpMJsPQ3cnV1tXNUQjivfBO47OxsGjVqlKtQiY+PJzs7W96YxWDZsmVs3ryZ6Oho+vTpQ0BAgL1DEkIUIWcuV/v378+ZM2fYtm0bTZs2pWXLlvYOSQinlm8fuJkzZ9r0ycqxe/duZs2aVaxBlVUqlYpu3boxfPhwSd6EcELOXq7Wrl2bUaNGSfImRAnIN4E7dOgQ3bt3z7X/sccey7MAEkIIUTApV4UQRSXfBE5R8l8W9taJPIUQQhSOlKtCiKKSbwLn7+/PsWPHcu0/duwY5cuXL9aghBDCGUm5KoQoKvkOYpgyZQoTJ06kf//+1tnBjx8/TmRkJHPnzi2xAIUQwllIuSqEKCr51sA1btyYZcuWoSgKq1atIjIyEoDZs2dbvxdCCFF4Uq4KIYpKgbMsBgQEMGHCBE6ePMnvv/9OZGQkf/75Jz169Cip+IQQwqlIuSqEKAr5JnAXLlxg7dq1rF27Fl9fX3r16oWiKPzwww8lGZ8QQjgNKVeFEEUl3wSuZ8+etGzZkgULFlC9enXAsradEEKIeyPlqhCiqOTbB27evHkEBATwzDPPMG3aNPbu3VvgEHghhBAFk3JVCFFU8q2B69atG926dSMjI4PNmzfz7bffEh8fz1tvvUW3bt3o2LFjScYphBAOT8pVIURRybcGLoeHhwePPfYYX331Fdu3b6d+/fpERESURGxCCOGUpFwVQtyvAkeh3s7X15chQ4YwZMiQ4opHCCHKFClXhRD34o41cEIIIYQQonSRBE4IIYQQwsFIAieEEEII4WAkgRNCCCGEcDCSwAkhhBBCOBhJ4IQQQgghHIwkcEIIIYQQDsYuCVxSUhIjRoyge/fujBgxguTk5FznXLt2jaeffpqePXvSu3dvvvvuu7u6XgghhBDCWdklgYuIiKBdu3Zs3LiRdu3a5TkDuUajYerUqaxfv56lS5eyZMkSoqKiCn29EEIIIYSzsksCt2XLFvr16wdAv3792Lx5c65zAgMDadCgAQBeXl7UqlWLmJiYQl8vhBBCCOGs7JLAxcfHExgYCFgStYSEhALPv3LlCqdOnaJJkyb3dL0QQgghhDO5q7VQ78bw4cOJi4vLtX/ixIl3dZ/09HQmTJjA66+/jpeXVxFFJ4QQQgjhuIotgfv222/zPebv709sbCyBgYHExsZSvnz5PM8zGAxMmDCBsLAwunfvftfXCyGEEEI4I7s0oYaGhhIZGQlAZGQkXbt2zXWOoii88cYb1KpVixEjRtz19UIIIYQQzsouCVx4eDi7d++me/fu7N69m/DwcABiYmJ4/vnnATh06BCrV69m37599O3bl759+7J9+/YCrxdCCCGEKAuKrQm1IH5+fjbzuuWoWLEiCxcuBKBly5acOXPmrq4XQgghhCgLZCUGIYQQQggHIwmcEEIIIYSDkQROCCGEEMLBSAInhBBCCOFgJIETQgghhHAwksAJIYQQQjgYSeCEEEIIIRyMJHBCCCGEEA5GErhSZPv27bRu3ZqgoCCmTJmC0Wi0d0hCCFEoiYmJDBs2jIoVK9KjRw+ioqLsHZIQTs0uKzGI3FJSUnjsscdISUkB4KOPPqJy5cpMmjTJzpEJIcSdvfTSSyxZsgSAjRs3MnjwYA4fPmznqIRwXlIDV0ocPHjQmrzl2LJli52iEUKIu3N7eXXkyBESExPtFI0Qzk8SuFKiYcOG6HQ6m33Nmze3UzRCCHF3WrRoYbNdq1YtfH197ROMEGWAJHClRGBgIIsWLaJChQqo1WoGDBjAf/7zH3uHJYQQhfLf//6XNm3aABASEsKPP/6ISqWyc1RCOC/pA1eKDBs2jCFDhpCdnY2Hh4e9wxFCiEKrUaMG+/btIzU1FS8vL0nehChmksCVMhqNRpI3IYTD8vb2tncIQpQJ0oQqhBBCCOFgJIETQgghhHAwksAJIYQQQjgYSeCEEEIIIRyMJHBCCCGEEA5GEjghhBBCCAcjCZwQQgghhIORBE4IIYQQwsFIAieEEEII4WAkgRNCCCGEcDCSwAkhhBBCOBhJ4IQQQgghHIwkcEIIIYQQDkYSOCGEEEIIByMJnBBCCCGEg5EETgghhBDCwUgCJ4QQQgjhYCSBE0IIIYRwMJLACSGEEEI4GEnghBBCCCEcjCRwQgghhBAORhI4IYQQQggHIwmcEEIIIYSDkQROCCGEEMLBSAInhBBCCOFgtPZ40KSkJCZNmkR0dDRBQUF8+umn+Pj42Jxz7do1pkyZQlxcHGq1msGDB/Pss88CMG/ePJYtW0b58uUBmDx5Ml26dCnx5yGEEEIIYQ92qYGLiIigXbt2bNy4kXbt2hEREZHrHI1Gw9SpU1m/fj1Lly5lyZIlREVFWY8PHz6c1atXs3r1aqdK3g4cOMCqVatIS0uzdyhCCHFXrl+/zvLly/n777/tHYoQTs8uCdyWLVvo168fAP369WPz5s25zgkMDKRBgwYAeHl5UatWLWJiYkoyzBL33HPP0aZNGwYMGEDt2rWlEBRCOIwNGzZQo0YNBg8eTL169Zg3b569QxLCqdklgYuPjycwMBCwJGoJCQkFnn/lyhVOnTpFkyZNrPt++uknwsLCeO2110hOTi7WeEvCqVOnWLRokXU7NjaWjz76yI4ROY+srCzMZrO9wxDCqb3xxhtkZ2cDoCgK06ZNQ6/X2zkqx2cymeR1FHkqtgRu+PDh9OnTJ9dXXrVtBUlPT2fChAm8/vrreHl5ATB06FA2bdrE6tWrCQwM5IMPPiiOp1Ci8kpiExMT7RCJ80hPT2fQoEF4enoSFBTEsmXL7B2SEE7r9vIqLS1NEo/79OWXX1KhQgW8vLx4/vnnMRqN9g5JlCLFNojh22+/zfeYv78/sbGxBAYGEhsbax2McDuDwcCECRMICwuje/fu1v0BAQHW7wcNGsSYMWOKLG57adeuHQ0aNODEiRMAqFQqRo4caeeoHNvs2bNZsWIFYOmb88wzz9C1a1f8/f3tHJkQzmfUqFG8/vrr1u0hQ4ZYP3SLu/f3338zbtw4FEUB4Ouvv6Z58+a88MILdo5MlBZ2GYUaGhpKZGQk4eHhREZG0rVr11znKIrCG2+8Qa1atRgxYoTNsZzkD2Dz5s3UqVOnROIuTmq1mj/++IN58+YRHR3Nk08+SWhoqL3DcmgHDx602c7Ozuavv/7ioYcesk9AQjix1157japVq7J582aaNm0qicZ9OnTokDV5y3F7mSbKNrskcOHh4UycOJEVK1ZQuXJlPvvsMwBiYmKYNm0aCxcu5NChQ6xevZoHHniAvn37Av9OF/LRRx9x+vRpAIKCgpgxY4Y9nkaRCwgI4J133rF3GE7j4YcfZv369dbtcuXK0aJFCztGJIRze+qpp3jqqafsHYZT6NixI1qt1qbZ9OGHH7ZjRKK0sUsC5+fnx3fffZdrf8WKFVm4cCEALVu25MyZM3leL537RWFMnDiRq1evsmTJEoKDg5kzZw7e3t72DksIIe6oatWqLF++nGnTppGYmEh4eDjDhg2zd1iiFLFLAidESXBxcWHu3LnMnTvX3qEIIcRd69evn3XKLSFuJ0tpCSGEEEI4GEnghBBCCCEcjCRwQgghhBAORhI4IYQQQggHIwmcEEIIIYSDkQROCCGEEMLBSAInhBBCCOFgJIETQgghhHAwksAJIYQQQjiYMrUSQ3R0NAMGDLB3GEKIEhQdHW3vEIqElF9ClD0FlV8qRVGUEoxFCCGEEELcJ2lCFUIIIYRwMJLACSGEEEI4GEnghBBCCCEcjCRwQgghhBAORhI4IYQQQggHU6amESltEhMTGT58OABxcXGo1Wri4uJ44IEHMBgMxMXF4eXlhbe3N35+fnz77bd2jdcR5PWali9fHoA+ffrw66+/otVqUavVjBw5kn79+tkvWAdx48YNZs2axV9//YVOpyMoKIjXX38dgFmzZnHx4kW0Wi0PPPAAb775JgEBAXaOWJQUKcOKlpRfRc+pyy9FlAr//e9/la+//tpm33/+8x9l/fr1dorI8d36mi5ZskQZOXKkkpqaqiiKoqSkpCgrV660Z3gOwWw2K4MHD1aWLFli3Xfy5Enlzz//VLp166Zs2bLFun/v3r3KmTNn7BGmKAWkDCtaUn7dP2cvv6QGTpQJX331Fd9//z1eXl4AeHt7079/fztHVfrt27cPrVbL0KFDrfvq16/PihUraNq0KaGhodb9bdu2tUeIQjg9Kb/ujbOXX9IHTji9tLQ00tPTqVatmr1DcThnz56lQYMGhd4vhChaUn7dO2cvvySBE2WCSqWydwhCCHFPpPwSeZEETjg9Ly8v3N3duXz5sr1DcTh16tThxIkTufaHhITkuV8IUbSk/Lp3zl5+SQInyoTw8HDeeecd0tLSAEuzxNKlS+0cVenXtm1b9Ho9y5Yts+47duwY1atX58iRI/zxxx/W/Tt27ODMmTN2iFII5ybl171x9vJLEjhRJjz55JO0adOGxx9/nD59+vDUU0/h5uZm77BKPZVKxeeff87u3bt55JFH6N27N59//jmBgYEsWLCAH374ge7du9OrVy9WrVqFv7+/vUMWwulI+XVvnL38UimKotg7CCGEEEIIUXhSAyeEEEII4WAkgRNCCCGEcDCSwAkhhBBCOBhJ4IQQQgghHIwkcEIIIYQQDkYSOOFQZs+ezaOPPkpYWBjjxo0jJSUFgN27dzNgwADCwsIYMGAAe/futV4zd+5cunTpQrNmzWzupdfrmThxIt26dWPQoEFcuXLF5nhaWhqdOnVixowZ1n1Tp04lNDSUvn370rdvX06dOlWMz1YI4Uyk/BJFSRI4USopioLZbM61v0OHDvz++++sWbOGGjVq8NVXXwHg5+fHl19+yZo1a/jggw+YMmWK9ZqHH36Y5cuX57rX8uXLKVeuHJs2bWL48OHMmTPH5vinn35K69atc103ZcoUVq9ezerVq6lfv/79PlUhhJOR8kuUBK29AxAix5UrV3j++edp06YNR48exdvbm8TERFQqFY8//jjDhw+nY8eO1vObNm3K//73PwAefPBB6/46deqg1+vR6/XodDqaNm2a5+Nt3bqVF198EYAePXowY8YMFEVBpVJx/Phx4uPj6dSpE8ePHy++Jy2EcApSfomSJjVwolS5cOEC/fr1Y+bMmWi1Wuun1QEDBuQ699dff6Vz58659m/YsIH69euj0+kKfKyYmBgqV64MgFartRa4ZrOZ2bNn23wKvtXcuXMJCwtj1qxZ6PX6e3iWQghnJOWXKEmSwIlSpUqVKjRt2pSqVaty+fJl3n33XXbs2IGXl5fNeV9++SUajYbHHnvMZv/Zs2eZM2eOTb+P/OS1CIlKpWLJkiV07tzZWjjeavLkyfzvf//j119/JTk5mYiIiLt8hkIIZyXllyhJ0oQqShUPDw8AfHx8WL16Nbt27WLJkiWsX7+e999/H4BVq1bxxx9/8O2336JSqazXXr9+nRdffJHZs2dTrVq1Oz5WpUqVuHbtGpUqVcJoNJKamoqvry9Hjhzh0KFD/Pzzz6Snp2MwGPDw8OCVV14hMDAQAJ1Ox4ABA1i0aFExvApCCEck5ZcoSZLAiVIpISEBnU5Hjx49qFatGlOnTgVgx44dLFy4kB9//BF3d3fr+SkpKYSHhzN58mRatGhRqMcIDQ1l1apVNGvWjA0bNtC2bVtUKhUff/yx9ZyVK1dy/PhxXnnlFQBiY2MJDAxEURQ2b95MnTp1ivBZCyGcgZRfoiRIAidKpdjYWF577TXrSK7JkycD8O6776LX6xkxYgQATZo0YcaMGfz4449cunSJL774gi+++AKARYsW4e/vz4cffsjvv/9OZmYmnTt3ZtCgQYwfP56BAwfy6quv0q1bN3x8fJg7d+4d43rllVdITExEURTq1avHO++8U0yvgBDCUUn5JUqCSsmrIV0IIYQQQpRaMohBCCGEEMLBSAInhBBCCOFgJIETQgghhHAwksAJIYQQQjgYSeCEEEIIIRyMJHBCCCGEEA5GEjghhBBCCAcjCZwQQgghhIP5f+4J/7b8bq4xAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, axes = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
+    "ax1, ax2 = axes\n",
+    "\n",
+    "im_coef, im_p = spearmanr(concat_imputed_df['genotype'], concat_imputed_df[genepair])\n",
+    "# sns.violinplot(x=concat_imputed_df['genotype'],  \n",
+    "#                y=concat_imputed_df[genepair], \n",
+    "#                ax=ax1,\n",
+    "#               inner=None)\n",
+    "sns.swarmplot(x=concat_imputed_df['genotype'],  \n",
+    "               y=concat_imputed_df[genepair], \n",
+    "               ax=ax1,\n",
+    "              color='black')\n",
+    "sns.regplot(x=concat_imputed_df['genotype'],  \n",
+    "               y=concat_imputed_df[genepair], \n",
+    "               ax=ax1, scatter=False)\n",
+    "ax1.set_title(f'Imputed r={im_coef:.2f}; pvalue {im_p:.4f}')\n",
+    "ax1.set_xticklabels([f'{refallele}{refallele}', \n",
+    "                     f'{refallele}{altallele}',\n",
+    "                     f'{altallele}{altallele}'])\n",
+    "ax1.set_xlabel(snp_id)\n",
+    "\n",
+    "coef, p = spearmanr(concat_df['genotype'], concat_df[genepair])\n",
+    "# sns.violinplot(x=concat_df['genotype'],  \n",
+    "#                y=concat_df[genepair], \n",
+    "#                ax=ax2,\n",
+    "#               inner=None)\n",
+    "sns.swarmplot(x=concat_df['genotype'],  \n",
+    "               y=concat_df[genepair], \n",
+    "               ax=ax2,\n",
+    "              color='black')\n",
+    "sns.regplot(x=concat_df['genotype'],  \n",
+    "               y=concat_df[genepair], \n",
+    "               ax=ax2, scatter=False)\n",
+    "ax2.set_xlabel('')\n",
+    "ax2.set_title(f'Not Imputed r={coef:.2f}; pvalue {p:.4f}')\n",
+    "ax2.set_xticklabels([f'{refallele}{refallele}', \n",
+    "                     f'{refallele}{altallele}',\n",
+    "                     f'{altallele}{altallele}'])\n",
+    "ax2.set_xlabel(snp_id)\n",
+    "plt.savefig(example_savedir/f'{snp_name}_ref{refallele}_alt{altallele}_{gene1}_{gene2}.{celltype}_{datasetname}.full.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/examples/rs221045_C_refT_altC_AC005076.5_ARHGEF19.monocyte_onemillionv2.full.pdf')"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_savedir/f'{snp_name}_ref{refallele}_alt{altallele}_{gene1}_{gene2}.{celltype}_{datasetname}.full.pdf'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/04_coeqtl_mapping/.ipynb_checkpoints/rb_celltypes-checkpoint.ipynb b/04_coeqtl_mapping/.ipynb_checkpoints/rb_celltypes-checkpoint.ipynb
new file mode 100644
index 0000000..834ede3
--- /dev/null
+++ b/04_coeqtl_mapping/.ipynb_checkpoints/rb_celltypes-checkpoint.ipynb
@@ -0,0 +1,2026 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['pdf.fonttype'] = 42\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from pathlib import Path\n",
+    "workdir = Path(\"./coeqtl_mapping/\")\n",
+    "\n",
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
+    "import matplotlib\n",
+    "def heatmap(data, row_labels, col_labels, ax=None,\n",
+    "            cbar_kw={}, cbarlabel=\"\", **kwargs):\n",
+    "    \"\"\"\n",
+    "    Create a heatmap from a numpy array and two lists of labels.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    data\n",
+    "        A 2D numpy array of shape (M, N).\n",
+    "    row_labels\n",
+    "        A list or array of length M with the labels for the rows.\n",
+    "    col_labels\n",
+    "        A list or array of length N with the labels for the columns.\n",
+    "    ax\n",
+    "        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If\n",
+    "        not provided, use current axes or create a new one.  Optional.\n",
+    "    cbar_kw\n",
+    "        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.\n",
+    "    cbarlabel\n",
+    "        The label for the colorbar.  Optional.\n",
+    "    **kwargs\n",
+    "        All other arguments are forwarded to `imshow`.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if not ax:\n",
+    "        ax = plt.gca()\n",
+    "\n",
+    "    # Plot the heatmap\n",
+    "    im = ax.pcolormesh(data, **kwargs)\n",
+    "\n",
+    "    # Create colorbar\n",
+    "    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n",
+    "    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n",
+    "\n",
+    "    # Let the horizontal axes labeling appear on top.\n",
+    "    ax.tick_params(top=True, bottom=False,\n",
+    "                   labeltop=True, labelbottom=False)\n",
+    "\n",
+    "    # Rotate the tick labels and set their alignment.\n",
+    "    plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n",
+    "             rotation_mode=\"anchor\")\n",
+    "\n",
+    "    # Turn spines off and create white grid.\n",
+    "#     ax.spines[:].set_visible(False)\n",
+    "\n",
+    "#     ax.set_xticks(np.arange(-0.5, data.shape[1]-2, 1), minor=True)\n",
+    "#     ax.set_yticks(np.arange(-0.5, data.shape[0]-2, 1), minor=True)\n",
+    "    # Show all ticks and label them with the respective list entries.\n",
+    "    ax.set_xticklabels([\"\"]+col_labels)\n",
+    "    ax.set_yticklabels([\"\"]+row_labels)\n",
+    "#     ax.grid(which='minor', color=\"white\", linestyle='-', linewidth=2)\n",
+    "#     ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
+    "    return im, cbar\n",
+    "\n",
+    "\n",
+    "def annotate_heatmap(im, data=None, valfmt=\"{x:.2f}\",\n",
+    "                     textcolors=(\"black\", \"white\"),\n",
+    "                     threshold=None, **textkw):\n",
+    "    \"\"\"\n",
+    "    A function to annotate a heatmap.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    im\n",
+    "        The AxesImage to be labeled.\n",
+    "    data\n",
+    "        Data used to annotate.  If None, the image's data is used.  Optional.\n",
+    "    valfmt\n",
+    "        The format of the annotations inside the heatmap.  This should either\n",
+    "        use the string format method, e.g. \"$ {x:.2f}\", or be a\n",
+    "        `matplotlib.ticker.Formatter`.  Optional.\n",
+    "    textcolors\n",
+    "        A pair of colors.  The first is used for values below a threshold,\n",
+    "        the second for those above.  Optional.\n",
+    "    threshold\n",
+    "        Value in data units according to which the colors from textcolors are\n",
+    "        applied.  If None (the default) uses the middle of the colormap as\n",
+    "        separation.  Optional.\n",
+    "    **kwargs\n",
+    "        All other arguments are forwarded to each call to `text` used to create\n",
+    "        the text labels.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Normalize the threshold to the images color range.\n",
+    "    if threshold is not None:\n",
+    "        threshold = im.norm(threshold)\n",
+    "    else:\n",
+    "        threshold = im.norm(data.max())/2.\n",
+    "\n",
+    "    # Set default alignment to center, but allow it to be\n",
+    "    # overwritten by textkw.\n",
+    "    kw = dict(horizontalalignment=\"center\",\n",
+    "              verticalalignment=\"center\")\n",
+    "    kw.update(textkw)\n",
+    "\n",
+    "    # Get the formatter in case a string is supplied\n",
+    "    if isinstance(valfmt, str):\n",
+    "        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)\n",
+    "\n",
+    "    # Loop over the data and create a `Text` for each \"pixel\".\n",
+    "    # Change the text's color depending on the data.\n",
+    "    texts = []\n",
+    "    for i in range(data.shape[0]):\n",
+    "        for j in range(data.shape[1]):\n",
+    "#             kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])\n",
+    "            text = im.axes.text(j+0.5, i+0.5, valfmt(data[i, j], None), **kw)#j+0.1, i+0.5\n",
+    "            texts.append(text)\n",
+    "\n",
+    "    return texts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## celltypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_res_df = pd.read_csv(workdir/'output/filtered_results/rb_calculations/summary.csv', index_col=0)\n",
+    "unfiltered_res_df = pd.read_csv(workdir/'output/unfiltered_results/rb_calculations/summary.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_res_df_clean = filtered_res_df[filtered_res_df['celltype_discovery']!='B']\n",
+    "filtered_res_df_clean = filtered_res_df_clean.dropna()\n",
+    "filtered_res_df_clean.to_excel(workdir/'output/summary/rb_values_replication_in_other_celltypes_filtered_results.xlsx')\n",
+    "\n",
+    "unfiltered_res_df_clean = unfiltered_res_df[unfiltered_res_df['celltype_discovery']!='B']\n",
+    "unfiltered_res_df_clean = unfiltered_res_df_clean.dropna()\n",
+    "unfiltered_res_df_clean.to_excel(workdir/'output/summary/rb_values_replication_in_other_celltypes_unfiltered_results.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### filtered results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# filtered results\n",
+    "rb_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "rbse_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "rbpvalue_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "numcoeqtl_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "num_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "rbse_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "for discovery_celltype in celltypes:\n",
+    "    # replication in other celltypes\n",
+    "    for replication_celltype in celltypes:\n",
+    "        if discovery_celltype != replication_celltype:\n",
+    "            rb_results = filtered_res_df[(filtered_res_df['celltype_discovery'] == discovery_celltype) &\n",
+    "                                         (filtered_res_df['celltype_replication'] == replication_celltype)]\n",
+    "            replicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/filtered_results/rb_calculations/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t',\n",
+    "                index_col=0\n",
+    "            ).shape[0]\n",
+    "            if rb_results['r'].values[0] < 10 and discovery_celltype != 'B':\n",
+    "                rb_df.loc[replication_celltype, discovery_celltype] = rb_results['r'].values[0]\n",
+    "                rbse_df.loc[replication_celltype, discovery_celltype] = rb_results['se_r'].values[0]\n",
+    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = rb_results['p'].values[0]\n",
+    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
+    "                rbvalue = rb_results['r'].values[0]\n",
+    "                rbsevalue = rb_results['se_r'].values[0]\n",
+    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"{rbvalue:.2f}\\nN={replicated_coeqtls_num}\"\n",
+    "            elif discovery_celltype == 'B':\n",
+    "                rb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                rbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
+    "                rbvalue = rb_results['r'].values[0]\n",
+    "                rbsevalue = rb_results['se_r'].values[0]\n",
+    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "            else:\n",
+    "                rb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                rbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
+    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "        else:\n",
+    "            rb_df.loc[replication_celltype, discovery_celltype] = 1\n",
+    "            rbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            replicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/filtered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t'\n",
+    "            ).shape[0]\n",
+    "            numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
+    "            num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "            rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "    \n",
+    "replicated_ratio_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                                   columns=celltypes, index=celltypes)\n",
+    "for discovery_celltype in numcoeqtl_df.columns:\n",
+    "    for replication_celltype in numcoeqtl_df.index:\n",
+    "        replicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "        numcoeqtl_df.loc[replication_celltype, discovery_celltype] / numcoeqtl_df.loc[discovery_celltype, discovery_celltype]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>monocyte</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>NK</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CD4T</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.971596</td>\n",
+       "      <td>0.759425</td>\n",
+       "      <td>0.773429</td>\n",
+       "      <td>0.953264</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD8T</th>\n",
+       "      <td>0.988285</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.847118</td>\n",
+       "      <td>1.002450</td>\n",
+       "      <td>0.966100</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>0.792142</td>\n",
+       "      <td>0.779688</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.797139</td>\n",
+       "      <td>0.960618</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>0.794745</td>\n",
+       "      <td>0.815816</td>\n",
+       "      <td>0.935905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.853924</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NK</th>\n",
+       "      <td>0.925802</td>\n",
+       "      <td>0.967842</td>\n",
+       "      <td>0.868747</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>0.918479</td>\n",
+       "      <td>0.952496</td>\n",
+       "      <td>0.948709</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              CD4T      CD8T  monocyte        DC        NK    B\n",
+       "CD4T      1.000000  0.971596  0.759425  0.773429  0.953264  NaN\n",
+       "CD8T      0.988285  1.000000  0.847118  1.002450  0.966100  NaN\n",
+       "monocyte  0.792142  0.779688  1.000000  0.797139  0.960618  NaN\n",
+       "DC        0.794745  0.815816  0.935905  1.000000  0.853924  NaN\n",
+       "NK        0.925802  0.967842  0.868747       NaN  1.000000  NaN\n",
+       "B         0.918479  0.952496  0.948709       NaN       NaN  1.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rb_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>monocyte</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>NK</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CD4T</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.126679e-35</td>\n",
+       "      <td>2.425843e-03</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD8T</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>7.557685e-59</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>1.052643e-121</td>\n",
+       "      <td>5.216640e-92</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.774726e-21</td>\n",
+       "      <td>1.393096e-317</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>3.609987e-25</td>\n",
+       "      <td>4.217830e-39</td>\n",
+       "      <td>5.947381e-316</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>4.322965e-05</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NK</th>\n",
+       "      <td>2.552726e-264</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>8.365584e-06</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>2.320757e-144</td>\n",
+       "      <td>1.610287e-212</td>\n",
+       "      <td>1.074123e-78</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   CD4T           CD8T       monocyte            DC  \\\n",
+       "CD4T       0.000000e+00   0.000000e+00   1.126679e-35  2.425843e-03   \n",
+       "CD8T       0.000000e+00   0.000000e+00   7.557685e-59  0.000000e+00   \n",
+       "monocyte  1.052643e-121   5.216640e-92   0.000000e+00  1.774726e-21   \n",
+       "DC         3.609987e-25   4.217830e-39  5.947381e-316  0.000000e+00   \n",
+       "NK        2.552726e-264   0.000000e+00   8.365584e-06  0.000000e+00   \n",
+       "B         2.320757e-144  1.610287e-212   1.074123e-78  0.000000e+00   \n",
+       "\n",
+       "                     NK    B  \n",
+       "CD4T       0.000000e+00  0.0  \n",
+       "CD8T       0.000000e+00  0.0  \n",
+       "monocyte  1.393096e-317  0.0  \n",
+       "DC         4.322965e-05  0.0  \n",
+       "NK         0.000000e+00  0.0  \n",
+       "B          0.000000e+00  0.0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rbpvalue_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "color_dict = {'CD4T': '#2E9D33',\n",
+    "              'CD8T': '#126725',\n",
+    "              'monocyte': '#EDBA1B',\n",
+    "              'NK': '#965EC8',\n",
+    "              'DC': '#E64B50',\n",
+    "              'B': '#009DDB',\n",
+    "              'cMono': 'peru',\n",
+    "              'ncMono': 'y',\n",
+    "              'CD4T_individual_100': '#2E9D33',\n",
+    "              'CD4T_individual_50': '#2E9D33',\n",
+    "              'CD4T_50': '#2E9D33',\n",
+    "              'CD4T_150': '#2E9D33',\n",
+    "              'CD4T_250': '#2E9D33'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-1-1162ad6d26ab>:60: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-1-1162ad6d26ab>:61: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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7IPsOHufMuctWg8gA3N1dAejVo5NFep+e2nPsI0dL1r1c3lycXYmOjbZKj42NwSWflnZBDh87yIVL54psJV+5GsmO3duK7B6/lVxsbIhJsW45x6al4ppPSzu3AGcXZvXozdHr17hr2S+0WLyQA1evMKZ5SwC87AueOaDX6Rhcux7hiYlcSUos20kohaoanzRFKSGDh4GMG9bPojNuZGBwL/pjbdvAFpt6NmRGZ4JeC96xW2LROenynYedcjoFJNg1qRrd3qA9iz515qJV+umzl2jcqPjzWn9e8hcGg4FhDw+w3kdjbZBWfq1WAJ2ucu/1G9RrxLkL1jcL5y+epUHdRsWu57c/l2AwGHhg0MOF5vtj9a9kZmYW2T1+KzV0c+dsPs+iz0VHW02vys+gOnXpH1Cbi7ExmPR6ApxdeHXHdnwdHIuch53dcyEK6oZSyoVqUVchUf9d59gzBzkx4QgZiZZBSGZKjj1zkMhV4WXaR8hPgRx75qDVT/hS6zmxiecTuPDpGY4/f4hTk44SviyErDTrVmxKWDKXpp/jxLjDnHzxCCHzAslIKGJAVxnZ1LUhPSKdjJib+8mIzSA9PB3busWbOiV0AoOHAYOrgcyETFLOpmDfMv8WRPKpZAzVDBirV52Rz4MGdGfv/mNcuhyakxYYFMauPUes5kIXJC0tneW/b6B/37vxrGb9pd6xfQu8vaqxactOi/SNW/8DoF2bpqU/gXLQt8cADh3dT1DI5Zy0kLAg9h/eYzEXujBpaWmsWvc7Pe/pazUNK68Vq5bRuGEzmjVuUabjLk/9agVw8OoVi4FfwfFx7IuMpF+tgGLVodfpaODmToCzC5GJiay6dIERTQr/v83IymL1pYv4OTpSvZCWd0UQQowUQkghRIwQwi3PNoN52/tl3MdUIcQx8z6ShBBnhBDvCCEKPFkhhKsQItK8/94F5SspFairoKzkTK6tj6yw+vVOBuq+2dDip1ofy1WIkkOSuDT9HAYnAwEv1sPrAV+id94gZF6gRb706DQufn6OrHRJrbF18H2iJgmn4gj85kKh05zKyq65HXpnPTGrYki5kELKxRRiVsWgd9Jj1+JmqzczLpPIryJJ2J2QkyYzJXHb4ki5kEJqcCqJhxO58csNDNUM+c6/Tr+STsaNjCoziCzb6BFDCajpw4OPv8hfa/9h9bp/ePDxl/D39eLZUTdbhkHB4di6t+bjadYrka3dsJ2o6FirQWTZDAYDn7z/Eus2/su4iR+xeesuZv+4jBcmf0K3ru3p0e2uCju/4nji4RH4+9bk6fGPs3HrWjb9vY5R4x/Hx9uXpx4ZlZMvNCyYms3c+WrmNKs6tmzbQExsNA8PKbyVfPzkEc6cP1WlWtMATzZqgr+TEyM2rmd94GU2BF5mxMb1+Dg6MrzxzWAbEh+Pz4+zmX5wf05aelYm7+z6j3WBl9gRFsrcE8fou/I3Grq5MzbX/Os/LpznuS2bWH7uDP+Fh7HywnkeXLOKo9ev8XYHy8cit5gL8FoF1e0MzAceBwYDvwBvAUsLKTONYo2UKZnbuutbCNEJmATcDVQD4oFDwGLzT1cg94TSFOAGcAz4A1gkpUwrpP7OwH9ow4uMUsoMIcRItP+8onwgpXy/hKcEgGNTZ65vvUa1Pl4YXcq/BaczCBzqOhaa58qqCIxuJmqNrYswiJxyIT8FktQ/Cfta2k3ltY1XkJmS2i/WRW9vXnjE1cilz88RdzgGl7ZuBe6jTOdg1OH+sDtx2+KI3RALUlu0xKm7EzrTzftPKSXIPANrBGRGZxJ3Jo6s1Cz0jnrsmtnh2MFRm6KVR/KpZNCBbaPKX+QkNwcHezatnsuUNz5n5Jg3kVLSs9tdTP/sVRwdb970SynJzMzMd4T2z0v/wt3NhYH3ditwP8MfH4JOp+PLGfNY+MufuLu58Pijg/jkvZcK7BK/VeztHVg+fzXvT32DF18bg5SSuzt144M3PsPB4eZnXGK+BtL6Gvy2aimuLm707n5vofv6bdVSDAYDDw56pNzPoywcjEZ+HzSEd3ftZMI/W5BAVx8/PurcxWJVMokkU0qycv0uCASX4mL54+J54lJTqeHgyLCGjXmpdRtM+puPgGo6OXE9JZkP9u4mJiUVO4OBVtWrs6z/IHr416QSbQJeEELMkFKWa+tGSpl33dit5tb060KIalLK67k3CiG6AE8CLwA/leexiNKMEK0KhBATgf8BfwMLgSDADegLjAKGAbFogfpFYD9gBHyAPsBTwGmgj5TyWj71G9GCfjXAm5uB2hOomytrG2Bmrn1kC5VShlKAlvPaWV34qP+uEzo/iLqvNeDSVxdwv9sD3ye0XwKZKTn+3CGq31cD7yE+RV2eAoX8FEjC6Tgaf1lw153MkJyYcBjPvl54P3hz5aas9CxOTjiCZ39vvO/XjuHM68exr+NIzedqW9Rx+pXjODZyxP8Zy/RsR58+IGq8XOP2/PCVk4jpESIj7vgdfQ0Mzs1F+Jm4O/oa+DRyFlfHjL+jr0H1OTNLdMeXq8F0D7ABmCelfMG8zQCkU4bGUiH7nQJ8AbhKKWNzpRuBw8CvwA60uNNHSrkl34pK6LZsUQsh7kEL0t9JKV/Ms3mVEOJ/gAOQ/dDttJRyT648vwohfkK7mPPQujXyegWtJT0PyFkn0BzUcwK7ECK7mZV3H6VmcDFSracn17dcxbOfF6Zq+Y/clJnF+N0W1ot7ZMRlcPKlI2QmZWLytMH97mp43uuVky/1WioyXWLja9nVqzPqMHnakBqeDEBWWhZp19Nw72rd0rT1tSUlvPAVwhRFUcooAvgOmCiE+FJKGZRfJnPwLoqUUlqtdmQuawt0BCaj3RTknW7xKmACPgfK/VnAbRmogdeBKLSLY0VKeRFACNG9oAqklLuFELPR/oPrZpcxl6uL9iyiP9Cz/A67+Dz7e3Nj+3Wu/BWB/9MBVtvTrqdy5rUTRdZj9DDR+PPmOf+2rWlHjQA/bH1syUqXxB2KIfKPMFKvpuA/UttPpnkgm8HeevSz3kFPZmLmzXwS9A7WHyO9g4HUSBWoFUWpcNOAMcB7wNN5NwohAoDLedPzEQQE5CnbDDieK2kR8FyePPWAt4H7pJSpFfE46LYL1EIIPdAd+FNKWdZIsA6YCHQBcs9zmQ2skFL+K4SolEBtcDTg2c+LK3+F4znAGxtPy1a1wdVIvXeKnn4iDJbjBT37WL7px7mFCzpbHdc3X6V6f29svGxvDoUoy+ftju7IUxTlVpFSRgkhpgPvCSGmYfldDhAOtLcuaSW/ZdwumMs6AJ2BN9Di5hO58swGVkkpN5f02IvrtgvUaM+M7dDufsoq2Pxnzst7hRBPAu2A4k/CLCYhxHOY78Z8h9fEo5tnofmr9anO9a1XufJnODWftXzWqzPosPMvxpSIYgRb1w7uXN98laTAJGy8bHMW/MhItF7zOjMxE6OPNkBFb2/QBmYlWk/FykzKyLelrSiKUgG+QhvE9SGWQRQpZZoQ4kgx6rBqXpgbg9kvZ98uhIgA5gshvpVS7hFCPILW0GsnhHA158sexegghHDJp5u8xO70b9LsMCYBhBDuwHTgTSnl1fLemZTyB+AHyH8wWV56Wz3VB3gTsTyU5HstW8Kl7fouTPbFMFW3QRgEqWHJFtuz0rNIu5aKSzttJLfORofJw5Tvs+iU8BQcGxY+slxRFKU8SCkThBCfoX1/f5F7W1m6vvORHbTrAXuAJmgNx5P55P0TbUCzazH2XajbMVDfAJKBWuVQl7/5zwjznx8DV4Dlue6OskdKuQghUqSUt3StPI8enlzfdIXIlZYLnZS26zs/0XuiQIBdbW0Osc6gw6mZMzEHovEa4pMzZSn2QDQyQ+Lc6uYr8ZxbuRK96waZSZnozc+0E88nkH4jDeeWrsU9zRxJJ5OI2xiHsBF4PuNp8eINmSW5MuMKDh0dcOpc+IpJhYnfEU/q5VQy4zORmVKbe93YDoe2DgjjzS6ItLA0ko4nkXElg4yoDHROOqqPrm5VX/KpZJKOJZERnYFMk+gcdNjUtMGxkyN6J+vn/EVZ+MsqRo97BxcXJ84fXY+b283lQDMyMrDzaMM7rz/Pu2/knT1SfGvWb+PXFes5ePgUFy4F07VLW7aunWeVb8bMn1n22zouB4aSmJSMn48Xgwf24PWXR+NhXl4UoF7zewkKzn8xnmdHPcysGe+U+lhz+3XlL0x+cxzOTi7s3nwUV5eb0/8yMjKo1dyDyeNf5+UJb5R6H5v+Wc+qtSs4euIwgcGX6NiuCysWrc03776Du/n4y3c5efoYTk7O3D/wYV6b+A52thU3537Z2TO8tP1vnE0m9g97Elebm4M5M7Ky8J37PVPatOOVdh1KvY/MrCy+PnKIJWdPcyUxEV9HJ0Y1bZaztGi2vZER/HLmFEeuXeNCTDQ+Do4cePypUu+3jGahDfb6OE96Wbq+88qex5jdvb4A2JYnTyu0Fv4UYG8x6izSbReozVOktgF9hBA2UsriXNyCDDT/mb3sUhOgOdrNQF7XgVXA/WXYX4npjDqqD65B2KJgy3SDDvsA68U5CpN2PZWQuYG4dHDDprotMiOL2EMxRO+6gXu3athUv/kc3GuIDxc+PUPQ95fw6OFJ+o00IpaH4tLW1WK/nvd6Eb3nBoHfXqD6AG8ykzOJ+C0Mu9r2OLdxLfV5y1RJ4v5EnLqWPiAXJCstC7umdtr7qA2QHp5Owt4E0q+k4zbk5hd/WnAa6WHpGL20rv6s9PzfFJWVkoXJ34RDOwd0NjoyojNI2JNAamAq1UZWs5jXXRKxsfF8MWMen34wsVTlC7NqzT8cPX6WDu2bk1LIG5aio2N5YHAvmjaph5OjA4ePneGTad+z7d997N2+LGcJ0d8Wf0VqmuWSBH/+tZXp3yxg8IDu5X78cfGxzJo7gzdf/qDc6964ZQ0nzxynTcv2pKYVfG1OnT3BsGfup9vdvVgw+1dCQoP4+Mt3iLwSzvdfLSj348orLi2Nb48c5p27yn/Bkdf++5dfz51hUpt2tK3uxX/hYXywZxeJ6elMbtMuJ9+OsFD2RETQ0tMTASSmp5f7sRSXeSDXh5h7LXOlp3GzJVwsQogWwJfAb8AlwAZtKthLwHop5W5z3YFAYJ6y2X89KqX8r6TnkZ/bLlCbTUW7i/kCbf6yBSFEbaDQb3jzYilj0AalZb9UdSLW3RQjgRFAb7TW9i3nfnc1rm28QtqVstyTgM5Wj95Bz7X1V8iISwcBNjVs8Rnmj0cPy+fldjXtqT2pPpErwgj8+gJ6ez1unT0s5lUDGN1M1HmlIRG/hhA46xI6g8C5lSs1HvUr08srTLVMJB1Owr6Nfb4vySgLl14uFv+2qWmDTNduDLKSs3LeNe3Q0QHHTlr3fcy6GNLC818bx6GN5Q2Tyd+E3llP9B/RpAWmYdugdAul9OnZmZk/LOHFcU/i7VX40pYlNefb93KCbLd+IwrM98HbEyz+3a1re+ztbBk/6SMOHz1D29ZNAGjd0vqd0O9++C3eXtXo26tzOR65+Ti69GTeLz8wevg4qnt6FV2gBL746Nuca3P/E/0KzDf928/w9vJhzlcLMZoXFjEaTUx843nGj55I86atyvW48uru589PJ48zpnnLcl3CMzQhnl/OnmZy67Y5Qbmbnz8JaWl8ffggo5o0w81W+0xPbtOOKW21xuq4vzezL7LiVlQspvloU2vrl7GeK2iNszfR1tFIQgvYU4C5Zay7xG7LQG0ejT0Z+J8QojFa90Mw2oInvYDRaMu+ZT/EbyyESEA73xpoi6I8BZwCns1V75G8+8o1xWu7lLJCF7B2v7sa7ndbfyELvaDRp4W/eq84DI4GAl6oV+z8jg2dqPdW0d3rdn521Hm5QZH5SsLxLkei/4gmcW8izj2L/yao0soOzrkX1S3LNIucLvsy3GO8+cqzDBw6ls++/JGvvyh9V25+yvIyDQ937UbHaCz46yM4JIJtO/YzcfxT6PXle6MF8NLzr/DEc0P5Zs6XfPz2F0UXKIHiXJv09HS2/beFZ0eOzwnSAIP7P8Cr777Ixr/XVXigntS6LY+tX8NXhw9avYKyLA5fvUqWlPSsafl0sYd/TeadOsHWkGAeqq/9vusqaWU6KeUCtO/9vOkZQJm/jKSUV9BiSGnKbqNsc2as3LZrfUspZ6AtHRqD1kXxN9p/XGO0lvLqXNm/AXYDW4AZgC8wHuiYdxk4pWrQOeiwb2VP0vEkMuOsR59nk1myWD8Flc1KyyI1KJXEg4nYNbVDZ1P6XwmZJZEZkvRr6cRtj8PgYcCmVunfXe3t5cm4Z4cxd8GKAp//gvZstqifzMyCr2FxZGRkkJSUzJ79R/nws9n07HaX1burc/tl2WqklPm+OrM8VPf0YtTjz/LL8gWEhgUXmK+irk1QyGVSUlNoVL+JRbqtjS21atbm3MWKf/2nl70DzzRtzuLTJwmJjy8wX0ZWVpE/mbmWl9Wbg68pzw2LjfmG60x0fk8GlYp0W7aos0kpdwG7CsmyjTLe2ZiXoHu/kO1l3oeSP4f2DiQdTyJhdwIu/VystmfEZnD9p6Lvs3TO1oPA0q+nc2PRzS8c2ya2OPcpW8v96vdXkSnaTYHBy4DbULecddJL65WJT/PjghV8NO175s780Gp7YFAY9VsU/ZaoWjV9uHB8Q6mOISEhCTffjjn/7turM8sWTi+0zC+/rqFVi0Y0b1q+PS25jRs9kcXLF/C/WdP43yczrbaHhAXRsXfRb7ny86nJ3q3Hi8yXW/Y7sPN757Wrixsx+bwjuyJMaNmaRadP8uXB/Xzd3XrJh+D4ONovXVxkPf6OTjmDwOq6ugJw8OoVmle7+UjswBWtWzu/d18rFeu2DtTK/286Ox0ObR1I2J2AQ3sH9K6WXah6Rz0ej3sUXVE+Pa8GVwMej3sg0yVpEWkk7kskNisW1wGupT5e94fdkemSjKgMEvclEv17NO6PuluMXC9xne4uTJownI+mfs8rE0dRt7a/xXafGtXZ/U9hL/PR2NiU/uUu9va27P5nKSmpqRw5doapX/7I/Y+9wMZVP2AwWH+F7Nl/lLPnA/nftIp6qZHGzdWd50ZO4KtZUxk3eiIB/pZrDXh51mDdb/8UUPomk6kUvR7mdyTk1/N7K9+f4GZry9gWrfjy4H5eaNWaAGfLG1pvewc2PvBQkfXkfgFHQzd3uvn68fmB/dRycqZNdS92hofxw4ljQOV1d9/JVKBWqjT7NvYkHU4iYVcCLgMsv4SEXmCoXrqPsDAIjN5a8DL5m9A56IjbGEdaqzRMPqZS1Wn0NNfnY8Lkb+L6T9dJOpaEY4eyzSd/adxTzJyzhA8+mcWiuZ9ZbDOZjLRq0bDIOsr0vF2ny3nv9N2d2tC8SX16D3qG3//czKMPWbfmFy9djdFo4LF8tpW350aMY/4vc/jym0/47gvLMT4mk4mmjYpuUZfm2mRPC4uJjbHaFhsXQ4N65b5eUoHGNG/J3BPHmXZgH7N79rHYZtLraeZR9EDEvFfg6+49Gff3Fh5bvwYAJ6OJd+/qxCv/bcfrFr97WlGBWqnidCYdDh0ciN8ej0M7y9HVZen6zit7ClZmTKb2frUyMrgYELZCq6+MHB3teXXyaF5960smvzjSYtut6PrOq21rLWhfvBxitS01NY3f/thI/75d8azmbrW9vDk4ODLh2cl8+PlbjH3GcgJIRXZ91/KvjY3JhrPnT1ukp6SmEBwSyKB+95eovrJwMBp5sVUb3t+zk/EtW1tsK03XN0ANB0dWDr6fyMREolNTCHB24VSU9qiog3eNgqpRKogK1EqVZ9/SnsSDicTvtBwwU5au77zSQrWpV3m710sr/Xo6MkWWW31jRz/K1zMX8d7H31qk34qu77z+3alNSa1T289q25r124iKjuWpYRUziCw/Ix4fzQ8LZ/L515brXFRk17fJZKJ7196s3rCSlye8kfMIYO3GVaSmpdK3Z8X3JuQ2qmkz5hw/wtT9lutrlKbr26K8gwPeDg5IKfnh+FHqu7rSxcc337xKxVGBWqnyhEHg2NGRuC1xlun6m93XxZV+LZ34f+OxrW+rBdEMbQWyxEOJmAJMFt3eWUlZOQE8Mz4T0iHlnLZcqsHDgMFD+/W5sewGtvVs0bvrEQZBxrUMEg8monPUYd+8fLoJbWxMvPXqGMa+ZDmgzGQy5nRLl0RQcDgHDmlL0EZFxaDT6fj9z00AtGvTjFo1fYiNjWfg0LEMe2Qg9evURAjB/kMnmPHdIlo0b8gDg3tb1bt42Wo83F0Z0O+eUpxl6diYbJg09lVefe8li3STyUTLZm1KXF9oWDBHThwCIDomCp3QsWbjnwC0atYGP1/tHfGTx7/OkGF9eH7SCEY8/iyhYcF8/OU7DOw3hBZNWxdUfYWw0euZ3KY9U3Zss0g36fW08iy8Nyk/C06dwEavp6aTM1eTklh+/ix7IyNYMfA+i2fU15OT2R2hzUgIS0ggKSOD1Ze0RbsauLnR0K3ie1XuBCpQK7cFu2Z2JB5ILHNXss5eh85WR+K+RDITMxFGgd5Fj1M3J+ybWQbV9BvpxKyJsUjL/nfuZUyNNYwkn0zOmUamc9Jh29AWh/YON+dnl4ORT97P/75ZyPmLZX8fzbYd+xk9znJZz8dGTAFg7qyPGPHEEGxtbWjUsA4zv19CWMQVDAYDtWr6MGnCcCY8/wQ2NpbP8q9dj2LD5p089/TDmEzl14IvjkcffJLZ877hclDeFyeV3M59O5j8puXyrGMmaovC/O/TWTz6gPbOh2aNW/DLj3/wyfT3GPH8Izg5OTP0vmG8MendMh9DaQxr2IhZxw5zKbbM74AgM0vy7fHDhCbEY2cw0LmGD+uGDKWRu2XgPRsdxegtGy3Ssv9d1mVMlZvErRyhqNxUnJdy/H929OkDosbLNe7oaxAxPUJkxB2/o6+Bwbm5CD8Td0dfA59GzuLqmPF39DWoPmemGkpeiNt2wRNFURRFuROoQK0oiqIoVZgK1IqiKIpShalArSiKoihVmArUiqIoilKFqUCtKIqiKFWYCtSKoiiKUoWpQK0oiqIoVZgK1IqiKIpShalArSiKoihVmArUiqIoilKFqUCtKIqiKFWYCtSKoiiKUoWpQK0oiqIoVZgK1IqiKIpShalArSiKoihVmKGyD+BOdfTpA5V9CJWmw8IuAETGRVbykVS+9OtbK/sQKpXBuTmrPjtd2YdRqcYuvAuZklrZh6FUYapFrSiKoihVmGpRV5LsVqWiKIqiFEa1qBVFURSlClOBWlEURVGqMBWoFUVRFKUKU4FaURRFUaowFagVRVEUpQpTgVpRFEVRqjAVqBVFURSlClOBWlEURVGqMBWoFUVRFKUKU4FaURRFUaowFagVRVEUpQpTgVpRFEVRqjAVqBVFURSlClOBWlEURVGqMPWay9tU6o1UQpZeJu5kLFKCc1MXaj5eGxsPm6LLXksh5NdArWymxKGOI/6PBuBQ29EiX3p8OqHLg4g5EkVmShb2/vb4PuCPS3O3ijotpYRCw6N59ZM/+fu/c0gkPTo34It3HsDfp+j/o5DwaD783zq277nAjehEfL1deXBAK14Z2wsH+5ufo0b3fEhwWLRV+WWzn+a+vs3L9XxKIzrxKptOLCL4xhlCo8+TnpnKRw/+gYejT5Fl0zNTWX34B/Zd3kByWgJ+bvW5v+146nu1tsiXJbPYdOJn/ju3krjkKLxcajKgxdO0rtWzok6rRMISE3h3/17+jQhDAvfU8OHDdh3xc3QssmxQfDwfHtzHjohw0mUWrT08ebdte1pV88zJs+zCOSbu2lFgHcceHkZ1O/vyOBUlHypQ34YyUzM5+/lJdAZB7dH1QAjC/gjm7NQTNP24FXobfYFlMxLSOf3JCfS2emqNrIvepCNyYzhnpp2gybstsPPRftmy0rM4O+0kGQnp+D1SC6OLiev/XuH8jDM0mNIE58Yut+p0lQIkJafR/8lZ2JgM/PDF4wgBH/5vPfc+MZN9a1+xCLZ5JSalMvCp2aRnZPLupP74+7hx8FgwH3+9kYuB1/j52xEW+Xt3bcTbL/WzSKtfp3qFnFdJXYsP4VDQVmp6NKJe9Vacjthb7LKLd33CidBdPNB2AtWcfNl+dgXfbZnIlP4/4u/eICff6iNz2HpyCYNbP09Nj0YcvLyZudvfYmzP6TTz61wRp1VsSRkZPLRpPSa9jm+63INAMPXIQYZuWsffgx/AwWgssGxUSgpDNqzB0Wjk845dsDcY+P7UCYZuWs/6AffRwNUVgN5+/qztP9iirEQy/O/N1HR0UkG6gqlAfRu6tv0KqVdTaD61NbZedgDY+9tz7LVDXPvnCt73FtySuPp3JOlxaTR642ZZpyYuHHvlEGErQ6g3viEAUftvkByaRMPXmuYEZZfmrpx85yihy4No8l6LCj5LpSjzl+3mcsgNjm5+g7oBWuuneSMfmvf6lJ+W7ubFZ7oXWHb3wctcCLzGXwvG0LtrIwC6dapPdGwSM+ZuIyk5DXs7U07+au4OdGgdUJGnU2r1vFoz7ZH1AOw8v6rYgTo06jz7L2/iqc5v06neIADqe7Xmo78eZ82RHxjb80sA4pOj2HpyCX2bDadP0ycAaOjdlmvxofx5aGalB+pfzp8hKCGenUMeorazMwCN3dzp/Odv/Hz+DM83KbjXY+G501xLSWZlv4E5Zbt41+Culcv54ughfuym9RhUs7Wjmq2dRdk9VyKJSk1lSss2FXRmSjb1jPo2FHM4Gse6TjmBFsDG0xan+s7EHI4qtGzCxQRsvewsyupt9Dg1cCL2aDQyUwKQeDEenUmHUyPnnHxCCJybuZJ4OYG06NRyPiulpNZuPUmHVrVygjRAgL8HndrWZs2WE4WWTUvPBMDZ0dYi3cXZjqwsiZSy/A+4guhE6b7GjoXuQK8z0Dagd06aXmegXUAfTofvJT0zDYBT4XvJyEqnQx3LHoUOde4lPOYi1+PDS3/w5WBjSDBtq3nmBFqAWk5OtK/uxYaQ4ELLHrx2jTrOzhZlHYxG7qruzZbQYDKysgosu/zieUw6HfcH1Cn7SSiFUoH6NpQcloSdn3VXk62vHcnhSYWWFToQBmGdbtCRlZZFytUULUEnEHqBEJZ5deayyaGF70epeKfOR9KkQQ2r9Mb1vTlzIbLQsj27NKBegCdvf76G0+cjSUhMZduu88xc8C+jH+9s1W2+butJPJq+imvjKXQbOoO/Nh0v13OpDBExl/Bw9MFksLxZqeFam4ysdK7Fh+bkM+hMeDr5W+UDiIi9fGsOuABnY2Jo5Go9JqGhiyvnYmIKLasXAqPO+lGZjV5PcmYmgfFx+ZZLzshgddBl+vjVxN3WNt88SvlRXd+3oczEDPT21v91BgcjGYkZhZa19bYj7mQsGQnpGBy1Z1cyS5J4OSGnbgA7b1sykzNJDk/KeW4NkHAxHqDI/SgVLzo2CTcXO6t0Nxd7omOTCy1ra2Nky68v8Pj4+bS9d1pO+shHOvLV+w9a5B3QsyltW9QkwN+dq9fj+f7n/3hs7Dx+mv4Ew+5vVz4nUwkSU+OwNzlZpTuYtNZlUqoWpBLT4rA3OVrdtNrnyVdZYtJScTFZj0dws7EhNq3wnq+6Li78GxFGVEpKTsDNkpLD16/l1J2fDSFBxKen80jdemU8eqU4VIv6dmXdKIZidFd69vBGZkku/XCelKsppMWkEfzLZVKvpVjU697RE4OTgcs/XiApJJH0+HTCV4cSf9b8pSTyOwDlVssbPIBidVunpKYz/MVFXLuRwE/Tn2DT0gl8+vp9/L7uMBPf+90i7//eH8oTD7anS/u6PNC/Fet+Hkeb5v68+8XacjuPyiER+fwiSfJcPykL+LxXnccD+R1ecY5uRINGZEl4Yed2AuPjuJKUxFv7dhOcoN2Q6/L9otG6vT1sbenl65/vdqV8qUB9G9I7GHJavrllJGVgcCi8k8S2ui11xjQgMTCR468e4ujEAyRciMe7rzYAzeiqDSAyOBioN6ERGQnpnHznKEde2M/1HVfxvV/7xTS5FjySVLk13JztiIqxfgQRE5ecb0s7twXL9/Lv3gus/Ok5ht3fjrs71GXisz347I0hzF2yi2Onwwosq9freLB/S8IiY4i4Glvm86gs9iZnEtOsW8NJaVqQsrdxzvkzKTXe6gYob77K4mIyEZNq3fKNSc2/pZ1bLSdnZnbtxrEbN+i48jdarljKwWtXea5xM4B8R3NfSUri34hwhtaui0GnQsitoLq+b0N2vnYkh1l/QaeEJVt0UxfEvb0Hbm3dSYlMRhh02Fa3JXDhRUzuJot52E4NnWn+eRtSr6QgsyS23nZErg9DZ9JhX6vo+ZlKxWpc35vT562fRZ+5EEmjet6Flj15Nhw3F3vq1Kpmkd6uZU0Azl64QovGvgWWzw5Z+bXobxc+rnU4GrKdtIwUi+fUETGXMeiMeDr5AVDDtQ4ZWWlciw+lurO/RT6AGi61b+2B59HQ1Y2z+TyLPhcbkzO9qjCDatWmv38tLsbFYdLrCHBy5tU9O/G1d8h3HvaKSxfIlJJH6tYvh6NXikPdDt2GXFu5k3Ax/ubAL7RFTBIuxOPaqniLkQidwM7HHtvqtqRFpxG17waePa2/3IUQ2HrbYedjT1ZaFte2X8Wjsyd624LnapfJOWAusAjI20jIMm87WI77iwPmm+vN3TjMAo4Ba4HFwEJgJXCW/PsUo4H1wALgZ2A7kJJPvnI0sHcz9h0J4nLw9Zy0oNAodh+8zMBeTQst6+XpTHRsEhcDr1mk7z8SBICPd8Hz5DMyMvlj3RH8fdzw9qzc1mRZNPfvSmZWBoeCtuakZWZlcDBoC418OmDUa71LTX06YtAZ2X95o0X5fZc24ONal2pORS+sUpH6+dXk4PWrBOUa+BWcEM/+q1fo51ezWHXodToauLoS4ORMZFIifwVeYkTDxvnm/e3SBZq4udPM3aNcjr80hBAjhRBSCBEjhHDLs81g3vZ+GfcxVQhxzLyPJCHEGSHEO0KIAltDQghXIUSkef+9C8pXUrd1i1oI0QmYBNwNVAPigUNoX62Lga7AP7mKpAA30L6C/wAWSSnT8tRpD7wGDAP8gevmOt6VUgYKIUaifbUX5QMp5fulPbfCeHb34urWSC58cwbfB2uCgLA/gjG5m/DscTPYpl5P4dirh/AZ4o/vEK0lkJWRRejyIJwaOqO3M5AclkTE2lDsfO2s5l+H/BaEQ4ADBkcjqVdTiFwfhtAL/B4q3i9/maQBR4EOFbyfXYAJyDv2KhM4AtQDmqH9poQAO4AY4K5ceRPRAroL0Avt2PcBm4DB5D+eoByMerQj3y/6j0fGzOPdyf0RQvDRV+vxq+HKM8Nuzu0NDouiaY9PeOOFvrz5gjbF6KmhHfh23jYeeOZHXh3XG38fNw4dD2HqzE20buZHp7ZaK3H5X4dYs+UE/bo3xq+GK1evxzNn8X8cPhHKwhlPVcyJlcKhoL8BCL5xBoCTYbtxtHXD0caVBt5tuJEQwXsrH2JAi6cZ0PIZAPzdG9A2oDe/7Z9BZlYGHo4+7Dj7BzfiIxh19wc5dTvZudOz8WNsPL4IW6M9/u4NORi4hXORBxnT4/Nbf7J5PFG/IfPOnmLEP1t4vVVbhIBpRw7h4+DI8AaNcvKFJMTTceVvTG7RmpdbaiuvpWdl8dHBfXTy8sbJaOJMTDTfnjhGQ1c3nm/SzGpfx25c50xMNO+3q+hfzGJzQfu+fr0C6nZG+64/i9Zs6Ay8BbQDhhRQZhoVMHjhtg3UQoiJwP+Av9H+o4IAN6AvMBvt6zS7jfQisB8wAj5AH2AmMEEI0UdKmbtZMRe4H3gPOADUBD4AtgohWqJ9JXfKlb+Nua7sfWQLLZcTzYfeRk/D15oSsuQyl344D0icG7tS8/EAy5auRGsZZt383AghSLmSwo0918lMysDkZqJaVy9qDPJFZ7DsYMmISyd4SSAZcekYnI24tXHH9wH/nNHiFcoXOIUWJCtq0aMLaLdhrYA9ebbpgUeA3DNPfNGC8CmgLTd/e46jXee+QPaTA3u0T0ogUEE9ow72Nqz/ZRyvfvwno6f8gpTQvVN9vnjnARwdbj7CkBIyM7PIyvU5qOXnzrYVE/nkmw188L/13IhOxK+GK08/2olXx/dBZ372GODvzrUb8bw19S+iYpOwtzXRtoU/q+aPoc89jayOqbLM3f6mxb+X7f0C0BYwaeA9G5BkyUyypOW84Kc6v81fh79n9eE5JKUl4Odejwm9v6Kmh+W53df6eWyMdvxzejlxyTeo7lyTZ7p9Qgv/rhV6XsXhYDSyos8A3juwlwk7tyMldK1Rgw/bd7RYlUwCmVKSletZuwAuxcXxx+WLxKWlUcPegWH16vNi81aY9Na9ZssvnscgBENrV5nR3puAF4QQM6SUhc9JLCEp5bg8SVvNDbnXhRDVpJTXc28UQnQBngReAH4qz2MRt9PCBtmEEPcA24DvpJQv5rO9LuAAuKO1hvtIKbfkydPJvG2zlHKwOc0OrVX+uZTyzVx570Xr2LxXSrkxTz3dC9pHYTos7HL7XfhytG/ETiGeFdbX4BzwLzAI2AA0QLuPBS0YzgNaowXKskgFVqC12KV5nw+j3Z8X5hRaK/wxIPvx3a9AdaBHnrzLgBpAt/yrkj9KkXxpxh39ObCrM1HMHrH3jr4GYxfeJa6MGH1HXwOvhXNL1O+Uq2fzHrRvinlSyhfM2wxAOhXQqymEmAJ8AbhKKWNzpRuBw2jfBjsoRUwozO36jPp1IAp4Nb+NUsqLUspjhVUgpdyN1vIeZA7soLWR9GhPLnOLMf95u16v248d0AQ4g3brVJCsYv7ktQ8tKJd0PEwkWld5dis/w3x8+Q0NcEV7dq0oSkWJAL4DnhNC1Cook/m5dVE/+Q68MW9zND9znox2U5B3usOraN8MFfIs5Lbr+jZfzO7An1LKsg7XWQdMBLoAF6WU8UKIn4EXhRB70bqya6HdQR0FthZUkVIBWqAF6sNo9815xaPdvxbFEa0FnC0SOA88UMLjCQUuobXms2/Zsge85TcLxgbLAWqKolSEacAYtMeVT+fdKIQIAIqzfFwQEJCnbDO0h1vZFgHP5clTD3gbuE9KmVoRMyFuu0CNNmjMDu2illX2Qri512EcBXyD9uw72160bgyLgWclJYR4DvN/cq2RdajevfApNHc8W6A52vDAFmhDO3Kzp+AhHbnlvk/OBP5De/Zdkrd1RqN1ZtUAWpagnKIoFUpKGSWEmA68J4SYBlzMkyUcaF+MqvJbhu2CuawD2kO4N9Di5hO58swGVkkpN5f02IvrdgzU5Sn71if386GP0QYETEFrUddEu1NbL4ToJqVMLO3OpJQ/AD+AekZdbM2Ak2hTsvI+A9YDJZ0hcgLt17EpN38ts9eOSUcbLGbKUyYObYSCI9owxNwPQLJb0vn9iqeSf0tbUZTy9hXaIK4PsQyiSCnThBBHilGH1Xeyudf2gPmf24UQEcB8IcS3Uso9QohH0Hpk2wkhXM35skevOAghXPLpJi+x2zFQ30CbTFPg84gSyF69IAJACNEU7fn3aCllzqg9czf4OWA08HU57Ddf13dc5fJPF9Db6WnxZVuLVcZkpuTAM7vxGeKH7wOlnx4VsjyI2GPRpN1IRWZKTO42eHSqhte9PlbvsY4/F0fI8iCSghLR2+nx6FQNv6E10Zks8yWHJRG85DIJF+IRBh1urd3wfyygfEaHG9FasHvRWtUWB0jJu75j0D49S/PJ9yfa8MPcS10noj0gMQL3Yh3EDYAT+T+LjsGyr6aYfl6xjzGvLcXFyZZT29/BzeXmsPeMjEycG07hzRf78fZL95a8crO1W0/w2+rDHDoewsWg69zdoQ4bl0ywyJOZmcW387azcdspTp+PJCklnboB1Rjz5N0Mf6hDzshwgKV/HuDHX3ZyIfAacQkpeHs606NLA956oR9+PiXpuijc7gtr+HnXx9gZHfnowT8sVgXLzMrghcV3M6DFMwxq9Wyp93EsZAcHLm8m+MZprsWHUs+rFZP6zc4374UrR1h5aCahUeewMzrQrnZf7mv9vNWLPkpr2YVzTNy1A2ejiX0PPoKrzc07v4ysLPwWz+flFq15pVXpXzW5KSSYlZcvcvTGdS7Hx9HRy5uV/Qbmm3fvlUg+OrSfE1E3cDKaeLB2HV5v3Q47w83vqna//0poYkK+5Yc3aMTnHbuU+lgLIqVMEEJ8BkxHe1SZoyxd3/nIDtr10OaKNEHr4T2ZT94/0R5+uRZj34W67QK1lDJDCLEN6COEsJFSluV9i9mfxp3mP7Nf3Jp7mhVSyvNCiBgg/xUAyllmciYRa8Pwf6Q87kXy1J2SQbWu1bH1tkNnFCScjyd8dSiJgQnUf+nm6SWFJHL2i1O4NHOlwaRGpF5LJeTXQNKi06g3rmFOvrToNM5MPYFtDTvqjW9IRlImIcsDSZ5xhsZvNkPoyuF5TRO0lnDehU5K0/XdEusBZKFoM+u7YznqOxktSAMMQPt1zE9NtGfeuVvjkUCCeVspxcan8L85W/no1cGlr6QAqzef4NjpMDq0qkVKanq+eZJT0pk2cxOPP9CecSPvwdHeho3bTzP+zeWcu3iVT9+4LydvVHQi3Ts3YNJzPXF1tuPcpatM/W4zW/49y6GNr+HkWD6BK+fY0hPYdOJn7m87vlzrBTga8i+h0ecI8GyW86rL/IRGn+fbLS/R2Ocuxvb8khsJ4fxx8Dtikq4xutsn5XpMcelpfHfiGG+3LU4PbsmsDwniRHQUbTyrk5qZWWC+U9FRPLplA919fPm5Z1+CE+L58OA+IpKS+MH83mqA+d17kZbn9ZhrgwOZdfI4fYu5AEspzUIb7PVxnvSydH3nlT2HI7t7fQHaDKTcWqG18KegNTHK7LYL1GZT0S7OF2jzly0IIWqjtXMKZJ6eNQZtUNolc3L2PLwOaF/d2XkboN0VFbwAcjlybubK1S0RePetkbP2dnkJGF7X4t/OTVzJTMsicm0Y6fHpGJ20VnDYyhBM7ibqjm+QM79aGASXf7xA4oAEHAK03p3I9WHIDEn9lxrn9ACY3Iyc+ewk0YeicG9XDqsX6dGmZP2XT7qndfZCuWJ9f5t98+/JzUCdgTbpIwFt2ZxE80/uerL/a1qgPcnahHYjkIZ2q+dJ0ffnhejdtSGzF/3H+FHdyn0FsFmfPpLTIu71yDf55rGzNXJy29u4uzrkpPXo0oDo2CRmL9rBO5Puxc5WuwjjR1nOQet6Vz1q+rozZNQctv53lvvvLd8H+41r3MW2s7/Ro8ljuNiV7wpZT3R6I+cd19PXP1dgvrVH5uJq78mz3T5Fr9M++3qdkUU7PyT4xlNWc7HLonsNX346e4rnmjTNd/3tspje6W505gFQ961fU2C+L44cooa9Az9264XR/Nkx6nS8uPNfJty4TgsPbTna5h7VrMp+dvgA1e3s6OFT8LK0ZWUeyPUh5seLudLTuNkSLhYhRAvgS+A3tCGkNmhDWl8C1ptnDSGlDERbLSF32ey/HpVS5v3WKpXbcrqRlPJftDunCUKIzUKIJ4QQXYUQ9wkhvkZrf+VeZqKxEKKjEOJuIcTDQogf0YYGnQZy95HtQBvdPV0I8YoQoocQYgTa0hWxaAtJVjifwdoaw+GrK2zNFAsGR+1LRui1D1hWRhaxx2Nwb+9hsQiKe/tqCIMg5nBUTlrM4ShcWrpZdNM7NXTB5GGyyFdmDbAeTFaRktEesmSi3RL+lecn91IHDmh9Mzq0eQE70bq8+1GmVcleG98HgM9nlv8YFV0xXqag1+ssgnS2ti1qkpqWwY2owodruLtqAcVgKP/lZvu3GAXAhmPFWSSwZLKDdGEyszI4Fb6HNgG9coI0QNuAXhh0Ro6F7CjXY5rYohUAM44dLdd6gZwgXZj0rCz+CQ/lvoDaOUEa4L6A2ph0OjaEBBdYNjQhgZ2RETxYuy76in+Jx3y0/q2yuoL2W/4msAZtYeBeaK3kks4XKbPbtUWNlHKGEGIf2hKiX3JzCdEDaC3l1dyc1JPdZEhF+/o9CowHfs49kltKmSmE6IX2n/Mc2sCE62hLXLwrpSz401iOjK5Gqvf25sqmCLz7+2BTLf9uQ5lZjPFogny7n2WmJCs9i4SL8VzZGE61rtUxmN9xnXo1BZmehZ2f5Z27zqTDprotyWHaeptZaZmkXk+lWjcvq/rtfO1JDrd+cUiRGph/8tKhrRRWEfLbpxPaiITickfrHi9H3p7OPP/U3Xy3YDsTn+1BTV/3fPNlZBTcXZlNCIFeXz5fkv/tvYirsx3e1a3vnDIzs8jIyOLc5au8/ukqGtf3onfXhvnUUjbOdh50a/gQ/5z+ld5Nn8DDMf/BAJlZRb83XSDQ6Up2M3EtPpT0zFR8XC17qIx6G6o5+RIRW5xHosXnZWfP0w0b8+Ppk4xt2gx/x/w7DDOy8ls0wJKAEgfMwPg4UjIzaeRqOd7AVm+glpMz52ILXjBgxaULSODRcnyJh5RyAVq3c970DPL/Bilp/VeAx0tZdhvlvHDwbRuoAaSUu9CCaEG2UcILJqW8Abxs/ilO/hLvozhqDPDl2j9XCF8VSu1nrJfrS72WwrFXDhVZj8nDhpbTLZfxSgpN5OTbN+/MPbp4EjDq5hdOhvkVmtmBOzeDgyFne0ZiJsj88+kdDKRE5F1AWympyWN68tPSXXzyzUbmTBtmtT0oNIrG3T4qsp6avm6c+ffdMh/P5n/P8Pu6I7w7qX++LeWAu97lRrTW0m7T3J81i8Zia1MxS872bfYU/537k3VHf+KpLm9bbb+REM47fzyYT0lL7g7efDz0zxLtOzFVWxPJ3mQdMB1MziSlWr8+s6wmNGvJz+fOMv3oYWZ0sV5YIDghng5/LC+yHj8HRw4MfbRE+85+jWZ+r810M9nk+5rNbCsuXaC5uweN3fK/0VSKdlsH6v/PDI5GvO/1IWxVCN4DfLGtbtmqNrqZaPJe3mHQ1oTB+h7CtrodTd5rQWZqJgkX4olYE4bMPE/d5/PciOZ3+yHz+UeR+ZTScnd14MVnevDptxt5eUwv6tS0fB5bo7ozO1ZOKrIeG5uy/6qfPh/JyIk/c89ddXl5TM9886z9eSzJyemcuXiFL2dvYfDw79my/EVcnQt/P3ZpONi40Kvp46w7+hN9mz1FNSfL558udp68NqDornGDvjQ3EtoHXOTz4ZcV9OF3s7Hh+abNmH70MBOatSDAybJHw9vOng0D7iug9E02+azhXZSc15rmu63g8z147SoX4mL5uH3HEu9TuUkF6irMq58PV7ZEELYymLpjLIOozqDDvqb180Mr+fxm6Uw6HGprg8GcG7lgcjFx+acLePWugWM9p5znzdkt59wykjJy3nmttzeAyD9fZlIGekf18SoPLzzdje8X7eCjr9Yz/6snLbaZTAZaNil6gE5ZV0u6HHydQSO+p5afO79+/0yBz52z32F9V5sAunWsR9MenzB3yU6mPF9ub/yz0LPxY2w78xurj/zAqK4fWGwz6I34uRfd3ZpfsC2KvUkLkolp1i3npLR4ariWdJRj8Yxp3Iyfzpzi8yOHmNW1u8U2k15frFdPluaT4GpuScekWbecY9LSaFjAe6+XXzyPUafj/tp1SrFXJZv6Jq3C9LZ6agzyI2RZIEn9LQfulKXrOy/72lrAT7magmM9J2yq2yIMIudZdLastCxSr6bg3l77MtDb6LGpZkNymPWz6OSwZJwa3b7vKq5KHB1smDK2F69/+hcTn7Vc9eVWdH2HRsQw4KnZODvasGr+GJydijfVKsDfA3dXey4GXS86cynZGu3p12w4fxz4hj5NLW9iKrLr29PJD4PORETMJYv09MxUrseH06ZWrxLVV1wORiMvNmvJ+wf2Mq5pc4ttFdn1HeDkhI1Oz9kYy2fRKZkZBMfHM7iW9SviUjMz+SvwMr18/ahmW/49KncSFairuOo9vYncGE7o75bj2MrS9Z1X/BmtVZDdva4z6HBp7kbUvuv43u+fMxo86sANZIbEtfXNZ02urdy5vvMqGUkZOc+q48/FkXYjFddW6plUeXnuibv55qftfPC/9RbpFd31fe1GAoOGa4t9rF44Fk8PxyJK3HTqXAQ3ohOpU9N6uk55uqfhULaeWsrqI3Ms0iuy69ugN9LEtyOHArcysOXonJHfh4L+JiMrrUJffzmyYWPmnDrBtCOWCwtUZNe3Sa+nh68vfwVeZkrLNhjMg9HWBAWSmpVJP3/r+dGbQoOJTkvlkXIcRHanUoG6itMZdfje50/gAsvla3WGm93XxZUUkkjIskDc21fDproNWemS+LNxXNkcgUsLVxzr3RwY43u/P6c+Ps6FWWfx6uWtLXiyPAi3dh45c6gBvAf4cGP3NS58fYYaA33JSM4kdHkgDnUccWurAnV5sbEx8MYLfZnwlmWLyWQy0LZFyReRCA6L4uAx7eYvKiYRnU6wcv0RQJt+VdPXneSUNO4b9T1BoVF8P+0xwiNjCI+MyamjUT3vnNZ1r0e+4b6+zWlQtzq2JiMnzobz9U/b8PV2ZdSjFft80qg3MaDF0yzZM9Ui3aA3UqtaydcoupEQQdCN0wAkpMWiQ8ehIG3p/1oejXNGmA9sOZov1z/L3H/folvDh7iREMHKg9/RulbPcp1DnZeNXs/kFq2YsmenRbpJr6dVtZJ3uYckxHPkhtbrEZ2WgkCwOkgbtd7Ko1rOCPMpLdswaP1qnvv3b0Y1bEKIecGTQbUCaJnP3OnfLl7A3caG3r7+VtuUklGB+jZQrWt1ItaHkXqlbC8LMzobMTgZCV8TSnpsunm6lQ3+j9bCM88UK/taDjSc0piQ5UGc+99p9PYGqnX2xPchy6BgcrOh4WtNCVkWyIXvziIMAtfW7tQcFlA+q5IpOYY/1IEZP/7DhcBrZa5r++4LjHnNch3VJyZoywTMmTaMpx7qwNXrCRw9qa3xM2rSYqs6Nvwynns6ajMS2reqxeLf9xMUFoWUEn8fNx4Z1IaJz/agmnvJbihLo1O9QWw5+QtX40PKXNe5yIP8vMtycau527XX0z/V+W061RsEgL97Ayb0nsGfB2cya+vL2JkcuKtuf4a0HlvmYyjKY/UaMOvkcS7Fl310+c7ICCbuspz3/ex27cZkRueuPGa+gW/m7sHS3v34+OB+nty6CSeTkYfr1ueN1u2s6ryekszfYSEMb9AYUyla8IolIaUanlsZ7vSXcuwbsVOIZ8UdfQ3kj1IkX5pxR18DuzoTxewRe+/oazB24V3iyojRd/Q18Fo4V93VF+K2XJlMURRFUe4UKlAriqIoShWmArWiKIqiVGEqUCuKoihKFaYCtaIoiqJUYSpQK4qiKEoVpgK1oiiKolRhKlAriqIoShWmArWiKIqiVGEqUCuKoihKFaYCtaIoiqJUYSpQK4qiKEoVpgK1oiiKolRhKlAriqIoShWmArWiKIqiVGEqUCuKoihKFWao7AO4U+0bsbOyD6HSdFjYpbIPQVEU5bahArVyy33e41MANk/aXMlHUvmSL35Z2YdQqezqTKTfC/Ur+zAqnTDoK/sQlCpMBepKsi14e2UfgqIoinIbUM+oFUVRFKUKU4FaURRFUaowFagVRVEUpQpTgVpRFEVRqjAVqBVFURSlClOBWlEURVGqMBWoFUVRFKUKU4FaURRFUaowFagVRVEUpQpTgVpRFEVRqjAVqBVFURSlClOBWlEURVGqMBWoFUVRFKUKU4FaURRFUaow9ZrL29TV8KvM/PA7Dv53ECklbbu0Zfx7E/Dy9Sq03IKv5rNwxsJ8txltjGw6d/Md0bFRMXz/6Rx2b91FcmIydRrXZdTkUXTo1qFcz6W0rkZc5ftp33Nw90GQ0LpTa8a9No7qPtULLbdo5iJ+nvVzvtuMJiPrDq/L+XdcTBw/z/6ZPdv2EHUtCvdq7nS4pwNPjXsKV3fX8jwdpZTCI8P45Ku3+G/vNkDSuX033nn5U3y8/YpRNpT/ff8pew78R3RMFN5ePgzoPYSxIydib+eQky8rK4s5C79m6cqFXLtxlTq16vHC6Cnc2/O+ijuxEghLSODdfbvZHhaKBO7x8eWjuzrj5+hYZNmg+Dg+3L+Xf8PDSM/KonU1T97r0JFW1Tyt8kYkJjL10H62hoYQm5qKl70D99epy9vtqsZ3wv9XKlDfhlKSU5g8bBJGk5HXp78OQjDvy5+Y/Ngk5m78CTt7uwLLDnxsoFWgTU5K4bURr9K5d5ectLTUNCYPm0xsVCxj3nged0931v26ljeffoMvF39Jq06tK+z8iiMlOYVXnn4Fk8nEq5++ihCC+d/MZ8rTU5jzx5xCr0H/of1pd3c7q/reHPMmnXp0ykmTUvLO+HcICwpjxIQR1KxTk6CLQSz4dgHnT53n61++RghRYeeoFC05JYknx92PyWjii/dnIoTgf7M/5Ynnh7B26b8WwTavpOREnhr/IBkZ6Ux6/g18vP04duowX/8wjcDgS3z72U85ef/3/af8tHgmk8e+RbPGLVmzaSUTXn+aH79aSo8ufW7FqRYoKSODoRvWYNLp+eaeHghg6qH9PLh+Nf/c/xAORmOBZaNSUrhv7V84Go180bkrdgYD3584xoPr1rDhvvtp4OqWkzc4Pp7Ba1dR09GJT+7qjKedHSEJCVyOi70FZ3lnU4H6NrRm6RoigiNY9M8ifAO0VkPdRnV5svsTrP5lNY88+0iBZT1rVMezhmWLc9Mfm8jMyKTf0H45advXbuPSmUt8teyrnKDcoXsHRt/7DHM+m8Psv76vgDMrvnUr1hEZGsm8NfPwreULQO0GtRk5YCRrl6/loZEPFVjW09sTT2/L1sLmvzaTmZFJ3yF9c9LCgsI4deQUE9+byMBHBgLQskNLhE7wzYffEBoYin9t/wo4O6W4lq38mZCwQDav2EuAfx0AGtVrSq+h7Vn6x0KeeWJcgWUPHt1HYPBFFny7gq4dewDQqV1XYmOjmfvLTJJTkrCzted61DV+WjyTMSNe4tmnJuTkCwq5xBfffVjpgXrx2dMExceza+gj1HZ2AaCJuzudVvzKz2dP83yzFgWWXXDmFNeSk/lzwOCcsnfX8KHDb8v44tBBfuzZOyfvq7t24G3vwB8DBmPUqaemt5K62rehXZt30bh1k5wgDVCjZg2atWvOzs07S1zfxhUbcPN0o0O39jlppw6fwsbWhpYdW+WkCSFo17UdZ46e4VrktTKdQ1nt/mc3jVo0ygnSADX8atC0dVN2/bOrxPVtXrUZNw832nW52dJOT08HwN7R3iKvo5PWnZiVlVWaQ1fK0dZ/19OqWbucIA3g71uLti3uYsv29YWWTU9PA8DRwbJ72NnJhaysLKSUAOzY8zdp6WkM6f+wRb77+z/M2QunCAkLKo9TKbWNwUG09ayeE2gBajk508HLmw3BhR/bwWtXqePsYlHWwWiko7c3m0OCyDB/xgPj4vgnLJTRTZqqIF0J1BW/DQWev0zthrWt0gPqBxB0PrBEdV2LuMqR3UfoPaQPesPNDhadXo/BaLDq2jWaTABcPnu55AdejoIuBFG7fj7XoG4AwReDS1TXtchrHN13lJ6DeqI36G/WVS+A5u2a88v3v3D2xFmSE5M5c+wMi2cvpn3X9tSqW6vM56GUzflLZ2lQt7FVev06Dblw+WyhZbt06EZAzbp8/u2HnL90hsSkBHbt/5cFy37g8QdH5nSbn794BpPJxuJmAKB+3UYARe6nop2NiaaRm5tVekNXN87FRBdaVi9EvoHXpNOTnJlJYHwcAPuuRAJgqzfw8Ia1+C+YS4PFC5iw/R+iUlLK4SyUwqhAfRuKj4nHycV6kIizqxPxsfElqmvTH5vJysqi30P9LNL96/iTGJ9I0HnLO/JTh07mHENlio+Nx9HZ+ho4uTgRH1eyY9uyegtZWVkW3d6g9SB8MvsT/Gr7MeHRCdzX4T5eGPYCNfxr8N6M98p0/Er5iI2LxsXJxSrdxcWN2PiYQsva2Njy649ryZJZ3PtoF1p0q8VT4x6gx919ef/Vz3PyxcTF4OzoYnXT6uLsZt5eeDCsaDGpqbiYbKzSXW1siElNLbRsPRcXLsfFWgTbLCk5fP0qANHm8pFJSQBM/G87dZ1dWNK3P2+3u4stocE8tnEdWebeB6ViqEB9m8pvEFNpflc2/b6R+k3rU7dxXYv03kN64erhytSXP+PSmUvERsWw+LvFHN13VNu/rvIHUeV7DSj5Rdj812bqNa5HnYZ1rLZ99d5XnD56mpfee4npC6fz0nsvce7EOT6c9KHq+q4i8v9dKPpzkJqawotvjuZG1DWmfzCbpXNW8/qLH7Buy0re+/wVi7pKu49bpbTHN7xhE7KAF/79h8C4OK4kJfHWnp0Ex2s3uzq0erPMv1edvWswtfPddPXxZXijxkztdDdHb1znn9CQ8jsZxYoaTHYbcnJxIi6fFm18bDxOLk7Fruf0kdMEXwxm/HsTrLY5ujjxwfcfMnXyZzzT72kAfGr5MHLiSOZNn4dHdY/Sn0A5cHRxzLf3ICE2ASfn4l+DM8fOEHIphLGvj7Xatnf7Xv5Z9w/TfppGm45tAGjRrgU1/Grw+rOvs2fbHjr37Fz6k1DKzNnZlZi4GKv0uLgYXJxcCy27fNVi9h78j79XHqCWn/YYpUObzjg5OvPWp5N4/MFRNG7QDFcXV2LjY6wCdpy5xe7qbN3tfCu5mGyISbXufo5NS8PVxrqlnVuAszOzuvXg9d07uWvFMgBaeFRjTNPmzDpxDC97bXyGu7mebr6WU966m/99POoGvfxrlvlclPypQH0bCqgfQOA562fEQRcCqVU/oNj1bFyxEb1BT+8hvfPd3qJDC37ZsYSwwDCyMjPxq+PPr3OWYWNrQ4Nm9Ut7+OUioG4AgRcCrdKDLgVRs27xvzA2rdqE3qCn58CeVtsum69xw2YNLdIbNdeeTQZfDFaBupLVr9OQ85fOWKVfuHyOerUb5lPiprMXT+Pi7JoTpLO1bKrdlF0IPEfjBs2oX6cRaWmpBIVetnhOff6S9my6qP1UtIZubpyNtu5+PxcTbTG9qiCDAurQv2YAF+NiMen0BDg78+quHfg6OOTMw27o6g5AQf1ougK3KOVBdX3fhjr36cypw6cIDw7PSYsMieDEgRN06V28wJGels4/q//mrh4dcfVwLTCfEAK/2n7UrFeL1OQU1ixdQ58H+2LnYF9gmVuhU49OnD52moiQiJy0yLBITh4+aTEXujDpaelsW7+NDl075Lt4iVs17Uvu7HHLwUKnj50GwMOrYnoVlmxKxKNvGLUfCCcm3rJ7PSNT4tE3jGmL4kpdf1xiFl8sjuPeideoNzSc2g+Ec+/Ea6zdmZxv/qWbEuk57iq17g+nwcMRPPjadXYft3z2efpyOpNnRNNz3FW8B4Th0Tes1MdXEr279ufIiQMEhwbmpIWGB3Pw6F563XNvoWU9PaoTGxdDYMgli/QjJw4C4O1ZA4B7OvXCZDTx14YVFvlWrf+NBnUb4+9buYMK+9WsxcFrVwmMu/mZCI6PZ9+VSPr5F+/Y9DodDVzdCHB2JjIpkVWXLjGiUZOc7W2rV6e6nR1/h4ValPvb3OXdytN6cZSKJIQYKYSQQogYIYRbnm0G87b3y7iPqUKIY+Z9JAkhzggh3hFCFPjlJ4RwFUJEmveffwuoFFSgLoIQ4n0hhHVzqxINHDYIbz9v3h79Fv9t+o+dm3fy1rNvU71GdQY/MTgnX2RoJL3q9GTh19Yrke3eupu4mDiLudN5/TjtB7av3caR3YdZu3QNYwaNwWAw8Oxrz1bIeZVE/4f64+3jzbsvvMuuv3ex6+9dvDfhPTy9PRn08KCcfFfCr9CvRb98VyLbs30P8bHx9BmS/zzYu/vcjUd1D6a9MY3Vy1ZzZO8RVi9bzedvfI6ntyd397q7ws4PIC5R8vXy8h+0F3Y1k3mrE+nc3MT3r7nz01vu1PU1MPyDKOb+lWCRd+HaRCZ8GUPrRiYWvOPOjEmupGdIhr5+nWMX0nLyHTmfxuZ9qfhW19Oqgancj7kgjz7wFL41ajJmypNs3r6OLdvXM2bKk9Tw8mXYgyNunnNECA06VufbH7/ISRs6aBiODo48M/Exfl+zlN0HdvDDz98y9Zt3ada4JW1b3gVANXdPRg17ntkLZvDTL7PYc/A/3pn6MrsP7GDKuLdv2bkW5MkGjfB3dGLE1o2sDwpkQ3AgI7ZsxMfBkeGNbo6ID0mIx2f+j0w/fDAnLT0ri3f27mJdUCA7wsOYe+oEfVetpKGbG2Nzzb826HTa4LGQYF7ZuYNtYaHMP32S13f/R2fvGnSt4XNLzzkXF+C1CqrbGZgPPA4MBn4B3gKWFlJmGpRioEwRVNd30d4DPgH+ruwDyWZnb8f/ln7FzA9n8tmkT5FS0qZLGya8O8GypSslWZlZyHwGPW38fSPOrs506lVw6zPqWjTfffgdMTdicPVwpWu/roycPApnV+eKOK0SsbO344t5XzB72mymvT4NKSWtO7Zm7OtjsXO4uSqZzL4G+Qys2bxqM04uTnTs1jHffTg4OvDNkm/4edbPLJ+3nBvXbuDh6UHH7h0ZPn64xX4qQo+2Nsz9M5HnH3DEy11fdIFiqumt5+BCL+xtb96n92xnS9i1TL75NYHR990cTb90cxLtm5iY/qJrTlrXVjbUfyiCP7cn06KeFpQf7W3PsL7adKZP5sex/9TNIF6R7O0c+GX2n3z8v7eY8t5YpJR0an8P70z+FAf7m+chpSQzM5MsefN3wc+nJivmbeSbHz/nf7M/JTo2ihpevjx6/wjGPz0ZXa5pSy+Pext7ewcWLJvD9RtXqV2rHt9+Nq/IVvut4GA08nv/Qby7dxcT/v0HKaGrjw8f3dXZYlUyKSFTSosR2gK4FBfHHxcvEpeWSg0HB4Y1aMhLLVtj0lt+5h6t3wCdgG+PHWXZ+bO42tgwtG593m7XoTJX6NsEvCCEmCGljCzPiqWUeVfL2WpuTb8uhKgmpbyee6MQogvwJPAC8BPlSFSlkYtVkRBCAp9IKcv11nlb8PY7+sJ3r9lNbDm15Y6+Br2b9BZRm/2srsGSTYm88GUMa6ZX45E3b/D4vfZMG+8KaF3fXv3DefVJJ14bXr43TO/PjWXWigSubri5iEzP8VfxcNHx26fVctIyMyUB90fwzBAH3h9tPTXqk/lx/G9pPDc2+Vpty8u9T6i4dCDqjv4c1GnnLq4+M+aOvgbVf5pTokgvhBiJ1tq9B9gAzJNSvmDeZgDSgQ+klO+X53EKIaYAXwCuUsrYXOlG4DDwK7AD+AfoI6XcUh77LVbXt7n7VwohGgkhNgohEoUQwUKIUebtT5n77xOEEP8IIermKmsUQnwshAgUQqSZ//zYfGLZeQLM9Y8RQnwohIgwPxdYLYTwy3MsRdZnzudgfsZwUQiRan5u8LsQwksI0da8vyH5nOsCIUSoEEJvDtIAb5nzWzz3EEJ0E0JsFULEm6/JRiFEs+JcU0Upipe7nmeGOLBoXSIhVzIKzJeRKYv8ycwsOg7sPp5KfX/LTrZnBjuw/VAqi9cnEpuQRfj1TF6dGYvBAE/eW7njFBQFiAC+A54TQhT4QN783Lqon3y7rczbHM3PnCej3RTkXeD8VcAEfG5VQTkoadf3b8CPwJfAOGCeEKI+0B14HTACXwNLgLvMZRYCjwCfAv8BnYC3gTpoff+5vQHsAp4GqgPT0Z4LdMuVp8j6hBAmYDPQCvgM2IP2LKMf4CalPCiE2A+MAVZlVyyEcDXX/bmUMlMI0QnYDSwA5pizhZrzDjSXXYvW3QHas5IdQogWUko1sVAps5cecWLh2kQ+XxzPty9bj+ANjsyg9fArRdbj76XnyM/eBW5fuDaRA6fT+f41y308ca/Wnf3KtzG89FUMAF7uOn6fWo16fgW/7EFRbqFpaN/l76HFDgtCiACgOEspBgEBeco2A47nSloEPJcnTz20GHSflDK1Ih4DlDRQfyGlXGQ+uANoD9jHALWllHHm9BrA1+a7GydgGJZdEJuEEJnAR0KIqVLKY7nqD5JS5gRvIYQn8IUQwkdKGW6+aMWp70m0AD5ESvlXrvpzD9ucBfwkhKglpcxefms42l3RXAAp5R7zRQ+TUu7Jcy2+BrZLKXNa5UKIf4BLwMvAxMIvpaIUzc1Zx/ihjny+OJ4XH3Gkto/lr6y3h54t3xU94tbGWPCXx39HU3ljVgyP9Lbj4V6WreR1u5J59bsYRgx04N5OtqSkSuasTOSxt2+w6vNqNK6tgrVSuaSUUUKI6cB7QohpwMU8WcKB9tYlreS3jNsFc1kHoDNaY9IAPJErz2xglZRys3Xx8lHSUd85q9xLKaOBq8Ce7CBtlj2p0R/t+QHA4jz1ZP+7W570tXn+nX0nkz0xtrj19QUi8wTpvJYBMUDuIcxjgLVSytB8S5iZexHqAr/k7joBktBa4PcUUO45IcQBIcSB1UtWF7YLRcnx/FBH3Jx0TF1kPQLcZBQ0r2ss8qdhzfzvyQ+dTeOJ927QtZUN30y2bE1LKZk8I4bBXe34bJwr3Vrb0q+jHcs+9sDRTvDpwtJPEVOUcvYVEAV8mHeDlDINOFKMn1P5lE2RUh6QUm6XUn4GvAg8LoToCCCEeAToAnxonprlCmSPYnQQQlgP4iiFkrao886qTysgDcAWcDf/PSJPnuzRee550qPy/Dv7Dsc2T/6i6vMACp3IKaVMEULMB54xP3fuBDQBphRWziz7PZE/kf/ovnzfCiGl/AH4AdRgMqX4HO10THzMiXd+iGXCw5brm5el6/vU5XQefvM6zesYWfCuB0aDZav7anQW12KyaJ1nupXJKGhax8i54IKfmyvKrSSlTBBCfIb2uPSL3NvK0vWdjwPmP+uhPVJtAtgBJ/PJ+ycQC7gWY9+FqujpWdmB1xvL7ojsb4wbFVTfdaA4g7pmow0OGAI8AAQCG4tRLns/bwD5jeor1dyUDb+tZ9qUaTg4O7D0v2UWy4FmZmTQu25vRkwcwchJo0pTPYnxiayYt4L92/YRcimEzKxMAuoF8Njzj3F3v65W+Y/vP8acT+dw/uR5HJwd6HVfb0a/OhobW8tlCXdv3cWSWUsIOh+ElJJa9c119i35POONKzfy5dtf4uDkwM8bf85zDTK5t+W9PDXuKYaPH17yC5B9vP/s5p91/3Du5DnCg8Np3q450xdMzzfvpj838ecvfxIWFIbRZNTe+z32SZq3bZ6T58k+T3IlPP9gOfCRgUx8b2KpjzXb04MdmPV7Ap8usGzFlrbr+2JYBkNfv04tbwNLPvLAzsa6a9zVUYeNUWt155aWLjlxMZ1aNW797M4Vq5fw2ocv4OTozPZVh3Fxds3ZlpGRQcNOXrz47Ku89FzpptbGJ8SxYOkctu/eyuXgC2RmZlKvdkOeG/4CfbsPtMg7b8ls/tqwgpDwIJKSk/Cu7kOfe/ozdtQk3FzztkHKz7LzZ3lpx3acTSb2PzzMYpnQjKwsfBfMZUqrNrzSpl0htRQuMyuLr48dYcm5s1xJSsTX0ZFRjZsypmlzi3x7IyP55dwZjly/xoXYGHwcHDjwSN6hR7fMLLTv84/zpJel6zuv7F7b7PizANiWJ08rtBb+FGBvMeosUkX/pm03//kY2lzkbNn9+/9WUH2bgMeEEIOllAX2MUspLwohNgGvoF3cD6WUeScdp6HdMeV2Fi2oN5VSTi3hORQpMS6RpbOX8NzrY8q13qvhV1j185/0f7g/T704HKET/P3XVt557h1e/PAlHhjxQE7ei6cvMuWJKbTv1p5P531GZEgE33/6PdevXOe9mTffHLVv217eeuYtut7blScnPAXA2qVrePe5d/jkp08LnaddmMT4RH796VdGTx5dtpPOx66/d3HxzEUat2xMWmrB91Rrl69lxgczGPToIJ6Z9AypKamsWLiC10a/xjdLvqFe43oAvP/N+6SlWdazc/NOls9fXuxV0opiYxK88qQTk2bEWKSbjMKqxVuUa9GZDH39OmkZkteHO3M2T8u4RV0jNiaBjUnw1AAH5q5KxM0phn532ZKcJpm7KpHgK5l8MvZmr15SShZb9mnfc+dDtPd4//WvttKZv7e+xMdYlPiEOOYs/JpXXyjft5iFR4axeMU8Hho8jAnPTEGn07F64++MfWU4778yjaceufl5jImNpl+PQTSo2xgHB0dOnT3Ot3O/YM/B//hz0VaLedgVIS4tjW+PHeGd9ncVnbmEXtv9H7+eP8ekVm1o61md/yLC+WDfHhLT05ncqk1Ovh0RYey5EkHLap4IAYnmd7hXBvNArg8x91rmSk/jZku4WIQQLdAGTf+GNu7IBu2R5kvAeinlbnPdgWixIHfZ7L8elVL+V9LzyE+FBmop5UkhxFLgffMz3F1oXczvAEvzDCQrz/oWoz17XmruDtmLNrCtHzBDSpl7ceBZaKO304F5+ez2FDBQCLEBrZs/3DywbTywyjzCfDlaK94LbcBBsJTyfyU5t9za3dOelQtW8tDTD+Feji+/8PavwZIdS7G1s81J69CtA1fDr7Hs+6UWgXrBV/PxrOHJ+7M+wGDUPiYGk5Gpkz9j2PPDaNC8AQAbf99ENe9qvDvzPfTmBRLa3dOOxzo/ypY/N5c6ULft3JZVS1bx4FMP4u5Zvq2TSR9MyvkSnfjkxALzbVq1iSatmvDSuy/lpLW6qxVDOw9l+4btOYE6+8/c5n89H/dq7rTrUvpWTV6P97Pnu9/iuRiWWaZ6zgZnEHJFq2PYO9adWocXeVHTW/s//+R5F+r5GVi8Poklm5KwNQka1TKw4lMPerS7+Tm6HpPFqI8tn1xl//uxPvbMfKV8A3XXjj1YtHwuo4Y9j2c1r3Kr19+3JttWHcTO9uaguns69STiShhzFn1jEagnj33TomzHtndja2vHO5+9zMmzx2jeuFW5HVd+uvv68dPpk4xp2pzq9uU3VS40IYFfzp1lcsvWOUG5m68fCelpfH30MKMaN8HNRvu/n9yqDVNatwVg3Pa/c95bXYnmozW8yvoygito3+lvovXYJqEF7CmYBxvfSrdiCdERaMPnnwbWAc+Y/z2isEJlqU9KmY42oGw22lD6dWgBuRrWz8HXov0nrCpgZZsJQCKwGthvrg8p5Tq0OywHtP+4jWhz6LzRBpSV2lMvaC3Txd/lHTNXNnb2dhZBOlvDFg25fuXmIjsZ6Rns27aP7gN75ARpgB4Du2M0Gdm5eWeuvOnY2tvmBGkAvV6PnYMdWVmlfwz/xPNaJ8mSH5aUuo6CFLelk56ejn2eNc1tbG0wGA2FvuLyavhVju47Ss9BPS2uS3E93teBG5t8qeNreR9t0Av2zffmxibfMi12cndLG25s8i3wJztIZ+/z2SGObP++OsGrfDj3Ww3++tLTIkgD1PQ2FFjfzFfK/+1S459+GYCZ80t9P5wvezsHiyCdrXmTVly9XnQQcnPRbiqNhoofDT+pZWsAvjp6uFzrPXztKllS0tPP8uU2PXz9ScnMZGuuV1rqKmlFMinlAimlkFJeyJOeIaVsYN72fhnqvyKlfFxKWVtKaSel9JBStpdSzpRSFtpFLqXcZt5/uSx2AsUM1FLK9807zsiTHiClfDJPmsVBSinTpZRvSylrSSmN5j/fNgfT7DKB5jJzC6hrW660Iusz50uQUr5i3m6SUtaQUj4kpbya5/R6AvbA9wWc+04pZVsppW3e/3wp5W4p5SAppZt5e4CU8rHsbpHS8qjuzv0jHmDN0jVEhhb85ZCZkVH0T2bRra9je49avHEqPCiMtNQ0aje0fKuQydYGn5o+BJ0PzEkb9PhgwgLDWPztz8RGxRBzI4aFXy8kMjTSooVeUu7V3Llv2H2s+21dgc9/QXtuXeRPMa5Bfu577D4O7TnE+t/XkxCXwPUr1/nuk+/QG/T0H9q/wHKbV29GSknfIX1LtV+laJ7VvHjqkWf4deUiwiIKXrIgIyOjyJ/ifD72H95NnVr5N9IyMjJITkni8PH9fD1nKp3b30Oj+k1LfW7F5WVvzzONm7L47GlCEgpeEz4jK6vIn8xcN556c/A16S3Dg435pvNMPm/qUirWHbvWt3n1tDpoD/0PSSm3VvIhWRg2dhirl/zFwhkLee1L64ExkSERDLt7WJH1ePl5sWznrwVuX71kNacOn+LNGW/lpGW/69rJxdEqv5Or5buw29/Tnk/mfsonL33MT19qA+DtHe358PuPaNGhhVX5knj0mUdZ+9tafp71M1M+th6MHxkWyVN9nyqyHi8fLxZvLnnvxL0Paus4f/PRN/zvXa3l5l7NnWk/TsMvwK/AcltXb6Ve43rUblC7wDxK2Y0Z/hJL/1jINz9+zrR3v7XaHhoeTLchrYusx7eGP//+daTA7Uv/WMjh4wf434fW9/KJSQm06HZzQayuHXvy3dT8nqBVjAktWrLo7Gm+PHyQr7t2t9oeHB9P+98Ke4eExt/RMWcQWF0XVwAOXr1Kc4+bS8ceuKrdMOf37mulYt2xgRrtufaTwFG0hU6qFGdXZx4Z/SgLv17AsLHD8K1l+XYaD69qfL86304AC0ZTwc8Gj+w+zLfvf0OfB/vS54Gbb5CS2eu/59OtlXdp+FOHTvLpxE+4q0dH+j7YB4Rg44oNfDDufT6d9xmtOxf9RVkQZ1dnHhrxED/P/plHn3kUH/8818DTg+9+/a7IekyFXIPC7Pp7F99+/C0DHxlIpx6dSEtJY+Xilbw19i2+mP8FtetbB+JTR08RcjmEcW/kXc9fKW+uLm4888R4vp37OWOGv0jNPO+Vru7pzcqFRfc+2phsCty25+B/fDj9De4f8AhD+j9std3O1p6VC7eQlpbKybPHmDXvK56b/ASLZv6BwVDxX69uNraMbdaCLw8f5IUWrQhwsnwk4m1vz8b7iu7ZMuluPqJp6OZGNx9fPj98gFpOTrTxrM7OiHB+OHkCqLzu7jvZHRuopZQjgZGVfBiFenj0Q6xc+Afz/zeft79+y2Kb0WSkXhPrQUxWCvilOnP0DG+Nfos2ndrw6uevWmzLfjtWfIx1d1pCbDwBDQJy/v3Ne98Q0CCAt7+5+c6SDt068MLQCcz6aCY/ri/buIuhw4eyaskqFn63kDemvWGxzWgyUq9Rca5ByfcrpeSr97+ia9+ujH9jfE56285teXrw0yz8diHvf/O+Vbktq7ZgMBjoMaBHyXeqlNjTjz/PouU/8tWcqXz10RyLbSajiSYNmhdQ8qaClnw8dvIQY15+gk7tujL1nW/yzaPT6WjRRLsZbdeqIw3rNeGJ54ew/u+/GNz3wRKeTemMadqcuadOMO3QAWZ3s3wjr0mvp5l70QNS816Br7t2Z9z2v3lsk7bGlZPRyLvt7+KVXf/hZafWeL/V7thAfTuwc7Dn8XFPMPvjWTw25lGLbWXp+r505hKvPvUK9ZrU44M5H1oMGAPwqeWD0cZI4DnLNQLSUlIJDwmn28DuN+s6e4khT95vtd9GLRuxavEqq/SSsnOw47HRjzHnizk8PMqyRVORXd/R16OJuRFDw2YNLdKNJiN1G9Yl+JL1mjZpaWls27CNDvd0wNXdtUT7U0rHwd6RsSMn8umMd3j2qQkW28rS9X32wilGvvgwjRs0Z9a0BcUeHJY90jso5FKx8pcHB6ORF1u04v19exjfvKXFttJ0fQPUcHBg5YDBRCYlEp2aSoCTM6eitHG4HbwKXjNeqRgqUFdxQ54awm9zl+c8/81W2q7v0MuhTHnyZWrUrMGn8z6zWrxEK2OkQ7cObFu7jZGTRqI3d+FtX7ed9NR0OvfpnJPX3dOds8fOWNVx+sgZPL2KXoijOAYPG8zvi35nwTcLLNIrsuvb0cURo8nI2eNnLdLT09K5eOYi3n7WX1Z7/tlDfGw8fYb0sdqmVJwnHnqan36Zxf9mf2qRXtqu78vBFxk+fij+vrX48X9LsLUt/nvH9x3aBWDVDV/RRjVuypyTx5l6cL9Femm6vi3LO+Bt74CUkh9OHqe+iytdavjkm1epOCpQV3EmGxPDXxrB9Ne/tEg3mow0bNGoRHVFX49mypMvk5GWwchJoyxGbwPUa1ofk40W1EZOHMn4B8bzwbgPGDL8fiJDI5nz6fd0G9CNhs1vtjIfHPEgsz+ZzUcvfJTznHvTHxs5efAEE95/oRRnbM1kMvHk80/y1ftfWaQbTUarFm9xXAm/khOA42Lj0Akd/27U1spp2LwhXj5emEwmBjw0gFVLVuHk4sRd3e4iLTWNVUtWERkWydjXx1rVu/mvzTi7OnPXPeW/AIVSMBuTDS+MfoW3Pp1kkW4ymnK6pYvretQ1RkwYSnpGGhOfe50Lly1v1Jo0bIGNyYb4hDhGvfgw9937EAH+dRFCcPTkQeYtmU3j+s3o12NQmc+rJGz0em1O884dFukmvZ5W1Up+w7zg9Cls9HpqOjlxNTmJ5efPs/dKJCv6D7R4Rn09OZndkdqKzmEJCSRlZLD6stab0MDVjYZu5T81706kAvVtoP/D9/LrnGWEXi70XSFFCjofyJVQbeTmm0+/YbV96X9L8favAWhB+/Ofv+CHz+bwxtOv4+jkSN+hfRn96rMWZR557lHcPN35Y8EffPKStnKfXx1/3v7mHXoN6VWm482t3wP9WD5/OWFBhS7hXixH9h7hy7ctb3w+mvwRAFM+nkK/B/oBMPa1sfgF+LH+9/VsXLkRk42JWvVq8dkPn1ktZBITFcP+//Yz6JFBGE3qjVK32kODH+fHxd8RGJz3xUklc+Hy2ZzpXqMnWT9a2r7qMH4+NTGZbKgb0ICFv/7IlasR6PV6/Hxq8swT4xjx6HOFDlCrKMPqN2TW8WNcisv7quSSy5RZfHvsOKGJCdjpDXSuUYN1g4fQyM1y8aGzMdGM/sey1yL731NateEVt/Jb8OdOJmTeYbzKLXGnv5Sje81uYsupLXf0NejdpLeI2ux3R18D9z6h4tKBqDv6GtRp5y6uPjPmjr4G1X+ao4aSF+JWrEymKIqiKEopqUCtKIqiKFWYCtSKoiiKUoWpQK0oiqIoVZgK1IqiKIpShalArSiKoihVmArUiqIoilKFqUCtKIqiKFWYCtSKoiiKUoWpQK0oiqIoVZgK1IqiKIpShalArSiKoihVmArUiqIoilKFqUCtKIqiKFWYCtSKoiiKUoWpQK0oiqIoVZihsg/gTtW9ZrfKPoRK83fQNgCCo4Ir90CqAL1zq8o+hEq38dvzlX0IlWrswruw637nfh8oRVMtakVRFEWpwlSLupJktyoVRVEUpTCqRa0oiqIoVZgK1IqiKIpShalArSiKoihVmArUiqIoilKFqUCtKIqiKFWYCtSKoiiKUoWpQK0oiqIoVZgK1IqiKIpShalArSiKoihVmArUiqIoilKFqUCtKIqiKFWYCtSKoiiKUoWpQK0oiqIoVZgK1IqiKIpShanXXN6mroZfZdZHMzn030GklLTp0pZx747Hy9er0HILv1rAoq8X5rvNaDKy4dymnH/HRsXyw2ffs3vrbpITk6nTqA4jJ4+ifbcO5XoupRV1JYpl3y7j1IFTSClp0q4Jj734GB5eHkWWvXHlBn/O/ZMzh86QEJuAm6cb7Xq2Y+CTA7GxswEgMjiSv1f+zdlDZ7kWcQ1bO1sCGgfwwOgH8K/nX9GnVyyhV5J5c8Yptu27jpTQrb0Hn01qir+3XZFlA8OTePfb02zbf52MDEmbJi589EJjWjd2LbDMik1hjH73CD6etpxa3ascz0Qpi9AbN3hjyc/8c+IEUkq6N23G1Ceewr9atSLLhly/zse/r2DH6VPcSIjHx82dB+66i5cH34eDjW1OvmaTXyL4+nWr8ktemsSgtu3K9XwUSypQ34ZSklOY8vhkjCYjr375OkLA/OnzeHnYZH7cMBc7+4K/pAc8NtAq0KYkJ/P6iNfo3KdzTlpaahovPz6ZuKhYnnt9DG6e7qxfvo63nnmTz3/+kladWlXU6RVLakoqX0z8AoPRwNNvPo0QgpVzV/LFi1/wwYIPcoJtvmWTU5k+cTqZmZncP/p+PLw8uHz6MqvmreJq6FWe/+B5AE7uP8nZQ2fp3L8ztRrUIikhifVL1vPxmI95Y9YbBDQMuEVnm7+klEzuG78HG5OOWe+2RAj4ZM45Bo/fw87FXXGwK/jXOyo2jf5jduFob2DGa82xs9Uzc+klBo/fw9afutCwtpNVmZj4dN6ccRovj4KvrXLrJaWmMmjqJ9gYjHz/3PMI4KPff2Pg1E/Y/clnFsE2r8TUFO6b9hkZmZm8NfQh/D08OHT5Ep/+8TuXIiNZMOFFi/y9mrfgjQcetEirX8OnIk5LyUUF6tvQ2qVriAiOYMHfi/AN8AWgTuO6DO/+JGuWrObh0Y8UWNazhieeNTwt0jb/sYnMjEz6Du2Xk7Z93XYun7nE9KVf5QTlDt078Gz/0fwwdQ6zVs0u/xMrgX9X/8u18Gt88ssnePlpvQh+df148/E32bZqG/0e61dg2fPHz3Ml9AqTpk+iWYdmADRq04jE+EQ2LttIakoqNrY2dOjVgZ4P9kQIkVO2UZtGvPbwa2z5bQuj3x5dsSdZhIWrggkMT+LAr92p4+8AQNN6zrR9eBvzVwYz4fE6BZb96fcgrkalsXZWp5yy97TzoNWD//DZ3PMs+KSNVZn3vjtNs/pOeHnYsn2/dctKqRwLtv1D4NWrHPz8S+p6eQPQtGZNWr/yMvP//psJ/QcUWHbPuXNcvBLJyldeo1fzFgDc06Qp0QmJfLN+LUmpqdjb3Lwx83ByokO9+hV7QooV9Yz6NrR7yy4at26cE6QBavjXoFnbZuzatLPE9W36fSNu1dxof8/Nlvbpw6ewsbWhZceWOWlCCNp1bcfZo2e4FnmtbCdRRkf+O0LdJnVzgjSAp48n9ZrV48h/Rwotm5meCYCdg2XPg72jPTJLgtT+7eTqZBGks/N4+XsRfS267CdRRut3XKF9U7ecQAsQ4GPPXS3cWLfjSqFlD5yMoa6fvUVZBzsDnVq5s/G/K2RkZFnk33M0iuUbwvhySrPyPQmlzNYfPkT7evVygjRAgGd1OtZvwNpDBwstm5aRAYCTneXvgou9PVlSIrN/GZRKpQL1bSjwXCABDWpbpQc0CCDoQlCJ6roWcY0ju4/Q6/7e6A36nHSdTofBaLAKVEaTUTuGs5dLceTlJzwwHN86vlbpPrV9CA8ML7Rsk3ZN8PLzYsXsFYRfDiclKYXTB0+z5bctdBvSrdBu84S4BMIuh+ETUPndfWcuJdC4rnUXdePajpy9nFBoWZ1OYDRa//rbGHUkp2ZxOSwpJy09I4uJU4/zwpN1LQK7UjWcDguliZ/1mIlGvn6cCQ8rtGyPps2o6+XNe78u40xYKAkpKWw/dZLZmzbydI9eVt3mGw4fwmv0KKo9PYKeH7zLmoMHyvVclPypru/bUHxsPE4u1l/QTq7OxMfGl6iuzX9sIisry6LbG8C/rj+J8YkEXQiiVr1aOemnDp3SjiGmZPspb4lxidg72VulOzg7kJSQlE+Jm4w2Rl6f9Tqz3p7FO8PfyUnvOqgrT0x6otCyS2YsAQm9H+5dugMvR9Fxabg6Ga3S3ZxNxMSnF1q2fk0Htu27RlRsGu4uJgCysiQHT8WY675Zfsaii6SmZzF5eN3yO3il3EQnJOBqb30D5eboQExiYqFlbU0mNr3zLk9+8zUd3ngtJ31Et+5MHz7CIu+9rdrQpk4dAjw9uRobyw9bNvP411/xw5ixPNbl7vI5GSVfKlDfpvK2dAGkLHk31eY/NlGvaX3qNrb8Eu51X28WzVjItJenMmXaK3hU92DN0tUc23dU27/Oev+3miCfYyjGJUhPTef7974nLjqO0W+Pxt3LncunL7N6wWr0ej1PTXkq33Jrf17L3s17Gfn6SIsu98qUz8egWJ+Dpx+sxZzfAnn+gyNMm9wUO1s90xdcICgiGYDs/95LIYlMX3iBxVPbYmujL6RGpTLl9zkozu9CSloaI2d+y7W4OH4YMxZ/Dw8OXrrEtD//wKDX89XIp3PyfpkncA9u156eH7zHB7/9qgJ1BVNd37chRxcn4mLirNITCmhpF+TMkdMEXwym79C++ezDkfdmf0BcVCzP3vsMD7a5nw3L1zNi4kgAPKoXPQWqIjk4OZAYb91aSIxPxN7RuqWd2461Ozh7+CwTv5hIp36daNiqIfcOu5dHxj/CtlXbCLkQYlVm25/b+OOHP3jg2QfoOrBruZ1HWbg6GS1avtli4tPzbWnnFuBrzw/vt+LImThaP7SNRoO2su94NOMe0x6peFXTujxf+99J7mnrQbtmbsTEpxMTn056RhYSSUx8OskpmeV/YkqJuDo4EJ1Pyzk6MRFXh8IfVSz6dxs7Tp9mxcuv8FiXu+nSqDEvDhjIJ8Oe4Ke/t3I8uOBHaXqdjgc6dCAsKorImMofs/H/mWpR34YC6gcQdD7QKj3ovGU3dVE2/r4RvUFPryH5d+O26NCCn//9hbDAMLIys/Cr48evc37FxtaG+s0alPbwy4VPbR/CLls/f4sIjCjy+XHopVDsneyp7lvdIr1OE22UdHhguMU86V0bdrH4f4vp+1hfBg0fVA5HXz4a1XHizCXrRxBnLifQsLZjkeWH9KzBoG7eXAhOwGTUUdvPgcnTjuPnZZszD/vM5QRCIpMJ6LPJqnxAn008/2gAUyc1LfvJFNPuC2v4edfH2Bkd+ejBP7C3cc7ZlpmVwQuL72ZAi2cY1OrZUu9j0c4P2XNxnVV6j8aP8nD7SRZpey6u5Z/Ty7kWH4JBZ8LPvT4DWjxDPa9Wpd5/STX29eN0aKhV+tnwMBr5WI/jyO1USAiuDg7U8bLsIWpbt665jnCa1yz4OyW78ybf3q0KJIQYCcwHYoHaUsroXNsMQDrwgZTy/TLsYzAwDGgH1AP+lVJ2LyDv3cDnQGvzMS0B3pJSJpd2/7mpFvVtqHPvzpw6fIrw4JuDpiJDIjlx8ASd+nQpVh3paen8s/of7up+F64ergXmE0LgV9uPmvVqkpqcyrpla+j9QB+rEdO3Wqsurbh06hLXwm+OPr8ecZ0Lxy/Q6u5WhZZ1cXchKT6JK6GWI6MvnboEgJunW07aoX8PMX/qfLoO6sqj4x8tvxMoB/27erH/ZAyBuQZ+BYUnsfdYNP3vLl7XvF4vaFjbidp+DkRcS+GPrRE8/eDNL+Z5H7dm9cyOFj+9Onri4Wpi9cyOPPdQQHmfVrEkpyew6cTPFVa/o60br/Sfa/HTs/FjFnn+O/cni3Z+REC1Jjzb7TOe6PQmGVkZfLP5RUJunK2wY8urf+s27L94gctXr+akBV27xp7z5xjQxnqaXW7VXVyJSUzk4pVIi/QDFy8C4OPmll8xADIyM/lz3178PTzwcnUt/QmUjQvwWpG5Sud+oBWwB7C+EzITQrQANgNXgUHA28AoYEF5HYhqUWNxd5YtCbgGHAaWAiuklFl5ylQDXgYGA7UBAVwE1gJfSykjKup4BwwbyJ+LVvLus28z6mVtsY/50+dRvUZ1Bj8+OCffldBInuz2BE+9OJzhL1k+X9rz927iY+Lo+1DB843nTvuR+s0b4OLmQlhQGMt/+BWDwcDo10rfWikv9wy+h7//+Jtv3/iWB0Y/kLPgiVt1N7rd1y0n3/XI67zx2BsMHjGY+0bdB0CX/l3Y9Osmvn7lawYOH4iHlweBZwJZvXA1tRrWol7zegCcPXKWOR/Mwa+OH136d+HiyYs59RqMBmo1KH7vRUUYMcSfH1cE8virB3hrTAMEgk9+OIuvly2jHqiZky84IonWD23j1afr89oz2hzY9Iws3v3uDF1au+PkYODMpXi+WnSRxrUdLeZft29m/UW9ZG0oNkYdXdtW3uOPxjXuYtvZ3+jR5DFc7Mr/OAw6A7U9C5+KtvviWmp7NmdYx5txomGNtrzyaz8OBm3F36NhuR9Xfkb26MEPWzYzbMZ03hn6MEIIPv59BX7u7ozqcXP1uODr12g5ZTKv3f8Ar9+vLVryRNd7mLlhHQ99+QVT7huCv4cHhy9f5vNVK2kdUJuO9bWes99272LtoYP0bdkKP3d3rsbF8eOWzRwOvMy8cRNuyXkWYBPwghBihpQyssjcJfNs9ve+EOK/QvJ9gBbIH5ZSppvzpwELhRDTpJSHynogKlBbehjtgtsANYGBaIH6OSHE4OxuDCFEE7QPiAC+AbLnKLQGxgANgQcq6iDt7O34csn/mP3RTKZO/gwpJa07t2H8uxMsWrpSQlZmljY3OI9NKzbi5OpMx56dCtxP9PUoZn34HTE3YnD1cOXufl0ZMWkkzq7OBZa5VWzsbJjy9RSWfbuMuR/PRUpJ47aNGfbiMGztc00pyb4GuQZYVatRjbe+f4tV81excu5KEmIScK/uzj333cOg4YPQ6bSOpjOHzpCRlkHw+WA+G/eZxf49vD34/LfPb8m5FsTBzsBf33XkzRmneP79o0gk97SrxmcTm+Bof/NXW0rIzJRk5focCLSBYis2hhGbkIFPdVueHOTP5JH1MOUzbauq6d9iFN9tnciGY/N59K4plXIMmVnpONq4WqSZ9LbodQby3NdXKAcbW9a8/iZvLFnMc3NmI4H/a++u46M4+geOf+Y07k4gwd3dihQvFChSSqlAC1X6UPenXkopLbQPdaQCRYtLobi7uwYJCXHPJXe3vz8udsnFLyH8mPfrlRdkd2Z29+5y353ZkW6NGvPFo4/h4pD7t6AoYDKbrT4HIb6+bPrgIyYv+5tPly4mJimJal7ePNmjJ689OCTnbyHE15foxETeXzCfuJQUnHQ6WtWsxd+vvUmvZs0q7Vpt+BRYD7wLTLRnwfkrZ7YIIbRAP+Dr7CCdZRHwCzAYKHegFmXpKfz/TZ4adV1FUS7m2zcMWAzMVBRlYtbzjxOAFuikKMrtfOk1QH9FUVYVdczNYVvv6Re+Z0h3MXvn7Hv6NRjXZZxI2Dfwnn4N3NuvFj88sa/Er0H2M+oPhyxm14UVbDmzkA+GLMLbJdDmM2qT2VhsmQKBSpXbo/33XR9z4MoGHLTOpGUk4+MSRKe6g+jV6FGrdLsvrGL+3i94pMObtAzpQYYxjXXH53DwykbeeGA2/m41bB2ugOd+ay+S/ph/T38OXB8bXaqH3Hm/s4HxwCSgnqIoYbaeUWdtK46iKIrN3pFZNWpj/mfUQogGwBlgtKIof+Xbdxo4pSjKiJJfmW2yRl0MRVGWCiFWAOOFEG9ieQbRAEszx20b6Y1AkUFakqTy69PkMXaeX87aY7N4rPN7BfbHJIfz/t8P2chpzcs5gE+HLc/5PdizHtW9GhDkUYtMUwbHrm1lxeEfuJ14nTGd3s1J16mu5THTgn1TmbfncwDcHL2Z2PvbEgdpyS6mYGnJ/AAYl3+nECIUKMkMTWFAaCmP7ZX1r61u77F59peLDNQlsxZLx4I2QC/AlLVNkqQ7xFnvzv2NR7P22Cz6NHkMH1frHs7ujr68OWBOIblzadTWQ9l6NrLuNNYkuBN6rRObzyygT5PH8MsKwseubWfh/q/oUm8Izap3JdNkYMuZhXy/6RUm9ZlJkKecIKYyKIoSK4SYBnwghJiCpa9QXuFA2xIUZSjD4bNbAmy1iNitK3zVfxhVNVzL+jcQqA5EKYpS9PRXNgghJgghDgohDq6eLyvdklRePRuOwknvxqqjPxfYp1FrCfaqW+xPoHvB6Xjza1OzNwBhMWcBy6Qy8/dOpmVID0a2e4UGgW1pGtyF53t+jV7rZPN8pAr1DZYa7Mf5dyiKkgEcLcHP6TIcNzbrX1s1Z888+8tF1qhLpqi7phJTFOVn4GeQz6glyR4ctE70bfI4fx/8lt6Nx1jtK2vTty3ZfXmyxwsnpseSlB5HiHdDq3QatZZgz7pEJFwt+UVI5aYoSrIQYjIwDZiad18FN31fwlITt5pMQAjhANTC0r+p3GSgLpns2S9uAdeB3kIIp7LUqktr/eL1TH19Cs6uzszb+ZfVzGMmo4k+dXrx+H+e4ImXnyxT+RHXI3i06yOF7n/32/fp+WDP3GOaTCyfu4y1C9dw8+pNHJwcqdu4Dm99806FzVa2c+1O5kyeg6OLI1MWWV6LnPMxmpjQYwIPjn2QweMGl+s4GYYM1s1bx96Ne4mNjMXJxYnQBqG88NkLaLQazCYzGxZt4MTeE4RfCceQbsA/2J+eD/Wk84DOOT1kK8q81dd54dPjuLloOP53TzzccptsjUYzPl3W8eZTdXl7fNknozGZFH5cdIU/V10nLDwNV2cNbZp48PbT9WhSN7e3/8y/LrNkQzhXb6aSmm4iyM+BAV39efXJOjlzh1eW++oPY9Ppv1h19Cer7WVt+rblwJUNCAQhPpbA7KRzRaPScTXauhJmNGVyI+48Pi5FTzRSHvN2bOO5X37G3cmJ49Om45ln9jGjyYTX2Md5a8hDvPPQsDIf44OFC9hw/Cg3YmLIMBoJ9vZmZMfOTOw/wGrZy/+tX8fiPbu5evs2qRkGgjy9eKBVK14dNBhv15LPkmgn3wOvYOkJnleFNX0ripIhhFgPjBRCfJjVRwlgOJbRQytLW6YtMlCXzANAOnAIS/P3eKA/sLSyTiAlKYUFP/zF+Lcm2LVcLz8vvvt7ZoHtc6bN4sSBE7S5r43V9i9e/pwD2w8y+oVHqd+0HilJKRzbd4wMQ4Zdz8uWtOQ01s1bx/Bnh9u9bKPRyPTXphN1K4oHxjxAYGggSfFJnD54GrPZMkojw5DB6t9X06lvJ3oN74XeUc+JvSf47cvfuBV2i5EvFL4OuD0lJhuZ/sclPnyhgd3L/uznc0z/4xKvPF6Hrm28iY3PYOqciwx6YS87/+xKNT/L8L+4xEwGdQ+gYS1XXJw0HD+fwJezL7DjUAxb53ZBVYlzwWvVOgY0G8f8vV9YbdeotTmBtaRikm/x286PaB3aC1+36hhNGRy7to29l9bQpd4QfF2Dc47Zue6DbDu3BGe9G02Cu5BpNLDt3GJikm8xvM3LxRyp/BJSU5m+ehUfPTyq+MSllJiWxqNdu1E3MBC9RsO+Cxf4atUKjly5zIKXX81JF5eczINt2tAouDouDg4cCwvjy+V/s+PMGbZ99EmF37zmpSiKQQjxMVmtlnm2Z5A7hLbEhBAh5AZ4b8AshMj+8jmgKEr2/KofAnuARUKImVhq5VOxzL9R9DqjJSQDdTGEEA8BD2KZxCRVCPE3cA6YIoTYrihKVL70GqCvoihr7Hkebbq2Yflvyxg2bjhefnbpSAiATq+jUatGVtvS09I5e+wsHXt1shozvXnlZrau2crM5d9Tr2nuZA6dSjgbWnk1btuYTUs30XtEb9y93e1a9oYFGwg7H8Ynv3+Cl3/u69ume+6Nik6v44uFX+Diljs9Z6M2jUhJSmHT35sY8vQQdPqKr032bO/Dz4uv8tyoUPy9HYrPUArz19zgoV5BvPds7vvbuI4b7UZtY8Ou24wdapnk5b1nrCfz6NraGycHNS9POcnx84m0aGDf96c4HesM5N9T87idVHCe9tJw0DrhpHdj46k/SUyLRQhBgHsII9q9wn31rWuow9tOwt89hN0XVrLn4hq0ah2BHjV5sdcMGgW1L9d5lETPJk35aeMGnu/bz+4zg33z5Fir37s3bkJahoGvV68iJikpp7b8/nDrkUddGzbCSadj0tzZHAsLo2XN4p//29kc4HUsw7bKqwfWE2FBblN2zsxjiqIcFUL0xdL7fA2WKUR/B96xwzkAMlDn1yJrxjEdlglPBmKZBGUj8DZYhl9lBe+NwFEhxAxy79aaAxOAs1jeMLt5dOJjvP3Em8yb+ScTP3rJnkUXsGP9DlKTUwssfbnyzxU0b9/cKkhXpoFPDOSb175h9e+ri12OsrS2LNtCmx5trIJ0fiq1yipIZ6vZoCa71u6yTJxSRH57eW1sXYZP2s9Xcy4y9bWiZ88qrYxMM67O1l8L7q6W383FTP+Q3eStUVdcbbpjnYF0rFNwvnW1SsOHQ8v/ONBZ786zPUo2kY1apaF7gxF0b1DuYbJl8sbgITw09UumrlxRYGWriuDlYgnOGnXRq6h5uVj+RrTFpCsPRVHmYmOKzqymZ7ssRFDYMQpJux0ofPaocpK9vq0txtKE8Q/wGZZnDKOAfoqipGcnUhTlNJag/AfwJLAaS2AeC/yNZUyfXXn7eTP48SGs+Ws1kTcKnynPZDQV/2MqesWjDUv/wcPHk3bd2uVsM2YaOXv0DKH1Qvlp8o8MbTmYPnV68cLg5ziyu9wT75SIu7c7PR/qyfZV24mOiC40XUleA7MpN+rERMYQezsW3yBf5k6Zywt9X+CZ+59h6n+mcu3CtUKPk+3c0XM4uTjZvZZfmABvPeOHh/Dbiutcu1V4Nwmj0Vzsj8lk3afxqWEhLFp/kzXbI0hMyeTqzVRe/fIk1fwceKhXoM1jpKabOHAyjsm/nKdbG2+rZ9lSxfH38GBC797M3bqZa9FRhaYzmkzF/pgKuQszmkwkp6ez5eRJ/rd+LY/d1w13p4Kr0xlNJlINBvZfvMDny5bSvVFjmtSQY8ntRdaoKd2dU5480cBbWT+VYtRzj7B6/ip+n/Ebr08tOA99cR3DsvlX82f+rgU290VFRHF09xGGjn0ItSb3jjgxLpHMjEz+WfIPgdUDeXXya2j1Whb+tJC3nniTb5f+j/rNKr6m3f/R/mxbuY2Vc1Yy7u0CcxsQfSuaN0cWP0d/3ilA46PjAVg3bx2hDUJ55sNnMGYaWTFrBV++9CUfzf0Ib3/bHeVO7jvJwS0HGfL0EKvXq6JNeqw2c5ZfY8qsC8x8r3mB/WHhqTR/aEux5VQPcOTE8tzOgu9OqI9eq+Kxtw7l1KDr1HBm9fcd8MzXSSw51Uhwz39yfr+/gy9zPyt6EQjJviY9MIg5WzbzxbJlfD++YP+VsKgomr46qdhyavj4cPLrGVbbTt+4Tod3cr/eHunclW/HPV0gb3J6OkETnsr5/f6mzfj9xYpt9bvXyEB9F3HzcGPE+JH8PuM3Rj37CEEh1j1Lvf29+X7lj8WWo9UV3sv13783Yjab6Zuv2ducNe2tMdPI53O/wMffB4Bm7Zoz5r7RLPppAe/P/KC0l1RqLm4u9H24LyvnrmTAowPwDfK12u/h48H7v7xfbDkabZ65sLPmPtY56HhpykvoHSy9WkPrh/L2I2+z+e/NjHiuYPNm+JVwfvroJ+q3rE//0f3Lc1ml5umu48XRtZgy6wKTHqtNzWrWtZxAXwe2zCm+70D+eb1nLQ3jq7kXeW1sXbq28iY2IYNvfr/E0P/sZ92PHQn0zX0m7uSgZsuczqQbzBw/n8C0uZcY9doBVnzXHo1GNtZVBi8XF17sN4Avlv/NywMHUtPPetW0QE9Ptn74SbHl6LUFQ0EtP3+2fvgJqRkG9l04z9erVmI0m5j13AtW6Zx0OrZ++AmGzEyOhV3lq1UrePibaax88+1im8mlkpGB+i4z/KkRLP9tGXO/nsM7M6ynTdTqtNRpVKf4Qop4hLjh7w3UaVSH2vnKcXV3RQhBSN2QnCAN4OjsSKOWjbhw+mL+oipM75G92bR0E8tnLWf8+9YreWm0Gqu1pAsjRO6L4OJueaZWp2mdnCAN4OXvRUCNAJvN31HhUUx7ZRq+gb68+PmLlVqbzvb8qJr8vPgqn/98nl8+amG1T6dV0bQETdB5X4e4hAzemXGaiY/W4p08Q7zua+1Ns6Fb+HbeZSZPyu14qFIJWjb0AKBjCy8a1XZj0At7WbElgmG9i14TXLKfF/r156eNG/hs6RJ+zRdEdRoNzUKKX+Ut7+cgm4NOR6talpXUujRoSICHB8/98jPP9O5Duzq5fbVUKlVOuo7169O4enUemPwZyw/sZ3iHCntse0+Rgfou4+jsyCPPjebHz35g5DPWwzLK2/R99thZrl0M4/n3XyiwT++gJ7BGoM0/aAVQ2dheURycHBgwZgCLZi6i3yP9rPaVpenbJ8gHnV6XM5mFFQVUwrp2GHs7lq8mfYWjsyMvf/XyHVub28VJw8uP1+a9b8/w0phaVvvK0vR98XoKhgwzrbKCbzZPdx2hwU6cv5pcZFktG1qe0V++nlKKq5DKy8XBgVcGPci7f83jpQHWHe3K0/SdX8uals/Y5chIq0BdeDp7rzp575KB+i704GODWTJrMXO+mmW1vbxN3xuW/oNao6bn4Ptt7u/StyvL5v5N1K0ofAMtTc6pyamcPnyKtnk6nlWGHkN7sGHRBpb9ssxqe1mavjUaDU07NuXCsQsY0gzoHS216pjIGCKuR9Cia4uctElxSUx7eRoAr3z9Cq6elT6pg5Wnh4Xw/V9X+PSn81bby9L07edlue7Dp+MZcF9uE2pcQgZXbqTQvH7RNfRdR2IAqBlcsLORVLHG39+LmevX8slS657v5Wn6zm/n2TMABZrXy5pOKjkZqO9COr2Ox156nK/fnma1XavTlrlDlzHTyJZVm2nXvT2ePp4204wYP5KNf2/g7bFv8dhLj6PValj0yyIMaQYeea74mrw9aXVaBj0xiN+n/m61XaPVENogtNTlDRk3hE+f+ZQZb8ygz6g+ZGZksnLOSpxcnOj5kKXGmWHI4OtXvyY6Ipqxb40lLiqOuKjcRXOCQoMqvXat16l5Y1xd/vPFCavtOq0qp1m6pEKCnOjb2Y9v511GqKBLS8sz6hl/XsaQYeaphyxNqAnJmQyftJ8RfatRu7ozQsChU/HMXHCFJnXdGNQ9wF6XJ5WQXqvlzcFDeWmO9c27TqPJaZYuqZPXrvHugnkMbdueUD8/DEYju8+e5YcN6+ndrDnt61pq0wmpqTw0dQojO3Wmtn+A5XNw+RL/W7+OpjVq8GCbkkwGJpWEDNR3qX4j+rPw54XcvHLDLuXt3bSHxLhE+gzrU2gaL18vvlk0gx8/+4Gpr09BMSs0atWIrxdOJ7RepU9sQJcBXfjnr3+IvBFZ7rKCagbx2vTXWPLjEn764CfUGjX1W9bnxc9fxN3L0qSbGJuY87z6l49/KVDG69++ToOW9p8trDiPDgzm23mXuWSHJuc5n7Xif/Mvs3RDODPnX8HVWUPz+m5M+6lTTuB30KmoF+rCT4uucisqHY1aUD3QkRcfqckzI2ui18kORHfCmPu6MWPtGi6Vs8nZz90dbxdXpq1aSWRCPI46PaF+fnz6yGie6NYjJ52DVkv9oGr8uOEfbsXFoVGrqOHjy8T+A3i2d1/02uKnZpVKRmRPNi9Vrnt9UY6eId3F7J2z7+nXYFyXcSJh38B7+jVwb79a/PDEvnv6NXjut/Yi6Y/59/Rr4PrY6Mrr5HIXkmMoJEmSJKkKk4FakiRJkqowGaglSZIkqQqTgVqSJEmSqjAZqCVJkiSpCpOBWpIkSZKqMBmoJUmSJKkKk4FakiRJkqowGaglSZIkqQqTgVqSJEmSqjAZqCVJkiSpCpOBWpIkSZKqMBmoJUmSJKkKk4FakiRJkqowGaglSZIkqQqTgVqSJEmSqjDNnT6Be1XPkO53+hTumM1hW+/0KUiSJN01hKIod/oc7lX37As/bu0EZg/4WQS/HnzPvgYAN6beECnnPrynXwPn+h+KO30OVcGtgUPv6c9B4Opl8nNQBFmjvkPGrZ1wp09BkiRJugvIZ9SSJEmSVIXJQC1JkiRJVZgM1JIkSZJUhclALUmSJElVmAzUkiRJklSFyUAtSZIkSVWYDNSSJEmSVIXJQC1JkiRJVZgM1JIkSZJUhclALUmSJElVmAzUkiRJklSFyUAtSZIkSVWYDNSSJEmSVIXJQC1JkiRJVZhc5vIulRyVzP5f9hJ+5AYoENiiGu0ndMTFz6XYvEkRiRyYvZ9bR29iNprxqedL26fa41PXNydNZmoGO2dsJ+ZSDGmxqag0KtyqudNoUGNq96xbkZdWYsZEIwlbEjCEGQDQh+hx7+GOxq34j7Ux3kjCtgQM1wxgAm2gFvdu7ugCdDlpUk6mEL8+vtAyAp4LQO2sLvd1lMeNWwm8Ofkftuy+jKIodO9Uiy/f7kf1IPdi814PT+CTGVvYvv8KMXGpVPN3Y2j/xrw2oQvOTpbX4c+/j/LsOysKLePSjlfx9y3+MydJUtnJQH0XMqYbWf/OGtRaNV1f6Q7A4T8Osv7t1QyeOQytg7bQvOmJ6ax9YxVaRy0dX+yCRq/h1LITrHt7DYO+HoxHDU8ATEYzKrWKZiOa4+LviinTxJUdl9k+bSvpCek0Htq0Mi61UOZMM9GLohFqgWd/TxCQuDOR6IXR+D3hh0pXeGORKc1E1IIoVDoVHr09EFpB8sFkohdG4zvGF6235fVzqOWA72hfq7wKCrHLYlG7q+94kE5Ny+SBJ39Hp1Pz0xdDEAI+nr6FAU/8xt4Vz+YEW1tSUjMYNPZ3Mo1m3n+pB8GB7hw+Gc5n323lUlgsv38zHIC+3euyecFTVnkVRWHEc39Rs7qnDNKSVAlkoL4LnfvnLMkRSTz00wjcsmpOXjW9WDp+EefWnaHJ0GaF5j279jRpcWn0/2JgTt7A5kEseWohR+YdosfbvQBwcHOg2xs9rfJWb1uDxJsJnN947o4H6tTjqZgSTPiP80fjafkYa320RM6KJOV4Cq5tXAvNm3I0BXOKGd+HfXPy6qvrifw1kqRdSXg96AWA2kmN2sk6GBtuGDCnmXHtVHj5lWXO4kNcuR7HkXUvUjvEcs5N6vvTvO93zF54iIljOxaad+/h61wMi2XFr2O4v0ttALp1qElcQhozZu8mNS0TJ0ctvl7O+Ho5W+XddTCM2Pg03p3YvcKuTZKkXPIZ9V3o+r4wfOv75QRaANcAN/wa+XNtb1iReaPO3sYtyN0qr9ZBi3/jAK4fuIbZZC4yv95Vj0pz5z826ZfS0QXqcgItgMZDg66ajvSL6UXmzbiVgcZTY5VXpVNZ8l5ORzErheZNPZUKanBs4Fj+iyintZvP0655cE6QBggN9qRDyxqs3nSuyLwZmSYAXF30VtvdXR0wmxUUpfDXYP7yY+i0aoYPaFKOs5ckqaTu/DeuVGrxYXF4hHgW2O5Zw5P4a/FF5hUqFSptwbddrVVhMphIupVotV1RFMwmM+mJ6Zxbd4abh2/QePCd/4LOjM5E41OwQUjrrcUYYywyrxACbLRaC41AMSoY423nVzIV0s6l4VDLAbXjnW32Bjhz8TYN6/oW2N6wri9nL0UVmbdHp1rUCfHi/a/+5czFKJJTMti69wrf/7GPp0a1KbTZPC09k2XrT9Ovez28PZ3sch2SJBVNNn3fhQzJBvT5akIAOlc9GcmGIvO6B7sTfvQG6YnpOLg5AKCYFaLOW77YDUnW+c+sPs2+H3cDoNKoaD+hE3Xur2ePyygXc7oZlUPBGw6VowpzetGtAhovDYYwA6Y0U07AVRSFjFsZOWXbknYxDSVDwalx1QhQcQlpeLoXrNl7ujsSn5hWZF4HvYYN88fx6EuLaDvw+5ztT45oydfvDyg036p/z5KYbODRoc3LfuKSJJWKDNR3K2FjW+GtlTka9G/ImZWn2DFtK+2f7YRGr+HYwiMkRyRZihXWBde6rxZ+DfxIT0jn+r4w9v20G6EWNOjf0A4XYX9FNdlmc27uTPLhZOLWxuFxvwdCI0jam4QpwdIcnP81yJZ6KhWVowqHWg52PefysPkxKMFrkG4w8sTLS4iOSeHXL4cSHOjOoeM3+eL7bajVKmZ8ONBmvvnLj+Hj5UTf+6pGz39JuhfIQH0X0rnoC9R8ATKSDehs1LTzcg10477Xe7D3+10sfXohAN61fWg8pCkn/z6Oo5d1bdHB3RGHrFpbcJvqGA1GDszaR73e9e/os2qVg+2as5Ku2Kxp56Xx0OD1gBfx/8YT+WskAFp/LS6tXUg+mIzKuWB+U7IJQ5gB51bOCJXtQF7ZPNwciU0oWHOOT0zHw63oZ+i/LTnMjv1XOb5hIrVqWJ5xd2kbgpurnon/Xc3To9rQtEGAVZ6I20ls2XOZZ8e0Q1MF+ilI0r1CBuq7kEcND+KvxRXYHn89Ho8aHsXmD+1ckxodQki8mYBKq8Yt0I3dM3fi7Otc7Dhs77q+XNx0gbT4VJx97tzQHI23xuaz6MyYTDTexX+sHes54lDHAWOcEaEWaDw0xG+MR+2qtjkOO/V0KihUmWZvgIZ1fDl7seCz6LMXo2hQu+Cz67xOnb+Np7tDTpDO1qZZNUsZl6ILBOoFq45jMik8OqRF+U5ckqRSkbfFd6Ea7UOIOnvbquNXUmQSkacjqNE+pERlqNQqPGp44hboRmpMCle2X6bBgEbF5os8cQuNozanln2nONZxJCM8w6rjlzHBSEZ4Bg61S9Y0LVQCrbcWjYcGU7KJ1HOpOLdwtpk29XQqGl8NOr/CxyZXtgd61mf/sRtcuZ570xZ2I569R67zQM+i+xH4+7gQl5DOpbBYq+0Hjt0EIMi/4PCz+cuP06S+P80aBhTYdycJIZ4UQihCiHghhGe+fZqsfR/a8Xi1hBCpWeXWsVe5tiy8HkbQmuU0+Gc18ZkZVvuMZjNBa5bz1fkz5TrGhshbPH/kIF22bqTamuUM27OjQBqTovDDpQuM2LuTZhvXUXf9avrs2ML8a1cx53vUsvTGdR7cvZ0mG9cSum4l7Tb/w6vHj3AzLbVc55lXZbznQogvhBDHs46RKoQ4K4R4XwhR6N26EMJDCBGRdfxe5Tl+XjJQZynNGy+E6G7rjcj6A76a9VOros61Xr8GuPi78u8nGwjbc5Vre8PY9MkGnH1cqJ/n2XHy7STmDvqVo/MP52wzG83s+3kPYXuuEn7sJqdXnmTlpOV4hnhYjY0+u+4MO77ZxqUtF4k4cYuru66wdcomru66QvOHW6DW3tlez05NnVC7q4lZHkPaxTTSLqYRszwGtasa5+a5wdaYYOTmtJsk7s69qVFMCvFb4km7kIbhmoHkw8nc/uM2Wm8tLm0KthJkRGZgjDZWqdo0wJMjWhFSzYOHn1/A6k1nWbP5HA+/sIDgADfGPdwmJ921m/G4N/6YyTO35WwbM7QFrs46hk2Yx7xlR9m29wrTZ+3i3S830LJxIB1b1bA61tFTtzh94TaPDqnSncjcgTcr4TjfAwmVcJwciUYjMy9dqJCy10fc4lRiAq08vAh0sH0Dnm4yMePiOeq7uvFl0xbMbtOezt4+vH7iKJ+ePWWVNi4zgy7evnzVtCV/tevES3XqszXqNg/u3kGyMdPep1+R77kbMAcYDQwC5gHvAn8VkWcKJeotVDqy6bug7Df+rdJkEkLUBzYB6UBPRVGuVcC5AZZxz/0+f4D9v+xhx7StKEBQ8yDaTeiI1jF3VjJFsfTotupcJCAxPIHL2y6RkWzA2ceZur3r0XxkS6vg6xnixbW9YRyYtRdDkgEHdwfcgz3o9UFfqrez/hK/E1Q6FT4jfUjYkkDc2jhQcqcQLTArmYL1n44AY5yRtDNpmA1m1C5qnJs649LeBaEu+Pw59VQqqMCpYdUK1M5OOtbMfYI3J//D+DeWoSjQvWNNprzdDxfn3Jq/ooDJpGDOMz48JNiDzQuf5vP/beXjGVuIiUslOMCNsSNb8/qzXVHlew4/b/lRNBoVIwfd2YluirEBmCiEmK4oSkRFHEAIMRpoCUwGvqmIY9jSzceP2VcvMz60Nn4O9u3M+FWzlqiyOlAO3r3dZhoHtZo9Pfrgqcv9XHX18SU+M5M5Vy/zer2GOKot3x9P16xtlbejtw/Bjo6M3r+HbVG3eSCwmj1Pv8Lec0VRns+3aVNWbfotIYSPoijReXcKIToDY4CJwCx7nosM1AWV+o0XQjTGEqTjgPsVRQmvyBMEcPFzoee7vYtM4+rvytg14622qdQqen/Yr9jy/Rv50+ej4tPdSRo3Dd6DvYtO466h2mvWXwxCJfB5yKfEx/Ho6YFHT4+ynGKFqx7kzvzvRhaZJiTYg+SzHxTY3rCOL39MH1Gi40x9tz9T3+1fpnOsRJ8C67HUeibau/CslravgdewORK/4kyqW4/R+/cw4+I5Pmti31YNVSGjHPJSC2EVpLO18PBk4Y1rxGYYqOZY+I2sp9aSV6OyeyNuhb7nNsRk/WvVNCCE0AI/AV8Al+19UNn0XdCnWf++W5LEQogWwFbgNtCtMoK0JEk23QL+B0wQQhTaWSPrUVZxP7YC8ZfAWUVR/qioCyiMn96BsSG1mHc9jBuphT/rNZrNxf6YSjB8r6T2xETjrtHiry9YyzcpCgaTidOJCXx45iT1XFzp5uNnt2Nnqej3PDuvS9ajzleA2Yqi5H/08Qagw/IZsTtZoy4o+42fJIT4SlGUoubkbAu8DlwFeiuKElNEWkmSKt4U4BngA2Bc/p1CiFDgSgnKCQNC8+TrAjyOpdn7jnihdl3+vHaFaRfO8k3zVgX2X09Nof2WjcWWE+zoyP6efct9PlujIll16yZv1Gtos6bcbOM64rI6wDV392Bh+844qCukIaJC3vOsvE2AE3k2/Q5MyJemDvAe8KCiKIbC5mEoDxmobSvyjc/jcyydSmSQlqQqQFGUWCHENOADIcQU4FK+JOFYbrCLkzNRgRBCh6VZ8xtFUU7b7WRLyVOn45ladfj6wjleqF2XUCfrEQr+Do6s69yt2HJ0dmh+Pp+UyPNHDtLJ24cXatue/GZRh86kmUxcSE7ifxfPM2rfLpZ36oq71r4jJyriPc/jYlZeZ6AT8DaWuPlonjQ/ACsURSn+LqmMZKC2oQRvfLY1wAPAVCHEU0oxU0IJISaQdTfW6cUuVj20JUmym2+wPK/8GOsvVBRFyRBCHC1BGXn/licBXsC3QgiPrG3ZD2RdhRCuiqIkleeES2pCzTrMvnqZqefPMrNFa6t9OpWKxm7Fr0Ne3hpfWGoKo/btprqjE7Nbty/0uXP2ubTx9KKztw8dt2zk97CrTKxTIVMQ2/s9z86bDhzM+nWbEOIWMEcI8Z2iKHuFECOBzkCbPJ+N7KEjzkIIdxvN5KUmA3XhCn3j85gO7Ac+wnI39lxRBSqK8jPwM8C4tRPs3oVfkiRQFCVZCDEZmAZMzbuvjM2gjYAA4KaNdIeBY0CLsp1t6ThrNEysXY+Pzpzk+VrWQ7gro+k7PC2NkXt34aLRML9dJ1y12uIzATWcnPHQ6riamlKm4xanAt7zwmQH7TrAXiyfDUfglI20y7G0uHqU4NhFkoG6EEW98fnSfSyE0APvCCHSFUV5uazHvLDxPDunb0PnrGP4rFHoXXOnAzWbzPz24CxajG5Fy0dbF1FK0a7tC+PKtktEX4gi8VYiAU0C6f+F7XmdI09FcGD2PmIvx6Bz0lGre21aPd4Wjd76Y3N9fxjHFx+zrNylKHjU8KTJsGaEdAwt9fmlnEwhfn08Qi8IGB9gNR2oYlYI/zoc146uuHV2K3XZ2dIupZF2Jo2MyAxMcSZ0wTp8R1nP5KWYFZIPJZN+OR1jjBElU0HjqcG5pTNOTZysaiWpp1NJOZqCMc6IOcOM2lmNPkSPa0dXm7OcldWffx/l2XdW4O6q5+S//7FakMNoNOPR5BPefqFbudaJNpnM/PDHPn5beoSwG/G4uuhp2zyY9yZ2p0l9f5t54hPTaTXgf9yOTmHV7Mfo0anCphAoje+xdPz5NN/2sjSDfgHMzbe/H5ZhnGOAotcUtbMnQmry05VLTMk30UlFN33HGAw8vG8XAAvad8ZbX/R0xXmdS0okLjOjQHO9ndnzPS9M9guc3co6F0tn4rxaYKnovQbsK0GZxZKBumiFvfFWFEV5VwjhALySFazfLs9BM1IyOLHkGG3GtitPMTZd23OVmMsx+Dbww5S1JrEtsVdi+Oe9tVRrFUyvD/qSHJnEgVn7SIlJpcdb9+eku3HwOv9+vIGQTjVp/rCln8259WfZ/NlGev237GOuFYNC0v4k3O8rvimvtNIvpJN5OxNdoI4MY4bNNIpRIWlvEk6NnHBp7YLQCgyXDcT/E48xxoh799zzMqeZ0dfQ49LOBZVehTHWSNLeJAxXDfiN9Ss4rrucEpIMfPPrLj5+1W4TH+X4ZMYWvv51F69O6EK3DjWJiUvlyx+2M+Dx39iz4lmqBRS8QXr/q43lbk61t6xOPR+T1YKVZ3sGubWikpZ1Fjibd1tWLQ1gn6IoF8txqqWmV6t5pW59Xj9x1Gq7TqWiuUfB5W+LcyM1laMJltnt4jIyUAnB6luWxoMW7p4EOzmRZjLxyP7d3EhLZVqzltxKT+NWeu488/VcXHNq14N3b6d/QCB1nF3Rq9WcSUzgpysXCXRwZHSNks2cWBb2fM+FEM2Ar4DFWIZb6YH7gP8A6xRF2ZNV9lUsnYnz5s3+7zFFUXaW9jpskYG6CIW98YWkfTWrZv2WEMKgKMqHZT1uUKtqnFl1ikaDm+DkZd9JNjq/dF/OohJrXl9ZaLoj8w7h5ONMj7d75Sy+odKo2PH1NqKHN8enjmUc8qXNF3Dydqb7mz1RqS3pglpWY/HYBVzacrHMgVofqiflcAourV1QO9u3p6hHX4+cP6aov2yv2yw0goCnA1A55gZZhxAHzAYzyUeScevshtBaynBpbT2bmb66HrWbmpilMRiuGnCsZ9/pVu/vXJsf/9zPC493wN/XvvOt/7nsKMP6N+aDST1ztjWp70/rATNZv/U8T41qY5V+z+FrLFx1gq/e68/z7xb+ebpD5mAZlfH/bqmvh4Nr8MPlC1xOKX9T8q6YKF4+fsRq24TDBwD4pllLHnYKIdqQzslEy6PWF48eKlDGkg6d6eRtaZVq5eHJohvXuZ6aioJCNUcnBgcF83ytunjrSl4LLyN7veeRQDTwDpbHHqlYAvZrwK/lLLvUZKAuXmne+IlY7rw+EEKkKYoypSwHbP5wSzb+dz3HFx6hw3Ody1JEoUqy8pPZaObmoRs0HtLUaoWs0K612PXtDq7vDcsJ1CajGa2DJidIg2VSFa2jpkTLLRbGtYMrMUtiSNqbhMf9HmUux5aS1P6ESiAcC6bTBmjhJJjSTGi0hf/55AT4Cpip4I3nujJ0/Dy+/HE704pYO7osMjJNuOZbgc3D1TJGNv+czpmZJl76YDWvjO9MaHDpa3L2oijKXAo2TaMoihGokJ5LhR3T3h6uHsLD1QvWQjUqFTu7Fz3hUXmPkVd1J2fCHxhSovI+aFTxs9dV9HuuKEoklqlDy5J3K7ZXoC0zOeFJFkVR5iqKIvI3YymKYlQUpV7Wvg+ztm3N+v3ffGkVRVHGZ+0rU5AGcPJyosHARpxbf5bk24V3JjWbzCX6Ka3EW4mYMkx4hlh/+Wp0GlwD3axW7qrfrwGJ4YkcW3CE9IR00hPSODr/MMmRyTQcWPwiH4VRO6txbulMyvEUjAkFV8nKppiVEv3YS8b1DIReoHYpWMtXzAqKUSEzKpOErQlovDU4hNp/7eoAX1eeebQtcxYd5trN+ELTGY3mYn9M+T4f40e3ZeGq46zedJbEZANXrscx6eM1VAtwY1j/JlZpv5m1i8wMEy8/bd+bSUmSrMkadRXVbERzzq8/y9H5h+kyqWAHkaTIJJaMW1BsOS5+LoyY80ipjp291rWtta31rnoMybl9Lqq1Cub+//Zh+9QtHP7D8hhI66ilx7u9CGgSWKrj5ufazpWU4ykk7UnCs1/BGpsxwUjkL5HFlqN2UxMwofwrPqVfSSftXBpuXdxstkxE/BCBOc0S+LT+WnxG+CA0FfPs9uWnuzB74SEmz9zGD58PLrA/7EY8jXvNKLacGkHunN48Kef391/qgV6nZvTERTlzg9cN9Wbd70/g5ZHbhH8pLJYvf9jBou9HodfJrxFJqkjyL6yK0rs60HhoU47OP0zT4c1xDbTuxOPk5cSg6UOKLUdVplWuLF/QNluI81VOb5+NZPtXWwluW53aPeoiBFz49wJbJ2+i1wd9CWweVIbjW6gcVbi0cSFpdxIu7VzQeFh/XNUuanzHFL3uMmBzoY3SyozOJHZNLLrqOlza2X4u7DPCB7PRjDHGSNK+JKIXR+P7iK9Vz3V78fJwZOLYjkyeuY2Xn+5cYF3pQD9Xti8eX0juXDqd9efjl78O8OUPO3jj2a7c197SmezrX3Yy+Kk/2fDnWAKzlr+c9NEaHri/Pj0717ZVrCRJdiQDdRXWeEhTzqw6xeE/D9Ht9R5W+9RaNV61il6Qoqz0WTXp7Jp1XoZkA541cmu3+37cjWeIB91ez+18VK11dda8vpL9v+5l8HcPletcXFq7kHI4haRdSXg+YF2rFmqB1q9k4zjLwxhvJHpJNBp3Dd5DvAt9zp99LvogPfrqeiJ/jSTlWAqu7Quu7WwPLz7RkR//3M+n321l9lTr11mnU5do3ei8N2Ox8Wm8Nfkf/vNUJ957Kffz1q1DTRrfP53ps3cx5e1+LF13ir2Hr7F9yQTiE9MBSEm19J5PScsgISkdd1f7N/lL0r1KBuoqTOuopdmIFuyftZemw5pZ7avIpm/XQDfUWjVxeZ5FAxgzjCRHJFGzS+442bircTR4oOAMaz51fTm3tnwL2oNlOUvX9q4kbE3Apa11TbYymr5NSSaiF0Wj0qnwHuaNSl+y2rHGQ4PKUYUxvvDn6+Xl4qzjtQldeHvKBiaN62S1ryxN3xevxmDIMNG6qfVqY14ejtSs4cW5S5ZV/c5ejCIt3Ujbgd8XKG/UCwtxd9Vz80CpVomVJKkIMlBXcQ0GNuLk8hMc/t16GGBFNn2rtWqqtQ7m6o7LtHy0dU6P7rCdVzBlmqjePnfIlaOnI9EXCg5xij4fhZO3fYaWObdwJvlQMok7E63Ps4Kbvk2pJqIXW4KTzwgf1E4lfy0zozMxp5kLNNfb2/jRbflu7l4+nrHZantZmr79fSw3QgeP3+SBnvVztsfGp3E5LJYWjSw3O2OGtqBru1Crck6cjeDNyf/w+Ru9adM8uKyXI0mSDTJQV3FqrZoWj7Ri93c7Cmz3qVt8kMov+XYS0ectgdWQaECo4OpOy/KpPvV8cfGzNNO2GN2aNa+tYMvkTTQc2Mgy4cns/YR2rml13IaDGnNg1j62fbmZWj0sUxpe2nyB22ciaf9MxzJdc35CI3Dt6Er8hnjr7WqBLqD0E/wbE4xkRliWkzWnmUFA2jnL5A3aAC0adw1KpkLMkhiMCUY8+3liSjJhSsqdIEbjrcmpXUf9FYVDHQe0XlrQgDHKSNLBJNSuapya2nccfH56nYa3nr+Pif9dbbVdp1PTqmnp+geEBHvQv3s9ZszajUol6NI2hNj4NKb/ugtDppGnR7XNSRcS7GGzjKYNAujUumxj5yVJsk0G6rtA3d71OLn0GInhicUnLsatY7fYOX2b1bYtkzcB0GVSN+r2tgRq79re9PmkPwfn7OffD/9B66yjzv11af249Ux8TR5qhqOnE6dXnmT71C0AuFVzp9vrPajV3Xou4vJwauJE0oEkTHGFz6ZWUobrBuLXx1tti10VC4BHPw807hpMqSYyb1uCedyauPxF4DPSB30Ny7N8XYCO1FOpmBIs56Z2VePUwAmXti6lqoWX1WMPtWTGrN1cDIstd1m/fTOcb+fsZvGak3w3Zw+uLnpaNApk4wcPlDrwS5JkH6I8k1JIZXevL8oxe8DPIvj14Hv6Nbgx9YZIOffhPf0aONf/sGrNPXqH3Bo49J7+HASuXiY/B0WQE55IkiRJUhUmA7UkSZIkVWEyUEuSJElSFSYDtSRJkiRVYTJQS5IkSVIVJgO1JEmSJFVhMlBLkiRJUhUmA7UkSZIkVWEyUEuSJElSFSYDtSRJkiRVYTJQS5IkSVIVJgO1JEmSJFVhMlBLkiRJUhUmA7UkSZIkVWEyUEuSJElSFSYDtSRJkiRVYZo7fQL3Kq1Ke6dP4Y5zdXC906dwxxljtt/pU5CqgMDVy8SdPoc7Tcy+qNzpc7iTlHF1Cv0MyBq1JEmSJFVhMlBLkiRJUhUmA7UkSZIkVWEyUEuSJElSFSYDtSRJkiRVYTJQS5IkSVIVJgO1JEmSJFVhMlBLkiRJUhUmA7UkSZIkVWEyUEuSJElSFSYDtSRJkiRVYTJQS5IkSVIVJgO1JEmSJFVhMlBLkiRJUhUmA7UkSZIkVWEyUEuSJElSFaa50ycglU1yVBJ7ftrFjSM3QFGo1jKYTs90wcXPtdi8iRGJ7Pt1NzeP3sBsNONb348OT3XCt55fTpr4G/GcXn2C8GM3SYxIROuow7eeH20fb4d3LZ+KvLQSy0zI5PbG26RcSQEFnGo64d/HH627tti8GXEZRG2KIuVKCopZwTHIEd/7fXEMcrRKd/G7ixgTjAXyVxtRDdf6xb/WFe1GZDrvfHuRrQdiURTo1saTyf+pS/UAh2LzXg1P478zL7H1YBxGo5lWDd345IXatGzoZpWu6bA9XI9IL5D/z8lNGHifr92uRZIk22SgvgsZ0zNZ/dZK1Fo1PV7tCUJw4Pd9rHpzBcN/eBitQ+GBKj0xnZWvLUPrqKXrxG5o9FqOLzvK6rdWMGT6MDxreAFw4/B1wo/dpF6vBvjU8cWQbODYkiMsn7SUB6cNxbeuX6HHqAzmTDPX/ryG0AgCHwwEIHprNNf+uEbNCTVR6QpvLDKlmrj2+zVUOhUBAwIQWkHcvjiu/3mdkHEh6H30Vumdaznjc5/1zYnOW2f/iyql1HQTD750FL1WxffvNUQI+OznKwyaeIRdv7fD2VFdaN7YhEz6P3cYFycN01+vh6ODmpkLrjNo4lE2/dqa+qHOVunvb+/FW+NCrbbVreFUEZclSVI+MlDfhc6sP01SRCIjfxmNe5A7AF41vVn41DzOrD1Fs4daFJr39JqTpMWlMujL3LxBLaqxYOyfHPrzAL3e6QtAnW51aDyoCUKInLzVWlRj/pN/cnLFcXq81qviLrAE4o/EkxmfSa3naqHzsgRNvZ+ey99fJv5wPF4dvArNG3coDmOy0Sqvc6gzl2ZeInpbNNWGVbNKr3ZS4xjsaKuoO+q3leFcDU/j4F/tqRVsCZqNa7vQetQ+5qy4yYujahSad9aym9yOy2TNzJY5ee9r7UmLEXuYPOsKcz9pYpXe211L2ybuFXcxkiQVSj6jvguF7b2KXwP/nEAL4BbgRkCjQML2XC0y7+2zkbhXc7fKq3XQEtA4kLD9YZhNZgAc3B2tgjSAzlmPezV3UqJT7HcxZZR8PhnHao45gRZA56nDsbojSeeTisybdjMNnZfOKq9Kp8KpuhPJF5JRzEqFnbc9rdsZTdvGbjmBFiA0yJH2Td1YuyO6yLwHTyVSO9jRKq+zo5qOzT34Z1cMRqO5ws5bkqTSkYH6LhR3LRbPkII1Rs8QT+KuxRaZV6gEKk3BJlG1Vo3JYCTxVkKhedOT0om7GotHDc/Sn7SdGaIM6H31BbbrffVkRGcUmVeoBEItCm5XCxSjQkacdf7kC8mc++Ic5yaf4+qcqySdK/pGoLKcvZJKw1ouBbY3rOnMuaupReZVqQRabcHXQK9VkWYwc+Wm9TPp9buiCey5Db/uW+k1/hCrt0eV7+QlSSoxGajvQoYkA3oXG0HK1QFDsqHIvO7BHiSEJ5CemPtFrJgVbp+/nVN2YXZ/vwMFaDqkedlO3I5MaSZUjgU/vmoHNaY0U5F5dd46MmIzMKXmplMUhbTwNADMabm1SZe6Lvj39af66OoEDglEqAU3F98k4UThNzSVJS4xEw/Xgk+vPN20xCcV7ACXV90ajly+nkZsQmbONrNZ4dCZREvZSbnb+3X2ZsrL9Vj6dXN++aARer2KMW+fZOE/EXa6EkmSiiID9V1KFKwMoSjFN9k2GtAYzApbvvqXxPAEUmNT2PXjDpIiErPKtVEwcGThIS5uvUDn57paNZvfjTxaeYAC4SvDyYjNwJhkJPKfSDLjs4JTnpcgoF8A7s3ccarhhFtDN2qMqYFDoANRm6tGjdLWu1WCjwHjhlTDrMCzn5zmyo00IqINvPnNBcJuWW7gVHkKnvpKPR7pH0CnFh4M7uHHyhktaNnAlY9/vGyfi5AkqUgyUN+F9C560m3UfDOSbde083ILdKfHG72IvhjFgqfm8eejv3H7TCRNh1pqyU5eBXvynl5zkgNz99Hm8XY06NvQPhdRTmpHtVXNN5sp3YS6iN7OYHmWHTgkkPRb6Vz+/jIXZ1wk7UYaXu0tjxM0LoX3sRQqgWtDV4xJRozF1Formoerhjgb5xCfZLumnVdoNUd+/m9Djp5LpuXDe2kweDf7TyXw/MhgAPy9C/8cqdWCIT19uXnbQER00S04kiSVn+z1fRfyDPEiLqzgs+i4a3E5w6uKUqtLbUI71iThZjxqjRq3IHd2fLcNZ1+XAuOwz286x86Z22n2UHNaPdLGbtdQXnofPYaogkHCEGVA51P80Cm3hm641nclIzYDoRLovHRErI1A46Yp0ThswHZ1thI1qOnM2SsFO/advZpK/dDih04N7uHHwPt8uXg9FZ1GRc1gR16Zeo5gf32x47Cza+2FNMBUKiHEk8AcIAGoqShKXJ59GiAT+EhRlA/LcYwvgAFADUAHXAPmAdMURSm6Q4CdVcb15jteLeAk4AjUVRTlYr79amAi8DRQB0gBjgCPKYpyyx7nUMDOpTDnLXB0hSlbwDlPK5/JCBMawoMTYfBLZT/GkqlwYhvEhIMpE7yCoMMg6PMU6POMAlnxLaz8rmD+Fr1g4g9lP34eMlDfhULah7L3190k3krALdDyAU2KTCTidATtx3YoURkqtSonqKfEpHB5+0WaDW9hlebKrsts+3ozDfo2osP4zna9hvJyqefC7X9vkxGXgc7TEpgz4jNIu5GGb8+STcIhVCJnzHRmUiaJpxPx7uhdZB7FrJB0JgmNm6bImndl6N/Fh/dnXuLqzTRCq1m+OMJupbHveAIfPFerRGWo1SJnzPStKAN/b7rNS6MLH9YFYDSaWb45imB/fZE17zvAHXgTeKsCynbDEhzPAQagE/Au0AYYXAHHK4mKvN68vsdyU1DYGMU/gL7A58DBrPPqBhQ/6055pSXBup9h+OsVUHYydB4GATVBo4NLh2H1D3D1JEz8sWD6txaAKk8jtbOH3U5FBuoyynNXm80MRAC7gPcVRTlXUcdu0L8Rp1ad4J+P19H28fYg4ODv+3HxdaHhgMY56ZIik1gw7k9ajW5D60fbWk7SaGLvrD0ENQ1C66QjLiyWo4sO4xniZTX++taJcDZP2YhXTW/q9a5P5JncjkNqrRqfOnd2RiqPlh7EHYzj5qKb+HT3AWGZ8ETrpsWzVW6v9Mz4TC7NvIRPV5+cSUsUk8LtTbdxCnFCpVNhiDYQuysWva/eavx14slEks4n4VLHBY2bBlOKibiDcaTfSidoaFClX3N+TzwYxC9LbzL6rRO8O6EmAsFnv16mmr+esYNzz+9aRDotR+7ljSdDeHNcTQAyjWb+O/MSnVt64Oqs4ezlFL75I4yGNZ158ZHqOXmXbIxk7Y5oenf0ppqfnqi4DH5depOj55KY9VGjSr/mYmwAJgohpiuKYteeboqiPJ9v0yYhhBPwlhDCR1GUosfDVYwKu95sQojRQEtgMvCNjf2jgJFAe0VRDuXZtbIizqeAxl1g0x/Q+0lwt/N30mMfWf/eqBNkpMPanyApFlzztV7Wag7qigmpMlCX3wjgBqAGagPvY/kjbqwoSoV0DdY6aBn4xWB2/7yLLVP/BSCoRTCdnumM1jFvs62CYlasO5kJQWJ4Ape2XsCQbMDZx4X6fRrS8uFWqLW5z3ZvHruBKdNEzKVoVr66zOr4Ln6ujP7tsYq4tBJT6VTUGFODyI2R3FphaV1zCnXCr4+f1axkCgoo+TraCciIzSDxVCLmdDMaVw3uzd3x7uJtNWxL66HFlGLi9qbbll7mWhUOgQ4EPxKMS+2Cw6Iqm7OjmpXftuCdby/y7MdnUBS4r40nk/9TBxen3D9tRVEwmRTM1i8Bl2+ksWRjJAnJRoJ89YwZGMgrj4eg0+a+fiGBDkTFZfDfmReJSzTi5KCiZUM3ln7djPvbF936cAd8CqzHUtOdWAnHi8n6N7PIVBWnQq9XCOEJfA28huX7zZbngW35gnTlGfg8fPOUpab76H8r/njZtWR1CR+P2YkM1OV3NM8zm11CiHBgI5amsXUVdVAXP1f6vNevyDSu/m5MWGddEVCpVfT76IFiy28zph1txrQr1zlWNK27luDhwUWm0XnoaPBeA6ttQiWoPqp6ITlyOQY7UuOxopuB77TqAQ788XmTItOEBDoSv6uH1TaNRsXCqc2KLb9tE3dWfdeyXOdYiW4B/wMmCSG+UhQlzFairOe4xVEURSkwzi8rrwPQAXgFmF1RN+QlUNHX+yVwVlGUP7JaEPOXqwXaAz8JIb4ExmFp9j4MvK0oyuaSX0oZuftBzzHw71zo+xT4VLOdzlSCjp9CgMrG/YjJCJkGuHwUNsyBLsPBycY8/6/fB4kx4BkA7R6wPB/X2af1XwZq+0vM+rdyb7kkSQKYAjwDfIAlcFgRQoQCV0pQThgQmi9vE+BEnk2/AxPKeJ72UiHXK4ToAjyOpdm7MN5YOtY9CVwGxmN5fv86sF4I0UlRlIMlOHb59J8A2xZYOnSN+6Lg/ugb8GaPgtvz864GX2613nbjPHyQp2LTaSg88al1Gr8QGPYa1GhkCfandsLGuXDtFLz6W2mvxiYZqMtPnXXHqgZqYelQcRvYeidPSpLuRYqixAohpgEfCCGmAJfyJQkH2pagKFvjzi5m5XXG0mL2Npbv0EfLfsblUxHXK4TQAT8B3yiKcrqIPNnPSLTAAEVRwrPyb8cSuF8HHi7RhZSHiwf0HQcr/wcDJoBvvlYwDz94/+/iy9HYGC3iH2LJa0iFi0dg7Y9Zvcq/zk3TMV9fwsZdLLXqBZ/B6V3QqPwdcWWgLr+z+X4PBwYqipKYP6EQYgJZd+BdJ3az6vglSZLdfIPlme3H5AuiiqJkCCGOlqCMAtPGKIqSjqVXM8A2IcQtYI4Q4jtFUfaW75TLxd7XOwnwAr4VQnhkbcse7+cqhHBVFCUJiMvKdzo7SGcdM1kIsYeia+P21XuspVPZ8hkwfpr1Po0Oqpdg/gdbYw21eghtavl//faWDmtz3oL7H4PaRVxe+0GWQH3lhAzUVcRQLJ3JBBAEvAisFULcpyjKmbwJFUX5GfgZ4Jn1L9wdKz9I0l0mK1BMBqYBU/PuK0/Ttw3ZQbsOcMcCdQVcbyMgALhpI91h4BjQQlGUNCHEZWzc1GD5Pqy8lV0cnGHAs7BoMvR72npfeZq+88sO2revFR2os9lpogEZqMvvZN4JAIQQG4DrwIfYodnn3MazbPt6MzpnHY/MGYPeNbdzgtlk5teBP9Lq0Tbl7vhlNBg5uvgwF7dcIPl2EnoXPb71/Oj9Xj+r3uDZDMkGFk2YT1pcGgM+H0Rwy+I7Z5VV/LF4IlZFoNKrqP1ibauZxxSzwrnPz+Hd1RvfbmUfnhG+MpzE4wUaQfBs54l/H3+rbQnHEog9EEtmbCZCI9D76/Hp6oNTBa/PPG/NLV74/CxuLhqOL+6Ah1tuNwij0YxPt228OS6Ut5+qWeZjPPfpGf5aV3Ckz7MjgvliUt2c32cuuM6SjZFcDU8jNd1MkK+eAV19ePXxELxKOmFMxfoeS2evfA8Uy9X0nV+3rH/zNzffCfa83i+Aufn298MyZnsMlrHk2ZZhGSIWrCjKDQAhhCvQEUuP9MrTYzRsmA3L8o0iK0/Td37n91v+zd+8nt/erNFpteyzLoIM1HaW5y6z+C61pZCRksHRxUdoP66jPYsFLGOr172/mqTIRFqMbIVnDS/SEtK4eeR6oUs+7pu9h8qemstsMBOzJwa/nn4VUr7aSU3wSOte5PknNYk/HE/E2gg8Wnng19MPc6aZ2H2xXJ93nZCxITgUM6OXPSQmG5k+7xofPle7Qsr38dDy15SmVtv8fawnNolLzGRQN18a1nLGxUnN8fPJfDn3KjsOx7F1VhtUqsr9bOSnKIpBCPExWS1YebZnkFsTLhEhRDPgK2AxlmeveuA+4D/AOkVR9tjlpMvBnterKMpZ8j3Sy6qZA+zLNzPZV8BjWFoRPwYysAzncsIS8CuPVg+DXoTf37PertHl1oRL6vpZWPQFtOkPvtXBmAHnD8C/v0HTblAnT236wwctncwCamZ1JtsFm/+AJvdBg5JNQFUcGajtLGsShNrAKXuWG9yqOqdWnqDpkOY25+Muj+N/HyP6YhQjfhqFi2/usINaXWwHgohTt7i4+TydnuvK9ulb7HouRXGu5UzcgTi82nlVyKxgQi1wDC5s8iWLhOMJOAY7EjAgIGebU6gTF6ZdIPF0YqUE6p7tPPl5yQ2eGxlcITOD6bQq2jYpeuGV9yZYz3zWtZUnTg4qXp56nuPnk2nRwMbwlco3B0uHprrFJSxGJBANvIOlSTgVS8B+Dfi1nGXbk72ut8QURYkUQtyHpdl9DpYOZnuAboqi2PU7sES6DIN/foXIq+Urx80HXD0tnccSokDnaAnYI9+CriOs0wbUsgTmhCgwmyy17UEvQL/x5TuHPGSgLr8WQggfLNXLQCzPqL0AG5O/ll3LR1qz7r3VHFlwiM7Pd7Vn0ZxafZJaXWtbBenCmI0mdny3jRYjW+EW6GbX8yiOdxdvUv9KJXpnNAH9AorPUAEUk1Jg0Q+VVmWZKKWSeh289kQow189xle/hTH1lXqVc9ASyG7y1mgqtzatKMpcCjbVoiiKESj3C6QoSiQwurzl2EtFX29pjpm17zwwqCKOW6guwyw/+ak18PnG8pfv7gMTCkzEZtuz08t/vGLIQF1+i/P8PwrL5PX9FEX5x54HcfJypvGgppxYfoxmw1rg6m87qJpNJeu/oVJbRlYk304iJSoZ1wA3ts/YwqVtFzEbzfg3CqDD+M741Paxynd08RHMRhPNh7ck8mzlrkescdHg2caTuP1xeHfwRuth+1loYc31+Yl8zbPGFCMXpl3AlG5C66nFo4UHXh28rNJ5tPYgYk0E8UficW3oijnDTMzOGIRK4NHCo8zXVhoBPjrGDwvmh0XXmTi6BjUKqcUbjcV/FoQQqNXWr0NUXAa1BuwkIdlIaJADjw0MZOIjNQqkyz5GhlHh1MVkJv96hW5tPGlS587P2iZJ/5/IQF1GRd1hVpTmI1tyZt0pDs07QPdXehbYnxSZyF9P/llsOXmnAE2Jsay+dGzxEXzr+XH/W30wZZo49Od+Vr+5nOHfP5yzolZCeAJHFhyi7wcDUOuKXkqyonh38ib+cDzRO6IJHBRYYH9GfAaX/1f8Oskadw11JtbJ+d3B3wGHQAf0PnoUk0LS2SSiNkeREZtB4MDc42QH48h1kUSssdyoqF3UVH+0OjrvEnRGsZNJY2owZ0U4U2ZfYeY7BYeehN1Ko/nw4jsiVw9w4MTS3H4PTeu60KK+Kw1qOWMwmFm9PYqPfrzMpetpfPe29QxvyalGgnvvyPn9/vZezP1EDjmUJHuTgfou4uDqQLOHWnBo3gFajGyZs3JWNicvZ4bOGF5sOXl7cWfPga1x0NDvwwFoHCy1VN96viwYN49Tq07S/inLF/nO/20jpENNgltVXA/v4qgd1Xh18CJ6ezRenbxyVs7KpnXVEjIupNhyRL7m2ey1qLO51HFBpVNZau+dvNF5WY6TdC6JyPWReLTywKWeC4pRIXZ/LDcW3KDGmBro/SpnNSlPNy0vjqrOlDlXmfRoCDWrWdeqA330bPm1dbHl6PLMiw7w/MPW722fTt44O6r5YdENJo2pQe3quf0jnBzUbPm1NekZZo6fT2ba72GMevMEK6Y3R6ORS91Lkr3IQH2XaTq0OSdXnuDgH/vp+UZvq31qrRrvfE3VxXFws3zB+zcKzAnSAC6+rnhU9yT6UhQAl7ZfJOJ0BA/NGI4h2TKSIzPNshaBMd1IRooBnXPlBCmv9l7EHYgjels0QUOsV7ESamG3Dl1ujd2I2x9Heng6Oi8diqIQsTYC1wau+PfNHbLlXMuZyz9cJmpbFMEjip573J6efziYn5fc4PNfr/DLB9a1ap1WRdO6xTdBixKM8xze258fFt3gyNkkq0CtUglaNrT0U+jY3INGtZ0ZNPEoK7ZGMayXf2HFSZJUSjJQ32W0jlpaPNyKvb/sovkw6wH3ZWn6dgtwQ63X2B5opSg5z2fjrsViMhhZ/OyCAsk2fLwOnbOOJ5c8XWBfRVDpVHh39ub2xtukd0i32lfWpu8iZb04phQTphQTDkHWNwJCbRlLnRGdUbLy7MTFScPLj4Xw3v8u8tJo65pwWZu+bcludSkuprfM6ul9+UZasceVJKnkZKC+CzV+oAkn/j7Kgd/3WW0vS9O3SqOmRtsQIk6Gk5meiTarVp18O4n4G/GEdLRMnlG/dwOCmlmvTBNzKZo9P++iw9Od8KtfuTUoj9YexO6NJXqr9TLAZW36tiXxpGUCFIdAS2BWOVh6d6eHW98cKCYFQ4QBrWflT/Tx9EPV+H7hdT79xXryqbI2fduyeONthIBWDYru5b/raDwANasVPcRNkqTSkYH6LqTWqWk1ui07vt1qvV2rxrde6ScDaTOmLcsmLWH9f9fQ7KEWmDKNHJp3EL2LnsaDLBMFuPq74epv+4vaq5Y3AU0KduyqSCqNCp+uPkSste55LtQCx6DSBYrM+EzCV4Tj1tgNnacOs8lM8rlkEo4l4NHKI+f5tEqjwr2lO/EH41E7qnGp44LZaCbuYByZCZn49amYiViKotepeGNsKP+Zcs5qu06rymmWLqlrEek88/FphvXyp1Y1RwyZls5k89dGMHZwEDWzxpgnJBsZ/soxRvTxp3Z1RwSCQ2cSmbngOk3quDCoHDPESZJUkAzUd6n6fRpwfOkREm6WfylczxAvBn4xmH2z9/DvFxtQqVUENa9Gn//2x8mzYqfFLA/3Fu7E7I0hMzazXOWo9CrUjmpidsdgSjGBAJ2PDv++/ni08bBK69/HH723nvij8SQcS0BoBDofHdUfqY5zbedynUdZPToggG/nX+PS9fI1Obs6qfF00zL9zzCi4jIRQL1QJ6ZMqsvTD+W2pjjoVNQLdeKnJTe4FWVAoxZUD3TgxUeq88zwYPQlqKVLklRyIvv5k1S57vVFOX7qN1M0fL/hPf0anPnkjEjY3fOefg3cO22+s3ONSlWGmH3xnv5bUMbVKfRvQd76SpIkSVIVJgO1JEmSJFVhMlBLkiRJUhUmA7UkSZIkVWEyUEuSJElSFSYDtSRJkiRVYTJQS5IkSVIVJgO1JEmSJFVhMlBLkiRJUhUmA7UkSZIkVWEyUEuSJElSFSYDtSRJkiRVYTJQS5IkSVIVJgO1JEmSJFVhMlBLkiRJUhUmA7UkSZIkVWEyUEuSJElSFSYURbnT5yBJkiRJUiFkjVqSJEmSqjAZqCVJkiSpCpOBWpIkSZKqMBmoJUmSJKkKk4FakiRJkqowGaglSZIkqQr7P+fJHvhukSyOAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 504x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "matplotlib.rcParams.update({'font.size': 16})\n",
+    "discovery_celltype = 'CD4T'\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
+    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
+    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "    im1, bar = heatmap(rb_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "            list(rb_df.index), \n",
+    "            [discovery_celltype],\n",
+    "                     cmap=cmap1, ax=axes[i], vmin=0.7, vmax=1)\n",
+    "    bar.remove()\n",
+    "    _ = annotate_heatmap(im1, \n",
+    "                         data=rbse_anno_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                         valfmt=\"{x:^}\", \n",
+    "                         textcolors=(\"white\", \"white\"),\n",
+    "                         threshold=1)\n",
+    "    if i > 0:\n",
+    "        axes[i].axis('off')\n",
+    "plt.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig('rb_values.filtered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cdict = {'red':   [[0.0,  0.0, 0.0],\n",
+    "#                    [0.5,  0.5, 0.5],\n",
+    "#                    [1.0,  1.0, 1.0]],\n",
+    "         \n",
+    "#          'green': [[0.0,  0.0, 0.0],\n",
+    "#                    [0.5, 0.5, 0.5],\n",
+    "#                    [1.0,  1.0, 1.0]],\n",
+    "         \n",
+    "#          'blue':  [[0.0,  0.0, 0.0],\n",
+    "#                    [0.5,  0.5, 0.5],\n",
+    "#                    [1.0,  1.0, 1.0]]}\n",
+    "\n",
+    "# cdict['alpha'] = ((0.0, 0.0, 0.0),\n",
+    "#                    (0.5, 0.5, 0.5),\n",
+    "#                    (1.0, 1.0, 1.0))\n",
+    "# newcmp = LinearSegmentedColormap('alpha', segmentdata=cdict, N=256)\n",
+    "\n",
+    "c_white = matplotlib.colors.colorConverter.to_rgba('white',alpha = 0)\n",
+    "c_black= matplotlib.colors.colorConverter.to_rgba('black',alpha = 1)\n",
+    "cmap_rb = matplotlib.colors.LinearSegmentedColormap.from_list('rb_cmap',[c_white,c_black],512)\n",
+    "\n",
+    "\n",
+    "\n",
+    "mpl.cm.register_cmap(cmap=cmap_rb, name='alpha')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-1-f7950d1936ff>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-1-f7950d1936ff>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "im, bar = heatmap(replicated_ratio_df.values, \n",
+    "                   list(rb_df.index), \n",
+    "                  celltypes,\n",
+    "                   cmap='alpha', \n",
+    "                   vmin=0, vmax=1)\n",
+    "_ = annotate_heatmap(im, \n",
+    "                 data=replicated_ratio_df.values, \n",
+    "                 valfmt=\"{x:.0%}\", \n",
+    "                 textcolors=(\"white\", \"white\"),\n",
+    "                 threshold=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "matplotlib.rcParams.update({'font.size': 16})\n",
+    "fig, ax = plt.subplots(figsize=(8, 7))\n",
+    "im, bar = heatmap(np.flip(rb_df.values, axis=0), \n",
+    "                   list(rb_df.index)[::-1], \n",
+    "                  celltypes,\n",
+    "                   cmap='alpha', \n",
+    "                   vmin=0.7, vmax=1)\n",
+    "_ = annotate_heatmap(im, \n",
+    "                 data=np.flip(rbse_anno_df.values, axis=0), \n",
+    "                 valfmt=\"{x:^}\", \n",
+    "                 textcolors=(\"white\", \"white\"),\n",
+    "                 threshold=1)\n",
+    "\n",
+    "plt.savefig('rb_values.filtered_results.varyingalpha.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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aTeoWpUndosxZup+NO09bk/VnwxbS/LVSVCyTN51693wK58tPxdKlU122ZvNmJgwdRv0aNahdpQq/z5/PnoMHaVirFmazmW5ffkG/jz8mMFeudG71s6tesjHVSza2KcufsxhdxzZm57G1NKv+Pln9cpHVz7Yvh8/uBKB2mabWsoNnttG48rtUCK5NheDabDm8nCPndluT9bSlo6hSrCGFc5dK2079C1WwPE4T9gCQsGUOplSStfnQerQz+3H4/A90hSsB4JCvNPGf1SRh5S8Y3htsiQs5hXn3Ugwdv0FfraW1fuMXjUhY9AO6nlMtcUe3oKv0OvqKTSzbPbgO87Gt1mSdsGg8KmsQ+oqvv/D+2s/hgXhmSvffjnB1TjqUQdkcIWsmDZ1j0ttB56jDbDQDlkQedSaCrC1zpKgroz3LPji36QSaWSOoZnCqy80mM3qHpKNFvaMBsykBgFunQrl74SbBTVL/4M9IOt2//xnvO3SJ8qXyWBM1QI6s3gQXyMrStUetZUZjAi7JRh1cnB2JizMBlkS+fe95hvd78R9EGSHeaMTF2fJl1GAw4OjgQGxcHAC//P034RER9O3yUUY28bl4unoDPPHod9PBJQRlL0KuLPmtZSaTEUdD0qk0J0dn4o2W/XLw9DaOX9xH+0Z90qbRz0A9xfvefGgDeGexJmoA5eqBrmRtEg6tt43TO6Arn/SFR+kN6Co0xnx8G1pi/0kwohyck2KcXCFxmfnqGRI2z8bQdshz9ix1kqxfEZpZQzNpxN+N59pfVwDwre5vXe4a6Mb9HXcxPjAScTyMmJBoXAPdMBvNhP59hYAWOTC4v7wDMVF3Iji77hjFW1RA95jhW5/cflzZd57Y8Ghu/XONsND7+OTORIIpgWOL9lG4cSkc3ZxSXdfe6fU6HB1T/v6cHA1cCLlLbJwRsBxt/7lwH9dvhbFu6ymOngqlfMncxMWZ6PP1AoZ+1gQ/H7f0bv5/1q7npzgF5iVLyRK07dGdkNBQ67LyJUvyy99/cff+fabPmU1EVBSlixXjzr17DP5uDBOGDsPR0TEDW//sEswJGE3xXLtzickLh+Dj4U+1Eq+lGnvq0kGu3w2hdummNuUFchVn48El3Au/zcEz27l47R8K5iqB0RTP1KUjaNewN55u3unQm+enhZ5F5cifolxlzw93r6HFRiXFZcqBcnJJGWcyot26bHkdWIKE/WvQbl7CfOk45hPbUUElATD9/hX6+h3QZQ1Mk768vJ++4pmETLlA2IEHABg8DeTtmR/nbElvzCxvZOXiD+c41cdylJWpYRbc8rlzc8k1DB4GfKv5pVbtS+PI/N0EFMuFf/6Ax8YUqF+C3b9sYO2QBQAE1QrGN08mTq85gqO7M7kq5Euv5r5w+QMzs/vgRYzGBBwSRw8iImM5dfY6mqZxPyyarJm9GNijAW9+8DNBlb4CoNeHtalQOi8jJqzG39edDq0rZmQ3npqnhwe9PvyQ6hUq4unuzqETJxg9eRJbm7/JvhUryezvz5gvB9H0g/cJKFUSg8HAt198Sa7s2enYty/1qlWnTtWq/74hO/PZpDacDz0BQFa/XAz7cAbe7qn/7W46uASD3pBi+LxNnU/4ekYX3h9ZA4A3q39Aodwlmb1+Ep5uvtQr1yJtO/EiRYWh/FOOCCo3L+tynN3Qoh6Aq2cqcd6WHyLDANDXbYf5+HbiP68LgK5CE3TlG5OwfSHa/RvoX/8kLXoBSLJ+ZQS0ykGmRgEY78Vzd+NtLo4/R2Df/LjmsRwlOfg4kn9IYeJvx6N31WNwNxB3O47ba24SNKAgWrxG6JwQwg89QDnqyFQ/C/517Ovc7eNc2X+BByF3qd2/6RPjXLxdqdm3CdF3I3FwccTRzYmouxGc23ySqt0akGBM4MSS/Vw/dgWDo57AGsEEViuUTr14Pl3bV2fhysN0HzSXQT0bkZBgpv/IJURGxwNJQ+nZA7zZs6IfF0Pu4uXpgp+PGxdD7jB+2ibWz+lBTKyRz0csZtnao7i4ONLjg5p83L56RnYtVaWKFqVU0aLW19UrVqRahfJUbtqUiTNnMLTvZxQtWJDTW7ZyISSEgEyZ8PTwYMf+fSxZs4ZjG9Zz5949egwaxIYd28nk68eQPn1o2bjxE7aa8Xq99Q3RsZHcvHeVxdtm8NW0Toz66E+y+Ga3iTOa4tl+dA1lC9XE083HZpmfVxbGf7qIG/eu4ObsiaebNzfuXmHR1hl889GfxBtj+XXFaHaf2ICTgzNNq7anSZX30rObz0ADlcqpMk1LWfYUccrFHccBf6PdCQW9AeWTBS0qHNPc0Rg++AYMjpjmjyVhx0LQQF+tBfo3ez7VkP2/kWT9inDK5ASZnCCvGx4lvDgz+CQ3Fl0jsFfSEJFSCqfMScO81/4Kwbe6Py45XbmxMJSYS9EUGBqM8b6R89+cximrc4rJa/bGFGfkxNL95K9dBL2DHmOMJTmhaWhmM8aYePSOBuvsbqUUbv5JsziPLdxH7gr58Mruy6mVh3hw5S61+r1ObFg02yeuwSOLF5kKZE1t03alUtlAxn3dkq/GLOf3eZZJObUqF+Dd5uWYvWQ/vl5J57KVUgTmTjpF0vvrhXRoXZHihbPz1XcrOHjsCvtW9efazQfUa/MjhfIFUKtKgXTv07MqXbQYBfLmZf+RpHP0BoOBAoGWYcuEhAS6fzmIwb16kTVzFtp+2oPI6CjObtvO3sOHadaxI8UKFaJgUFBGdeFf5cxsaVvBXCUoXbAanUfXY8GWX/jkzSE2cXtObiAqNjzFEPhDSimbyWhTl46gXrmW5M1WiD/W/MC5qyf4secS7obfZMCUtuTMEkSJfJVSrStDuXlbjpofoUUnTsZNPMJWbl5od6+lEmc5osbdy6Zc+Sd9+TEtGIsuX2n0JWuRsHk2CbuW4DhwNgDxo95B+edAX6P1c3dFkvUrSGfQ4ZLDhZgrMY+NCTt4n5grMeTqYvkgizgejk8VPwweDhg8HHAv4knE8XC7T9bxUXHER8ZxauVhTq08bLMs5vBlrh2+TLn3a5C1WMoZv9ePhhAeeo+ybasBltniOcsF4eTujJO7M5kLZOPWP9deimQN0OW9qnRoVZHzl2/j6e5Mjmw+NH1/CuVK5LYOjT9qyZqjHD0Vyu/j2wGwbusp2rYoTyY/dzL5uVOnakHWbT31UiRrsFzKo1I7ggJ+nDEDnU7RtX17ANZu2cKv343F08ODutWqEVwgPxt2bLfrZJ2cu4snAX65uH43JMWyjQeW4OnmQ5lC/z4qsuv4ei5e/4e+74wF4NCZ7dQu3Qwvd1+83H0plb8KB89st8tkrbLnx3x8W4pyLfQc+GVDObslxR1YhxYXY3PeWgs9BwYHVObcqdZvvnQc864lOA5faXl9bBv6sg1RmXICoC/XCPOxrZKsxX9jjjMTfSkapwDnxy6/Nusq2drkQO+itym3+TmVkSR74+ThQuVP6qUoP/DHNjyyelOgbjE8ArxTLDfFmzi+ZD9FmpW1uSY7Id6ULMaYJm1OS05OBoITv1wcP32NTTvPMG3Mu6nGRsfE02/4Ir79ohke7knvlaiHoxNAVHRcqiOK9mj/0aOcuXiRlo2bpFh2/dZNho8fz/LffkOvT3rPR0VH2/ysvSydBR5E3CH09gVqlGySovzw2Z00qtgGgz7l/QaSi4uP4dflo+jY5HNcnZImFsYak77ox8RHpz6sbAd0Jetg3jYf8z970BWqAIAWE4H58EZ0yS6v0pWsQ8Ki8Zj3rUJftbklLsGEee9KdEWqohxSTizVzGbLpLLXP0H5ZUsqj4tJ9nM0L+qDUpL1S+7B/vsAxFy2zGqMOBaO3sOAwcOAe0EPrv5+Gb2bAdfcrug9DBjvxnNn4y1MYUZydcqTap03l1/HKcAJ73JJdzBzL+zB3Y23cApwxvggnshT4WSqnyXN+/c0rh2xzNR8cPUeADdPheLk7oyjmzP++bLgny/lpDKdQY+Tu0uqywDOrD2KeyZPspfMYy3LlD8rF7efxj2zJ7HhMdw5e+Oxl4Glt0WrDgNw6Lhlpv+aLafI5OuOv6871Srk4+r1B/zy1w4qlsmDk6OBQ8evMGbyeprWL07rN8qkWuc3E9dQIDAzLRonXU9bu0oBpvyxjQKBmbl+K5xNO8/So2OtVNfPSG0/7UHenDkpVbQo3p6eHD5xgtGTJ5M9IICuHTqkiP9s2HBaNmlsc012nSpVGTXxR7w8PNh35AjnL1+mVuXK6diLpzfy9+4EZQ8mT0ABXJzduXbnEku3/45eZ6BpNds7bG05vJwEs4naZZr9a71zNv5E9kx5qVo86Q5mJfJVYuXOv8mRKS/3wm9z9PxumlXr8IJ79HQS9q0CwHzJMqnOfGwLePiiPHzRFaqArlQdVL5SGKf2wdC6P8rNE9PyKYCG4bUPrfXocgejK98Y09/DLZdnZcpJwsa/LDdT6TI21W2bt8yB2Cj09ZP2r65IZUxzvyWhQFlLzO5lGNoMeCF9lWT9kgv56YLN69A/LUNebgXdce9XENe8btzbdod7W25jjjPj4OOAa6AbOTrkwSWHS4r6Yq/HcnfjLfIPLmxTnvn1rJgiTFydcQnlqCNri+x4FLWPIfD9v221eX1swV4A/IKy4J+v/jPXF3EzjEs7zlC9t+0lLwXqFyMuMpbDs3ehd9BTuHEpMhfM9pha0te73WbavO45eD4A1SoEsebv7jg46Nl/5DLTZ+8kIiqWwFz+DOjegK4dUh8GPX3+Jj//uZ0di/valPfv1oBbdyP5uP9snJ0dGPZZE+ra4SS7ogUKMnvZUib99hvRMTEEZMpEs4YN+apXL/x9bW+ju2nnDjbs2M6JjZtsyscNGULXL77gne7d8PfxYfrYsQTnt8/h/oK5SrDj2GqWbJuJMcGIv1cAxQLL0aJm5xSTyzYeWELuLPkJyv7kL5pXb11g5a5ZfN99nk1569of8SDyLj/O/xJHB2faNexFqQJVHlNL2jJN6m77+nfLVQyqYHkcB/yN0ulw6PkLpjnfYPrjKzDGoYJK4fD5nzZHwwCGTqNJWDAW04Jx1tuNOvSZbrlD2iO0iHuYFnyPQ/dJKEPS6ISuZhv0t0IwzR4Jmoa+5tvoqrd6IX1VL9Owzv8T1zxu2qMJ8VWTJ8w+jkoz0uymqR/Vvkoc9G9mdBMy3MrZURndhAzXOuDluqY9rcS2D0p1UoXcFEUIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDv3UidrpVQlpdRcpdQ1pVS8UuquUmqdUqq9UkqvlKqplNKSPWKUUleVUiuVUp2UUo6p1OmqlPpaKXUmMf6KUup3pVSexOUdHqnzcY8h6b0/hBBC/H8yZHQD/iulVE/ge2Aj8DlwGfAB6gM/AQ+AsMTwHsA+wAHIBtQDJgHdlFL1NE27nazqaUAz4CtgP5AL+BrYoJQqAawAKiWLL51Y18NtPHT1hXRUCCHEK++lTNZKqepYEvVETdN6PLJ4iVLqe8AN8E0sO6Vp2u5kMXOUUr8Cm4DpwOuJ9boArYFvNU0bk2x7N4FVQBVN09YAt5Mtc37MNoQQQogX4mUdBu8P3AP6pbZQ07TzmqYdfVIFmqbtwnIE3kQpFZRYbAD0QPgj4Q8Sn1/W/SWEEOIl9tIlH6WUHqgJrNU0LfY5q1uZ+FwFQNO0COAPoIdSqpZSyl0pVQQYAxwBNjzn9oQQQohn9tIla8AfcMFyjvp5hSQ+Z01W9j6wCMu58AjgOJZz3fU0TYt/AdsUQgghnslLec76BVKJz1qysuHAe0BfLBPGcmGZbLZKKVVD07So/7wxpToDnQG8snjRsmDz/1rV/4Uf/piZ0U3IcMpQPaObkPG0fw/5fxdxOy6jm5DxAlJcnCOSeRmT9V0gBsj9AurKmfh8HSBxyLs/0EnTtF8fBiml9gBngE7A+P+6MU3TpgJTAbIXyi4fUUIIIZ7KSzcMrmmaCdgM1FNKOT1ndY0Tn3ckPhdLfE5+CRaapp3FMsms8HNuTwghhHhmL12yTvQN4Idl4lcKSqm8SqniT6pAKVUJ6AIs1jTtQmLxjcTn8o/EFgC8gdDnaLMQQgjxn7yMw+BomrZVKdUb+F4pVRiYiWWymA9QB8tw9Tsk3RSlsFIqEkt/s2K5cUpb4CTwYbKqt2GZ9T1WKeVD0k1Rvkys67e07ZkQQgiR0kuZrAE0TftBKbUX6AV8h2WWeASWBNsFWAY8nL0zIfE5Dss57yNAV+CP5DO8NU1LUErVAQZimQg2FLgD7AQGa5r2cPa4EEIIkW5e2mQNoGnaTiyJ9HE2kzTj+2nrvAv0SXw8Tfwzb0MIIYR4Fi/rOWshhBDilSHJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzhoxugPhvTmw6zrH1Rwn9J5So+5F4ZfEmuEYw1dvVxMnVCYDz+89zaOUBrhy/QsSdcDz8PclXPh+1OtbB3cfdWld8bDwrxy3n1NaTOHu4ULdLPYrVKW6zvW1/beXo2iN89Osn6A36dO3r48RfiCDuXAQJt+MwxySgczfgmNcdl1K+KMek76GmO3HE7LmD8UYMKIVDNhdcK/mj93K0xmhGM1E7bmO8FIly0uNSzg+nfB4224s5fI/4sxF4tsiF0ql06+eTrNtynLE/reafc9e5HxaNv687FcsE8WXPNyhcIJs17n5YFANHzGfZ2sPExMZToXQQ3w5uTdFCOawx0TFx9B48i6VrDuHt5cbX/d6k1evlbLY3dspq5izew87lX2Kwk/dBauq89RZb9+xOdVn96jVY8fvv3Ll3j48H9Gfjzp3kzJqVcUOGUKtyFZvYbl9+QUhoKEtnzEyHVj+9uxE3Wb53BhdvniTk9lniTbGM67SCTF7ZbOKiYsP5e8s4DpzbjNEUS75sxXmvZl9yZspvExdvimP+jsnsOLWS6LgIcmcqQJvqn1IoRxlrjNmcwNztE9l6fAkGvSOvlW1LwzLv2tSz+/Ra/tw0hm/fX4SrkzvpzXxqF6aFP6BdOg6OzuiK18TQZgDKy98aY/ylH+YdC1NdXwUE4vjNWgC0uBhMf36N+eA6cPXE0LIv+gqNbeJNK6di3rUUhyGLUfq0TaeSrF9SO2ZtxyuLF3W71MMzkxfXz15j8/SNXDx4kU5TOqPT6di/eC9xMXHUaF8Tn2y+3Lt6h42/buDcnrN88lt3a1Lf9udWzu87x5tftODGuRssGDqPbAWy4ZfT8gYPuxXG1t8203Zse7tJ1AAxRx6gdzfgUt4PnZuBhDtxxBy4h/FaDJ7NcqCUIiEsnvClV9D7OOFeJwDMGjEH7hG+9CpeLXOhc7H8CcQcvo8pNBq3mllIuBdP1KYbGDI5WRO6OdJIzMH7eLyWzW4SNcC9B1GUKpabzm1rkcnPnSvX7vHd5FXUeHMU+9YMIXcOPzRNo2XHiVy6coexX7fBx8uNMZNX0bDNWHavGkSOrL4AfDd5NRu2n2Lq2Pc5fuoqH/T8lVJFc5EvbxYArl6/x+gfV7Dkt0/tOlED/Dh8GOERkTZluw8e5LPhw2hSry4AfYcP4/zlEGZNnMSKjRto/dFHnNm2HR8vLwAOHDvGnwsXcnD1mnRv/7+5+eAKe86sI2+WwhTMXopjl3eliNE0jbGLe3I7LJR2tfvh5uzJsr3TGTG3MyPazcbPI4s1dtqarzl8cRtvV+9FJq/srD88l9ELujLk7d/InbkgANtOLGPj0fm8X/cLouMi+G3DaHJlLkhwzrIAxMZH89fmsbxTo3fGJOrT+zB+9z66otXQd5sEkfcxLRyH8du2lmTqYPm8MzTtilbrbZt1tTuhmKb0RFeqjrUsYcXPmE/swNBpNNqV05im9kHlLoIuII9lnXvXSVg6CYc+M9I8UYMk65fWu6Pb4ubjZn2dt1ReXD1cWThiPpcOXSSwTBBN+ryRIsYvpz/Tu03jxMZjlG5i+SM7u/sM5VtUpFDVwhSqWpij645wfv95a7JeNX4FRWoXJVex3OnbyX/h0TCrNdkCOGRzRTnridp0E9O1GByyuxJ7+D5KKTxey4bOyZJgDJmdeTD7MrFHHuBa0dJH45UonIp44ZjHHfJA3NlwjFejrck6audtnILccQhwSfd+PslbTSvwVtMKNmVlS+SlRO1BLFp5gJ6d67N83RF27jvH6ll9qFG5EAAVSgdSuOoAvp+yhu+/tnxwrdl8jI/a16JJvZI0qVeS2Yv3sHH7KWuy7jtkDi0al6VS2Xzp28n/IDh/gRRlv86ehaOjI2+9/gYAazZvZsLQYdSvUYPaVarw+/z57Dl4kIa1amE2m+n25Rf0+/hjAnPlSu/m/6tCOUoz+eMNAGw6ujDVZH3w/GbOhB5iYKupBOeyjJDkz1acXtOasGLfTNrV/hyAy7dOs/OfVXzYYAg1ijYFoHDOMnw+syXzd0ymz5vjAThyaQeVCjWiUqGGAOw/t5mjF3dYk/WCnT+RzTcPlQs3StvOP4ZpyY/glw1Dj5+syVNlDcI4tDnmrfPQ13nPUpY5Nyqz7WeZ6cQOAHRV37SWmY9tQV+3LfpSdaFUXcy7lqKd3AGJydr013B05V9Dl790OvROzlm/tJIn4YeyF84OQPjt8CfEWIY9w++EW8sSjAk4ODlYXzs4OWCKNwGWRH7p8EXqf9zwxTX+BUmeqB8yZHIGwBxlab/pZiyGLM7WRA2gc3dA7+NI/MVkR14JGsqQ9OegDDpI0ACID4nCdD0GlwpJQ2n2zDfx9+7gYOnzinWHyZrF25qoAbw8XXmtbgmWrztsLTMaE3BJ9j5wdXEkNs4IwNrNx9m+5wzDB7RIhx68eDGxsSxYuZImderg6+0NQLzRiIuz5f1iMBhwdHAgNi4OgF/+/pvwiAj6dvkoo5r8RDr17x/dB89vwcc9kzVRA7g6eVAqsDoHzm22idPrDFQsWN9aptcZqFSwAccu78JoigfAlGDE0eBkjXF2cCbeZNlfV+6cY+PRBbSvM+B5u/afaecPoytS1eYoVxdYHNx9SDi47onrmncuQuUpii57si95JiPKwTnptaMzmtHSX/PRLZhP78XQut8L7cOTSLL+P3Lp8EUAMuXJ9PiYQ4kxuTNby3IE5+DwqoNE3Ann7J6z3Dh3nRxFcmKKN7Hih+XU+6g+rl6uadv4F8R4PRoAvU/i+WgFpDJsrfQKc7gRzWQGLEfbcafDMUeZiL8SRcLdOPSZndESzETvuI1LeX90zvY79JuQYCY+3sS5izfpNuBPAjJ5Wc83nzx7jSIFs6VYp3CBbFwJvUdkVCwA5Urm5a8Fu7h+8wHrthznyMkrlC8VSFyckd5fzWLY583x80n/4c0XYdHqVURERtK2RUtrWfmSJfnl77+4e/8+0+fMJiIqitLFinHn3j0GfzeGCUOH4ejo+IRa7dvVuxfI4ZdyFCSHXxB3I24QG2/5Wwm9e55MXtlxcrAdNcruH4QpwcjNB1cACMpajH1nN3DjfggXb57k2KXd5MtaDICZ60fSsPS7ZPPNk7adehKdDmVwSFlucES7euaxq5nPHkC7eRl9leY25SqwBAk7FqI9uIX52Fa0kFPogkqiGeMw/TUUQ6u+KHefF92Lx5Jh8P8T4bfD2DhtA4Flg8iebNJQcnHRcayasIJMeTJRqFpha3mtD2rzR9/fGNNsNABV3qlGrqK52DR9A27ebtbhcntnjjIRs+8ehuwu1iNsvbcjxpuxaAkaSm9J2lq8mYT7lqMFLc6MMuhwKetHxMpQHvxp+TLjXMIHhwAXYvbfReeix6mQZ8Z06ilVbzqSg8cuAxCUJzOrZvUhs7+lzfcfRJE7R8pRAV8vyxH4/bBo3N2cGdjzdZq1H09g+c8A6NWlARXLBDHih6X4+3rQoU3VdOrNi/fngoVk9venYc2a1rIxXw6i6QfvE1CqJAaDgW+/+JJc2bPTsW9f6lWrTp2qL29/AaJiw8jkmTVFuZuzZ+LycJwdXYmMDbeWJeeeWBYZGwZA/VJtOHZpF32nW4bKKxZsQMVCDdh6Yin3Im/RtELHtOrKU1EBgZjPH7Yp0+6EQtgteMI55YQdi0DvgK5iE5tyQ7PuGMd2JL5nZQD0jT5El680psUTwMMXXfXWL7wPTyLJ+v9AXHQcf/f/E51ex5sDUx+mTDAlMG/IHMJvh9Pppy42E8U8M3nxyczu3L92D2d3F1y9XLkXeo8ds7bTaXJnjHFGVv+4klNbT+Lg7Ejlt6pQsWWl9OreU9GMZiJWXwMduNdMmjjjVMyb+AtXidp2C9dyfmhmjehdd9CMliNqEg+6dW4GPFvmwhxuRDnp0TnrSQg3EnP0Pp5Nc4JJI2rXLeIvRaEMCufiPjgX9U7/jj7Gr+M6Eh4Zw8WQ24yfupbG733PxvmfkzunP5oGKpU5cRqazevsAT7sXf0VF0Nu4+Xpip+POxdDbvPD1LVsmP85MbHxfD5sLkvXHMLF2ZEenerxyft1UlZsZ67dvMmGHdvp/v77GAxJH3lFCxbk9JatXAgJISBTJjw9PNixfx9L1qzh2Ib13Ll3jx6DBrFhx3Yy+foxpE8fWjZu/IQt2RdN07C+wZ8cmGqUZvv2wMXRjS/fmsad8GvodQZ83DMTFRvB7K3j+bDBEAwGR+Zun8i2E8sAjepFmtKiysdPNWT/Iujrtcc0tQ+mBd+jr9ceLfIBpplfgtJZHqnQjHGY965EV7IWysPXZpnyCcBh2HK4HQKunih3H7RbISSs/hWHgbMhPhbjrJGYD64FRxcMDT5AX69dmvVPkvVLzhhn5O/+f3L/2n0+mNgJr8xeKWLMZjOLRizgwv7zvPttOwLyBaSIUUrhm93P+nrFD8so83pZAvJnZf3Pa7n2Tyjdfv+U8Dth/PrJL2TKk5mgskFp2renpZksidocYcTj9Rzo3JOdfw9wwbVqJmL23uXBact5ekN2FxwLeBJ/NgKV7Fy2Usrmcq7oHbdwKuSFwc+J6L13MN2Ow6tVLsxRCZYZ5t6OOOSwj9MDhfJbjqDKlwqkQc1iFKran+9+WsWPI9vi4+3G/QdRKda5H2YZBvVJdopDKUVgslMkvQfPokObahQPzslX3y7i4NHL7F/7Nddu3Kduq28pnD8btaoWTlG3Pfl70SLMZrPNEPhDBoOBAoGBACQkJND9y0EM7tWLrJmz0PbTHkRGR3F223b2Hj5Ms44dKVaoEAWD7ON9/2/cnb2Iig1PUf6w7OHRtJuzF3cibqSMiwu31pOcv2fSKZV52yeSP1sJSgVWY9PRhew4uYJBbaYDMGJOJzJ5ZaNmsTdJD/rKTdGuXyBh9TQSlk0GpdCVb4wqXgNz6NlU1zEfWg/R4eiqpN5GpRQkm4xm+nMo+uqt0OUqjGn+WLRLx3AcvhLt/k2Mo95GZc+HLrhymvRPzlm/xBJMCcz+8m9CT13lvTHtyBKUMgkDLBuzhOMbj9FqyFtPlWBPbjnBjbPXqd3JconL2T1nKdmoNG4+bmTNn4185fNzbs/jzwGlJy1BI3LddUy3YvFolA2Dn1OKGOci3ni3y4tnq1x4vZsHzyY50KJNGDI7WYfGHxV/MRLTnThcy1q+wBivRONU0BOdiwGDvxMOOVwxXkmZAO2Bt5crQbkzc/7SbQCC82fj5JlrKeL+OXuNnNl9cXdzTrEMYMnqgxw5eYXBvS3Dnuu2HOfdFpXI5OdBiSK5qFOtCGu3HE+7jrwgfy5cQPHCwZQIDn5i3I8zZqDTKbq2bw/A2i1b6Pzue3h6eFC3WjWCC+Rnw47t6dHkFyK7XxBX755PUR567wJ+HgE4O1q+pOXwD+R2WChxxhjbuLsXMOgdyOKdM9X6L948yY5TK2lby3La5OilHZQvUJfMXtnJ7JWd8gXqcvTSzhfcqycztOiF48R9OAxbgeMPu3D4+Ae0m5fR5S+Tarx5+yLw8EFXvOa/1p1wYC3mkFPo3+xpWffYVvRVmqM8/dDlDkZXpCrmo1tfYG9sSbJ+SZnNZuZ/PZeLBy7w9qj3yFk09ctLVv+4koPLD9BsQHMKV3/yhxVYbpCyasJKGnVvbL0OGyA+Jt76c1xMXIohsoygaRpRG29gDI3Bo0E2DFkef1mV0usw+Dqhd3fAdDcOY2g0TkW8U6/XaCZ6521cK2eyubmKdegc0Ix2sAMe4+btcE6fv0FgbstEw8b1SnDtxgO27T5tjQmPiGHl+qM0rlsi1TqiY+L4bOgcvh3cGg/3pGQelex9EBUdaxfvgyfZf/QoJ86coW2LJ89iv37rJsPHj2fi8BHo9UmjLVHR0TY/a/be4WRKB9XgfuQtTl3Zby2Ljovk0PmtlA6qkSyuJglmE3vOJM2YTjCb2HN6LcVyV8LBkHKSnVkzM3P9KJpW6Ih/svPiyRN+rDEmQ/aXcnJFl7Mgyssf89EtaNfPo6/1Too4LewO5hPb0Vd8I/WJaclj42Iw/T0cwzsDUS7uycqjk/0cBaRdf2UY/CW14vtlnNh0nOrtauLo7MCV4yHWZZ6ZvfDK7MW2P7eyc84OSjcug18OP5sYNx83m2Hvh7bM3IR/Ln+K1ilmLQsqG8TehbvJlDsTEXfCuXjgAlXsYLJR9PbbxF+IxLmUD8pBYbqZ9EGhczOgc3fAHGkk9mQYhiwuKL3CdCeW2EP3cczjnuIOZQ/FHLyHztsRp6Ck5Q7ZXYk9EYbe2xFztAlTaDQuxb3Tuov/qnXnSZQqkpuihXPg6e7M2Ys3+fHX9RgMOj79sB4ATeqVoELpIN7v+SsjB7bEx8uVMZNXoWkavT9K/ZK8URNWUCAwgJZNki77qVW1MFN+20jBoACu33zAph3/8OmH9VNd3178uXABBoOBt5s2fWLcZ8OG07JJYyqWTrpmtk6Vqoya+CNeHh7sO3KE85cvU6ty2gxx/hd7E5PrpVunADhycTuerj54uPhQOGdZSuerQf6sxflp5Ze8XaMnbs6eLN0zHQ2NJuU6WOvJnbkgFQvW589N35GQYCKTV3Y2HJnH7bBQPn5tRKrb3nR0IbHGaJs7mBXJVYHZ2yZQMIdlH+46tYp3avZOo96nZL58AvPRLehyF7G8PnuAhFW/oH+tc6rXQifsWgIJpscOgdvELp2ICsiLvnzSnAVdkSokbPgDlTUIHtxEO7kLXcO0m2QnyfoldXa3ZRh66++b2fr7ZptlNd+vTe2OdawxB1cc4OCKAzYxJRuVovkXtufwbl++zd6Fe/jo109symt0qEXUgygWj1qIwclA3S71yVfe9naFGcEYYhmGjj10n9hD922WOZfxtQxh6xSmW7HEnQpDi9fQeTrgXNoX52LeqdaZcD+e2BNheLWwHfpzKeOLOcZE1JaboFe4VPDHIWfK69jTW/lSgSxcvp/x09YSH59Ajmw+VK9YkM8+aUTuxJva6HQ6Fs7ozoDh8+j55V/ExpmoUDqQ1bP7kjObb4o6T5+7zs+/b2Ln8i9tygf0aMLtuxF89NlMnJ0dGfZ5c+pWL5Iu/fwvjEYjc5YupUGNGmTJ9PjLGTft3MGGHds5sXGTTfm4IUPo+sUXvNO9G/4+PkwfOzbVm61klAnLbK/xnblhFACFcpThy7emoVM6+rw5gb+3jGPmhlEYTfHky1acga2m4udpe8qsc4OvmbtjIvN3TCY6LoJcmQrwWYtJ5M2Scj5CRPR95m2fRM+m32HQJx2R1i7eglsPrvLX5rGgadQu0ZKaRZu9+I4/jsEB89EtJKz8BUzxqGxBGNoPQ18t5VwFAPOORagcBdDlKfrEas3XzpOw4U8chiyxKde/0RUt/C6m6f3BwRl9q8/QFa32wrrzKPUyDev8P8leKLv20a9dM7oZGeqHP2ZmdBMyXOjA9Lupgr0yaPZ9dJ4e5vx4L6ObkOE6Fkt9pOtVE9s+KNWJNHLOWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJWgghhLBzkqyFEEIIOyfJ+l8opYYopWpndDuEEEK8uiRZ/7uvAEnWQgghMowkayGEEMLOPVWyThwK1pRShZRSa5RSUUqpEKXU+4nL2yql/lFKRSqlNimlgpKt66CUGq6UuqSUik98Hq6UckgWkyex/i5KqaFKqetKqQdKqWVKqRyPtOVf60uMc1NKfaOUOq+UilNK3VBKLVBKZVFKlUncXtNU+jpTKXVVKaVXSmmJxV8kxmtKqSHJYmsopTYopSIS98kapVTRp9mnQgghxNN61iPrecAKoBlwAJiulBoJfAz0B94HCgJ/J1vnt8RlvwNNgBnA54nljxoA5AM+AD4FKgF/PRLzr/UppRyBdUAPYGZiXDfgHuCjadoBYB/QJXnFSilvoDUwTdO0hMTtk1hHpcTHtMTYxsAGIBJ4D3gH8AC2KaVyptI3IYQQ4j8xPGP8GE3TfgdQSu0HXseS8PJqmhaeWJ4VGK+Uyo0leb0NfK1p2pDEOtYqpRKAYUqpbzRNO5qs/suapr3z8IVSKhMwRimVTdO0a4lHrU9T33tYEmtTTdOWJqt/frKfJwO/KqVya5p2ObGsHeBIYkLWNG23UgogVNO03Y/si/HAFk3TrEfnSqlNwAWgD9Dz0Z2nlOoMdAbwz+pPAd/8j4a8Uoa3GpDRTchw0acHZXQTMpyzOXdGNyHDeWTK8e9B4pX2rEfWqx7+oGnafeAWsPthok70T+JzTqB64s9/PlLPw9c1Hilf8cjrY4nPuRKfn7a++sCNRxL1o2YDD4APk5V1AVZomnb1CeuhlMoPBAF/KaUMDx9ANLArWTttaJo2VdO0spqmlfX09XzSJoQQQgirZ03W9x95Hf+YMgBnwDfx5+uPxNxIfPZ9pPzeI6/jktWVPP7f6vMDQnkCTdNisQyhd0xMttWAYGDKk9ZLlDnx+VfA+MijSeL2hRBCiBfiWYfBn9XD5BsAnE9WHpD4fDeN6rsDPM1Er5+A3kBT4E3gErDmKdZ7uJ0BwPpUlsenUiaEEEL8J2l96daWxOc2j5S/m/i8NY3qWwsEKKVef1JlmqadT4z9DGgJ/KJpmvmRsHjA5ZGy01gSexFN0/an8jiKEEII8YKk6ZG1pmknlFKzgCGJ53R3Ypn4NQiY9axJ7Rnq+xPLuehZSqlRwB4sk90aAD9omvZPsmonA0uwDGFPT2WzJ4HGSqnVWIb8ryVOdusKLEmceT4Xy9F8FqAyEKJp2vfP0jchhBDicdLjpijtgdFYLsdaCXRMfN0+rerTNM2IZZLZT1hmX6/EkpT9SXlefAWWiWFLNE27QUrdgChgGZbLvTonbmMllolkblhmj68BvsUyJL/rP/ZNCCGESEFpmvbvUf/HlFL1sAyF19U0bUN6bTeoaJA2ct436bU5u3TvWmRGNyHDvWWWS7eczTMyugkZbv1huXSrdYBjRjfBLsS2D1Kplaf1BDO7lXiXtUBgHHAwPRO1EEII8Sxe5XuDD8Jy3XgclpuhCCGEEHbplT2y1jStA9Ahg5shhBBC/KtX+chaCCGEeClIshZCCCHsnCRrIYQQws5JshZCCCHsnCRrIYQQws5JshZCCCHsnCRrIYQQws5JshZCCCHsnCRrIYQQws5JshZCCCHsnCRrIYQQws5JshZCCCHsnCRrIYQQws5JshZCCCHs3Cv7LzL/H9y9cZel05Zw4cR5Lp++THxsPBPWTSRz9szWmFuht+hRr1uq6/+6ewZunm4AxMXEMWPEdPat34ublztter5N5UaVbeKX/rqE7cu3M2reN+gN+rTr2DO4f/sea2evIuTMJa5euIIxLp5hf36LX4C/NebujTsMeq9fqut/t3giru6uAMTHxjFn4l8c2XEIV3dX3vigBWVrlbeJXztnFfs27KL/T1+h12f8Pti4P5bxcyI4E2LiQaQZPy8d5YOd6NfWg0K5HaxxDyLMfPVLGCt3xhIbp1E22JERH3kRnDcpJjrWTP9JYazYGYO3u44v3/fkzZquNtubMDeC+Ruj2TgpMwa9Srd+Jhd6+zbfz5vLwTNnOHbxAjFxcZyc+Tu5AwJs4u5HRPDFtF9YvmsnMXFxlC8czOguH1E0b16buNj4eIb+NpPZGzcSFhVJ8cAghnXsSNVixa0xCQkJfDVzBn+sXYOTgwM9WrSk25vNbepZsHUL/ab8xKFffsXTzS3tdkAqdhxbw7bDKzkXepywyHv4e2elUtG6tKzVBVcnS1vGzx3IxoOLU10/e6a8TO6zAoC4+BimLh3B7hPrcXfx4r0GPalWopFN/MItv7Ll0HK+7z4Pvd4+0kjCvlWYdy/HfOkYhN9F+WVDV6Y++iYfo1zcrXHmyycxzRuDdvYAKIWuUAUMbw9EZcljjdHiYjD9+TXmg+vA1RNDy77oKzS22Z5p5VTMu5biMGQxKh32gX3sZfGf3Ai5we41u8gbHEihMoU5uuPIY2ObftiMsrXL2pS5uLlYf14ybTHHdh7l45FdCTlzmUmf/0jewnnJmicrYPlisGjKQvpPHWg3iRrgdugtDm7ZR64CuclXND+nDpx4bGyDtxtTvFJJmzJnF2frz2tmr+SfAydp99kHhF64ysxvfiFX/txkzpEFsHwxWP3XMrqO6m0XiRrgfoSZkvkd6fi6O37eOq7eSmD8nAgafHqb7T9nJmcWA5qm8e5Xd7l8w8Q3Xb3wdtfxw+wImn52h80/ZSZ7Jktfxs+JZPPBOCb29eHEBSMfjb5P8fyOBGW3fEyE3k5g7N8RzBvhl2GJGuD8tWss2LqFUvnzU7lIUTYcPJAiRtM0Wg/5iks3b/Ddx13x9nBn7JzZvPb5Z+ya9BPZM2Wyxn48bixr9u5lRKcPyROQlanLltL0i4FsHDeeEkFBAPy5fh3TV65gfPcehEVF0XvSRIoHBlG9RAkAImNi6D/1Z0Z92CXdEzXA4q0zyOSdlfca9MTfK4AL104xe/0kjp3fy+iP/0an09G6zkc0rPiWzXo374cydlZfyheuZS2bv3kah8/upEerkVy+foZxcz4nKHthsvnnAeBO2A3mbpzCVx9MtZtEDZCwahrKLxuGFn1QvgGYL58kYfEEzKd24/DlPJROh/nGJYyj3kZlz4+hy1hISCBhyY/Ej3oHx6HLUJ5+lrpW/Iz5xA4MnUajXTmNaWofVO4i6ALyAKDdu07C0kk49JmRLokaJFm/1AqXLczP234BYOP8DU9M1llyZiF/iQKPXX5422EavNuQsrXLUrZ2WbYv386x3cesyfq3UTOo2LASBUsVfLGdeE75ihdg9PwfANixcusTk7V/1kzkDQ567PITe49Ro1kdilcuRfHKpdi7cTf/HDxpTdbzJs2idI1yBBXJ90L78Dxa1HKlRS3bstIFHajY8RZLt8XQtaUHq3bFsvt4PIu/9adaSScAygU7UqrdDX6cG8E3Xb0BWL8vlk5N3WhUyYVGlVyYvzGGLQdjCcpuOSoZ+NMDmlV3oXwRp/TsYgpVixXj0uy5AMxctSrVZL1i9y52njjOytHfUqNESQAqFA6mSPt2jJs3l+8+6QrA0QvnmbtpEz/17kO7+g0AqFa8OGU7f8jw339j3tdDAVi3bx+ta9WiVU3Lzl62cydr9++zJuvhf/xOgRw5aV3rkV9GOvmy/WS83H2tr4sGlsPdxYvx8wZw/MJeiuerSFa/XGT1y2Wz3uGzOwGoXaaptezgmW00rvwuFYJrUyG4NlsOL+fIud3WZD1t6SiqFGtI4dyl0r5jz8Ch51RrsgXQFaqAcvfG9MtnaP/sQQVXImHlz6B0OPSejnLztMQFlSC+Xx0SVk3D8NbnAJiPbUFfty36UnWhVF3Mu5aindwBicna9NdwdOVfQ5e/dLr1T85Zv8R0uhf36zMZTTg6OVpfOzk7YoyLByyJ/NS+U7zT590Xtr0X5UXugwSTCQfHpGFhRydHjPFGwJLIzx49TbNOrV7Y9tKKr6dlnzw8+l29K5YAP501UQN4uuloUMGZVbtirWVGk4azY9IRs6uTIvEtwIZ9sew8Gs9XnTzToQdP9jS/8xW7dpHVz8+aqAG83NxoVLECy3fvspat3LULB4OBltVrWMsMej0ta9Zk/cEDxMVbdkC8yYSzY9L+c3N2JjZx2YlLF5m+cgXjuqZ+uik9JE/UD+XPWRSAu+G3HrvepoNLCMpehFxZ8lvLTCYjjoakvjo5OhNvjAPg4OltHL+4j/aN+ryopr8wyRO1tSxvMQC0+zcsz+cPo/KVsiZqAOWbFZWjAOaDa5NWNBlRDkmjbjg6oyXuA/PRLZhP78XQOvVTa2lFkvUrYta4v3mnWBveL9+eMV1HE3ImxGZ5vuL52LpkC/dv3+fI9sNc+ucS+UsUwBhvZOaI6bzd+x08vD0yqPUvxpJf59Otfid6v9GVnwZNIPTCVZvleQoFsmfdDsLuPuDkvuNcPR9C3uBAjPFG5k76i2adWuLu5f6Y2jNWQoJGvFHjfKiJ3uMfkMVXR/OaltMc/1w2UjiPQ4p1CuVx4OqtBCJjzACUKejI7HXR3LibwMb9sRy7YKRsYQfi4jX6T3rA4I6e+Hrax/D/vzkVcpng3HlSlBfOnYcrt24RGRNjjcuTJQBXZ+dH4nITbzRy/to1AMoVKsSS7ds5fy2UQ2fPsOHgAcoXKgxAr4k/0rXZmxTImTNtO/WMjl/YB0COzIGpLj916SDX74ZQu3RTm/ICuYqz8eAS7oXf5uCZ7Vy89g8Fc5XAaIpn6tIRtGvYG08377Ru/guh/bMXAJUtcTRMp0cZUv4tYHBEuxWCFm9JyCqwBAk7FqI9uIX52Fa0kFPogkqiGeMw/TUUQ6u+KHef9OqGpYnpujWR7hwcHajbui7Fq5TAw8eTaxdDWTx1EYPf+ZIRc0aSPSgHAC0/acU3XUbycY0uALz+wRsUKFmA+ZPm4eHrSa0WtTOyG8/F4GCgapOaFC5TBA9vD26EXGfNrBV89+kI+k0cRNbc2QBo3K4pEweMY8BbvQGo27ohgcH5WPH7Ety9PKjcqFpGduOJ6vW4zZGzllGAwGx6Fn3rTyYfS2J9EGEmV5aUf+o+Hpbv6mERZtxddHzW1pO3vrhDkbctRyHdW7lTLtiJb/8Ix89bz3sNXVPUYa/uR0SQO0uWFOW+7pYvnA8iI3B3ceFeRATeHim/gPkkxt2PjADgozeasuHAAYp/8D4ALWvUpGWNGvy5bi2hd+7Q7+130qor/8ndsJvMWjeREvkqkT9H0VRjNh1cgkFvoHpJ24lTbep8wtczuvD+SMtow5vVP6BQ7pLMXj8JTzdf6pVrkebtfxG0+zcwLfoBVaQKusQjbBWQF/O5g2gmozVpazGRaKFnQdMgOgwcM2No1h3j2I7E97RMstU3+hBdvtKYFk8AD1901Vune38kWf+f88nkQ6chna2vC5ctTImqJfnsjT4s+nkh3b7tAYBvFl9GLxrDzSs3cfN0w8Pbg5tXbrJ8xjK+/nMo8bHx/DH6N/Zt2IejsyON2zeh4XuNHrdZu+Ll5807PdtZX+crVoDgcsUY3ulLVv+9nPcHWPaPt78PX0z9mjvXb+Pi5oq7lzt3rt1i/bzV9PlhAMa4eOZPmcOR7QdxdHakdov61HqzbkZ1y8ZPn/sQEaVx+YaJSfMiadH/Diu/z0SuAAOaBiqV+WCaZvs6m7+erVMyc+l6Al7uCl9PPZeum5g4P5KV32ciJk5j0M8PWLEjFhcnxcct3OnczD5HGjRNA1J2WkNLEaeeIs7D1ZXVY74j5OZNHAwGsvr58SAyki9/ncaUXn1wcnBgyMwZ/LV+HZqm0bZefQa1a/9CT9M8rZi4KEb83g2dTk+PViNSjTGa4tl+dA1lC9XE0832CNHPKwvjP13EjXtXcHP2xNPNmxt3r7Bo6wy++ehP4o2x/LpiNLtPbMDJwZmmVdvTpMp76dG1p6bFRmEc/xHoDTh0/MZarq/fAfO+VZh+G4SheU9ISMA0eyTERVsClOX3pXwCcBi2HG6HgKsnyt0H7VYICat/xWHgbIiPxThrpGXo3NEFQ4MP0Ndrl0pLXhxJ1q8g/6z+FCxdiPPHz9uUK6UIyJV0+cvMEdOp1bI2uQvlYfYPs7hw4gJjlozl3s17DGk7mOxBOShWqVh6N/+F8M3sS1DR/Fw+fdGmXClFpmxJl77Nmfg3VRpVJ0dQLpZMX0DImYt8OW0oD+484Pteo8iaOxuFSgend/NTKJjLcpRQtrAjdcs5U7LtDcbPiWDspz54e+i4H2FOsc6DSEuZl0dSQlFKkTdb0sdC/0kPaNvQlaJBDgyfEcahM0a2T83M9bsJNO59h4K5DdQo5Zyi7ozm4+HB/YiIFOX3IyMB8E48cvb18OTqrdsp4h4kxj08wn4oV7Kj9a9nzqBicDANK1RgxqqVzN64gXXfjQWgwWd9yRMQQPuG6fuFNt4Yx4jfunHz3hVGdPkdf6+AVOP2nNxAVGx4iiHwh5RSNpPRpi4dQb1yLcmbrRB/rPmBc1dP8GPPJdwNv8mAKW3JmSWIEvkqpUmfnpUWH4dxfBe021dw6P83yjerdZkufxkMbYdgmv8d8dvmA6CCK6Or8ibmXUvAzcsaq5SCzLmtr01/DkVfvRW6XIUxzR+LdukYjsNXot2/mTjDPB+6YNvLXV8kOWf9itI0zfJmfIy96/dy6Z9LtO5uudTjyPbDVG9aA09fT/IUzkPxKsU5sv1wOrU2bfzbPji8/QBXz4fQpEMzAE7uO06FelXw8PYkZ75cFC5TlJP7jqdTa5+el7uOvNkMXLiWAFjOTf9z2Zgi7vRlEzky63F3Sf1jYPn2GI6dN9K/vWUyzsZ9cbSp54q/t55iQY7UKuPExn1xadeR51A4dx5OhVxOUf7P5cvkzJwZdxeXxLjcXLp5g+jY2BRxjg4OBGXLlmr9h86eYfbGDYz56BMA1u3fT7Oq1cgTkJU8AVl5s1p11u3f/4J79WSmBCPf/PkpZ68eZfD7P5Mn4PFXf2w8sARPNx/KFKr+r/XuOr6ei9f/4Z363QE4dGY7tUs3xcvdl8BshSmVvwoHz2x/Yf14HprJiGliV7QLR3HoNQ1dzpRXr+jrvIfjhD04jFiJ49itOPb7He3BLVRgydTPZwMJB9ZiDjmF/s2eAJiPbUVfpTnK0w9d7mB0RapiPro1LbsmyfpVdOfaHc4cOk2+4qlfghQXE8fvo2bS7vP2Ntdix8UkfTDHRsclDjW+nO7dvMuFE+fIUyj1yTfxsXHMnzyblh+3wdnVxab8obiY2BTDpfbg1v0Ezl0xkTer5Zx1w4rOXL9jZsfRpLaHR5lZszuGhpVSPyqOjjXzxZQwRnzkhYerLll5Un+jYuyx9xaNK1bk2p07bDt61FoWHhXFqj27aVwx6QjwtYqVMJpMLNyW9EFrSkhgwdYt1CldGidHRx5lNpvpOfFHPnv7HXJmThqFSZ7wo2Ji0vW9YTab+X52P46e383AdhMpmKvEY2MfRNzh8NmdVC/RGIM+9eT0UFx8DL8uH0XHJp9bb64CEGuMsf4cEx+d8pxKBtDMZkw/98Z8cicOn05Bl+/xl5YpByd02Qug/LJhvnIa7eRO9LVTn3egxcVg+ns4hncG2txcRXs4dA5ocVGQxr9vGQZ/ye1esxuACycuAJbLrDx9PPH09SS4XDB/jP4ds2amQIkCePp6cu3iNZb8shilUzTr3DzVOhf+tICsebNRKdkdzIpWKsaav1eTLW827t++z/Hdx2jcoUnad/ApHNxqOYIJOXMJsFxm5e7tgbuXBwVKFGTBlNloZo28wUG4e3tw88oN1s5aiVKKhu80TrXOlX8uI3POAMrUTLqDWaHSwWxZspEsubISducBpw+dok6rBmnevydpO+QuJfI5EBzogIerjvNXTfy0MBK9Hj5paflgaVTJmXLBjnz0zX2+/tATbw/LTVE0oEer1M85f/dXBPlyGGhWI2lSWfXSTkxbGkn+nAZu3E1g66E4urbImHPWixKT66FzZwFYu38f/l5e+Ht5U614cRpXrESFwsF0/PYbRnT6EG93D8bOmY2mQa9WSZfflQgKomWNGnz+8xRMpgRyBwQwbcUyLt24wa/9+qe67RmrVhIVE0v3ZHcwq1WqFIOm/0rlopbJXHM3b2LUh51TXT8t/LxkGDuOraFVrS44O7pyOiTpngt+XllshsO3HF5OgtlE7TLN/rXeORt/InumvFQtnjScXyJfJVbu/JscmfJyL/w2R8/vplm1Di+yO/+J6Y8hmPetQv/6J+DogvncIesy5RuA8s1quZnJxr9R+UqjHBwxXzpOwvKfLHc6q/h6qvUmLJ2ICsiLvnzSZ4WuSBUSNvyByhoED26indyFrmHHNO2fepmPjl5mQUWDtJHzvvn3wH/RJjj1WYmFywXz1W9D2LRgI+vmrONmyA1iomLw8PagSMWitPykFdnyphziC70QypdvDWTkvG+sN0QBiI2KZcaI6ezfaJlg9lq7xrz+wRvP1fZ71yKfa/2HPqn7Qarl+YsXpNf3n7Nz1Ta2LtvE7Wu3iIuOxd3LnQIlC9O43RtkyZk1xXo3Qq7zbbdh9J/8lfWGKACxMbHMnfgXR3cexsHRgdot6lOvdcPnavtb5kHPtf74OREs2RLDxesmjCbIlklP1eKO9GzjQa6ApO/i98PNDP4ljJU7Y4iLt5zbHt7Fi6JBKY+szoQYqdfjNhsnZbbevQwgMsZyO9JVu2JwdlR83Nydbq2e/3I+Z/OMZ17HrWH9VMurFSvO6jHfAXAvIpyBv/zC8p07iTXGU75wYb7p3IXigbY3xomJi2PIzBnM3byJsMhIigUGMuyDTtYbniR3JyyM0h925O9Bg1PcjvTL6b8ya8N6yx3j6tZj2Acdn/pOd+sP53jarqfqw2/qcuvBtVSXtanzCW8nu+Xwpz+8iaaZmdBryRPrvHrrAn0nvcX33edZb4gClglsU5eOYO/JjTg6OPNG1Xa8WT31v8Fn0Tog5SjGs4jrUwPuhqa6TN+0O4Y3P0ULu4Px595oIacgNhKVORe6aq3Q1++Q6p3IzNfOYxzaHIchS6x3LwPLBDbTn0MxH1oPDs7oG7yPoVGn52r/Q7Htg1I9NyfJOoO8qGT9MntRyfpl9rzJ+v/Bf0nW/2+eN1n/P3jeZP3/4nHJWs5ZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZCyGEEHZOkrUQQghh5yRZA0qpDkopLdkjSil1SSm1SCnVWimVYj8ppfyVUqOUUscT46OVUseUUt8opbJmRD+EEEL8fzJkdAPsTCvgKuAE5AIaA7OAzkqp1zVNiwFQSgUDawEFTAD2J65fCugCFATeTN+mCyGE+H8lydrWYU3TziV7/YdSah4wD/gW6K6UMgALgFigsqZpt5LFb1BK/QA0Sq8GCyGE+P8nw+D/QtO0BcAS4EOllCvQHCgE9H8kUT+MN2matiydmymEEOL/mCTrp7MSy9B4WaAukJBYJoQQQqQ5SdZPJyTxOSuQE7itaVp0BrZHCCHEK0TOWT8dlfisPVclSnUGOgNkz5mditkqPG+7XmoJfuaMbkKG89D9ltFNyHB3OkzM6CZkuNKjZ2Z0EzLcIi+HjG6CXZMj66eTM/H5OnAFyJR4/vqZaJo2VdO0spqmlfX1832hDRRCCPH/S5L102mMZfb3AWA9oEdmfAshhEgnkqz/hVKqOfAGMCXxPPVC4DQwWimVKZV4g1KqcTo3UwghxP8xOWdtq6RSyh9wxHJTlCZYbpSyDhgAlkuzEhP4OuCwUmo8STdFKYHlnPQ/wIp0brsQQoj/U5Ksbc1LfI4FbgEHgTbAfE3TrJPLNE07qZQqAfQFOgBDsExCO4vlyHt8+jVZCCHE/ztJ1oCmaTOBmc+4zh2gf+JDCCGESDNyzloIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5KshRBCCDsnyVoIIYSwc5Ks/4+0a9GOPN65+W74GGvZji3b6dn5U6qXrEbBgAJUL1mNL3p/wZ3bd2zWjYmOoV+3zyiRpzjVS1Zj2cJlKeqfMn4KDas0xGQypXlf/qsObdoTlCUvY0d9Zy27GnKVoCx5U32Eh4Vb42KiY+jf83NKFyxJrfI1WL54eYr6f544hca1GmXYPrh67TY9+/9ItUbd8crVGMdMdbkUciNF3P0HEXTpOZasBZvjnbsJDVt8xrGTF1LExcbG03/Iz+Qq0hrPnK9RrVF3tu08ahOTkJDAgKG/kL1wSwJLvM2EKQtS1DNv8WZyF32L8IioF9fZ5/DO3p1kW7GY0adP2pQ/MMbT5+ghiqxdSdDqZbTevYNT4WE2MdEJJnofOUjw2hVU2rSWJdeupqh/0vmz1N26EZPZnKb9eFqtOjQmZ1HvVB/vdWmR6jr9v+5JzqLe9Pi8s015TEw0fQd1o2jlPFRpWJKlqxamWPen6eOp37yK3X0WHNm5hb7N69Isnx+ti+ZgzKcduX/7pk3M970681pOt1QfnWuWssbFxkTzQ9+PaV00Bx9UKcqWpfNTbG/eT9/TtX4FEtJhPxjSfAsiXSyZv4RTx0+mKP9r+l9ERUXRrW93cuXJxaULFxk3ahxbN2xh9Y41uLm7AfDTuMls37yd7yaP5Z8Tp+jVuSdFSxQlb1BeAK6HXmfidz/y2/zfMBjs822zdOFS/jlx6rHLP+7xCXUa1LUpe9h/gCk//sT2rdv5dvwY/jn5D3269qJI8SLkDUzcB9euM3ncJKbPnplh++D8xVDmL91C6eIFqFqhKOs2H0gRo2kazd8bxKWQG4wb1Q0fL3e+HT+b+m/2Zd+mn8mRLZM1tnPP71i1bg/fDOlM3txZ+Wn6Uhq/1Z+tKydQslg+AP6YvZZpvy1n4nc9CQuL5NP+P1KiWBA1qpQEIDIyhn6DpzB6aBc8PdxStCe9LQq9yslHEjBY9kuHfXu4EhPF8CLF8XZw4MfzZ2i5ewfrqtUim4sLABPPnWXrnduMK1GaU+HhdD98gGJe3gS6uQNwLSaG8edO81e5Shh09nG8M2LQWCIjI2zKDhzZy9Bvv6BerUYp4vcf2sOi5fPwcPdMsWzSr+PYtmsz34+YzKkzJ/h0QGeKBZcgb+4gAK7fCGXCz9/xx5T5dvVZcHzPDr589w1K16jLF1P/Ivz+PX4fM5SBbRozYeUOHJycAHj70/681raTzbo3r1xmdLcOVKj3mrVs3qSxHNq2kd7f/8zFU8f57tOO5CtWkux5LX8Xd66HMnvCtwz7YzH6dNgP9rOnxX8W9iCMYQOHMWjkID7t1MNm2bCxw/Hz97O+rli1InmDAnmrcWtWLFpO67ZvAbB5/Wbafdieeq/Vo95r9Vg8bzHbN2+3Juuv+w+hcbPGlKlQNv069gzCw8IZMXgYXwwdRK+PP001JmfunJQqWyrVZQBbNm6h7QdtqduwHnUb1mPpwiXs3LrDmqyHfTmU1954jTLlyqRJH55GtUrFuXrS8g1/+h8rU03Wy1bvZMee46xd9B01q5YEoGK5YAqUacvYH+cwblQ3AI4cP8/sBRv5ZXxf2r/TEIDqlUtQompHvh79G4v+HAbA6g37aNOiNm+9WQuApSt3sHrDPmuyHvrtbxTMn5M2zWunZdefSpgxniGnjjGkcDG6Ht5vs2zNzRvsvX+XeRWqUMXf8oWljI8vFTetZfKFswwvUhyATbdv8n6eQBpkyUqDLFlZGHqFbXduW5P14JNHeT1rdsr5+mEvCgQVSlH29/zfcHRw5I1GtkfWRqORz7/uSffOffhr3owU623atp4O73xI/VqvUb/WayxePo9tuzZbk/VX3/SnSYNmlC1VIW068x/9/cNIMmfPxeBpc6zJM2dQQXq+Xp01s3+jSXvLCELWPIFkzRNos+7BrRsBqNvyXWvZ/k1radKhCxXrN6Zi/cZsXjyHw9s2WZP1lK8+o1qT5gSXrZge3ZNh8P8H33w1igKF8tO0ZdMUy5In6odKlC4BwI3rScOn8fFGnJ2drK9dXFyIi40DLIl8z4499P96wItu+gvzzdBR5C9YgDeav/Gf6zDGx+Ps7Gx97ZxsH2zZuIW9u/bQb1D/527r89A9xZHc8tW7yBbgZ03UAF6e7jSuX5Flq3cmxa3ZhYODgVbNalrLDAY9rd+sxbpN+4mLiwcg3mjEJdl7w9XVmbhYy7Ljpy7yy2/LGT/a9ktiRhl+6gQF3T14M3uOFMvW3rxOgJOzNVEDeDo4UC9zAGtuXreWGc1mnJPtZxe9nriEBAA23brJrrt3+aJQkTTsxfOLiY1hxdol1K3ZEB8vH5tlU2ZMwJyQQOcO3VJd12iMx9kp6e/AxcWFuPhYADZtX8/u/TsY2PvrtGv8f/TPwX2Uqlbb5ii3QMkyePr4sXPN0ieuu3HB3+QrVorcBYOtZSZjPE7OLtbXTi4uxMdZ9sP+TWs5vns7Hwwc9oJ78XiSrF9y+3btY8HshQwbO/yp19m9YzcA+Qrms5aVKluSBbMWcOvGTbZs2MLJYycpVa4UcXFxDOn3FZ8P+RwfX5/HVZmh9u/Zx6J5Cxk6eugT474bOYYC2fJRIl9xOrftxOmT/9gsL1G6JAvnLuTWzVts3bSFU8dPUrKMZR98PXAIn33Rz273QXInT1+iSKE8KcqDC+Uh5OotIiNjLHH/XCJPrgBcXZ1t4wrmJj7eyLmL1wAoX7owi5Zv49yFUA4eOcP6zQcoX7YwAD36TaB7l+YUzJczbTv1FPbcu8v80CuMLFoi1eVnIiMo6JFy2LeAhyehMTFEJZ53LOXtw7zQK9yMjWXz7ZucCA+jtI8vcQkJfHniKAMLBePr6JimfXleq9cvIzIqgpZN37YpvxRykQlTv2PEoO9wdEi9D6WKl2XeklncvH2DzTs2cOKfY5QuXo64+DgGj+zHgJ5D8PH2TY9uPBOdXo/B0SFFuYOjI5dPpzxF+NCJfbu4duk8dVu9a1NesFQ51s/7i3s3r3Ng8zounDhKodLlMcbFMWVwXzoMGIqnT/qNrsgw+EvMaDQysNcAOnf/kKD8QU+1TmREJEMHDCVfwXzUb9zAWv7p5z3p0LI95QuVB6BLjy6UKV+GH775AV9/X95q2yZN+vC8jEYjX372BZ0+/pDAfKnvA0cnR95u9w7ValbD18+X82fP89P4ybRq0pKFqxeTr4DlS0uPvp/ywdsdqFTcMrz3YdfOlC5XmgnfjcfXz5fW776Vbv16HvfuR5A7Z0CKcl9vDwDuh0Xg7u7C/QcR+Hi7p4zzSYy7bzkH2vXDZqzfvJ/gCu0BaP1mLVo3q8nvs9cSev02A3q9m6KO9GY0m/n82GE+CsxHPnePVGMeGOPJ4eKaotzbwcG63M1goHeBQry3dxelNqwG4OPAfJT18WXsmX/wdXTinZy5064jL8j8pbPx981Erar1bMoHDu1FozqvU7l89ceu2/Pjz2n3UUvK1rIMrX/0fg/KlCzPuMnf4OfjT5sWbdO07f9VjsD8/HNwn03Zzash3Lt1A71DyiT+0MYFf2NwcKBG01Y25e/0HMjgds14r6zl86HFRz0pXKYCf40biZefPw3atH/xnXgCSdYvsSk//ERsTCzd+nR/qniTyUSPTt25ef0G81cvsJkcEpAtgFU7VhNyKQRPL098fH0IuRTCLxOnMm/VfGJjYhn2xTDWLl+Ds4sznbp2okOX99Oqa0/t5x+nEBsTS9eeqQ/pAWTOkpnhY0ZYX5erWJ7qtWvQqHoDJv8wke8n/wBAQNYAVmxaRcjlEDw9k/bBtMm/MGfZXGJjYhnx1XDWrlyDi4sLH3zUkfadOqRxD/8DDZRSqRRrtq81LfU42zA83F1Zv+R7Ll+5iYODnmwB/jwIi2Tg0KlM/aEvTk4ODBoxnT/nrEXTNNq93YAh/Ts81ZD9izLp/FliExLoka/gY2M0DVLpbgpZnV1YX60Wl6Oj8XRwwNfRkcvRUUy5cI7FlasRY07g65PHWXXjOi56PZ3zBtEx79N9WU4PN25dZ/vuzXzw3kc2f+MLl83hyImDbFq27wlrQ9Ys2Vi7cAeXr1zCy9MLH29fLl+5xM8zJ7Lw91XExsYw9NsvWL1hOS4uLnzYrivvv9slrbv1r5p2/IQxPTry27df0/SDj4l4cJ8Jn3dH6XToVOrvRWNcHNuWL6R8nUZ4+frbLPPPmo1Ja/dw4/JF3Ly88PTx4/rliyz8eTxjFq4jLjaGX4b2Z9fqpTi5uPLmh9154/2P06x/kqxfUqFXQpk4diKjJ4wmLi6OuLg467L4uHjCHoTh7uGOXq8HwGw20+fj3mzfvIMZc6dTuGjhFHUqpcidN+mo4at+g3mrbRuCiwUzZti3HDt0lDW71nLj2g1av9aK/IXyU6VG1bTv7GNcuxrK5PGTGDX2G+Lj44mPj7cui4+PJzwsHDd3N+s+SC5b9myUqVCWo4dtL1NSSpE7T9I++PqLIbR+9y0KFwnmu5FjOHbkGKu2rOHmjZu0eaM1+Qrkp0r1KmnXyf/Ax8eD+/fDU5TffxBpWe5lOfL08fYg5OqtVOIirPUklztnFuvPg0dMp2K5IrxWvyK//rGCWfPXs3HZOADqNu1N3lxZef+9lLOQ08LVmGgmnDvNd8VLEW9OIN6cYF0WZzYTZozH3eCAt6MjD+KNKdZ/YLSUeScbFlZKkcctaWb7l8eP8k6u3BTx9OKbf05yJOwBm6rX5npsDG/u2k4BD0+qJTsXnpEWLZ+L2Wym1RtJQ+BR0ZEMHfMFH3/QEydHJ8LCHwBgNmsYTUbCwh/g6uKGQ+IRqFKKPLnyWtcfPLIfbVq0JbhQMUaPH8aRE4dYv3gXN25do0W718gfVIiqFWukaz8fVevNNlw5d4aFP49nzo/fopSi+ustKFerAZfPpD4MvmvtciLDHlCnZeqjQ0opm8loUwb3oUGb9gQGF+e30UM4e+QgP63fx50b1+jXoj658heiZNVaadI/OWf9kgq5FEJcbBw9O/ekRJ7i1gfA1B+nUiJPcf45kXRO9oteA1m+cDk//vrjUyXY1ctWc/LYSXoP7A3AlvVbaPF2C/z8/ShSvAjValVjy/otadO5pxRy+QpxsXH07tqLUgVKWB8A0yb/QqkCJTh96vRj13/ckeVDa1au4dTxk/T8vBcAWzdtpXnr5vj5+xFcNJiqNauxdVPG7oPUBBfMzcnTl1OUnzp9mVw5MuPubpk0E1woD5dCbhAdHWsbd+Yyjo4O5MubLdX6Dx45w9/z1/P9iE8AWLNhH81fr07e3FnJmzsrLd6owZqNTz56e5FCoqOINZvpdvgAhdeutD4Aplw4R+G1KzkVHkYBdw/ORKb8EnM2MpzsLi64Pebym1U3rnEiPIzPCliGhTfdvkmr7Dnxc3KiqJc3NTJlYtMj1/JmpPlLZxNcsCjBhYpZy+7dv8vde3cYPX4oRSvnsT6u3bjK8jWLKFo5Dxu2rkm1vlXrl3Hi9DH6dhsIwJYd62nV9G38fP0pUqg41SvXYvP29enSt3/T7rPBzD4awqS1e/jzwHk+n/Qb1y6dJ7hcpVTjN8z/C09ff8rVbpDq8uR2rlrKhRNHea/vIAAObFlH3Vbv4uWXiaAiJShVvQ4HNq97of1JTo6sX1LBxYKZtWx2ivK3X2/Dm63fpHXbt8gTmAeA4V8MY/bvsxn70/c0aPLvb8qY6BiGDRjKoJGDcfdIOqcZHR1j/TkqKhrt0fHSdBZcNJi/Fs5KUf5u87dp1rIZrd55y2akILlrV0M5uPcA9V6rn+rymOgYhg8ayhdDv8Td3d2m/KHoqKgM3wepadKwMr/NWsPWHUeoXsXy5SU8IooVa3fZXF7VpEElho7+jflLt9KujWU/mEwJzFu8hbo1y+DklHICktlspnu/CfTv9S65ciQdaUclS/iRUTHpul+KeHoxv2LK0Y2Wu3fQInsO3s6Zm7xu7tTPEsCcqyHsunuHSn6WIc8Io5F1N2/QLFvK2eNguUHK4JPHGBJcDHeDQ7LypKP3KFNCilMHGeXI8UOcOXeKwf1G2JRn8s/C3Okpb3TU9bOOFMofTPfOfSiYPzjF8piYaL4ePYCv+o3E3S1ppCU6Jtr6c1S0ff0dOLu6kbdwUcAya/vKudN8OmZyirj7t29ycOsGGrf7EMMTzmmD5QYpP3/djw+/Go1rsjkRsdFJNwGKjYpM0/0gyfol5eXtRaVqqX9bzJ4ru3XZTz/8xLRJ02j9XmvyBOXh4L6D1jg/f79Uk9mEMRMIzB9IkzebWMuq1KzK77/8RlD+IG7duMnOLTv4sNuHL7hXz8bTy5OKVVK/xjFbjuzWZSO/Go7ZrFGqbCl8/fy4eP4CP02YjNIpPvn0k1TXn/j9jwQGBdK4abJ9UL0Kf0z/ncB8Qdy6eZOd23bS8eNOqa6flhYs3QrAwaNnAFizYS/+ft5k8vOiepUSvN6wEhXLBdPhk28YNaRz4k1RZqFp0Kd70iS5ksXy0apZTfp+ORmT0USe3AH8PGMZl0Ku8/tPqV+m9+sfK4mMiuHTj5Ku3a1TozQDh06jSkXLkdzsBRv5dmj6ncP0cnCksl/qQ9A5XFytyxpkyUoZb1+6HT7AoMJFLDdFOXcGDfgkKH+q6/9w9jRBbu68kS27tayaf2ZmXr5APnd3bsbGsv3ubboE5kt1/fS2YOksDAYDzV6znSzl7ORMpfLVUsQ7OTnh75c51WUA46eMITBPfl5v+Ka1rGrFmsyc9QtBefNz8/YNduzZQuf2j58zkl7OHz/M/k3rCEq8GuDEvl0s+PkHWn7cK9VroTctmkOCyWRzbfXjzBr/DTkC81P99aT3fcmqtVg282dyBBXk3s3rHN6xmead0+4SRknW/+c2r9sMwNw/5zL3z7k2y1q83ZKxP421KTt35hx/TPudZZttb7XZ47Me3L19l37dPsPZxZnPh3xO9dqPn1FqT/IXLMBfv/3FgjnziYqMwsfXh0pVK9Ojb49UZ5CfP3ueP2f8weJ1ttdmduvdnbt37tK/Vz+cnZ357Mt+VKuZ/vvg7Y62l6h17zcBgOqVi7N+yffodDoW/zWcz7/6mR79JhAbF0/FssGsXfQdObNntll32oTPGDxyOl+NmsGD8EiKFwli+ZxRlCqRMnnduRvG4JHTmTtjCA4OSR8dndo15sKl6/Qb/BOaBp07NOH9d9PnfPWz0CnF7+UqMvTUcQYeP0JsgpmyPj7Mq1iV7KnMEj8bGcHMyxdZXbWmTXmv/AW5Ex9Hn6OHcNbpGVgwmJqZMqdYP70ZjUaWrFpAzSp1yOT//O05d+EMv82exsq5m23KP/3oM+7cu03fQd1wdnahf88h1KiS8TfEMTg4sm/TGuZPGYcxLo6c+QvSbeR46r/VLtX4DfP/InfBYPIVe/yNkgCunDvN8t+mMmHldpvytz/tz4M7t/mh78c4OTvzfv+hlK5R9zG1PD9lT8MXr5LipYprjybEV01CnH3cVzkj5dSdz+gmZLg7HSZmdBMyXMLomRndhAx3zOvJQ9GvikY5XFOdSCMTzIQQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8laCCGEsHOSrIUQQgg7J8k6kVKqg1JKU0o9UEr5PLLMkLhsSOLrmomv6z4SF6iUupT4CEzH5gshhPg/Jsk6JS/g82ddSSlVENgKmIDqmqZdeNENE0II8WqSZJ3SWqC7UirgaVdQShUBtgARWBJ1SFo1TgghxKtHknVKwxOfv3iaYKVUSWAzcAuooWnatbRplhBCiFeVJOuUrgMTgc5Kqdz/ElsO2AhcAWppmnYrrRsnhBDi1WPI6AbYqdFAF+Ar4IMnxI0EwoB6mqbd/bdKlVKdgc4AWXNkJTw+/AU09eV149a/7rL/ezmzaBndhAyXZfYfGd2EDPdz1xMZ3YQM16u2f0Y3wS7Etg9KtVyOrFOhado9YCzQLnHi2OOswDIhbYxSSj1FvVM1TSuraVpZHz+ffwsXQgghAEnWTzIOuAcMfULMD1iOvt8HJqdDm4QQQryCJFk/hqZpkcAooBVQ8glxQ7EMh3+klBqXPq0TQgjxKpFz1k82GehN0gzxVGma9oVSyhnorZSK1TRtQLq0TgghxCtBkvUTaJoWp5QaCkx9itg+SiknoL9SKk7TtCFp3kAhhBCvBBkG/3czgLNPGdsdmAZ8pZR65rugCSGEEKmRI+tEmqbNBGamUm4CCjxSthlIMftb0zQN+DDxIYQQQrwQcmQthBBC2DlJ1kIIIYSdk2QthBBC2DlJ1kIIIYSdk2QthBBC2DlJ1kIIIYSdk2QthBBC2DlJ1kIIIYSdk2QthBBC2DlJ1kIIIYSdk2QthBBC2DlJ1kIIIYSdk2QthBBC2DlJ1kIIIYSdk2QthBBC2DlJ1kIIIYSdk2QthBBC2DlJ1i+5beu30uGN9lTIU55KgRVpU+8t9mzbY10e/iCMr3p9RfVC1SifpzwftujEmZNnbOqIiY7hq56DqVqgCq+Va8TqxatTbGf6j9NpWbMFJpMpzfv0OLdv3GLSyPF8+u4nvF6uAfWL1eRG6PUUcRFhEXz/1be0rPYGr5dvyOedenPxzIUUcfFxcUwd+xNtajWnSdn6fPruJxzdf8QmJiEhgWnf/0yrGs14p24rFv4xL0U9W1Zvok3tFkRFRr24zj6HJm8NwDFzPQaPmvHYmE/6/IBj5nq0//gbm/Lo6Fg69xxLlgLNKVSuHXMXb06x7nc/zqFMzS6YTAkvuun/2dXQUHr07UuV2rVxz5wZvYcHly5ftol5v0sX9B4eqT6CS5e2xkVHR9Ppk0/wz5WL/MWLM2fBghTbGzNuHKUqVcrQv4dHnb91hAnretBvbiN6z6rNqOXt2Hl2mXX57zuG8snvFVN9fL34LWtcvCmWP3aOoO/s+gxe2IL9F9el2Nba438wYtl7JJjtp/8A5lO7iB/xFnEfFiGuaxmMP/dBC7tjE2P8pR9xHfKl+ojvX98ap8XFYPy1P3FdyxD3WS0S9qxIsT3TyqnED2qClpD2+8GQ5lsQaWbeb3MZNWAUbTq2oUvvLpjNGqeP/0NsTCwAmqbRvW0PQkOu0n/kADy9Pfl1/DQ6Ne/I3I3zCMgWAMD0Cb+ya8suhk0YztmTZxj4yQAKFy9M7sDcANy4doNfxk3lp9lTMBgy7i1z7UooW9ZsJn9wAYqWLs6BnftSxGiaxlc9BnLj6nW6DvgUd093Zk/7m8869uSnedPIFJDZGjt28Bj2btvFh70/JmuOrCydvZiBH33G+D8nEVQoPwDrlq5hxfyl9BjUm6iISCaOHE9QoXyUKFcKgJjoaH4eM4kufT/Bzd0tfXbEE8xeuJGjJ1J+MUlu194TzFqwAU8P1xTLvp0wmw1bDjBtQl+OnbxIh0++oVTxfOQPzAHA1Wu3GTXub5bPHonBoE+TPvwX5y5cYN7ChZQuVYqqlSuzbsOGFDFffv45XTp2tCm7FBLCu++/z+uvvWYtG/3996zftInpP/3EsRMnaNepE6VLlCB/vnyA5YvBiDFjWLlwYYb+PSR39f5ZJqzrQR7/IrxbcQCOBicOXd7En7tGYDLHU71gCxoV/4BqBZrbrHc38jrTtw2iWM6q1rI1x3/nn2t7aVflS0Lvn2Pm9iHk8itIZs9cANyPusXqYzPoWucH9Dr76D+A+fQ+jN+9j65oNfTdJkHkfUwLx2H8ti0OQxajHJwAMDTtilbrbZt1tTuhmKb0RFeqjrUsYcXPmE/swNBpNNqV05im9kHlLoIuII9lnXvXSVg6CYc+M1D6tN8P9rOnxTMJDQnl20Hf0uur3rTt0tZaXqV2FevPm1dv4tCeg0xb+Cvlq5YHoETZEjQq25CZE2fQf+QAALZv3E6bD96mVsNa1GpYixULVrB7y25rsh79xWjqN21AyfIl06+DqShWpgRztywCYNWC5akm612bdnD84DG+/XUcJctbEmpwiSK0a/g2c2fMpuuAHgCcP32OTSvX02fo5zR4sxEAxcuW4MM33+e3STMY+uNIAPZt20Ot1+pSq5Hlj3jnxu3s27bXmqx/nzSTnHlzUeu1Oo82Jd09CIvks0FTGDPsI9p9NCrVGKPRxMd9f6B/r3eY9nvKI4U1G/fx8QdNeb1hZV5vWJlZCzawccsha7Lu/cVkWjatTqXyRdK0L8+qepUqXL9g+ZIybebMVJN1UGAgQYGBNmXrN20CoN2771rLVq9bR9fOnXmjcWPeaNyYv+fMYf3mzdZk3bNfP1q9+SaVK1ZMq+48swMX12PWEvi49nc4O1i+hBXOVoGr98+y5/wqqhdsQSaPHGTyyGGz3qnrewGoGNTYWnYidBc1CrWkeM7qFM9Znb0X1/DP9X3WZD1v3/eUzl2HoMzF06l3T8e05Efwy4ahx0/W5KmyBmEc2hzz1nno67xnKcucG5U5t+26J3YAoKv6prXMfGwL+rpt0ZeqC6XqYt61FO3kDkhM1qa/hqMr/xq6/KVJDzIM/pJa/PcilE5H6/atHxuzec1mMgdktiZqAA9PD2rUr8mm1ZusZcZ4I84uTtbXzi7OxMfFAZZEfmDXfnoN6pUGvXg2Ot2/v113bd6JX2Z/a6IGcPNwp0LNyuzatCMpbtNODAYDNRrWspbpDQZqNqzNgR37iI+PB8BkNOHklHzfuFiXXTx7gRXzltLti57P27UXYsDQXwgulJs2zWs/NmbspLkkJCTQ6+OWqS6Pjzfhkuy94OriTGycpb9rNu5j266jjBz04Ytt+AvwNO+N1PwxaxZlSpWiSOHC1rL4+HhcXFysr11dXYmLtYxWrV63jq3bt/PN0KHP1+AXzGQ2otcZcNQ72ZS7OnqgYX7senvOrySXXyGyeSd9iUkwG3FIVo+j3hljguU9cCJ0F2dvHqJZ6W4vuAfPTzt/GF2RqjZHubrA4uDuQ8LBlEP5yZl3LkLlKYoue4GkQpMR5eCc9NrRGc1o+Vw0H92C+fReDK37vdA+PIkk65fUwb2HyJsvD6sXr+K1co0olbUkjcu/xuxfZ1ljzp0+T75C+VKsG1QwiOtXrxMdGQ1AsTLFWDpnKbdv3mbHxh2cPn6a4mWKEx8XzzcDRvHplz3x9vVOr649l8vnL5InX94U5XmC8nDr+k1ioqOtcQE5suLs4mwTlzsoD0ajkWshoQAUKl6Y7eu3EBpylTMnTnNg5z4KFw8G4MfhP/Dmey3JmTdXGvfq3+3YfZw/565jwugej405f/Eao8b9zY+je+Do6JBqTPkyhfhjzlqu37zL2o37OHL8PBXKFCYuLp5eAyYy4suO+Pl6plU30tWOXbs4d/487d55x6a8fNmy/P7331y/cYM169dz+OhRKpQrR1xcHJ9+9hkjv/4aPz+/DGp16iolHhnP3fc9D6JvEx0fwfYzi/nn+j5qF3471XXO3zrC7YirVAh8zaY8j38R9lxYSVj0HU6G7ubq/bPkzVQUY0I8c/eOpVnpT3B39krzPj0znQ5lSOV9bXBEu3omZXki89kDaDcvo69ie4pABZYgYcdCtAe3MB/bihZyCl1QSTRjHKa/hmJo1Rfl7vOie/FYMgz+krp94xa3b9zm+6+/p/vAHuTMk5O1S9cycsBITAkJvNf5PcLvh5E9Z7YU63r5WP7QwsPCcXV35aO+H/NJm4+pU8xyRNah6/uUKFeSn8b8hI+fD83fbZ6iDnsVERZBlsRz8cl5eHlYlodH4uLqSkRYBO6e7o+PC4sAoOk7zTmwcx/vN7YModVsVJsaDWuxdslq7ty8zTud26aoI70ZjSY++ewHen3SioL5cj42rttn42n2WlVqVi352Jgv+7bl9TYDyV2sDQC9u7amYrlgho35A38/b95/t9GLbn6G+WPWLBwcHGjT0naUYfCAATRu3pwc+S3zFvp++imVKlRg6KhRZPLzo2P79hnR3CfK5hNEz/qTmbr5c7aetkyI0+sMvF3xc8rmrZfqOnvOr0KvM1Aub32b8sYlOjJxfS8GzG8CQN0i7xKYqRgrjkzD3dmHyvneSNvO/EcqIBDz+cM2ZdqdUAi7BU84p5ywYxHoHdBVbGJTbmjWHePYjsT3rAyAvtGH6PKVxrR4Anj4oqv++FHNtCDJ+iWlmTWiIqMYNmE4dZvUBaBCtQpcuxLKr+On8e6H76JpGiiVcl1Ns3mdJWsW5m9ewNVLV/Hw8sDb15url67w2+SZ/Lbsd2JjYvlu8Bg2rNyAs6sL7T5qyzud3k1Rrz3QNA2Vap9TiePf41zdXPluxnhuXruBwWDAL7M/keERTPv+Z/oM64eDowMzJkxj3ZLVaED9pg1p3+2D/zws+1+M+XEOsTFxDOj5zmNj/pq3nv2HT3Nsx/Qn1pU9qz8HNv/MhUvX8fZyx8/XkwuXrjNu8jw2LRtHTEwcnw2ewpKVO3B1debTj1rQtVOzF9yjtBcXF8e8RYto3LAh/v7+NsuyZ8vGoV27uHDxIt5eXvj5+XHh4kXGTpjA1rVriYmJoc+AASxetgxXV1d6detGt48+yqCeWNwKD+GXzQPI6hXI2xU/x0HvxNErW5m1ezQOekfKBza0iTcmxHPg8gaK5qiCu7O3zTJv18x88fqf3IkMxcXBA3dnL+5EhLL+xN/0afgzxoQ45u8fz5GQzTganKld+G1qFU7fxJUafb32mKb2wbTge/T12qNFPsA080tQOssjFZoxDvPelehK1kJ5+NosUz4BOAxbDrdDwNUT5e6DdiuEhNW/4jBwNsTHYpw1EvPBteDogqHBB+jrtUuz/kmyfkk9PDquVLOSTXnlmpXZsXEHt2/extPHi/D7YSnWDX8QDoCnV9JwplKKnHmTjspGDRxF8/eaU7BoQSaMnMCJIydYuHURt67fosMb7QksEETF6vYzweYhDy9P61FxcpHhljKPxKNpTy9Pbt+49fi4xCPsh5Ifrc+Y8CvBJYtQoXolVs5fzobl6xj7248A9H3/UwJyZKVR88akh5Crt/jmh7+Z8n1v4uKNxMUbrcvi44w8CItEKUW/r36mb/e3cHZ25EFYJABmsxmjycSDsEjcXJ1xcEiclKMUQXmTRmR6DZzIB+81okTRIAaNnM6BI2c4tPUXQq/fofYbvSlcIBe1q6fPJJsXZcmKFTx48L927js+5vsP4PjrsqNGJtIQI0NIRBBBrIhZs6hRe29q1lYSsVdVKaVU0RoVmwwhsWqVxqa2iNUKstf9/ji9OHehKuP4vZ+Pxz1y9/5+vp/7fL655H3fz/fz+cZqDYH/Q6FQaExGGzpqFL26daNC+fJMnDqVU6dPE3XsGNExMdRp1Iiyrq7U8/XNpdZr23b6OwwNjBhYb556hrarXRXik5+y6cQCvEo1xOClhBV1J5LElOdUK637c6pQKDQmo204Po8azi0oZuXMttNLuf3XRSa2WE9swiPm7+2PnUUpXO2q5Gwn38DQpyXKmOuk711B+o4loFBg4N0UhUcdMqKv6twn43QYJDzDoEYrndsVCgW8NBktba0/hrXbYuBQlrTN81DePIvJtN0onzwgdcbnKOydMCjnkyP9k2vW7ylHHdeiIfOs2UBhgFMZR/68/KdWmetXrmFXzI58+bWX7gDs27WPy+cuM2iMahLJ4fBDtGjfAisbK1zLu1L9xRcCfVTSsSS3/ryhFb917SaF7Ypgnk/V5xKOJbl/N0a9zE1d7vpNjI2N+djBXmf9V85fZt+uUAaOHQLAycPHqdWgDnbF7LArZkfthr6cPHQ8m3uVtRu3YkhKSqH7wJkUdm6lfgDMX7KJws6tuHErhkePY5kU+INGmTvRj9i8LYLCzq3YHXpMZ/1bdx3ij3PX+GqMaug3JPwkXdo3xNbGAs/yTtT3rUxI+Mlc6292WbNuHTbW1jRp1OiNZYO2b+ePs2eZOmECAMFhYXTt2BFbW1s8PTxo4OdHcOjrJzDltHtPrmFv5aS1lKqEjRvxyU95nvREI/7btd3kN7XAvdibE8uZ2we4+/dVmnmqJhZeiP6NqqWbUMDMkuJWLpT92JsL0b9lX2fegVGb4ZgsPoFxwC5MFh7FeMBClA9uYeBcWWf5jENBUMASAw/fN9adfiqEjNsXMWw1TLXv2UgMa7RGUdAagxLlMHCrSUZUZDb2RpMk6/dUvRdLhQ7v10yaR/YfocjHRbApYoNvI18exjzk5EtLnOKexxEREoFvI1+d9SYmJDJ70ixGB4zWWDecmJCY+Tw+QWsoXV9U8/Xh8cPHRJ04o47Fx8XzW8RRqvtm/mOqXteHtLQ0IkMOqGPpaWlE7N1PJR8vTExMtOrOyMjgm8CFdOzTmcJ2RdTxpMSXjk1CIkpy79hUcHckNGiu1gOg42f1CA2ai1Mpe51lithaUq92JUKD5lKjqrtW3QkJSYyatJQ5AQMo8NIXu/iEzC848fGJevtZyMqDhw8JDQ/n83btMDbWPdHuHwkJCYwYO5Z5M2dSoEDmaEt8fOYNcOLi4/P8GBQ0t+Lu31dJS0/ViN98dB5jQ1M+MskcRXuW+BcX7x3Dq1TDN66TTklLYvOJhXxW5QvMjD/SiP8jOTV3P/NvojDNh0HxMigK2ZARFYEy5hqGdbVHUJRPH5Nx/hCG1Vronpj2ctnkRNLWT8Oo43gU5vlfiie89DwecvA4yDD4e6pW/VpUqelNwCh/Yv+KpViJYoTuDOHIgSMELAoAwLdxXSp4VWDcwHGM+GokBQsVZOWiFSiVSnoM7qmz3uXzl1HSsSSNWmZe46pWuxo/r/yZUk6leHj/EccOHqPrgLyZZPNPcr364i5sJw4dp5BlISwsLfCo4kn1ujUoV8GNmeMC6TOyPwUKFuCXFetAqaRtz8xZsY6uztRpXJfvZi0mPS2NovZ27Ni4jfvRMYydOVHne+/evJOkhERad2mrjlWsVpmVC5bhXkm15nT/7jD6jhqYQ73XZlEoP3VqVNC5zaF4EfU2XWXMzEwobGuZ5f6B89fh4liMti3rqGN+tSuydOU2yjgVJ+b+X4QfPM2wLJaB5bbNW7cC8PuZMwDsCQ3F1sYGWxsb6tTMvOnH+g0bSEtLy3II/GXTZs3CxdmZdq0zJ1nW8/Xl2+XLKePiQsz9+4QfOMCIIUOytS9vq45rW1ZEjGdp+Chql2mDsZEpZ+8c5OTNEPzKdsDIMDMZnbgRTIYynWqOTV5To8ruqB8oXNCByiXrq2OudlWIuLyJIoVK8DThEZfvn6Se25uPZU7LuHWejKgIDEqo7gGQcfUU6Xu+x7BJX51rodOPboP0tCyHwDXKbl+MomgpDL0zLxsYuNUgfd9PKOwcIfYBygtHMWjc6zW1vBtFXn8j/H/l5umm/CV0wzvVEfc8jq+nLSR0RyjPnj6jlFMpeg7tRdM2mR+op0+eMm/KXML3hJOSnIKHVwVGTx1NGfcyWvXduHqdjo068kvYBvUNUQAS4hKYMX46+/fux8zMjM79utB9UPd3ajvA/Qd/vfU+Dcv76ox7eFVg7qqvAdUs9+/nLuVI+CFSUlIoW8GNfqMH4lhG89JBclIyqxatYP/uMOKex1G6jBO9h/dV3/DkZU+fxNKrRVcmLwjAwyszuaWnp7NywTLCdoSiREmDFo3oNawvhob/7u5evkVS31zoPzAp3ICxwzviP65HlmWcK3fGx9udH5eO1dp26eptajQawm9h36pviAIQF5fIsPGL2bH3KOZmJgzt14YRg9pq7f82DM2zZ+6DYYECOuN1atYkfM8e9euK1auTkZHBH8d0D/3/49Lly1SrW5cTkZHqG6IAxMXFMXTUKLbv3o25mRnDBg1i5BdfvFPblw06/077A5yPPkLIuZ+Iib1BanoKtgXsqeHcklourTAwyPw8Bu7ojFKpZGKLda+t7/7Tm8ze3ZOxTVerb4gCkJSawMbj84i6cxBjQ1P8ynWggdu7Tzgd7mfz5kKvkRF9hbTVk1TLtNJSUHzsiGH9rhjWyuKeApOagTIDk2m7X1/vvWuk+rfGeMo29d3LAJRJ8aSt9Vdd9zY2w7BRD4w+6f1OfQBI6uaoPfMVSdZ5JjuS9fvuvyTrD01OJev3SXYl6/dZdiTr9927JusPRVbJWq5ZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZCyGEEHpOkrUQQgih5yRZ/0cKhaK7QqFQvvRIVygU0QqFYqNCoSiT1+0TQgjx4TDK6wZ8ANoCdwFDwBGYBOxTKBRuSqXyaZ62TAghxAdBkvW7O6NUKv988fywQqG4B4QCPsCevGuWEEKID4UMg2e/Zy9+GudpK4QQQnww5Mz63RkqFAojVMPgpYHpwEPgQF42SgghxIdDkvW7u/TK63tAM6VS+ezVggqFoi/QF8C+uD3W5la50Dz9lb9IwbxuQp4zMHue103IcwpDw7xuQp7z6+OY103Ic8lXYvO6CXpNhsHfXSugCuANfApcAHYrFIqyrxZUKpXLlUqll1Kp9LK2sc7dVgohhHhvyZn1uzv30gQzFApFCHAHmAK0z6tGCSGE+HDImXU2UyqVicB1wCOv2yKEEOLDIMk6mykUinyo1ls/yuu2CCGE+DDIMPi781QoFDaAArADBgNWwDd52iohhBAfDEnW727TS88fAeeAxkqlMjiP2iOEEOIDI8n6P1IqlauB1XncDCGEEP8H5Jq1EEIIoeckWQshhBB6TpK1EEIIoeckWQshhBB6TpK1EEIIoeckWQshhBB6TpK1EEIIoeckWQshhBB6TpK1EEIIoeckWQshhBB6TpK1EEIIoeckWQshhBB6TpK1EEIIoeckWQshhBB6TpK1EEIIoeckWQshhBB6TpK1EEIIoeckWb8H7kXfY+KoiTT3a45jYUfsC9hz59YdrXKxT2IZNWgU7iXccSriRPvm7bl4/qJWuaSkJAImBFDRqSKOto4092vOb4d+0yiTnp5O4KRAPEp54OXqxffffq9Vz/Yt26nkXInnz55nX2ffwueftqd04RI6H93bdwXg7u07WZZ59vSpuq7EhETGDBtNRRcPfKvUYufWHVrvt+yb72ji25i0tLRc6+N/0aRVP4wKujPJf5FG/EzUJZq06kchuypY2lfl0/aD+fPabY0yCQmJ9Bk0CVsHH1w8GrPx1z1a9c9Z+AOVfFrr1XG4e/cuQ0aMwMfXl4+srTHIl4+bt25plOnRty8G+fLpfJT19FSXS0hIoFf//ljb2+Pk5saGzZu13m/2/Pl4Vq2qN8egy5CWuNay1fnoPbKdzn0mzxmJay1bRvsP0IgnJiUwYeYXVG3iTIP2Vdi9L0hr3xXrvqFld1+96b/apd9gRgfo7w5DvOD7UfD0sWaZlV9CL2fdjwmNMsslJ8Kqcap6xvrB8V3a77dnOXzVHNJz/jgY5fg7iHd28/pNdmzZgUdFD6r6VCViX4RWGaVSSY/2Pbh96zbT5k6jkEUhFs9bTNsmbQk5EsLH9h+ry44aNIp9wfuYGDARh1IO/Lj8Rzq16sS2fdtw93AHYNO6TaxdtZaZC2fy9OlTJo6ciJuHGz61fACIj4tn6ripTJ4+mQIFC+TOgXiF/6wA4p7HacR+P/k7gZMDqNeovkZ8wBeDqP9K7KP8+dXPv1u0hEMRh5izaB6XLlxkxMBhuHm4U6p0KQBi7sXw7YJvWPXLjxgZ6e+fzS+bdhN17rJW/Oqft/Bt3BW3ss78tGIWaWnpBMxcQt1PunHq8GYK21oDMGv+SsL2H+WH7wKJOneFrn3GUbFCOZydSgBwN/o+0+csY9eW7/TqOPx5/TqbtmyhsqcntWrUICQsTKvMxLFj6de7t0bs5q1bdOzWjeZNm6pjM+fOJSw8nFXLlhF17hxdevakkqcnzk5OgOqLQeCsWezZtk1vjsFXI2YTl6D5pfnMuZPMXDwJv5qNtcr/fvY4O0M3k/8j7b/d79cu4siJCGaM/4bL187zZcBAyrl4ULK4IwD3H95j6Zr5fD93g970H4ArJ2B+D3CrCQMXQ1wsBC2AuV1hchAYm6rKNR8Evp9r7vs4GpYPB0+/zNieZXDhMPSaBXcuwYpRUMINipRUbf87BnYugeE/gGHOHwc9OtIiK9VqVOOP638AsH71ep3JOmRXCMePHmfjro3UqF0DgMrelalevjpLFy4lYE4AAOfPnidoYxDzl8ynfZf2AFSvWZ26Veoyd9pcVm9cDUB4aDiftv2Ulp+1BCB4ZzD7Q/ark/XcwLk4uTjxadtPc7Lrr+VcxkUr9svanzExMaF5qxYacYcSxanoVSnLug6EH6Brz27Ub9yA+o0bsO3XrRyOOKRO1v4TptCkZVMqe3tlbyeyUWzsM0aOm8W8GWPo3OtLjW1zFq7E0NCQXb8uxcKiIADeXuUp49mEeYtWMytgJADBoQcZ2LcjzZvUpXmTuvy8cSf7DhxVJ+vhY2bStlUjfKpWzN3OvUHtmjW5f/MmACtWrdKZrB1Ll8axdGmNWOi+fQB069RJHdsbEsKg/v1p0awZLZo1Y/2GDYSFh6uT9bDRo2nbujU+1arlUG/enlOpMlqxTTt+wtjYhCb1WmnEU9NS+WrOSPp1Gc6G7Wu09os8to9OrXvhV7MxfjUbsyP0V46ejFQn68BF4/mkbksqlffOmc78V9sXg/XHMHhpZvK0Kw3T2sDBzeD34ndcuITq8bILh1U/fVpnxs5Ggl9n8Kynehzbrir3T7L+eRpUaQJOWf9fyU4yDP4eMDB4868pZHcIRe2KqhM1QMFCBanfuD7Bu4I1yhkbG9OiTWYyMzIyouVnLYnYF0FycjIAqSmpmJmZqcvky5dPve3ShUusXbWWwHmB79y37JSUmMSe7bvxa1gPC0uLt9o3NSUVM3NT9Wtzc3N1fyPCD3D86DHGTBqXnc3NdmMnz6dcWSc6tG2ite3YiSiqVamgTtQAxeyL4l7OiW079qljKampmJtlHod8+cxJSkoBYG/oISIPnWTG1BE52Iv/5t/8jejy0/r1VK5YEbdy5dSxlNRUzM3N1a/zmZuT9OKzsDckhIhDh5g1bdq7NTiHJSUnsnf/dur6NMSioKXGtpXrF5OekU6PDgN17puamoqpaebfvpmpOckpqv4fPLaPE2eOMnLA5Jxr/H91/QyUq6F5llvKA/JbwumQ1+97ZCuUcAd758xYWiqYZB4HTMwhVfW3wNlI1Zn8Z6Ozq/VvJMn6A3H54mXKlNP+dl2mbBmi70QTHxcPwJWLVyheojjm+cw1yrmUdSElJYWb128CUNGrIru37+bGtRtEnY4iIjyCSlVU3yDHDx9P74G9cXJxytlOvaW9u/YQFxdHm/afaW2bEzgbZ7vSeDi606dLLy5duKSx3bOyJ79u+JWHDx4QGR7BhXMXqFi5IsnJyUwZ9xVfThyDpZWlVr364tDR3/np5+0snj9R53ZDQwNMTIy14iYmJly7cYekJNU/Y28vD376eRsx9x8RHHaYM1GXqFrFg+TkFIaNns70qcOwtrbIya7kmsNHj/LntWt07dxZI+7t5cWatWuJiYkhODSUM1FRVPP2Jjk5maEjRzLD3x9ra+s8avW/Exqxi/iEOD79pING/Hb0Db5bs4DJI2ZjYmyic1+PcpXYuncDDx/f5+CxcC79eY4KbpVJSUlm2sJxjOw3EctCVrnRjbdjYACG2p9xjEwg+mrW+109BQ9vQQ3NEQhKV4DDQRD7EM4dhNsXobQnpCbDen9oM0r1RSCXyDD4ByL2SSzFSxTXiv9zhhkbG8tH+T8i9kkshSwKZV3u71gAevbvSWR4JDU9awLQ8rOWtGjTgo3rNhJzL4aho4fmSD/eRdDGLVjb2FCnnq86ZmJqwuddO1HLtxZWNtZcv3qNJV8vpm3T1gQFb8PJRfVNeuioYfTo0I1qL4b2+g7qR6Uqlfl6zkKsra1o16mDrrfUC6mpqQz8YiojhnSnjHMpnWVcnEtx9NgZUlNTMTZW/UN7/jyeC5euoVQqeRL7DLuitkwaO4BmbfpT3KUuACO/6EH1qp74z1iCjY0lPbu2ybV+5bQ169ZhbGzM523basS/Gj+eJq1aYe+oGvYdNXw41atWZWpgILY2NvTq3j0PWvt2tu7dgLWlLbWr1tOIT5k7mga1m1KtUs0s9x3cYzR9RnWgdqvyAPT6fDAV3auweNUcLC1s+KxZ5yz3zVNFS6vOrl/2OBqePnz9NeWjQaok791MM95iMCzsDSNfjFY27g1OFWHbN1DACmq11a4rB0my/kAoUaJQKLTjSqXWa13l0CxG/gL52bxnM3dv38XI2IiidkV5GvuUwEmBzFsyD1NTU2ZOncmm9ZtACe06t2P0xNH/eTjyXT24/4DDkYfo3qeHxqSXwkWKEDh3uvq1dzVvavvVoXGtBny7YDELln4NQFG7ouw+sJfbN29TsFBBLK0suX3zNiuWLGfjjs0kJSYxbXIAIbuDMc9nRq/+venWu0eu91OX2Qt+IDEpmfGj+2ZZZsiATmwOCmbgMH+mTBhMWlo6oyfMIS4uAQADA9Vnwv7jIvx+ZAvXb9zBolBBrK0tuH7jDvO/WU1E8BoSE5MYNX42W3fsI18+c4YN6srg/p2yfF99lZyczKYtW2j2ySfY2NhobLO3t+fMsWNcv3EDi0KFsLa25vqNG8z7+msOhoWRmJjIiDFj2LpjB/nMzRk+dChDBgzI4p1y34PH9zl6KpIun/XV+FvYHryJs5dOs3vtkdfuX8TWjm2rD3Dn3k0K5C+EZSEr7ty7yQ8/f8v6JTtJSk5k5uLJhEXuxszMnO7t+tPlsz453a03q99VNft7y3yo3w3iY+HHiaAwUD10SU2GE3uggq8qAb/MsihM2QGPbkO+gqqz6Ee3IXgljPsZUpJgw3Q4HaoaIm/YA+p1zbHuSbL+QFhaWhL7JFYr/jRWtTzJwsJC9dPSgug70VrlYmNV+1pYWWjEizkUUz+f5T8Lr6pe1G9cn3Wr1rFlwxaCglXLOtp80gaHEg583u2VWZa5ZOumIDIyMnQOgb/qY/uPqVzVi6gzURpxhUJBiVKZE0+mjJ9Mu84dKOtejrnTZ3P2jyj2RobwIOY+7Vu0xcnFmRq1sz5DyQ2378QwY+5yln8zleTkFJKTU9TbklNSiI19RoECH1GjWiW+mTeRCVMXsuon1e/Mz7caXTu2YN2GnVhZZo62KBQKHEs7qF9/MXo6Pbu2oUJ5Vyb6f82p0+f549hWou89xLdxV8q6OlLPV38mW/0b23buJDY2lq6ddH/RUB2DzMloQ0eMoFf37lTw8GDCV19x6vffOXviBNH37lG7QQPKubpSr27d3Gr+a+0I3kRGRgatPmmvjsUnxDFz8WR6dxyCqYkZz56r/i8oMzJIS0/l2fOnmJvnw9hINeqiUChwsM8cpZm2YBxtm3XG1cmdBcsDOXfpDDt+jOTB4xg6DWqOU8kyVPeqnbsdfVW1lhBzXZVMdy0FhUI1Aax8HYi+onufM/sg4ZnmxLKXKRSak9HW+UPttlC8rOpLwc1z4L8LnjyAmR3BzgnK+WR/35Br1h8MF1cXLl/UXrJz5dIV7Ivb81H+jwDVNew7t+6QmJCoUe7qpauYmJhQsnRJnfVHnY5iy4Yt+M/2B+BA2AGatmyKQ0kHHEo60OzTZuwP25+9nXoLQZt+paxbOcq6l3tzYYCsRhheCN61l4vnLjB8jGoyVWR4BG3at8Haxppy5d2o6VuLyHDtWfm57fpN1fXmrn3GYuPgo34AzF+0GhsHH86eV12vG9CnAzHXIvnj2FZuXAglZPsK7sU8wtvLQz00/qqtO8L44+xlpk4YDEBI2GG6fN4SWxsrPD1caeDnQ3DYodzpbDZas3YtNjY2NGmsvazpVUHbtnEmKgr/SZMACA4NpWvnztja2uJZoQIN69Vjb2hoTjf5X9sWvBFXJzdcndzVsSdP/+bv2McsWB6IdxMn9SPmYTR7wrfh3cSJiCO6+xAauYuLf55jSO+xABw8Fs6njdtjZWlDWefy1PD25eCx8Fzp2xu1Gg5fH4epO2HeEei3EB7eBOcsVnEc3qI6Yy5f5811/x6iWsLV8gvV63ORquvcBazBoRy41VDFcoicWX8gGjZpyIa1Gzh66CjVa1YH4Pmz54TuCaVVu8yJEw2aNGBu4Fx2BO2gXSfVzRLS0tLY/ut2avvVxtTUVKvujIwMxo8Yz9BRQ7Evbq+OJyQkqJ/Hx8drDbnnlqgzUVy5dIUJ/pP+Vfnou9GcOn6Khk0a6dyemJBIwCR/JgZMJv9La7ETXvqCkxCfkGf9fZlneVfCdv2gFa/ftCed2jejR9fWOL10lmxqaoJbWdXEwLPnr7DvwG+sXjZda39Q9XfE2FnMm/ElBQp8pI7Hv3Qc4uL04zi8jQcPHhCybx8D+vbN8kvKPxISEhj+5ZfMnz2bAgUy1yTHx8ern8fl4Wf/VWcvneHqjUuMHRygEbe1KsyPi7ZqlR85pQ8upcvRr+twXEq5am1PTEpgxqKJjBsSQP58+TXi/0hIiEf56nW0vGSaD4q9mGx7NlJ1tt19hna5p49VS7F8O4LR6z8HJCfCz4HQYTyY59eMq58naF1OzE6SrN8TO7fuBFAP3YaHhmNtY421jTXVa1anYdOGVPauzJDeQ5g0bZL6pigAA4ZlXk9z93CnRZsWTBk7hbTUNIqXLM6aFWu4c+sOi1cu1vne61avIz4unj6DM69L1axbk+mTp1PVpyoAWzdtZXJg3iznCNr4K0ZGRrRo3VJrW+DkADKUSip5VcLK2orrf15n6aIlKAwMGDhskM76vpm/iNKOpWnaMnPCiU/tmvy08kccnRx5cP8BRw4epveAvL9OZ2FREN9aute7OhT/WL3tbvR9vlu5gerenpiamvD7mQvMnPc9rVrU07nUC2Da7O9wcSpJ29aZZ59+vtVYsvxnXF1KcS/mEeERxxg+pFv2d+w/2BykGt4/dfo0AHuCg7G1tcXWxoY6tWqpy63bsIG0tDSNtdVZCZg5kzIuLrRrkzmxrp6fH99+9x2uLi7ci4lh3/79jBiqHxMut+3dgJGhEc0aaA7rmpqaUbViDa3yJiZmWFva6twGsGT1PEo6OPGJ36fqmI9XHdZtWUlpB2ce/nWf334/mOUysFx167zqzNbBTfX66kkIXgGN++heC/3bdtWdx2pkMQT+sp3fQtFSqmH1f5T1gfCfVBPbYh/CxaPQsGf29EUHSdbviX5d+mm8Hj98PKC6ocnmPZsxMDDgx00/EjAhgPEjxpOUlERl78ps3LUR+2L2GvvOXzqfWVNnMTtgNs+ePqNc+XKsDVpLec/yWu/79+O/mTV1FivWrdA4C+ncozO3rt9i6ripKJVKuvTskifXq1NTU9kRtJ3afnWwLWyrtd3Z1YV1q9fy6y+biI+Lx9LKiuq1qvPFqGGUdnLUKn/t6p+s/WEN28J2asSHjBjKX4//Ysyw0ZiZmfHlxDHUqpvH1+jegrGxEcdPnuX7HzbxPC4ex1LFmTimP0MH6J7Ze+nKdZZ+/wvHIzZqxCd+2Z9Hj/6m98BJmJubMX3qMBrW0/2PPre1eyX5Dho2DIA6tWqxPzjzXgNr1q3D3c2NShVff2OXS5cvs2TZMk4ePqwRnzR2LI8ePqTXgAGYm5kxIyCAhvXrZ1FL7klNS2VXWBA1q/phY1X4neu7fusq64N+4NcVmjeYGdBtBH89ecSEmV9gamrGiH6TqOmtB9frjUwgKgL2fA9pKWDnCF38oWYW81iOBIG9i+quZK8Tcw3C18LkrZrx5oPg+V+qW5KamKmWcrnX0llFdlDoy/DN/5sKlSoo90Rq33f5/0lyop7dVzgPOJjlzX3V9YmBUek3F/rAXTkV/+ZCHzjXK7F53QS9oOzppHMyjUwwE0IIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCzymUSmVet+H/hkKh6Av0ffGyDHA5D5ujD0oolUrbvG6EEELoO0nWQgghhJ6TYXAhhBBCz0myFkIIIfScJGshhBBCz0myFkIIIfScJGshhBBCz/0PiMdSHlQbYZ0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 504x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "discovery_celltype = 'CD4T'\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
+    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
+    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "    im1, bar = heatmap(np.flip(replicated_ratio_df[discovery_celltype].values.reshape((6, 1)),\n",
+    "                               axis=0), \n",
+    "            list(rb_df.index)[::-1], \n",
+    "            [discovery_celltype],\n",
+    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
+    "    bar.remove()\n",
+    "    _ = annotate_heatmap(im1, \n",
+    "                         data=replicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                         valfmt=\"{x:.0%}\", \n",
+    "                         textcolors=(\"white\", \"white\"),\n",
+    "                         threshold=1)\n",
+    "    if i > 0:\n",
+    "        axes[i].axis('off')\n",
+    "        \n",
+    "plt.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig('replicated_ratio.filtered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 36x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(0.5, 6))\n",
+    "fig.subplots_adjust(bottom=0.5)\n",
+    "\n",
+    "colors = [\"white\", 'black']\n",
+    "cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "norm = mpl.colors.Normalize(vmin=0.7, vmax=1)\n",
+    "\n",
+    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),\n",
+    "             cax=ax, orientation='vertical')\n",
+    "plt.savefig('colorbar.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 36x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(0.5, 6))\n",
+    "fig.subplots_adjust(bottom=0.5)\n",
+    "\n",
+    "colors = [\"white\", 'black']\n",
+    "cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "norm = mpl.colors.Normalize(vmin=0, vmax=1)\n",
+    "\n",
+    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),\n",
+    "             cax=ax, orientation='vertical')\n",
+    "plt.savefig('colorbar.replication_ratio.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### celltype comparison for unfiltered results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# filtered results\n",
+    "unrb_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "unrbse_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                       columns=celltypes, index=celltypes)\n",
+    "unrbpvalue_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                           columns=celltypes, index=celltypes)\n",
+    "unnumcoeqtl_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                           columns=celltypes, index=celltypes)\n",
+    "unanno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                           columns=celltypes, index=celltypes)\n",
+    "unnum_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                           columns=celltypes, index=celltypes)\n",
+    "\n",
+    "for discovery_celltype in celltypes:\n",
+    "    for replication_celltype in celltypes:\n",
+    "        if discovery_celltype != replication_celltype:\n",
+    "            unrb_results = unfiltered_res_df[(unfiltered_res_df['celltype_discovery'] == discovery_celltype) &\n",
+    "                                           (unfiltered_res_df['celltype_replication'] == replication_celltype)]\n",
+    "            unreplicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/unfiltered_results/rb_calculations/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t',\n",
+    "                index_col=0\n",
+    "            ).shape[0]\n",
+    "            if rb_results['r'].values[0] < 10 and discovery_celltype != 'B':\n",
+    "                unrb_df.loc[replication_celltype, discovery_celltype] = unrb_results['r'].values[0]\n",
+    "                unrbse_df.loc[replication_celltype, discovery_celltype] = unrb_results['se_r'].values[0]\n",
+    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = unrb_results['p'].values[0]\n",
+    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
+    "                unrbvalue = unrb_results['r'].values[0]\n",
+    "                unrbsevalue = unrb_results['se_r'].values[0]\n",
+    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"{unrbvalue:.2f}\\nN={unreplicated_coeqtls_num}\"\n",
+    "            elif discovery_celltype == 'B':\n",
+    "                unrb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                unrbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
+    "                unrbvalue = unrb_results['r'].values[0]\n",
+    "                unrbsevalue = unrb_results['se_r'].values[0]\n",
+    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "            else:\n",
+    "                unrb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                unrbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
+    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "        else:\n",
+    "            unrb_df.loc[replication_celltype, discovery_celltype] = 1\n",
+    "            unrbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            unreplicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/unfiltered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t'\n",
+    "            ).shape[0]\n",
+    "            unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
+    "            unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "            unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "            \n",
+    "unreplicated_ratio_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                                   columns=celltypes, index=celltypes)\n",
+    "for discovery_celltype in unnumcoeqtl_df.columns:\n",
+    "    for replication_celltype in unnumcoeqtl_df.index:\n",
+    "        unreplicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "        unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] / unnumcoeqtl_df.loc[discovery_celltype, \n",
+    "                                                                                          discovery_celltype]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "matplotlib.rcParams.update({'font.size': 14})\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
+    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
+    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "    im1, bar = heatmap(np.flip(unreplicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                               axis=0), \n",
+    "            list(rb_df.index)[::-1], \n",
+    "            [discovery_celltype],\n",
+    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
+    "    bar.remove()\n",
+    "    _ = annotate_heatmap(im1, \n",
+    "                         data=unreplicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                         valfmt=\"{x:.0%}\", \n",
+    "                         textcolors=(\"white\", \"white\"),\n",
+    "                         threshold=1)\n",
+    "    if i > 0:\n",
+    "        axes[i].axis('off')\n",
+    "        \n",
+    "plt.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig('replication_ratio.unfiltered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "matplotlib.rcParams.update({'font.size': 14})\n",
+    "discovery_celltype = 'CD4T'\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
+    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
+    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "    im1, bar = heatmap(np.flip(unrb_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                               axis=0),\n",
+    "            list(rb_df.index)[::-1], \n",
+    "            [discovery_celltype],\n",
+    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
+    "    bar.remove()\n",
+    "    _ = annotate_heatmap(im1, \n",
+    "                         data=unanno_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                         valfmt=\"{x:^}\", \n",
+    "                         textcolors=(\"white\", \"white\"),\n",
+    "                         threshold=1)\n",
+    "    if i > 0:\n",
+    "        axes[i].axis('off')\n",
+    "        \n",
+    "plt.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig('rb_values.unfiltered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BIOS replication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bios_replication_filtered_df = pd.read_csv(\n",
+    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/filtered_results/replication_summary.csv', \n",
+    "    index_col=0\n",
+    ").set_index('celltype')\n",
+    "bios_replication_unfiltered_df = pd.read_csv(\n",
+    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/unfiltered_results/replication_summary.csv', \n",
+    "    index_col=0\n",
+    ").set_index('celltype')\n",
+    "color_dict = {'CD4T': '#2E9D33',\n",
+    "              'CD8T': 'darkgreen',\n",
+    "              'monocyte': '#EDBA1B',\n",
+    "              'NK': '#E64B50',\n",
+    "              'DC': '#965EC8',\n",
+    "              'B': '#009DDB',\n",
+    "              'cMono': 'peru',\n",
+    "              'ncMono': 'y',\n",
+    "              'CD4T_individual_100': '#2E9D33',\n",
+    "              'CD4T_individual_50': '#2E9D33',\n",
+    "              'CD4T_50': '#2E9D33',\n",
+    "              'CD4T_150': '#2E9D33',\n",
+    "              'CD4T_250': '#2E9D33'}\n",
+    "\n",
+    "bios_replication_filtered_df['color'] = [color_dict.get(celltype) for celltype in \n",
+    "                                         bios_replication_filtered_df.index]\n",
+    "bios_replication_unfiltered_df['color'] = [color_dict.get(celltype) for celltype in \n",
+    "                                          bios_replication_unfiltered_df.index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bios_replication_filtered_df_clean = bios_replication_filtered_df.drop(index=['B'])\n",
+    "bios_replication_filtered_df_clean = bios_replication_filtered_df_clean.drop(columns=['color'])\n",
+    "bios_replication_filtered_df_clean.to_excel(workdir/'output/summary/rb_values_bios_replication_filtered_results.xlsx')\n",
+    "\n",
+    "bios_replication_unfiltered_df_clean = bios_replication_unfiltered_df.drop(index=['B'])\n",
+    "bios_replication_unfiltered_df_clean = bios_replication_unfiltered_df_clean.drop(columns=['color'])\n",
+    "bios_replication_unfiltered_df_clean.to_excel(workdir/'output/summary/rb_values_bios_replication_unfiltered_results.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-08dd41d53262>:3: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax2.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
+      "<ipython-input-12-08dd41d53262>:8: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax2.errorbar(y=bios_replication_unfiltered_df.loc[sorted_celltypes]['r'].values,\n",
+      "<ipython-input-12-08dd41d53262>:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax2.set_xticklabels(['', 'CD4T', '', 'CD8T', '', 'monocyte', '', 'DC', '', 'NK'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK']\n",
+    "fig, ax2 = plt.subplots()\n",
+    "ax2.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
+    "             x=[ind for ind in range(len(sorted_celltypes))],\n",
+    "             yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
+    "             fmt='.', markersize=6, marker='o', color='black', label = 'Filtered results')\n",
+    "bios_replication_unfiltered_df.loc['DC'] = [np.nan, np.nan, np.nan, np.nan]\n",
+    "ax2.errorbar(y=bios_replication_unfiltered_df.loc[sorted_celltypes]['r'].values,\n",
+    "             x=[ind+0.05 for ind in range(len(sorted_celltypes))],\n",
+    "             yerr=bios_replication_unfiltered_df.loc[sorted_celltypes]['se_r'].values,\n",
+    "             fmt='.', markersize=6, marker='o', markerfacecolor='white', color='black', label = 'Unfilter results')\n",
+    "ax2.set_xticklabels(['', 'CD4T', '', 'CD8T', '', 'monocyte', '', 'DC', '', 'NK'])\n",
+    "plt.legend()\n",
+    "plt.ylabel(\"rb (SE)\")\n",
+    "plt.savefig('sf20.comparison_rb_values_bios_replication.pdf')\n",
+    "plt.savefig('sf20.comparison_rb_values_bios_replication.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-31-c42524d09425>:14: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x360 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare between filtered and unfiltered\n",
+    "fig, axes = plt.subplots(1, 5, figsize=(4, 5), sharey=True)\n",
+    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK']\n",
+    "# ax1.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
+    "#              x=[ind-0.1 for ind in range(len(sorted_celltypes))],\n",
+    "#              yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
+    "#              fmt='.', markersize=6, marker='o', \n",
+    "#              ecolor=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values,\n",
+    "#              color=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values[0])\n",
+    "# ax1.set_xticklabels([\"\"]+sorted_celltypes)\n",
+    "# ax1.plot([0, 5], [0.5, 0.5], linestyle='--', color='black')\n",
+    "for ind, celltype in enumerate(sorted_celltypes):\n",
+    "    ax = axes[ind]\n",
+    "    ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
+    "             x=[0.4],\n",
+    "             yerr=bios_replication_filtered_df.loc[celltype]['se_r'],\n",
+    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
+    "             markeredgecolor='black', markerfacecolor='black'\n",
+    "             )\n",
+    "    ax.set_xlim([0, 1])\n",
+    "    ax.spines['bottom'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.spines['top'].set_color(bios_replication_filtered_df.loc[celltype]['color']) \n",
+    "    ax.spines['right'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.spines['left'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.set_xticklabels([])\n",
+    "    ax.set_xlabel(celltype)\n",
+    "    \n",
+    "\n",
+    "plt.savefig('bios_replication.filtered_results.pdf')\n",
+    "plt.savefig('bios_replication.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-32-cb6cab60ed6f>:14: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
+      "<ipython-input-32-cb6cab60ed6f>:20: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax.errorbar(y=bios_replication_unfiltered_df.loc[celltype]['r'],\n",
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
+      "  return array(a, dtype, copy=False, order=order)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare between filtered and unfiltered\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(12, 6), sharey=True)\n",
+    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
+    "# ax1.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
+    "#              x=[ind-0.1 for ind in range(len(sorted_celltypes))],\n",
+    "#              yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
+    "#              fmt='.', markersize=6, marker='o', \n",
+    "#              ecolor=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values,\n",
+    "#              color=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values[0])\n",
+    "# ax1.set_xticklabels([\"\"]+sorted_celltypes)\n",
+    "# ax1.plot([0, 5], [0.5, 0.5], linestyle='--', color='black')\n",
+    "for ind, celltype in enumerate(sorted_celltypes):\n",
+    "    ax = axes[ind]\n",
+    "    ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
+    "             x=[0.4],\n",
+    "             yerr=bios_replication_filtered_df.loc[celltype]['se_r'],\n",
+    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
+    "             markeredgecolor='black', markerfacecolor='black'\n",
+    "             )\n",
+    "    ax.errorbar(y=bios_replication_unfiltered_df.loc[celltype]['r'],\n",
+    "             x=[0.6],\n",
+    "             yerr=bios_replication_unfiltered_df.loc[celltype]['se_r'],\n",
+    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
+    "             markeredgecolor='black', markerfacecolor='white')\n",
+    "    ax.set_xlim([0, 1])\n",
+    "    ax.spines['bottom'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.spines['top'].set_color(bios_replication_filtered_df.loc[celltype]['color']) \n",
+    "    ax.spines['right'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.spines['left'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.set_xticklabels([])\n",
+    "    ax.set_xlabel(celltype)\n",
+    "    \n",
+    "\n",
+    "plt.savefig('bios_replication_comparison.filter_and_unfilter.pdf')\n",
+    "plt.savefig('bios_replication_comparison.filter_and_unfilter.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare between filtered and unfiltered\n",
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'B', 'NK', 'DC']\n",
+    "fig, axes = plt.subplots(6, 2, figsize=(12, 12), sharex=True)\n",
+    "for i, celltype in enumerate(celltypes):\n",
+    "    replication_celltypes = [ct for ct in celltypes]\n",
+    "    ax1, ax2 = axes[i, :]\n",
+    "    ax1.scatter(x=replication_celltypes,\n",
+    "                 y=numcoeqtl_df[celltype].loc[replication_celltypes])\n",
+    "    ax1.scatter(x=replication_celltypes,\n",
+    "                y=unnumcoeqtl_df[celltype].loc[replication_celltypes])\n",
+    "    ax2.errorbar(x=replication_celltypes, fmt='.', markersize=12,\n",
+    "                 y=rb_df[celltype].loc[replication_celltypes],\n",
+    "                 yerr=rbse_df[celltype].loc[replication_celltypes], label='filtered')\n",
+    "    ax2.errorbar(x=replication_celltypes, fmt='.', markersize=12,\n",
+    "                 y=unrb_df[celltype].loc[replication_celltypes],\n",
+    "                 yerr=unrbse_df[celltype].loc[replication_celltypes], label='Unfiltered')\n",
+    "    ax1.set_ylabel(celltype)\n",
+    "ax2.legend()\n",
+    "\n",
+    "plt.savefig('celltype_rb.comparison_filtered_unfiltered_results.pdf')\n",
+    "plt.savefig('celltype_rb.comparison_filtered_unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sub celltypes in monocytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>r</th>\n",
+       "      <th>se_r</th>\n",
+       "      <th>p</th>\n",
+       "      <th>celltype_discovery</th>\n",
+       "      <th>celltype_replication</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.971431</td>\n",
+       "      <td>0.048402</td>\n",
+       "      <td>1.351820e-89</td>\n",
+       "      <td>ncMono</td>\n",
+       "      <td>cMono</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.929081</td>\n",
+       "      <td>0.088678</td>\n",
+       "      <td>1.101982e-25</td>\n",
+       "      <td>ncMono</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.936797</td>\n",
+       "      <td>0.025409</td>\n",
+       "      <td>1.468276e-297</td>\n",
+       "      <td>cMono</td>\n",
+       "      <td>ncMono</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.999726</td>\n",
+       "      <td>0.000613</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>cMono</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.896203</td>\n",
+       "      <td>0.036240</td>\n",
+       "      <td>5.115902e-135</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>ncMono</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.949824</td>\n",
+       "      <td>0.008640</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>cMono</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          r      se_r              p celltype_discovery celltype_replication\n",
+       "1  0.971431  0.048402   1.351820e-89             ncMono                cMono\n",
+       "2  0.929081  0.088678   1.101982e-25             ncMono             monocyte\n",
+       "3  0.936797  0.025409  1.468276e-297              cMono               ncMono\n",
+       "4  0.999726  0.000613   0.000000e+00              cMono             monocyte\n",
+       "5  0.896203  0.036240  5.115902e-135           monocyte               ncMono\n",
+       "6  0.949824  0.008640   0.000000e+00           monocyte                cMono"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_mono_res_df = pd.read_csv(workdir/'output/filtered_results/rb_calculations/monocyte_subcelltypes/summary.csv', \n",
+    "                                   index_col=0)\n",
+    "filtered_mono_res_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# filtered results\n",
+    "mono_subcelltypes = ['monocyte', 'cMono', 'ncMono']\n",
+    "monorb_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                     columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "monorbse_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "monorbpvalue_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "mononumcoeqtl_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "monoanno_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "mononum_anno_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                     columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "\n",
+    "for discovery_celltype in mono_subcelltypes:\n",
+    "    # replication in other celltypes\n",
+    "    for replication_celltype in mono_subcelltypes:\n",
+    "        if discovery_celltype != replication_celltype:\n",
+    "            monorb_results = filtered_mono_res_df[(filtered_mono_res_df['celltype_discovery'] == discovery_celltype) &\n",
+    "                                         (filtered_mono_res_df['celltype_replication'] == replication_celltype)]\n",
+    "            monoreplicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/filtered_results/rb_calculations/monocyte_subcelltypes/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t',\n",
+    "                index_col=0\n",
+    "            ).shape[0]\n",
+    "            if monorb_results['r'].values[0] < 10:\n",
+    "                monorb_df.loc[replication_celltype, discovery_celltype] = monorb_results['r'].values[0]\n",
+    "                monorbse_df.loc[replication_celltype, discovery_celltype] = monorb_results['se_r'].values[0]\n",
+    "                monorbpvalue_df.loc[replication_celltype, discovery_celltype] = monorb_results['p'].values[0]\n",
+    "                mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
+    "                monorbvalue = monorb_results['r'].values[0]\n",
+    "                monorbsevalue = monorb_results['se_r'].values[0]\n",
+    "                monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"rb={monorbvalue:.2f}\\nN={monoreplicated_coeqtls_num}\"\n",
+    "                mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={monoreplicated_coeqtls_num}\"\n",
+    "            else:\n",
+    "                monorb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                monorbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                monorbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
+    "                monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"rb=NA\\nN={monoreplicated_coeqtls_num}\"\n",
+    "                mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={monoreplicated_coeqtls_num}\"\n",
+    "        else:\n",
+    "            monorb_df.loc[replication_celltype, discovery_celltype] = 1\n",
+    "            monorbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            monorbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            monoreplicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/filtered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t'\n",
+    "            ).shape[0]\n",
+    "            mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
+    "            monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={monoreplicated_coeqtls_num}\"\n",
+    "            mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={monoreplicated_coeqtls_num}\"\n",
+    "    \n",
+    "monoreplicated_ratio_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "for discovery_celltype in mononumcoeqtl_df.columns:\n",
+    "    for replication_celltype in mononumcoeqtl_df.index:\n",
+    "        monoreplicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "        mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] / mononumcoeqtl_df.loc[discovery_celltype, discovery_celltype]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>monocyte</th>\n",
+       "      <th>cMono</th>\n",
+       "      <th>ncMono</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.826087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cMono</th>\n",
+       "      <td>0.996441</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.826087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ncMono</th>\n",
+       "      <td>0.985765</td>\n",
+       "      <td>0.980645</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          monocyte     cMono    ncMono\n",
+       "monocyte  1.000000  1.000000  0.826087\n",
+       "cMono     0.996441  1.000000  0.826087\n",
+       "ncMono    0.985765  0.980645  1.000000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monoreplicated_ratio_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-1-1162ad6d26ab>:60: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-1-1162ad6d26ab>:61: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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1f5887wS8T+FMoTECmCIiI+oddjewVFVHAxfhLDaPiPQBfgOMU9VROEtlTfbrTRtjQkZbYliwCb4SGWNCRhXS6lczxuPMzbbe2zw3Czi93jEjgE+hZhTmQO/cauB0UYkRkXAgFmfeN2OMaZSf41eHsyTPGBMQzvQDfp8MuQ/OJLjVNnu3+VoGnAUgIuOBAUBfdRabfwhnbrMsoFBVP27/nRpjQlFbYliwCb4SdTARucrK4LBy1GXlaG8Z2txcmyoii3xeV9W5aEP1+4RNA5JFZClwA7AEqBCRZJxav0FAOhAnIhe2/r6CRzD8bYCVI9jKAFaO+joqhgWb4CtRxwuGP8BgKANYOeqzctRqdRmqpx9o7QvIU9VxPq/pPpfdDPTzed+Xek2uqlqkqpeq6hicPnk9gA3AccAGVc1VZ8H4d4BDW3tfQSYY/jbAyuErGMoAVo76OiSGBRubQsUYEzCV/l8S6DtgiHeVhS04AyfO9z1ARJKA3d4+e1cAX6pqkYhsAg4WkVigGJgALPJ3AY0xoSMAMaxDdckkz1ttexXA+WfFH1C1dUinTgHwzIM96OwyWDkaGs7+HB82ycrhLUNrVS/u7U+qWiEi1wNzcEbHzvAuYXWNd/+zwD7AKyJSCawCLvfuWyAibwGLgQqcZtzpjXxMUAu2+AXB8282GMoRDGUA6H/YORxwxSNWjlpXt/aEQMSwjiZdfYqjYPjHaILTxPQxnV2EoPJJ1ZuteqQdvG+c3v9u/dlNmjd5yKLvVXVcq0/sgix+mT058He/7uwiBJXvn7+l1VVybYlhwRa/umRNnjEm8KpHphljjBuFQgyzJM8YExCKuL4/izGm6wqFGGZJnjEmYIJxtJkxxrSU22OYJXnGmIBQJSjnjTLGmJYIhRhmSZ4xJkCCc5kfY4xpGffHMEvyjDEBobj/KdgY03WFQgyzJM8YEzBuH5lmjOna3B7D3F16Y4wxxhjTKKvJM8YEhCJUuXz6AWNM1xUKMcySPGNMwLi9qcMY07W5PYZZkmeMCQgFqlzeadkY03WFQgyzJM8YEyBCpcunHzDGdGXuj2HuTlGNMUGr+im4tS9jjAkGbYlhLSEiJ4rIGhFZKyJ3NrI/WUTeFZHlIrJQREb57LtZRFaKyA8i8pqIRDf1WRZRjTEBU+l9Em7NyxhjgoW/45eIeICngJOAEcAUERlR77C7gaWqOhq4CHjMe24f4DfAOFUdBXiAyU19njXXGmMCQlWsZs4Y41oBimHjgbWquh5ARGYBpwOrfI4ZAfzVKYP+KCIDRSTNuy8ciBGRciAWyGzqwywCG2MCplLDWv0yxphgEYD41QfI8Hm/2bvN1zLgLAARGQ8MAPqq6hbgIWATkAUUqurHTX2YRVRjTEAoUOVd+7E1L2OMCQZtiWFAqogs8nldVe+yjQU5rfd+GpAsIkuBG4AlQIWIJOPU+g0C0oE4EbmwqXuw5lpjTICI1cwZY1ysTTEsT1XHNbF/M9DP531f6jW5qmoRcCmAiAiwwfuaCGxQ1VzvvneAQ4F/7enDLMkzxgSEMzLNauaMMe4UoBj2HTBERAYBW3AGTpzve4CIJAG7VbUMuAL4UlWLRGQTcLCIxALFwARgUVMfZkmeMSZg3D5bvDGma/N3DFPVChG5HpiDMzp2hqquFJFrvPufBfYBXhGRSpwBGZd79y0QkbeAxUAFTjPu9KY+z5I8Y0xAhMK6j8aYritQMUxVZwOz62171ufn+cCQPZz7B+APLf0sS/KMMQFTZTV5xhgXc3sMsyTPGBMQqlBpNXnGhRIHr6No3eBWn7ctv5Jzr9zKd0tLuPi8RJ64v0ejx+UXVDL5mq1szKhgQL9wXv9HL5KTPABMezyfGa/twOOBR+9LZeIxce26l/Za+vJdjLn4r206d+uyT9m2ZgGEhdHv4DNI7Du8wTG7t2WS8fVbVFaUEhnfnUFHX4AnMppduZvY9NWb3qOU3mMnkjRw33bcSeuFQgxzd4pqjAlqVSqtfjWnnUsCJYnIWyLyo4isFpFD/HzLxuVUlaqqtp0bHS388bfdefD3qU0e98CTBUw4PJY13wxgwuGxPPBkAQCr1pTx+vs7WTGvP7NnpnP9XblUVtafXaPjqCqqbfv84oKtFKxfwj5n/5a9J17Jpm/eQRv5xW766g3SD/wVI866naSBo8he8TkAMcm9GH76Texz5q3sPfEqNn39FlpV2a77aQt/x6+OZkmeMcY12rMkkNdjwEeqOhzYD1gd+FKbYPdLRjkjj9jIdXfmMO6EDIpLlNvuzWPc8RkcP2kLuXktSy7iYsM4/KAYoqOb/rL/YM4uLjo3AYCLzk3g/Y92ebfv5LzT44mKEgb1j2DwwAgWLilp3821UumOfFa+9QCbvn6bH997BK0sZ/OCD1j93iP8PPsZyot3tug6hZtWkrzXWMI84UQlpBCVmMKu3E0NjispzCG+114AJKYPZfsvKwAIC49EwpzazarKcj/dXddjzbXGmIBwOi0H1ZJAxcCRwCXefWVAmb8LaNxpzbpyXng0jaem9cTTey1j943ioXtTue+RfP70SD5P3N+Dh54uYOY7Oxqce8TBMTz258abZhuTnVtJ7zTn67d3Wjg53iRyy9ZKDtq/dr35vunhbNna8bVXpYW5DDxyMv0PO5vFL9xKbEof+h50GllLPmbrko/pd+hZZC//nPx1ixucG99rL/odcibluwqJ7TmgZntkXBLluwsbHB+T3IvCTStJGjCKgg3LKdu1vWbfrpyNbPzf65TtLGDgUefXJH0dJUAxrENZkmeMCZiWLNjdSo0tCXRQvWOqlwT6yndJIKASyAVeFJH9gO+BG1V1l78LadxnQN9wDj7ASbDCwuC80+MBuODsBM65PAuA265N5rZrkwNWhsZaRjujATAyPpm46gRNhOS9xgDQffABrP/0JQDSRh9D2uhj9ngNbbCIAzR2NwOOOI+M+e+RteQTkvqPqJPIxfUcwIizf0vx9mw2fvEaiX2HExYe0dbbapMAxLAO5e4UNQhdfnM2vUZtYPTRtdXS+QWVnHDeFoYdupETzttCwfbaJ7Npj+cz9JCN7HP4RuZ87nzXlJYqJ03JZPTRm3jmpdonn6tvy2HJitKOuxk/sN/Hnm3Un5ivHzNfP2aFLqBSK1mnP/CtfsK3+gmL9UtKtRiA7ZrHt/oJC/VTdqvTXFKuZSzW/7W5z0ygVU8k2oY+LU0tC9TmJYFwHmr3B55R1bHALqBBn76uxtN7Lbfdm1fz/uFnCvjjQ9tadO7SH0o57JQM9j1qE2OO3cTr79fWcn36v92MOz6D/Y/bxJGnbWbtBqfS9MefyzjslAxiBqzl4WcK/Hsz7RAXu+evQ/H+1T30dAH7H7epwevG3+W26rPSenjIyq4AICu7gp6pTmLTt7eHzZm1TZObMytI79WxtVfgNJU2J3v556x+9+EGr4z57wLemjufWrmyXduJiE1scJ3opDSGnHQ1+5xxM8mD9ycqIaXBMTFJaYRFRFJcsLXtN9UGbYlhwcbVSZ6IXCIi6Z1dDl8Xn5vI7Jm962xrbSfbOfN2c8DoKJZ+1o/n/uUkNctWllKlMHbfqA6/p/aw30fjSrSYDNYyngkcIiegKNlkMIBhHCzHc7AcTyq9We/tMraRnxjNwQxmFJtZB8AGVjOI4YgEX2BxOE0drX3hXRbI5+U72WeLlgRS1UtVdQxOn7weOEsCbQY2q+oC76Fv4SR9nSJY4ldUlPDu7J3kbWt9s2BsjPDS42ms+ML5N3vL7/PYXuhc57o7c/nnU2ksntufKWcl8JdHnX/n3ZPDePTPPbj1msDViLVXVRW89aHzMPXaOzs4bHwM4NTkLZ7bv8GrNU21AKeeEMcrbzgJ8Stv7OC0ic4I2lMnxvH6+zspLVU2bCpn7YZyxo+NbupSgadKwYblAOSvW0x8r0GAU5O3z5m3Nnj1O+RMALr1H0nB+iVUVVZQumMbpUV5xPXo3+Dy5cU7vB9Txdaln5C6jzMWqnTHtpqBFqU78iktzCUqoaP/Zlofw4KN25trLwF+oF6Q70xHHhLDLxl1O4l+MGcXn73dB3A62R579ham/S51j51sI8KhuKSKioraa/z+wW0880DPjrwVv7Dfx54pShWViApVVBBFNOFS2xRRSe2XbhhhVHr/J4SxW3dSSjHJ0rovl45W5f+mjjYvCQQUiUiGiAxT1TU4SwKtovNcQhDEr3APXHlhIo9O386f72pYi9KUoYNra3zSe4XTM9VD7rZKkrp5EIGinc5oysKiStK9fdB6pobTMzWc2XODt5U8LlZYtaaMA0/IoFtiGK8926vF5+514C8U7ayirEx5/6OdfPRaH0YMi+TKW3O4emoi48ZEc8f1yUy+eiszXiuif59wXp/uXH/ksCgmnRrPqKM2Eh4uPHF/Dzyezn2ICwuPpGT7Vla/93c8kdEMOmZqi86LSe5F0qAxrHr7QSQsjH6HnIWEOUnQxv+9TurwQ4nr0Y+CdUvIXf01AEkD9yVlyHgAdm7dQPbyz5zmWxH6HXIW4dHxgbnJJgQghnWoZpM8ERkIfAR8BRyM09/lReCPQE/gAmAtMAPYC9gNXKWqy0XkXqC/d3t/4FFVfdx73VuAy7wf87yqPurdfhFwG05N6XLgWu//D1XVchFJ9L6/HRgHvCoixcAhOB2uHwHigTzgElXNatuvxn9a28n2jBPj+NfbOzjkVxncdm0yH8zZxQGjo0nv5fac3GG/D4iWGAboUL7iP4ThIYU0UsQJ9Gv1B7LYSDgRHMBRAAxkOKtZjAcPIzmQn1nOYEZ25i00KxBzTLVhSaBfgL1E5Hmc+LUJ+EBE+uLEmNNFpDtdPH5de2kSY47dxO3XJdXZ/urbOxptUh08MII3n69bQ79wSQllZcrggc6DyvSHenLKhZnERAuJ8WF8859+Da4TLAb2i2D5vNpapuo58v50R+uSXoD13w1sdPtzD9c+lKZ09/DJm30aPe7um7pz903dW/25/hKV0J0RZ99e8756jrz0A05q9bV6jzmO3mOOa7B9wBHn1fzcc9SR9Bx1ZINjUoaMI2XIuFZ/pj+Fwjx5Lf2W3BuYBFyF8yR9PnA4cBrOdAUZwBJVPUNEjgVeAcZ4zx0OHAMkAGtE5BlgNHApTodpARaIyBc4I93uAQ5T1TwR6a6qO0RkHvAr4D2cJ/e3VfVNEbkOuE1VF4lIBPAEcLqq5orIecBfqA3EQWdPnWzDw4VXn3a+8MvLnf5o773cm1v/kMumLRVMnZRYU70fSrrS76Ncy8glk8M4mXAiWMG3ZOlGessA9pZR7M0oNuiPZLCWwYwkQZIYz7EAFGguUcSgwAr9FiGMIYwmSjq5WacRgWi+aM2SQN6H1LXA2dTGr69x1oI8DafPXpePX4kJYUydlMATzxcSE1P7pXbB2QlccHZCs+dnZVdw8Q3ZvPhYGmFhzvmPTt/Oh/9K56D9o3no6QJuvTevTqJjjBsEYxNsa7S09BtUdYWqVgErgU/V6e29AhiIk/D9E0BVPwNSRKSb99z/qGqpquYBOUCa9/h3VXWXqu4E3gGOAI4F3vIei6rme6/xPE5Qxfv/LzZSxmHAKOATb4fr3+H012lARK6q7tQ9/Z8Nh3T7W3s62T7zUiFTz01g/qISIiOFWf/oxf2P5uNm9vuAfHKIIY5IiSJMwuhBHwqp29m9F/3IYUudbarq7Yu3DxtYxV6MoBf9yWBtRxa/RarXfQyCjssWv1rgxiuTmPFaEbt21z5tvfr2jkYHGky6oraCsWhHFademMWf7kipGZ2am1fJ8lWlNTXz554Wz/zviv1WVmM6QltiWLBpaZLnO4Sxyud9FU5tYFMj3nzPrWzieLzbG9TnqOrXwEAROQrwqOoPezh3paqO8b72VdUTGvsQVZ1e3an7qqndGjvEr9raybZgeyUfzt3FRZMS2F1cRZgIIlBSGpyjKVvKfh8QTQyF5FOpFagqBeQQSyK7tXZ0Yi5ZxFG3FiWLjaTSmwiJ9PbZEwSopIJgVIW0+hUAFr9aoHuyh0mnxTNjZlHNtgvOTmh0oEF1U21ZmXL2ZVlMnZTApFNr+0slJ4VRWFTFT+ucEbWffFnM8CHNj9gMBu0ZbQxw0pRMug9bz6lT63a1vPTGbAaP/6UmUV76g/OnNe+b3SQPXV+z/b5HgvehdfELt7J5wQc177NXfE7m4jktPn/tR9NZ9s97WPvx83W278j8mdXvPcKqt//GL1+81ikrW+xJEMSvdvFXp6Yvcfrm3SciR+OMjitqYtTfl8BLIjINJ7idCUzFae54V0T+rqrbvM0d1X/xrwCvAff5XGcH1HwLrgF6iMghqjrf2/wxVFVX+ukeW+T8X2/li2+KycuvpP/+G/jDbSlt7mR73yP53HNjd0SEiUfH8vSLhex3TAZXX9RwGHqwst9H47pJCj21Dwv4FEFIIIm+DGIFC9mtOxCEaGIZ7jP4s1IryGIjYzkCgP4MYTnzCSOMUQ2miut81dMPuIDFL69brkniqRktrx1844OdfPltMdsKKnn5DSc5nPFoGmNGRfGPh3oy6YqthIVBcrcwnv97GgBbcyoYf2IGRTuqCAsTHntuOz98MYDEhOBoFqsebXznDcmkprR++pLbrk1id7HSWC3rA79P5ZxTGg4eOPygaP79z04faN0s8YSz/ZcV9Nrv2DYNgug5+miqKsrJ+3F+zTbVKn758jWGnPRrorv1IPP7j9j28yJSh3V+THNRDNsjfyV59+JMMLocp+PyxU0drKqLReQlYKF30/OqugRARP4CfOHtNL0E7+z0wKvAn3ECZbWXgGd9Oi6fAzzubWoJBx7FaZ7pMDOfaXwUVls62T7yp9qRk9HRYcx5vfFrBDP7fezZYBnZYPDEfux5KVWPhNcMxABIlh4cQqOVPUHDJf1Z7qULx6/qQQYAaT3C2blhcBNH13XhOQlceE7jffbOPDmeM09umAj06hnOpsWDWl/QDtKe0cYAE46IZd43uwNQss4nEkbq8IPJ/uFL+ow7udXnJ6YPZUdW3a4lFSW7CQsLJ7qbE98T+wxl67JPgyLJA9fEsD1qNslT1V9w+opUv79kD/tOb+Tce+u9973OIzgjyeqf8zLwciNFORynv8t2n2PfBt72OWYpzrJFxpjOFgR9VCx+mbbwx2jjxvzftG38+ZF8jj08hr/ek0pUlPPv49vvSxg7YRO908L52x9SGDkseOf/7LHPYax+9yF61VvtIn/t92SvmNfg+KjEVPaasOfnpvDoOLSqil25Gc6UKvWWNutUQRDD2ssVc1CIyBM4C5K3/tHBGNMpFPfPMeUPFr/cp72jjRtz/90p9OrpoawMrr49hwefKuD/bunO/vtGs+G7gcTHhTH7012cdelW1nwzoPkLdhJPZDTd9x5HzsqvCAuvTSG6730A3fc+oNXXExEGHnMhmxe8j1ZWkNhnGCLBUXsWCjHMFUmeqt7Q2WUwxrSe25+C/cHilzvdeGUS407I4JLJtX1+21OTVz03aFQUXDI5gYef2Q5Qpy/iyRPiuP7OXPK2VbapP2BH6TnqSH587xFShhxYs62tNXkA8WkDGXbK9QAUbV5DSVHrlokLJLfHMFckecYY9wmFTsum6/IdbXzpFKf2rj01eVnZFfROC0dVef+/uxg13BltvDWngrQeHkSEhUtKqKqClO7BUZO1J+FRsSQN2o+8nxaSMtRZoaKtNXngLG0WEZNAVWUF2cs/o1cjEyh3hlCIYZbkGWMCxu0B0nRtrR1tDHDU6Zv5cW0ZO3cr/fffwHMP92TiMXFMvS6b3G2VqMJ+IyN55kFnYui3P9zJsy8XER4OMdHCzGfTgng96lpp+x5N7qqvW3XOmg+fpLQwh8ryUla89icGHHEuiX2Hk71iHoWbVgFKj+GHkpA+JCBlbgu3xzBL8owxxhiv9ow2Bvji/UbnsGbuW43PBnDdZUlcd1lSqz6js1QvcQYQEZPA2Eumter86ibZ+vqOP5W+409tV9lM4yzJM8YERPVs8cYY40ahEMMsyTPGBIzbR6YZY7o2t8cwS/KMMYGh7u/PYozpwkIghlmSZ4wJiFAYmWaM6bpCIYZZkmeMCRi3B0hjTNfm9hhmSZ4xJiBCodOyMabrCoUYZkmeMSZg1OUB0hjTtbk9hlmSZ4wJGLePTDPGdG1uj2GW5BljAkJDYGSaMabrCoUYZkmeMSZg3N7UYYzp2twewyzJM8YEiPs7LRtjujL3xzBL8owxAeP2p2BjTNfm9hgW1tkFMMaEpuqJRFv7ao6InCgia0RkrYjc2cj+ZBF5V0SWi8hCERlVb79HRJaIyIf+u1tjTKhpSwwLNpbkGWMCQ52Oy619NUVEPMBTwEnACGCKiIyod9jdwFJVHQ1cBDxWb/+NwGp/3KIxJoT5OX51BkvyjDEBU4W0+tWM8cBaVV2vqmXALOD0eseMAD4FUNUfgYEikgYgIn2BXwHP+/M+jTGhyc/xq8NZkmeMCQjF6c/S2lcz+gAZPu83e7f5WgacBSAi44EBQF/vvkeB3wJV7bs7Y0yoa0sMCzY28MIYEyBt7qOSKiKLfN5PV9XpNRdtqH4jyTTgMRFZCqwAlgAVInIKkKOq34vI0W0pmDGmKwnOfnatYUmeMSbY5KnquD3s2wz083nfF8j0PUBVi4BLAUREgA3e12TgNBE5GYgGEkXkX6p6oZ/Lb4wxQcGaa40xAePvgRfAd8AQERkkIpE4idsHvgeISJJ3H8AVwJeqWqSqd6lqX1Ud6D3vM0vwjDFNcfvAC6vJM8YEjL/7qKhqhYhcD8wBPMAMVV0pItd49z8L7AO8IiKVwCrgcr8WwhjTZQRjP7vW6LSaPBG5V0RURPb22Xazd9uemmqMMS7hPNn6feAFqjpbVYeq6mBV/Yt327PeBA9Vna+qQ1R1uKqepaoFjVxjnqqe0tZ7s/hlTOhrSwwLNp3dXLsCp9mk2jk4T97GmBAQiMmQg4jFL2NCnNvjV4cleSJykXcG+mUi8k/v5vfwznElInsBhUCuzzlTRGSFiPwgIg/4bN8pIn/xXutbnzmwBojIp97P+VRE+nfU/RljGgpAn7xOYfHLmK7J7fGrQ5I8ERkJ3AMcq6r74cw4D1AEZHiXHZoCvO5zTjrwAHAsMAY4UETO8O6OA771XutL4Erv9ieBV7wz3b8KPB7A2zLGNCMQzbUdzeKXMV1XIOJXe5Zm9A4se0tEfhSR1SJySFOf1VEDL44F3lLVPABVzXdmNgCcGesnAxOBCXinPgAOBOapai6AiLwKHInz9FwGVK87+T1wvPfnQ/BOggr8E3iwscKIyFXAVQCPPpDEpRfGt/sGQ0F8WHRnF8GEECU4k7Y2CNr41X3y2SQcfnC7bzAUrD/7H51dhKDS75K1nV0E1wtEDPNZmvF4nCmhvhORD1TVt6tH9dKMZ4rIcO/xE7z7HgM+UtVzvLMIxDb1eR3VXCs0nLC02r+BqcAm7/xWvufsSblqTcVoJXtOVhv9TFWdrqrjVHWcJXjGBI624RWEgjZ+WYJnTGAFIH61eWlGEUnEeVh8wbuvTFW3N/VhHZXkfQqcKyIpACLSvXqHqhYDdwB/qXfOAuAoEUn1Zr5TgC+a+ZxvqO0IfQHwlR/KboxpiwCNru0EFr+M6YoCM7q2PUsz7oXT7/dFEVkiIs+LSFxTH9YhzbXeeaz+AnzhnbtqCfCLz/5ZjZyTJSJ3AZ/jPBXPVtX3m/mo3wAzROR2nF/Epc0cb4wJpCCtmmsNi1/GdGGtj2FNLcsI7ViaEYgA9gduUNUFIvIYcCfwf3sqTIdNhqyqLwMvt+C4o31+ngnMbOSYeJ+f3wLe8v78C07/GWNMEAjSmrlWs/hlTNfUhhjW1LKM0L6lGWOBzaq6wHvoWzhJ3h519jx5xpgQFipTqBhjuqYAxK/2LM24FWdE/zDvvgk0MzenLWtmjAkIJXRq8owxXU8gYpgflma8AXjVmwSup5luHZbkGWMCQwFL8owxbhWgGKaqs4HZ9bY96/PzfGDIHs5dCrR46URL8owxAWPNr8YYN3N7DLMkzxgTOC4PkMaYLs7lMcwGXhhjjDHGhCCryTPGBEjQTm5sjDEt4P4YZkmeMSZwXN7UYYzp4lwewyzJM8YEhtoUKsYYFwuBGGZJnjEmcFz+FGyM6eJcHsMsyTPGBJC7n4KNMV2du2OYJXnGmMBx+VOwMaaLc3kMsyTPGBM4Lg+QxpguzuUxzJI8Y0xg2LJmxhg3C4EYZpMhG2MCRrX1r+aIyIkiskZE1orInY3sTxaRd0VkuYgsFJFR3u39RORzEVktIitF5Eb/37ExJpT4O351NEvyjDGBo214NUFEPMBTwEnACGCKiIyod9jdwFJVHQ1cBDzm3V4B3Kqq+wAHA9c1cq4xxtTyY/zqDJbkGWMCR6X1r6aNB9aq6npVLQNmAafXO2YE8CmAqv4IDBSRNFXNUtXF3u07gNVAH3/erjEmxPg3fnU4S/KMMQEj2vpXM/oAGT7vN9MwUVsGnAUgIuOBAUDfOuUSGQiMBRa0+eaMMSHPz/Grw9nAC2NMYLS9+SJVRBb5vJ+uqtO9Pzf2qFz/U6YBj4nIUmAFsASnqda5gEg88DZwk6oWtamEIWbTLffQ/5G/tOncwjmfsXP+QggLo/s5pxMzYliDY8o2Z7Jt1ttoaRnhKcmkXnw+YTHRVGzLJ/PPfyO8Zw8AogYOIGXK2e26F+NfH57wHKd8fGWbzv3pn4vZ9J/VECaMvvFweh7Uv8ExhWvzWPbQl1QUlxPbK4EDfn8cEXGRZHz8E2tfW1pzXNG6bRz9wiS6DUlt6620XpA2wbaGJXnGmABpc/NFnqqO28O+zUA/n/d9gUzfA7yJ26UAIiLABu8LEYnASfBeVdV32lK4UKPt6DFelpXNrsVLSb/nNioKi8h58h+k//4OJKxuI9G2mW+SfOYpRA8ZzM75Cyn6dB5Jp5wIQHhqCul33dLu+zD+p6pQ1bZzizbks+XTtRzzymRK8nbxzc3/5riZUxBP3b+NpQ/MY+S1h5I6Np2N/1nN2teWss8V4+l3wlD6nTDUuda6bSy4678dm+AB7YhhQcOSPGNM4Pj/Kfg7YIiIDAK2AJOB830PEJEkYLe3z94VwJeqWuRN+F4AVqvqI34vmYtUbMsn++kXiB46mNING9HycvLf+TelP60jLDaG1EsvwJMQ3+x1ipevJG7/MUhEOBGp3QlPTaXsl01E7TWwznHlOblE7b0XANHDh5Lz1HM1SZ4JLruziph/+39IHduHgpXZVJZW8MOT35C3eAsRCVGMu/d4opJjmr3O1q9+oc+EvfFEeohLTySuTzcKVufQfVSvOsft3LSdlDG9Aeg5rh/f3Poh+1wxvs4xm+f+TJ/jhvjvJlvDavKMMWYP/BwgVbVCRK4H5gAeYIaqrhSRa7z7nwX2AV4RkUpgFXC59/TDgKnACm9TLsDdqjrbv6V0h4qcXOIvPJeU885i4/W3E9mvD93POpXt//2Ewv9+Qvdzz6Rw7jx2fbe4wbnRe+9F90lnUFlYSOTA2iY4T3I3KgqLiKp3fGTvXhSvWEns6FHsXryMioLC2nJsyydz2t8Ji44m6ZSJRHuTQdN5dm7azti7jmG/W4/k/SOeodvQVEZdfyhrXlzEmpcWMfrmI/h55hI2f/Jzg3NT9ktn9E2HU5K3i+QRaTXbY3rGUZK7q8HxCXt1Z+tXv9D7iEFs+XwdxTk7Gxyz5bN1HPTXTnoosCTPGGM6jjcpm11v27M+P88HGjz2q+pXuH0hSj/ydE8iatAA540IcfvvB0DcgfuT+9zLAHQ77mi6HXf0Hq+hLWzmTbngXPLfeo/C/84lZt8RiMfjlCExkT5/ugdPfBylmzaTO/0l0u+5jbCY6LbfmGm32F4JdB/prXELE/ocuzcAfScOZeE9HwEw5PyxDDl/7B6v0ejfRiP/+sbeeQwrHvuKNS8totfhAwmLqNucm78yG090OIl7pbTtZro4S/KMMYHj8qfgUBYWGbnnneJ8GzdXkxeelESlT61cZUEh4d0SGxwf0asnaddfBUB5di7FK390PiYiHE+E8zUU1b8v4akpTtPugH4NrmE6jid6z6mBeP82mqvJi+kRX6dWrjhnF9GpcQ2OTxiQzKGPnAo4NYjZ8zfV2b/l07X0nbB3m+7DL1wewyzJ87Prbsnno7kl9EgN49vPnCeh/IIqLv31NjZlVNK/n4eXnk0hOcl5Wnn4iSL+OWsXnjDhgfuSOO7oaEpLlSmX5ZGZVcnlF8Vz5SVO35jf/LaAyy+KY79RTQTnIHP5zdn855Pd9Ez1sHye06yTX1DJ5Gu2sjGjggH9wnn9H71ITnKe7Kc9ns+M13bg8cCj96Uy8Zg4SkuVMy7JYktWBb++pBu/vqQbAFfflsM1F3dj7L71G4fcYaP+RCa/ABBPN0Ywjl9YTS5ZAEQSxUgOJEpi2K55/MgSwghjFAcRK/GUaxkrWMBYDq8JvEElBJYE6jJU2b1kBXHjxrBr0RKi9xoENF+TFzN6BHkvzSTx2COpKCyiIjevTvNttcodO/EkxKNVVRTOmUvC4QfXbA+Li0XCwijP20ZFbh7hqZ1bY+PpvZabr07ioXudTv4PP1PAzl1V/OG2lpXrpCmZLFhcwmHjo/n3P9Nrtl96YzZfzi+mW6IT+2c8msaYUVHM+2Y3Z16ylUH9na/jM0+O5/9u6e7nu2qHKiVz3jr6HjeEzZ/8TPd9ne+15mryeh0+kO//OJfB5+1HSd4udm3eTvI+PRscV1qwm6jkWLRKWfPK9ww8vXZ+cvV+9uFPnuH322qREIhhnTZPnogMFBEVkft8tqWKSLmIPNlZ5Wqv88+N4+1X644A+vtTRRx1eBRLvu7FUYdH8fennFkbfvypnHfeL2bBZ714+9VUbr27gMpK5dMvShizbyTfzE3j5VedPgwrVpZRVaWuSvAALj43kdkze9fZ9sCTBUw4PJY13wxgwuGxPPBkAQCr1pTx+vs7WTGvP7NnpnP9XblUVipz5u3mgNFRLP2sH8/9y6k1WLaylCrFtQleiRaTwVrGM4FD5AQUJZsMBjCMg+V4DpbjSaU361kNwEZ+YjQHM5hRbGYdABtYzSCGB2eC5xWAefKCQqjFL4mMpHzrVrIeeJSSn9bS7aTjWnReZO9exI3dj8y//I2cp5+n+7ln1oys3fbqm5RudKY03LVoCVv++ACZ9/0NT7duxB18IAAla9eTdf8jZP71EfKe/yfdJ5+NJy42MDfZQlFRwruzd5K3rbJN5992bRIvP5HW6L4Hfp/K4rn9WTy3P2NG1cauww+KrtkeVAke4IkJZ8cvBcy7/E3yFm9m2CV7GvheV+Kg7qQfO5jPps5i/m0fMvqWI2pG1i6Z9jkFP+YAsHnuWuZOmcmnF7xGdGoc/U8eXnONbcsyiekRR1x6w9rhjuL2+NXZNXnrgVOA//O+nwSs7LzitN9hB0exMaOizrbZc0r4z1vOPFDnT4rjV+fk8qd74D9zijnr9BiiooSB/cPZa2A43y8pIyIcSkqUCp/L/OVvRfx9WnJH3opfHHlIDL9klNfZ9sGcXXz2tjN/7UXnJnDs2VuY9rtUPpizk/NOjycqShjUP4LBAyNYuKSEiHAoLqmq8/v4/YPbeOaBhk+FbqIoVVQiKlRRQRTRhEtEzf5Kar9kwgij0vs/IYzdupNSikmWHp1R9JYLwqDnR66NX+Ep3Um/57aa99Vz5LVlxGu3EyfQ7cQJDbanXDCp5ufEY44g8ZgjGhwTN3Y0cWNHt/ozAyncA1demMij07fz57taX6s44YhY5n2zOwAl6xixvRM59pXJNe+r58irP+K1JYZddADDLjqgwfaxdx5T8/PgSaMZPKnxv4HUsX048h+dPG+iy2OYX2vyvE+3q0XkOe8C4B+LSIyI7C0ic0VkmYgsFpHB3lOKgdUiUv1ocB7whs/1BojIp96Fxj8Vkf7e7S+JyOMi8o2IrBeRc7zbRUT+JiI/iMgKETnPn/fXVrl5lfRKc5oje6V5yPU+IWZtraRvuqfmuPTeHjK3VnLMkdFk51Zy7Ck53HhtArM/LmbM6Eh69/I0en23yc6tpHea83zROy2cnDzn97FlayV902uTnL7p4WzZWsnxR8WyNbeSQ36VwW3XJvPBnF0cMDqa9F6d/YzSdtESwwCG8hX/4X98SDgRpIjTDLJWf+B/+h+2sonBjARgIMNZzWIy+Jl+DGYdP9TsM/5h8ctUu/bSJGa+s4PCorq1ea++vYP9j9vU4DXpiqwWXff/pm1jzLGbuOX3uZSW1mYP335fwtgJmzj5/ExWrin1672Yri0Q35JDgCmqeqWIvAGcDdwATFPVd0UkGie5rK6GmQVMFpGtQCXOxKbVHRmeBF5R1ZdF5DLgceAM777ewOHAcOAD4C2cpYzGAPsBqcB3IvKlqrbsX2AHa3TwkUB4uPDCU84TZHm5cub5ecx6KYW7791OxpZKpkyK5eQTmp+nyG32NBgrPFx49WknASovV06aksl7L/fm1j/ksmlLBVMnJXLaxIYdeoNZuZaRSyaHcTLhRLCCb8nSjfSWAewto9ibUWzQH8lgLYMZSYIkMZ5jASjQXKKIQYEV+i1CGEMYTZQE34jEYGy+aIbFL0NiQhhTJyXwxPOFxMTUdoe44OwELjg7oU3XvP/uFHr19FBWBlffnsODTxXwf7d0Z/99o9nw3UDi48KY/ekuzrp0K2u+GeCvWzHt5MIYVkcg+uRtUNWl3p+/BwYBfVT1XQBVLVFV37rsj4DjgSnA6/WudQgw0/vzP3GCYrX3VLVKVVcB1R0gDgdeU9VKVc0GvgAOrF9AEblKRBaJyKIX/9VwTh5/65HqYWu280S4NbuSHilOjVx6bw+bM2ufFDOzKumdVre27vmXd3L+pFgWfl9GRKTw0rPd+dtj7l6JKa2Hh6xsp+01K7uCnqnOPfft7WFzZm3T7ubMCtLr1V4+81IhU89NYP6iEiIjhVn/6MX9j+Z3XOH9JJ8cYogjUqIIkzB60IdCttU5phf9yGFLnW2q6u2Ltw8bWMVejKAX/clgbUcWv+Vau7h353dydlX82vHVt229T9OMG69MYsZrRezaXfst356avN5p4YgIUVHCJZMTWLikBHASyvg456v45AlxlJdrm/sDmgBwV/xqIBA1eb51zZVAkx3JVLVMRL4HbgVGAqc2dfgePkfq/X+TvOtgTgcoyuwX8Dz9pBOimfnmLm65PpGZb+7i5IlOjcvJJ8RwxXX5XH9VAlnZlazbUMEBY2sHVhRsr+KjuSW891oqsz8uIUycmr5Sl9fmn3pCHK+8sYM7bkjmlTd21NTCnToxjguvzebmq5PJzK5g7YZyxo+trZ0q2F7Jh3N3MWdWOh/M2UWYCCJQUuq+R61oYigkn0qtIAwPBeSQQDK7dQex4tQU5JJFHHVrDbLYSCq9iZBIKrUSEASopKLhh3Q2d6776Kr4NfCph9r9G954/e0kHHsk3c9yil44dx5aWkbSr05o9tyyzVvYNusdtKQUwoRuEycQd8AYAIrX/Mz2d/+DahVhUVGkTD2PiB6pqCoFb71P8cofkcgIUqaeR1S/vu29Db/rnuxh0mnxzJhZxKVTnH+H7anJy8quoHdaOKrK+//dxajhTqzfmlNBWg8PIsLCJSVUVUFK904bE9mk9494hsHn7ceo6w8FYO1rS6koLmf4ZQ2eRRo1/9YPyV+VTcq+vTn4wZNrtud+v5mVT82nqqKSpGE9GHPHMYSFB8HvwJ0xrI6O+C0WAZtF5AwAEYkSkfrDpx4G7lDVbfW2f4OzbBHABcBXzXzWl8B5IuIRkR7AkcDC9hS+tS67dhvHn5bDz+sq2OeALF55bRe3XJfA51+WMvawrXz+ZSk3X+eMFNpnWARnnBrD+GO2cvYFeTz8lyQ8nto4/+Dfi7j9xkREhAlHRbNkeRmHTMjm4vPd0zR5/q+3ctgpm1mzroz++2/ghZlF3HF9MnO/3M2wQzcy98vd3HG98z06clgUk06NZ9RRGzn5/EyeuL9Hnd/HfY/kc8+N3RERJh4dy6JlJex3TAZXXNB5I6/aqpuk0JM+LOBTvuUTFKUvg/iZH5ivH/OtfkI+2QxlTM05lVpBFhvpi9MlrD9DWM581vJDzbago214BZfQj1/h4exetoLKnQ1XI2iORESSetFk0n93Gz2vvYKCtz+gancxAPmz3iH1kimk33ULcePGUvjRXABKVv1IeW4e6X+4g5Qp55A/K3iXEL7lmiTy8ltXq3bU6Zs578qtfPZVMf3338Ccz53f69TrstnvmE3sd0wGefmV3HOTM4r27Q93MvroDMZO2MRNv8tl5rNpQTtiPizSQ9aX6yndXtym8/eeMoYDfld3kI5WKYvv/4xx9x7Psa9MJiYtgYyP1vijuP7h7vjVYaNrpwL/EJE/AeU4o9Bqlj1W1ZU0PirtN8AMEbkdyMW76HgT3sVpIlmG8+v+rapubX/xW27G042Pxvr3G42Pgrz9xkRuv7HxJOWvf0yq+Tk6WnjvtSAfSdmImc/0anT7J2/2aXT73Td15+6bGp9C4JE/1d5/dHQYc15v/BpuMVhGNhg8sR+H7PF4j4RzAEfVvE+WHhxC87Utncnt/Vm8Qjp+SVgYCYceTNFnX5J82kmtOjcirfbfZHhSN8IS4qncuZOw2BgQoarEqbCsKi7B082Z33L38pXEjz/AabocNICq4hIqCosanUS5MxStq31gSusRzs4NrXuA+uL9xmsl577VeLy67rIkrrssqVWf0VnEIww4dQTr3ljOiKsOavX5Pcb1JW9J3S4oZYUleCI8xPdPAqDngX356V9LGHDKPv4ocru5PYb5NclT1V+AUT7vH/LZfWwjp4yqv0FVXwJe8rleg/NU9ZJ67+O9/6/A7d6XMaazuShAduX4lXDkoWT+9RG6HX90ne07v1tM0dx5DY6P6JFKjysuqrOt9JdNaEVlzWTGKeefQ87TLyCREYRFR9Hr1hsAqNxehCc5qea88KRuVG5vfKUME3wGnTWKzy95gyHnj6mzPePjn1j72tIGx8f16cb4P0/c4/Uik6Kpqqii4Mcckof3JHPe+kbXr+00LophjXHvHBTGmODn8gDZVYTFRBM//gCK5n2FRNROYxR/4P7EH7h/s+dXFBaR98osUqeeVzMZctHn/6PntZcTNbA/hXPnUfDOv71z5+1hWgHjChFxkfSbOJT1b63AE1WbQvQ7YSj9Thja6uuJCOPuPZ4fnviaqvIqeh7YlzBPEPTHq+byGGZJnjEmIIJ1BnjTuIRjjiDrgUeJP7h2RYOW1ORVFZeQ+8wMkk6ZSNQgZ+qPyh07Kd+SSZR3ibO4/fcj5+nnAfAkdaOyYHvNtSq2F+KxWjxXGXzuaOZd/hb9Tx5Ws62tNXkA3Uf14oinzgQgZ2EGOzMKmzy+o4RCDLMkzxgTOEE4pYBpnCculrj9R7Nz/nfEe5cda64mTysqyH3uZeIOOoC4/fer2R4WG0NVcQnl2blEpPWg+MefiUhzphaM2XckO778mtgDxlD2yybCYqKtqdZlIhOj6XPMYDZ9+CP9f+UsQ9bWmjyoXb+2sqySn19dwtCLmq897jAuj2GW5BljAsflT8FdTeKxR7Hji29afPyuxcsoWbueyl272PntdwCkTj2PyL59SDn/HHKffwXChLCYGFIuPBeAmJHDKV65msw/TkMiImu2G3cZPHk/NrzzQ6vO+d9177Jz43YqisuZc9YrjL3jaHoe1J+1M5eydf5GtEoZdMZIehwQRFPquDyGWZJnjAkYtzd1dAXV69YCeBIT6P/3+1t8bvz4A4gf33BtUoDY/fYldr99G2wXEVLOO6v1BTWdrnodW4Do7rGcMvfKJo5uqLpJtr6R1x3KyOsObVfZAsXtMSyIejcaY4wxxhh/sZo8Y0zguPwp2BjTxbk8hlmSZ4wJjBAYmWaM6cJCIIZZkmeMCRyXB0hjTBfn8hhmSZ4xJnBcHiCNMV2cy2OYJXnGmIBxe1OHMaZrc3sMs9G1xhhXEZETRWSNiKwVkTsb2Z8sIu+KyHIRWSgio1p6rjHGhBJL8owxgaNteDVBRDzAU8BJwAhgioiMqHfY3cBSVR0NXAQ81opzjTGmlh/jV7X2PKh693tEZImIfNjcZ1mSZ4wJDK1d+7E1r2aMB9aq6npVLQNmAafXO2YE8CmAqv4IDBSRtBaea4wxDv/Hr3Y9qPq4EVjdkluwJM8YEzh+rskD+gAZPu83e7f5WgacBSAi44EBQN8WnmuMMbX8X5PXngdVRKQv8Cvg+ZZ8mCV5xpjAaVuSlyoii3xeV/lcsbHVwuuH1mlAsogsBW4AlgAVLTzXGGNq+T/Ja8+DKsCjwG+BqpZ8mI2uNcYEhNDmkWl5qjpuD/s2A/183vcFMn0PUNUi4FIAERFgg/cV29y5xhhTrY0xLFVEFvm8n66q0+tdtr7GHlQf8z6orsD7oCoipwA5qvq9iBzdksJYkmeMCRz/15N9BwwRkUHAFmAycL7vASKSBOz2NoVcAXypqkUi0uy5xhhTR+tjWFMPqdC+B9XJwGkicjIQDSSKyL9U9cI9fZg11xpjAiMAAy9UtQK4HpiD0/H4DVVdKSLXiMg13sP2AVaKyI84nZtvbOrcQNy6MSYEBGDgBT4PqiISiZO4feB7gIgkefeBz4Oqqt6lqn1VdaD3vM+aSvDAavKMMYEUgB5vqjobmF1v27M+P88HhrT0XGOM2SM/xzBVrRCR6odNDzCj+kHVu/9ZnAfVV0SkElgFXN7Wz7MkzxgTODaswRjjZkH2oOpzzDxgXnOfZUmeMSZg3L4kkDGma3N7DLM+ecYYY4wxIchq8owxgePyp2BjTBfn8hhmSZ4xJjBaPjmoMcYEnxCIYZbkGWMCxu39WYwxXZvbY5glecaYwHF5gDTGdHEuj2GW5BljAsbtT8HGmK7N7TGsyyd5a8ptgHG13Mqozi5CUJmTubSzi+B+Lg+QwW7IDQs6uwhBY99N13Z2EYLKipuf7uwihAaXx7AumeGIyFUiskhEFr07c2dnF8eY0KRtfJkm+cavzbq+s4tjTOgKgfjVJWvyVHU6MB3gu40DgvA/izHuJ96X8S/f+HV82CSLX8YESCjEsC6Z5BljOoilIMYYN3N5DLMkzxgTMG7vtGyM6drcHsMsyTPGBI7LA6QxpotzeQyzJM8YEzguD5DGmC7O5THMkjxjTGCo+5s6jDFdWAjEMEvyjDGB4/IAaYzp4lwewyzJM8YEjNufgo0xXZvbY5glecaYwHF5gDTGdHEuj2GW5BljAsbtT8HGmK7N7TGsSy5rZowxxhgT6qwmzxgTGEG6lqMxxrRICMQwS/KMMYHj8gBpjOniXB7DrLnWGBMQgtOfpbWvZq8rcqKIrBGRtSJyZyP7u4nIv0VkmYisFJFLffbd7N32g4i8JiLRfr1pY0zIaEsMCzaW5BljAkfb8GqCiHiAp4CTgBHAFBEZUe+w64BVqrofcDTwsIhEikgf4DfAOFUdBXiAye2+R2NM6PJj/OoM1lxrjAkYUb9HvfHAWlVdDyAis4DTgVU+xyiQICICxAP5QIV3XzgQIyLlQCyQ6e8CGmNCRwBiWIeymjxjTGC0pRav+XjaB8jweb/Zu83Xk8A+OAncCuBGVa1S1S3AQ8AmIAsoVNWP23ZzxpiQ5//41eEsyTPGBEwb++Slisgin9dVvpds5GPqh9aJwFIgHRgDPCkiiSKSjFPrN8i7L05ELvTvHRtjQonb++RZc60xJnDaFvTyVHXcHvZtBvr5vO9LwybXS4FpqqrAWhHZAAwHBgAbVDUXQETeAQ4F/tWmUhpjQl8QJm6tYTV5xpiACcDo2u+AISIySEQicQZOfFDvmE3ABAARSQOGAeu92w8WkVhvf70JwGr/3a0xJtRYTZ4xxuyJn4OeqlaIyPXAHJzRsTNUdaWIXOPd/yxwH/CSiKzAad69Q1XzgDwReQtYjDMQYwkw3b8lNMaElCBM3FrDkjxjTGAE6MlWVWcDs+tte9bn50zghD2c+wfgD/4vlTEm5ARp7VxrWJJnjAkclwfIruJzfZdj5Mw2nbtBfySTDQjCMMaQIr0aHLNDt/Mji6mgghjiGMV4wiWixed3tNV/v5N9bp7WpnPzvp1LwfIFiITR67gziR80vMExJTlbyPr4LarKSono1p0+p1yIJ6p2Xu7yogLWvvAAPQ6bSOr4Y9p8H8YPXB7DrE+eMSYgArXihfEvVW3z99hOLSKbDA7hBMZyBD+yBG1kXrHVfM/e7MshcgI9SGcja1p1fkdSVWhjGUrztlK4egmDL7uD/pOuIuuTt9GqqgbHZX70Bj2P/BWDL/stCUP2ZdvCz+vs3/rZe8TvtU+bymD8JxRWvLCaPGNM4Lh8ItFQVay7WMJXdKcH28mnikp+0mUUkEs4EezLwURKVLPXySWTNPoRJh5iiCNG4ykknyRS6hy3ix0kkQpACmks4X8MZlSLzw+0ssJ8Nr05nbj+e1OcuRGtKGfrZ++ze9NawqJj6XvaVMJj45u9zo61P9Btn7GEhYcTmZRCZFIqxVmbiO0zsO7n5ecQ228wAPEDh7Lxzen0POIkAIp+XkFktxQkItLv92nawOUxzJI8Y0zABOOTrXHsZgcjGcdw2Z+5+hYJJDNU9mO9rmI9qxjOWH7RNWxlU4Nzk+nBMBlDKcV0o3vN9mhiKKW4wfHxJJJLFj1JJ5vNlHiPaen5HaEsP5f0kyfT+4RzWPXgLcSk9aXXsaeT+/Uccr+eQ+/jzyZvwWcUrl7c4Ny4vnvR67izKN9RSEz6gJrtEQndqNhZ2OD4qNTe7Fy7koQhoyhas4yKou0AVJWVsm3BZww49xry6tXumc7h9hhmSZ4xJjCCdAZ444gmlm5SW2OWRl8AetGf5cwHYKAMYyDD2v1ZIxjHGpayQVfRg3TCgrCnUES3ZGLTBzpvREjcZwwA3UaOI+O9FwFIPehYUg86tt2flX7SeWz99F1yv/mYhL1HIh4PADlfz6H7uKMIi2y+FtV0gBCIYZbkGWNMF+RpMvw7C4s0V5MXRUxNrRxACcVEEdPg+DhJZH+OBGCX7iCPLIAWn98RwppoHhXv76O5mryIhG5U7Nhes718RyHh8d0aHB+VksaAc68BoDQ/hx3rnKWXi7M2smPNMnLm/ZvK0mIQISw8nO77H9GeWzNdmCV5ATZrRhHvzdqFKpwxOY4plyfy06oypt2TT/FupXdfD396NJX4hDCWLSrlgd/lExEp/PnxFPoNjGBHYRV3X5/H46/0wJm/1d3ef3Ebc14vQIGJ5yVzxqUprFtVzFP/l0VZqeLxwLV/6s2w/WJZtWg3T/0+k4hI4beP9iV9YBQ7iyp54DcZ/OnFAa78fVx+czb/+WQ3PVM9LJ/XH4D8gkomX7OVjRkVDOgXzuv/6EVykvNkP+3xfGa8tgOPBx69L5WJx8RRWqqccUkWW7Iq+PUl3fj1Jc6XyNW35XDNxd0Yu2/w1AJIwz7nJkjlsIVe9GMrGTV94pqryetBb35gIQN0CKWUUMzOOs2v1cq0hEiJRlXZwGr6sFerzu9wqhStWU63fcZSuGoxMX0HAc3X5MXvPYot//4n3ccdTcXOQsoKconp3b/BcRW7dhAel4BqFXnz55I85lAABp1/Q80xOV99RFhkVFAleJ7ea7n56iQeutfpX/nwMwXs3FXFH25rvg/l0h9Kue7OHIp2OHH+rhuTOe/0BACuuCWb75eVogpD9orgxcfSiI8Ljtpet8ew4Pgthqh1a8p4b9YuXno/jVf/24uvPitm04Zy/nJnPtffkcRrc3pz9MRY/jW9CIBXnyti2jOpXHt7N97+104AXniikEuvS3RlQlPfL2tKmPN6AY+8uxdPfjiYhZ/tYMuGUl58IJvzb+jBkx8O5sKbevLiA9kAvPNCHnc/1Y+Lb0tj9swCAGY9mcu5v3ZvwnvxuYnMntm7zrYHnixgwuGxrPlmABMOj+WBJ517XbWmjNff38mKef2ZPTOd6+/KpbJSmTNvNweMjmLpZ/147l9Of59lK0upUoIqwQNCYoHvrsCDh10UskDnUkAOezGiRefFSzfS6Mt8PmYJ/2MYY2r+ba7SRRRpPgBbyeAb/Yj5zCGKGNIZ2Oz5nUkiIinN28r6lx9h16af6XFoo9MuNhCd2ovE4WNYN+MBNr05nV7Hn42EOV+zmf99neKsDAAKVy9h7XN/Zd3zDxAen0jSvuMDdi/+FBUlvDt7J3nbKlt9bmyM8NLjaaz4wolnt/w+j+2FznUe+WMPlnzan6Wf9ad/n3CemtGwH2OncXn8spq8ANqwtoJRYyOJjnH+ke9/UDTz5hSzaX05Yw9yvowPOjya31yUwzW3JhEeLpSWKCXFSni4sHljOblbK9n/4OimPsY1MtaVMmxsTM3vY9/xscz/eAcisHun87i0a0cV3Xs682eFhwtlpUpJcRWecCFrYxnbtpaz70FxnXYP7XXkITH8klFeZ9sHc3bx2dt9ALjo3ASOPXsL036XygdzdnLe6fFERQmD+kcweGAEC5eUEBEOxSVVVFTUXuP3D27jmQd6duSttIjbOy2HqhiJ4xCf+aKr58gbzKhWX2uQ7MMgGk73MUJqlx/uL0Poz5BWnd+RIrt1Z/Blv615Xz1HXvWI19boccjx9Djk+Abb0086r+bnlHFHkjLuyCav0/PwE1v92YEW7oErL0zk0enb+fNdrRsBPXRwbXN4eq9weqZ6yN1WSVI3D4kJzneCqlJcogRBnl/D7THMavICaPCwCJYsLGV7QSUlxVV8/Xkx2VkV7DU0gi8/cfqhzJ29m+ws52nmkmsT+etd+cyasYNJF8fzzN8KufrWhv053GrA0Gh+WLibooIKSoqrWPTFTnKzyrnyd72ZMS2biw9bw4xpW7nkdidZmfTrVJ64J5P3X9zGqVO788rD2Vx4c/AlMu2VnVtJ7zTneat3Wjg5ec7fw5atlfRNj6g5rm96OFu2VnL8UbFsza3kkF9lcNu1yXwwZxcHjI4mvVeQPbMpzvQDrX0ZY4LWtZcmMfOdHRQW1a3Ne/XtHex/3KYGr0lXZDW4xsIlJZSVKYMH1sa3y27KJn30L6xZW871lwXJ915bYliQCbJvhdAyaO8ILromkRsuzCEmLowh+0Ti8Qj/92AKD/+xgBceL+SI42IJj3AeW4aOjGTGe85s74sXlJCa5kEV7r4uj/AIuPGeZFJ6eDrzltql/95RnHN1Kr+7eCPRsWEMGh6NJxxmv5rPlb/rxWEnJvK//xTy6J2Z3P/PgQweEcMjbzt9d35YuIvuaU5AmHZDBp4I4Yq7e5GcGrp/wo3FC8Gp4Xz1aefvpLxcOWlKJu+93Jtb/5DLpi0VTJ2UyGkTg6O20+1PwcaYuhITwpg6KYEnni8kJqa2yu2CsxO44OyEZs/Pyq7g4huyefGxNMLCas+f8WgalZXKb+7J5fUPdnLp5MSAlL+13B7DQvcbsgkichVwFcBd93fnzPObn+SyrU4/L57Tz3Ou//SD2+nZ28PAvSN44p9OjdTG9eV8/XndeaFUlRefLOIvT6byt9/nc9XN3cjaXMHrL+3g2tuTAlbWjjDx3GQmnpsMwMsPZZPSK4KX/5bN1b93kpbDT07ksbsz65yjqsx6Kpc7Hu/Hs/dmccFNPcneXMYHL23j4tvSOvwe/C2th4es7Ap6p4WTlV1Bz1Qnke/b28PmzNqm3c2ZFaT3qpvkP/NSIVPPTWD+ohIiI4VZ/+jFYadsDpokLxj7qLidb/wazv70lb3adb25+hb9GcJQ2Q+AjbqGCioYLCObPdd3uTJBGMhwekk/ABbp51Tg9Ckoo5RudGc/ObTOiF1F2UURR3EaERIck/+uevAWuo87il7Hng5A3sLPqSorbXHz6cY3/0Fx5kZi++xF/3OuqNm+ZfZr7M5YR5h3+bI+J00hOq0PhSu/J2/hZwCERUTR+4Szie7Zx8935V83XpnEuBMyuMQnEXv17R08/ExBg2MHD4zgzeedfshFO6o49cIs/nRHCgcf0LAbkscjnHtaAg89UxA0SZ7bY1iXTPJUdTowHeC7jQMC+p8wP6+S7qketm6p4POPdvPCu71qtlVVKTOeLOSsC+ommf95axeHHRNNYrcwSooVCQMJg5Jil/+1AdvzKkhKDScns4xv5hTx0Ft78e9XtrFiwW5GHxzHsm92kT6gbrCf+/Z2Djw6gYRuHkqLqwgLg7AwobTE5cOevE49IY5X3tjBHTck88obO2oStFMnxnHhtdncfHUymdkVrN1QzvixtYGxYHslH87dxZxZ6XwwZxdhIohASWlw/J1ULwlk/Ms3fh0fNqndv+EwwshhCwN1eItWuah7roeRHEisJFCqxSzgU1I0jQiJZJzUrrm6TOfTg3Sg7ojdXM1kEz8HTYIHIJ5wdvy8gtSDJ7RolYv6UsYfg5aXU7B0foN9aUefSuKw/epsi0jqzsAp1+GJjmXH+tVkznmTvabe1Nbid4juyR4mnRbPjJlFXDrFqb1rriavrEw5+7Ispk5KYNKptb9XVWXdL+XsPSgSVeXDT3YxfO/g+HsIhRjWJZO8jnTHr/MoKqjEEy7cfl93EruFMWtGEW/+0xk9e8zEWE6dVFvrUlJcxX/e3lVT03f+FQnc+es8IiLgvsdTO+Ue/On+6zIo2l5JeDj8+t7eJHTz8Jv70/nHn7ZSValERIVxw1/Sa44vKa7i03e28+eXBwJwxuUp/OXaDMIjhDse69tJd9F25/96K198U0xefiX999/AH25L4Y7rk5l89VZmvFZE/z7hvD7dqdUcOSyKSafGM+qojYSHC0/c3wOPp7Z5475H8rnnxu6ICBOPjuXpFwvZ75gMrr4oWJ6Ag7OPiqlLEPqwF5v4mb1bOfAiTmq/1KMkhkiNopxSIqj9kq7QcgrIYSTjGpy/lQx60a/thQ+EsDCS9zuY/EVf0vPIk1t9evyAoezatLbFx8f2GVT7c/qAOvPsBbNbrklq1SjYNz7YyZffFrOtoJKX33BmlJjxaBqjR0Ry6Y05FO2oQhVGj4jk6WAZRBagGCYiJwKPAR7geVWdVm9/MjADGAyUAJep6g8i0g94BegFVAHTVfWxpj7LkrwAe+7Nhs2Jky9LZPJljX8RR8eE8cys2nPGjo/mtTm9Gz3WjR58fVCDbSPHxfH4B4MbPT46JoxpM2vPGXVgHE//d++AlS/QZj7Tq9Htn7zZePPM3Td15+6bGp837JE/9aj5OTo6jDmvB18Tj9ufgruKfgzmWz5hoNadEy9LN7GRNQ2OjyWe0XJInW2Fmk8VVcRQt/Yrh0y605NwiaizvVIr2MZWhjPWT3fhP8ljD2f9i38j5aBj6mwvXPk9ed81XG4sMimVfmdc0ux1c76cTe43HxPXfwg9jzqFsPC6X8EFyxcQP6hzRxo3pWhdbZxO6xHOzg2Nx+3GXHhOAhee03hN3/8+CN4Hdn/HMBHxAE8BxwObge9E5ANVXeVz2N3AUlU9U0SGe4+fAFQAt6rqYhFJAL4XkU/qnVuHJXnGmMCxJM8VwiWC3jqATfyMh9p+n72lP71pOJlvfaVazEoWMpIDG8xzl80m0mn4cJdLFkmkBlVTbTVPVDTdRo4j//v/IeG1yWm3kQfQbeQBbbpmzyN/5UyAXFlJ1pw32LbgU3ocNrFm/66NP7N9+QIGXnBDE1cxHc7/MWw8sFZV1wOIyCzgdMA3URsB/BVAVX8UkYEikqaqWeAsF6OqO0RkNdCn3rl1WJJnjAkYq8lzj/4MYQFzayYqhpbV5FVoOUv5msGMqrMWLkCZllJEAaM5tME1soOxqdZHyrgjWf/yI3UmKm5PTV5EvNN6I+HhJO07nm0L59XsK8nJJHPOG/Q/50rCY4Jk0JQB2hTDUkVkkc/76d5+tNX6ABk+7zcDB9W7xjLgLOArERkPDAD6Atk15RIZCIwFFjRVGEvyjDGBoUCVZXluESGRpGlftrChJtFrriavSqtYxjf0ZgBp0rDJLYfNpNIbj9QdFe7008tlFMG70oMnJo7E4WMoWL6gJtFrT01e+c4iIuITUVV2/LyCqB7eaZCKCsh470X6/Op8oroHSV8042hbDMtT1YYdUGs1NtVz/Q+ZBjwmIkuBFcASoGb6exGJB94GblLVoqYKY0meMSZwLMdzlf4MJYN1LT4+mwy2k0c5ZWTqLwCM5EASJAlwBlYMZHiD83LYQgppeCS4v4JSDjya/MVfteqcDTOfoGxbDlXlpfz09B9JP+k84gcNZ8uH/6JytzPgLrpnOr1PmARA7tcfU1m8m6xP3gZAJIy9Lr7Fvzdi2s7/MWwz1KnC7gvUmTfMm7hdCiBO/4cN3hciEoGT4L2qqu8092HB/S/MGONq1lwb/KqXNAOIkmiO5cwmjq6rtwygNwP2uH+cHN3o9nQZWKdZOJhUL2kGEB6XwD63PNCq8wed33ifuoGTr210e/pJ59VZ8swElwDEsO+AISIyCNgCTAbOr/OZIknAblUtA64AvlTVIm/C9wKwWlUfacmHWZJnjAkcm0LFGONmfo5hqlohItcDc3CmUJmhqitF5Brv/meBfYBXRKQSZ1DF5d7TDwOmAiu8TbkAd6vq7D19niV5xpiAsZo8Y4ybBSKGeZOy2fW2Pevz83xgSCPnfUXjffr2KKyNZTTGmE4hIieKyBoRWSsidzayv5uI/FtElonIShG51Gdfkoi8JSI/ishqkXqTvRljTAixmjxjTGAofu+03MKJRK8DVqnqqSLSA1gjIq96+7c8BnykqueISCQQ698SGmNCRgBiWEezJM8YExDOuo9+j5AtmUhUgQRvJ+V4IB+oEJFE4EjgEgBv0lfm7wIaY0JDgGJYh7LmWmNM4FS14dW0xiYSrb+e25M4HZczceaYulFVq4C9gFzgRRFZIiLPi4jNPGuM2TP/xq8OZ0meMSZgRLXVL7wzxvu8rvK9ZCMfU/9ReyKwFEgHxgBPemvxwoH9gWdUdSywC2jQp88YY6q1IX4FFWuuNcYERtv7szQ1Y3yzE4niTCI6TVUVWCsiG4DhwCZgs6pWLwP0FpbkGWP2JAT65FlNnjEmQNSZY6q1r6bVTCTqHTgxGfig3jGbgAkAIpIGDAPWq+pWIENEhnmPm0ATC3sbY7o6v8evDmc1ecaYgPH3HFMtnEj0PuAlEVmB07x7h6rmeS9xA/CqN0Fcj3fpIGOMaYzb5/q0JM8YEzgBeLJtwUSimcAJezh3KdDU4uHGGFMrCGvnWsOSPGNMYChIEI42M8aYFgmBGGZJnjEmcFz+FGyM6eJcHsMsyTPGBI6746MxpqtzeQyzJM8YEzDBOG+UMca0lNtjmCV5xpjAcXmANMZ0cS6PYZbkGWMCQwnKZX6MMaZFQiCGWZJnjAkIITiX+THGmJYIhRhmSZ4xJnBcHiCNMV2cy2OYLWtmjDHGGBOCrCbPGBM4Ln8KNsZ0cS6PYZbkGWMCIwQ6LRtjurAQiGGW5BljAsbtnZaNMV2b22OYJXnGmMBxeYA0xnRxLo9hXTLJE5GrgKu8b69W1emdWR5jQpO6PkAGI4tfpmVu7uwChAD3x7AumeR5g6IFRmMCSXF9gAxGFr+M6SAhEMO6ZJJnjOkgLu+0bIzp4lwewyzJM8YEjNs7LRtjuja3xzBL8owxgePyAGmM6eJcHsMsyTPGBIYCVe4OkMaYLiwEYpglecaYAHH/yDRjTFfm/hhmSZ4xJnBcHiCNMV2cy2OYJXnGmMBxeYA0xnRxLo9hYZ1dAGNMiKruz9LaVzNE5EQRWSMia0Xkzkb2dxORf4vIMhFZKSKX1tvvEZElIvKh/27WGBNy2hLDgozV5BljAkRB/TvJlIh4gKeA44HNwHci8oGqrvI57DpglaqeKiI9gDUi8qqqlnn33wisBhL9WjhjTIjxfwzraFaTZ4wJHNXWv5o2Hlirquu9Sdss4PT6nwokiIgA8UA+UAEgIn2BXwHP+/M2jTEhyr/xq8NZkmeMcZM+QIbP+83ebb6eBPYBMoEVwI2qNY/jjwK/xfXz2BtjTPMsyTPGBEbb++Slisgin9dVPleVPXySr4nAUiAdGAM8KSKJInIKkKOq3/v9Xo0xocf65BljTBPa1nyRp6rj9rBvM9DP531fnBo7X5cC01RVgbUisgEYDhwGnCYiJwPRQKKI/EtVL2xLIY0xXUAQNsG2htXkGWMCx/998r4DhojIIBGJBCYDH9Q7ZhMwAUBE0oBhwHpVvUtV+6rqQO95n1mCZ4xpksv75FlNnjEmQPwf9FS1QkSuB+YAHmCGqq4UkWu8+58F7gNeEpEVOM27d6hqnl8LYozpAoIzcWsNS/KMMYGhQJX/xzeo6mxgdr1tz/r8nAmc0Mw15gHz/F44Y0zoCFAM60iW5BljAsflT8HGmC7O5THMkjxjTOC4PEAaY7o4l8cwS/KMMQESnFMKGGNMy7g/htnoWmNMYCioVrX6ZYwxQaENMawlWrD+drKIvCsiy0VkoYiMaum59VmSZ4wJnLZNhmyMMcHBz/HLZ/3tk4ARwBQRGVHvsLuBpao6GrgIeKwV59ZhSZ4xJnD8P0+eMcZ0HP/Hr5asvz0C+NT5eP0RGOid87Ml59ZhSZ4xJjBUnekHWvsyxphg0JYY1ryWrL+9DDgLQETGAwNwVvdpybl12MALY0zgWM2cMcbNWh/DUkVkkc/76ao63ed9S9bfngY8JiJLgRXAEqCihefWYUmeMSZg1GrmjDEu1oYY1tTa29CC9bdVtQhnDW5ERIAN3ldsc+fWZ821xpgAaUN/PKv5M8YEjYDEr2bX3xaRJO8+gCuAL72JX0vW7q7DavKMMYGh2GhZY4x7BSCGtXD97X2AV0SkElgFXN7UuU19niV5xpjAsXnvjDFuFoAY1oL1t+cDQ1p6blOsudYYY4wxJgRZTZ4xJiAUUGuuNca4VCjEMEvyjDGBoWrNtcYY9wqBGGZJnjEmYNz+FGyM6drcHsMsyTPGBI7Ln4KNMV2cy2OYqM1LZYwJABH5CEhtw6l5qnqiv8tjjDGt0cYYFlTxy5I8Y4wxxpgQZFOoGGOMMcaEIEvyjDHGGGNCkCV5xhhjjDEhyJI8Y4wxxpgQZEmeMcYYY0wI+n80WGfUtBBqkQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax1, ax2 = axes\n",
+    "\n",
+    "im1, bar = heatmap(monoreplicated_ratio_df.values, \n",
+    "        list(monorb_df.index), \n",
+    "        list(monorb_df.columns),\n",
+    "                 cmap=\"viridis\",\n",
+    "                 ax=ax1)\n",
+    "\n",
+    "\n",
+    "_ = annotate_heatmap(im1, \n",
+    "                     data=monoreplicated_ratio_df.values, \n",
+    "                     valfmt=\"{x:.0%}\", \n",
+    "                     color=\"black\",\n",
+    "                     threshold=1)\n",
+    "\n",
+    "im2, bar = heatmap(monorb_df.values, \n",
+    "        list(monorb_df.index), \n",
+    "        list(monorb_df.columns),\n",
+    "                 cmap=\"viridis\",\n",
+    "                 ax=ax2)\n",
+    "\n",
+    "\n",
+    "_ = annotate_heatmap(im2, \n",
+    "                     data=monoanno_df.values, \n",
+    "                     valfmt=\"{x:^}\", \n",
+    "                     color=\"black\",\n",
+    "                     threshold=1)\n",
+    "\n",
+    "plt.savefig('cmono_ncmono_mono.filtered_results.pdf')\n",
+    "plt.savefig('cmono_ncmono_mono.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Non-zero ratio and co-expression mean and variances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "celltype = 'monocyte'\n",
+    "annotated_coeqtl_df = pd.DataFrame()\n",
+    "for celltype in celltypes:\n",
+    "    celltype_annotated_coeqtl_df = pd.read_csv(\n",
+    "        workdir/f'output/filtered_results/UT_{celltype}/coeqtls_fullresults_fixed.all.annotated.tsv.gz',\n",
+    "                                     compression='gzip',\n",
+    "                                     sep='\\t'\n",
+    "    )[['mean_onemillionv2', 'var_onemillionv2', \n",
+    "       'gene2_nonzeroratio_onemillionv2',\n",
+    "       'eqtlgene_nonzeroratio_onemillionv2',\n",
+    "       'gene2_isSig']]\n",
+    "    celltype_annotated_coeqtl_df['celltype'] = celltype\n",
+    "    annotated_coeqtl_df = pd.concat([annotated_coeqtl_df, \n",
+    "                                     celltype_annotated_coeqtl_df],\n",
+    "                                   axis=0)\n",
+    "    \n",
+    "annotated_coeqtl_df_clean = annotated_coeqtl_df.replace([np.inf, -np.inf], np.nan, inplace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='celltype', ylabel='mean_onemillionv2'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
+    "            y=abs(annotated_coeqtl_df_clean['mean_onemillionv2']),\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "            fliersize=1,\n",
+    "              palette='viridis',\n",
+    "              showfliers = False)\n",
+    "# plt.savefig('mean_onemillionv2.filtered_results.pdf')\n",
+    "# plt.savefig('mean_onemillionv2.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='celltype', ylabel='var_onemillionv2'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
+    "            y=annotated_coeqtl_df_clean['var_onemillionv2'],\n",
+    "              hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "              palette='viridis', fliersize=1,\n",
+    "              showfliers = False)\n",
+    "# plt.savefig('var_onemillionv2.filtered_results.pdf')\n",
+    "# plt.savefig('var_onemillionv2.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='celltype', ylabel='gene2_nonzeroratio_onemillionv2'>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
+    "            y=annotated_coeqtl_df_clean['gene2_nonzeroratio_onemillionv2'],\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "            palette='viridis', fliersize=1, showfliers = False)\n",
+    "# plt.savefig('gene2_nonzeroratio_onemillionv2.filtered_results.pdf')\n",
+    "# plt.savefig('gene2_nonzeroratio_onemillionv2.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='celltype', ylabel='eqtlgene_nonzeroratio_onemillionv2'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
+    "            y=annotated_coeqtl_df_clean['eqtlgene_nonzeroratio_onemillionv2'],\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "           palette='viridis', fliersize=1, showfliers = False)\n",
+    "# plt.savefig('eqtlgene_nonzeroratio_onemillionv2.filtered_results.pdf')\n",
+    "# plt.savefig('eqtlgene_nonzeroratio_onemillionv2.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### unfiltered results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CD4T\n",
+      "CD8T\n",
+      "monocyte\n",
+      "DC\n",
+      "NK\n",
+      "B\n"
+     ]
+    }
+   ],
+   "source": [
+    "celltype = 'monocyte'\n",
+    "annotated_coeqtl_df = pd.DataFrame()\n",
+    "for celltype in celltypes:\n",
+    "    print(celltype)\n",
+    "    celltype_annotated_coeqtl_df = pd.read_csv(workdir/f'output/unfiltered_results/UT_{celltype}/coeqtls_fullresults_fixed.all.annotated.tsv.gz',\n",
+    "                                     compression='gzip',\n",
+    "                                     sep='\\t')[['mean_onemillionv2', 'var_onemillionv2', \n",
+    "                                                'gene2_nonzeroratio_onemillionv2',\n",
+    "                                                'eqtlgene_nonzeroratio_onemillionv2',\n",
+    "                                                'gene2_isSig']]\n",
+    "    celltype_annotated_coeqtl_df['celltype'] = celltype\n",
+    "    annotated_coeqtl_df = pd.concat([annotated_coeqtl_df, \n",
+    "                                     celltype_annotated_coeqtl_df],\n",
+    "                                   axis=0)\n",
+    "    \n",
+    "annotated_coeqtl_df_clean = annotated_coeqtl_df.replace([np.inf, -np.inf], np.nan, inplace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
+    "            y=abs(annotated_coeqtl_df_clean['mean_onemillionv2']),\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "            fliersize=1,\n",
+    "              palette='Paired',\n",
+    "              showfliers = False)\n",
+    "plt.savefig('mean_onemillionv2.unfiltered_results.pdf')\n",
+    "plt.savefig('mean_onemillionv2.unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
+    "            y=annotated_coeqtl_df_clean['var_onemillionv2'],\n",
+    "              hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "              palette='Paired', fliersize=1,\n",
+    "              showfliers = False)\n",
+    "plt.savefig('var_onemillionv2.unfiltered_results.pdf')\n",
+    "plt.savefig('var_onemillionv2.unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
+    "            y=annotated_coeqtl_df_clean['gene2_nonzeroratio_onemillionv2'],\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "            palette='Paired', fliersize=1, showfliers = False)\n",
+    "plt.savefig('gene2_nonzeroratio_onemillionv2.unfiltered_results.pdf')\n",
+    "plt.savefig('gene2_nonzeroratio_onemillionv2.unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
+    "            y=annotated_coeqtl_df_clean['eqtlgene_nonzeroratio_onemillionv2'],\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "           palette='Paired', fliersize=1, showfliers = False)\n",
+    "plt.savefig('eqtlgene_nonzeroratio_onemillionv2.unfiltered_results.pdf')\n",
+    "plt.savefig('eqtlgene_nonzeroratio_onemillionv2.unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/04_coeqtl_mapping/README.md b/04_coeqtl_mapping/README.md
new file mode 100644
index 0000000..0b4821e
--- /dev/null
+++ b/04_coeqtl_mapping/README.md
@@ -0,0 +1,44 @@
+# 04_coeqtl_mapping
+
+*plot_effect_concordance_across_cohorts.R*:  compares effect sizes (Z-scores) calculated in each individual dataset (before the meta-analysis)
+
+*plot_celltype_overlap_upset.R* : upset plot overlap of significant co-eQTLs between cell types
+
+*power_analyis_coeqtls.R* : explores how the number of tests reduce the power to detect co-eQTLs, taking estimates for number of tests based on how many genes are expressed above different cutoffs for Oelen v3 dataset
+
+Rb calculations are these files:
+*prepare_for_rb_calculation.py* : prepare the input files for rb calculation <br />
+*Rb.R* : rb function <br />
+*calculate_rb_for_sc_and_bios.R* : execute rb functions<br />
+*rb_celltypes.ipynb*: examine the rb values for each cell type; also include scripts for examining different characteristics for coeQTLs compared to non-coeQTLs<br />
+
+Co-eQTL pipeline are these files:<br />
+all files in the betaqtl_scripts (incl. templates)<br />
+*individual_networks.py*: make co-expression files for each individual<br />
+*prepare_genelist_and_annotation_for_betaqtl.py*: prepare input files for the qtl mapping pipeline<br />
+*createBatches.sh*: create batches for qtl mapping pipeline<br />
+*submit_process_betaqtl_results.sh*: submit the jobs for concatenating qtl mapping and perform multiple testing procedures<br />
+*concat_betaqtl_results.fixed.py*: concat qtl mapping results<br />
+*screen_permutation_p_values.py*: concat permutation files<br />
+*multipletesting_correction.fixed.py*: perform multiple testing correction<br />
+
+Other co-eQTL analysis:<br />
+*filtering_strategy.py*: filter for gene pairs<br />
+*individual_networks_cmono_ncmono.py*: create co-expression files for each individual for sub cell types in monocytes<br />
+*individual_networks_maxcell.py*: create co-expression files for each individual with a limit of cell number<br />
+*merge_coexpression_for_betaeqtl_maxcell.py*: merge the co-expression files for each individual with a limit of cell number<br />
+*merge_coexpression_for_betaqtl.subsampleindividuals.py*: create co-expression files for each individual with a limit of sample number<br />
+
+BIOS replication are these files:<br />
+*replication_in_bios.py*: perform bios replication<br />
+*select_snps_from_vcf.sh*: select SNP from vcf file<br />
+*examine_bios_replication.ipynb*: examine the bios replication results<br />
+
+Annotating coeQTL results:<br />
+*annotate_coeqtl_files.py*: annotate the coeqtl results for nonzero ratio, mean and var of gene pair<br />
+*collect_nonzeroratio.py*: collect non zero ratio annotation for all genes in all datasets<br />
+
+
+
+
+
diff --git a/04_coeqtl_mapping/Rb.R b/04_coeqtl_mapping/Rb.R
new file mode 100644
index 0000000..eb22df3
--- /dev/null
+++ b/04_coeqtl_mapping/Rb.R
@@ -0,0 +1,70 @@
+
+#' Function for Rb analysis
+#'
+#' @param b1 Beta from first dataset.
+#' @param se1 Standard error of beta from first dataset.
+#' @param b2 Beta from second dataset.
+#' @param se2 Standard error of beta from second dataset.
+#' @param theta Variable representing sample overlap between two datasets. Should be set 0 if no sample overlap.
+#'
+#' @return Data frame with Rb, SE(Rb) and corresponding P-value.
+#' @export
+#'
+#' @note This function is slightly adapted from the script shared by Ting Qi.
+#'
+#' @examples
+calcu_cor_true <- function(b1, se1, b2, se2, theta) {
+  idx <- which(is.infinite(b1) | is.infinite(b2) | is.infinite(se1) | is.infinite(se2))
+  if (length(idx) > 0) {
+    b1 <- b1[-idx]
+    se1 <- se1[-idx]
+    b2 <- b2[-idx]
+    se2 <- se2[-idx]
+    theta <- theta[-idx]
+  }
+
+  var_b1 <- var(b1, na.rm = T) - mean(se1^2, na.rm = T)
+  var_b2 <- var(b2, na.rm = T) - mean(se2^2, na.rm = T)
+  if (var_b1 < 0) {
+    var_b1 <- var(b1, na.rm = T)
+  }
+  if (var_b2 < 0) {
+    var_b2 <- var(b2, na.rm = T)
+  }
+  cov_b1_b2 <- cov(b1, b2, use = "complete.obs") - mean(theta, na.rm = T) * sqrt(mean(se1^2, na.rm = T) * mean(se2^2, na.rm = T))
+  r <- cov_b1_b2 / sqrt(var_b1 * var_b2)
+
+  r_jack <- c()
+  n <- length(b1)
+  for (k in 1:n) {
+    b1_jack <- b1[-k]
+    se1_jack <- se1[-k]
+    var_b1_jack <- var(b1_jack, na.rm = T) - mean(se1_jack^2, na.rm = T)
+    b2_jack <- b2[-k]
+    se2_jack <- se2[-k]
+    var_b2_jack <- var(b2_jack, na.rm = T) - mean(se2_jack^2, na.rm = T)
+    if (var_b1_jack < 0) {
+      var_b1_jack <- var(b1_jack, na.rm = T)
+    }
+    if (var_b2_jack < 0) {
+      var_b2_jack <- var(b2_jack, na.rm = T)
+    }
+    theta_jack <- theta[-k]
+    cov_e1_jack_e2_jack <- mean(theta_jack, na.rm = T) * sqrt(mean(se1_jack^2, na.rm = T) * mean(se2_jack^2, na.rm = T))
+    cov_b1_b2_jack <- cov(b1_jack, b2_jack, use = "complete.obs") - cov_e1_jack_e2_jack
+    r_tmp <- cov_b1_b2_jack / sqrt(var_b1_jack * var_b2_jack)
+    r_jack <- c(r_jack, r_tmp)
+  }
+  r_mean <- mean(r_jack, na.rm = T)
+  idx <- which(is.na(r_jack))
+  if (length(idx) > 0) {
+    se_r <- sqrt((n - 1) / n * sum((r_jack[-idx] - r_mean)^2))
+  } else {
+    se_r <- sqrt((n - 1) / n * sum((r_jack - r_mean)^2))
+  }
+
+  p <- pchisq((r / se_r)**2, df = 1, lower.tail = FALSE)
+
+  res <- cbind(r, se_r, p)
+  return(res)
+}
diff --git a/04_coeqtl_mapping/annotate_coeqtl_files.py b/04_coeqtl_mapping/annotate_coeqtl_files.py
new file mode 100644
index 0000000..5d6e71e
--- /dev/null
+++ b/04_coeqtl_mapping/annotate_coeqtl_files.py
@@ -0,0 +1,84 @@
+import pandas as pd
+from pathlib import Path
+import numpy as np
+import argparse
+
+
+def parse():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--celltype', dest='celltype')
+    parser.add_argument('--networkcelltype', dest='networkcelltype')
+    parser.add_argument('--filtertype', dest='filtertype')
+    return parser
+
+args = parse().parse_args()
+celltype = args.celltype
+filtertype = args.filtertype
+networkcelltype = args.networkcelltype
+# filtertype = 'filtered_results'
+workdir = Path("./coeqtl_mapping/")
+coeqtl_filepath = workdir/f'output/{filtertype}/UT_{celltype}/coeqtls_fullresults_fixed.all.tsv.gz'
+
+def find_gene2(genepair, eqtlgene):
+    gene1, gene2 = genepair.split(';')
+    if gene1 == eqtlgene:
+        return gene2
+    else:
+        return gene1
+
+coeqtl_df = pd.read_csv(coeqtl_filepath, sep='\t', compression='gzip')
+coeqtl_df['gene2'] = [find_gene2(item[0], item[1]) for item in coeqtl_df[['Gene', 'eqtlgene']].values]
+unique_genepairs = list(set(coeqtl_df['Gene']))
+
+
+network_prefix = Path("./coeqtl_mapping/input/individual_networks/UT/")
+def annotate_by_datasets(datasetname, coeqtl_df, unique_genepairs):
+    def read_numpy(prefix):
+        data = np.load(f'{prefix}.npy')
+        columns = [item.strip() for item in open(f'{prefix}.cols.txt', 'r').readlines()]
+        rows = [item.strip() for item in open(f'{prefix}.rows.txt', 'r').readlines()]
+        return pd.DataFrame(data=data, columns=columns, index=rows)
+    print(f"Loading {datasetname}.")
+    network_df = read_numpy(network_prefix / datasetname / f'UT_{networkcelltype}.zscores')
+    individual_ids = network_df.columns.copy()
+    common_genepairs = list(set(unique_genepairs) & set(network_df.index))
+    selected_network_df = network_df.loc[common_genepairs]
+    selected_network_df[f'var_{datasetname}'] = np.nanvar(selected_network_df[individual_ids].values, axis=1)
+    selected_network_df[f'mean_{datasetname}'] = np.nanmean(selected_network_df[individual_ids].values, axis=1)
+    var_mean_dic = selected_network_df[[f'var_{datasetname}', f'mean_{datasetname}']].T.to_dict()
+    get_var = lambda x:var_mean_dic.get(x)[f'var_{datasetname}'] if x in var_mean_dic else np.nan
+    get_mean = lambda x:var_mean_dic.get(x)[f'mean_{datasetname}'] if x in var_mean_dic else np.nan
+    coeqtl_df[f'var_{datasetname}'] = [get_var(genepair) for genepair in coeqtl_df['Gene']]
+    coeqtl_df[f'mean_{datasetname}'] = [get_mean(genepair) for genepair in coeqtl_df['Gene']]
+    return coeqtl_df
+
+for datasetname in ['onemillionv2', 'onemillionv3', 'stemiv2', 'ng']:
+    coeqtl_df = annotate_by_datasets(datasetname, coeqtl_df, unique_genepairs)
+
+
+def annotate_with_nonzero(df, celltype, datasetname, condition='UT'):
+    nonzeroratio_prefix = Path(
+        "./coeqtl_mapping/input/gene_pair_selection/annotations/")
+    nonzeroratio_path = nonzeroratio_prefix/f'{datasetname}.genes_nonzeroratio.tsv'
+    nonzero_df = pd.read_csv(nonzeroratio_path, sep='\t', index_col=0)
+    if condition == 'UT' and datasetname == 'stemiv2':
+        colname = f'{datasetname}_t8w_{celltype}'
+    elif condition == 'UT' and datasetname.startswith('onemillion'):
+        colname = f'{datasetname}_UT_{celltype}'
+    elif condition == 'UT' and datasetname.startswith('ng'):
+        colname = f'{datasetname}_{celltype}'
+    else:
+        raise NotImplementedError(f"{datasetname} {celltype} not understood")
+    nonzero_dict = nonzero_df[colname].T.to_dict()
+    df[f'eqtlgene_nonzeroratio_{datasetname}'] = [nonzero_dict.get(genename) for genename in df['eqtlgene']]
+    df[f'gene2_nonzeroratio_{datasetname}'] = [nonzero_dict.get(genename) for genename in df['gene2']]
+    return df
+
+for datasetname in ['onemillionv2', 'onemillionv3', 'stemiv2', 'ng']:
+    print(datasetname)
+    coeqtl_df = annotate_with_nonzero(coeqtl_df, networkcelltype, datasetname)
+
+
+coeqtl_df.to_csv(workdir/f'output/{filtertype}/UT_{celltype}/coeqtls_fullresults_fixed.all.annotated.tsv.gz',
+                 compression='gzip', sep='\t', index=False)
+
diff --git a/04_coeqtl_mapping/betaqtl_scripts/createBatches.py b/04_coeqtl_mapping/betaqtl_scripts/createBatches.py
new file mode 100644
index 0000000..76763b6
--- /dev/null
+++ b/04_coeqtl_mapping/betaqtl_scripts/createBatches.py
@@ -0,0 +1,166 @@
+import gzip
+import sys
+import os
+import glob
+
+if len(sys.argv) < 6:
+	print("Usage: createbatches.py expfile.txt.gz gte.txt genotype.vcf.gz genelist.txt.gz annotation.txt.gz template.sh nrmaxgenesperbatch outdir")
+	sys.exit(0)
+
+expfile = sys.argv[1]
+gte = sys.argv[2]
+genotype = sys.argv[3]
+genelist = sys.argv[4]
+annotation = sys.argv[5]
+template = sys.argv[6]
+nrgenes = int(sys.argv[7])
+out = sys.argv[8]
+condition = sys.argv[9]
+celltype = sys.argv[10]
+
+if not out.endswith("/"):
+	out = out + "/"
+
+def writeJob(exp, gte, gt, template, batchfile, jobfile, outprefix, logprefix, chr, condition, celltype):
+	print("Writing job: "+jobfile)
+	fh = open(template,'r')
+	lines = fh.readlines()
+	fh.close()
+	fho = open(jobfile,'w')
+	for line in lines:
+		line = line.replace("GENOTYPE",gt)
+		line = line.replace("GTE",gte)
+		line = line.replace("EXPRESSION",exp)
+		line = line.replace("CHROM",str(chr))
+		line = line.replace("BATCHFILE",batchfile)
+		line = line.replace("OUTPREFIX",outprefix)
+		line = line.replace("LOGPREFIX",logprefix)
+		line = line.replace("CONDITION", condition)
+		line = line.replace("CELLTYPE", celltype)
+		fho.write(line)
+	fho.close()
+
+def checkDir(path):
+	if os.path.exists(path):
+		# delete contents
+		files = glob.glob(path+"*")
+		for file in files:
+			print("Removing: "+file)
+			os.remove(file)
+	else:
+		print("Creating dir: "+path)
+		os.mkdir(path)
+
+abspath = os.path.abspath(out)
+checkDir(abspath+"/batches/")
+checkDir(abspath+"/output/")
+checkDir(abspath+"/jobs/")
+checkDir(abspath+"/logs/")
+
+# read expression file
+fh = None
+genesinfile=genelist
+print("Reading: "+genesinfile)
+if genesinfile.endswith(".txt.gz"):
+	fh = gzip.open(genesinfile,'rt')
+else:
+	fh = open(genesinfile,'r')
+genesInExp = set()
+fh.readline()
+for line in fh:
+#	gene = line.split("\t", maxsplit=1)[0]
+	gene = line.strip()
+	genesInExp.add(gene)
+#	print(gene)
+
+fh.close()
+print("{} genes in {}".format(len(genesInExp),expfile))
+
+# read gene set
+geneset = set()
+fh = None
+print("Genelist: "+genelist)
+if genelist.endswith(".txt.gz"):
+	fh = gzip.open(genelist,'rt')
+else:
+	fh = open(genelist,'r')
+for line in fh:
+	gene  = line.strip()
+	if gene in genesInExp:
+		geneset.add(line.strip())
+fh.close()
+print("Genes in genelist: {}".format(len(geneset)))
+
+# read annotation
+print("Annotation: "+annotation)
+fh = None
+if annotation.endswith(".txt.gz"):
+	fh = gzip.open(annotation,'rt')
+else:
+	fh = open(annotation,'r')
+fh.readline()
+genesPerChr = {}
+annotread = 0
+for line in fh:
+	elems = line.strip().split("\t")
+	gene = elems[1]
+	if gene in geneset:
+		chr = -1
+		try:
+			chr = int(elems[3])
+		except:
+			print(gene+" has non-numeric chromosome: "+elems[3])
+		if chr < 23 and chr > 0:
+			pos = int(elems[4])
+			chrgenes = genesPerChr.get(chr)
+			if chrgenes is None:
+				chrgenes = []
+			chrgenes.append(gene)
+			genesPerChr[chr] = chrgenes
+			annotread = annotread + 1
+fh.close()
+print("Annotation read for {} genes".format(annotread))
+
+# create batches
+for chr in genesPerChr.keys():
+	bctr = 1
+	chrgenes = genesPerChr.get(chr)
+	gctr = 0
+	bgctr = 0
+	batchname = "chr"+str(chr)+"-batch-"+str(bctr)
+	# write job script for first batch
+	batchfile = abspath+"/batches/"+batchname+".txt"
+	print("Writing batch: "+batchfile)
+	jobfile = abspath+"/jobs/"+batchname+".sh"
+	outprefix = abspath+"/output/"+batchname
+	logprefix = abspath+"/logs/"+batchname
+	print()
+	print("Writing job: "+template+"\n"+batchfile+"\n"+jobfile+"\n"+outprefix+"\n"+str(chr))
+	# exp, gte, gt, template, batchfile, jobfile, outprefix, chr
+	chrgenotype = genotype.replace("CHR",str(chr))
+	writeJob(expfile, gte, chrgenotype, template, batchfile, jobfile, outprefix, logprefix, chr, condition, celltype)
+	bgout = open(batchfile,'w')
+	while gctr < len(chrgenes):
+		bgout.write(chrgenes[gctr]+"\n")
+		bgctr = bgctr + 1
+		if bgctr == nrgenes:
+			# start new batch
+			bgout.close()
+			bctr = bctr + 1
+			# write job script for new batch
+			batchname = "chr"+str(chr)+"-batch-"+str(bctr)
+			batchfile = abspath+"/batches/"+batchname+".txt"
+			print("Writing batch: "+batchfile)
+			jobfile = abspath+"/jobs/"+batchname+".sh"
+			outprefix = abspath+"/output/"+batchname
+			logprefix = abspath+"/logs/"+batchname
+#			writeJob(template, batchfile, jobfile, outprefix, chr)
+			writeJob(expfile, gte, chrgenotype, template, batchfile, jobfile, outprefix, logprefix, chr, condition, celltype)
+
+			bgout = open(batchfile,'w')
+			bgctr = 0
+		gctr = gctr + 1
+	# if there are any genes left, close batch
+	if bgctr > 0:
+		bgout.close()
+
diff --git a/04_coeqtl_mapping/betaqtl_scripts/createBatches.sh b/04_coeqtl_mapping/betaqtl_scripts/createBatches.sh
new file mode 100644
index 0000000..ce5ca12
--- /dev/null
+++ b/04_coeqtl_mapping/betaqtl_scripts/createBatches.sh
@@ -0,0 +1,58 @@
+condition=$1
+celltype=$2
+workdir="/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/"
+coexpressionfile=${workdir}/"input/individual_networks/${condition}/${condition}_${celltype}.onemillionv23stemiv2ng.zscores.tsv.gz"
+gtefile=${workdir}/"input/summary/gte-fix.tsv"
+gtfile=${workdir}/"output/genotypevcfs/chrCHR/GenotypeData.bgz.vcf.gz"
+batchsize=100000
+
+genelist=${workdir}/"output/${condition}_${celltype}/genelist.noduplicated.txt"
+geneannotation=${workdir}/"input/summary/${condition}_${celltype}.genepairs.annotation.gene1position.noduplicated.tsv"
+jobtemplatefile=${workdir}/"output/betaqtl_scripts/jobtemplate.noduplicated.sh"
+outputfile=${workdir}/"output/${condition}_${celltype}/noduplicated/"
+mkdir -p ${outputfile}
+python createBatches.py \
+	${coexpressionfile} \
+	${gtefile} \
+	${gtfile} \
+	${genelist} \
+	${geneannotation} \
+	${jobtemplatefile} \
+	${batchsize} \
+	${outputfile} \
+	${condition} \
+	${celltype}
+
+genelist=${workdir}/"output/${condition}_${celltype}/genelist.duplicatedversion1.txt"
+geneannotation=${workdir}/"input/summary/${condition}_${celltype}.genepairs.annotation.gene1position.duplicatedversion1.tsv"
+jobtemplatefile=${workdir}/"output/betaqtl_scripts/jobtemplate.duplicatedversion1.sh"
+outputfile=${workdir}/"output/${condition}_${celltype}/duplicatedversion1/"
+mkdir -p ${outputfile}
+python createBatches.py \
+	${coexpressionfile} \
+	${gtefile} \
+	${gtfile} \
+	${genelist} \
+	${geneannotation} \
+	${jobtemplatefile} \
+	${batchsize} \
+	${outputfile} \
+	${condition} \
+	${celltype}
+
+genelist=${workdir}/"output/${condition}_${celltype}/genelist.duplicatedversion2.txt"
+geneannotation=${workdir}/"input/summary/${condition}_${celltype}.genepairs.annotation.gene1position.duplicatedversion2.tsv"
+jobtemplatefile=${workdir}/"output/betaqtl_scripts/jobtemplate.duplicatedversion2.sh"
+outputfile=${workdir}/"output/${condition}_${celltype}/duplicatedversion2"
+mkdir -p ${outputfile}
+python createBatches.py \
+	${coexpressionfile} \
+	${gtefile} \
+	${gtfile} \
+	${genelist} \
+	${geneannotation} \
+	${jobtemplatefile} \
+	${batchsize} \
+	${outputfile} \
+	${condition} \
+	${celltype}
diff --git a/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.duplicatedversion1.sh b/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.duplicatedversion1.sh
new file mode 100644
index 0000000..cd8864f
--- /dev/null
+++ b/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.duplicatedversion1.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+#SBATCH --ntasks=1
+#SBATCH --time=1:00:00
+#SBATCH --mem=24g
+#SBATCH --cpus-per-task=11
+#SBATCH -o LOGPREFIX.log
+#SBATCH -e LOGPREFIX.err
+
+set -e
+set -u
+
+
+
+
+ml Java/11-LTS
+# ml Java/11.0.2
+
+# CHROM, BATCHFILE, OUTPREFIX
+# EXP, GTE, GENOTYPE
+# CONDITION CELLTYPE
+threads=11
+java -Xmx17g \
+	-Djava.util.concurrent.ForkJoinPool.common.parallelism=$threads \
+	-Dmaximum.threads=$threads -Dthread.pool.size=$threads \
+	-jar /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/BetaQTL-1.0-SNAPSHOT-jar-with-dependencies.jar \
+	-m betaqtl \
+	--maf 0.1\
+	-a /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/summary/CONDITION_CELLTYPE.genepairs.annotation.gene1position.duplicatedversion1.tsv \
+	-e /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/individual_networks/CONDITION/CONDITION_CELLTYPE.onemillionv23stemiv2ng.zscores.tsv.gz \
+	-sgl /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/snp_genepair_selection/CONDITION_CELLTYPE.baseline.duplicatedversion1.tsv \
+	-gl BATCHFILE \
+	-g /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/summary/gte-fix.tsv \
+	-v GENOTYPE \
+	--chr CHROM \
+	-o OUTPREFIX \
+	--perm 100 \
+	--outputall \
+	 --snplog \
+	 --outputallpermutations
diff --git a/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.duplicatedversion2.sh b/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.duplicatedversion2.sh
new file mode 100644
index 0000000..9792bc9
--- /dev/null
+++ b/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.duplicatedversion2.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+#SBATCH --ntasks=1
+#SBATCH --time=1:00:00
+#SBATCH --mem=24g
+#SBATCH --cpus-per-task=11
+#SBATCH -o LOGPREFIX.log
+#SBATCH -e LOGPREFIX.err
+
+set -e
+set -u
+
+
+
+
+ml Java/11-LTS
+# ml Java/11.0.2
+
+# CHROM, BATCHFILE, OUTPREFIX
+# EXP, GTE, GENOTYPE
+# CONDITION CELLTYPE
+threads=11
+java -Xmx17g \
+	-Djava.util.concurrent.ForkJoinPool.common.parallelism=$threads \
+	-Dmaximum.threads=$threads -Dthread.pool.size=$threads \
+	-jar /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/BetaQTL-1.0-SNAPSHOT-jar-with-dependencies.jar \
+	-m betaqtl \
+	--maf 0.1\
+	-a /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/summary/CONDITION_CELLTYPE.genepairs.annotation.gene1position.duplicatedversion2.tsv \
+	-e /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/individual_networks/CONDITION/CONDITION_CELLTYPE.onemillionv23stemiv2ng.zscores.tsv.gz \
+	-sgl /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/snp_genepair_selection/CONDITION_CELLTYPE.baseline.duplicatedversion2.tsv \
+	-gl BATCHFILE \
+	-g /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/summary/gte-fix.tsv \
+	-v GENOTYPE \
+	--chr CHROM \
+	-o OUTPREFIX \
+	--perm 100 \
+	--outputall \
+	 --snplog \
+	 --outputallpermutations
diff --git a/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.noduplicated.sh b/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.noduplicated.sh
new file mode 100644
index 0000000..6373cd7
--- /dev/null
+++ b/04_coeqtl_mapping/betaqtl_scripts/jobtemplate.noduplicated.sh
@@ -0,0 +1,38 @@
+#!/bin/bash
+#SBATCH --ntasks=1
+#SBATCH --time=1:00:00
+#SBATCH --mem=24g
+#SBATCH --cpus-per-task=11
+#SBATCH -o LOGPREFIX.log
+#SBATCH -e LOGPREFIX.err
+
+set -e
+set -u
+
+
+
+
+ml Java/11-LTS
+
+# CHROM, BATCHFILE, OUTPREFIX
+# EXP, GTE, GENOTYPE
+# CONDITION CELLTYPE
+threads=11
+java -Xmx17g \
+	-Djava.util.concurrent.ForkJoinPool.common.parallelism=$threads \
+	-Dmaximum.threads=$threads -Dthread.pool.size=$threads \
+	-jar /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/BetaQTL-1.0-SNAPSHOT-jar-with-dependencies.jar \
+	-m betaqtl \
+	--maf 0.1 \
+	-a /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/summary/CONDITION_CELLTYPE.genepairs.annotation.gene1position.noduplicated.tsv \
+	-e /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/individual_networks/CONDITION/CONDITION_CELLTYPE.onemillionv23stemiv2ng.zscores.tsv.gz \
+	-sgl /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/snp_genepair_selection/CONDITION_CELLTYPE.baseline.noduplicated.tsv \
+	-gl BATCHFILE \
+	-g /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/summary/gte-fix.tsv \
+	-v GENOTYPE \
+	--chr CHROM \
+	-o OUTPREFIX \
+	--perm 100 \
+	--outputall \
+	--snplog \
+	 --outputallpermutations
diff --git a/04_coeqtl_mapping/calculate_rb_for_sc_and_bios.R b/04_coeqtl_mapping/calculate_rb_for_sc_and_bios.R
new file mode 100644
index 0000000..40415e6
--- /dev/null
+++ b/04_coeqtl_mapping/calculate_rb_for_sc_and_bios.R
@@ -0,0 +1,89 @@
+# Title     : TODO
+# Objective : TODO
+# Created by: Shuang
+# Created on: 1/19/2022
+
+source("Rb.R")
+library(glue)
+library(data.table)
+
+calculate_rb_bios_replication_summary <- function(biostype, filtertype){
+    print(biostype)
+    print(filtertype)
+    resdf <- c()
+    for ( celltype in c('CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC') ){
+        df <- read.csv(glue('./coeqtl_mapping/bios/{biostype}/{filtertype}/UT_{celltype}/replication_parameters.csv'))
+        res <- calcu_cor_true(df$flipped_bios_beta, df$std.err_bios, df$MetaBeta, df$MetaSE, df$theta)
+        res <- cbind(res, celltype)
+        resdf <- rbind(res, resdf)
+    }
+    write.csv(resdf, glue('./coeqtl_mapping/bios/{biostype}/{filtertype}/replication_summary.csv'))
+}
+
+# BIOS replication
+args = commandArgs(trailingOnly=TRUE)
+calculate_rb_bios_replication_summary(args[1], args[2])
+
+
+# coeQTLs
+filtertype = 'filtered_results'
+workdir = './coeqtl_mapping/'
+resdf <- c()
+for ( celltype_discovery in c('CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC') ){
+    for ( celltype_replication in c('CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC') ){
+        if ( celltype_discovery != celltype_replication ){
+            df <- fread(glue('{workdir}/output/{filtertype}/rb_calculations/discovery_{celltype_discovery}_replication_{celltype_replication}.tsv.gz'))
+            print(c(celltype_discovery, celltype_replication, nrow(df)))
+            if ( nrow(df) < 5 ){
+                resdf <- rbind(resdf, c(NA, NA, 0, celltype_discovery, celltype_replication))
+            }else{
+                res <- calcu_cor_true(df$MetaBeta, df$MetaSE, df$MetaBeta_replication, df$MetaSE_replication, df$theta)
+                res <- cbind(res, celltype_discovery, celltype_replication)
+                resdf <- rbind(res, resdf)
+            }
+        }
+    }
+}
+write.csv(resdf, glue('{workdir}/output/{filtertype}/rb_calculations/summary.csv'))
+
+# coeQTLs monocyte sub celltypes
+filtertype = 'filtered_results'
+workdir = './coeqtl_mapping/'
+resdf <- c()
+for ( celltype_discovery in c('monocyte', 'cMono', 'ncMono') ){
+    for ( celltype_replication in c('monocyte', 'cMono', 'ncMono') ){
+        if ( celltype_discovery != celltype_replication ){
+            df <- fread(glue('{workdir}/output/{filtertype}/rb_calculations/monocyte_subcelltypes/discovery_{celltype_discovery}_replication_{celltype_replication}.tsv.gz'))
+            print(c(celltype_discovery, celltype_replication, nrow(df)))
+            if ( nrow(df) < 5 ){
+                resdf <- rbind(resdf, c(NA, NA, 0, celltype_discovery, celltype_replication))
+            }else{
+                res <- calcu_cor_true(df$MetaBeta, df$MetaSE, df$MetaBeta_replication, df$MetaSE_replication, df$theta)
+                res <- cbind(res, celltype_discovery, celltype_replication)
+                resdf <- rbind(res, resdf)
+            }
+        }
+    }
+}
+write.csv(resdf, glue('{workdir}/output/{filtertype}/rb_calculations/monocyte_subcelltypes/summary.csv'))
+
+
+# eQTLs
+workdir = './coeqtl_mapping/'
+resdf <- c()
+for ( celltype_discovery in c('CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC') ){
+    for ( celltype_replication in c('CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC') ){
+        if ( celltype_discovery != celltype_replication ){
+            df <- fread(glue('{workdir}/input/snp_selection/rb_calculations/discovery_{celltype_discovery}_replication_{celltype_replication}.tsv.gz'))
+            print(c(celltype_discovery, celltype_replication, nrow(df)))
+            if ( nrow(df) < 5 ){
+                resdf <- rbind(resdf, c(NA, NA, 0, celltype_discovery, celltype_replication))
+            }else{
+                res <- calcu_cor_true(df$metabeta, df$SE, df$metabeta_replication, df$SE_replication, df$theta)
+                res <- cbind(res, celltype_discovery, celltype_replication)
+                resdf <- rbind(res, resdf)
+            }
+        }
+    }
+}
+write.csv(resdf, glue('{workdir}/input/snp_selection/rb_calculations/summary.csv'))
\ No newline at end of file
diff --git a/04_coeqtl_mapping/cell-type_specific_eQTLmapping/template_config.xml b/04_coeqtl_mapping/cell-type_specific_eQTLmapping/template_config.xml
new file mode 100644
index 0000000..6684866
--- /dev/null
+++ b/04_coeqtl_mapping/cell-type_specific_eQTLmapping/template_config.xml
@@ -0,0 +1,94 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+
+<settings>
+
+  <defaults>
+
+    <qc>
+        <snpqccallratethreshold>0.95</snpqccallratethreshold>
+        <snpqchwethreshold>0.0001</snpqchwethreshold>
+        <snpqcmafthreshold>0.1</snpqcmafthreshold>
+    </qc>
+
+    <analysis>
+        <analysistype>cis</analysistype>
+        <cisanalysisprobedistance>100000</cisanalysisprobedistance>
+        <correlationtype>nonparametric</correlationtype>
+        <!-- <regressOutEQTLEffects>/groups/umcg-bios/scr01/umcg-aclaringbould/eqtlpipeline_lld/cis-eQTLs/conditional/output_round18_RegressedOut/toRegressOut.txt</regressOutEQTLEffects> -->
+        <threads>10</threads>
+        <createdotplot>false</createdotplot>
+        <createqqplot>false</createqqplot>
+    </analysis>
+
+    <multipletesting>
+        <type>fdr</type>
+        <threshold>0.05</threshold>
+        <fdrtype>probe-level</fdrtype>
+        <permutations>10</permutations>
+    </multipletesting>
+
+    <output>
+        <outputdirectory>/path/to/output_dir/</outputdirectory>
+        <outputplotthreshold>0.1</outputplotthreshold>
+        <outputplotdirectory>/path/to/plotsoutput_dir/</outputplotdirectory>
+        <maxnreqtlresults>50000000</maxnreqtlresults>
+        <generatesnpsummarystatistics>false</generatesnpsummarystatistics>
+        <generateeqtlpvaluetable>false</generateeqtlpvaluetable>
+        <binaryoutput>false</binaryoutput>
+        <textoutput>true</textoutput>
+    </output>
+
+    <confine>
+        <snp/>
+	      <snpProbe>/path/to/SNPconfinement/file.tsv</snpProbe>
+        <confineSNPsToSNPsPresentInAllDatasets>false</confineSNPsToSNPsPresentInAllDatasets>
+        <confineSNPsSelectSNPInStrongestLD>false</confineSNPsSelectSNPInStrongestLD>
+        <confineProbesThatMapToKnownChromosome>false</confineProbesThatMapToKnownChromosome>
+    </confine>
+
+  </defaults>
+
+  <datasets>
+    <dataset>
+        <name>van_der_Wijst</name>
+        <location>/path/to/van_der_Wijst/genome/trityper/</location>
+        <expressiondata>/path/to/van_der_Wijst/cell_type_specific_donor_aggregated_matrix/expression.tsv</expressiondata>
+        <probeannotation>/path_to_snp_annotation_file/singleCell-annotation-stripped.tsv</probeannotation>
+        <quantilenormalize>false</quantilenormalize>
+        <logtranform>false</logtranform>
+    </dataset>
+    <dataset>
+        <name>van_Blockland_v2</name>
+        <location>/path/to/van_Blockland_v2/genome/trityper/</location>
+        <expressiondata>/path/to/van_Blockland_v2/cell_type_specific_donor_aggregated_matrix/expression.tsv</expressiondata>
+        <probeannotation>/path_to_snp_annotation_file/singleCell-annotation-stripped.tsv</probeannotation>
+        <quantilenormalize>false</quantilenormalize>
+        <logtranform>false</logtranform>
+    </dataset>
+    <dataset>
+        <name>van_Blockland_v3</name>
+        <location>/path/to/van_Blockland_v3/genome/trityper/</location>
+        <expressiondata>/path/to/van_Blockland_v3/cell_type_specific_donor_aggregated_matrix/expression.tsv</expressiondata>
+        <probeannotation>/path_to_snp_annotation_file/singleCell-annotation-stripped.tsv</probeannotation>
+        <quantilenormalize>false</quantilenormalize>
+        <logtranform>false</logtranform>
+    </dataset>
+    <dataset>
+        <name>Oelen_v2</name>
+        <location>/path/to/Oelen_v2/genome/trityper/</location>
+        <expressiondata>/path/to/Oelen_v2/cell_type_specific_donor_aggregated_matrix/expression.tsv</expressiondata>
+        <probeannotation>/path_to_snp_annotation_file/singleCell-annotation-stripped.tsv</probeannotation>
+        <quantilenormalize>false</quantilenormalize>
+        <logtranform>false</logtranform>
+    </dataset>
+    <dataset>
+        <name>Oelen_v3</name>
+        <location>/path/to/Oelen_v3/genome/trityper/</location>
+        <expressiondata>/path/to/Oelen_v3/cell_type_specific_donor_aggregated_matrix/expression.tsv</expressiondata>
+        <probeannotation>/path_to_snp_annotation_file/singleCell-annotation-stripped.tsv</probeannotation>
+        <quantilenormalize>false</quantilenormalize>
+        <logtranform>false</logtranform>
+    </dataset>
+  </datasets>
+
+</settings>
diff --git a/04_coeqtl_mapping/cell-type_specific_eQTLmapping/template_job_file.sh b/04_coeqtl_mapping/cell-type_specific_eQTLmapping/template_job_file.sh
new file mode 100644
index 0000000..3c99f30
--- /dev/null
+++ b/04_coeqtl_mapping/cell-type_specific_eQTLmapping/template_job_file.sh
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+#SBATCH --job-name=B_1m_v2
+#SBATCH --output=/groups/umcg-bios/tmp01/projects/1M_cells_scRNAseq/ongoing/GRN_reconstruction/EMP_mapping/B/err/B_1m_v2.out
+#SBATCH --error=/groups/umcg-bios/tmp01/projects/1M_cells_scRNAseq/ongoing/GRN_reconstruction/EMP_mapping/B/err/B_1m_v2.err
+#SBATCH --time=05:59:00
+#SBATCH --cpus-per-task=10
+#SBATCH --mem=64gb
+#SBATCH --nodes=1
+#SBATCH --open-mode=append
+#SBATCH --export=NONE
+#SBATCH --get-user-env=L
+
+set -e
+ml Java/1.8.0_144
+
+java -jar -Xmx40g -Xms20g -XX:StringTableSize=10000019 -XX:MaxPermSize=512m /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/cis_eqtl_single_cell/EMP_mapping_30_11_2021/eqtl-mapping-pipeline-1.4.9a-SNAPSHOT/eqtl-mapping-pipeline.jar --mode metaqtl --settings /groups/umcg-bios/tmp01/projects/1M_cells_scRNAseq/ongoing/GRN_reconstruction/EMP_mapping/B/config/1m_v2.xml
diff --git a/04_coeqtl_mapping/collect_nonzeroratio.py b/04_coeqtl_mapping/collect_nonzeroratio.py
new file mode 100644
index 0000000..b86dc08
--- /dev/null
+++ b/04_coeqtl_mapping/collect_nonzeroratio.py
@@ -0,0 +1,95 @@
+from pathlib import Path
+import numpy as np
+import scanpy as sc
+import re
+import pandas as pd
+
+
+prefix = Path('./seurat_objects')
+data_path_dic = {'onemillionv2':prefix/'1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad',
+                 'stemiv2': prefix / 'cardio.integrated.20210301.stemiv2.h5ad',
+                 'onemillionv3': prefix / "1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad",
+                 'ng': prefix / 'pilot3_seurat3_200420_sct_azimuth.h5ad'}
+
+
+# extract timepoint from timepoint - stimulation annotation
+def get_time(x):
+    if x == 'UT':
+        return x
+    else:
+        pattern = re.compile(r'\d+h')
+        return re.findall(pattern, x)[0]
+
+
+def count_nonzeroratio(data_sc):
+    df = pd.DataFrame(data=data_sc.X.toarray(),
+                      index=data_sc.obs.index,
+                      columns=data_sc.var.index)
+    nonzerocounts = np.count_nonzero(df.values, axis=0)/df.shape[0]
+    return nonzerocounts
+
+
+def load_onemillion(data_name, data_sc):
+    var_df = pd.DataFrame(index=data_sc.var.index.values)
+    data_sc.obs['time'] = [get_time(x) for x in data_sc.obs['timepoint']]
+    for condition in data_sc.obs['time'].unique():
+        for celltype in data_sc.obs['cell_type_lowerres'].unique():
+            print(condition, celltype)
+            subset_sc = data_sc[(data_sc.obs['time']==condition) &
+                                (data_sc.obs['cell_type_lowerres']==celltype)]
+            var_df[f'{data_name}_{condition}_{celltype}'] = count_nonzeroratio(subset_sc)
+    return var_df
+
+
+def load_ng(data_sc):
+    var_df = pd.DataFrame(index=data_sc.var.index.values)
+    celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',
+                       'other T': 'otherT', 'other': 'other', 'B': 'B'}
+    data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in
+                                                     data_sc.obs['predicted.celltype.l1']]
+    for celltype in data_sc.obs['cell_type_mapped_to_onemillion'].unique():
+        print(celltype)
+        subset_sc = data_sc[(data_sc.obs['cell_type_mapped_to_onemillion']==celltype)]
+        var_df[f'ng_{celltype}'] = count_nonzeroratio(subset_sc)
+    return var_df
+
+
+def load_stemi(dataname, data_sc):
+    var_df = pd.DataFrame(index=data_sc.var.index.values)
+    for condition in data_sc.obs['timepoint.final'].unique():
+        for celltype in data_sc.obs['cell_type_lowerres'].unique():
+            print(condition, celltype)
+            subset_sc = data_sc[(data_sc.obs['timepoint.final']==condition) &
+                           (data_sc.obs['cell_type_lowerres']==celltype)]
+            var_df[f'{dataname}_{condition}_{celltype}'] = count_nonzeroratio(subset_sc)
+    return var_df
+
+
+def get_expressed_ratio(datasetname):
+    data_sc = sc.read_h5ad(data_path_dic[datasetname])
+    if datasetname.startswith('onemillion'):
+        var_df = load_onemillion(datasetname, data_sc)
+    elif datasetname.startswith('stemi'):
+        var_df = load_stemi(datasetname, data_sc)
+    else:
+        var_df = load_ng(data_sc)
+    return var_df
+
+
+def calculate_genes_withnonzeroratio(datasetname, savepath):
+    print("Processing ", datasetname)
+    var_df = get_expressed_ratio(datasetname)
+    var_df.to_csv(savepath, sep='\t')
+    return var_df
+
+
+work_dir = Path('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/')
+nonzero_savepath = work_dir/'coeqtl_mapping/input/gene_pair_selection/annotations/'
+for datasetname in ['stemiv2', 'ng', 'onemillionv2', 'onemillionv3']:
+    print('Processing ', datasetname)
+    savepath = nonzero_savepath/f'{datasetname}.genes_nonzeroratio.tsv'
+    var_df = calculate_genes_withnonzeroratio(datasetname, savepath)
+
+
+
+
diff --git a/04_coeqtl_mapping/concat_all6majorcelltypes_coeqtls.py b/04_coeqtl_mapping/concat_all6majorcelltypes_coeqtls.py
new file mode 100644
index 0000000..a2de1f7
--- /dev/null
+++ b/04_coeqtl_mapping/concat_all6majorcelltypes_coeqtls.py
@@ -0,0 +1,38 @@
+import pandas as pd
+from pathlib import Path
+
+def find_eqtlsnp_gene(snp_genepair, eqtl_snp_gene_set):
+    snp = snp_genepair.split('_')[0]
+    gene1, gene2 = snp_genepair.split('_')[1].split(';')
+    if '_'.join([snp, gene1]) in eqtl_snp_gene_set:
+        return gene1
+    else:
+        return gene2
+
+
+workdir = Path("/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output")
+eqtl_prefix = Path("/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/snp_selection/eqtl")
+writer = pd.ExcelWriter(workdir/'summary/coeQTLs_6majorcelltypes.unfiltered.xlsx', engine='xlsxwriter')
+for celltype in ['CD4T', 'CD8T', 'monocyte', 'B', 'DC', 'NK']:
+    eqtls_path = eqtl_prefix/f'UT_{celltype}_eQTLProbesFDR0.05-ProbeLevel.tsv'
+    eqtl_df = pd.read_csv(eqtls_path, sep='\t')
+    eqtl_df['snp_gene'] = ['_'.join(item) for item in eqtl_df[['SNPName', 'genename']].values]
+    eqtl_snp_gene_set = set(eqtl_df['snp_gene'])
+    df = pd.read_csv(workdir/f'unfiltered_results/UT_{celltype}/coeqtls_fullresults.sig.tsv.gz', sep='\t', compression='gzip')
+    df['eqtlgene'] = [find_eqtlsnp_gene(item, eqtl_snp_gene_set) for item in df['snp_genepair']]
+    print(celltype, df.shape[0], len(df['eqtlgene'].unique()))
+    df.to_excel(writer, sheet_name=celltype)
+writer.save()
+
+
+writer = pd.ExcelWriter(workdir/'summary/coeQTLs_6majorcelltypes.filtered.xlsx', engine='xlsxwriter')
+for celltype in ['CD4T', 'CD8T', 'monocyte', 'B', 'DC', 'NK']:
+    eqtls_path = eqtl_prefix/f'UT_{celltype}_eQTLProbesFDR0.05-ProbeLevel.tsv'
+    eqtl_df = pd.read_csv(eqtls_path, sep='\t')
+    eqtl_df['snp_gene'] = ['_'.join(item) for item in eqtl_df[['SNPName', 'genename']].values]
+    eqtl_snp_gene_set = set(eqtl_df['snp_gene'])
+    df = pd.read_csv(workdir/f'filtered_results/UT_{celltype}/coeqtls_fullresults.sig.tsv.gz', sep='\t', compression='gzip')
+    df['eqtlgene'] = [find_eqtlsnp_gene(item, eqtl_snp_gene_set) for item in df['snp_genepair']]
+    print(celltype, df.shape[0], len(df['eqtlgene'].unique()))
+    df.to_excel(writer, sheet_name=celltype)
+writer.save()
\ No newline at end of file
diff --git a/04_coeqtl_mapping/concat_betaqtl_results.fixed.py b/04_coeqtl_mapping/concat_betaqtl_results.fixed.py
new file mode 100644
index 0000000..7d698f7
--- /dev/null
+++ b/04_coeqtl_mapping/concat_betaqtl_results.fixed.py
@@ -0,0 +1,65 @@
+import pandas as pd
+from pathlib import Path
+import os
+import argparse
+from tqdm import tqdm
+from statsmodels.stats.multitest import multipletests
+
+
+def concat_results(prefix, savepath):
+    concated_df = pd.DataFrame()
+    coeqtl_annotation_path = f'{args.annotation_prefix}.genepairs.annotation.gene1position.noduplicated.tsv'
+    coeqtl_annotation_df = pd.read_csv(coeqtl_annotation_path, sep='\t')
+    coeqtl_annotation_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in
+                                       coeqtl_annotation_df[['Chr', 'ChrStart', 'ChrEnd']].values]
+    coeqtl_annotation_dict = coeqtl_annotation_df.set_index('ArrayAddress')['chr_pos'].T.to_dict()
+    for filename in tqdm(os.listdir(prefix/'noduplicated/output')):
+        if filename.endswith("-TopEffects.txt"):
+            df = pd.read_csv(prefix/'noduplicated/output'/filename, sep='\t')
+            df['chr_pos'] = [coeqtl_annotation_dict.get(gene) for gene in df['Gene']]
+            concated_df = pd.concat([concated_df, df], axis=0)
+    concated_df['snp_genepair'] = ['_'.join(item) for item in concated_df[['SNP', 'Gene']].values]
+    version1 = pd.DataFrame()
+    coeqtl_annotation_path = f'{args.annotation_prefix}.genepairs.annotation.gene1position.duplicatedversion1.tsv'
+    coeqtl_annotation_df = pd.read_csv(coeqtl_annotation_path, sep='\t')
+    coeqtl_annotation_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in
+                                       coeqtl_annotation_df[['Chr', 'ChrStart', 'ChrEnd']].values]
+    coeqtl_annotation_dict = coeqtl_annotation_df.set_index('ArrayAddress')['chr_pos'].T.to_dict()
+    for filename in tqdm(os.listdir(prefix/'duplicatedversion1/output')):
+        if filename.endswith("-TopEffects.txt"):
+            df = pd.read_csv(prefix/'duplicatedversion1/output'/filename, sep='\t')
+            df['chr_pos'] = [coeqtl_annotation_dict.get(gene) for gene in df['Gene']]
+            version1 = pd.concat([version1, df], axis=0)
+    version1['snp_genepair'] = ['_'.join(item) for item in version1[['SNP', 'Gene']].values]
+    version2 = pd.DataFrame()
+    coeqtl_annotation_path = f'{args.annotation_prefix}.genepairs.annotation.gene1position.duplicatedversion2.tsv'
+    coeqtl_annotation_df = pd.read_csv(coeqtl_annotation_path, sep='\t')
+    coeqtl_annotation_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in
+                                       coeqtl_annotation_df[['Chr', 'ChrStart', 'ChrEnd']].values]
+    coeqtl_annotation_dict = coeqtl_annotation_df.set_index('ArrayAddress')['chr_pos'].T.to_dict()
+    for filename in tqdm(os.listdir(prefix/'duplicatedversion2/output')):
+        if filename.endswith("-TopEffects.txt"):
+            df = pd.read_csv(prefix/'duplicatedversion2/output'/filename, sep='\t')
+            df['chr_pos'] = [coeqtl_annotation_dict.get(gene) for gene in df['Gene']]
+            version2 = pd.concat([version2, df], axis=0)
+    version2['snp_genepair'] = ['_'.join(item) for item in version2[['SNP', 'Gene']].values]
+    concated_versions = pd.concat([concated_df, version1, version2], axis=0)
+    concated_versions = concated_versions.sort_values(by=['GeneChr', 'GenePos'])
+    concated_versions = concated_versions.set_index('snp_genepair')
+    # add multiple test significance
+    concated_versions['multipletestP'] = multipletests(concated_versions['BetaAdjustedMetaP'],
+                                                       alpha=0.05, method='fdr_bh',
+                                                       is_sorted=False, returnsorted=False)[1]
+    concated_versions.to_csv(savepath, sep='\t')
+    return concated_versions
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--prefix', type=str, dest='prefix')
+    parser.add_argument('--savepath', type=str, dest='savepath')
+    parser.add_argument('--annotation_prefix', type=str, dest='annotation_prefix')
+    return parser
+
+if __name__ == '__main__':
+    args = argumentsparser().parse_args()
+    concat_results(Path(args.prefix), args.savepath)
\ No newline at end of file
diff --git a/04_coeqtl_mapping/examine_bios_replication.ipynb b/04_coeqtl_mapping/examine_bios_replication.ipynb
new file mode 100644
index 0000000..8c5b906
--- /dev/null
+++ b/04_coeqtl_mapping/examine_bios_replication.ipynb
@@ -0,0 +1,1013 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import spearmanr\n",
+    "from pathlib import Path\n",
+    "from scipy.stats import t, norm\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "def flip_zscore(zscore, coeqtlallele, altaf, altallele):\n",
+    "    if not pd.isnull(zscore):\n",
+    "        if coeqtlallele == altallele:\n",
+    "            coeqtlaf = altaf\n",
+    "        else:\n",
+    "            coeqtlaf = 1 - altaf\n",
+    "        if coeqtlaf > 0.5:\n",
+    "            return -zscore\n",
+    "        else:\n",
+    "            return zscore\n",
+    "    else:\n",
+    "        return np.nan\n",
+    "    \n",
+    "def flip_allele(altaf, altallele, refallele):\n",
+    "    if altaf > 0.5:\n",
+    "        return refallele\n",
+    "    else:\n",
+    "        return altallele"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_withbios_prefix = Path(\n",
+    "    \"./coeqtl_mapping/output\"\n",
+    ")\n",
+    "filter_type = 'filtered_results'\n",
+    "\n",
+    "def flip_direction(allele1, allele2, zscore2):\n",
+    "    if allele1 == allele2:\n",
+    "        return zscore2\n",
+    "    else:\n",
+    "        return -1*zscore2\n",
+    "\n",
+    "\n",
+    "def get_z_score(t_statistic, num):\n",
+    "    prob = t.cdf(t_statistic, num - 2)\n",
+    "    z_score = norm.ppf(prob)\n",
+    "    return z_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "color_dict = {'CD4T': '#2E9D33',\n",
+    "              'CD8T': 'darkgreen',\n",
+    "              'monocyte': '#EDBA1B',\n",
+    "              'NK': '#E64B50',\n",
+    "              'DC': '#965EC8',\n",
+    "              'B': '#009DDB',\n",
+    "              'cMono': 'peru',\n",
+    "              'ncMono': 'y',\n",
+    "              'CD4T_individual_100': '#2E9D33',\n",
+    "              'CD4T_individual_50': '#2E9D33',\n",
+    "              'CD4T_50': '#2E9D33',\n",
+    "              'CD4T_150': '#2E9D33',\n",
+    "              'CD4T_250': '#2E9D33'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "workdir = Path(\"./coeqtl_mapping/\")\n",
+    "bios_replication_filtered_df = pd.read_csv(\n",
+    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/filtered_results/replication_summary.csv', \n",
+    "    index_col=0\n",
+    ").set_index('celltype')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "celltype = 'CD4T'\n",
+    "eqtldf = pd.read_csv(\n",
+    "    workdir/f'input/snp_selection/eqtl/UT_{celltype}_eQTLProbesFDR0.05-ProbeLevel_withAF.tsv',\n",
+    "    sep='\\t'\n",
+    "    )\n",
+    "eqtldf['snp_eqtlgene'] = ['_'.join(item) for item in eqtldf[['SNPName', 'genename']].values]\n",
+    "eqtl_allele_af_df = eqtldf.drop_duplicates(subset=['snp_eqtlgene', 'AlleleAssessed', 'AF'])\n",
+    "eqtl_allele_af_dict = eqtl_allele_af_df.set_index('snp_eqtlgene')[['AlleleAssessed', 'AF', 'alt_allele', 'ref_allele']].T.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "biostype = 'onlyRNAAlignMetrics_rmLLD'\n",
+    "celltype = 'CD4T'\n",
+    "filter_type = 'filtered_results'\n",
+    "\n",
+    "coeqtl_df = pd.read_csv(\n",
+    "    coeqtl_withbios_prefix/filter_type/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbios{biostype}.tsv.gz',\n",
+    "    compression='gzip', \n",
+    "    index_col=0, \n",
+    "    sep='\\t')\n",
+    "coeqtl_df = coeqtl_df.dropna(subset=['t_bios'])\n",
+    "coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
+    "                            coeqtl_df[['t_bios', \n",
+    "                                       'num_individuals_bios']].values]\n",
+    "coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
+    "                                  coeqtl_df[['SNPEffectAllele', \n",
+    "                                             'assessed_allele_bios',\n",
+    "                                             'zscore_bios']].values]\n",
+    "\n",
+    "isConcordant = lambda x:True if x[0]*x[1] > 0 else False\n",
+    "coeqtl_df['is_concordant'] = [isConcordant(item) for item in \n",
+    "                              coeqtl_df[['MetaPZ', 'flipped_zscore_bios']].values]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>snp_genepair</th>\n",
+       "      <th>Gene</th>\n",
+       "      <th>GeneChr</th>\n",
+       "      <th>GenePos</th>\n",
+       "      <th>GeneStrand</th>\n",
+       "      <th>GeneSymbol</th>\n",
+       "      <th>SNP</th>\n",
+       "      <th>SNPChr</th>\n",
+       "      <th>SNPPos</th>\n",
+       "      <th>SNPAlleles</th>\n",
+       "      <th>...</th>\n",
+       "      <th>gene1_bios</th>\n",
+       "      <th>gene2_bios</th>\n",
+       "      <th>assessed_allele_bios</th>\n",
+       "      <th>num_individuals_bios</th>\n",
+       "      <th>isinteractionterm_bios</th>\n",
+       "      <th>snp_genepair_bios</th>\n",
+       "      <th>corrected_p_bios</th>\n",
+       "      <th>zscore_bios</th>\n",
+       "      <th>flipped_zscore_bios</th>\n",
+       "      <th>is_concordant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>snp_gene1_gene2</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_NPM1</th>\n",
+       "      <td>rs7605824_NPM1;SH3YL1</td>\n",
+       "      <td>NPM1;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NPM1;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>NPM1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_NPM1;SH3YL1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.617874</td>\n",
+       "      <td>-3.617874</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_CD48</th>\n",
+       "      <td>rs7605824_CD48;SH3YL1</td>\n",
+       "      <td>CD48;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CD48;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>CD48</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_CD48;SH3YL1</td>\n",
+       "      <td>0.784422</td>\n",
+       "      <td>-0.446946</td>\n",
+       "      <td>-0.446946</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_RPS13</th>\n",
+       "      <td>rs7605824_RPS13;SH3YL1</td>\n",
+       "      <td>RPS13;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPS13;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>RPS13</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_RPS13;SH3YL1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.489377</td>\n",
+       "      <td>-3.489377</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_RPL31</th>\n",
+       "      <td>rs7605824_RPL31;SH3YL1</td>\n",
+       "      <td>RPL31;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPL31;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>RPL31</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_RPL31;SH3YL1</td>\n",
+       "      <td>0.349601</td>\n",
+       "      <td>-1.325633</td>\n",
+       "      <td>-1.325633</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs7605824_SH3YL1_RPL3</th>\n",
+       "      <td>rs7605824_RPL3;SH3YL1</td>\n",
+       "      <td>RPL3;SH3YL1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>217730</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPL3;SH3YL1</td>\n",
+       "      <td>rs7605824</td>\n",
+       "      <td>2</td>\n",
+       "      <td>280819</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SH3YL1</td>\n",
+       "      <td>RPL3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs7605824_RPL3;SH3YL1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.854851</td>\n",
+       "      <td>-3.854851</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_ACTB</th>\n",
+       "      <td>rs4147638_ACTB;SMDT1</td>\n",
+       "      <td>ACTB;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ACTB;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>ACTB</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_ACTB;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.748326</td>\n",
+       "      <td>3.748326</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_RPS25</th>\n",
+       "      <td>rs4147638_RPS25;SMDT1</td>\n",
+       "      <td>RPS25;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPS25;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>RPS25</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_RPS25;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.773036</td>\n",
+       "      <td>-5.773036</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_RPS3A</th>\n",
+       "      <td>rs4147638_RPS3A;SMDT1</td>\n",
+       "      <td>RPS3A;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPS3A;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>RPS3A</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_RPS3A;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.434777</td>\n",
+       "      <td>-4.434777</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_RPS18</th>\n",
+       "      <td>rs4147638_RPS18;SMDT1</td>\n",
+       "      <td>RPS18;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPS18;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>RPS18</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_RPS18;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.128733</td>\n",
+       "      <td>-7.128733</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rs4147638_SMDT1_RPL11</th>\n",
+       "      <td>rs4147638_RPL11;SMDT1</td>\n",
+       "      <td>RPL11;SMDT1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42475695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>RPL11;SMDT1</td>\n",
+       "      <td>rs4147638</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42487900</td>\n",
+       "      <td>G/A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SMDT1</td>\n",
+       "      <td>RPL11</td>\n",
+       "      <td>G</td>\n",
+       "      <td>2491.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>rs4147638_RPL11;SMDT1</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.896748</td>\n",
+       "      <td>-5.896748</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>497 rows × 55 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  snp_genepair          Gene  GeneChr  \\\n",
+       "snp_gene1_gene2                                                         \n",
+       "rs7605824_SH3YL1_NPM1    rs7605824_NPM1;SH3YL1   NPM1;SH3YL1        2   \n",
+       "rs7605824_SH3YL1_CD48    rs7605824_CD48;SH3YL1   CD48;SH3YL1        2   \n",
+       "rs7605824_SH3YL1_RPS13  rs7605824_RPS13;SH3YL1  RPS13;SH3YL1        2   \n",
+       "rs7605824_SH3YL1_RPL31  rs7605824_RPL31;SH3YL1  RPL31;SH3YL1        2   \n",
+       "rs7605824_SH3YL1_RPL3    rs7605824_RPL3;SH3YL1   RPL3;SH3YL1        2   \n",
+       "...                                        ...           ...      ...   \n",
+       "rs4147638_SMDT1_ACTB      rs4147638_ACTB;SMDT1    ACTB;SMDT1       22   \n",
+       "rs4147638_SMDT1_RPS25    rs4147638_RPS25;SMDT1   RPS25;SMDT1       22   \n",
+       "rs4147638_SMDT1_RPS3A    rs4147638_RPS3A;SMDT1   RPS3A;SMDT1       22   \n",
+       "rs4147638_SMDT1_RPS18    rs4147638_RPS18;SMDT1   RPS18;SMDT1       22   \n",
+       "rs4147638_SMDT1_RPL11    rs4147638_RPL11;SMDT1   RPL11;SMDT1       22   \n",
+       "\n",
+       "                         GenePos  GeneStrand    GeneSymbol        SNP  SNPChr  \\\n",
+       "snp_gene1_gene2                                                                 \n",
+       "rs7605824_SH3YL1_NPM1     217730         NaN   NPM1;SH3YL1  rs7605824       2   \n",
+       "rs7605824_SH3YL1_CD48     217730         NaN   CD48;SH3YL1  rs7605824       2   \n",
+       "rs7605824_SH3YL1_RPS13    217730         NaN  RPS13;SH3YL1  rs7605824       2   \n",
+       "rs7605824_SH3YL1_RPL31    217730         NaN  RPL31;SH3YL1  rs7605824       2   \n",
+       "rs7605824_SH3YL1_RPL3     217730         NaN   RPL3;SH3YL1  rs7605824       2   \n",
+       "...                          ...         ...           ...        ...     ...   \n",
+       "rs4147638_SMDT1_ACTB    42475695         NaN    ACTB;SMDT1  rs4147638      22   \n",
+       "rs4147638_SMDT1_RPS25   42475695         NaN   RPS25;SMDT1  rs4147638      22   \n",
+       "rs4147638_SMDT1_RPS3A   42475695         NaN   RPS3A;SMDT1  rs4147638      22   \n",
+       "rs4147638_SMDT1_RPS18   42475695         NaN   RPS18;SMDT1  rs4147638      22   \n",
+       "rs4147638_SMDT1_RPL11   42475695         NaN   RPL11;SMDT1  rs4147638      22   \n",
+       "\n",
+       "                          SNPPos SNPAlleles  ... gene1_bios  gene2_bios  \\\n",
+       "snp_gene1_gene2                              ...                          \n",
+       "rs7605824_SH3YL1_NPM1     280819        G/A  ...     SH3YL1        NPM1   \n",
+       "rs7605824_SH3YL1_CD48     280819        G/A  ...     SH3YL1        CD48   \n",
+       "rs7605824_SH3YL1_RPS13    280819        G/A  ...     SH3YL1       RPS13   \n",
+       "rs7605824_SH3YL1_RPL31    280819        G/A  ...     SH3YL1       RPL31   \n",
+       "rs7605824_SH3YL1_RPL3     280819        G/A  ...     SH3YL1        RPL3   \n",
+       "...                          ...        ...  ...        ...         ...   \n",
+       "rs4147638_SMDT1_ACTB    42487900        G/A  ...      SMDT1        ACTB   \n",
+       "rs4147638_SMDT1_RPS25   42487900        G/A  ...      SMDT1       RPS25   \n",
+       "rs4147638_SMDT1_RPS3A   42487900        G/A  ...      SMDT1       RPS3A   \n",
+       "rs4147638_SMDT1_RPS18   42487900        G/A  ...      SMDT1       RPS18   \n",
+       "rs4147638_SMDT1_RPL11   42487900        G/A  ...      SMDT1       RPL11   \n",
+       "\n",
+       "                        assessed_allele_bios  num_individuals_bios  \\\n",
+       "snp_gene1_gene2                                                      \n",
+       "rs7605824_SH3YL1_NPM1                      A                2491.0   \n",
+       "rs7605824_SH3YL1_CD48                      A                2491.0   \n",
+       "rs7605824_SH3YL1_RPS13                     A                2491.0   \n",
+       "rs7605824_SH3YL1_RPL31                     A                2491.0   \n",
+       "rs7605824_SH3YL1_RPL3                      A                2491.0   \n",
+       "...                                      ...                   ...   \n",
+       "rs4147638_SMDT1_ACTB                       G                2491.0   \n",
+       "rs4147638_SMDT1_RPS25                      G                2491.0   \n",
+       "rs4147638_SMDT1_RPS3A                      G                2491.0   \n",
+       "rs4147638_SMDT1_RPS18                      G                2491.0   \n",
+       "rs4147638_SMDT1_RPL11                      G                2491.0   \n",
+       "\n",
+       "                        isinteractionterm_bios       snp_genepair_bios  \\\n",
+       "snp_gene1_gene2                                                          \n",
+       "rs7605824_SH3YL1_NPM1                     True   rs7605824_NPM1;SH3YL1   \n",
+       "rs7605824_SH3YL1_CD48                     True   rs7605824_CD48;SH3YL1   \n",
+       "rs7605824_SH3YL1_RPS13                    True  rs7605824_RPS13;SH3YL1   \n",
+       "rs7605824_SH3YL1_RPL31                    True  rs7605824_RPL31;SH3YL1   \n",
+       "rs7605824_SH3YL1_RPL3                     True   rs7605824_RPL3;SH3YL1   \n",
+       "...                                        ...                     ...   \n",
+       "rs4147638_SMDT1_ACTB                      True    rs4147638_ACTB;SMDT1   \n",
+       "rs4147638_SMDT1_RPS25                     True   rs4147638_RPS25;SMDT1   \n",
+       "rs4147638_SMDT1_RPS3A                     True   rs4147638_RPS3A;SMDT1   \n",
+       "rs4147638_SMDT1_RPS18                     True   rs4147638_RPS18;SMDT1   \n",
+       "rs4147638_SMDT1_RPL11                     True   rs4147638_RPL11;SMDT1   \n",
+       "\n",
+       "                        corrected_p_bios  zscore_bios flipped_zscore_bios  \\\n",
+       "snp_gene1_gene2                                                             \n",
+       "rs7605824_SH3YL1_NPM1           0.000000    -3.617874           -3.617874   \n",
+       "rs7605824_SH3YL1_CD48           0.784422    -0.446946           -0.446946   \n",
+       "rs7605824_SH3YL1_RPS13          0.000000    -3.489377           -3.489377   \n",
+       "rs7605824_SH3YL1_RPL31          0.349601    -1.325633           -1.325633   \n",
+       "rs7605824_SH3YL1_RPL3           0.000000    -3.854851           -3.854851   \n",
+       "...                                  ...          ...                 ...   \n",
+       "rs4147638_SMDT1_ACTB            0.000000    -3.748326            3.748326   \n",
+       "rs4147638_SMDT1_RPS25           0.000000     5.773036           -5.773036   \n",
+       "rs4147638_SMDT1_RPS3A           0.000000     4.434777           -4.434777   \n",
+       "rs4147638_SMDT1_RPS18           0.000000     7.128733           -7.128733   \n",
+       "rs4147638_SMDT1_RPL11           0.000000     5.896748           -5.896748   \n",
+       "\n",
+       "                       is_concordant  \n",
+       "snp_gene1_gene2                       \n",
+       "rs7605824_SH3YL1_NPM1           True  \n",
+       "rs7605824_SH3YL1_CD48           True  \n",
+       "rs7605824_SH3YL1_RPS13          True  \n",
+       "rs7605824_SH3YL1_RPL31          True  \n",
+       "rs7605824_SH3YL1_RPL3           True  \n",
+       "...                              ...  \n",
+       "rs4147638_SMDT1_ACTB            True  \n",
+       "rs4147638_SMDT1_RPS25           True  \n",
+       "rs4147638_SMDT1_RPS3A           True  \n",
+       "rs4147638_SMDT1_RPS18           True  \n",
+       "rs4147638_SMDT1_RPL11           True  \n",
+       "\n",
+       "[497 rows x 55 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coeqtl_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# flip direction according to AF\n",
+    "coeqtl_df['eqtl_effect_allele'] = [eqtl_allele_af_dict.get(eqtl)['AlleleAssessed'] for eqtl in \n",
+    "                                       coeqtl_df['snp_eqtlgene']]\n",
+    "coeqtl_df['eqtl_alt_af'] = [eqtl_allele_af_dict.get(eqtl)['AF'] for eqtl in coeqtl_df['snp_eqtlgene']]\n",
+    "coeqtl_df['eqtl_alt_allele'] = [eqtl_allele_af_dict.get(eqtl)['alt_allele'] for eqtl in \n",
+    "                                    coeqtl_df['snp_eqtlgene']]\n",
+    "coeqtl_df['eqtl_ref_allele'] = [eqtl_allele_af_dict.get(eqtl)['ref_allele'] for eqtl in \n",
+    "                                    coeqtl_df['snp_eqtlgene']]\n",
+    "coeqtl_df[f'MetaPZ_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
+    "                                                   for zscore, coeqtlallele, altaf, altallele in\n",
+    "                                                   coeqtl_df[[f'MetaPZ',\n",
+    "                                                                  f'SNPEffectAllele',\n",
+    "                                                                  'eqtl_alt_af',\n",
+    "                                                                  'eqtl_alt_allele']].values]\n",
+    "coeqtl_df[f'flipped_zscore_bios_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
+    "                                                   for zscore, coeqtlallele, altaf, altallele in\n",
+    "                                                   coeqtl_df[[f'flipped_zscore_bios',\n",
+    "                                                                  f'SNPEffectAllele',\n",
+    "                                                                  'eqtl_alt_af',\n",
+    "                                                                  'eqtl_alt_allele']].values]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9637681159420289\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(3, -5, 'Concordance = 0.96\\nrb = 0.61')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
+    "coeqtl_nonsig = coeqtl_df[coeqtl_df['corrected_p_bios']>0.05]\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.scatter(coeqtl_nonsig['MetaPZ_flippedforAF'], \n",
+    "            coeqtl_nonsig['flipped_zscore_bios_flippedforAF'], \n",
+    "            label='Insignificant',\n",
+    "            edgecolor='gray',\n",
+    "            facecolor='white', alpha=1)\n",
+    "plt.scatter(coeqtl_sig['MetaPZ_flippedforAF'],\n",
+    "            coeqtl_sig['flipped_zscore_bios_flippedforAF'],  \n",
+    "            label='Significant',\n",
+    "            edgecolor=color_dict[celltype],\n",
+    "            facecolor=color_dict[celltype], alpha=1)\n",
+    "plt.plot([-15, 12], [0, 0], linestyle='--', color='lightgray')\n",
+    "plt.plot([0, 0], [-6.5, 4], linestyle='--', color='lightgray')\n",
+    "plt.legend()\n",
+    "\n",
+    "concordance_rate = coeqtl_sig[coeqtl_sig['is_concordant']].shape[0] / coeqtl_sig.shape[0]\n",
+    "print(concordance_rate)\n",
+    "\n",
+    "celltype_rb = bios_replication_filtered_df.loc[celltype]['r']\n",
+    "plt.text(3, -5, f'Concordance = {concordance_rate:.2f}\\nrb = {celltype_rb:.2f}')\n",
+    "\n",
+    "# plt.savefig('bios_replication.cd4t.filtered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_ci_manual(t, s_err, n, x, x2, y2, ax=None):\n",
+    "    \"\"\"Return an axes of confidence bands using a simple approach.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    .. math:: \\left| \\: \\hat{\\mu}_{y|x0} - \\mu_{y|x0} \\: \\right| \\; \\leq \\; T_{n-2}^{.975} \\; \\hat{\\sigma} \\; \\sqrt{\\frac{1}{n}+\\frac{(x_0-\\bar{x})^2}{\\sum_{i=1}^n{(x_i-\\bar{x})^2}}}\n",
+    "    .. math:: \\hat{\\sigma} = \\sqrt{\\sum_{i=1}^n{\\frac{(y_i-\\hat{y})^2}{n-2}}}\n",
+    "    \n",
+    "    References\n",
+    "    ----------\n",
+    "    .. [1] M. Duarte.  \"Curve fitting,\" Jupyter Notebook.\n",
+    "       http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/CurveFitting.ipynb\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    if ax is None:\n",
+    "        ax = plt.gca()\n",
+    "    \n",
+    "    ci = t * s_err * np.sqrt(1/n + (x2 - np.mean(x))**2 / np.sum((x - np.mean(x))**2))\n",
+    "    ax.fill_between(x2, y2 + ci, y2 - ci, alpha=0.1, color='gray')\n",
+    "    return ax\n",
+    "\n",
+    "from scipy import stats\n",
+    "def equation(a, b):\n",
+    "    \"\"\"Return a 1D polynomial.\"\"\"\n",
+    "    return np.polyval(a, b) \n",
+    "\n",
+    "x=coeqtl_df['MetaPZ_flippedforAF']\n",
+    "y=coeqtl_df['flipped_zscore_bios_flippedforAF']\n",
+    "\n",
+    "p, cov = np.polyfit(x, y, 1, cov=True)                     # parameters and covariance from of the fit of 1-D polynom.\n",
+    "y_model = equation(p, x)     \n",
+    "# Statistics\n",
+    "n = y.size                                           # number of observations\n",
+    "m = p.size                                                 # number of parameters\n",
+    "dof = n - m                                                # degrees of freedom\n",
+    "t = stats.t.ppf(0.975, n - m)                              # used for CI and PI bands\n",
+    "# Estimates of Error in Data/Model\n",
+    "resid = y - y_model                           \n",
+    "chi2 = np.sum((resid / y_model)**2)                        # chi-squared; estimates error in data\n",
+    "chi2_red = chi2 / dof                                      # reduced chi-squared; measures goodness of fit\n",
+    "s_err = np.sqrt(np.sum(resid**2) / dof)                    # standard deviation of the error\n",
+    "\n",
+    "# Plotting --------------------------------------------------------------------\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "# Data\n",
+    "ax.scatter(\n",
+    "    x, y\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Fit\n",
+    "ax.plot(x, y_model, \"-\", color=\"0.1\", linewidth=1.5, alpha=0.5, label=\"Fit\")  \n",
+    "\n",
+    "x2 = np.linspace(np.min(x), np.max(x), 100)\n",
+    "y2 = equation(p, x2)\n",
+    "\n",
+    "# Confidence Interval (select one)\n",
+    "plot_ci_manual(t, s_err, n, x, x2, y2, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-46-55674587bbd9>:19: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  coeqtl_sig['celltype'] = celltype\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f6de56225b0>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# withbiostechnicalandcelltypePICs\n",
+    "sig_df = pd.DataFrame()\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharex=True, sharey=True)\n",
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC']\n",
+    "for i in range(2):\n",
+    "    for j in range(3):\n",
+    "        celltype = celltypes[i*3+j]\n",
+    "        coeqtl_df = pd.read_csv(\n",
+    "            coeqtl_withbios_prefix/filter_type/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbiosonlyRNAAlignMetrics_rmLLD.tsv.gz',\n",
+    "            compression='gzip', index_col=0, sep='\\t')\n",
+    "        coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
+    "                                    coeqtl_df[['t_bios', \n",
+    "                                               'num_individuals_bios']].values]\n",
+    "        coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
+    "                                          coeqtl_df[['SNPEffectAllele', \n",
+    "                                                     'assessed_allele_bios',\n",
+    "                                                     'zscore_bios']].values]\n",
+    "        # flip the direction according to AF\n",
+    "        coeqtl_df['eqtl_effect_allele'] = [eqtl_allele_af_dict.get(eqtl)['AlleleAssessed'] for eqtl in \n",
+    "                                       coeqtl_df['snp_eqtlgene']]\n",
+    "        coeqtl_df['eqtl_alt_af'] = [eqtl_allele_af_dict.get(eqtl)['AF'] for eqtl in coeqtl_df['snp_eqtlgene']]\n",
+    "        coeqtl_df['eqtl_alt_allele'] = [eqtl_allele_af_dict.get(eqtl)['alt_allele'] for eqtl in \n",
+    "                                            coeqtl_df['snp_eqtlgene']]\n",
+    "        coeqtl_df['eqtl_ref_allele'] = [eqtl_allele_af_dict.get(eqtl)['ref_allele'] for eqtl in \n",
+    "                                            coeqtl_df['snp_eqtlgene']]\n",
+    "        coeqtl_df[f'MetaPZ_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
+    "                                                           for zscore, coeqtlallele, altaf, altallele in\n",
+    "                                                           coeqtl_df[[f'MetaPZ',\n",
+    "                                                                          f'SNPEffectAllele',\n",
+    "                                                                          'eqtl_alt_af',\n",
+    "                                                                          'eqtl_alt_allele']].values]\n",
+    "        coeqtl_df[f'flipped_zscore_bios_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
+    "                                                           for zscore, coeqtlallele, altaf, altallele in\n",
+    "                                                           coeqtl_df[[f'flipped_zscore_bios',\n",
+    "                                                                          f'SNPEffectAllele',\n",
+    "                                                                          'eqtl_alt_af',\n",
+    "                                                                          'eqtl_alt_allele']].values]\n",
+    "        ## end flip\n",
+    "        coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
+    "        coeqtl_sig['celltype'] = celltype\n",
+    "        sig_df = pd.concat([coeqtl_sig, sig_df], axis=0)\n",
+    "        significant_ratio = coeqtl_sig.shape[0] / coeqtl_df.shape[0]\n",
+    "        coeqtl_sig_samedirection = coeqtl_sig[((coeqtl_sig['MetaPZ']>0) & (coeqtl_sig['flipped_zscore_bios']>0)) | \n",
+    "                                              ((coeqtl_sig['MetaPZ']<0) & (coeqtl_sig['flipped_zscore_bios']<0))]\n",
+    "        consistent_ratio = coeqtl_sig_samedirection.shape[0] / coeqtl_sig.shape[0]\n",
+    "        # draw\n",
+    "        ax = axes[i][j]\n",
+    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']>0.05], \n",
+    "                   coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']>0.05], alpha=0.5,\n",
+    "                   label='Non-sig')\n",
+    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']<=0.05],\n",
+    "                    coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']<=0.05], alpha=0.5,\n",
+    "                    label='Sig')\n",
+    "        ax.set_xlabel('single cell')\n",
+    "        ax.set_ylabel('BIOS')\n",
+    "        ax.set_title(celltype)\n",
+    "        ax.text(-2, -8, \n",
+    "                f'Significant ratio: {significant_ratio:.2f}\\nConcordance ratio: {consistent_ratio:.2f}')\n",
+    "ax.legend(loc='upper left')\n",
+    "        \n",
+    "# plt.savefig('bios_replication.filtered_results.scatterplots.pdf')\n",
+    "# plt.savefig('bios_replication.filtered_results.scatterplots.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-18-0693f2cb76ec>:19: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  coeqtl_sig['celltype'] = celltype\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f6dedb25820>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# unfiltered results \n",
+    "# withbiosonlyRNAAlignMetrics_rmLLD\n",
+    "sig_df = pd.DataFrame()\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharex=True, sharey=True)\n",
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC']\n",
+    "for i in range(2):\n",
+    "    for j in range(3):\n",
+    "        celltype = celltypes[i*3+j]\n",
+    "        coeqtl_df = pd.read_csv(\n",
+    "            coeqtl_withbios_prefix/'unfiltered_results'/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbiosonlyRNAAlignMetrics_rmLLD.tsv.gz',\n",
+    "            compression='gzip', index_col=0, sep='\\t')\n",
+    "        coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
+    "                                    coeqtl_df[['t_bios', \n",
+    "                                               'num_individuals_bios']].values]\n",
+    "        coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
+    "                                          coeqtl_df[['SNPEffectAllele', \n",
+    "                                                     'assessed_allele_bios',\n",
+    "                                                     'zscore_bios']].values]\n",
+    "        coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
+    "        coeqtl_sig['celltype'] = celltype\n",
+    "        sig_df = pd.concat([coeqtl_sig, sig_df], axis=0)\n",
+    "        # draw\n",
+    "        ax = axes[i][j]\n",
+    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']>0.05], \n",
+    "                   coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']>0.05], alpha=0.5,\n",
+    "                   label='Non-sig')\n",
+    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']<=0.05],\n",
+    "                    coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']<=0.05], alpha=0.5,\n",
+    "                    label='Sig')\n",
+    "        ax.set_xlabel('single cell')\n",
+    "        ax.set_ylabel('BIOS')\n",
+    "        ax.set_title(celltype)\n",
+    "ax.legend(loc='upper left')\n",
+    "# plt.savefig('bios_replication.unfiltered_results.scatterplots.pdf')\n",
+    "# plt.savefig('bios_replication.unfiltered_results.scatterplots.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/04_coeqtl_mapping/filtering_strategy.py b/04_coeqtl_mapping/filtering_strategy.py
new file mode 100644
index 0000000..f901ab4
--- /dev/null
+++ b/04_coeqtl_mapping/filtering_strategy.py
@@ -0,0 +1,56 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import argparse
+from scipy.stats import norm
+from time import time
+
+
+sig_thres_zscore = norm.ppf(1-0.025)
+individual_network_prefix = Path("./input/individual_networks")
+saveprefix = Path("./input/gene_pair_selection/annotations/")
+def read_numpy(prefix):
+    data = np.load(f'{prefix}.npy')
+    rows = [item.strip() for item in open(f'{prefix}.rows.txt', 'r').readlines()]
+    cols = [item.strip() for item in open(f'{prefix}.cols.txt', 'r').readlines()]
+    return pd.DataFrame(data=data, columns=cols, index=rows)
+
+
+def merge_datasets(celltype, condition):
+    res_df = pd.DataFrame()
+    for datasetname in ['stemiv2', 'onemillionv2', 'onemillionv3', 'ng']:
+        data_path = individual_network_prefix / condition / datasetname / f'{condition}_{celltype}.zscores'
+        startime = time()
+        df = read_numpy(data_path)
+        res_df = pd.concat([res_df, df], axis=1)
+        print(f'Merged {datasetname}, it took', time() - startime)
+    return res_df
+
+
+def calculate_significance_freq(zscore_df, thres=sig_thres_zscore):
+    freqs = (abs(zscore_df.values) > thres).sum(axis=1)
+    assert len(freqs) == zscore_df.shape[0]
+    return freqs
+
+
+def parse():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--celltype', dest='celltype')
+    parser.add_argument('--condition', dest='condition')
+    return parser
+
+
+def main():
+    args = parse().parse_args()
+    celltype, condition = args.celltype, args.condition
+    celltype_condition_df = merge_datasets(celltype, condition)
+    celltype_condition_df['sig_count'] = calculate_significance_freq(celltype_condition_df)
+    celltype_condition_df['sig_freq'] = [item/celltype_condition_df.shape[1] for item in celltype_condition_df['sig_count']]
+    print(celltype, celltype_condition_df[celltype_condition_df['sig_freq']>=0.1].shape)
+    celltype_condition_df[['sig_count', 'sig_freq']].to_csv(saveprefix/f'{condition}_{celltype}.significance_frequency.tsv',
+                                               sep='\t')
+    return celltype_condition_df
+
+
+if __name__ == '__main__':
+    _ = main()
\ No newline at end of file
diff --git a/04_coeqtl_mapping/individual_networks.py b/04_coeqtl_mapping/individual_networks.py
new file mode 100644
index 0000000..82dd60a
--- /dev/null
+++ b/04_coeqtl_mapping/individual_networks.py
@@ -0,0 +1,270 @@
+import os
+import re
+from itertools import combinations
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import scanpy as sc
+from scipy.stats import spearmanr
+from scipy.stats import t, norm
+from tqdm import tqdm
+import argparse
+from scipy.stats import rankdata
+from collections import namedtuple
+
+
+def get_time(x):
+    if x == 'UT':
+        return x
+    else:
+        pattern = re.compile(r'\d+h')
+        return re.findall(pattern, x)[0]
+
+class DATASET:
+    def __init__(self, datasetname):
+        self.name = datasetname
+        self.path_prefix = Path("./seurat_objects")
+        self.information = self.get_information()
+    def get_information(self):
+        if self.name == 'onemillionv2':
+            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'
+            self.individual_id_col = 'assignment'
+            self.timepoint_id_col = 'time'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 'UT',
+                                     'stimulated': '3h'}
+        elif self.name == 'onemillionv3':
+            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'
+            self.individual_id_col = 'assignment'
+            self.timepoint_id_col = 'time'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 'UT',
+                                     'stimulated': '3h'}
+        elif self.name == 'stemiv2':
+            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'
+            self.individual_id_col = 'assignment.final'
+            self.timepoint_id_col = 'timepoint.final'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 't8w',
+                                     'stimulated': 'Baseline'}
+        elif self.name == 'ng':
+            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'
+            self.individual_id_col = 'snumber'
+            self.celltype_id = 'cell_type_mapped_to_onemillion'
+        else:
+            raise IOError("Dataset name not understood.")
+
+    def load_dataset(self):
+        self.get_information()
+        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')
+        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)
+        if self.name.startswith('onemillion'):
+            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]
+        elif self.name == 'ng':
+            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',
+                               'other T': 'otherT', 'other': 'other', 'B': 'B'}
+            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in
+                                                          self.data_sc.obs['predicted.celltype.l1']]
+
+
+def corr_to_z(coef, num):
+    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))
+    prob = t.cdf(t_statistic, num - 2)
+    z_score = norm.ppf(prob)
+    positive_coef_probs = 1 - prob
+    positive_coef_probs[coef < 0] = 0
+    negative_coef_probs = prob
+    negative_coef_probs[coef > 0] = 0
+    probs = negative_coef_probs + positive_coef_probs
+    return z_score, probs
+
+
+# def z_to_corr(z, num):
+#     prob = norm.cdf(z)
+#     t_statistic = t.ppf(prob, num - 2)
+#     corr = t_statistic / np.sqrt(num - 2 + t_statistic ** 2)
+#     return corr
+
+
+def save_numpy(data_df, prefix):
+    np.save(f'{prefix}.npy', data_df.values)
+    with open(f'{prefix}.cols.txt', 'w') as f:
+        f.write('\n'.join(data_df.columns))
+    with open(f'{prefix}.rows.txt', 'w') as f:
+        f.write('\n'.join(data_df.index))
+    return None
+
+def _contains_nan(a, nan_policy='propagate'):
+    '''
+    From scipy: https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L4343-L4525
+    '''
+    policies = ['propagate', 'raise', 'omit']
+    if nan_policy not in policies:
+        raise ValueError("nan_policy must be one of {%s}" %
+                         ', '.join("'%s'" % s for s in policies))
+    try:
+        with np.errstate(invalid='ignore'):
+            contains_nan = np.isnan(np.sum(a))
+    except TypeError:
+        try:
+            contains_nan = np.nan in set(a.ravel())
+        except TypeError:
+            contains_nan = False
+            nan_policy = 'omit'
+
+    if contains_nan and nan_policy == 'raise':
+        raise ValueError("The input contains nan values")
+
+    return contains_nan, nan_policy
+
+
+def _chk_asarray(a, axis):
+    '''
+    From scipy: https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L4343-L4525
+    '''
+    if axis is None:
+        a = np.ravel(a)
+        outaxis = 0
+    else:
+        a = np.asarray(a)
+        outaxis = axis
+
+    if a.ndim == 0:
+        a = np.atleast_1d(a)
+
+    return a, outaxis
+
+
+def spearmanr_withnan(a, axis=0, nan_policy='propagate'):
+    '''
+    Modified from scipy: https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L4343-L4525
+    '''
+    SpearmanrResult = namedtuple('SpearmanrResult', ('correlation', 'pvalue'))
+    if axis is not None and axis > 1:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays, supplied axis argument {}, "
+                         "please use only values 0, 1 or None for axis".format(axis))
+    a, axisout = _chk_asarray(a, axis)
+    if a.ndim > 2:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays")
+    n_vars = a.shape[1 - axisout]
+    n_obs = a.shape[axisout]
+    if n_obs <= 1:
+        # Handle empty arrays or single observations.
+        return SpearmanrResult(np.nan, np.nan)
+    a_contains_nan, nan_policy = _contains_nan(a, nan_policy)
+    variable_has_nan = np.zeros(n_vars, dtype=bool)
+    if a_contains_nan:
+        if nan_policy == 'propagate':
+            if a.ndim == 1 or n_vars <= 2:
+                return SpearmanrResult(np.nan, np.nan)
+            else:
+                variable_has_nan = np.isnan(a).sum(axis=axisout)
+    a_ranked = np.apply_along_axis(rankdata, axisout, a)
+    rs = np.corrcoef(a_ranked, rowvar=axisout)
+    dof = n_obs - 2  # degrees of freedom
+    # rs can have elements equal to 1, so avoid zero division warnings
+    with np.errstate(divide='ignore'):
+        t_ = rs * np.sqrt((dof/((rs+1.0)*(1.0-rs))).clip(0))
+    prob = 2 * t.sf(np.abs(t_), dof)
+    # For backwards compatibility, return scalars when comparing 2 columns
+    if rs.shape == (2, 2):
+        return SpearmanrResult(rs[1, 0], prob[1, 0])
+    else:
+        rs[variable_has_nan, :] = np.nan
+        rs[:, variable_has_nan] = np.nan
+        return SpearmanrResult(rs, prob)
+
+
+def get_individual_networks_selected_genepairs(data_sc, individual_colname, selected_genepairs):
+    data_df = pd.DataFrame(data=data_sc.X.toarray(),
+                           index=data_sc.obs.index,
+                           columns=data_sc.var.index)
+    selected_genes = list(set([ele for item in selected_genepairs for ele in item.split(';')]) & set(data_sc.var.index))
+    selected_genes_sorted_genepairs = [';'.join(sorted(item)) for item in combinations(selected_genes, 2)]
+    common_genepairs = list(set(selected_genes_sorted_genepairs) & set(selected_genepairs))
+    coef_df = pd.DataFrame(index=common_genepairs)
+    coef_p_df = pd.DataFrame(index=common_genepairs)
+    zscore_df = pd.DataFrame(index=common_genepairs)
+    zscore_p_df = pd.DataFrame(index=common_genepairs)
+    data_selected_df = data_df[selected_genes]
+    print(f"Begin calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals")
+    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):
+        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]
+        if cell_num > 10:
+            individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]
+            individual_coefs, individual_coef_ps = spearmanr_withnan(individual_df.values, axis=0)
+            try:
+                individual_coefs_flatten = pd.DataFrame(data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],
+                                                        index=selected_genes_sorted_genepairs).loc[common_genepairs]
+                individual_coef_ps_flatten = pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],
+                                                          index=selected_genes_sorted_genepairs).loc[common_genepairs]
+                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(individual_coefs_flatten, cell_num)
+                coef_df[ind_id] = individual_coefs_flatten
+                coef_p_df[ind_id] = individual_coef_ps_flatten
+                zscore_df[ind_id] = individual_zscores_flatten
+                zscore_p_df[ind_id] = individual_zscore_ps_flatten
+            except:
+                continue
+        else:
+            print("Deleted this individual because of low cell number", cell_num)
+    return coef_df, coef_p_df, zscore_df, zscore_p_df
+
+
+def get_individual_networks_given_celltype_condition_datasetname(celltype, datasetname, condition='UT'):
+    # load the data and data information
+    dataset = DATASET(datasetname)
+    dataset.load_dataset()
+    print(f"{datasetname} loaded.")
+    # calculate the individual network for specific condition and celltype
+    print(datasetname, celltype, condition)
+    work_prefix = Path('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing')
+    selected_genepairs_path = work_prefix / 'coeqtl_mapping/input/snp_genepair_selection' / f'{condition}_{celltype}.baseline.tsv'
+    selected_genepairs = pd.read_csv(selected_genepairs_path, sep='\t')['genepair_sorted'].values
+    if datasetname == 'ng':
+        data_selected = dataset.data_sc[(dataset.data_sc.obs[dataset.celltype_id] == celltype)]
+    else:
+        data_selected = dataset.data_sc[(dataset.data_sc.obs[dataset.celltype_id] == celltype) &
+                                        (dataset.data_sc.obs[dataset.timepoint_id_col] == dataset.chosen_condition[condition])]
+    individual_coefs_df, individual_coefs_p_df, individual_zscores_df, individual_zscores_p_df = get_individual_networks_selected_genepairs(
+        data_selected,
+        dataset.individual_id_col,
+        selected_genepairs
+    )
+    print(individual_coefs_df.head())
+    save_prefix = Path('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input')
+    if not os.path.exists(save_prefix / 'individual_networks' / condition / datasetname):
+        os.mkdir(save_prefix / 'individual_networks' / condition / datasetname)
+    ## not saving the coefficients
+    # save_numpy(individual_coefs_df,
+    #            save_prefix / 'individual_networks' /  condition / datasetname / f'{condition}_{celltype}.coefs')
+    # save_numpy(individual_coefs_p_df,
+    #            save_prefix / 'individual_networks' /  condition / datasetname / f'{condition}_{celltype}.coef_ps')
+    save_numpy(individual_zscores_df,
+               save_prefix / 'individual_networks' /  condition / datasetname / f'{condition}_{celltype}.zscores')
+    save_numpy(individual_zscores_p_df,
+               save_prefix / 'individual_networks' /  condition / datasetname / f'{condition}_{celltype}.zscore_ps')
+    print("Saved ")
+    return individual_coefs_df, individual_coefs_p_df, individual_zscores_df, individual_zscores_p_df
+
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--datasetname', type=str, dest='datasetname')
+    parser.add_argument('--celltype', type=str, dest='celltype')
+    parser.add_argument('--condition', type=str, dest='condition')
+    parser.add_argument('--nonzeroratio', type=float, dest='nonzeroratio')
+    return parser
+
+def run_get_individual_networks_given_celltype_condition_datasetname():
+    args = argumentsparser().parse_args()
+    print(f"Starting to calculate individual network for {args.datasetname}, {args.celltype}, {args.condition}, "
+          f"for genes {args.nonzeroratio}.")
+    _ = get_individual_networks_given_celltype_condition_datasetname(celltype=args.celltype,
+                                                                 condition=args.condition,
+                                                                 datasetname=args.datasetname)
+    return None
+
+
+if __name__ == '__main__':
+    run_get_individual_networks_given_celltype_condition_datasetname()
diff --git a/04_coeqtl_mapping/individual_networks_cmono_ncmono.py b/04_coeqtl_mapping/individual_networks_cmono_ncmono.py
new file mode 100644
index 0000000..e01e855
--- /dev/null
+++ b/04_coeqtl_mapping/individual_networks_cmono_ncmono.py
@@ -0,0 +1,309 @@
+import argparse
+import os
+import re
+from collections import namedtuple
+from itertools import combinations
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import scanpy as sc
+from scipy.stats import rankdata
+from scipy.stats import t, norm
+from tqdm import tqdm
+
+
+def get_time(x):
+    if x == 'UT':
+        return x
+    else:
+        pattern = re.compile(r'\d+h')
+        return re.findall(pattern, x)[0]
+
+
+class DATASET:
+    def __init__(self, datasetname):
+        self.name = datasetname
+        self.path_prefix = Path(
+            "./seurat_objects")
+        self.information = self.get_information()
+    def get_information(self):
+        if self.name == 'onemillionv2':
+            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'
+            self.individual_id_col = 'assignment'
+            self.timepoint_id_col = 'time'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 'UT',
+                                     'stimulated': '3h'}
+        elif self.name == 'onemillionv3':
+            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'
+            self.individual_id_col = 'assignment'
+            self.timepoint_id_col = 'time'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 'UT',
+                                     'stimulated': '3h'}
+        elif self.name == 'stemiv2':
+            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'
+            self.individual_id_col = 'assignment.final'
+            self.timepoint_id_col = 'timepoint.final'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 't8w',
+                                     'stimulated': 'Baseline'}
+        elif self.name == 'ng':
+            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'
+            self.individual_id_col = 'snumber'
+            self.celltype_id = 'cell_type_mapped_to_onemillion'
+        else:
+            raise IOError("Dataset name not understood.")
+    def load_dataset(self):
+        self.get_information()
+        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')
+        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)
+        if self.name.startswith('onemillion'):
+            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]
+        elif self.name == 'ng':
+            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',
+                               'other T': 'otherT', 'other': 'other', 'B': 'B'}
+            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in
+                                                                  self.data_sc.obs['predicted.celltype.l1']]
+    def get_cMono_ncMono(self):
+        def tell_cmono_foronemillion(x):
+            if x == 'mono 1' or x == 'mono 3' or x == 'mono 4':
+                return 'cMono'
+            elif x == 'mono 2':
+                return 'ncMono'
+        if self.name.startswith('onemillion'):
+            self.data_sc.obs['sub_monocytes'] = [tell_cmono_foronemillion(x) for x in
+                                                 self.data_sc.obs['cell_type']]
+            self.cmono = self.data_sc[self.data_sc.obs['sub_monocytes'] == 'cMono']
+            self.ncmono = self.data_sc[self.data_sc.obs['sub_monocytes'] == 'ncMono']
+        elif self.name.startswith('stemi'):
+            self.cmono = self.data_sc[self.data_sc.obs['cell_type'] == 'cMono']
+            self.ncmono = self.data_sc[self.data_sc.obs['cell_type'] == 'ncMono']
+        elif self.name == 'ng':
+            self.cmono = self.data_sc[self.data_sc.obs['predicted.celltype.l2'] == 'CD14 Mono']
+            self.ncmono = self.data_sc[self.data_sc.obs['predicted.celltype.l2'] == 'CD16 Mono']
+        else:
+            raise IOError("Dataset name not understood.")
+
+
+
+def save_numpy(data_df, prefix):
+    np.save(f'{prefix}.npy', data_df.values)
+    with open(f'{prefix}.cols.txt', 'w') as f:
+        f.write('\n'.join(data_df.columns))
+    with open(f'{prefix}.rows.txt', 'w') as f:
+        f.write('\n'.join(data_df.index))
+    return None
+
+
+def corr_to_z(coef, num):
+    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))
+    prob = t.cdf(t_statistic, num - 2)
+    z_score = norm.ppf(prob)
+    positive_coef_probs = 1 - prob
+    positive_coef_probs[coef < 0] = 0
+    negative_coef_probs = prob
+    negative_coef_probs[coef > 0] = 0
+    probs = negative_coef_probs + positive_coef_probs
+    return z_score, probs
+
+
+def _contains_nan(a, nan_policy='propagate'):
+    policies = ['propagate', 'raise', 'omit']
+    if nan_policy not in policies:
+        raise ValueError("nan_policy must be one of {%s}" %
+                         ', '.join("'%s'" % s for s in policies))
+    try:
+        with np.errstate(invalid='ignore'):
+            contains_nan = np.isnan(np.sum(a))
+    except TypeError:
+        try:
+            contains_nan = np.nan in set(a.ravel())
+        except TypeError:
+            contains_nan = False
+            nan_policy = 'omit'
+    if contains_nan and nan_policy == 'raise':
+        raise ValueError("The input contains nan values")
+    return contains_nan, nan_policy
+
+
+def _chk_asarray(a, axis):
+    if axis is None:
+        a = np.ravel(a)
+        outaxis = 0
+    else:
+        a = np.asarray(a)
+        outaxis = axis
+    if a.ndim == 0:
+        a = np.atleast_1d(a)
+    return a, outaxis
+
+
+def spearmanr_withnan(a, axis=0, nan_policy='propagate'):
+    SpearmanrResult = namedtuple('SpearmanrResult', ('correlation', 'pvalue'))
+    if axis is not None and axis > 1:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays, supplied axis argument {}, "
+                         "please use only values 0, 1 or None for axis".format(axis))
+    a, axisout = _chk_asarray(a, axis)
+    if a.ndim > 2:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays")
+    n_vars = a.shape[1 - axisout]
+    n_obs = a.shape[axisout]
+    if n_obs <= 1:
+        # Handle empty arrays or single observations.
+        return SpearmanrResult(np.nan, np.nan)
+    a_contains_nan, nan_policy = _contains_nan(a, nan_policy)
+    variable_has_nan = np.zeros(n_vars, dtype=bool)
+    if a_contains_nan:
+        if nan_policy == 'propagate':
+            if a.ndim == 1 or n_vars <= 2:
+                return SpearmanrResult(np.nan, np.nan)
+            else:
+                variable_has_nan = np.isnan(a).sum(axis=axisout)
+    a_ranked = np.apply_along_axis(rankdata, axisout, a)
+    rs = np.corrcoef(a_ranked, rowvar=axisout)
+    dof = n_obs - 2  # degrees of freedom
+    # rs can have elements equal to 1, so avoid zero division warnings
+    with np.errstate(divide='ignore'):
+        t_ = rs * np.sqrt((dof / ((rs + 1.0) * (1.0 - rs))).clip(0))
+    prob = 2 * t.sf(np.abs(t_), dof)
+    # For backwards compatibility, return scalars when comparing 2 columns
+    if rs.shape == (2, 2):
+        return SpearmanrResult(rs[1, 0], prob[1, 0])
+    else:
+        rs[variable_has_nan, :] = np.nan
+        rs[:, variable_has_nan] = np.nan
+        return SpearmanrResult(rs, prob)
+
+
+def get_individual_networks_selected_genepairs(data_sc, individual_colname, selected_genepairs):
+    data_df = pd.DataFrame(data=data_sc.X.toarray(),
+                           index=data_sc.obs.index,
+                           columns=data_sc.var.index)
+    selected_genes = list(set([ele for item in selected_genepairs for ele in item.split(';')]) & set(data_sc.var.index))
+    selected_genes_sorted_genepairs = [';'.join(sorted(item)) for item in combinations(selected_genes, 2)]
+    common_genepairs = list(set(selected_genes_sorted_genepairs) & set(selected_genepairs))
+    coef_df = pd.DataFrame(index=common_genepairs)
+    coef_p_df = pd.DataFrame(index=common_genepairs)
+    zscore_df = pd.DataFrame(index=common_genepairs)
+    zscore_p_df = pd.DataFrame(index=common_genepairs)
+    data_selected_df = data_df[selected_genes]
+    print(f"Begin calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals")
+    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):
+        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]
+        if cell_num > 10:
+            individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]
+            individual_coefs, individual_coef_ps = spearmanr_withnan(individual_df.values, axis=0)
+            try:
+                individual_coefs_flatten = \
+                pd.DataFrame(data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],
+                             index=selected_genes_sorted_genepairs).loc[common_genepairs]
+                individual_coef_ps_flatten = \
+                pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],
+                             index=selected_genes_sorted_genepairs).loc[common_genepairs]
+                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(individual_coefs_flatten, cell_num)
+                coef_df[ind_id] = individual_coefs_flatten
+                coef_p_df[ind_id] = individual_coef_ps_flatten
+                zscore_df[ind_id] = individual_zscores_flatten
+                zscore_p_df[ind_id] = individual_zscore_ps_flatten
+            except:
+                continue
+        else:
+            print("Deleted this individual because of low cell number", cell_num)
+    return coef_df, coef_p_df, zscore_df, zscore_p_df
+
+
+def get_individual_networks_UT_subcelltypesMonocytes(celltype, datasetname, condition='UT'):
+    # load the data and data information
+    dataset = DATASET(datasetname)
+    dataset.load_dataset()
+    dataset.get_cMono_ncMono()
+    print(f"{datasetname} loaded.")
+    # calculate the individual network for specific condition and celltype
+    print(datasetname, celltype, condition)
+    work_prefix = Path('./')
+    selected_genepairs_path = work_prefix / 'coeqtl_mapping/input/snp_genepair_selection' / f'{condition}_monocyte_{datasetname}.baseline.tsv'
+    selected_genepairs = pd.read_csv(selected_genepairs_path, sep='\t')['genepair_sorted'].values
+    if celltype == 'cMono':
+        data_celltype = dataset.cmono
+    elif celltype == 'ncMono':
+        data_celltype = dataset.ncmono
+    else:
+        raise IOError("Celltype not understood. select from cMono or ncMono.")
+    if datasetname == 'ng':
+        data_selected = data_celltype
+    else:
+        data_selected = data_celltype[
+            data_celltype.obs[dataset.timepoint_id_col] == dataset.chosen_condition[condition]]
+    individual_coefs_df, individual_coefs_p_df, individual_zscores_df, individual_zscores_p_df = get_individual_networks_selected_genepairs(
+        data_selected,
+        dataset.individual_id_col,
+        selected_genepairs
+    )
+    print(individual_coefs_df.head())
+    save_prefix = Path('./coeqtl_mapping/input')
+    if not os.path.exists(save_prefix / 'individual_networks' / condition / datasetname):
+        os.mkdir(save_prefix / 'individual_networks' / condition / datasetname)
+    # save_numpy(individual_coefs_df,
+    #            save_prefix / 'individual_networks' / condition / datasetname / f'{condition}_{celltype}.coefs')
+    # save_numpy(individual_coefs_p_df,
+    #            save_prefix / 'individual_networks' / condition / datasetname / f'{condition}_{celltype}.coef_ps')
+    save_numpy(individual_zscores_df,
+               save_prefix / 'individual_networks' / condition / datasetname / f'{condition}_{celltype}.zscores')
+    save_numpy(individual_zscores_p_df,
+               save_prefix / 'individual_networks' / condition / datasetname / f'{condition}_{celltype}.zscore_ps')
+    print("Saved.")
+    return individual_coefs_df, individual_coefs_p_df, individual_zscores_df, individual_zscores_p_df
+
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--datasetname', type=str, dest='datasetname')
+    parser.add_argument('--celltype', type=str, dest='celltype')
+    parser.add_argument('--condition', type=str, dest='condition')
+    return parser
+
+
+def run_get_individual_networks_given_celltype_condition_datasetname():
+    args = argumentsparser().parse_args()
+    print(f"Starting to calculate individual network for {args.datasetname}, {args.celltype}, {args.condition}.")
+    _ = get_individual_networks_UT_subcelltypesMonocytes(celltype=args.celltype,
+                                                         condition=args.condition,
+                                                         datasetname=args.datasetname)
+    return None
+
+
+if __name__ == '__main__':
+    run_get_individual_networks_given_celltype_condition_datasetname()
+
+
+# dataset = DATASET('stemiv2')
+# dataset.load_dataset()
+# dataset.get_cMono_ncMono()
+# celldf = dataset.data_sc.obs
+# cellnum = celldf[(celldf[dataset.celltype_id]=='CD4T') & (celldf[dataset.timepoint_id_col]==dataset.chosen_condition['UT'])][dataset.individual_id_col].value_counts()
+# print('CD4T, ', cellnum[cellnum>10].mean())
+# cellnum = celldf[(celldf[dataset.celltype_id]=='CD8T') & (celldf[dataset.timepoint_id_col]==dataset.chosen_condition['UT'])][dataset.individual_id_col].value_counts()
+# print('CD8T, ', cellnum[cellnum>10].mean())
+# cellnum = celldf[(celldf[dataset.celltype_id]=='monocyte') & (celldf[dataset.timepoint_id_col]==dataset.chosen_condition['UT'])][dataset.individual_id_col].value_counts()
+# print('Monocyte, ', cellnum[cellnum>10].mean())
+# cellnum = dataset.cmono.obs[dataset.cmono.obs[dataset.timepoint_id_col]==dataset.chosen_condition['UT']][dataset.individual_id_col].value_counts()
+# print('cMono, ', cellnum[cellnum>10].mean())
+# cellnum = dataset.ncmono.obs[dataset.ncmono.obs[dataset.timepoint_id_col]==dataset.chosen_condition['UT']][dataset.individual_id_col].value_counts()
+# print('ncMono, ', cellnum[cellnum>10].mean())
+#
+# dataset = DATASET('ng')
+# dataset.load_dataset()
+# dataset.get_cMono_ncMono()
+# celldf = dataset.data_sc.obs
+# cellnum = celldf[(celldf[dataset.celltype_id]=='CD4T')][dataset.individual_id_col].value_counts()
+# print('CD4T, ', cellnum[cellnum>10].mean())
+# cellnum = celldf[(celldf[dataset.celltype_id]=='CD8T')][dataset.individual_id_col].value_counts()
+# print('CD8T, ', cellnum[cellnum>10].mean())
+# cellnum = celldf[(celldf[dataset.celltype_id]=='monocyte') ][dataset.individual_id_col].value_counts()
+# print('Monocyte, ', cellnum[cellnum>10].mean())
+# cellnum = dataset.cmono.obs[dataset.individual_id_col].value_counts()
+# print('cMono, ', cellnum[cellnum>10].mean())
+# cellnum = dataset.ncmono.obs[dataset.individual_id_col].value_counts()
+# print('ncMono, ', cellnum[cellnum>10].mean())
\ No newline at end of file
diff --git a/04_coeqtl_mapping/individual_networks_maxcell.py b/04_coeqtl_mapping/individual_networks_maxcell.py
new file mode 100644
index 0000000..72566f6
--- /dev/null
+++ b/04_coeqtl_mapping/individual_networks_maxcell.py
@@ -0,0 +1,315 @@
+import os
+import re
+from itertools import combinations
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import scanpy as sc
+from scipy.stats import spearmanr
+from scipy.stats import t, norm
+from tqdm import tqdm
+import argparse
+from scipy.stats import rankdata
+from collections import namedtuple
+
+
+def get_time(x):
+    if x == 'UT':
+        return x
+    else:
+        pattern = re.compile(r'\d+h')
+        return re.findall(pattern, x)[0]
+
+
+class DATASET:
+    def __init__(self, datasetname):
+        self.name = datasetname
+        self.path_prefix = Path("./seurat_objects")
+        self.information = self.get_information()
+    def get_information(self):
+        if self.name == 'onemillionv2':
+            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'
+            self.individual_id_col = 'assignment'
+            self.timepoint_id_col = 'time'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 'UT',
+                                     'stimulated': '3h'}
+        elif self.name == 'onemillionv3':
+            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'
+            self.individual_id_col = 'assignment'
+            self.timepoint_id_col = 'time'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 'UT',
+                                     'stimulated': '3h'}
+        elif self.name == 'stemiv2':
+            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'
+            self.individual_id_col = 'assignment.final'
+            self.timepoint_id_col = 'timepoint.final'
+            self.celltype_id = 'cell_type_lowerres'
+            self.chosen_condition = {'UT': 't8w',
+                                     'stimulated': 'Baseline'}
+        elif self.name == 'ng':
+            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'
+            self.individual_id_col = 'snumber'
+            self.celltype_id = 'cell_type_mapped_to_onemillion'
+        else:
+            raise IOError("Dataset name not understood.")
+    def load_dataset(self):
+        self.get_information()
+        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')
+        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)
+        if self.name.startswith('onemillion'):
+            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]
+        elif self.name == 'ng':
+            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',
+                               'other T': 'otherT', 'other': 'other', 'B': 'B'}
+            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in
+                                                          self.data_sc.obs['predicted.celltype.l1']]
+
+
+
+def select_gene_nonzeroratio(df, ratio):
+    nonzerocounts = np.count_nonzero(df.values, axis=0) / df.shape[0]
+    selected_genes = df.columns[nonzerocounts > ratio]
+    return selected_genes
+
+
+def corr_to_z(coef, num):
+    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))
+    prob = t.cdf(t_statistic, num - 2)
+    z_score = norm.ppf(prob)
+    positive_coef_probs = 1 - prob
+    positive_coef_probs[coef < 0] = 0
+    negative_coef_probs = prob
+    negative_coef_probs[coef > 0] = 0
+    probs = negative_coef_probs + positive_coef_probs
+    return z_score, probs
+
+
+def z_to_corr(z, num):
+    prob = norm.cdf(z)
+    t_statistic = t.ppf(prob, num - 2)
+    corr = t_statistic / np.sqrt(num - 2 + t_statistic ** 2)
+    return corr
+
+
+def get_om_name(filename):
+    pattern = re.compile(r'LLDeep_\d\d\d\d')
+    return re.findall(pattern, filename)[0]
+
+
+def get_stemi_name(filename):
+    pattern = re.compile(r'TEST_\d.')
+    return re.findall(pattern, filename)[0]
+
+
+def save_numpy(data_df, prefix):
+    np.save(f'{prefix}.npy', data_df.values)
+    with open(f'{prefix}.cols.txt', 'w') as f:
+        f.write('\n'.join(data_df.columns))
+    with open(f'{prefix}.rows.txt', 'w') as f:
+        f.write('\n'.join(data_df.index))
+    return None
+
+def _contains_nan(a, nan_policy='propagate'):
+    policies = ['propagate', 'raise', 'omit']
+    if nan_policy not in policies:
+        raise ValueError("nan_policy must be one of {%s}" %
+                         ', '.join("'%s'" % s for s in policies))
+    try:
+        with np.errstate(invalid='ignore'):
+            contains_nan = np.isnan(np.sum(a))
+    except TypeError:
+        try:
+            contains_nan = np.nan in set(a.ravel())
+        except TypeError:
+            contains_nan = False
+            nan_policy = 'omit'
+    if contains_nan and nan_policy == 'raise':
+        raise ValueError("The input contains nan values")
+    return contains_nan, nan_policy
+
+
+def _chk_asarray(a, axis):
+    if axis is None:
+        a = np.ravel(a)
+        outaxis = 0
+    else:
+        a = np.asarray(a)
+        outaxis = axis
+    if a.ndim == 0:
+        a = np.atleast_1d(a)
+    return a, outaxis
+
+
+def spearmanr_withnan(a, axis=0, nan_policy='propagate'):
+    SpearmanrResult = namedtuple('SpearmanrResult', ('correlation', 'pvalue'))
+    if axis is not None and axis > 1:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays, supplied axis argument {}, "
+                         "please use only values 0, 1 or None for axis".format(axis))
+    a, axisout = _chk_asarray(a, axis)
+    if a.ndim > 2:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays")
+    n_vars = a.shape[1 - axisout]
+    n_obs = a.shape[axisout]
+    if n_obs <= 1:
+        # Handle empty arrays or single observations.
+        return SpearmanrResult(np.nan, np.nan)
+    a_contains_nan, nan_policy = _contains_nan(a, nan_policy)
+    variable_has_nan = np.zeros(n_vars, dtype=bool)
+    if a_contains_nan:
+        if nan_policy == 'propagate':
+            if a.ndim == 1 or n_vars <= 2:
+                return SpearmanrResult(np.nan, np.nan)
+            else:
+                variable_has_nan = np.isnan(a).sum(axis=axisout)
+    a_ranked = np.apply_along_axis(rankdata, axisout, a)
+    rs = np.corrcoef(a_ranked, rowvar=axisout)
+    dof = n_obs - 2  # degrees of freedom
+    # rs can have elements equal to 1, so avoid zero division warnings
+    with np.errstate(divide='ignore'):
+        t_ = rs * np.sqrt((dof/((rs+1.0)*(1.0-rs))).clip(0))
+    prob = 2 * t.sf(np.abs(t_), dof)
+    # For backwards compatibility, return scalars when comparing 2 columns
+    if rs.shape == (2, 2):
+        return SpearmanrResult(rs[1, 0], prob[1, 0])
+    else:
+        rs[variable_has_nan, :] = np.nan
+        rs[:, variable_has_nan] = np.nan
+        return SpearmanrResult(rs, prob)
+
+def read_numpy(prefix):
+    data = np.load(f'{prefix}.npy')
+    columns = [item.strip() for item in open(f'{prefix}.rows.txt', 'r').readlines()]
+    return pd.DataFrame(data=data, columns=columns, index=columns)
+
+
+def read_all_files(prefix, genepairs):
+    res_df = pd.DataFrame(index=genepairs)
+    for filename in os.listdir(prefix):
+        if filename.endswith('_coefs.npy'):
+            data = np.load(f'{prefix}/{filename}')
+            if len(data.shape) > 1:
+                data_uppertria = data[np.triu_indices_from(data, 1)]
+                individual_id = get_stemi_name(filename)
+                res_df[individual_id] = data_uppertria
+    return res_df
+
+
+def get_unique_genepairs(genepair_list, sep=';'):
+    unique_pairs = set()
+    for genepair in genepair_list:
+        reverse_genepair = sep.join(genepair.split(sep))
+        if genepair in unique_pairs or reverse_genepair in unique_pairs:
+            continue
+        else:
+            unique_pairs.add(genepair)
+    return unique_pairs
+
+
+def get_genes(genepair_list, sep=';'):
+    genes = list(set([gene for genepair in genepair_list for gene in genepair.split(sep)]))
+    return genes
+
+
+def get_genepairs(genelist_path):
+    genelist = [item.strip() for item in open(genelist_path, 'r').readlines()]
+    genepairs = [';'.join(sorted(item)) for item in combinations(genelist, 2)]
+    return genelist, genepairs
+
+
+def get_individual_networks_selected_genepairs(data_sc, individual_colname, selected_genepairs, maxcell):
+    data_df = pd.DataFrame(data=data_sc.X.toarray(),
+                           index=data_sc.obs.index,
+                           columns=data_sc.var.index)
+    selected_genes = list(set([ele for item in selected_genepairs
+                               for ele in item.split(';')]) & set(data_sc.var.index))
+    selected_genes_sorted_genepairs = [';'.join(sorted(item)) for item in combinations(selected_genes, 2)]
+    common_genepairs = list(set(selected_genes_sorted_genepairs) & set(selected_genepairs))
+    coef_df = pd.DataFrame(index=common_genepairs)
+    coef_p_df = pd.DataFrame(index=common_genepairs)
+    zscore_df = pd.DataFrame(index=common_genepairs)
+    zscore_p_df = pd.DataFrame(index=common_genepairs)
+    data_selected_df = data_df[selected_genes]
+    print(f"Begin calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals.")
+    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):
+        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]
+        if cell_num > 10:
+            if maxcell>0 and cell_num >= maxcell:
+                individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id].sample(maxcell, random_state=5)
+                cell_num = maxcell
+            else:
+                individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]
+            # individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]
+            individual_coefs, individual_coef_ps = spearmanr_withnan(individual_df.values, axis=0)
+            try:
+                individual_coefs_flatten = pd.DataFrame(data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],
+                                                        index=selected_genes_sorted_genepairs).loc[common_genepairs]
+                individual_coef_ps_flatten = pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],
+                                                          index=selected_genes_sorted_genepairs).loc[common_genepairs]
+                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(individual_coefs_flatten.values,
+                                                                                     cell_num)
+                coef_df[ind_id] = individual_coefs_flatten
+                coef_p_df[ind_id] = individual_coef_ps_flatten
+                zscore_df[ind_id] = individual_zscores_flatten
+                zscore_p_df[ind_id] = individual_zscore_ps_flatten
+            except:
+                continue
+        else:
+            print("Deleted this individual because of low cell number", cell_num)
+    return coef_df, coef_p_df, zscore_df, zscore_p_df
+
+
+def get_individual_networks_given_celltype_condition_datasetname(celltype, datasetname, condition='UT', maxcell=-1):
+    # load the data and data information
+    dataset = DATASET(datasetname)
+    dataset.load_dataset()
+    print(f"{datasetname} loaded.")
+    # calculate the individual network for specific condition and celltype
+    print(datasetname, celltype, condition)
+    work_prefix = Path('./')
+    selected_genepairs_path = work_prefix / f'coeqtl_mapping/input/snp_genepair_selection/{condition}_{celltype}_{datasetname}.baseline.tsv'
+    selected_genepairs = pd.read_csv(selected_genepairs_path, sep='\t')['genepair_sorted'].values
+    if datasetname == 'ng':
+        data_selected = dataset.data_sc[(dataset.data_sc.obs[dataset.celltype_id] == celltype)]
+    else:
+        data_selected = dataset.data_sc[(dataset.data_sc.obs[dataset.celltype_id] == celltype) &
+                                        (dataset.data_sc.obs[dataset.timepoint_id_col] == dataset.chosen_condition[condition])]
+    individual_coefs_df, individual_coefs_p_df, individual_zscores_df, individual_zscores_p_df = \
+        get_individual_networks_selected_genepairs(
+        data_selected,
+        dataset.individual_id_col,
+        selected_genepairs,
+        maxcell
+    )
+    print(individual_coefs_df.head())
+    save_prefix = Path('./coeqtl_mapping/input')
+    if not os.path.exists(save_prefix / 'individual_networks' / condition / datasetname):
+        os.mkdir(save_prefix / 'individual_networks' / condition / datasetname)
+    save_numpy(individual_zscores_df,
+               save_prefix / 'individual_networks' /  condition / datasetname / f'{condition}_{celltype}.max{maxcell}cells.zscores')
+    print("Saved ")
+    return individual_coefs_df, individual_coefs_p_df, individual_zscores_df, individual_zscores_p_df
+
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--datasetname', type=str, dest='datasetname')
+    parser.add_argument('--celltype', type=str, dest='celltype')
+    parser.add_argument('--condition', type=str, dest='condition')
+    parser.add_argument('--maxcell', type=float, dest='maxcell')
+    return parser
+
+def run_get_individual_networks_given_celltype_condition_datasetname():
+    args = argumentsparser().parse_args()
+    print(f"Starting to calculate individual network for {args.datasetname}, {args.celltype}, {args.condition}, "
+          f"for max cell number {args.maxcell}.")
+    _ = get_individual_networks_given_celltype_condition_datasetname(celltype=args.celltype,
+                                                                     condition=args.condition,
+                                                                     datasetname=args.datasetname,
+                                                                     maxcell=int(args.maxcell))
+    return None
+
+if __name__ == '__main__':
+    run_get_individual_networks_given_celltype_condition_datasetname()
\ No newline at end of file
diff --git a/04_coeqtl_mapping/launch_sbatch_files.sh b/04_coeqtl_mapping/launch_sbatch_files.sh
new file mode 100644
index 0000000..27633da
--- /dev/null
+++ b/04_coeqtl_mapping/launch_sbatch_files.sh
@@ -0,0 +1,48 @@
+# Calculate individual networks
+working_dir=/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping
+condition='UT'
+for celltype in 'CD4T' 'CD8T' 'B' 'NK' 'DC'
+do
+for dataset in 'stemiv2' 'onemillionv2' 'onemillionv3' 'ng'
+do
+  echo ${dataset}_${condition}_${celltype}
+  sbatch --parsable --job-name ${dataset}_${condition}_${celltype} \
+  --output ${working_dir}/input/individual_networks/logs/${dataset}_${condition}_${celltype}.out \
+  --error ${working_dir}/input/individual_networks/logs/${dataset}_${condition}_${celltype}.err \
+  ${working_dir}/input/individual_networks/submit_individual_networks.sh ${dataset} ${celltype} ${condition}
+done
+done # decided not to save into tsv after saving in numpy
+
+# merge individual networks and create gene list and annotation file for betaqtl
+for celltype in 'CD4T' 'CD8T' 'B' 'NK' 'DC'
+do
+  echo ${condition}_${celltype}
+  sbatch --parsable --job-name merge_${condition}_${celltype} \
+  --output ${working_dir}/input/individual_networks/logs/merge_${condition}_${celltype}.out \
+  --error ${working_dir}/input/individual_networks/logs/merge_${condition}_${celltype}.err \
+  ${working_dir}/input/individual_networks/submit_merge_coexpression.sh ${celltype} ${condition}
+done
+
+
+# rsync the betaqtl_scripts to gearshift: /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/${condition}_${celltype}
+# make batches for betaqtl
+for celltype in 'CD4T' 'CD8T' 'B' 'NK' 'DC'
+do
+cd ${working_dir}/output/${condition}_${celltype} ||exit
+./createBatches.sh ${condition} ${celltype}
+# submit betaqtl jobs
+./suball.sh ${working_dir}/output/${condition}_${celltype}/noduplicated/jobs
+./suball.sh ${working_dir}/output/${condition}_${celltype}/duplicatedversion1/jobs
+./suball.sh ${working_dir}/output/${condition}_${celltype}/duplicatedversion2/jobs
+done
+
+# concate and process output from betaqtl
+for celltype in 'CD4T' 'CD8T' 'B' 'NK' 'DC'
+do
+  cd ${working_dir}/output/${condition}_${celltype} ||exit
+  echo ${condition}_${celltype}
+  sbatch --parsable --job-name process_betaqtl_results_${condition}_${celltype} \
+  --output ${working_dir}/input/individual_networks/logs/process_betaqtl_results_${condition}_${celltype}.out \
+  --error ${working_dir}/input/individual_networks/logs/process_betaqtl_results_${condition}_${celltype}.err \
+  ${working_dir}/output/submit_process_betaqtl_results.sh ${condition} ${celltype}
+done
\ No newline at end of file
diff --git a/04_coeqtl_mapping/merge_coexpression_for_betaeqtl.py b/04_coeqtl_mapping/merge_coexpression_for_betaeqtl.py
new file mode 100644
index 0000000..e1bb6fd
--- /dev/null
+++ b/04_coeqtl_mapping/merge_coexpression_for_betaeqtl.py
@@ -0,0 +1,40 @@
+import pandas as pd
+from pathlib import Path
+import numpy as np
+import argparse
+
+
+def read_numpy(prefix):
+    data = np.load(f'{prefix}.npy')
+    columns = [item.strip() for item in open(f'{prefix}.cols.txt', 'r').readlines()]
+    rows = [item.strip() for item in open(f'{prefix}.rows.txt', 'r').readlines()]
+    return pd.DataFrame(data=data, columns=columns, index=rows)
+
+
+def concat_numpy_files(celltype, condition, res_prefix):
+    allres = pd.DataFrame()
+    for dataset in ['onemillionv2', 'onemillionv3', 'stemiv2', 'ng']:
+        if condition =='stimulated' and dataset == 'ng':
+            continue
+        else:
+            numpyfile_path = res_prefix/condition/dataset/f'{condition}_{celltype}.zscores'
+            df = read_numpy(numpyfile_path)
+            allres = pd.concat([df, allres], axis=1)
+            print(f'Adding {dataset}, it has shape:', allres.shape)
+    allres.to_csv(res_prefix/condition/f'{condition}_{celltype}.onemillionv23stemiv2ng.zscores.tsv', sep='\t')
+    return allres
+
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--celltype', type=str, dest='celltype')
+    parser.add_argument('--condition', type=str, dest='condition')
+    return parser
+
+
+workdir = Path("/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping")
+res_prefix = workdir/'input/individual_networks/'
+
+args = argumentsparser().parse_args()
+celltype, condition = args.celltype, args.condition
+_ = concat_numpy_files(celltype, condition, res_prefix)
\ No newline at end of file
diff --git a/04_coeqtl_mapping/merge_coexpression_for_betaeqtl_maxcell.py b/04_coeqtl_mapping/merge_coexpression_for_betaeqtl_maxcell.py
new file mode 100644
index 0000000..319f6a2
--- /dev/null
+++ b/04_coeqtl_mapping/merge_coexpression_for_betaeqtl_maxcell.py
@@ -0,0 +1,42 @@
+import pandas as pd
+from pathlib import Path
+import numpy as np
+import argparse
+
+
+def read_numpy(prefix):
+    data = np.load(f'{prefix}.npy')
+    columns = [item.strip() for item in open(f'{prefix}.cols.txt', 'r').readlines()]
+    rows = [item.strip() for item in open(f'{prefix}.rows.txt', 'r').readlines()]
+    return pd.DataFrame(data=data, columns=columns, index=rows)
+
+
+def concat_numpy_files(celltype, condition, res_prefix, maxcell):
+    allres = pd.DataFrame()
+    for dataset in ['onemillionv2', 'onemillionv3', 'stemiv2', 'ng']:
+        if condition =='stimulated' and dataset == 'ng':
+            continue
+        else:
+            numpyfile_path = res_prefix/condition/dataset/f'{condition}_{celltype}.max{maxcell}cells.zscores'
+            df = read_numpy(numpyfile_path)
+            allres = pd.concat([df, allres], axis=1, join='outer')
+            print(f'Adding {dataset}, it has shape:', allres.shape)
+    allres.to_csv(res_prefix/condition/f'{condition}_{celltype}.max{maxcell}cells.onemillionv23stemiv2ng.zscores.tsv.gz',
+                  compression='gzip', sep='\t')
+    return allres
+
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--celltype', type=str, dest='celltype')
+    parser.add_argument('--condition', type=str, dest='condition')
+    parser.add_argument('--maxcell', type=str, dest='maxcell')
+    return parser
+
+
+workdir = Path("./coeqtl_mapping")
+res_prefix = workdir/'input/individual_networks/'
+
+args = argumentsparser().parse_args()
+celltype, condition, maxcell = args.celltype, args.condition, int(args.maxcell)
+_ = concat_numpy_files(celltype, condition, res_prefix, maxcell)
diff --git a/04_coeqtl_mapping/merge_coexpression_for_betaqtl.subsampleindividuals.py b/04_coeqtl_mapping/merge_coexpression_for_betaqtl.subsampleindividuals.py
new file mode 100644
index 0000000..d0e2dc0
--- /dev/null
+++ b/04_coeqtl_mapping/merge_coexpression_for_betaqtl.subsampleindividuals.py
@@ -0,0 +1,44 @@
+import pandas as pd
+from pathlib import Path
+import numpy as np
+import argparse
+
+
+def read_numpy(prefix):
+    data = np.load(f'{prefix}.npy')
+    columns = [item.strip() for item in open(f'{prefix}.cols.txt', 'r').readlines()]
+    rows = [item.strip() for item in open(f'{prefix}.rows.txt', 'r').readlines()]
+    return pd.DataFrame(data=data, columns=columns, index=rows)
+
+
+def concat_numpy_files(celltype, condition, res_prefix, num):
+    allres = pd.DataFrame()
+    for dataset in ['onemillionv2', 'onemillionv3', 'stemiv2', 'ng']:
+        if condition =='stimulated' and dataset == 'ng':
+            continue
+        else:
+            numpyfile_path = res_prefix/condition/dataset/f'{condition}_{celltype}.zscores'
+            df = read_numpy(numpyfile_path)
+            allres = pd.concat([df, allres], axis=1, join='outer')
+            print(f'Adding {dataset}, it has shape:', allres.shape)
+    allres.sample(num, axis=1).to_csv(res_prefix/condition/f'{condition}_{celltype}.onemillionv23stemiv2ng.{num}randompeople.zscores.tsv.gz',
+                  sep='\t', compression='gzip')
+    # allres.sample(50).to_csv(res_prefix / condition / f'{condition}_{celltype}.onemillionv23stemiv2ng.50randompeople.zscores.tsv.gz',
+    #                           sep='\t', compression='gzip')
+    return allres
+
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--celltype', type=str, dest='celltype')
+    parser.add_argument('--condition', type=str, dest='condition')
+    parser.add_argument('--num', type=str, dest='num')
+    return parser
+
+
+workdir = Path("./coeqtl_mapping")
+res_prefix = workdir/'input/individual_networks/'
+
+args = argumentsparser().parse_args()
+celltype, condition, number = args.celltype, args.condition, int(args.num)
+_ = concat_numpy_files(celltype, condition, res_prefix, number)
\ No newline at end of file
diff --git a/04_coeqtl_mapping/multipletesting_correction.fixed.py b/04_coeqtl_mapping/multipletesting_correction.fixed.py
new file mode 100644
index 0000000..bf7a9c4
--- /dev/null
+++ b/04_coeqtl_mapping/multipletesting_correction.fixed.py
@@ -0,0 +1,129 @@
+import pandas as pd
+from statsmodels.stats.multitest import multipletests
+import numpy as np
+import argparse
+from scipy.optimize import minimize
+from scipy.stats import beta
+from scipy import special
+from pathlib import Path
+
+
+def read_numpy(prefix):
+    data = np.load(f'{prefix}.npy')
+    columns = [f'perm{item.strip()}' for item in open(f'{prefix}.cols.txt', 'r').readlines()]
+    rows = [item.strip() for item in open(f'{prefix}.rows.txt', 'r').readlines()]
+    return pd.DataFrame(data=data, columns=columns, index=rows)
+
+
+def beta_distribution_mle_function(x, p):
+    k, n = x
+    ll = (k - 1) * np.sum(np.log(p)) + (n - 1) * np.sum(np.log(1 - p)) - np.size(p) * special.betaln(k, n)
+    return -1 * ll
+
+
+def beta_distribution_initial_guess(x):
+    """
+    https://stats.stackexchange.com/questions/13245/which-is-a-good-tool-to-compute-parameters-for-a-beta-distribution
+    """
+    mean = np.mean(x)
+    var = np.var(x)
+    a = mean * ((mean * (1 - mean) / var) - 1)
+    b = (1 - mean) * ((mean * (1 - mean) / var) - 1)
+    return a, b
+
+
+def fit_beta_distribution(p, a_bnd=(0.1, 10), b_bnd=(1, 1000000)):
+    a, b = beta_distribution_initial_guess(p)
+    x0 = np.array([min(max(a, a_bnd[0]), a_bnd[1]), min(max(b, b_bnd[0]), b_bnd[1])])
+    res = minimize(beta_distribution_mle_function,
+                   x0=x0,
+                   args=(p, ),
+                   method='nelder-mead',
+                   bounds=(a_bnd, b_bnd),
+                   options={"maxiter": 10000, "disp": True})
+    return res.x, res.nfev, res.nit
+
+
+def arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--permutation_pvalue_path', dest='permutation_pvalue_path')
+    parser.add_argument('--coeqtl_path', dest='coeqtl_path')
+    parser.add_argument('--eqtl_path', dest='eqtl_path')
+    parser.add_argument('--save_prefix', dest='saveprefix')
+    return parser
+
+
+def find_eqtlsnp_gene(snp, genepair, eqtl_snp_gene_set):
+    gene1, gene2 = genepair.split(';')
+    if '_'.join([snp, gene1]) in eqtl_snp_gene_set:
+        return '_'.join([snp, gene1])
+    else:
+        return '_'.join([snp, gene2])
+
+
+def find_eqtl_gene(coeqtl_chrpos, annotation_dict):
+    annotation_eqtlgene = annotation_dict.get(coeqtl_chrpos)
+    return annotation_eqtlgene
+
+def main():
+    args = arguments().parse_args()
+    coeqtl_path = args.coeqtl_path
+    eqtls_path = args.eqtl_path
+    saveprefix = args.saveprefix
+    permutation_pvalue_path = args.permutation_pvalue_path
+    permutation_cols = [f'Perm{ind}' for ind in range(0, 100)]
+    permutation_pvalues_df = pd.read_csv(permutation_pvalue_path, sep='\t',
+                                         compression='gzip', index_col=0)
+    eqtl_df = pd.read_csv(eqtls_path, sep='\t')
+    eqtl_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in eqtl_df[['ProbeChr', 'ProbeCenterChrPos']].values]
+    eqtl_snp_gene_set = set(['_'.join(item) for item in eqtl_df[['SNPName', 'genename']].values])
+    annotation_path = '/groups/umcg-bios/tmp01/projects/1M_cells_scRNAseq/ongoing/eQTL_mapping/probeannotation/singleCell-annotation-stripped.tsv'
+    mappingdic = pd.read_csv('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/resources/features_v3_reformated_names.tsv',
+                              sep='\t', names=['geneid', 'genename']).set_index('geneid')['genename'].T.to_dict()
+    annotation_df = pd.read_csv(annotation_path, sep='\t')
+    annotation_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in annotation_df[['Chr', 'ChrStart', 'ChrEnd']].values]
+    annotation_df['genename'] = [mappingdic.get(ensemblid) for ensemblid in annotation_df['Ensembl']]
+    annotation_dict = annotation_df.set_index('chr_pos')['genename'].T.to_dict()
+    # eqtl_df['snp_gene'] = ['_'.join(item) for item in eqtl_df[['SNPName', 'genename']].values]
+    # eqtl_snp_gene_set = set(eqtl_df['snp_gene'])
+    coeqtls = pd.read_csv(coeqtl_path, sep='\t', index_col=0, compression='gzip')
+    coeqtls['eqtlgene'] = [find_eqtl_gene(chr_pos, annotation_dict) for (chr_pos) in coeqtls['chr_pos']]
+    coeqtls['snp_eqtlgene'] = ['_'.join(item) for item in coeqtls[['SNP', 'eqtlgene']].values]
+    coeqtls_lowest_nominalP = coeqtls.sort_values(by='MetaP', ascending=True).drop_duplicates(subset=['snp_eqtlgene'])
+    coeqtls_lowest_nominalP_dict = coeqtls_lowest_nominalP.set_index('snp_eqtlgene')['MetaP'].T.to_dict()
+    permutation_pvalues_df['SNP'] = [item.split('_')[0] for item in permutation_pvalues_df.index]
+    permutation_pvalues_df['nominalP'] = [coeqtls_lowest_nominalP_dict.get(snp) for snp in
+                                          permutation_pvalues_df.index]
+    permutation_pvalues_df = permutation_pvalues_df.dropna(subset=['nominalP'])
+    permutation_pvalues_df['beta_shape1'], permutation_pvalues_df['beta_shape2'] = \
+        zip(*[fit_beta_distribution(x)[0] for x in permutation_pvalues_df[permutation_cols].values])
+    permutation_pvalues_df['pval_beta'] = [1-beta.sf(x[0], x[1], x[2]) for x in
+                                               permutation_pvalues_df[['nominalP', 'beta_shape1', 'beta_shape2']].values]
+    assert permutation_pvalues_df['pval_beta'].isnull().sum() == 0
+    # over all eqtls, perform BH-FDR
+    permutation_pvalues_df['qval'] = multipletests(permutation_pvalues_df['pval_beta'].values, method='fdr_bh')[1]
+    permutation_pvalues_df.to_csv(f'{saveprefix}.eqtls_betaadjustedPs.tsv.gz', sep='\t', compression='gzip')
+    ub = permutation_pvalues_df[permutation_pvalues_df['qval']>=0.05].sort_values(by=['pval_beta'], ascending=True)['pval_beta'].values[0]
+    lb = permutation_pvalues_df[permutation_pvalues_df['qval']<=0.05].sort_values(by=['pval_beta'], ascending=False)['pval_beta'].values[0]
+    pthreshold = (ub + lb) / 2
+    print('Minimum p-value threshold', pthreshold)
+    permutation_pvalues_df['threshold_per_betadistribution'] = [beta.ppf(pthreshold, x[0], x[1]) for x in
+                                                                permutation_pvalues_df[['beta_shape1', 'beta_shape2']].values]
+    permutation_pvalue_threshold_dict = permutation_pvalues_df.T.to_dict()
+    coeqtls['snp_beta_shape1'] = [permutation_pvalue_threshold_dict.get(snp)['beta_shape1'] for snp in coeqtls['snp_eqtlgene'].values]
+    coeqtls['snp_beta_shape2'] = [permutation_pvalue_threshold_dict.get(snp)['beta_shape2'] for snp in coeqtls['snp_eqtlgene']]
+    coeqtls['snp_pvalbeta'] = [permutation_pvalue_threshold_dict.get(snp)['pval_beta'] for snp in coeqtls['snp_eqtlgene']]
+    coeqtls['snp_qval'] = [permutation_pvalue_threshold_dict.get(snp)['qval'] for snp in coeqtls['snp_eqtlgene']]
+    coeqtls['gene2_pthreshold'] = [permutation_pvalue_threshold_dict.get(snp)['threshold_per_betadistribution']
+                                   for snp in coeqtls['snp_eqtlgene']]
+    issig = lambda x:True if x[0] <= x[1] else False
+    coeqtls['gene2_isSig'] = [issig(item) for item in coeqtls[['MetaP', 'gene2_pthreshold']].values]
+    significant_coeqtls = coeqtls[(coeqtls['snp_qval']<=0.05) & (coeqtls['gene2_isSig'])]
+    print('Significant results:', significant_coeqtls.shape[0])
+    coeqtls.to_csv(f'{saveprefix}.all.tsv.gz', sep='\t', compression='gzip')
+    significant_coeqtls.to_csv(f'{saveprefix}.sig.tsv.gz', sep='\t', compression='gzip')
+    return coeqtls
+
+
+if __name__ == '__main__':
+    _ = main()
diff --git a/04_coeqtl_mapping/plot_celltype_overlap_upset.R b/04_coeqtl_mapping/plot_celltype_overlap_upset.R
new file mode 100644
index 0000000..8e5cd48
--- /dev/null
+++ b/04_coeqtl_mapping/plot_celltype_overlap_upset.R
@@ -0,0 +1,46 @@
+# ------------------------------------------------------------------------------
+# Generate an upset plot of overlap between cell types
+# Input: significant co-eQTL results per cell type
+# Output: upset plot
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(UpSetR)
+  
+coeqtls_mono<-fread("coeqtl_mapping/output/filtered_results/UT_monocyte/coeqtls_fullresults_fixed.sig.tsv.gz")
+coeqtls_cd4t<-fread("coeqtl_mapping/output/filtered_results/UT_CD4T/coeqtls_fullresults_fixed.sig.tsv.gz")
+coeqtls_cd8t<-fread("coeqtl_mapping/output/filtered_results/UT_CD8T/coeqtls_fullresults_fixed.sig.tsv.gz")
+coeqtls_nk<-fread("coeqtl_mapping/output/filtered_results/UT_NK/coeqtls_fullresults_fixed.sig.tsv.gz")
+coeqtls_dc<-fread("coeqtl_mapping/output/filtered_results/UT_DC/coeqtls_fullresults_fixed.sig.tsv.gz")
+coeqtls_b<-fread("coeqtl_mapping/output/filtered_results/UT_B/coeqtls_fullresults_fixed.sig.tsv.gz")
+
+pdf(paste0(outdir, "grn_plot_snp_gene_gene.pdf"))
+
+upset(fromList(list(Monocyte = coeqtls_mono$snp_genepair,
+                    `CD4+ T` = coeqtls_cd4t$snp_genepair,
+                    `CD8+ T` = coeqtls_cd8t$snp_genepair,
+                    NK = coeqtls_nk$snp_genepair,
+                    DC = coeqtls_dc$snp_genepair,
+                    B = coeqtls_b$snp_genepair)), 
+      set_size.show = T,set_size.scale_max = 600, 
+      mainbar.y.label = "SNP-Gene-Gene", 
+      nintersects = 40, nsets = 10,
+      text.scale = 1.5)
+
+dev.off()
+
+#Identify all elements that are in at least four of the six cell types
+all_coeqtls<-c(unique(coeqtls_mono$snp_genepair),
+               unique(coeqtls_cd4t$snp_genepair),
+               unique(coeqtls_cd8t$snp_genepair),
+               unique(coeqtls_nk$snp_genepair),
+               unique(coeqtls_dc$snp_genepair),
+               unique(coeqtls_b$snp_genepair))
+
+occurrence<-data.frame(table(all_coeqtls))
+
+#Show all coeQTLs part of at least three different cell types:
+most_occ<-occurrence[occurrence$Freq > 2,]
+#How many of the frequent coeQTls are associated with the RPS26 locus:
+mean(startsWith(as.character(most_occ$all_coeqtls),"rs1131017"))
+
diff --git a/04_coeqtl_mapping/plot_co-eQTL.R b/04_coeqtl_mapping/plot_co-eQTL.R
new file mode 100644
index 0000000..0357de4
--- /dev/null
+++ b/04_coeqtl_mapping/plot_co-eQTL.R
@@ -0,0 +1,152 @@
+############################################################################################################################
+# Code Author: Dylan de Vries
+# Name: plot_co-eQTL.R
+# Function: Plot co-eQTLs
+############################################################################################################################
+#
+# Libraries
+#
+############################################################################################################################
+library(data.table)
+library(ggplot2)
+library(ggbeeswarm)
+library(gridExtra)
+
+############################################################################################################################
+#
+# Functions
+#
+############################################################################################################################
+# Name: get.expression.data
+# Function: Get the expression data and calculate the co-expression for plotting purposes
+# Input:
+#   Name 	            Type          Description
+#   sample				character	  sample name
+#	cell.type			character	  cell type to get the data for
+# 	gene1 				character 	  first gene to get data for
+# 	gene2 				character 	  second gene to get data for
+# 	genotype			character 	  the genotype of the co-eQTL for this sample 
+#
+# Output:
+# 	A list with two data frames of one sample. The first is for making the boxplots and the second for the personalized expression regression plot
+get.expression.data <- function(sample, cell.type, gene1, gene2, genotype){
+	sample.gene1.expression <- data@assays$SCT@data[gene1, rownames(data@meta.data[data@meta.data$cell_type_lowerres == cell.type & data@meta.data$assignment == sample,])]
+	sample.gene2.expression <- data@assays$SCT@data[gene2, rownames(data@meta.data[data@meta.data$cell_type_lowerres == cell.type & data@meta.data$assignment == sample,])]
+
+	sample.co.expression <- cor(sample.gene1.expression, sample.gene2.expression, method="spearman")
+	expr.plot.data <- data.frame(gene1.expression=sample.gene1.expression, gene2.expression=sample.gene2.expression, sample=sample, genotype=genotype)
+	plot.data <- list(sample.co.expression, expr.plot.data)
+	return(plot.data)
+}
+
+# Name: prepare.plot.data
+# Function: Combine the data of all samples into data.frames
+# Input:
+#   Name 	            Type          Description
+# 	gene1 				character 	  first gene to get data for
+# 	gene2 				character 	  second gene to get data for
+# 	SNP.name			character 	  the rs-ID for the co-eQTL SNP
+#	cell.type			character	  cell type to get the data for
+#
+# Output:
+# 	A list with two data frames. The first is for making the boxplots and the second for the personalized expression regression plot
+prepare.plot.data <- function(gene1, gene2, SNP.name, cell.type){
+	co.expressions <- c()
+	genotypes <- c()
+	expr.plot.data <- data.frame(gene1.expression=numeric(0), gene2.expression=numeric(0), sample=character(0), genotype=character(0))
+	for (sample in samples){
+		genotypes <- c(genotypes, genotypes_all[SNP.name, sample])
+		plot.data <- get.expression.data(sample, cell.type, gene1, gene2, genotypes_all[SNP.name, sample])
+		expr.plot.data <- rbind(expr.plot.data, plot.data[[2]])
+		co.expressions <- c(co.expressions, plot.data[[1]])
+	}
+	plot.data <- data.frame(co.expression=co.expressions, sample=samples, genotype=genotypes)
+	combined.plot.data <- list(plot.data, expr.plot.data)
+	return(combined.plot.data)
+}
+
+# Name: plot.co.eQTL.boxplot
+# Function: Make a plot for the co-eQTL
+# Input:
+#   Name 	            Type          Description
+#   plot.data 			data.frame 	  the data for the boxplot
+#   expr.plot.data		data.frame 	  the data for the expression regression plot
+# 	gene1 				character 	  first gene to get data for
+# 	gene2 				character 	  second gene to get data for
+# 	SNP.name			character 	  the rs-ID for the co-eQTL SNP
+#	cell.type			character	  cell type to get the data for
+#	meta.z				numeric		  meta z-score
+#	QTL.type			character	  indicates whether it's amongst the strongest, middle or weakest co-eQTLs
+#	QTL.type.index		character	  the index of the co-eQTL within its type
+#
+# Output:
+# 	A list with two data frames. The first is for making the boxplots and the second for the personalized expression regression plot
+plot.co.eQTL.boxplot <- function(plot.data, expr.plot.data, gene1, gene2, SNP.name, cell.type, meta.z, QTL.type, QTL.type.index){
+	genotype.colors <- c("#57a350", "#fd7600", "#383bfe", "white")
+	names(genotype.colors) <- c("0/0", "0/1", "1/1", "white")
+
+	sample.color <- c(colorRampPalette(c("#9efc95", "#57a350"))(length(which(plot.data$genotype=="0/0"))),
+		colorRampPalette(c("#fabb84", "#fd7600"))(length(which(plot.data$genotype=="0/1"))),
+		colorRampPalette(c("#acadfc", "#383bfe"))(length(which(plot.data$genotype=="1/1"))))
+	names(sample.color) <- c(as.character(plot.data$sample[plot.data$genotype == "0/0"]), as.character(plot.data$sample[plot.data$genotype == "0/1"]), as.character(plot.data$sample[plot.data$genotype == "1/1"]))
+
+	expr.plot <- ggplot(expr.plot.data, aes(x=gene1.expression, y=gene2.expression, fill=sample, color=sample)) + geom_point(size=0.5) +
+		geom_smooth(method = "lm", fullrange = T, se=F) +
+		scale_fill_manual(values=sample.color) +
+		scale_color_manual(values=sample.color) +
+		xlab(paste0(gene1, " expression")) +
+		ylab(paste0(gene2, " expression")) +
+		ggtitle(paste0(SNP.name, " effect on ", gene1, " - ", gene2, "\nco-expression")) +
+		guides(fill=FALSE, color=FALSE) +
+		theme(axis.text.x = element_text(angle = 90, hjust = 1, size=7), panel.border = element_rect(color="black", fill=NA, size=1.1), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), strip.background = element_rect(colour="white", fill="white"))
+
+	box.plot <- ggplot(plot.data) + geom_boxplot(aes(x=genotype, y=co.expression, fill=genotype), outlier.shape=NA, alpha=0.6) + 
+		geom_quasirandom(aes(x=genotype, y=co.expression, color=genotype, fill="white"), pch=21, size=2, alpha=1, dodge.width=0.4, alpha=0.6) +
+		scale_fill_manual(values=genotype.colors) + 
+		scale_color_manual(values=genotype.colors) +
+		xlab("Genotype") +
+		ylab(paste0(gene1, " - ", gene2, " co-expression")) +
+		ggtitle(paste0(SNP.name, " co-eQTL\n", QTL.type, " ", QTL.type.index)) +
+		guides(fill=FALSE, color=FALSE) +
+		theme(axis.text.x = element_text(angle = 90, hjust = 1, size=7), panel.border = element_rect(color="black", fill=NA, size=1.1), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), strip.background = element_rect(colour="white", fill="white"))
+
+	pdf(paste0("/groups/umcg-bios/tmp01/projects/1M_cells_scRNAseq/ongoing/co-eQTLs/plots/", cell.type, "/", cell.type, "_co-eQTL_", SNP.name, "_", gene1, "-", gene2, ".pdf"))
+	grid.arrange(expr.plot, box.plot, ncol=2)
+	dev.off()
+}
+
+############################################################################################################################
+#
+# Main code
+#
+############################################################################################################################
+data <- readRDS("/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/seurat_objects/1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.rds")
+vcf <- fread('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/genotypes/LL_trityper_plink_converted.vcf.gz')
+target.QTLs <- read.table("/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/filtered_results/UT_monocyte/coeqtls_fullresults.sig.tsv.gz", header=T, sep="\t", stringsAsFactors=F)
+target.QTLs <- target.QTLs[order(abs(target.QTLs$MetaPZ), decreasing=T),]
+genotypes_all <- as.data.frame(vcf[, 10:ncol(vcf)])
+rownames(genotypes_all) <- vcf$ID
+
+#Get the 10 strongest, 10 middling and 10 weakest of the input co-eQTLs
+QTL.selection <- target.QTLs[c(1:10, floor(nrow(target.QTLs)/2):(floor(nrow(target.QTLs)/2)+10), (nrow(target.QTLs)-10):nrow(target.QTLs)),]
+samples <- unique(data@meta.data$assignment)
+
+for (QTL.index in 1:nrow(QTL.selection)){
+	print(QTL.index)
+	if (QTL.index <= 10){
+		type <- "strong"
+		QTL.type.index <- QTL.index
+	} else if (QTL.index <= 20){
+		type <- "medium"
+		QTL.type.index <- QTL.index - 10
+	} else {
+		type <- "poor"
+		QTL.type.index <- QTL.index - 20
+	}
+	genes <- unlist(strsplit(QTL.selection$Gene[QTL.index], ";"))
+	combined.plot.data <- prepare.plot.data(genes[1], genes[2], QTL.selection$SNP[QTL.index], "monocyte")
+	plot.data <- combined.plot.data[[1]]
+	expr.plot.data <- combined.plot.data[[2]]
+
+	plot.co.eQTL.boxplot(plot.data, expr.plot.data, genes[1], genes[2], QTL.selection$SNP[QTL.index], "monocyte", QTL.selection$MetaPZ[QTL.index], type, QTL.type.index)
+}
diff --git a/04_coeqtl_mapping/plot_effect_concordance_across_cohorts.R b/04_coeqtl_mapping/plot_effect_concordance_across_cohorts.R
new file mode 100644
index 0000000..b876047
--- /dev/null
+++ b/04_coeqtl_mapping/plot_effect_concordance_across_cohorts.R
@@ -0,0 +1,73 @@
+################################################################################
+# Compare effect sizes (Z-scores) calculated in each individual dataset  
+# (before the meta-analysis)
+# Input: coeqtls results of the respective cell type
+# Output: pairwise plot showing the differences for each combination of cohorts
+################################################################################
+
+library(GGally) #to generate pairwise comparison plots
+library(viridis)
+
+coeqtl_dir<-"coeqtl_mapping/output/filtered_results/"
+plot_dir<-"coeqtl_interpretation/plots_filtered/"  
+
+cell_type<-"CD4T"
+
+# Load current set of coeQTL
+coeqtls<-fread(paste0(coeqtl_dir,"UT_",
+                      cell_type,"/coeqtls_fullresults.all.tsv.gz"))
+coeqtls$gene1<-gsub(";.*","",coeqtls$Gene)
+coeqtls$gene2<-gsub(".*;","",coeqtls$Gene)
+
+# Gene 1 and 2 should be ordered alphabetically, but there is an issue regarding
+# small and capital letters (so order them again!)
+coeqtls$swap<-ifelse(coeqtls$gene1 > coeqtls$gene2,coeqtls$gene1,coeqtls$gene2)
+coeqtls$gene1<-ifelse(coeqtls$gene1 > coeqtls$gene2,coeqtls$gene2,coeqtls$gene1)
+coeqtls$gene2<-coeqtls$swap
+coeqtls$swap<-NULL
+
+# Filter for significant coeQTLs
+sign_coeqtls<-coeqtls[coeqtls$gene2_isSig == "TRUE" &
+                        coeqtls$snp_qval <= 0.05,]
+
+print(paste(nrow(sign_coeqtls),"significant coeQTLs from",
+            nrow(coeqtls),"pairs"))
+print(paste("CoeQTLs consisting of:",
+            length(unique(sign_coeqtls$GeneSymbol)), "unique gene pairs from",
+            length(unique(c(sign_coeqtls$gene1,sign_coeqtls$gene2))),"unique genes",
+            "and",length(unique(sign_coeqtls$SNP)),"unique SNPs"))
+
+# Check Z score distribution
+z_scores<-strsplit(sign_coeqtls$`DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)`,
+                   split=";")
+z_scores<-matrix(as.numeric(unlist(z_scores)),ncol=4,byrow=TRUE)
+z_scores<-as.data.frame(z_scores)
+colnames(z_scores)<-c("ng","onemillionv2","onemillionv3","stemiv2")
+z_scores$coeqtl<-sign_coeqtls$snp_genepair
+z_scores$meta_z<-sign_coeqtls$MetaPZ
+
+#Flip the Z-scores so that AF is always representing the minor allele
+z_scores$AF<-sign_coeqtls$SNPEffectAlleleFreq
+for(colN in c("ng","onemillionv2","onemillionv3","stemiv2","meta_z")){
+  z_scores[,colN]<-ifelse(z_scores$AF>=0.5,z_scores[,colN]*(-1),z_scores[,colN])
+}
+
+#Rename Z score columns
+colnames(z_scores)[1:4]<-c("van der Wijst","Oelen (v2)","Oelen (v3)", "van Blokland (v2)")
+z_scores<-z_scores[,c("Oelen (v2)","Oelen (v3)", "van Blokland (v2)",
+                      "van der Wijst","coeqtl","meta_z","AF")]
+#Plot comparison of Z scores between cohorts
+lowerfun <- function(data,mapping){
+  ggplot(data = data, mapping = mapping)+
+    geom_bin2d()+
+    scale_fill_viridis("Density",breaks=c(2,7),labels = c("Low", "High"))+
+    geom_hline(yintercept=0)+geom_vline(xintercept=0)
+}
+
+g<-ggpairs(z_scores[1:4],
+           lower=list(continuous=wrap(lowerfun)),
+           legend=c(2,1))+
+  theme(legend.position = "bottom")
+ggsave(g,file=paste0(plot_dir,cell_type,
+                     "_zscore_dist_cohorts.pdf"),
+       height=6,width=6)
\ No newline at end of file
diff --git a/04_coeqtl_mapping/plot_example_imputed_zero.ipynb b/04_coeqtl_mapping/plot_example_imputed_zero.ipynb
new file mode 100644
index 0000000..fc28e77
--- /dev/null
+++ b/04_coeqtl_mapping/plot_example_imputed_zero.ipynb
@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "import re\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "from scipy.stats import spearmanr, pearsonr\n",
+    "from scipy.stats import t, norm\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "\n",
+    "def get_time(x):\n",
+    "    if x == 'UT':\n",
+    "        return x\n",
+    "    else:\n",
+    "        pattern = re.compile(r'\\d+h')\n",
+    "        return re.findall(pattern, x)[0]\n",
+    "\n",
+    "\n",
+    "class DATASET:\n",
+    "    def __init__(self, datasetname):\n",
+    "        self.name = datasetname\n",
+    "        self.path_prefix = Path(\"./seurat_objects\")\n",
+    "        self.information = self.get_information()\n",
+    "    def get_information(self):\n",
+    "        if self.name == 'onemillionv2':\n",
+    "            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'\n",
+    "            self.individual_id_col = 'assignment'\n",
+    "            self.timepoint_id_col = 'time'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 'UT',\n",
+    "                                     'stimulated': '3h'}\n",
+    "        elif self.name == 'onemillionv3':\n",
+    "            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'\n",
+    "            self.individual_id_col = 'assignment'\n",
+    "            self.timepoint_id_col = 'time'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 'UT',\n",
+    "                                     'stimulated': '3h'}\n",
+    "        elif self.name == 'stemiv2':\n",
+    "            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'\n",
+    "            self.individual_id_col = 'assignment.final'\n",
+    "            self.timepoint_id_col = 'timepoint.final'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 't8w',\n",
+    "                                     'stimulated': 'Baseline'}\n",
+    "        elif self.name == 'ng':\n",
+    "            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'\n",
+    "            self.individual_id_col = 'snumber'\n",
+    "            self.celltype_id = 'cell_type_mapped_to_onemillion'\n",
+    "        else:\n",
+    "            raise IOError(\"Dataset name not understood.\")\n",
+    "    def load_dataset(self):\n",
+    "        self.get_information()\n",
+    "        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')\n",
+    "        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)\n",
+    "        if self.name.startswith('onemillion'):\n",
+    "            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]\n",
+    "        elif self.name == 'ng':\n",
+    "            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',\n",
+    "                               'other T': 'otherT', 'other': 'other', 'B': 'B'}\n",
+    "            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in\n",
+    "                                                          self.data_sc.obs['predicted.celltype.l1']]\n",
+    "    def get_cMono_ncMono(self):\n",
+    "        def tell_cmono_foronemillion(x):\n",
+    "            if x == 'mono 1' or x == 'mono 3' or x == 'mono 4':\n",
+    "                return 'cMono'\n",
+    "            elif x == 'mono 2':\n",
+    "                return 'ncMono'\n",
+    "        if self.name.startswith('onemillion'):\n",
+    "            self.data_sc.obs['sub_monocytes'] = [tell_cmono_foronemillion(x) for x in\n",
+    "                                                 self.data_sc.obs['cell_type']]\n",
+    "            self.cmono = self.data_sc[self.data_sc.obs['sub_monocytes'] == 'cMono']\n",
+    "            self.ncmono = self.data_sc[self.data_sc.obs['sub_monocytes'] == 'ncMono']\n",
+    "        elif self.name.startswith('stemi'):\n",
+    "            self.cmono = self.data_sc[self.data_sc.obs['cell_type'] == 'cMono']\n",
+    "            self.ncmono = self.data_sc[self.data_sc.obs['cell_type'] == 'ncMono']\n",
+    "        elif self.name == 'ng':\n",
+    "            self.cmono = self.data_sc[self.data_sc.obs['predicted.celltype.l2'] == 'CD14 Mono']\n",
+    "            self.ncmono = self.data_sc[self.data_sc.obs['predicted.celltype.l2'] == 'CD16 Mono']\n",
+    "        else:\n",
+    "            raise IOError(\"Dataset name not understood.\")\n",
+    "\n",
+    "example_savedir = Path(\n",
+    "    \"/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/examples\"\n",
+    ")\n",
+    "\n",
+    "import subprocess\n",
+    "bashfile_path = '/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/bios/select_snps_from_vcf.sh'\n",
+    "def get_snps_from_vcffile(bashfile_path, vcf_path, snps_path, savepath):\n",
+    "    response = subprocess.run([bashfile_path, vcf_path, snps_path, savepath])\n",
+    "    print(response)\n",
+    "    return None\n",
+    "\n",
+    "# sample id mapping\n",
+    "gtefile = pd.read_csv(\n",
+    "    '/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/summary/gte-fix.tsv',\n",
+    "    sep='\\t'\n",
+    ")\n",
+    "gte_dict = gtefile.set_index(\"expressionsampleID\")[\"genotypesampleID\"].T.to_dict()\n",
+    "\n",
+    "\n",
+    "def corr_to_z(coef, num):\n",
+    "    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))\n",
+    "    prob = t.cdf(t_statistic, num - 2)\n",
+    "    z_score = norm.ppf(prob)\n",
+    "    positive_coef_probs = 1 - prob\n",
+    "    positive_coef_probs[coef < 0] = 0\n",
+    "    negative_coef_probs = prob\n",
+    "    negative_coef_probs[coef > 0] = 0\n",
+    "    probs = negative_coef_probs + positive_coef_probs\n",
+    "    return z_score, probs\n",
+    "\n",
+    "\n",
+    "def get_individual_networks_selected_genepairs(data_df, data_sc, individual_colname, genepair, fillna=False):\n",
+    "#     data_df = pd.DataFrame(data=data_sc.X.toarray(),\n",
+    "#                            index=data_sc.obs.index,\n",
+    "#                            columns=data_sc.var.index)\n",
+    "    gene1, gene2 = genepair.split(';')\n",
+    "    sorted_genepair = [';'.join(sorted([gene1, gene2]))]\n",
+    "    coef_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    coef_p_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    zscore_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    zscore_p_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    data_selected_df = data_df[[gene1, gene2]]\n",
+    "    print(\n",
+    "        f\"Calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals and;\\n{genepair}\"\n",
+    "    )\n",
+    "    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):\n",
+    "        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]\n",
+    "        if cell_num > 10:\n",
+    "            individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]\n",
+    "            individual_coefs, individual_coef_ps = spearmanr(individual_df.values, axis=0)\n",
+    "            if data_selected_df.shape[1] == 2:\n",
+    "                individual_coefs_flatten = pd.DataFrame(data = [individual_coefs],\n",
+    "                                                        index = sorted_genepair)\n",
+    "                individual_coef_ps_flatten = \\\n",
+    "                pd.DataFrame(data=[individual_coef_ps],\n",
+    "                             index=sorted_genepair)\n",
+    "            else:\n",
+    "                individual_coefs_flatten = pd.DataFrame(\n",
+    "                    data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],\n",
+    "                    index=sorted_genepair).loc[sorted_genepair]\n",
+    "                individual_coef_ps_flatten = \\\n",
+    "                pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],\n",
+    "                             index=sorted_genepair).loc[sorted_genepair]\n",
+    "            coef_df[ind_id] = individual_coefs_flatten\n",
+    "            coef_p_df[ind_id] = individual_coef_ps_flatten\n",
+    "            try:\n",
+    "                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(\n",
+    "                    individual_coefs_flatten.values,\n",
+    "                    cell_num\n",
+    "                )\n",
+    "                zscore_df[ind_id] = individual_zscores_flatten\n",
+    "                zscore_p_df[ind_id] = individual_zscore_ps_flatten\n",
+    "            except:\n",
+    "                continue\n",
+    "        else:\n",
+    "            print(\"Deleted this individual because of low cell number\", cell_num)\n",
+    "    if fillna:\n",
+    "        zscore_df = zscore_df.fillna(0)\n",
+    "    return data_selected_df, zscore_df, zscore_p_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset onemillionv2 from /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/seurat_objects 1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad\n"
+     ]
+    }
+   ],
+   "source": [
+    "datasetname = 'onemillionv2'\n",
+    "dataset = DATASET(datasetname)\n",
+    "dataset.load_dataset()\n",
+    "data_sc = dataset.data_sc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CompletedProcess(args=['/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/bios/select_snps_from_vcf.sh', '/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/genotypevcfs/chr1/GenotypeData.vcf.gz', PosixPath('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/examples/snplist.rs221045'), PosixPath('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/examples/rs221045.vcf')], returncode=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>#CHROM</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>REF</th>\n",
+       "      <th>ALT</th>\n",
+       "      <th>QUAL</th>\n",
+       "      <th>FILTER</th>\n",
+       "      <th>INFO</th>\n",
+       "      <th>FORMAT</th>\n",
+       "      <th>1_LLDeep_1191</th>\n",
+       "      <th>...</th>\n",
+       "      <th>s21</th>\n",
+       "      <th>s43</th>\n",
+       "      <th>s24</th>\n",
+       "      <th>s23</th>\n",
+       "      <th>s45</th>\n",
+       "      <th>s26</th>\n",
+       "      <th>s25</th>\n",
+       "      <th>s28</th>\n",
+       "      <th>s27</th>\n",
+       "      <th>s29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>16530049</td>\n",
+       "      <td>rs221045</td>\n",
+       "      <td>T</td>\n",
+       "      <td>C</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>GT:DS</td>\n",
+       "      <td>0/0:0.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>0/0:0.010000000000000009</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 182 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   #CHROM       POS        ID REF ALT QUAL FILTER INFO FORMAT 1_LLDeep_1191  \\\n",
+       "0       1  16530049  rs221045   T   C    .      .    .  GT:DS      0/0:0.03   \n",
+       "\n",
+       "   ...      s21                       s43      s24      s23      s45      s26  \\\n",
+       "0  ...  0/1:1.0  0/0:0.010000000000000009  0/1:1.0  0/0:0.0  0/0:0.0  1/1:2.0   \n",
+       "\n",
+       "       s25      s28      s27      s29  \n",
+       "0  0/0:0.0  0/1:1.0  0/0:0.0  0/1:1.0  \n",
+       "\n",
+       "[1 rows x 182 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "celltype = 'monocyte'\n",
+    "snp_id = 'rs221045'\n",
+    "chromosome = '1'\n",
+    "snp_vcf_path = example_savedir/f'{snp_id}.vcf'\n",
+    "with open(example_savedir/f'snplist.{snp_id}', 'w') as f:\n",
+    "    f.write(f'{snp_id}\\n')\n",
+    "vcf_path = f'/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/genotypevcfs/chr{chromosome}/GenotypeData.vcf.gz'\n",
+    "get_snps_from_vcffile(bashfile_path, vcf_path, example_savedir/f'snplist.{snp_id}', snp_vcf_path)\n",
+    "gt = pd.read_csv(snp_vcf_path, sep='\\t', skiprows=6)\n",
+    "gt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating networks for 72 individuals and;\n",
+      "AC005076.5;ARHGEF19\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/72 [00:00<?, ?it/s]/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/scipy/stats/stats.py:4264: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
+      "  warnings.warn(SpearmanRConstantInputWarning())\n",
+      "100%|██████████| 72/72 [00:00<00:00, 210.51it/s]\n",
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 42.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Not Imputed SpearmanrResult(correlation=-0.028018282506059713, pvalue=0.8942369051146191)\n",
+      "Imputed SpearmanrResult(correlation=-0.24638574744096847, pvalue=0.03833253459364005)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# genepair = 'RP1-29C18.10;ZNF501'\n",
+    "# genepair = 'CCDC15;UNC5B'\n",
+    "# genepair = 'GSTM3;RP1-29C18.10'\n",
+    "# genepair = 'MMEL1;SARS2'\n",
+    "genepair = 'AC005076.5;ARHGEF19'\n",
+    "gene1, gene2 = genepair.split(';')\n",
+    "\n",
+    "if datasetname == 'ng':\n",
+    "    ut_celltype = data_sc[data_sc.obs[dataset.celltype_id]==celltype]\n",
+    "else:\n",
+    "    ut_celltype = data_sc[(data_sc.obs[dataset.celltype_id]==celltype) &\n",
+    "                          (data_sc.obs[dataset.timepoint_id_col]==dataset.chosen_condition['UT'])]\n",
+    "\n",
+    "ut_celltype_df = pd.DataFrame(data=ut_celltype.X.toarray(),\n",
+    "                              columns=ut_celltype.var.index,\n",
+    "                              index=ut_celltype.obs.index)\n",
+    "selected_expression_df, ut_zscore_df, ut_zscore_p_df = get_individual_networks_selected_genepairs(\n",
+    "    data_df = ut_celltype_df,\n",
+    "    data_sc = ut_celltype,\n",
+    "    individual_colname = dataset.individual_id_col,\n",
+    "    genepair = genepair,\n",
+    "    fillna=False\n",
+    ")\n",
+    "\n",
+    "ut_t = ut_zscore_df.T\n",
+    "ut_t['gt_sampleid'] = [gte_dict.get(name) for name in ut_t.index]\n",
+    "ut_t = ut_t.set_index('gt_sampleid')\n",
+    "common_individuals = list(set(gt.columns) & set(ut_t.index))\n",
+    "gt_t = gt[common_individuals].T\n",
+    "gt_t['genotype'] = [item.split(':')[0].count('1') for item in gt_t[0]]\n",
+    "concat_df = pd.concat([gt_t, ut_t], axis=1).replace([np.inf, -np.inf], np.nan).dropna()\n",
+    "print('Not Imputed', spearmanr(concat_df['genotype'], concat_df[genepair]))\n",
+    "\n",
+    "ut_t_imputed = ut_zscore_df.fillna(0).T\n",
+    "ut_t_imputed['gt_sampleid'] = [gte_dict.get(name) for name in ut_t_imputed.index]\n",
+    "ut_t_imputed = ut_t_imputed.set_index('gt_sampleid')\n",
+    "common_individuals_imputed = list(set(gt.columns) & set(ut_t_imputed.index))\n",
+    "gt_t_imputed = gt[common_individuals_imputed].T\n",
+    "gt_t_imputed['genotype'] = [item.split(':')[0].count('1') for item in gt_t_imputed[0]]\n",
+    "concat_imputed_df = pd.concat([gt_t_imputed, ut_t_imputed], axis=1).replace([np.inf, -np.inf], np.nan).dropna()\n",
+    "print('Imputed', spearmanr(concat_imputed_df['genotype'], concat_imputed_df[genepair]))\n",
+    "\n",
+    "# dosage_dict = gt_t['genotype'].T.to_dict()\n",
+    "# selected_expression_df_withsample = pd.concat([selected_expression_df,\n",
+    "#                                                ut_celltype.obs[[dataset.individual_id_col]]],\n",
+    "#                                              axis=1)\n",
+    "# selected_expression_df_withsample['gt_sampleid'] = [gte_dict.get(name) for name in\n",
+    "#                                                     selected_expression_df_withsample[dataset.individual_id_col]]\n",
+    "# selected_expression_df_withsample['genotype'] = [dosage_dict.get(gt_sampleid) for gt_sampleid in\n",
+    "#                                                selected_expression_df_withsample['gt_sampleid']]\n",
+    "\n",
+    "sns.set_style('white')\n",
+    "refallele = gt['REF'].values[0]\n",
+    "altallele = gt['ALT'].values[0]\n",
+    "snp_name = f'{snp_id}_{altallele}'\n",
+    "\n",
+    "_, axes = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
+    "ax1, ax2 = axes\n",
+    "\n",
+    "im_coef, im_p = spearmanr(concat_imputed_df['genotype'], concat_imputed_df[genepair])\n",
+    "sns.violinplot(x=concat_imputed_df['genotype'],  \n",
+    "               y=concat_imputed_df[genepair], \n",
+    "               ax=ax1,\n",
+    "              inner=None)\n",
+    "sns.swarmplot(x=concat_imputed_df['genotype'],  \n",
+    "               y=concat_imputed_df[genepair], \n",
+    "               ax=ax1,\n",
+    "              color='black')\n",
+    "ax1.set_title(f'Imputed r={im_coef:.2f}; pvalue {im_p:.4f}')\n",
+    "# ax1.set_xticklabels([f'{refallele}{refallele}', \n",
+    "#                      f'{refallele}{altallele}',\n",
+    "#                      f'{altallele}{altallele}'])\n",
+    "ax1.set_xlabel(snp_id)\n",
+    "\n",
+    "coef, p = spearmanr(concat_df['genotype'], concat_df[genepair])\n",
+    "sns.violinplot(x=concat_df['genotype'],  \n",
+    "               y=concat_df[genepair], \n",
+    "               ax=ax2,\n",
+    "              inner=None)\n",
+    "sns.swarmplot(x=concat_df['genotype'],  \n",
+    "               y=concat_df[genepair], \n",
+    "               ax=ax2,\n",
+    "              color='black')\n",
+    "ax2.set_xlabel('')\n",
+    "ax2.set_title(f'Not Imputed r={coef:.2f}; pvalue {p:.4f}')\n",
+    "# ax2.set_xticklabels([f'{refallele}{refallele}', \n",
+    "#                      f'{refallele}{altallele}',\n",
+    "#                      f'{altallele}{altallele}'])\n",
+    "ax2.set_xlabel(snp_id)\n",
+    "plt.savefig(example_savedir/f'{snp_name}_ref{refallele}_alt{altallele}_{gene1}_{gene2}.{celltype}_{datasetname}.full.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 42.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, axes = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
+    "ax1, ax2 = axes\n",
+    "\n",
+    "im_coef, im_p = spearmanr(concat_imputed_df['genotype'], concat_imputed_df[genepair])\n",
+    "# sns.violinplot(x=concat_imputed_df['genotype'],  \n",
+    "#                y=concat_imputed_df[genepair], \n",
+    "#                ax=ax1,\n",
+    "#               inner=None)\n",
+    "sns.swarmplot(x=concat_imputed_df['genotype'],  \n",
+    "               y=concat_imputed_df[genepair], \n",
+    "               ax=ax1,\n",
+    "              color='black')\n",
+    "sns.regplot(x=concat_imputed_df['genotype'],  \n",
+    "               y=concat_imputed_df[genepair], \n",
+    "               ax=ax1, scatter=False)\n",
+    "ax1.set_title(f'Imputed r={im_coef:.2f}; pvalue {im_p:.4f}')\n",
+    "ax1.set_xticklabels([f'{refallele}{refallele}', \n",
+    "                     f'{refallele}{altallele}',\n",
+    "                     f'{altallele}{altallele}'])\n",
+    "ax1.set_xlabel(snp_id)\n",
+    "\n",
+    "coef, p = spearmanr(concat_df['genotype'], concat_df[genepair])\n",
+    "# sns.violinplot(x=concat_df['genotype'],  \n",
+    "#                y=concat_df[genepair], \n",
+    "#                ax=ax2,\n",
+    "#               inner=None)\n",
+    "sns.swarmplot(x=concat_df['genotype'],  \n",
+    "               y=concat_df[genepair], \n",
+    "               ax=ax2,\n",
+    "              color='black')\n",
+    "sns.regplot(x=concat_df['genotype'],  \n",
+    "               y=concat_df[genepair], \n",
+    "               ax=ax2, scatter=False)\n",
+    "ax2.set_xlabel('')\n",
+    "ax2.set_title(f'Not Imputed r={coef:.2f}; pvalue {p:.4f}')\n",
+    "ax2.set_xticklabels([f'{refallele}{refallele}', \n",
+    "                     f'{refallele}{altallele}',\n",
+    "                     f'{altallele}{altallele}'])\n",
+    "ax2.set_xlabel(snp_id)\n",
+    "plt.savefig(example_savedir/f'{snp_name}_ref{refallele}_alt{altallele}_{gene1}_{gene2}.{celltype}_{datasetname}.full.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/examples/rs221045_C_refT_altC_AC005076.5_ARHGEF19.monocyte_onemillionv2.full.pdf')"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_savedir/f'{snp_name}_ref{refallele}_alt{altallele}_{gene1}_{gene2}.{celltype}_{datasetname}.full.pdf'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/04_coeqtl_mapping/power_analysis_coeqtls.R b/04_coeqtl_mapping/power_analysis_coeqtls.R
new file mode 100644
index 0000000..ff2e515
--- /dev/null
+++ b/04_coeqtl_mapping/power_analysis_coeqtls.R
@@ -0,0 +1,79 @@
+################################################################################
+# Evaluate how number of triplets decreases the power to detect
+# co-expression QTLs by calculating the power dependent on the sample size
+# (N=173), heritability (Rsq: 0.1-0.3), Bonferroni multiple testing correction,
+# and different number of tests
+# The number of tests is estimated based on different expression cutoffs for
+# the Oelen v3 dataset, assuming that all pairwise combinations are tested for
+# all genes above the respective cutoff and one SNP per pair
+# Input: Seurat object with data from Oelen v3
+# Output: line plot visualizing power for different number of tests
+################################################################################
+
+library(Seurat)
+library(scPower)
+library(ggplot2)
+
+theme_set(theme_bw())
+
+################################################################################
+# Getting expression distribution for Oelen v3 dataset
+################################################################################
+
+#Load complete seurat object
+seurat<-readRDS("seurat_objects/1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.rds")
+
+#Filter for monocytes and UT timepoint
+seurat<-seurat[,seurat$cell_type_lowerres == "monocyte"]
+seurat<-seurat[,seurat$timepoint == "UT"]
+
+#Calculate for each gene the non-zero ratio
+nonzero_ratio<-rowMeans(as.matrix(seurat@assays$SCT@counts)>0)
+
+#Get cumulative ratio
+thresholds<-seq(0,1,0.05)
+num_genes<-sapply(thresholds,function(i)sum(nonzero_ratio>i))
+
+#Save results in a file
+nonzero_count<-data.frame(nonzero_ratio=thresholds,num_genes)
+
+################################################################################
+# Performing power calculation
+################################################################################
+
+#Samples in meta-analysis
+nSamples<-173
+
+bonfLevel<-function(nTests){
+  return(0.05/nTests)
+}
+
+#Test different heritabilities
+Rsq<-seq(0.1,0.3,0.05)
+
+#Number tests
+nonzero_count$genepairs<-nonzero_count$num_genes*(nonzero_count$num_genes-1)/2
+
+res<-NULL
+for(her in Rsq){
+  for(i in 1:(nrow(nonzero_count)-1)){
+    res<-rbind(res,
+               data.frame(her,
+                          numTests=nonzero_count$genepairs[i],
+                          cutoff=nonzero_count$nonzero_ratio[i],
+                          power=scPower:::power.eqtl.ftest(her,
+                                        bonfLevel(nonzero_count$genepairs[i]),
+                                                           nSamples)))
+  }
+}
+
+#Plot results
+g<-ggplot(res,aes(x=numTests,y=power,color=as.factor(her)))+
+  geom_line()+
+  scale_color_discrete("Heritability")+
+  scale_x_log10()+
+  xlab("Number tests")+ylab("Power")
+print(g)
+ggsave(g,file="power_calculation/power_effect_nonzeroratio.png",
+       height=5,width=6)
+
diff --git a/04_coeqtl_mapping/prepare_for_rb_calculation.py b/04_coeqtl_mapping/prepare_for_rb_calculation.py
new file mode 100644
index 0000000..9eaf191
--- /dev/null
+++ b/04_coeqtl_mapping/prepare_for_rb_calculation.py
@@ -0,0 +1,307 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from scipy.stats import pearsonr
+import argparse
+
+
+def argumentsparser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--filtertype', type=str, dest='filtertype')
+    return parser
+
+def prepare_for_rb_BIOS_replication(celltype, filtertype, bios_replication_type='onlyRNAAlignMetrics'):
+    '''
+    Rb Calculation preparation for BIOS replication
+    '''
+    workdir = Path("./coeqtl_mapping")
+    coeqtl_path = workdir/f'output/{filtertype}/UT_{celltype}/coeqtls_fullresults_fixed.sig.withbios{bios_replication_type}.tsv.gz'
+    coeqtl_df = pd.read_csv(coeqtl_path, sep='\t', compression='gzip')
+    coeqtl_df['theta'] = 0
+    def flip_direction(allele1, allele2, coef2):
+        if allele1 == allele2:
+            return coef2
+        else:
+            return -1*coef2
+    coeqtl_df['flipped_bios_beta'] = [flip_direction(item[0],
+                                                     item[1],
+                                                     item[2]) for item in
+                                      coeqtl_df[['SNPEffectAllele',
+                                                 'assessed_allele_bios',
+                                                 'coef_bios']].values]
+    coeqtl_df[['snp_genepair', 'snp_eqtlgene',
+               'flipped_bios_beta', 'std err_bios',
+               'MetaBeta', 'MetaSE', 'theta']].dropna().to_csv(
+        workdir/f'bios/{bios_replication_type}/{filtertype}/UT_{celltype}/replication_parameters.csv'
+    )
+    return coeqtl_df
+
+
+def find_gene2(genepair, eqtlgene):
+    gene1, gene2 = genepair.split(';')
+    if gene1 == eqtlgene:
+        return gene2
+    else:
+        return gene1
+
+
+def flip_direction(df, flipcol, allele1_col, allele2_col):
+    df = df.rename({flipcol: f'{flipcol}_ori'}, axis=1)
+    def flip(x1, x2, x3):
+        if not pd.isnull(x1):
+            if x2 == x3:
+                return x1
+            else:
+                return -x1
+        else:
+            return x1
+    df[f'{flipcol}'] = [flip(score, allele1, allele2) for (score, allele1,allele2)
+                        in df[[f'{flipcol}_ori', allele1_col, allele2_col]].values]
+    return df
+
+
+# coeQTLs
+args = argumentsparser().parse_args()
+filtertype = args.filtertype
+workdir = Path("/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping")
+celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'B', 'NK']
+for celltype_replication in celltypes:
+    print(f"Discovery: {celltype_replication}")
+    replication_coeqtl_path = workdir / f'output/{filtertype}/UT_{celltype_replication}/coeqtls_fullresults_fixed.all.tsv.gz'
+    replication_coeqtl_df = pd.read_csv(replication_coeqtl_path, sep='\t', compression='gzip')
+    replication_coeqtl_df['gene2'] = [find_gene2(x[0], x[1]) for x in
+                                      replication_coeqtl_df[['Gene',
+                                                             'eqtlgene']].values]
+    replication_coeqtl_df['snp_eqtlgene_gene2'] = ['_'.join([item[0], item[1]]) for item in
+                                                replication_coeqtl_df[['snp_eqtlgene',
+                                                                       'gene2']].values]
+    replication_coeqtl_df = replication_coeqtl_df.set_index('snp_eqtlgene_gene2')
+    replication_coexpression_df = pd.read_csv(workdir/f'input/individual_networks/UT/UT_{celltype_replication}.sigcoeQTLs.tsv.gz',
+                         compression='gzip', sep='\t', index_col=0)
+    for celltype_discovery in celltypes:
+        if celltype_replication != celltype_discovery:
+            print(f"Replication: {celltype_discovery}")
+            discovery_coeqtl_path = workdir / f'output/{filtertype}/UT_{celltype_discovery}/coeqtls_fullresults_fixed.sig.tsv.gz'
+            discovery_coeqtl_df = pd.read_csv(discovery_coeqtl_path, sep='\t', compression='gzip')
+            discovery_coeqtl_df['gene2'] = [find_gene2(x[0], x[1]) for x in
+                                              discovery_coeqtl_df[['Gene',
+                                                                     'eqtlgene']].values]
+            discovery_coeqtl_df['snp_eqtlgene_gene2'] = ['_'.join([item[0], item[1]]) for item in
+                                                           discovery_coeqtl_df[['snp_eqtlgene',
+                                                                                  'gene2']].values]
+            discovery_coeqtl_df = discovery_coeqtl_df.set_index('snp_eqtlgene_gene2')
+            tested_coeqtls = list(set(replication_coeqtl_df.index) & set(discovery_coeqtl_df.index))
+            merged_coeqtl_df = pd.concat([replication_coeqtl_df.loc[tested_coeqtls],
+                                          discovery_coeqtl_df.loc[tested_coeqtls].add_suffix('_replication')], # todo: here is wrong.. should be discovery
+                                         axis=1)
+            merged_coeqtl_df = flip_direction(merged_coeqtl_df,
+                                              'MetaBeta_replication',
+                                              'SNPEffectAllele',
+                                              'SNPEffectAllele_replication') # MetaBeta, MetaSE, MetaBeta_replication, MetaSE_replication
+            disovery_coexpression_df = pd.read_csv(
+                workdir / f'input/individual_networks/UT/UT_{celltype_discovery}.sigcoeQTLs.tsv.gz',
+                compression='gzip', sep='\t', index_col=0)
+            # find overlapping individuals
+            tested_genepairs = list(merged_coeqtl_df['Gene'].unique())
+            tested_coexpression_discovery_df = disovery_coexpression_df.loc[tested_genepairs]
+            tested_coexpression_discovery_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+            tested_coexpression_replication_df = replication_coexpression_df.loc[tested_genepairs]
+            tested_coexpression_replication_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+            other_col_dict = {genepair:np.nan for genepair in tested_genepairs}
+            for genepair in tested_genepairs:
+                tested_coexpression_discovery_genepair_nonan = tested_coexpression_discovery_df.loc[genepair].dropna()
+                tested_coexpression_replication_genepair_nonan = tested_coexpression_replication_df.loc[genepair].dropna()
+                common_individuals = list(set(tested_coexpression_discovery_genepair_nonan.index) & set(tested_coexpression_replication_genepair_nonan.index))
+                num_common = len(common_individuals)
+                num_discovery = tested_coexpression_discovery_genepair_nonan.shape[0]
+                num_replication = tested_coexpression_replication_genepair_nonan.shape[0]
+                rho = pearsonr(tested_coexpression_discovery_genepair_nonan[common_individuals],
+                               tested_coexpression_replication_genepair_nonan[common_individuals])[0]
+                other_col_dict[genepair] = rho * num_common / np.sqrt(num_discovery * num_replication)
+            merged_coeqtl_df['theta'] = [other_col_dict.get(genepair) for genepair in merged_coeqtl_df['Gene']]
+            merged_coeqtl_df[['MetaBeta',
+                              'MetaBeta_replication',
+                              'MetaSE',
+                              'MetaSE_replication',
+                              'theta']].to_csv(workdir/f'output/{filtertype}/rb_calculations/discovery_{celltype_discovery}_replication_{celltype_replication}.tsv.gz',
+                                               sep='\t',
+                                               compression='gzip')
+        else:
+            continue
+
+
+# cmono ncmono and monocyte
+filtertype = 'filtered_results'
+workdir = Path("./coeqtl_mapping")
+celltypes = ['monocyte', 'cMono', 'ncMono']
+for celltype_replication in celltypes:
+    print(f"Discovery: {celltype_replication}")
+    replication_coeqtl_path = workdir / f'output/{filtertype}/UT_{celltype_replication}/coeqtls_fullresults_fixed.all.tsv.gz'
+    replication_coeqtl_df = pd.read_csv(replication_coeqtl_path, sep='\t', compression='gzip')
+    replication_coeqtl_df['gene2'] = [find_gene2(x[0], x[1]) for x in
+                                      replication_coeqtl_df[['Gene',
+                                                             'eqtlgene']].values]
+    replication_coeqtl_df['snp_eqtlgene_gene2'] = ['_'.join([item[0], item[1]]) for item in
+                                                replication_coeqtl_df[['snp_eqtlgene',
+                                                                       'gene2']].values]
+    replication_coeqtl_df = replication_coeqtl_df.set_index('snp_eqtlgene_gene2')
+    replication_coexpression_df = pd.read_csv(workdir/f'input/individual_networks/UT/monocyte_subcelltypes/UT_{celltype_replication}.sigcoeQTLs.tsv.gz',
+                         compression='gzip', sep='\t', index_col=0)
+    for celltype_discovery in celltypes:
+        if celltype_replication != celltype_discovery:
+            print(f"Replication: {celltype_discovery}")
+            discovery_coeqtl_path = workdir / f'output/{filtertype}/UT_{celltype_discovery}/coeqtls_fullresults_fixed.sig.tsv.gz'
+            discovery_coeqtl_df = pd.read_csv(discovery_coeqtl_path, sep='\t', compression='gzip')
+            discovery_coeqtl_df['gene2'] = [find_gene2(x[0], x[1]) for x in
+                                              discovery_coeqtl_df[['Gene',
+                                                                     'eqtlgene']].values]
+            discovery_coeqtl_df['snp_eqtlgene_gene2'] = ['_'.join([item[0], item[1]]) for item in
+                                                           discovery_coeqtl_df[['snp_eqtlgene',
+                                                                                  'gene2']].values]
+            discovery_coeqtl_df = discovery_coeqtl_df.set_index('snp_eqtlgene_gene2')
+            tested_coeqtls = list(set(replication_coeqtl_df.index) & set(discovery_coeqtl_df.index))
+            merged_coeqtl_df = pd.concat([replication_coeqtl_df.loc[tested_coeqtls],
+                                          discovery_coeqtl_df.loc[tested_coeqtls].add_suffix('_replication')], # todo: also here it is wrong...
+                                         axis=1)
+            merged_coeqtl_df = flip_direction(merged_coeqtl_df,
+                                              'MetaBeta_replication',
+                                              'SNPEffectAllele',
+                                              'SNPEffectAllele_replication') # MetaBeta, MetaSE, MetaBeta_replication, MetaSE_replication
+            disovery_coexpression_df = pd.read_csv(
+                workdir / f'input/individual_networks/UT/monocyte_subcelltypes/UT_{celltype_discovery}.sigcoeQTLs.tsv.gz',
+                compression='gzip', sep='\t', index_col=0)
+            # find overlapping individuals
+            tested_genepairs = list(merged_coeqtl_df['Gene'].unique())
+            tested_coexpression_discovery_df = disovery_coexpression_df.loc[tested_genepairs]
+            tested_coexpression_discovery_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+            tested_coexpression_replication_df = replication_coexpression_df.loc[tested_genepairs]
+            tested_coexpression_replication_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+            other_col_dict = {genepair:np.nan for genepair in tested_genepairs}
+            for genepair in tested_genepairs:
+                tested_coexpression_discovery_genepair_nonan = tested_coexpression_discovery_df.loc[genepair].dropna()
+                tested_coexpression_replication_genepair_nonan = tested_coexpression_replication_df.loc[genepair].dropna()
+                common_individuals = list(set(tested_coexpression_discovery_genepair_nonan.index) & set(tested_coexpression_replication_genepair_nonan.index))
+                num_common = len(common_individuals)
+                num_discovery = tested_coexpression_discovery_genepair_nonan.shape[0]
+                num_replication = tested_coexpression_replication_genepair_nonan.shape[0]
+                rho = pearsonr(tested_coexpression_discovery_genepair_nonan[common_individuals],
+                               tested_coexpression_replication_genepair_nonan[common_individuals])[0]
+                other_col_dict[genepair] = rho * num_common / np.sqrt(num_discovery * num_replication)
+            merged_coeqtl_df['theta'] = [other_col_dict.get(genepair) for genepair in merged_coeqtl_df['Gene']]
+            merged_coeqtl_df[['MetaBeta',
+                              'MetaBeta_replication',
+                              'MetaSE',
+                              'MetaSE_replication',
+                              'theta']].to_csv(workdir/f'output/{filtertype}/rb_calculations/monocyte_subcelltypes/discovery_{celltype_discovery}_replication_{celltype_replication}.tsv.gz',
+                                               sep='\t',
+                                               compression='gzip')
+        else:
+            continue
+
+
+# eQTLs
+workdir = Path("./cis_eqtl_single_cell/EMP_mapping_1_12_2021_perm1000/output/")
+celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'B', 'NK']
+genename_dict = pd.read_csv('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/resources/features_v3_reformated_names.tsv',
+                            sep='\t', names=['ensemblid', 'genename']).set_index('ensemblid')['genename'].T.to_dict()
+
+def read_alldataset_celltype_expression_df(celltype):
+    expression_prefix = Path('./expression_files/sources_for_coeqtl')
+    df = pd.DataFrame()
+    for datasetname in ['1m_v2', '1m_v3', 'NG', 't8w']:
+        dataset_df = pd.read_csv(expression_prefix/f'{datasetname}/{celltype}_expression.tsv', sep='\t', index_col=0)
+        dataset_df['genename'] = [genename_dict.get(geneid) for geneid in dataset_df.index]
+        dataset_df = dataset_df.dropna(subset=['genename']).set_index('genename')
+        df = pd.concat([df, dataset_df], axis=1)
+    return df
+
+for celltype_replication in celltypes:
+    print(f"Discovery: {celltype_replication}")
+    replication_coeqtl_path = workdir / f'{celltype_replication}/eQTLsFDR-ProbeLevel.txt.gz'
+    replication_coeqtl_df = pd.read_csv(replication_coeqtl_path, sep='\t', compression='gzip')
+    replication_coeqtl_df['genename'] = [genename_dict.get(ensemblid) for ensemblid in replication_coeqtl_df['ProbeName']]
+    replication_coeqtl_df['snp_gene'] = ['_'.join(item) for item in replication_coeqtl_df[['SNPName', 'genename']].values]
+    replication_coeqtl_df = replication_coeqtl_df.set_index('snp_gene')
+    replication_coeqtl_df['metabeta'] = [float(item.split(' (')[0]) for item in replication_coeqtl_df['Meta-Beta (SE)']]
+    replication_coeqtl_df['SE'] = [float(item.split(' (')[1][:-2]) for item in replication_coeqtl_df['Meta-Beta (SE)']]
+    replication_coexpression_df = read_alldataset_celltype_expression_df(celltype_replication)
+    for celltype_discovery in celltypes:
+        if celltype_replication != celltype_discovery:
+            print(f"Replication: {celltype_discovery}")
+            discovery_coeqtl_path = workdir / f'{celltype_discovery}/eQTLsFDR0.05-ProbeLevel.txt.gz'
+            discovery_coeqtl_df = pd.read_csv(discovery_coeqtl_path, sep='\t', compression='gzip')
+            discovery_coeqtl_df['genename'] = [genename_dict.get(ensemblid) for ensemblid in
+                                                 discovery_coeqtl_df['ProbeName']]
+            discovery_coeqtl_df['snp_gene'] = ['_'.join(item) for item in
+                                                 discovery_coeqtl_df[['SNPName', 'genename']].values]
+            discovery_coeqtl_df = discovery_coeqtl_df.set_index('snp_gene')
+            discovery_coeqtl_df['metabeta'] = [float(item.split(' (')[0]) for item in discovery_coeqtl_df['Meta-Beta (SE)']]
+            discovery_coeqtl_df['SE'] = [float(item.split(' (')[1][:-2]) for item in discovery_coeqtl_df['Meta-Beta (SE)']]
+            tested_eqtls = list(set(replication_coeqtl_df.index) & set(discovery_coeqtl_df.index))
+            merged_coeqtl_df = pd.concat([replication_coeqtl_df.loc[tested_eqtls],
+                                          discovery_coeqtl_df.loc[tested_eqtls].add_suffix('_replication')], # todo here it is wrong...
+                                         axis=1)
+            merged_coeqtl_df = flip_direction(merged_coeqtl_df,
+                                              'metabeta_replication',
+                                              'AlleleAssessed',
+                                              'AlleleAssessed_replication')
+            discovery_coexpression_df = read_alldataset_celltype_expression_df(celltype_discovery)
+            # find overlapping individuals
+            tested_genepairs = list(merged_coeqtl_df['genename'].unique())
+            tested_coexpression_discovery_df = discovery_coexpression_df.loc[tested_genepairs]
+            tested_coexpression_discovery_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+            tested_coexpression_replication_df = replication_coexpression_df.loc[tested_genepairs]
+            tested_coexpression_replication_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+            other_col_dict = {genepair:np.nan for genepair in tested_genepairs}
+            for genepair in tested_genepairs:
+                tested_coexpression_discovery_genepair_nonan = tested_coexpression_discovery_df.loc[genepair].dropna()
+                tested_coexpression_replication_genepair_nonan = tested_coexpression_replication_df.loc[genepair].dropna()
+                common_individuals = list(set(tested_coexpression_discovery_genepair_nonan.index) & set(tested_coexpression_replication_genepair_nonan.index))
+                num_common = len(common_individuals)
+                num_discovery = tested_coexpression_discovery_genepair_nonan.shape[0]
+                num_replication = tested_coexpression_replication_genepair_nonan.shape[0]
+                rho = pearsonr(tested_coexpression_discovery_genepair_nonan[common_individuals],
+                               tested_coexpression_replication_genepair_nonan[common_individuals])[0]
+                other_col_dict[genepair] = rho * num_common / np.sqrt(num_discovery * num_replication)
+            merged_coeqtl_df['theta'] = [other_col_dict.get(genepair) for genepair in merged_coeqtl_df['genename']]
+            merged_coeqtl_df[['metabeta',
+                              'metabeta_replication',
+                              'SE',
+                              'SE_replication',
+                              'theta']].to_csv(f'./coeqtl_mapping/input/snp_selection/rb_calculations/discovery_{celltype_discovery}_replication_{celltype_replication}.tsv.gz',
+                                               sep='\t',
+                                               compression='gzip')
+        else:
+            continue
+
+
+
+filtertype = 'filtered_results'
+workdir = Path("./coeqtl_mapping")
+celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'B', 'NK']
+for celltype_replication in celltypes:
+    for celltype_discovery in celltypes:
+        if celltype_replication != celltype_discovery:
+            print(celltype_discovery, celltype_replication)
+            merged_coeqtl_df = pd.read_csv(workdir/f'output/{filtertype}/rb_calculations/discovery_{celltype_discovery}_replication_{celltype_replication}.tsv.gz',
+                                                   sep='\t',
+                                                   compression='gzip')
+            merged_coeqtl_df = merged_coeqtl_df.rename({
+                'MetaBeta_replication': 'MetaBeta_discovery',
+                'MetaSE_replication': 'MetaSE_discovery'
+            },
+            axis=1)
+            merged_coeqtl_df = merged_coeqtl_df.rename({
+                'MetaBeta': 'MetaBeta_replication',
+                'MetaSE': 'MetaSE_replication'
+            },
+                axis=1)
+            merged_coeqtl_df = merged_coeqtl_df.rename({
+                'MetaBeta_discovery': 'MetaBeta',
+                'MetaSE_discovery': 'MetaSE'
+            },
+                axis=1)
+            merged_coeqtl_df.to_csv(
+                workdir / f'output/{filtertype}/rb_calculations/discovery_{celltype_discovery}_replication_{celltype_replication}.fixed.tsv.gz',
+                sep='\t',
+                compression='gzip')
\ No newline at end of file
diff --git a/04_coeqtl_mapping/prepare_genelist_and_annotation_for_betaqtl.py b/04_coeqtl_mapping/prepare_genelist_and_annotation_for_betaqtl.py
new file mode 100644
index 0000000..c5ea9f3
--- /dev/null
+++ b/04_coeqtl_mapping/prepare_genelist_and_annotation_for_betaqtl.py
@@ -0,0 +1,73 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import argparse
+import os
+
+
+def parse():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--condition', dest = 'condition')
+    parser.add_argument('--celltype', dest='celltype')
+    return parser
+
+args = parse().parse_args()
+condition, celltype = args.condition , args.celltype
+
+# old code for creating the annotation file..
+workdir = Path("/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/")
+eqtl_annotations_path = workdir/f'input/snp_genepair_selection/annotations/{condition}_{celltype}.baseline.annotatedeQTL.tsv'
+savepath = workdir/f'output/{condition}_{celltype}/'
+if not os.path.isdir(savepath):
+    os.mkdir(savepath)
+
+eqtl_annotations = pd.read_csv(eqtl_annotations_path, sep='\t')
+
+annotation_cols = ['Platform', 'ArrayAddress', 'Symbol', 'Chr', 'ChrStart', 'ChrEnd', 'Probe', 'Seq']
+gene_annotation_dict = pd.read_csv('/groups/umcg-bios/tmp01/projects/1M_cells_scRNAseq/ongoing/eQTL_mapping/probeannotation/singleCell-annotation-stripped.tsv',
+                                   sep='\t').set_index('Ensembl').T.to_dict()
+genename_ensembl_mapping = pd.read_csv(workdir/'../resources/features_v3_reformated_names.tsv',
+                                       sep='\t', names=['Ensembl', 'genename']).set_index('genename')['Ensembl'].T.to_dict()
+
+
+eqtl_annotations['ArrayAddress'] = eqtl_annotations['genepair_sorted']
+eqtl_annotations['Symbol'] = eqtl_annotations['genepair_sorted']
+eqtl_annotations['Probe'] = eqtl_annotations['genepair_sorted']
+eqtl_annotations['Seq'] = 'NNNNNNN'
+getchr = lambda x:gene_annotation_dict.get(x)['Chr'] if x in gene_annotation_dict else np.nan
+getchrstart = lambda x:int(gene_annotation_dict.get(x)['ChrStart']) if x in gene_annotation_dict else np.nan
+getchrend = lambda x:int(gene_annotation_dict.get(x)['ChrEnd']) if x in gene_annotation_dict else np.nan
+eqtl_annotations['Platform'] = 'SingleCell'
+
+eqtl_annotations['eqtlgene'] = [item.split(';')[0] for item in eqtl_annotations['eqtlgene1_gene2']]
+eqtl_annotations['eqtlgene_ensembl'] = [genename_ensembl_mapping.get(genename) for genename in eqtl_annotations['eqtlgene']]
+
+eqtl_annotations['Chr'] = [getchr(gene) for gene in eqtl_annotations['eqtlgene_ensembl']]
+eqtl_annotations['ChrStart'] = [getchrstart(gene) for gene in eqtl_annotations['eqtlgene_ensembl']]
+eqtl_annotations['ChrEnd'] = [getchrend(gene) for gene in eqtl_annotations['eqtlgene_ensembl']]
+counts = eqtl_annotations['genepair_sorted'].value_counts()
+duplicated_genepairs_set = set(counts[counts>1].index.values)
+isdup = lambda x:True if x in duplicated_genepairs_set else False
+eqtl_annotations['isdup'] = [isdup(genepair) for genepair in eqtl_annotations['genepair_sorted']]
+eqtl_annotations[eqtl_annotations['isdup']==False][['snp', 'genepair_sorted']].to_csv(workdir/f'input/snp_genepair_selection/{condition}_{celltype}.baseline.noduplicated.tsv',
+                                                     sep='\t', index=False)
+eqtl_annotations[eqtl_annotations['isdup']==False][annotation_cols].to_csv(workdir/f'input/summary/{condition}_{celltype}.genepairs.annotation.gene1position.noduplicated.tsv',
+                                                     sep='\t', index=False)
+eqtl_annotations[eqtl_annotations['isdup']==False][['genepair_sorted']].to_csv(savepath/'genelist.noduplicated.txt', header=None, index=False)
+
+
+duplicated = eqtl_annotations[eqtl_annotations['isdup']].drop_duplicates(subset=['genepair_sorted'], keep='first')
+duplicated[['snp', 'genepair_sorted']].to_csv(workdir/f'input/snp_genepair_selection/{condition}_{celltype}.baseline.duplicatedversion1.tsv',
+                                                     sep='\t', index=False)
+duplicated[annotation_cols].to_csv(workdir/f'input/summary/{condition}_{celltype}.genepairs.annotation.gene1position.duplicatedversion1.tsv',
+                                                     sep='\t', index=False)
+duplicated[['genepair_sorted']].to_csv(savepath/'genelist.duplicatedversion1.txt', header=None, index=False)
+
+
+duplcated_version2 = eqtl_annotations[eqtl_annotations['isdup']].drop_duplicates(subset=['genepair_sorted'], keep='last')
+duplcated_version2[['snp', 'genepair_sorted']].to_csv(workdir/f'input/snp_genepair_selection/{condition}_{celltype}.baseline.duplicatedversion2.tsv',
+                                                     sep='\t', index=False)
+duplcated_version2[annotation_cols].to_csv(workdir/f'input/summary/{condition}_{celltype}.genepairs.annotation.gene1position.duplicatedversion2.tsv',
+                                                     sep='\t', index=False)
+duplcated_version2[['genepair_sorted']].to_csv(savepath/'genelist.duplicatedversion2.txt', header=None, index=False)
+
diff --git a/04_coeqtl_mapping/rb_celltypes.ipynb b/04_coeqtl_mapping/rb_celltypes.ipynb
new file mode 100644
index 0000000..834ede3
--- /dev/null
+++ b/04_coeqtl_mapping/rb_celltypes.ipynb
@@ -0,0 +1,2026 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['pdf.fonttype'] = 42\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from pathlib import Path\n",
+    "workdir = Path(\"./coeqtl_mapping/\")\n",
+    "\n",
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
+    "import matplotlib\n",
+    "def heatmap(data, row_labels, col_labels, ax=None,\n",
+    "            cbar_kw={}, cbarlabel=\"\", **kwargs):\n",
+    "    \"\"\"\n",
+    "    Create a heatmap from a numpy array and two lists of labels.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    data\n",
+    "        A 2D numpy array of shape (M, N).\n",
+    "    row_labels\n",
+    "        A list or array of length M with the labels for the rows.\n",
+    "    col_labels\n",
+    "        A list or array of length N with the labels for the columns.\n",
+    "    ax\n",
+    "        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If\n",
+    "        not provided, use current axes or create a new one.  Optional.\n",
+    "    cbar_kw\n",
+    "        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.\n",
+    "    cbarlabel\n",
+    "        The label for the colorbar.  Optional.\n",
+    "    **kwargs\n",
+    "        All other arguments are forwarded to `imshow`.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if not ax:\n",
+    "        ax = plt.gca()\n",
+    "\n",
+    "    # Plot the heatmap\n",
+    "    im = ax.pcolormesh(data, **kwargs)\n",
+    "\n",
+    "    # Create colorbar\n",
+    "    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n",
+    "    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n",
+    "\n",
+    "    # Let the horizontal axes labeling appear on top.\n",
+    "    ax.tick_params(top=True, bottom=False,\n",
+    "                   labeltop=True, labelbottom=False)\n",
+    "\n",
+    "    # Rotate the tick labels and set their alignment.\n",
+    "    plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n",
+    "             rotation_mode=\"anchor\")\n",
+    "\n",
+    "    # Turn spines off and create white grid.\n",
+    "#     ax.spines[:].set_visible(False)\n",
+    "\n",
+    "#     ax.set_xticks(np.arange(-0.5, data.shape[1]-2, 1), minor=True)\n",
+    "#     ax.set_yticks(np.arange(-0.5, data.shape[0]-2, 1), minor=True)\n",
+    "    # Show all ticks and label them with the respective list entries.\n",
+    "    ax.set_xticklabels([\"\"]+col_labels)\n",
+    "    ax.set_yticklabels([\"\"]+row_labels)\n",
+    "#     ax.grid(which='minor', color=\"white\", linestyle='-', linewidth=2)\n",
+    "#     ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
+    "    return im, cbar\n",
+    "\n",
+    "\n",
+    "def annotate_heatmap(im, data=None, valfmt=\"{x:.2f}\",\n",
+    "                     textcolors=(\"black\", \"white\"),\n",
+    "                     threshold=None, **textkw):\n",
+    "    \"\"\"\n",
+    "    A function to annotate a heatmap.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    im\n",
+    "        The AxesImage to be labeled.\n",
+    "    data\n",
+    "        Data used to annotate.  If None, the image's data is used.  Optional.\n",
+    "    valfmt\n",
+    "        The format of the annotations inside the heatmap.  This should either\n",
+    "        use the string format method, e.g. \"$ {x:.2f}\", or be a\n",
+    "        `matplotlib.ticker.Formatter`.  Optional.\n",
+    "    textcolors\n",
+    "        A pair of colors.  The first is used for values below a threshold,\n",
+    "        the second for those above.  Optional.\n",
+    "    threshold\n",
+    "        Value in data units according to which the colors from textcolors are\n",
+    "        applied.  If None (the default) uses the middle of the colormap as\n",
+    "        separation.  Optional.\n",
+    "    **kwargs\n",
+    "        All other arguments are forwarded to each call to `text` used to create\n",
+    "        the text labels.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Normalize the threshold to the images color range.\n",
+    "    if threshold is not None:\n",
+    "        threshold = im.norm(threshold)\n",
+    "    else:\n",
+    "        threshold = im.norm(data.max())/2.\n",
+    "\n",
+    "    # Set default alignment to center, but allow it to be\n",
+    "    # overwritten by textkw.\n",
+    "    kw = dict(horizontalalignment=\"center\",\n",
+    "              verticalalignment=\"center\")\n",
+    "    kw.update(textkw)\n",
+    "\n",
+    "    # Get the formatter in case a string is supplied\n",
+    "    if isinstance(valfmt, str):\n",
+    "        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)\n",
+    "\n",
+    "    # Loop over the data and create a `Text` for each \"pixel\".\n",
+    "    # Change the text's color depending on the data.\n",
+    "    texts = []\n",
+    "    for i in range(data.shape[0]):\n",
+    "        for j in range(data.shape[1]):\n",
+    "#             kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])\n",
+    "            text = im.axes.text(j+0.5, i+0.5, valfmt(data[i, j], None), **kw)#j+0.1, i+0.5\n",
+    "            texts.append(text)\n",
+    "\n",
+    "    return texts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## celltypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_res_df = pd.read_csv(workdir/'output/filtered_results/rb_calculations/summary.csv', index_col=0)\n",
+    "unfiltered_res_df = pd.read_csv(workdir/'output/unfiltered_results/rb_calculations/summary.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_res_df_clean = filtered_res_df[filtered_res_df['celltype_discovery']!='B']\n",
+    "filtered_res_df_clean = filtered_res_df_clean.dropna()\n",
+    "filtered_res_df_clean.to_excel(workdir/'output/summary/rb_values_replication_in_other_celltypes_filtered_results.xlsx')\n",
+    "\n",
+    "unfiltered_res_df_clean = unfiltered_res_df[unfiltered_res_df['celltype_discovery']!='B']\n",
+    "unfiltered_res_df_clean = unfiltered_res_df_clean.dropna()\n",
+    "unfiltered_res_df_clean.to_excel(workdir/'output/summary/rb_values_replication_in_other_celltypes_unfiltered_results.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### filtered results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# filtered results\n",
+    "rb_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "rbse_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "rbpvalue_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "numcoeqtl_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "num_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "rbse_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "for discovery_celltype in celltypes:\n",
+    "    # replication in other celltypes\n",
+    "    for replication_celltype in celltypes:\n",
+    "        if discovery_celltype != replication_celltype:\n",
+    "            rb_results = filtered_res_df[(filtered_res_df['celltype_discovery'] == discovery_celltype) &\n",
+    "                                         (filtered_res_df['celltype_replication'] == replication_celltype)]\n",
+    "            replicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/filtered_results/rb_calculations/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t',\n",
+    "                index_col=0\n",
+    "            ).shape[0]\n",
+    "            if rb_results['r'].values[0] < 10 and discovery_celltype != 'B':\n",
+    "                rb_df.loc[replication_celltype, discovery_celltype] = rb_results['r'].values[0]\n",
+    "                rbse_df.loc[replication_celltype, discovery_celltype] = rb_results['se_r'].values[0]\n",
+    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = rb_results['p'].values[0]\n",
+    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
+    "                rbvalue = rb_results['r'].values[0]\n",
+    "                rbsevalue = rb_results['se_r'].values[0]\n",
+    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"{rbvalue:.2f}\\nN={replicated_coeqtls_num}\"\n",
+    "            elif discovery_celltype == 'B':\n",
+    "                rb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                rbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
+    "                rbvalue = rb_results['r'].values[0]\n",
+    "                rbsevalue = rb_results['se_r'].values[0]\n",
+    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "            else:\n",
+    "                rb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                rbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
+    "                num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "                rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "        else:\n",
+    "            rb_df.loc[replication_celltype, discovery_celltype] = 1\n",
+    "            rbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            rbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            replicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/filtered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t'\n",
+    "            ).shape[0]\n",
+    "            numcoeqtl_df.loc[replication_celltype, discovery_celltype] = replicated_coeqtls_num\n",
+    "            num_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "            rbse_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={replicated_coeqtls_num}\"\n",
+    "    \n",
+    "replicated_ratio_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                                   columns=celltypes, index=celltypes)\n",
+    "for discovery_celltype in numcoeqtl_df.columns:\n",
+    "    for replication_celltype in numcoeqtl_df.index:\n",
+    "        replicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "        numcoeqtl_df.loc[replication_celltype, discovery_celltype] / numcoeqtl_df.loc[discovery_celltype, discovery_celltype]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>monocyte</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>NK</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CD4T</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.971596</td>\n",
+       "      <td>0.759425</td>\n",
+       "      <td>0.773429</td>\n",
+       "      <td>0.953264</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD8T</th>\n",
+       "      <td>0.988285</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.847118</td>\n",
+       "      <td>1.002450</td>\n",
+       "      <td>0.966100</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>0.792142</td>\n",
+       "      <td>0.779688</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.797139</td>\n",
+       "      <td>0.960618</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>0.794745</td>\n",
+       "      <td>0.815816</td>\n",
+       "      <td>0.935905</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.853924</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NK</th>\n",
+       "      <td>0.925802</td>\n",
+       "      <td>0.967842</td>\n",
+       "      <td>0.868747</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>0.918479</td>\n",
+       "      <td>0.952496</td>\n",
+       "      <td>0.948709</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              CD4T      CD8T  monocyte        DC        NK    B\n",
+       "CD4T      1.000000  0.971596  0.759425  0.773429  0.953264  NaN\n",
+       "CD8T      0.988285  1.000000  0.847118  1.002450  0.966100  NaN\n",
+       "monocyte  0.792142  0.779688  1.000000  0.797139  0.960618  NaN\n",
+       "DC        0.794745  0.815816  0.935905  1.000000  0.853924  NaN\n",
+       "NK        0.925802  0.967842  0.868747       NaN  1.000000  NaN\n",
+       "B         0.918479  0.952496  0.948709       NaN       NaN  1.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rb_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>monocyte</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>NK</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CD4T</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.126679e-35</td>\n",
+       "      <td>2.425843e-03</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD8T</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>7.557685e-59</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>1.052643e-121</td>\n",
+       "      <td>5.216640e-92</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.774726e-21</td>\n",
+       "      <td>1.393096e-317</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>3.609987e-25</td>\n",
+       "      <td>4.217830e-39</td>\n",
+       "      <td>5.947381e-316</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>4.322965e-05</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NK</th>\n",
+       "      <td>2.552726e-264</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>8.365584e-06</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>2.320757e-144</td>\n",
+       "      <td>1.610287e-212</td>\n",
+       "      <td>1.074123e-78</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   CD4T           CD8T       monocyte            DC  \\\n",
+       "CD4T       0.000000e+00   0.000000e+00   1.126679e-35  2.425843e-03   \n",
+       "CD8T       0.000000e+00   0.000000e+00   7.557685e-59  0.000000e+00   \n",
+       "monocyte  1.052643e-121   5.216640e-92   0.000000e+00  1.774726e-21   \n",
+       "DC         3.609987e-25   4.217830e-39  5.947381e-316  0.000000e+00   \n",
+       "NK        2.552726e-264   0.000000e+00   8.365584e-06  0.000000e+00   \n",
+       "B         2.320757e-144  1.610287e-212   1.074123e-78  0.000000e+00   \n",
+       "\n",
+       "                     NK    B  \n",
+       "CD4T       0.000000e+00  0.0  \n",
+       "CD8T       0.000000e+00  0.0  \n",
+       "monocyte  1.393096e-317  0.0  \n",
+       "DC         4.322965e-05  0.0  \n",
+       "NK         0.000000e+00  0.0  \n",
+       "B          0.000000e+00  0.0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rbpvalue_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "color_dict = {'CD4T': '#2E9D33',\n",
+    "              'CD8T': '#126725',\n",
+    "              'monocyte': '#EDBA1B',\n",
+    "              'NK': '#965EC8',\n",
+    "              'DC': '#E64B50',\n",
+    "              'B': '#009DDB',\n",
+    "              'cMono': 'peru',\n",
+    "              'ncMono': 'y',\n",
+    "              'CD4T_individual_100': '#2E9D33',\n",
+    "              'CD4T_individual_50': '#2E9D33',\n",
+    "              'CD4T_50': '#2E9D33',\n",
+    "              'CD4T_150': '#2E9D33',\n",
+    "              'CD4T_250': '#2E9D33'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-1-1162ad6d26ab>:60: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-1-1162ad6d26ab>:61: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "matplotlib.rcParams.update({'font.size': 16})\n",
+    "discovery_celltype = 'CD4T'\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
+    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
+    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "    im1, bar = heatmap(rb_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "            list(rb_df.index), \n",
+    "            [discovery_celltype],\n",
+    "                     cmap=cmap1, ax=axes[i], vmin=0.7, vmax=1)\n",
+    "    bar.remove()\n",
+    "    _ = annotate_heatmap(im1, \n",
+    "                         data=rbse_anno_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                         valfmt=\"{x:^}\", \n",
+    "                         textcolors=(\"white\", \"white\"),\n",
+    "                         threshold=1)\n",
+    "    if i > 0:\n",
+    "        axes[i].axis('off')\n",
+    "plt.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig('rb_values.filtered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cdict = {'red':   [[0.0,  0.0, 0.0],\n",
+    "#                    [0.5,  0.5, 0.5],\n",
+    "#                    [1.0,  1.0, 1.0]],\n",
+    "         \n",
+    "#          'green': [[0.0,  0.0, 0.0],\n",
+    "#                    [0.5, 0.5, 0.5],\n",
+    "#                    [1.0,  1.0, 1.0]],\n",
+    "         \n",
+    "#          'blue':  [[0.0,  0.0, 0.0],\n",
+    "#                    [0.5,  0.5, 0.5],\n",
+    "#                    [1.0,  1.0, 1.0]]}\n",
+    "\n",
+    "# cdict['alpha'] = ((0.0, 0.0, 0.0),\n",
+    "#                    (0.5, 0.5, 0.5),\n",
+    "#                    (1.0, 1.0, 1.0))\n",
+    "# newcmp = LinearSegmentedColormap('alpha', segmentdata=cdict, N=256)\n",
+    "\n",
+    "c_white = matplotlib.colors.colorConverter.to_rgba('white',alpha = 0)\n",
+    "c_black= matplotlib.colors.colorConverter.to_rgba('black',alpha = 1)\n",
+    "cmap_rb = matplotlib.colors.LinearSegmentedColormap.from_list('rb_cmap',[c_white,c_black],512)\n",
+    "\n",
+    "\n",
+    "\n",
+    "mpl.cm.register_cmap(cmap=cmap_rb, name='alpha')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-1-f7950d1936ff>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-1-f7950d1936ff>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "im, bar = heatmap(replicated_ratio_df.values, \n",
+    "                   list(rb_df.index), \n",
+    "                  celltypes,\n",
+    "                   cmap='alpha', \n",
+    "                   vmin=0, vmax=1)\n",
+    "_ = annotate_heatmap(im, \n",
+    "                 data=replicated_ratio_df.values, \n",
+    "                 valfmt=\"{x:.0%}\", \n",
+    "                 textcolors=(\"white\", \"white\"),\n",
+    "                 threshold=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "matplotlib.rcParams.update({'font.size': 16})\n",
+    "fig, ax = plt.subplots(figsize=(8, 7))\n",
+    "im, bar = heatmap(np.flip(rb_df.values, axis=0), \n",
+    "                   list(rb_df.index)[::-1], \n",
+    "                  celltypes,\n",
+    "                   cmap='alpha', \n",
+    "                   vmin=0.7, vmax=1)\n",
+    "_ = annotate_heatmap(im, \n",
+    "                 data=np.flip(rbse_anno_df.values, axis=0), \n",
+    "                 valfmt=\"{x:^}\", \n",
+    "                 textcolors=(\"white\", \"white\"),\n",
+    "                 threshold=1)\n",
+    "\n",
+    "plt.savefig('rb_values.filtered_results.varyingalpha.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "discovery_celltype = 'CD4T'\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
+    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
+    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "    im1, bar = heatmap(np.flip(replicated_ratio_df[discovery_celltype].values.reshape((6, 1)),\n",
+    "                               axis=0), \n",
+    "            list(rb_df.index)[::-1], \n",
+    "            [discovery_celltype],\n",
+    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
+    "    bar.remove()\n",
+    "    _ = annotate_heatmap(im1, \n",
+    "                         data=replicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                         valfmt=\"{x:.0%}\", \n",
+    "                         textcolors=(\"white\", \"white\"),\n",
+    "                         threshold=1)\n",
+    "    if i > 0:\n",
+    "        axes[i].axis('off')\n",
+    "        \n",
+    "plt.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig('replicated_ratio.filtered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 36x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(0.5, 6))\n",
+    "fig.subplots_adjust(bottom=0.5)\n",
+    "\n",
+    "colors = [\"white\", 'black']\n",
+    "cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "norm = mpl.colors.Normalize(vmin=0.7, vmax=1)\n",
+    "\n",
+    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),\n",
+    "             cax=ax, orientation='vertical')\n",
+    "plt.savefig('colorbar.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 36x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(0.5, 6))\n",
+    "fig.subplots_adjust(bottom=0.5)\n",
+    "\n",
+    "colors = [\"white\", 'black']\n",
+    "cmap = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "norm = mpl.colors.Normalize(vmin=0, vmax=1)\n",
+    "\n",
+    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),\n",
+    "             cax=ax, orientation='vertical')\n",
+    "plt.savefig('colorbar.replication_ratio.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### celltype comparison for unfiltered results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# filtered results\n",
+    "unrb_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                     columns=celltypes, index=celltypes)\n",
+    "unrbse_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                       columns=celltypes, index=celltypes)\n",
+    "unrbpvalue_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                           columns=celltypes, index=celltypes)\n",
+    "unnumcoeqtl_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                           columns=celltypes, index=celltypes)\n",
+    "unanno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                           columns=celltypes, index=celltypes)\n",
+    "unnum_anno_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                           columns=celltypes, index=celltypes)\n",
+    "\n",
+    "for discovery_celltype in celltypes:\n",
+    "    for replication_celltype in celltypes:\n",
+    "        if discovery_celltype != replication_celltype:\n",
+    "            unrb_results = unfiltered_res_df[(unfiltered_res_df['celltype_discovery'] == discovery_celltype) &\n",
+    "                                           (unfiltered_res_df['celltype_replication'] == replication_celltype)]\n",
+    "            unreplicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/unfiltered_results/rb_calculations/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t',\n",
+    "                index_col=0\n",
+    "            ).shape[0]\n",
+    "            if rb_results['r'].values[0] < 10 and discovery_celltype != 'B':\n",
+    "                unrb_df.loc[replication_celltype, discovery_celltype] = unrb_results['r'].values[0]\n",
+    "                unrbse_df.loc[replication_celltype, discovery_celltype] = unrb_results['se_r'].values[0]\n",
+    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = unrb_results['p'].values[0]\n",
+    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
+    "                unrbvalue = unrb_results['r'].values[0]\n",
+    "                unrbsevalue = unrb_results['se_r'].values[0]\n",
+    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"{unrbvalue:.2f}\\nN={unreplicated_coeqtls_num}\"\n",
+    "            elif discovery_celltype == 'B':\n",
+    "                unrb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                unrbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
+    "                unrbvalue = unrb_results['r'].values[0]\n",
+    "                unrbsevalue = unrb_results['se_r'].values[0]\n",
+    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "            else:\n",
+    "                unrb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                unrbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
+    "                unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "                unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "        else:\n",
+    "            unrb_df.loc[replication_celltype, discovery_celltype] = 1\n",
+    "            unrbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            unrbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            unreplicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/unfiltered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t'\n",
+    "            ).shape[0]\n",
+    "            unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] = unreplicated_coeqtls_num\n",
+    "            unnum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "            unanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={unreplicated_coeqtls_num}\"\n",
+    "            \n",
+    "unreplicated_ratio_df = pd.DataFrame(data=np.zeros((len(celltypes), len(celltypes))), \n",
+    "                                   columns=celltypes, index=celltypes)\n",
+    "for discovery_celltype in unnumcoeqtl_df.columns:\n",
+    "    for replication_celltype in unnumcoeqtl_df.index:\n",
+    "        unreplicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "        unnumcoeqtl_df.loc[replication_celltype, discovery_celltype] / unnumcoeqtl_df.loc[discovery_celltype, \n",
+    "                                                                                          discovery_celltype]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "matplotlib.rcParams.update({'font.size': 14})\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
+    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
+    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "    im1, bar = heatmap(np.flip(unreplicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                               axis=0), \n",
+    "            list(rb_df.index)[::-1], \n",
+    "            [discovery_celltype],\n",
+    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
+    "    bar.remove()\n",
+    "    _ = annotate_heatmap(im1, \n",
+    "                         data=unreplicated_ratio_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                         valfmt=\"{x:.0%}\", \n",
+    "                         textcolors=(\"white\", \"white\"),\n",
+    "                         threshold=1)\n",
+    "    if i > 0:\n",
+    "        axes[i].axis('off')\n",
+    "        \n",
+    "plt.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig('replication_ratio.unfiltered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-21c20a3160c2>:62: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-67-21c20a3160c2>:63: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "matplotlib.rcParams.update({'font.size': 14})\n",
+    "discovery_celltype = 'CD4T'\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(7, 7), sharey=True)\n",
+    "for i, discovery_celltype in enumerate(['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']):\n",
+    "    colors = [\"white\", color_dict[discovery_celltype]]\n",
+    "    cmap1 = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
+    "    im1, bar = heatmap(np.flip(unrb_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                               axis=0),\n",
+    "            list(rb_df.index)[::-1], \n",
+    "            [discovery_celltype],\n",
+    "                     cmap=cmap1, ax=axes[i], vmin=0, vmax=1)\n",
+    "    bar.remove()\n",
+    "    _ = annotate_heatmap(im1, \n",
+    "                         data=unanno_df[discovery_celltype].values.reshape((6, 1)), \n",
+    "                         valfmt=\"{x:^}\", \n",
+    "                         textcolors=(\"white\", \"white\"),\n",
+    "                         threshold=1)\n",
+    "    if i > 0:\n",
+    "        axes[i].axis('off')\n",
+    "        \n",
+    "plt.subplots_adjust(wspace=0, hspace=0)\n",
+    "plt.savefig('rb_values.unfiltered_results.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BIOS replication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bios_replication_filtered_df = pd.read_csv(\n",
+    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/filtered_results/replication_summary.csv', \n",
+    "    index_col=0\n",
+    ").set_index('celltype')\n",
+    "bios_replication_unfiltered_df = pd.read_csv(\n",
+    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/unfiltered_results/replication_summary.csv', \n",
+    "    index_col=0\n",
+    ").set_index('celltype')\n",
+    "color_dict = {'CD4T': '#2E9D33',\n",
+    "              'CD8T': 'darkgreen',\n",
+    "              'monocyte': '#EDBA1B',\n",
+    "              'NK': '#E64B50',\n",
+    "              'DC': '#965EC8',\n",
+    "              'B': '#009DDB',\n",
+    "              'cMono': 'peru',\n",
+    "              'ncMono': 'y',\n",
+    "              'CD4T_individual_100': '#2E9D33',\n",
+    "              'CD4T_individual_50': '#2E9D33',\n",
+    "              'CD4T_50': '#2E9D33',\n",
+    "              'CD4T_150': '#2E9D33',\n",
+    "              'CD4T_250': '#2E9D33'}\n",
+    "\n",
+    "bios_replication_filtered_df['color'] = [color_dict.get(celltype) for celltype in \n",
+    "                                         bios_replication_filtered_df.index]\n",
+    "bios_replication_unfiltered_df['color'] = [color_dict.get(celltype) for celltype in \n",
+    "                                          bios_replication_unfiltered_df.index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bios_replication_filtered_df_clean = bios_replication_filtered_df.drop(index=['B'])\n",
+    "bios_replication_filtered_df_clean = bios_replication_filtered_df_clean.drop(columns=['color'])\n",
+    "bios_replication_filtered_df_clean.to_excel(workdir/'output/summary/rb_values_bios_replication_filtered_results.xlsx')\n",
+    "\n",
+    "bios_replication_unfiltered_df_clean = bios_replication_unfiltered_df.drop(index=['B'])\n",
+    "bios_replication_unfiltered_df_clean = bios_replication_unfiltered_df_clean.drop(columns=['color'])\n",
+    "bios_replication_unfiltered_df_clean.to_excel(workdir/'output/summary/rb_values_bios_replication_unfiltered_results.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-12-08dd41d53262>:3: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax2.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
+      "<ipython-input-12-08dd41d53262>:8: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax2.errorbar(y=bios_replication_unfiltered_df.loc[sorted_celltypes]['r'].values,\n",
+      "<ipython-input-12-08dd41d53262>:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax2.set_xticklabels(['', 'CD4T', '', 'CD8T', '', 'monocyte', '', 'DC', '', 'NK'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK']\n",
+    "fig, ax2 = plt.subplots()\n",
+    "ax2.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
+    "             x=[ind for ind in range(len(sorted_celltypes))],\n",
+    "             yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
+    "             fmt='.', markersize=6, marker='o', color='black', label = 'Filtered results')\n",
+    "bios_replication_unfiltered_df.loc['DC'] = [np.nan, np.nan, np.nan, np.nan]\n",
+    "ax2.errorbar(y=bios_replication_unfiltered_df.loc[sorted_celltypes]['r'].values,\n",
+    "             x=[ind+0.05 for ind in range(len(sorted_celltypes))],\n",
+    "             yerr=bios_replication_unfiltered_df.loc[sorted_celltypes]['se_r'].values,\n",
+    "             fmt='.', markersize=6, marker='o', markerfacecolor='white', color='black', label = 'Unfilter results')\n",
+    "ax2.set_xticklabels(['', 'CD4T', '', 'CD8T', '', 'monocyte', '', 'DC', '', 'NK'])\n",
+    "plt.legend()\n",
+    "plt.ylabel(\"rb (SE)\")\n",
+    "plt.savefig('sf20.comparison_rb_values_bios_replication.pdf')\n",
+    "plt.savefig('sf20.comparison_rb_values_bios_replication.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-31-c42524d09425>:14: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x360 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare between filtered and unfiltered\n",
+    "fig, axes = plt.subplots(1, 5, figsize=(4, 5), sharey=True)\n",
+    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK']\n",
+    "# ax1.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
+    "#              x=[ind-0.1 for ind in range(len(sorted_celltypes))],\n",
+    "#              yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
+    "#              fmt='.', markersize=6, marker='o', \n",
+    "#              ecolor=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values,\n",
+    "#              color=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values[0])\n",
+    "# ax1.set_xticklabels([\"\"]+sorted_celltypes)\n",
+    "# ax1.plot([0, 5], [0.5, 0.5], linestyle='--', color='black')\n",
+    "for ind, celltype in enumerate(sorted_celltypes):\n",
+    "    ax = axes[ind]\n",
+    "    ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
+    "             x=[0.4],\n",
+    "             yerr=bios_replication_filtered_df.loc[celltype]['se_r'],\n",
+    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
+    "             markeredgecolor='black', markerfacecolor='black'\n",
+    "             )\n",
+    "    ax.set_xlim([0, 1])\n",
+    "    ax.spines['bottom'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.spines['top'].set_color(bios_replication_filtered_df.loc[celltype]['color']) \n",
+    "    ax.spines['right'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.spines['left'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.set_xticklabels([])\n",
+    "    ax.set_xlabel(celltype)\n",
+    "    \n",
+    "\n",
+    "plt.savefig('bios_replication.filtered_results.pdf')\n",
+    "plt.savefig('bios_replication.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-32-cb6cab60ed6f>:14: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
+      "<ipython-input-32-cb6cab60ed6f>:20: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \".\" (-> marker='.'). The keyword argument will take precedence.\n",
+      "  ax.errorbar(y=bios_replication_unfiltered_df.loc[celltype]['r'],\n",
+      "/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/envs/scpy3.8/lib/python3.8/site-packages/numpy/core/_asarray.py:102: UserWarning: Warning: converting a masked element to nan.\n",
+      "  return array(a, dtype, copy=False, order=order)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 6 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare between filtered and unfiltered\n",
+    "fig, axes = plt.subplots(1, 6, figsize=(12, 6), sharey=True)\n",
+    "sorted_celltypes = ['CD4T', 'CD8T', 'monocyte', 'DC', 'NK', 'B']\n",
+    "# ax1.errorbar(y=bios_replication_filtered_df.loc[sorted_celltypes]['r'].values,\n",
+    "#              x=[ind-0.1 for ind in range(len(sorted_celltypes))],\n",
+    "#              yerr=bios_replication_filtered_df.loc[sorted_celltypes]['se_r'].values,\n",
+    "#              fmt='.', markersize=6, marker='o', \n",
+    "#              ecolor=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values,\n",
+    "#              color=bios_replication_filtered_df.loc[sorted_celltypes]['color'].values[0])\n",
+    "# ax1.set_xticklabels([\"\"]+sorted_celltypes)\n",
+    "# ax1.plot([0, 5], [0.5, 0.5], linestyle='--', color='black')\n",
+    "for ind, celltype in enumerate(sorted_celltypes):\n",
+    "    ax = axes[ind]\n",
+    "    ax.errorbar(y=bios_replication_filtered_df.loc[celltype]['r'],\n",
+    "             x=[0.4],\n",
+    "             yerr=bios_replication_filtered_df.loc[celltype]['se_r'],\n",
+    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
+    "             markeredgecolor='black', markerfacecolor='black'\n",
+    "             )\n",
+    "    ax.errorbar(y=bios_replication_unfiltered_df.loc[celltype]['r'],\n",
+    "             x=[0.6],\n",
+    "             yerr=bios_replication_unfiltered_df.loc[celltype]['se_r'],\n",
+    "             fmt='.', markersize=6, marker='o', ecolor='black',\n",
+    "             markeredgecolor='black', markerfacecolor='white')\n",
+    "    ax.set_xlim([0, 1])\n",
+    "    ax.spines['bottom'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.spines['top'].set_color(bios_replication_filtered_df.loc[celltype]['color']) \n",
+    "    ax.spines['right'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.spines['left'].set_color(bios_replication_filtered_df.loc[celltype]['color'])\n",
+    "    ax.set_xticklabels([])\n",
+    "    ax.set_xlabel(celltype)\n",
+    "    \n",
+    "\n",
+    "plt.savefig('bios_replication_comparison.filter_and_unfilter.pdf')\n",
+    "plt.savefig('bios_replication_comparison.filter_and_unfilter.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# compare between filtered and unfiltered\n",
+    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'B', 'NK', 'DC']\n",
+    "fig, axes = plt.subplots(6, 2, figsize=(12, 12), sharex=True)\n",
+    "for i, celltype in enumerate(celltypes):\n",
+    "    replication_celltypes = [ct for ct in celltypes]\n",
+    "    ax1, ax2 = axes[i, :]\n",
+    "    ax1.scatter(x=replication_celltypes,\n",
+    "                 y=numcoeqtl_df[celltype].loc[replication_celltypes])\n",
+    "    ax1.scatter(x=replication_celltypes,\n",
+    "                y=unnumcoeqtl_df[celltype].loc[replication_celltypes])\n",
+    "    ax2.errorbar(x=replication_celltypes, fmt='.', markersize=12,\n",
+    "                 y=rb_df[celltype].loc[replication_celltypes],\n",
+    "                 yerr=rbse_df[celltype].loc[replication_celltypes], label='filtered')\n",
+    "    ax2.errorbar(x=replication_celltypes, fmt='.', markersize=12,\n",
+    "                 y=unrb_df[celltype].loc[replication_celltypes],\n",
+    "                 yerr=unrbse_df[celltype].loc[replication_celltypes], label='Unfiltered')\n",
+    "    ax1.set_ylabel(celltype)\n",
+    "ax2.legend()\n",
+    "\n",
+    "plt.savefig('celltype_rb.comparison_filtered_unfiltered_results.pdf')\n",
+    "plt.savefig('celltype_rb.comparison_filtered_unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sub celltypes in monocytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>r</th>\n",
+       "      <th>se_r</th>\n",
+       "      <th>p</th>\n",
+       "      <th>celltype_discovery</th>\n",
+       "      <th>celltype_replication</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.971431</td>\n",
+       "      <td>0.048402</td>\n",
+       "      <td>1.351820e-89</td>\n",
+       "      <td>ncMono</td>\n",
+       "      <td>cMono</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.929081</td>\n",
+       "      <td>0.088678</td>\n",
+       "      <td>1.101982e-25</td>\n",
+       "      <td>ncMono</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.936797</td>\n",
+       "      <td>0.025409</td>\n",
+       "      <td>1.468276e-297</td>\n",
+       "      <td>cMono</td>\n",
+       "      <td>ncMono</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.999726</td>\n",
+       "      <td>0.000613</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>cMono</td>\n",
+       "      <td>monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.896203</td>\n",
+       "      <td>0.036240</td>\n",
+       "      <td>5.115902e-135</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>ncMono</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.949824</td>\n",
+       "      <td>0.008640</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>cMono</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          r      se_r              p celltype_discovery celltype_replication\n",
+       "1  0.971431  0.048402   1.351820e-89             ncMono                cMono\n",
+       "2  0.929081  0.088678   1.101982e-25             ncMono             monocyte\n",
+       "3  0.936797  0.025409  1.468276e-297              cMono               ncMono\n",
+       "4  0.999726  0.000613   0.000000e+00              cMono             monocyte\n",
+       "5  0.896203  0.036240  5.115902e-135           monocyte               ncMono\n",
+       "6  0.949824  0.008640   0.000000e+00           monocyte                cMono"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_mono_res_df = pd.read_csv(workdir/'output/filtered_results/rb_calculations/monocyte_subcelltypes/summary.csv', \n",
+    "                                   index_col=0)\n",
+    "filtered_mono_res_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# filtered results\n",
+    "mono_subcelltypes = ['monocyte', 'cMono', 'ncMono']\n",
+    "monorb_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                     columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "monorbse_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "monorbpvalue_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "mononumcoeqtl_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "monoanno_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "mononum_anno_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                     columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "\n",
+    "for discovery_celltype in mono_subcelltypes:\n",
+    "    # replication in other celltypes\n",
+    "    for replication_celltype in mono_subcelltypes:\n",
+    "        if discovery_celltype != replication_celltype:\n",
+    "            monorb_results = filtered_mono_res_df[(filtered_mono_res_df['celltype_discovery'] == discovery_celltype) &\n",
+    "                                         (filtered_mono_res_df['celltype_replication'] == replication_celltype)]\n",
+    "            monoreplicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/filtered_results/rb_calculations/monocyte_subcelltypes/discovery_{discovery_celltype}_replication_{replication_celltype}.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t',\n",
+    "                index_col=0\n",
+    "            ).shape[0]\n",
+    "            if monorb_results['r'].values[0] < 10:\n",
+    "                monorb_df.loc[replication_celltype, discovery_celltype] = monorb_results['r'].values[0]\n",
+    "                monorbse_df.loc[replication_celltype, discovery_celltype] = monorb_results['se_r'].values[0]\n",
+    "                monorbpvalue_df.loc[replication_celltype, discovery_celltype] = monorb_results['p'].values[0]\n",
+    "                mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
+    "                monorbvalue = monorb_results['r'].values[0]\n",
+    "                monorbsevalue = monorb_results['se_r'].values[0]\n",
+    "                monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"rb={monorbvalue:.2f}\\nN={monoreplicated_coeqtls_num}\"\n",
+    "                mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={monoreplicated_coeqtls_num}\"\n",
+    "            else:\n",
+    "                monorb_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                monorbse_df.loc[replication_celltype, discovery_celltype] = np.nan\n",
+    "                monorbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "                mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
+    "                monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"rb=NA\\nN={monoreplicated_coeqtls_num}\"\n",
+    "                mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={monoreplicated_coeqtls_num}\"\n",
+    "        else:\n",
+    "            monorb_df.loc[replication_celltype, discovery_celltype] = 1\n",
+    "            monorbse_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            monorbpvalue_df.loc[replication_celltype, discovery_celltype] = 0\n",
+    "            monoreplicated_coeqtls_num = pd.read_csv(\n",
+    "                workdir/f'output/filtered_results/UT_{discovery_celltype}/coeqtls_fullresults_fixed.sig.tsv.gz',\n",
+    "                compression='gzip',\n",
+    "                sep='\\t'\n",
+    "            ).shape[0]\n",
+    "            mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] = monoreplicated_coeqtls_num\n",
+    "            monoanno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={monoreplicated_coeqtls_num}\"\n",
+    "            mononum_anno_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "                f\"N={monoreplicated_coeqtls_num}\"\n",
+    "    \n",
+    "monoreplicated_ratio_df = pd.DataFrame(data=np.zeros((len(mono_subcelltypes), len(mono_subcelltypes))), \n",
+    "                       columns=mono_subcelltypes, index=mono_subcelltypes)\n",
+    "for discovery_celltype in mononumcoeqtl_df.columns:\n",
+    "    for replication_celltype in mononumcoeqtl_df.index:\n",
+    "        monoreplicated_ratio_df.loc[replication_celltype, discovery_celltype] = \\\n",
+    "        mononumcoeqtl_df.loc[replication_celltype, discovery_celltype] / mononumcoeqtl_df.loc[discovery_celltype, discovery_celltype]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>monocyte</th>\n",
+       "      <th>cMono</th>\n",
+       "      <th>ncMono</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>monocyte</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.826087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cMono</th>\n",
+       "      <td>0.996441</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.826087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ncMono</th>\n",
+       "      <td>0.985765</td>\n",
+       "      <td>0.980645</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          monocyte     cMono    ncMono\n",
+       "monocyte  1.000000  1.000000  0.826087\n",
+       "cMono     0.996441  1.000000  0.826087\n",
+       "ncMono    0.985765  0.980645  1.000000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monoreplicated_ratio_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-1-1162ad6d26ab>:60: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels([\"\"]+col_labels)\n",
+      "<ipython-input-1-1162ad6d26ab>:61: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_yticklabels([\"\"]+row_labels)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax1, ax2 = axes\n",
+    "\n",
+    "im1, bar = heatmap(monoreplicated_ratio_df.values, \n",
+    "        list(monorb_df.index), \n",
+    "        list(monorb_df.columns),\n",
+    "                 cmap=\"viridis\",\n",
+    "                 ax=ax1)\n",
+    "\n",
+    "\n",
+    "_ = annotate_heatmap(im1, \n",
+    "                     data=monoreplicated_ratio_df.values, \n",
+    "                     valfmt=\"{x:.0%}\", \n",
+    "                     color=\"black\",\n",
+    "                     threshold=1)\n",
+    "\n",
+    "im2, bar = heatmap(monorb_df.values, \n",
+    "        list(monorb_df.index), \n",
+    "        list(monorb_df.columns),\n",
+    "                 cmap=\"viridis\",\n",
+    "                 ax=ax2)\n",
+    "\n",
+    "\n",
+    "_ = annotate_heatmap(im2, \n",
+    "                     data=monoanno_df.values, \n",
+    "                     valfmt=\"{x:^}\", \n",
+    "                     color=\"black\",\n",
+    "                     threshold=1)\n",
+    "\n",
+    "plt.savefig('cmono_ncmono_mono.filtered_results.pdf')\n",
+    "plt.savefig('cmono_ncmono_mono.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Non-zero ratio and co-expression mean and variances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "celltype = 'monocyte'\n",
+    "annotated_coeqtl_df = pd.DataFrame()\n",
+    "for celltype in celltypes:\n",
+    "    celltype_annotated_coeqtl_df = pd.read_csv(\n",
+    "        workdir/f'output/filtered_results/UT_{celltype}/coeqtls_fullresults_fixed.all.annotated.tsv.gz',\n",
+    "                                     compression='gzip',\n",
+    "                                     sep='\\t'\n",
+    "    )[['mean_onemillionv2', 'var_onemillionv2', \n",
+    "       'gene2_nonzeroratio_onemillionv2',\n",
+    "       'eqtlgene_nonzeroratio_onemillionv2',\n",
+    "       'gene2_isSig']]\n",
+    "    celltype_annotated_coeqtl_df['celltype'] = celltype\n",
+    "    annotated_coeqtl_df = pd.concat([annotated_coeqtl_df, \n",
+    "                                     celltype_annotated_coeqtl_df],\n",
+    "                                   axis=0)\n",
+    "    \n",
+    "annotated_coeqtl_df_clean = annotated_coeqtl_df.replace([np.inf, -np.inf], np.nan, inplace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='celltype', ylabel='mean_onemillionv2'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
+    "            y=abs(annotated_coeqtl_df_clean['mean_onemillionv2']),\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "            fliersize=1,\n",
+    "              palette='viridis',\n",
+    "              showfliers = False)\n",
+    "# plt.savefig('mean_onemillionv2.filtered_results.pdf')\n",
+    "# plt.savefig('mean_onemillionv2.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='celltype', ylabel='var_onemillionv2'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
+    "            y=annotated_coeqtl_df_clean['var_onemillionv2'],\n",
+    "              hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "              palette='viridis', fliersize=1,\n",
+    "              showfliers = False)\n",
+    "# plt.savefig('var_onemillionv2.filtered_results.pdf')\n",
+    "# plt.savefig('var_onemillionv2.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='celltype', ylabel='gene2_nonzeroratio_onemillionv2'>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
+    "            y=annotated_coeqtl_df_clean['gene2_nonzeroratio_onemillionv2'],\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "            palette='viridis', fliersize=1, showfliers = False)\n",
+    "# plt.savefig('gene2_nonzeroratio_onemillionv2.filtered_results.pdf')\n",
+    "# plt.savefig('gene2_nonzeroratio_onemillionv2.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='celltype', ylabel='eqtlgene_nonzeroratio_onemillionv2'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
+    "            y=annotated_coeqtl_df_clean['eqtlgene_nonzeroratio_onemillionv2'],\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "           palette='viridis', fliersize=1, showfliers = False)\n",
+    "# plt.savefig('eqtlgene_nonzeroratio_onemillionv2.filtered_results.pdf')\n",
+    "# plt.savefig('eqtlgene_nonzeroratio_onemillionv2.filtered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### unfiltered results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CD4T\n",
+      "CD8T\n",
+      "monocyte\n",
+      "DC\n",
+      "NK\n",
+      "B\n"
+     ]
+    }
+   ],
+   "source": [
+    "celltype = 'monocyte'\n",
+    "annotated_coeqtl_df = pd.DataFrame()\n",
+    "for celltype in celltypes:\n",
+    "    print(celltype)\n",
+    "    celltype_annotated_coeqtl_df = pd.read_csv(workdir/f'output/unfiltered_results/UT_{celltype}/coeqtls_fullresults_fixed.all.annotated.tsv.gz',\n",
+    "                                     compression='gzip',\n",
+    "                                     sep='\\t')[['mean_onemillionv2', 'var_onemillionv2', \n",
+    "                                                'gene2_nonzeroratio_onemillionv2',\n",
+    "                                                'eqtlgene_nonzeroratio_onemillionv2',\n",
+    "                                                'gene2_isSig']]\n",
+    "    celltype_annotated_coeqtl_df['celltype'] = celltype\n",
+    "    annotated_coeqtl_df = pd.concat([annotated_coeqtl_df, \n",
+    "                                     celltype_annotated_coeqtl_df],\n",
+    "                                   axis=0)\n",
+    "    \n",
+    "annotated_coeqtl_df_clean = annotated_coeqtl_df.replace([np.inf, -np.inf], np.nan, inplace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
+    "            y=abs(annotated_coeqtl_df_clean['mean_onemillionv2']),\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "            fliersize=1,\n",
+    "              palette='Paired',\n",
+    "              showfliers = False)\n",
+    "plt.savefig('mean_onemillionv2.unfiltered_results.pdf')\n",
+    "plt.savefig('mean_onemillionv2.unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
+    "            y=annotated_coeqtl_df_clean['var_onemillionv2'],\n",
+    "              hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "              palette='Paired', fliersize=1,\n",
+    "              showfliers = False)\n",
+    "plt.savefig('var_onemillionv2.unfiltered_results.pdf')\n",
+    "plt.savefig('var_onemillionv2.unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'],\n",
+    "            y=annotated_coeqtl_df_clean['gene2_nonzeroratio_onemillionv2'],\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "            palette='Paired', fliersize=1, showfliers = False)\n",
+    "plt.savefig('gene2_nonzeroratio_onemillionv2.unfiltered_results.pdf')\n",
+    "plt.savefig('gene2_nonzeroratio_onemillionv2.unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=annotated_coeqtl_df_clean['celltype'], \n",
+    "            y=annotated_coeqtl_df_clean['eqtlgene_nonzeroratio_onemillionv2'],\n",
+    "            hue=annotated_coeqtl_df_clean['gene2_isSig'],\n",
+    "           palette='Paired', fliersize=1, showfliers = False)\n",
+    "plt.savefig('eqtlgene_nonzeroratio_onemillionv2.unfiltered_results.pdf')\n",
+    "plt.savefig('eqtlgene_nonzeroratio_onemillionv2.unfiltered_results.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/04_coeqtl_mapping/replication_in_bios.py b/04_coeqtl_mapping/replication_in_bios.py
new file mode 100644
index 0000000..e20c8c2
--- /dev/null
+++ b/04_coeqtl_mapping/replication_in_bios.py
@@ -0,0 +1,233 @@
+import argparse
+import os
+import subprocess
+from pathlib import Path
+
+import pandas as pd
+import statsmodels.api as sm
+from statsmodels.stats.multitest import multipletests
+from tqdm import tqdm
+
+workdir = Path("./coeqtl_mapping")
+bios_exp_path = 'BIOS_NoRNAPhenoNA_NoSexNA_NoMixups_NoMDSOutlier_20RNAseqAlignemntMetrics/data/gene_read_counts_BIOS_and_LLD_passQC.tsv.SampleSelection.ProbesWithZeroVarianceRemoved.TMM.SampleSelection.ProbesWithZeroVarianceRemoved.Log2Transformed.ProbesCentered.SamplesZTransformed.CovariatesRemovedOLS.txt.gz'
+
+unique_mappingfile = "./resources/features_v3_reformated_names.tsv"
+bios_gt_prefix = Path('./genotypes-hrc-imputed-vcf/')
+gte_mapping_path = "./coeqtl_mapping/bios/gte.tsv"
+
+
+def get_snps_from_vcffile(bashfile_path, vcf_path, snps_path, savepath):
+    response = subprocess.run([bashfile_path, vcf_path, snps_path, savepath])
+    print(response)
+    return None
+
+
+def get_genes_from_gzipfile(expression_path, gene_path, savepath):
+    print("Loading exp dataframe...")
+    exp_df = pd.read_csv(expression_path, sep='\t', index_col=0, compression='gzip')
+    print("Full exp loaded.")
+    genes = pd.read_csv(gene_path, sep='\t')['ensembl']
+    common_genes = list(set(genes) & set(exp_df.index.values))
+    print(f"Selecting {len(common_genes)} to save. {len(genes) - len(common_genes)} genes not found in BIOS")
+    selected_exp_df = exp_df.loc[common_genes]
+    genes_dic = pd.read_csv(gene_path, sep='\t').set_index('ensembl')['symbol'].T.to_dict()
+    common_genes_names = [genes_dic.get(geneid) for geneid in common_genes]
+    selected_exp_df.index = common_genes_names
+    selected_exp_df.to_csv(savepath, sep='\t')
+    print(f"Selected {selected_exp_df.shape[0]} genes in {savepath}.")
+    return selected_exp_df
+
+
+def make_snps_genes_files_for_coeqtls(coeqtl_path):
+    significant_coeqtls = pd.read_csv(coeqtl_path, sep='\t', compression='gzip', index_col=0)
+    mappings = pd.read_csv(unique_mappingfile, sep='\t', names=['geneid', 'genename', 'type']).set_index('genename')[
+        'geneid'].T.to_dict()
+    snps = significant_coeqtls['SNP'].unique()
+    genes = [ele for item in significant_coeqtls['Gene'].values for ele in item.split(';')]
+    genes = list(set([item for item in genes if item]))
+    genes_df = pd.DataFrame(data=[[item, mappings.get(item)] for item in genes],
+                            columns=['symbol', 'ensembl']).dropna(subset=['ensembl'])
+    print(f"Writing {len(snps)} snps and {len(genes)} genes from coeQTLs.")
+    with open(f"{str(coeqtl_path)[:-len('.tsv.gz')]}.snps.txt", 'w') as f:
+        f.write('\n'.join(snps))
+    genes_df.to_csv(f"{str(coeqtl_path)[:-len('.tsv.gz')]}.genes.tsv",
+                    sep='\t', index=False)
+    return snps, genes_df
+
+
+def replicate(annotated_coeqtl_path,
+              bios_gt_path,
+              bios_gene_path,
+              saveprefix,
+              gte_mapping_path,
+              vcf_header_rows=6):
+    import warnings
+    def find_gene2(eqtlgene, genepair):
+        gene1, gene2 = genepair.split(';')
+        if eqtlgene == gene1:
+            return gene2
+        else:
+            return gene1
+    warnings.simplefilter(action='ignore', category=FutureWarning)
+    gte_mapping = pd.read_csv(gte_mapping_path, sep='\t').set_index('gt')['exp'].T.to_dict()
+    # transform the GT columns into expression ids
+    gt = pd.read_csv(bios_gt_path, skiprows=vcf_header_rows, sep='\t')
+    sc_individuals = pd.read_csv(
+        './coeqtl_mapping/input/summary/gte-fix.tsv',
+        sep='\t'
+    )['genotypesampleID']
+    # remove LLD individuals
+    remove_individuals = list(set(sc_individuals) & set(gt.columns))
+    gt = gt.drop(remove_individuals, axis=1)
+    gt_snp_set = set(gt['ID'].values)
+    # map genotype and expression individual names
+    find_name = lambda x: gte_mapping.get(x) if x in gte_mapping else x
+    gt = gt.rename({item: find_name(item) for item in gt.columns}, axis=1)
+    # load expression data
+    exp = pd.read_csv(bios_gene_path, index_col=0, sep='\t', compression='gzip')
+    genename_mapping = pd.read_csv(unique_mappingfile, sep='\t', names=['gene_id', 'gene_name']).set_index('gene_id')[
+        'gene_name'].T.to_dict()
+    exp['genename'] = [genename_mapping.get(geneid) for geneid in exp.index]
+    exp = exp.dropna(subset=['genename']).set_index('genename')
+    expression_gene_name_set = set(exp.index)
+    common_indidvidauls = list(set(exp.columns) & set(gt.columns))
+    gt_df = gt.set_index('ID')
+    exp_common = exp[common_indidvidauls]
+    coeqtls = pd.read_csv(annotated_coeqtl_path, sep='\t', compression='gzip', index_col=0)
+    coeqtls['gene1'] = [item.split('_')[1] for item in coeqtls['snp_eqtlgene']]
+    coeqtls['gene2'] = [find_gene2(gene1, genepair) for (gene1, genepair) in coeqtls[['gene1', 'Gene']].values]
+    coeqtl_pairs = coeqtls[['SNP', 'gene1', 'gene2']].values
+    # start solving the interaction models
+    i = 0
+    res_df = pd.DataFrame()
+    for snp, gene1, gene2 in tqdm(coeqtl_pairs):
+        if snp in gt_snp_set and gene1 in expression_gene_name_set and gene2 in expression_gene_name_set:  # todo: ESNG id to genename
+            i += 1
+            gt_selected = gt_df.loc[snp]
+            gene1_selected = exp_common.loc[gene1]
+            gene2_selected = exp_common.loc[gene2]
+            x_df = pd.concat([gt_selected[common_indidvidauls], gene2_selected], axis=1)
+            x_df[f'{snp}_dosage'] = [float(item.split(':')[1]) for item in x_df[snp]]
+            x_df[f'{snp}_{gene2}'] = x_df[f'{snp}_dosage'] * x_df[gene2]
+            X = sm.add_constant(x_df[[f'{snp}_dosage', gene2, f'{snp}_{gene2}']])
+            model = sm.OLS(gene1_selected.T, X)
+            results_data = model.fit().summary().tables[1].data
+            results = pd.DataFrame(data=results_data[1:], columns=results_data[0]).set_index('')
+            results['gene1'] = gene1
+            results['gene2'] = gene2
+            results['assessed_allele'] = gt_selected['ALT']
+            results['num_individuals'] = len(common_indidvidauls)
+            res_df = pd.concat([res_df, results], axis=0)
+            if len(coeqtl_pairs) > 10000:
+                if i % 10000 == 0 and i > 1:
+                    res_df.to_csv(f"{saveprefix}.part{int(i / 10000)}.tsv", sep='\t')
+                    res_df = pd.DataFrame()
+                    print(f"results part {int(i / 10000)} has been saved in {saveprefix}.part{int(i / 10000)}.tsv")
+    part_ind = 1 + int(i / 10000)
+    res_df.to_csv(f"{saveprefix}.part{part_ind}.tsv.gz", sep='\t', compression='gzip')
+    print(f"results part {part_ind} has been saved in {saveprefix}.part{part_ind}.tsv")
+    return res_df
+
+
+def make_gte_mapping_file():
+    prefix = Path("./tmp03boxy/input/")
+    gtm = pd.DataFrame()
+    for filename in os.listdir(prefix / "hrcGTM"):
+        sub_gtm = pd.read_csv(prefix / f"hrcGTM/{filename}", compression='gzip', sep='\t', names=['gt', 'met'])
+        gtm = pd.concat([gtm, sub_gtm], axis=0)
+    gtm = gtm.set_index('met')
+    mte_path = prefix / 'hrcMTE/CODAM_LLDeep_LLS660Q_LLSOmni_NTR_RS_MTE.txt'
+    mte = pd.read_csv(mte_path, sep='\t', names=['met', 'exp']).set_index('met')
+    all_mapping = pd.concat([gtm, mte], axis=1)
+    gte = all_mapping[['gt', 'exp']]
+    gte = gte.dropna()
+    gte.to_csv('./coeqtl_mapping/bios/gte.tsv',
+               sep='\t', index=False)
+    return gte
+
+
+def examine_replicated_in_bios(replication_res_path, savepath):
+    from statsmodels.stats.multitest import multipletests
+    bios_replication = pd.read_csv(replication_res_path, sep='\t')
+    bios_replication['snp_genepair'] = ['_'.join(item) for item in bios_replication[['Unnamed: 0', 'gene1']].values]
+    tobesave = lambda x: True if 'dosage' not in x and 'const' not in x and x.startswith('rs') else False
+    bios_replication['isinteractionterm'] = [tobesave(item) for item in bios_replication['snp_genepair']]
+    bios_interactions_df = bios_replication[bios_replication['isinteractionterm']]
+    bios_interactions_df['corrected_p'] = multipletests(bios_interactions_df['P>|t|'], method='fdr_bh')[1]
+    bios_interactions_df.to_csv(savepath, sep='\t')
+    significant_res = bios_interactions_df[bios_interactions_df['corrected_p'] <= 0.05]
+    print("Significantly replicated coeQTLs: ", significant_res.shape[0])
+    return bios_interactions_df
+
+
+def arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--saveprefix', type=str, dest='saveprefix')
+    parser.add_argument('--coeqtlpath', type=str, dest='coeqtlpath')
+    parser.add_argument('--selection', type=str, dest='selection')
+    parser.add_argument('--chromosome', type=str, dest='chr')
+    parser.add_argument('--replicate', type=str, dest='replicate')
+    parser.add_argument('--bios_selected_vcf', type=str, dest='bios_selected_vcf')
+    return parser
+
+
+if __name__ == '__main__':
+    # _ = make_gte_mapping_file() # make the GTE files for BIOS
+    args = arguments().parse_args()
+    print("Arguments:")
+    print(args)
+    # get snps and genes from coeQTL results
+    coeqtl_path = args.coeqtlpath
+    savedirectory = Path(args.saveprefix)
+    if not os.path.isdir(savedirectory):
+        os.makedirs(savedirectory)
+    if args.selection == 'snp':
+        if not os.path.exists(f"{coeqtl_path[:-len('.tsv.gz')]}.snps.txt"):
+            _ = make_snps_genes_files_for_coeqtls(coeqtl_path)
+        # get snps from vcf
+        chromosome = args.chr
+        snp_bashfile_path = workdir / 'bios/select_snps_from_vcf.sh'
+        dataname = f"chr{chromosome}"
+        bios_gt_path = bios_gt_prefix / f"chr{chromosome}/GenotypeData.vcf.gz"
+        snp_savepath = savedirectory / f'bios_selected.chr{chromosome}.vcf'
+        get_snps_from_vcffile(snp_bashfile_path, bios_gt_path, f"{coeqtl_path[:-len('.tsv.gz')]}.snps.txt",
+                              snp_savepath)
+    elif args.selection == 'gene':
+        if not os.path.exists(f"{coeqtl_path[:-len('.tsv.gz')]}.snps.txt"):
+            _ = make_snps_genes_files_for_coeqtls(coeqtl_path)
+        # get genes from gzip files
+        gene_savepath = savedirectory / f'bios_selected_gene_expression.tsv'
+        _ = get_genes_from_gzipfile(bios_exp_path, f"{coeqtl_path[:-len('.tsv.gz')]}.genes.tsv", gene_savepath)
+    if args.replicate:
+        # perform replication in bios
+        work_prefix = Path("./coeqtl_mapping/")
+        bios_gt_path = args.bios_selected_vcf
+        vcf_header_rows = 6
+        bios_gene_path = bios_exp_path
+        saveprefix = savedirectory / 'bios_replication_results.eqtlgene1_gene2'
+        replicate(annotated_coeqtl_path=coeqtl_path,
+                  bios_gt_path=bios_gt_path,
+                  bios_gene_path=bios_gene_path,
+                  saveprefix=saveprefix,
+                  gte_mapping_path=gte_mapping_path,
+                  vcf_header_rows=vcf_header_rows)
+        # concatenate replication results saved in parts
+        res_df = pd.DataFrame()
+        for filename in os.listdir(savedirectory):
+            if filename.startswith('bios_replication_results.eqtlgene1_gene2.') and 'part' in filename and filename.endswith('gz'):
+                print(filename)
+                df = pd.read_csv(savedirectory / filename, sep='\t', compression='gzip')
+                res_df = pd.concat([res_df, df], axis=0)
+        tobesave = lambda x: True if 'dosage' not in x and 'const' not in x and x.startswith('rs') else False
+        res_df['isinteractionterm'] = [tobesave(item) for item in res_df['Unnamed: 0']]
+        bios_interactions_df = res_df[res_df['isinteractionterm']]
+        bios_interactions_df['snp_genepair'] = ['_'.join([item[0].split('_')[0],
+                                                          ';'.join(sorted([item[0].split('_')[1], item[1]]))]) for item
+                                                in
+                                                bios_interactions_df[['Unnamed: 0', 'gene1']].values]
+        bios_interactions_df['corrected_p'] = multipletests(bios_interactions_df['P>|t|'], method='fdr_bh')[1]
+        bios_interactions_df.to_csv(savedirectory / 'bios_replication_results.eqtlgene1_gene2.all.tsv.gz',
+                                    sep='\t', index=False, compression='gzip')
+        bios_interactions_df[bios_interactions_df['corrected_p'] <= 0.05].to_csv(
+            savedirectory / 'bios_replication_results.eqtlgene1_gene2.sig.tsv.gz',
+            sep='\t', index=False, compression='gzip')
diff --git a/04_coeqtl_mapping/screen_permutation_p_values.py b/04_coeqtl_mapping/screen_permutation_p_values.py
new file mode 100644
index 0000000..2812dc8
--- /dev/null
+++ b/04_coeqtl_mapping/screen_permutation_p_values.py
@@ -0,0 +1,130 @@
+import argparse
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import gzip
+
+
+workdir = Path('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping')
+annotation_path = '/groups/umcg-bios/tmp01/projects/1M_cells_scRNAseq/ongoing/eQTL_mapping/probeannotation/singleCell-annotation-stripped.tsv'
+mappingdic = pd.read_csv('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/resources/features_v3_reformated_names.tsv',
+                          sep='\t', names=['geneid', 'genename']).set_index('geneid')['genename'].T.to_dict()
+annotation_df = pd.read_csv(annotation_path, sep='\t')
+annotation_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in annotation_df[['Chr', 'ChrStart', 'ChrEnd']].values]
+annotation_df['genename'] = [mappingdic.get(ensemblid) for ensemblid in annotation_df['Ensembl']]
+annotation_dict = annotation_df.set_index('chr_pos')['genename'].T.to_dict()
+
+def update_perm(old_p_list, new_p_list):
+    return np.min([old_p_list, new_p_list], axis=0)
+
+
+def find_eqtlsnp_gene(snp, genepair, coeqtl_annotation_dic):
+    genepair_chrpos = coeqtl_annotation_dic.get(genepair)
+    eqtlgene = annotation_dict.get(genepair_chrpos)
+    snp_genepair = '_'.join([snp, eqtlgene])
+    return snp_genepair
+
+
+def loop_through_one_batch_perm(batch_perm_path, snpgene1_minpvalues_dict, coeqtl_annotation_dict):
+    with gzip.open(batch_perm_path, 'rb') as f:
+        f.readline()
+        while True:
+            line = f.readline().decode('utf-8')
+            if not line:
+                break
+            else:
+                linecontent = line.strip().split('\t')
+                perm_ps = [float(ele) for ele in linecontent[2:102]]
+                snp_gene1 = find_eqtlsnp_gene(linecontent[1], linecontent[0], coeqtl_annotation_dict)
+                snpgene1_minpvalues_dict[snp_gene1] = update_perm(snpgene1_minpvalues_dict[snp_gene1],
+                                                                      perm_ps)
+    return snpgene1_minpvalues_dict
+
+
+def update_dictionary_per_permutation_batch(batch_perm_path, snpgene1_minpvalues_df, coeqtl_annotation_dict):
+    batch_perm_df = pd.read_csv(batch_perm_path, compression='gzip', sep='\t')
+    # print(batch_perm_df.head())
+    batch_perm_df['chr_pos'] = [coeqtl_annotation_dict.get(genepair) for genepair in batch_perm_df['Gene']]
+    batch_perm_df['eqtlgene'] = [annotation_dict.get(chrpos) for chrpos in batch_perm_df['chr_pos']]
+    batch_perm_df['snp_eqtlgene'] = ['_'.join(item) for item in batch_perm_df[['SNP', 'eqtlgene']].values]
+    # print(batch_perm_df.head())
+    merge_columns = ['snp_eqtlgene'] + [f'Perm{ind}' for ind in range(100)]
+    merged_df = pd.concat([batch_perm_df[merge_columns], snpgene1_minpvalues_df[merge_columns]],
+                          axis=0)
+    # print(merged_df.head())
+    reduced_df = merged_df.groupby(by='snp_eqtlgene').agg(min)
+    reduced_df['snp_eqtlgene'] = reduced_df.index
+    # print(reduced_df.head())
+    return reduced_df
+
+
+def save_numpy(data_df, prefix):
+    np.save(f'{prefix}.npy', data_df.values)
+    with open(f'{prefix}.cols.txt', 'w') as f:
+        f.write('\n'.join([str(ele) for ele in data_df.columns]))
+    with open(f'{prefix}.rows.txt', 'w') as f:
+        f.write('\n'.join([str(ele) for ele in data_df.index]))
+    return None
+
+
+def arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--eqtl_path', dest='eqtl_path')
+    parser.add_argument('--result_prefix', dest='result_prefix')
+    parser.add_argument('--save_prefix', dest='save_prefix')
+    parser.add_argument('--annotation_prefix', dest='annotation_prefix')
+    return parser
+
+
+
+def main():
+    args = arguments().parse_args()
+    eqtl_path, results_prefix, save_prefix = args.eqtl_path, Path(args.result_prefix), Path(args.save_prefix)
+    # load eqtl path
+    eqtl_df = pd.read_csv(eqtl_path, sep='\t')
+    eqtl_df['snp_gene1'] = ['_'.join(item) for item in eqtl_df[['SNPName', 'genename']].values]
+    unique_snpgene1 = eqtl_df['snp_gene1'].values
+    # initialize the dict to contain the
+    # snpgene1_minpvalues_dict = {item: np.ones(100) for item in unique_snpgene1}
+    snpgene1_minpvalues_df = pd.DataFrame(data=np.ones((len(unique_snpgene1), 100)),
+                                          columns=[f'Perm{ind}' for ind in range(100)])
+    snpgene1_minpvalues_df['snp_eqtlgene'] = unique_snpgene1
+    # loop through all batch permutation files
+    coeqtl_annotation_path = f'{args.annotation_prefix}.genepairs.annotation.gene1position.noduplicated.tsv'
+    coeqtl_annotation_df = pd.read_csv(coeqtl_annotation_path, sep='\t')
+    coeqtl_annotation_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in
+                                       coeqtl_annotation_df[['Chr', 'ChrStart', 'ChrEnd']].values]
+    coeqtl_annotation_dict = coeqtl_annotation_df.set_index('ArrayAddress')['chr_pos'].T.to_dict()
+    for filename in tqdm(os.listdir(results_prefix / 'noduplicated/output')):
+        if '-Permutations.txt.gz' in filename:
+            snpgene1_minpvalues_df = update_dictionary_per_permutation_batch(results_prefix / 'noduplicated/output'/filename,
+                                                                       snpgene1_minpvalues_df, coeqtl_annotation_dict)
+    coeqtl_annotation_path = f'{args.annotation_prefix}.genepairs.annotation.gene1position.duplicatedversion1.tsv'
+    coeqtl_annotation_df = pd.read_csv(coeqtl_annotation_path, sep='\t')
+    coeqtl_annotation_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in
+                                       coeqtl_annotation_df[['Chr', 'ChrStart', 'ChrEnd']].values]
+    coeqtl_annotation_dict = coeqtl_annotation_df.set_index('ArrayAddress')['chr_pos'].T.to_dict()
+    for filename in tqdm(os.listdir(results_prefix / 'duplicatedversion1/output')):
+        if '-Permutations.txt.gz' in filename:
+            snpgene1_minpvalues_df = update_dictionary_per_permutation_batch(results_prefix / 'duplicatedversion1/output'/filename,
+                                                                   snpgene1_minpvalues_df, coeqtl_annotation_dict)
+    coeqtl_annotation_path = f'{args.annotation_prefix}.genepairs.annotation.gene1position.duplicatedversion2.tsv'
+    coeqtl_annotation_df = pd.read_csv(coeqtl_annotation_path, sep='\t')
+    coeqtl_annotation_df['chr_pos'] = ['_'.join([str(ele) for ele in item]) for item in
+                                       coeqtl_annotation_df[['Chr', 'ChrStart', 'ChrEnd']].values]
+    coeqtl_annotation_dict = coeqtl_annotation_df.set_index('ArrayAddress')['chr_pos'].T.to_dict()
+    for filename in tqdm(os.listdir(results_prefix / 'duplicatedversion2/output')):
+        if '-Permutations.txt.gz' in filename:
+            snpgene1_minpvalues_df = update_dictionary_per_permutation_batch(results_prefix / 'duplicatedversion2/output'/filename,
+                                                                   snpgene1_minpvalues_df, coeqtl_annotation_dict)
+    # snpgene1_minpvalues_df = pd.DataFrame.from_dict(snpgene1_minpvalues_dict)
+    snpgene1_minpvalues_df.to_csv(save_prefix / 'concated_alltests_permutations_fixed.tsv.gz',
+                                    sep='\t', compression='gzip')
+    return snpgene1_minpvalues_df
+
+
+if __name__ == '__main__':
+    _ = main()
diff --git a/04_coeqtl_mapping/select_snps_from_vcf.sh b/04_coeqtl_mapping/select_snps_from_vcf.sh
new file mode 100644
index 0000000..cc710c4
--- /dev/null
+++ b/04_coeqtl_mapping/select_snps_from_vcf.sh
@@ -0,0 +1,8 @@
+#!/usr/bin/env bash
+
+vcfpath=$1
+snpspath=$2
+savepath=$3
+
+ml BCFtools
+bcftools view --include ID==@${snpspath} ${vcfpath} > ${savepath}
\ No newline at end of file
diff --git a/04_coeqtl_mapping/submit_individual_networks.sh b/04_coeqtl_mapping/submit_individual_networks.sh
new file mode 100644
index 0000000..7a57463
--- /dev/null
+++ b/04_coeqtl_mapping/submit_individual_networks.sh
@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+#SBATCH --time=16:00:00
+#SBATCH --mem=80gb
+#SBATCH --nodes=1
+#SBATCH --export=NONE
+#SBATCH --get-user-env=L
+
+module purge
+
+conda init bash
+source /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/etc/profile.d/conda.sh
+conda activate scpy3.8
+
+
+python /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/individual_networks/individual_networks.py \
+--datasetname $1 \
+--celltype $2 \
+--condition $3
\ No newline at end of file
diff --git a/04_coeqtl_mapping/submit_merge_coexpression.sh b/04_coeqtl_mapping/submit_merge_coexpression.sh
new file mode 100644
index 0000000..52f6789
--- /dev/null
+++ b/04_coeqtl_mapping/submit_merge_coexpression.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+#SBATCH --time=8:00:00
+#SBATCH --mem=80gb
+#SBATCH --nodes=1
+#SBATCH --export=NONE
+#SBATCH --get-user-env=L
+
+module purge
+
+conda init bash
+source /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/etc/profile.d/conda.sh
+conda activate scpy3.8
+
+
+python /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/individual_networks/merge_coexpression_for_betaeqtl.py \
+--celltype $1 \
+--condition $2
+
+python /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/input/individual_networks/prepare_genelist_and_annotation_for_betaqtl.py \
+--celltype $1 \
+--condition $2
\ No newline at end of file
diff --git a/04_coeqtl_mapping/submit_process_betaqtl_results.sh b/04_coeqtl_mapping/submit_process_betaqtl_results.sh
new file mode 100644
index 0000000..e3c45c8
--- /dev/null
+++ b/04_coeqtl_mapping/submit_process_betaqtl_results.sh
@@ -0,0 +1,51 @@
+#!/usr/bin/env bash
+#SBATCH --time=01:00:00
+#SBATCH --mem=20gb
+#SBATCH --nodes=1
+#SBATCH --open-mode=append
+#SBATCH --export=NONE
+#SBATCH --get-user-env=L
+
+module purge
+
+conda init bash
+source /groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/tools/Beeline/miniconda/etc/profile.d/conda.sh
+conda activate scpy3.8
+
+
+celltype=$1
+
+condition='UT'
+workdir="./"
+
+# unfiltered results
+python ${workdir}/output/concat_betaqtl_results.fixed.py \
+--prefix ${workdir}/output/unfiltered_results/${condition}_${celltype_individual} \
+--savepath ${workdir}/output/unfiltered_results/${condition}_${celltype_individual}/concated_alltests_output_fixed.tsv.gz \
+--annotation_prefix ${workdir}/input/summary/${condition}_${celltype}
+python ${workdir}/output/screen_permutation_p_values.py \
+--eqtl_path ${workdir}/input/snp_selection/eqtl/${condition}_${celltype}_eQTLProbesFDR0.05-ProbeLevel.tsv \
+--result_prefix ${workdir}/output/unfiltered_results/${condition}_${celltype_individual} \
+--save_prefix ${workdir}/output/unfiltered_results/${condition}_${celltype_individual} \
+--annotation_prefix ${workdir}/input/summary/${condition}_${celltype}
+python ${workdir}/output/multipletesting_correction.fixed.py \
+--permutation_pvalue_path ${workdir}/output/unfiltered_results/${condition}_${celltype_individual}/concated_alltests_permutations_fixed.tsv.gz \
+--coeqtl_path ${workdir}/output/unfiltered_results/${condition}_${celltype_individual}/concated_alltests_output_fixed.tsv.gz \
+--eqtl_path ${workdir}/input/snp_selection/eqtl/${condition}_${celltype}_eQTLProbesFDR0.05-ProbeLevel.tsv \
+--save_prefix ${workdir}/output/unfiltered_results/${condition}_${celltype_individual}/coeqtls_fullresults_fixed
+
+# filtered results
+python ${workdir}/output/concat_betaqtl_results.fixed.py \
+--prefix ${workdir}/output/filtered_results/${condition}_${celltype_individual} \
+--savepath ${workdir}/output/filtered_results/${condition}_${celltype_individual}/concated_alltests_output_fixed.tsv.gz \
+--annotation_prefix ${workdir}/input/summary/${condition}_${celltype}
+python ${workdir}/output/screen_permutation_p_values.py \
+--eqtl_path ${workdir}/input/snp_selection/eqtl/${condition}_${celltype}_eQTLProbesFDR0.05-ProbeLevel.tsv \
+--result_prefix ${workdir}/output/filtered_results/${condition}_${celltype_individual} \
+--save_prefix ${workdir}/output/filtered_results/${condition}_${celltype_individual} \
+--annotation_prefix ${workdir}/input/summary/${condition}_${celltype}
+python ${workdir}/output/multipletesting_correction.fixed.py \
+--permutation_pvalue_path ${workdir}/output/filtered_results/${condition}_${celltype_individual}/concated_alltests_permutations_fixed.tsv.gz \
+--coeqtl_path ${workdir}/output/filtered_results/${condition}_${celltype_individual}/concated_alltests_output_fixed.tsv.gz \
+--eqtl_path ${workdir}/input/snp_selection/eqtl/${condition}_${celltype}_eQTLProbesFDR0.05-ProbeLevel.tsv \
+--save_prefix ${workdir}/output/filtered_results/${condition}_${celltype_individual}/coeqtls_fullresults_fixed
diff --git a/05_coeqtl_interpretation/.ipynb_checkpoints/LDTRAIT-checkpoint.ipynb b/05_coeqtl_interpretation/.ipynb_checkpoints/LDTRAIT-checkpoint.ipynb
new file mode 100644
index 0000000..659b6fa
--- /dev/null
+++ b/05_coeqtl_interpretation/.ipynb_checkpoints/LDTRAIT-checkpoint.ipynb
@@ -0,0 +1,944 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import requests\n",
+    "from tqdm import tqdm\n",
+    "import os\n",
+    "from io import StringIO\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "savedir = Path(\"./annotated_coeqtl_snps/ldtrait\")\n",
+    "\n",
+    "celltypesnps = {}\n",
+    "merged_dict = pd.read_excel('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/summary/coeQTLs_6majorcelltypes.filtered.xlsx',\n",
+    "                         sheet_name=None)\n",
+    "for celltype in merged_dict.keys():\n",
+    "    celltypesnps[celltype] = list(merged_dict[celltype]['SNP'].unique())\n",
+    "allcelltypes_snps = list(set([ele for l in celltypesnps.values() for ele in l]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "72"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(allcelltypes_snps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 17%|█▋        | 12/72 [05:53<34:19, 34.33s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs62480001\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 18%|█▊        | 13/72 [06:52<39:48, 40.48s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs817352\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 19%|█▉        | 14/72 [07:11<33:43, 34.89s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs80164297\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 24%|██▎       | 17/72 [09:23<37:43, 41.16s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs11772922\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 26%|██▋       | 19/72 [10:43<35:14, 39.89s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs3758833\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 29%|██▉       | 21/72 [11:39<28:28, 33.49s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs11047696\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 31%|███       | 22/72 [12:09<27:15, 32.70s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs9971029\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 32%|███▏      | 23/72 [12:35<24:55, 30.53s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs4949655\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 42%|████▏     | 30/72 [16:56<26:11, 37.41s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs6007595\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 43%|████▎     | 31/72 [17:27<24:21, 35.64s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs7309189\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 44%|████▍     | 32/72 [18:20<27:11, 40.79s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs9657360\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 49%|████▊     | 35/72 [19:43<20:09, 32.70s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs731835\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 53%|█████▎    | 38/72 [26:08<51:53, 91.57s/it]   "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs260503\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 58%|█████▊    | 42/72 [28:04<22:59, 46.00s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs13140099\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 60%|█████▉    | 43/72 [28:34<19:51, 41.10s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs2235910\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 71%|███████   | 51/72 [42:19<40:20, 115.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs1628955\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 74%|███████▎  | 53/72 [43:55<25:05, 79.24s/it] "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs12443580\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 82%|████████▏ | 59/72 [48:18<09:34, 44.16s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs150458741\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 85%|████████▍ | 61/72 [49:39<07:29, 40.88s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs62423804\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 86%|████████▌ | 62/72 [50:16<06:36, 39.69s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs2267989\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 89%|████████▉ | 64/72 [50:56<03:58, 29.82s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs7605964\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 99%|█████████▊| 71/72 [54:21<00:24, 24.54s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs1261896\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 72/72 [54:53<00:00, 45.75s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# curl -k -H \"Content-Type: application/json\" -X POST -d '{\"snps\": \"rs3\\nrs4\", \"pop\": \"YRI\", \"r2_d\": \"r2\", \"r2_d_threshold\": \"0.1\", \"window\": \"500000\", \"genome_build\": \"grch37\"}' 'https://ldlink.nci.nih.gov/LDlinkRest/ldtrait?token=faketoken123'\n",
+    "# snp = \"rs10276099\"\n",
+    "for snp in tqdm(allcelltypes_snps):\n",
+    "    if os.path.exists(savedir/f'{snp}.tsv'):\n",
+    "        continue\n",
+    "    else:\n",
+    "        params = {\"snps\": snp, \n",
+    "                  \"pop\": \"CEU\", \n",
+    "                  \"r2_d\": \"r2\", \n",
+    "                  \"r2_d_threshold\": \"0.8\", \n",
+    "                  \"window\": \"500000\", \n",
+    "                  \"genome_build\": \"grch37\"}\n",
+    "        r = requests.request(headers={\"Content-Type\": \"application/json\"},\n",
+    "                             method='POST',\n",
+    "                             json=params, \n",
+    "                             url=f'https://ldlink.nci.nih.gov/LDlinkRest/ldtrait?token={token}')\n",
+    "        try:\n",
+    "            if \"No entries in the GWAS Catalog are identified using the LDtrait search criteria.\" in r.text:\n",
+    "                print('no GWAS:', snp)\n",
+    "                continue\n",
+    "            else:\n",
+    "                r_df = pd.read_csv(StringIO(r.text), sep='\\t')\n",
+    "                r_df.to_csv(savedir/f'{snp}.tsv', sep='\\t', index=False)\n",
+    "        except:\n",
+    "            print('failed entry:', snp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 72/72 [00:00<00:00, 298.96it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "allsnps_inld_gwas_df = pd.DataFrame()\n",
+    "for snp in tqdm(allcelltypes_snps):\n",
+    "    if os.path.exists(savedir/f'{snp}.tsv'):\n",
+    "        df = pd.read_csv(savedir/f'{snp}.tsv', sep='\\t')\n",
+    "        if 'error' not in df.iloc[0].values[0]:\n",
+    "            allsnps_inld_gwas_df = pd.concat([allsnps_inld_gwas_df, df],\n",
+    "                                            axis=0)\n",
+    "            \n",
+    "allsnps_inld_gwas_df.to_csv(savedir/'summary.tsv', sep='\\t', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allsnps_inld_gwas_df = pd.read_csv(savedir/'summary.tsv', sep='\\t')\n",
+    "magma_df = pd.read_csv(savedir/'coeqtl_with_gwas_and_magma.tsv', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VARIABLE</th>\n",
+       "      <th>celltype</th>\n",
+       "      <th>SNP</th>\n",
+       "      <th>gene</th>\n",
+       "      <th>TYPE</th>\n",
+       "      <th>NGENES</th>\n",
+       "      <th>BETA</th>\n",
+       "      <th>BETA_STD</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>P</th>\n",
+       "      <th>...</th>\n",
+       "      <th>non_effect_allele</th>\n",
+       "      <th>current_build</th>\n",
+       "      <th>frequency</th>\n",
+       "      <th>sample_size</th>\n",
+       "      <th>zscore</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>effect_size</th>\n",
+       "      <th>standard_error</th>\n",
+       "      <th>imputation_status</th>\n",
+       "      <th>n_cases</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>38</td>\n",
+       "      <td>-0.199320</td>\n",
+       "      <td>-0.008952</td>\n",
+       "      <td>0.12542</td>\n",
+       "      <td>0.943980</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>54612</td>\n",
+       "      <td>0.138937</td>\n",
+       "      <td>0.889500</td>\n",
+       "      <td>0.002200</td>\n",
+       "      <td>0.015600</td>\n",
+       "      <td>original</td>\n",
+       "      <td>17008.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0.201320</td>\n",
+       "      <td>0.009042</td>\n",
+       "      <td>0.12905</td>\n",
+       "      <td>0.059382</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>53293</td>\n",
+       "      <td>1.735682</td>\n",
+       "      <td>0.082620</td>\n",
+       "      <td>0.023902</td>\n",
+       "      <td>0.013700</td>\n",
+       "      <td>original</td>\n",
+       "      <td>19099.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0.163610</td>\n",
+       "      <td>0.007256</td>\n",
+       "      <td>0.12608</td>\n",
+       "      <td>0.097201</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>29344</td>\n",
+       "      <td>-2.348664</td>\n",
+       "      <td>0.018841</td>\n",
+       "      <td>-0.010569</td>\n",
+       "      <td>0.004363</td>\n",
+       "      <td>original</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>38</td>\n",
+       "      <td>-0.010395</td>\n",
+       "      <td>-0.000467</td>\n",
+       "      <td>0.11668</td>\n",
+       "      <td>0.535490</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>15954</td>\n",
+       "      <td>-0.324182</td>\n",
+       "      <td>0.745800</td>\n",
+       "      <td>-0.009950</td>\n",
+       "      <td>0.025700</td>\n",
+       "      <td>original</td>\n",
+       "      <td>7387.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0.282350</td>\n",
+       "      <td>0.012677</td>\n",
+       "      <td>0.11706</td>\n",
+       "      <td>0.007937</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>337159</td>\n",
+       "      <td>-1.597883</td>\n",
+       "      <td>0.110069</td>\n",
+       "      <td>-0.000210</td>\n",
+       "      <td>0.000132</td>\n",
+       "      <td>original</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 44 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            VARIABLE celltype        SNP   gene TYPE  NGENES      BETA  \\\n",
+       "0  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      38 -0.199320   \n",
+       "1  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      38  0.201320   \n",
+       "2  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      37  0.163610   \n",
+       "3  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      38 -0.010395   \n",
+       "4  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      38  0.282350   \n",
+       "\n",
+       "   BETA_STD       SE         P  ... non_effect_allele current_build  \\\n",
+       "0 -0.008952  0.12542  0.943980  ...                 G          hg38   \n",
+       "1  0.009042  0.12905  0.059382  ...                 G          hg38   \n",
+       "2  0.007256  0.12608  0.097201  ...                 G          hg38   \n",
+       "3 -0.000467  0.11668  0.535490  ...                 G          hg38   \n",
+       "4  0.012677  0.11706  0.007937  ...                 G          hg38   \n",
+       "\n",
+       "   frequency sample_size    zscore    pvalue effect_size standard_error  \\\n",
+       "0   0.580808       54612  0.138937  0.889500    0.002200       0.015600   \n",
+       "1   0.580808       53293  1.735682  0.082620    0.023902       0.013700   \n",
+       "2   0.580808       29344 -2.348664  0.018841   -0.010569       0.004363   \n",
+       "3   0.580808       15954 -0.324182  0.745800   -0.009950       0.025700   \n",
+       "4   0.580808      337159 -1.597883  0.110069   -0.000210       0.000132   \n",
+       "\n",
+       "   imputation_status  n_cases  \n",
+       "0           original  17008.0  \n",
+       "1           original  19099.0  \n",
+       "2           original      NaN  \n",
+       "3           original   7387.0  \n",
+       "4           original      NaN  \n",
+       "\n",
+       "[5 rows x 44 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "magma_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VARIABLE                                       B_rs1131017_RPS26\n",
+       "celltype                                                       B\n",
+       "SNP                                                    rs1131017\n",
+       "gene                                                       RPS26\n",
+       "TYPE                                                         SET\n",
+       "NGENES                                                        38\n",
+       "BETA                                                    -0.19932\n",
+       "BETA_STD                                               -0.008952\n",
+       "SE                                                       0.12542\n",
+       "P                                                        0.94398\n",
+       "prefix                                  results/current/magma/AD\n",
+       "trait                                                         AD\n",
+       "FDR                                                     0.973479\n",
+       "Tag                                               IGAP_Alzheimer\n",
+       "PUBMED_Paper_Link    http://www.ncbi.nlm.nih.gov/pubmed/24162737\n",
+       "Phenotype                                              Alzheimer\n",
+       "RSID                                                  rs10876864\n",
+       "RSALIAS                                               rs57455456\n",
+       "CHR                                                           12\n",
+       "POS1                                                    56435929\n",
+       "POS2                                                    56401085\n",
+       "DIST                                                      -34844\n",
+       "R2                                                      0.991789\n",
+       "D                                                       0.240643\n",
+       "DPRIME                                                  0.995886\n",
+       "MAJOR                                                          A\n",
+       "MINOR                                                          G\n",
+       "MAF                                                     0.408549\n",
+       "CMMB                                                    0.155229\n",
+       "CM                                                     71.092406\n",
+       "panel_variant_id                          chr12_56007301_G_A_b38\n",
+       "chromosome                                                 chr12\n",
+       "position                                                56007301\n",
+       "effect_allele                                                  A\n",
+       "non_effect_allele                                              G\n",
+       "current_build                                               hg38\n",
+       "frequency                                               0.580808\n",
+       "sample_size                                                54612\n",
+       "zscore                                                  0.138937\n",
+       "pvalue                                                    0.8895\n",
+       "effect_size                                               0.0022\n",
+       "standard_error                                            0.0156\n",
+       "imputation_status                                       original\n",
+       "n_cases                                                  17008.0\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "magma_df.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.041321, 'Inflammatory Bowel Disease'],\n",
+       "       [0.030935, 'Non-cancer illness code, self-reported: psoriasis'],\n",
+       "       [0.0090688,\n",
+       "        'Non-cancer illness code, self-reported: schizophrenia'],\n",
+       "       [0.0042454,\n",
+       "        'Overall breast cancer in Europeans, imputed genotype'],\n",
+       "       [0.032584, 'Diagnoses - main ICD10: G40 Epilepsy'],\n",
+       "       [0.0013766,\n",
+       "        'Estrogen-receptor-negative breast cancer in Europeans, imputed genotype'],\n",
+       "       [0.025212,\n",
+       "        'Non-cancer illness code, self-reported: high cholesterol']],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "magma_df[(magma_df['SNP']=='rs4147638') & (magma_df['P']<0.05)][['P', 'Phenotype']].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Query</th>\n",
+       "      <th>GWAS Trait</th>\n",
+       "      <th>RS Number</th>\n",
+       "      <th>Position (GRCh37)</th>\n",
+       "      <th>Alleles</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>D'</th>\n",
+       "      <th>Risk Allele</th>\n",
+       "      <th>Effect Size (95% CI)</th>\n",
+       "      <th>Beta or OR</th>\n",
+       "      <th>P-value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rs2954654</td>\n",
+       "      <td>Type 2 diabetes</td>\n",
+       "      <td>rs2294120</td>\n",
+       "      <td>chr8:146003567</td>\n",
+       "      <td>A=0.52, G=0.48</td>\n",
+       "      <td>0.846295</td>\n",
+       "      <td>0.957895</td>\n",
+       "      <td>0.455879299759268</td>\n",
+       "      <td>0.04430</td>\n",
+       "      <td>0.029-0.06</td>\n",
+       "      <td>2.000000e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rs4840568</td>\n",
+       "      <td>Albumin-globulin ratio</td>\n",
+       "      <td>rs2409780</td>\n",
+       "      <td>chr8:11337587</td>\n",
+       "      <td>C=0.237, T=0.763</td>\n",
+       "      <td>0.897156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NR</td>\n",
+       "      <td>0.04604</td>\n",
+       "      <td>0.035-0.057</td>\n",
+       "      <td>1.000000e-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rs4840568</td>\n",
+       "      <td>Non-albumin protein levels</td>\n",
+       "      <td>rs2409780</td>\n",
+       "      <td>chr8:11337587</td>\n",
+       "      <td>C=0.237, T=0.763</td>\n",
+       "      <td>0.897156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NR</td>\n",
+       "      <td>0.04456</td>\n",
+       "      <td>0.034-0.055</td>\n",
+       "      <td>1.000000e-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>rs4840568</td>\n",
+       "      <td>Rheumatoid arthritis</td>\n",
+       "      <td>rs2618444</td>\n",
+       "      <td>chr8:11338370</td>\n",
+       "      <td>A=0.763, C=0.237</td>\n",
+       "      <td>0.897156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NR</td>\n",
+       "      <td>0.10050</td>\n",
+       "      <td>0.072-0.129</td>\n",
+       "      <td>7.000000e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>rs4840568</td>\n",
+       "      <td>Systemic lupus erythematosus</td>\n",
+       "      <td>rs2618444</td>\n",
+       "      <td>chr8:11338370</td>\n",
+       "      <td>A=0.763, C=0.237</td>\n",
+       "      <td>0.897156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NR</td>\n",
+       "      <td>1.36000</td>\n",
+       "      <td>1.22-1.51</td>\n",
+       "      <td>7.000000e-09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Query                    GWAS Trait  RS Number Position (GRCh37)  \\\n",
+       "0  rs2954654               Type 2 diabetes  rs2294120    chr8:146003567   \n",
+       "1  rs4840568        Albumin-globulin ratio  rs2409780     chr8:11337587   \n",
+       "2  rs4840568    Non-albumin protein levels  rs2409780     chr8:11337587   \n",
+       "3  rs4840568          Rheumatoid arthritis  rs2618444     chr8:11338370   \n",
+       "4  rs4840568  Systemic lupus erythematosus  rs2618444     chr8:11338370   \n",
+       "\n",
+       "            Alleles        R2        D'        Risk Allele  \\\n",
+       "0    A=0.52, G=0.48  0.846295  0.957895  0.455879299759268   \n",
+       "1  C=0.237, T=0.763  0.897156  1.000000                 NR   \n",
+       "2  C=0.237, T=0.763  0.897156  1.000000                 NR   \n",
+       "3  A=0.763, C=0.237  0.897156  1.000000                 NR   \n",
+       "4  A=0.763, C=0.237  0.897156  1.000000                 NR   \n",
+       "\n",
+       "   Effect Size (95% CI)   Beta or OR       P-value  \n",
+       "0               0.04430   0.029-0.06  2.000000e-08  \n",
+       "1               0.04604  0.035-0.057  1.000000e-16  \n",
+       "2               0.04456  0.034-0.055  1.000000e-15  \n",
+       "3               0.10050  0.072-0.129  7.000000e-12  \n",
+       "4               1.36000    1.22-1.51  7.000000e-09  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allsnps_inld_gwas_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allsnps_inld_gwas_df.to_excel('./coeqtl_mapping/output/snps_in_ld_with_gwas_catelogue.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05_coeqtl_interpretation/.ipynb_checkpoints/TEM_NAIVE-checkpoint.ipynb b/05_coeqtl_interpretation/.ipynb_checkpoints/TEM_NAIVE-checkpoint.ipynb
new file mode 100644
index 0000000..314c2f6
--- /dev/null
+++ b/05_coeqtl_interpretation/.ipynb_checkpoints/TEM_NAIVE-checkpoint.ipynb
@@ -0,0 +1,1437 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import re\n",
+    "from itertools import combinations\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "from scipy.stats import spearmanr\n",
+    "from scipy.stats import t, norm\n",
+    "from tqdm import tqdm\n",
+    "import argparse\n",
+    "from scipy.stats import rankdata\n",
+    "from collections import namedtuple\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "def get_time(x):\n",
+    "    if x == 'UT':\n",
+    "        return x\n",
+    "    else:\n",
+    "        pattern = re.compile(r'\\d+h')\n",
+    "        return re.findall(pattern, x)[0]\n",
+    "\n",
+    "\n",
+    "class DATASET:\n",
+    "    def __init__(self, datasetname):\n",
+    "        self.name = datasetname\n",
+    "        self.path_prefix = Path(\"./seurat_objects\")\n",
+    "        self.information = self.get_information()\n",
+    "    def get_information(self):\n",
+    "        if self.name == 'onemillionv2':\n",
+    "            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'\n",
+    "            self.individual_id_col = 'assignment'\n",
+    "            self.timepoint_id_col = 'time'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 'UT',\n",
+    "                                     'stimulated': '3h'}\n",
+    "        elif self.name == 'onemillionv3':\n",
+    "            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'\n",
+    "            self.individual_id_col = 'assignment'\n",
+    "            self.timepoint_id_col = 'time'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 'UT',\n",
+    "                                     'stimulated': '3h'}\n",
+    "        elif self.name == 'stemiv2':\n",
+    "            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'\n",
+    "            self.individual_id_col = 'assignment.final'\n",
+    "            self.timepoint_id_col = 'timepoint.final'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 't8w',\n",
+    "                                     'stimulated': 'Baseline'}\n",
+    "        elif self.name == 'ng':\n",
+    "            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'\n",
+    "            self.individual_id_col = 'snumber'\n",
+    "            self.celltype_id = 'cell_type_mapped_to_onemillion'\n",
+    "        else:\n",
+    "            raise IOError(\"Dataset name not understood.\")\n",
+    "    def load_dataset(self):\n",
+    "        self.get_information()\n",
+    "        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')\n",
+    "        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)\n",
+    "        if self.name.startswith('onemillion'):\n",
+    "            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]\n",
+    "        elif self.name == 'ng':\n",
+    "            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',\n",
+    "                               'other T': 'otherT', 'other': 'other', 'B': 'B'}\n",
+    "            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in\n",
+    "                                                          self.data_sc.obs['predicted.celltype.l1']]\n",
+    "\n",
+    "def corr_to_z(coef, num):\n",
+    "    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))\n",
+    "    prob = t.cdf(t_statistic, num - 2)\n",
+    "    z_score = norm.ppf(prob)\n",
+    "    positive_coef_probs = 1 - prob\n",
+    "    positive_coef_probs[coef < 0] = 0\n",
+    "    negative_coef_probs = prob\n",
+    "    negative_coef_probs[coef > 0] = 0\n",
+    "    probs = negative_coef_probs + positive_coef_probs\n",
+    "    return z_score, probs\n",
+    "\n",
+    "\n",
+    "def get_individual_networks_selected_genepairs(data_df, data_sc, individual_colname, genepair):\n",
+    "#     data_df = pd.DataFrame(data=data_sc.X.toarray(),\n",
+    "#                            index=data_sc.obs.index,\n",
+    "#                            columns=data_sc.var.index)\n",
+    "    gene1, gene2 = genepair.split(';')\n",
+    "    sorted_genepair = [';'.join(sorted([gene1, gene2]))]\n",
+    "    coef_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    coef_p_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    zscore_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    zscore_p_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    data_selected_df = data_df[[gene1, gene2]]\n",
+    "    print(\n",
+    "        f\"Begin calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals and;\\n{genepair}\"\n",
+    "    )\n",
+    "    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):\n",
+    "        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]\n",
+    "        if cell_num > 10:\n",
+    "            individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]\n",
+    "            individual_coefs, individual_coef_ps = spearmanr(individual_df.values, axis=0)\n",
+    "            if data_selected_df.shape[1] == 2:\n",
+    "                individual_coefs_flatten = pd.DataFrame(data = [individual_coefs],\n",
+    "                                                        index = sorted_genepair)\n",
+    "                individual_coef_ps_flatten = \\\n",
+    "                pd.DataFrame(data=[individual_coef_ps],\n",
+    "                             index=sorted_genepair)\n",
+    "            else:\n",
+    "                individual_coefs_flatten = pd.DataFrame(\n",
+    "                    data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],\n",
+    "                    index=selected_genes_sorted_genepairs).loc[sorted_genepair]\n",
+    "                individual_coef_ps_flatten = \\\n",
+    "                pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],\n",
+    "                             index=selected_genes_sorted_genepairs).loc[sorted_genepair]\n",
+    "            coef_df[ind_id] = individual_coefs_flatten\n",
+    "            coef_p_df[ind_id] = individual_coef_ps_flatten\n",
+    "            try:\n",
+    "#                 print(individual_coefs_flatten.values, cell_num)\n",
+    "                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(\n",
+    "                    individual_coefs_flatten.values, \n",
+    "                    cell_num\n",
+    "                )\n",
+    "                zscore_df[ind_id] = individual_zscores_flatten\n",
+    "                zscore_p_df[ind_id] = individual_zscore_ps_flatten\n",
+    "            except:\n",
+    "                continue\n",
+    "        else:\n",
+    "            print(\"Deleted this individual because of low cell number\", cell_num)\n",
+    "    return data_selected_df, zscore_df, zscore_p_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### One million data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the GT data\n",
+    "gt = pd.read_csv('./coeqtl_interpretation/rs1131017_TEM_ratio/rs1131017.vcf',\n",
+    "                 skiprows=6, sep='\\t')\n",
+    "change_colnames = lambda col:'_'.join(col.split('_')[1:]) if 'LLDeep' in col else col\n",
+    "gt = gt.rename({col:change_colnames(col) for col in gt.columns}, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>#CHROM</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>REF</th>\n",
+       "      <th>ALT</th>\n",
+       "      <th>QUAL</th>\n",
+       "      <th>FILTER</th>\n",
+       "      <th>INFO</th>\n",
+       "      <th>FORMAT</th>\n",
+       "      <th>LLDeep_1191</th>\n",
+       "      <th>...</th>\n",
+       "      <th>s21</th>\n",
+       "      <th>s43</th>\n",
+       "      <th>s24</th>\n",
+       "      <th>s23</th>\n",
+       "      <th>s45</th>\n",
+       "      <th>s26</th>\n",
+       "      <th>s25</th>\n",
+       "      <th>s28</th>\n",
+       "      <th>s27</th>\n",
+       "      <th>s29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12</td>\n",
+       "      <td>56435929</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>GT:DS</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>0/0:0.06000000000000005</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 182 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   #CHROM       POS         ID REF ALT QUAL FILTER INFO FORMAT LLDeep_1191  \\\n",
+       "0      12  56435929  rs1131017   C   G    .      .    .  GT:DS     0/0:0.0   \n",
+       "\n",
+       "   ...      s21      s43      s24      s23      s45      s26      s25  \\\n",
+       "0  ...  1/1:2.0  1/1:2.0  1/1:2.0  0/1:1.0  1/1:2.0  0/1:1.0  0/0:0.0   \n",
+       "\n",
+       "       s28                      s27      s29  \n",
+       "0  1/1:2.0  0/0:0.06000000000000005  1/1:2.0  \n",
+       "\n",
+       "[1 rows x 182 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gt.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CD4T+CD8T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # load onemillion v2 data\n",
+    "# dataset = DATASET('onemillionv2')\n",
+    "# dataset.load_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>assignment</th>\n",
+       "      <th>predicted.celltype.l1</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">LLDeep_0022</th>\n",
+       "      <th>B</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD4 T</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD8 T</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mono</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   0\n",
+       "assignment  predicted.celltype.l1   \n",
+       "LLDeep_0022 B                      0\n",
+       "            CD4 T                  0\n",
+       "            CD8 T                  0\n",
+       "            DC                     0\n",
+       "            Mono                   0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>assignment</th>\n",
+       "      <th>predicted.celltype.l2</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">LLDeep_0022</th>\n",
+       "      <th>ASDC</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B intermediate</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B memory</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B naive</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD14 Mono</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   0\n",
+       "assignment  predicted.celltype.l2   \n",
+       "LLDeep_0022 ASDC                   0\n",
+       "            B intermediate         0\n",
+       "            B memory               0\n",
+       "            B naive                0\n",
+       "            CD14 Mono              0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T TEM</th>\n",
+       "      <th>CD4T Naive</th>\n",
+       "      <th>CD8T TEM</th>\n",
+       "      <th>CD8T Naive</th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>CD4T TCM</th>\n",
+       "      <th>CD8T TCM</th>\n",
+       "      <th>all_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1370</th>\n",
+       "      <td>20</td>\n",
+       "      <td>85</td>\n",
+       "      <td>74</td>\n",
+       "      <td>13</td>\n",
+       "      <td>224</td>\n",
+       "      <td>89</td>\n",
+       "      <td>85</td>\n",
+       "      <td>8</td>\n",
+       "      <td>827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0434</th>\n",
+       "      <td>42</td>\n",
+       "      <td>417</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "      <td>989</td>\n",
+       "      <td>209</td>\n",
+       "      <td>406</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1319</th>\n",
+       "      <td>110</td>\n",
+       "      <td>132</td>\n",
+       "      <td>223</td>\n",
+       "      <td>4</td>\n",
+       "      <td>922</td>\n",
+       "      <td>236</td>\n",
+       "      <td>546</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0269</th>\n",
+       "      <td>21</td>\n",
+       "      <td>58</td>\n",
+       "      <td>80</td>\n",
+       "      <td>7</td>\n",
+       "      <td>211</td>\n",
+       "      <td>88</td>\n",
+       "      <td>101</td>\n",
+       "      <td>8</td>\n",
+       "      <td>529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0471</th>\n",
+       "      <td>27</td>\n",
+       "      <td>242</td>\n",
+       "      <td>81</td>\n",
+       "      <td>14</td>\n",
+       "      <td>570</td>\n",
+       "      <td>103</td>\n",
+       "      <td>254</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1241</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
+       "LLDeep_1370        20          85        74          13   224    89        85   \n",
+       "LLDeep_0434        42         417        95          93   989   209       406   \n",
+       "LLDeep_1319       110         132       223           4   922   236       546   \n",
+       "LLDeep_0269        21          58        80           7   211    88       101   \n",
+       "LLDeep_0471        27         242        81          14   570   103       254   \n",
+       "\n",
+       "             CD8T TCM  all_num  \n",
+       "LLDeep_1370         8      827  \n",
+       "LLDeep_0434        29     1496  \n",
+       "LLDeep_1319        19     1504  \n",
+       "LLDeep_0269         8      529  \n",
+       "LLDeep_0471        10     1241  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onemillionv2 = dataset.data_sc.obs.copy()\n",
+    "onemillionv2_celltype_df = pd.read_csv(\n",
+    "    './1M_v2_20201029_azimuth.tsv',\n",
+    "    sep='\\t', index_col=0\n",
+    ")\n",
+    "onemillionv2 = pd.concat([onemillionv2, onemillionv2_celltype_df], axis=1)\n",
+    "onemillionv2 = onemillionv2[onemillionv2['timepoint']=='UT']\n",
+    "onemillionv2_l1_cellratio_df = onemillionv2.groupby(['assignment', 'predicted.celltype.l1']).size().to_frame()\n",
+    "display(onemillionv2_l1_cellratio_df.head())\n",
+    "onemillionv2_celltyperatio = onemillionv2.groupby(['assignment', 'predicted.celltype.l2']).size().to_frame()\n",
+    "display(onemillionv2_celltyperatio.head())\n",
+    "onemillionv2_allcells = onemillionv2['assignment'].value_counts()\n",
+    "\n",
+    "# caluclate the individual CD4T TEM and NAIVE ratio\n",
+    "individual_ratio = pd.DataFrame()\n",
+    "for individual in onemillionv2['assignment'].unique():\n",
+    "    tem_num = onemillionv2_celltyperatio.loc[individual, \"CD4 TEM\"].values[0]\n",
+    "    naive_num = onemillionv2_celltyperatio.loc[individual, \"CD4 Naive\"].values[0]\n",
+    "    cd8t_tem_num = onemillionv2_celltyperatio.loc[individual, \"CD8 TEM\"].values[0]\n",
+    "    tcm_num = onemillionv2_celltyperatio.loc[individual, \"CD4 TCM\"].values[0]\n",
+    "    cd8t_tcm_num = onemillionv2_celltyperatio.loc[individual, \"CD8 TCM\"].values[0]\n",
+    "    cd8t_naive_num = onemillionv2_celltyperatio.loc[individual, \"CD8 Naive\"].values[0]\n",
+    "    cd4t_num = onemillionv2_l1_cellratio_df.loc[individual, 'CD4 T'].values[0]\n",
+    "    cd8t_num = onemillionv2_l1_cellratio_df.loc[individual, 'CD8 T'].values[0]\n",
+    "    all_num = onemillionv2_allcells.loc[individual]\n",
+    "    individual_ratio[individual] = [tem_num,  naive_num, \n",
+    "                                    cd8t_tem_num, cd8t_naive_num,\n",
+    "                                    cd4t_num, cd8t_num,\n",
+    "                                    tcm_num, cd8t_tcm_num, all_num]\n",
+    "\n",
+    "individual_ratio_df = individual_ratio.T\n",
+    "individual_ratio_df = individual_ratio_df.rename({0: 'CD4T TEM', 1:'CD4T Naive', \n",
+    "                                                  2: 'CD8T TEM', 3: 'CD8T Naive',\n",
+    "                                                  4: 'CD4T', 5: 'CD8T',\n",
+    "                                                  6: 'CD4T TCM', 7: 'CD8T TCM',\n",
+    "                                                  8: 'all_num'}, \n",
+    "                                                 axis=1)\n",
+    "display(individual_ratio_df.head())\n",
+    "\n",
+    "\n",
+    "common_individuals = list(set(individual_ratio_df.index) & set(gt.columns))\n",
+    "common_individuals_individual_ratio_df = individual_ratio_df.loc[common_individuals]\n",
+    "common_individuals_individual_ratio_df['gt'] = [float(gt[col].values[0].split(':')[1]) for col in \n",
+    "                                                common_individuals_individual_ratio_df.index]\n",
+    "common_individuals_individual_ratio_df['chemistry'] = 'v2'\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax1, ax2, ax3 = axes\n",
+    "cd4ydata = (common_individuals_individual_ratio_df['CD4T TEM']) / common_individuals_individual_ratio_df['CD4T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD4 TEM / CD4T')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_df['CD4T Naive']) / common_individuals_individual_ratio_df['CD4T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax2)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax2.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax2.set_ylabel('CD4 Naive / CD4T')\n",
+    "ax2.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_df['CD4T Naive']) / common_individuals_individual_ratio_df['CD4T TEM']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax3)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax3.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax3.set_ylabel('CD4 Naive / CD4T TEM')\n",
+    "ax3.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax1, ax2, ax3 = axes\n",
+    "cd4ydata = (common_individuals_individual_ratio_df['CD8T TEM']) / common_individuals_individual_ratio_df['CD8T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD8 TEM / CD8T')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_df['CD8T Naive']) / common_individuals_individual_ratio_df['CD8T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax2)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax2.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax2.set_ylabel('CD8 Naive / CD8T')\n",
+    "ax2.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_df['CD8T Naive']) / common_individuals_individual_ratio_df['CD8T TEM']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax3)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax3.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax3.set_ylabel('CD8 Naive / CD8T TEM')\n",
+    "ax3.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cd8tydata = (common_individuals_individual_ratio_df['CD8T TEM'] + \\\n",
+    "            common_individuals_individual_ratio_df['CD4T TEM']) / (\n",
+    "    common_individuals_individual_ratio_df['CD8T Naive'] + \\\n",
+    "    common_individuals_individual_ratio_df['CD4T Naive']\n",
+    ")\n",
+    "fig, ax = plt.subplots()\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax)\n",
+    "r, p = stats.spearmanr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax.set_ylabel('CD8+CD4 TEM / CD8+CD4 Naive')\n",
+    "ax.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Begin calculating networks for 72 individuals and;\n",
+      "RPS26;RUNX3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 72/72 [00:00<00:00, 207.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SpearmanrResult(correlation=0.29651955126400936, pvalue=0.011433091246178868)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='gt', ylabel='RPS26;RUNX3'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onemillionv2_datasc = dataset.data_sc\n",
+    "onemillionv2_monocytes_ut = onemillionv2_datasc[(onemillionv2_datasc.obs['cell_type_lowerres']=='CD4T') & \n",
+    "                                                (onemillionv2_datasc.obs['time']=='UT')]\n",
+    "onemillionv2_monocytes_ut_df = pd.DataFrame(\n",
+    "    data=onemillionv2_monocytes_ut.X.toarray(),\n",
+    "    columns=onemillionv2_monocytes_ut.var.index,\n",
+    "    index=onemillionv2_monocytes_ut.obs.index\n",
+    ")\n",
+    "data_selected_df, zscore_df, zscore_p_df = \\\n",
+    "get_individual_networks_selected_genepairs(onemillionv2_monocytes_ut_df, \n",
+    "                                           onemillionv2_monocytes_ut, \n",
+    "                                           'assignment', \n",
+    "                                           ';'.join(['RPS26', 'RUNX3']))\n",
+    "concated_df = pd.concat([zscore_df.T,\n",
+    "           gt.T],\n",
+    "          axis=1).dropna()\n",
+    "concated_df['gt'] = [item.split(':')[0].count('1') for item in concated_df[0]]\n",
+    "print(spearmanr(concated_df['RPS26;RUNX3'], concated_df['gt']))\n",
+    "sns.regplot(x='gt', y='RPS26;RUNX3', data=concated_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Begin calculating networks for 72 individuals and;\n",
+      "RPS26;RUNX3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 72/72 [00:00<00:00, 214.76it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SpearmanrResult(correlation=0.2201691525430256, pvalue=0.06311568667519006)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='gt', ylabel='RPS26;RUNX3'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onemillionv2_datasc = dataset.data_sc\n",
+    "onemillionv2_monocytes_ut = onemillionv2_datasc[(onemillionv2_datasc.obs['cell_type_lowerres']=='CD8T') & \n",
+    "                                                (onemillionv2_datasc.obs['time']=='UT')]\n",
+    "onemillionv2_monocytes_ut_df = pd.DataFrame(\n",
+    "    data=onemillionv2_monocytes_ut.X.toarray(),\n",
+    "    columns=onemillionv2_monocytes_ut.var.index,\n",
+    "    index=onemillionv2_monocytes_ut.obs.index\n",
+    ")\n",
+    "data_selected_df, zscore_df, zscore_p_df = \\\n",
+    "get_individual_networks_selected_genepairs(onemillionv2_monocytes_ut_df, \n",
+    "                                           onemillionv2_monocytes_ut, \n",
+    "                                           'assignment', \n",
+    "                                           ';'.join(['RPS26', 'RUNX3']))\n",
+    "concated_df = pd.concat([zscore_df.T,\n",
+    "           gt.T],\n",
+    "          axis=1).dropna()\n",
+    "concated_df['gt'] = [item.split(':')[0].count('1') for item in concated_df[0]]\n",
+    "print(spearmanr(concated_df['RPS26;RUNX3'], concated_df['gt']))\n",
+    "sns.regplot(x='gt', y='RPS26;RUNX3', data=concated_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### onemillion v3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # load onemillion v2 data\n",
+    "# datasetv3 = DATASET('onemillionv3')\n",
+    "# datasetv3.load_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T TEM</th>\n",
+       "      <th>CD4T Naive</th>\n",
+       "      <th>CD8T TEM</th>\n",
+       "      <th>CD8T Naive</th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>CD4T TCM</th>\n",
+       "      <th>CD8T TCM</th>\n",
+       "      <th>all_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0117</th>\n",
+       "      <td>39</td>\n",
+       "      <td>248</td>\n",
+       "      <td>70</td>\n",
+       "      <td>75</td>\n",
+       "      <td>625</td>\n",
+       "      <td>170</td>\n",
+       "      <td>289</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1300</th>\n",
+       "      <td>34</td>\n",
+       "      <td>149</td>\n",
+       "      <td>73</td>\n",
+       "      <td>39</td>\n",
+       "      <td>658</td>\n",
+       "      <td>119</td>\n",
+       "      <td>450</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0615</th>\n",
+       "      <td>68</td>\n",
+       "      <td>84</td>\n",
+       "      <td>175</td>\n",
+       "      <td>23</td>\n",
+       "      <td>550</td>\n",
+       "      <td>212</td>\n",
+       "      <td>347</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0923</th>\n",
+       "      <td>39</td>\n",
+       "      <td>80</td>\n",
+       "      <td>205</td>\n",
+       "      <td>40</td>\n",
+       "      <td>316</td>\n",
+       "      <td>249</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6</td>\n",
+       "      <td>907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0705</th>\n",
+       "      <td>13</td>\n",
+       "      <td>114</td>\n",
+       "      <td>123</td>\n",
+       "      <td>61</td>\n",
+       "      <td>322</td>\n",
+       "      <td>188</td>\n",
+       "      <td>166</td>\n",
+       "      <td>3</td>\n",
+       "      <td>947</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
+       "LLDeep_0117        39         248        70          75   625   170       289   \n",
+       "LLDeep_1300        34         149        73          39   658   119       450   \n",
+       "LLDeep_0615        68          84       175          23   550   212       347   \n",
+       "LLDeep_0923        39          80       205          40   316   249       140   \n",
+       "LLDeep_0705        13         114       123          61   322   188       166   \n",
+       "\n",
+       "             CD8T TCM  all_num  \n",
+       "LLDeep_0117        26     1599  \n",
+       "LLDeep_1300        10     1382  \n",
+       "LLDeep_0615        19     1277  \n",
+       "LLDeep_0923         6      907  \n",
+       "LLDeep_0705         3      947  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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3OQcyux64HuCSSy5Z52JKq9F3i3Qyfb5FOsOGavETEVnCCLCz4vEO4LHKDdx93N0nk/u3A2kzO6/6QO5+m7vvdfe9559/fj3LLCIiIrIkBX4iInOOAbvN7HIzywDXAYcrNzCzC83Mkvv7iOvRUw0vqYiIiMgKbLiuniIiC3H3opm9BbgDCIGPuPtxM3tzsv5W4GeAXzKzIjANXOfu1d1BRURERFqKAj8RkQpJ983bq5bdWnH/fwP/u9HlEhEREVkLdfUUERERERHpcAr8REREREREOpwCPxERERERkQ6nwE9ERERERKTDKfATERERERHpcAr8REREREREOpwCPxERERERkQ6nwE9ERERERKTDKYG7iEgLOHJilINHhxgey7JzSy837N/FgT3bm10sERER6RBq8RMRabIjJ0a56fBxRidybO5JMzqR46bDxzlyYrTZRRMREZEOocBPRKTJDh4dIh0avZkUZvHfdGgcPDrU7KKJSIsxs51m9iUz+5aZHTezG5Pl7zKzR83svuT28maXVURai7p6iog02fBYls096XnLetIhI2PZJpVIRFpYEfh1d7/XzAaAe8zsi8m6P3b39zWxbCLSwtTiJyLSZDu39DJdKM1bNl0osWNLb5NKJCKtyt0fd/d7k/sTwLeAi5tbKhFpBwr8RESa7Ib9uyiUnGy+iHv8t1Bybti/q9lFE5EWZmaXAc8H/jVZ9BYz+7qZfcTMtjSvZCLSihT4iYg02YE927n52ivZPtDN2ekC2we6ufnaKzWrp4gsyMz6gU8Db3P3ceBPgacDzwMeB/7nAvtdb2Z3m9ndJ0+ebFRxRaQFaIyfiEgLOLBnuwI9EVkWM0sTB31/5e6fAXD3JyvW/xnw97X2dffbgNsA9u7d6/UvrYi0CrX4iYiIiLQJMzPgw8C33P2PKpZfVLHZK4FvNLpsItLa1OInIiIi0j5+GHgd8O9mdl+y7J3Aa8zseYADDwM3NKNwItK6FPiJiIiItAl3/wpgNVbd3uiyiEh7UVdPERERERGRDqfAT0REREREpMMp8BMREREREelwCvxEREREREQ6XF0ndzGza4BbgBD4kLu/p2r9zwNvTx5OAr/k7vcn6x4GJoASUHT3vfUsq4iIiIhIJzlyYpSDR4cYHsuyc0svN+zfpZyxG1jdWvzMLAQ+ALwMuIJ4muErqjZ7CPhRd38u8G6ShKIVXuzuz1PQJyIiIiKyfEdOjHLT4eOMTuTY3JNmdCLHTYePc+TEaLOLJk1Sz66e+4AH3X3I3fPAIeAVlRu4+7+4+1jy8GvAjjqWR0RERERkQzh4dIh0aPRmUpjFf9OhcfDoULOLJk1Sz8DvYmC44vFIsmwhbwQ+V/HYgS+Y2T1mdn0dyiciIiIi0pGGx7L0pMN5y3rSISNj2SaVSJqtnmP8aiUX9Zobmr2YOPD7kYrFP+zuj5nZduCLZnbC3Y/W2Pd64HqASy65ZO2lFhERERFpczu39DI6kaM3M/dzf7pQYseW3iaWSpqpni1+I8DOisc7gMeqNzKz5wIfAl7h7qfKy939seTvKPBZ4q6j53D329x9r7vvPf/889ex+CIiIiIi7emG/bsolJxsvoh7/LdQcm7Yv6vZRZMmqWfgdwzYbWaXm1kGuA44XLmBmV0CfAZ4nbt/p2J5n5kNlO8DPwl8o45lFRERERHpGAf2bOfma69k+0A3Z6cLbB/o5uZrr9SsnhtY3bp6unvRzN4C3EGczuEj7n7czN6crL8VuAnYBnzQzGAubcMFwGeTZSngr9398/Uqq4iIiIhIpzmwZ7sCPZlV1zx+7n47cHvVslsr7r8JeFON/YaAq+pZNhERERERkY2inl09RUREREREpAUo8BMREREREelwCvxEREREREQ6nAI/ERERERGRDqfAT0REREREpMMp8BMREREREelwdU3nICIiy3PkxCgHjw4xPJZl55Zebti/S7mXREREZN2oxU9EpMmOnBjlpsPHGZ3IsbknzehEjpsOH+fIidFmF01EREQ6hAI/EZEmO3h0iHRo9GZSmMV/06Fx8OhQs4smIiIiHUKBn4hIkw2PZelJh/OW9aRDRsayTSqRiIiIdBoFfiIiTbZzSy/ThdK8ZdOFEju29DapRCIiItJpFPiJiDTZDft3USg52XwR9/hvoeTcsH9Xs4smIiIiHUKBn4hIBTO7xsy+bWYPmtk7FtnuajMrmdnPrPU5D+zZzs3XXsn2gW7OThfYPtDNzddeqVk9RUREZN0onYOISMLMQuADwEuAEeCYmR1292/W2O69wB3r9dwH9mxXoCciIiJ1o8BPRGTOPuBBdx8CMLNDwCuAb1Zt91+BTwNXN7Z40qqUh1FERFqdunqKiMy5GBiueDySLJtlZhcDrwRubWC5pIUpD6OIiLQDBX4iInOsxjKvevwnwNvdvVRj27kDmV1vZneb2d0nT55cr/JJC1IeRhERaQfq6ikiMmcE2FnxeAfwWNU2e4FDZgZwHvByMyu6+99WbuTutwG3Aezdu7c6eJQOMjyWZXNPet4y5WEUEZFWo8BPRGTOMWC3mV0OPApcB7y2cgN3v7x838w+Cvx9ddAnG8vOLb2MTuTozcx9pSoPo4iItBp19RQRSbh7EXgL8Wyd3wI+6e7HzezNZvbm5pZOWpXyMIqISDtQi5+ISAV3vx24vWpZzYlc3P0XGlEmaW0H9mznZuKxfiNjWXZoVk8REWlBCvxERETWSHkYpVHMbCfwceBCIAJuc/dbzGwr8H+Ay4CHgZ9z97FmlVNEWo+6eoqIiIi0jyLw6+7+bOCFwK+Y2RXAO4B/dPfdwD8mj0VEZinwExEREWkT7v64u9+b3J8gHo98MfAK4GPJZh8DfropBRSRlqXAT0RERKQNmdllwPOBfwUucPfHIQ4OAfU9FpF5FPiJiIiItBkz6wc+DbzN3cdXsN/1Zna3md198uTJ+hVQRFqOAj8RERGRNmJmaeKg76/c/TPJ4ifN7KJk/UXAaK193f02d9/r7nvPP//8xhRYRFqCAj8RERGRNmFmBnwY+Ja7/1HFqsPAG5L7bwD+rtFlE5HWpnQOIiIiIg1kZq9abH1FK14tPwy8Dvh3M7svWfZO4D3AJ83sjcD3gJ9dh6KKSAdR4CciIiLSWJ8C7ktuAFaxzoEFAz93/0rV9pV+fB3KJiIdSoGfiIiISGO9GviPwHOJu2R+wt0fbG6RRKTTKfATERERaSB3/yzwWTPrI86/9z/NbBvwW+7+5eaW7lxHToxy8OgQw2NZdm7p5Yb9uziwR9kiRNqNJncRERERaY4ccBYYB/qA7uYW51xHToxy0+HjjE7k2NyTZnQix02Hj3PkRM1JQ0WkhSnwExEREWkgM3uxmd0G3AO8GLjF3Z/v7nc0uWjnOHh0iHRo9GZSmMV/06Fx8OhQs4smIiukrp4iIiIijfWPwNeBrwBdwOvN7PXlle7+1mYVrNrwWJbNPel5y3rSISNj2SaVSERWq66Bn5ldA9wChMCH3P09Vet/Hnh78nAS+CV3v385+4qIiIi0qf9MPHtny9u5pZfRiRy9mbmfjNOFEju29DaxVCKyGnUL/MwsBD4AvAQYAY6Z2WF3/2bFZg8BP+ruY2b2MuA24AeWua+IiIhI23H3jy60zsxaqjfWDft3cdPh42TzRXrSIdOFEoWSc8P+Xc0umoisUD3H+O0DHnT3IXfPA4eIZ66a5e7/4u5jycOvATuWu6+IiEirOHJilNfc9jV+5L138prbvqaJL2RRZvaVivt/UbX6rgYXZ1EH9mzn5muvZPtAN2enC2wf6Obma6/UrJ4ibaieV5UuBoYrHo8AP7DI9m8EPrfKfUVERJqiPOthOrR5sx7eDPpxLAvpq7h/ZdW6hZKzN82BPdv1WRbpAPVs8atVcdXsz25mLyYO/Mrj/Vay7/VmdreZ3X3y5MlVFVREOoOZvbDZZZCNR7MeyiosNr6vLcb+iUj7qWeL3wiws+LxDuCx6o3M7LnAh4CXufuplewL4O63EY8NZO/evaosRTa2DwIvaHYhZGMZHssSGgydnCRfisiEAef1ZzTroSxms5m9kvgC/GYze1Wy3IBNzSuWiHSyegZ+x4DdZnY58ChwHfDayg3M7BLgM8Dr3P07K9lXRESkFfRnQh48OUVoRmhGseQ8eibHM87vW3pn2ai+DFxbcf+nKtYdbXxxRGQjqFvg5+5FM3sLcAdxSoaPuPtxM3tzsv5W4CZgG/BBMwMouvvehfatV1lFpGPsMrPDC61092sXWieyWsn3V9xWUx6o4BXLRc71/7r7Z5pdCBHZWBYM/Mzs9939nWs5uLvfDtxetezWivtvAt603H1FRJZwEvifzS6EbCwTM0Uu3tzNU5P52a6eFw52MTlTbHbRpHX9NnGPJxGRhlmsxe8aYE2Bn4hIg024+5ebXQjZWMoJrned3z+7LJsvsn2gu4mlEhERmW+xwC80sy0sMK2wu5+uT5FERFbt4WYXYK1GJ3J0pUL6u1KEgboKtgMluJZV2GNmX6+x3AB39+c2ukAi0vkWC/z2APewcGoFfaOJSEtx91dVL1uPbuuNlC9GTOaKnJ7K05uJA8DeTKjxYi3swJ7t3Eyc1mFkLMuOLb3csH+X8p7JYh5i/oQuIiJ1t1jg9013f37DSiIiskZm9v7qRcDrzKwfwN3f2vhSrY67MzVTZGqmSBgYfV0pBrpTdKXCZhdNalCCa1mhvLs/0uxCiMjGUs90DiIijfYq4AjwBeZ6K1xH3HuhbZUiZ3y6wPh0gUwqYKArTV9XSCoMml00SRw5McrBo0MMj2XZqRY/Wdo/N7sAIrLxLPar4ZaGlUJEZH08G3iKeHKq/8/dP0Y84cvHkvttL1+MODU1w/dOZ3n87DTjuQKlyJtdrA3tyIlRbjp8nNGJHJt70oxO5Ljp8HGOnBhtdtGkRbn7W5pdBhHZeBZs8XP3j1YvM7PvuPsz61oiEZFVcvcJ4G1m9v3AX5rZP7D4Ba62Np0vMZ0v8RQzZFIB3ekwvqUCtQY20MGjQ6RDozcTf6X2ZlJk80UOHh1Sq5+IiLSMxfL4TRBP4gJzXaZ6y8vdfbDehRMRWQ13v8fMfgz4ZeArzS5PI+SLEflixPh0AYB0GNCVToLBVEgmpUCwXobHsmzuSc9b1pMOGRnLNqlE0urM7IXu/rVml0NENpbFxvh9FNgE/Ka7PwlgZg+5++WNKJiIyGqZ2WZgN3AX8JfNLU1zFEoRhVI8QyhAGNhsENiVDuhKBZopdJ2U8/iVW/wApgsldmzpbWKppMV9EHhBswshIhvLgpeA3f2/Eo/z+4SZvdXMAuZaAEVEWo6ZZczso8T5/G4D/gx42Mw+YmaZZpat2UpRPEvoqakZHjszzSOnsoyO55gplppdtLZ3w/5dFEpONl/EPf6rPH4iItJqFp3VM+ku9RPAW4AvA90NKZWIyOr8NpAGdibj/TCzAeADwO8kNwEidyZnikzOFOnNpNjSl1aqiFVSHj9ZhV1mdnihle5+bSMLIyIbw5LpHNw9At5vZn8DKK+fiKxaA6a8fxWwz91nB1e5+4SZ/TLwNRT41ZTNF8nmi/R3pdjUqwBwNZTHr701IR3HSeB/1vMJRESqLRr4mdk24LXAnmTRt8xsm7ufqnvJRKSjlKe8T4c2b8r7m2E9f2BFlUFfmbtPmpm6qi+h3ALYkwkZ6E7Tkw4JA40DlM7WoLqp2oS7f7leBxcRqWXBMX5m9mzgG8D3A98BHgCuBv7dzPYstJ+ISC2VU96bxX/ToXHw6NB6Po2b2RYz21p9A6L1fKJONp0vMTqem80VeDZboFDSyyedqUF1U7WH63lwEZFaFmvxezdwo7t/snKhmb0a+H+AV9ezYCLSWRo05f0m4B7mUtBUUovfCrn7bK7AU1Nxioi+rhS9mThfoEgnaEY6Dnd/VfUyM/t9d39n3Z5URDa8xQK/73P3n6le6O6fNrPfr2OZRKQDNWLKe3e/bN0OJucolCLOZPOcycbpIXozcRDYmwmVGkLaVjPScZjZ+6sXAa8zs34Ad39r3Z5cRDasxTL6Tq1ynYjIORox5b2ZvdTMzrlgZWavNbOXrNsTCaXImcgVeHI8x8OnsjxxNsd4rkApUsOqtJcmpeN4FbAVuJu4l8LdQCG5f089n1hENq7FWvy2m9mv1VhuwPl1Ko+IdKgGTXn/e8BP1Vh+J/BZ4Ivr+WQSK/9YzuaLnLI8PemQnqQlMB0udn1RpPmalI7j2cRDaq4BftPdHzWz33X3j9XzSUVkY1ss8PszYGCBdR+qQ1lEpMM1YMr7Xnc/Wb3Q3Z8ws756PrHE5gWBxOMC4+6gKXoyGhcoranR6TiSPKNvM7PvB/7SzP6BxXthiYis2YKBn7v/XiMLIiKyDrrNLOXuxcqFZpYGeppUpg2tUIo4Ox1xdrpAKgjo7Qrp70ppchgRwN3vMbMfA34Z+EqzyyMinW2xdA5/YGZvrrH8V83svfUtlojIqnwG+LPK1r3k/q3JOmmiYhQxPl3gsTPTPHJqitGJeFxgvqhUEbIxmdlmYC9wF/ArzS2NiHS6xboV/F/AbTWW3wL8h/oUR0RkTX4beBJ4xMzuMbN7iPNlnUzWLcnMrjGzb5vZg2b2jhrrX2FmXzez+8zsbjP7kfU8gY2iFDmTuSJPTcwwMpZl+HSWU5Mz5AqlZhdNpO7MLGNmHyWun24jHl7zsJl9xMwyy9j/I2Y2ambfqFj2LjN7NKmb7jOzl69nmYvK5SnS9hYb4+fufs5/ubtHpnm7RaQFJV0832Fmvwc8I1n8oLtPL2d/MwuBDwAvAUaAY2Z22N2/WbHZPwKH3d3N7LnAJ4E963YSG1Rll9AwsGRymBS96ZAg0FeOdJzfBtLAzmS8H2Y2QFz//E5yW8xHgf8NfLxq+R+7+/vWt6ixqXyJ8ekc2/oz81JfiEj7WOw/N2tmu939gcqFZrYbWNaPKBGRZkgCvX9fxa77iAPFIQAzOwS8ApgN/Nx9smL7PpQYft2VWwMnc0XMjO50nDi+L5MiVBAoneFVwD53n80S7+4TZvbLwNdYIvBz96Nmdll9i3iuQiniibNxzsOtfRkyKc1HI9JOFvuPvQn4nJn9gpl9X3L7ReAfknUiIp3mYmC44vFIsmweM3ulmZ0grg//c60Dmdn1SVfQu0+ePGeiUVkmd2c6X+KpiRm+dzrLk+M5JmeKRMoXKO0tqgz6ypILS2v5cL8l6Yr+ETPbUmuD9aibsvkiI2NZTk7MKHenSBtZMPBz988BPw28mLhLwUeBA8Cr3f32+hdNRGRlzGyt/Y9qNSed86vG3T/r7nuI68h31zqQu9/m7nvdfe/55yv16Xpwd6ZmioyO53jkdJbHz04zNpXXuEBpR25mW8xsa/UNWO1guj8Fng48D3gc+J81n3gd66aJXIHh01nOZPO4KwAUaXWL/khy928Ab2hQWURE1uprZjYCfB74vLs/vML9R4CdFY93AI8ttHHS3erpZnaeuz+14tLKqpVbAqfzJcayzI4L7EnHYwPVJVRa3CbgHpZ5sWk53P3J8n0z+zPg71dXtJWJ3Dk9lWciV2RLX4b+Lo3/E2lV+u8UkY7h7nvN7FLgZcCfmNnFxLmxPgd82d1nljjEMWC3mV0OPApcB7y2cgMzewbw3WRylxcAGeDUOp+KrFDluECYIZMK6E6H9ClxvLQgd79svY9pZhe5++PJw1cC31hs+5U4cmKUD3zpQb43luWiwR6uu3on+3ZtnbdNoRQxOp7jbDpka29G/3ciLUiBn4h0FHd/hDhv361J4vYXAdcA/93MTrr7gulo3L1oZm8B7gBC4CPufryc09TdbwVeDbzezArEE139R1cfp5aTL0bki3HewFQQJLOEhnSnQ7UGyjmKpYiZYkRfg1qrzOylwIC7f6pq+WuBk+7+xSX2/wTx8Jvzkl4OvwscMLPnEbcYPgzcsB5lPXJilJsOHycwGOxOcWpqhlvufIAb2X1O8AcwUyjx+NlputMhWxQAirSUBWs4M3sN8AV315VsEWlL7l4A7kxuJC2AS+1zO3B71bJbK+6/F3jv+pZU6qkYRUzkIiZyBQBSQUA6ZfSk40CwO60fphuJuzNTjJgpRMwUS8wUIwqlCDPj8sZ1U/w94KdqLL8T+CywaODn7q+psfjD61Cucxw8OkQ6NLpSIcVSRE86ZLpQ4tCx4ZqBX1kuCQB7MnEAqP8zkeZbrIa7FPib5Ir5PxJ3lbpLV7ZFpF25+6PNLoM0XzGKKOZhOh9PCpMKAnq74m6h3ekApartLOXWvFwhDvJmilErTETS6+7nTKnp7k+YWV8zCrSQ4bEsm3vSVE7e2Z0OeGJ8eZm94rG4cQC4ra9LKSBEmmjBwM/d3wO8J0ko+hPEU5bfambfIp444Y7KgcQiIiLtqBhFjE/H3ULDwOjNpOjriieKURDYXmZb84oRM4W51rwW1G1mKXcvVi5MLrb3NKlMNe3c0svoRI6u1FyLXa4QceHgyoo5nS/xaGGawe4UW3ozBOpyLdJwS152cfeJZOryG9z9+cB/B84HPl730omIrEGrXTmX1leKnIlcgSfO5vje6SyjE3HewBZoIZIaiqWIqZkipyZneOzMNA+fyvLYmWlOTc4wOVNs1aAP4DPAn1XWUcn9W5N1LeOG/bsolJxsvojjTBdKFCPnuqt3Lr1zFXfn7HSBkbFpJmeKS+8gIutqxZ3Z3f2bwDdZID+MiEizmdkPAR8C+oFLzOwq4AZ3/+XmlkzaSeVMoYEZfV0p+rvUHbRZFhqbtx7uGjrNobuHeWpyhp1berlh/y4O7Nm+LsdewG8TX0h/xMweSZZdQjxO73fq+cQrdWDPdm4GPvClBxkey3LhArN6rkQximcAnVD3T5GG0qyeItKJ/hh4KXAYwN3vN7P9zS2StLPI45bAiVwBM6M7HcSBYCalLmt14h63Lk3n6zs2766h09xy5wOkAmNzT5rRiRw3HT7OzVC34C/p4vkOM/s94BnJ4gfdfXkD5xrswJ7tPP/SLZyaXCojzsqUu39u6kmzpTetCyoidVbXwM/MrgFuIZ4W/UPJuMHK9XuAPwdeAPyWu7+vYt3DwARQAoruvreeZRWRzuLuw1U/IkrNKstyHDkxysGjQzz01OS6XFGX+qlMHn/K8nSnA+59eIyPf/URRs5kuWRrXyNajDpSKXKm8kWyMyWmC6WGdLE9dGyYYqnEmWyJJ8ZzZMKAwZ4UB48O1f09TAK9f6/rk7Q4d+dMNs/UTJFt/Rl6M2qTEKmXxdI5LPqLw91PL7bezELgA8BLgBHgmJkdTrqKlp0G3gr89AKHebG7P7XY84iI1DCcdPd0M8sQ1zPfanKZFlTOk5UOjcHu9JJ5sqR1uDtfPnFytsWoNxPy6Jks7/zsv/PfXvosfvRZ20mnAlKBkQkDtQ4myt02C6WIYskpJDNvNmNM3iOnp5iYLmCBEQZGMXKemshTKE00vCwbWaEU8cTZHP1dKbb1dynfpkgdLHZZ5SnigK08+rbyP9CBXUscex9xt4UhADM7BLyCeHxgfBD3UWDUzBZMqCwisgpvJu5tcDFxPfYF4FeaWqJFlPNk9WZS5IvLz5MlreHQsWFSQZwXEKA7FTLtJT76L4/w3J2b521bziHYnQrp7QrnzZTYaaLIKUZO5E4puZ8vRuRLEfnWSKkAQL4YgUFghmGYQWRxWeul1oyeEpucKTJdKLG1L8NAd7rZxRHpKIsFfv8LOAD8M/AJ4CsrzOF3MTBc8XgE+IEV7O/AF8zMgYPuftsK9hWRjc3c/eebXYjlKufJqrSSPFnSXI+Px1PUV1ro/avMITiWhXQY0JsJ6cmEZMKAVNh+k1y4O/mk5a5YcnLFErlCiVLUGoHdUtKhMV2AYhLoGRAEkAnr2uL0NTMbIU6P9Xl3f7ieT9ZuSpFzcmKGqZk4ANTkLyLrY7E8fjdaPEDmAPA64H+Z2ReAP3X3h5Zx7Fo15kq+BX7Y3R8zs+3AF83shLsfPedJzK4Hrge45JJLVnB4Eelg/2JmDwH/B/i0u59pcnkWVc6TVTm2ZTV5sqQ5Lhrs4dTUzGyLHyz//SuUIs5OR5ydLgAQBkYmFZAJAzKpgHQYdxMNzJraTbTcJTNXKJErlnCPy1qKvFUSoq/a1t4MZ6fnN755BOf1d9XtOd19r5ldCrwM+BMzuxj4CvA54Mvuvr6zqLSpbD5u/RtIcv+p+6fI2iw6gjZp4fuSmf0bcB3wbuAB4M+WcewRoDLJyw7gseUWzN0fS/6OmtlnibuOnhP4JS2BtwHs3bu3fb95RGTduPtuM9tHXG/9lpl9Ezjk7n/Z5KLVdMP+Xdx0+DjZfJHQbE15sqTxrrt6J7fc+QDThRLd6YBcIVr1+1eKkoljFpiLyMziFimLuyQGgRGazf4g9uT6apBsF3ncIldyJ/K4+2Xkc9tAPGNp5BAk3R2DIN7XgVIp3redA7slmcXnnozxc4/fh3rPMOnujxDn7bs1Sdz+IuAa4L+b2Ul31zAY4s/v+HSByVyRLX0ZNvWo+6fIai02uUsf8Zi8/0icsP0zwAvcfXihfaocA3ab2eXAo8Q/wF67nB2T5w7cfSK5/5PAzct8XhER3P0u4C4z+33gj4CPAS0Z+JXzZB08OsTDT01ygWb1bCv7dm3lRnZz6NgwT4xP13VWVvc4tCsHb2uZq7ZU1Qmn5Mmylp7/dv1N5YtcMNjFWLZAMXIyYcCFg10NTTDu7gXgzuRG0gIoFSJ3Tk3OMJErcF5/F93pzh0fK1Ivi7X4jRK37n0CeJD44t/VZnY1gLt/ZrEDu3vRzN4C3EGczuEj7n7czN6crL/VzC4E7gYGgcjM3gZcAZwHfDa52pYC/trdP7/qsxSRDcXMBoFXEl9wejpQ7jXQsg7s2c6BPdsZGcvWdVIJqY99u7YqUG9T5a66O7f2zk62k80X2T7Q3bQyufujTXvyFpcvRjx2ZpqB7jRb+9T9U2QlFgv8/oY42NuT3Co5cQvgotz9duD2qmW3Vtx/grgLaLVx4Kqlji8isoD7gb8Fbnb3rza5LCLSwma76uZLZMKA6UKJQsm5Yf9Sk5dLM03kCkzNFNnSm2GwJ6Xk7yLLsNjkLr/QwHKIiKynXSuchVhENqjZrrp3D3NqcoYdW3q5Yf+uuidvr2Rmfe4+1bAn7BCRO6emZjg7XWBTb5rBbgWAIotZcH5cM/uTivs3Vq37aP2KJCKyOhX11mEzO+fWzLKJSOtr9NUiM/uhZPKpbyWPrzKzDza4GG2vGEWcmpxh+PQ0Z7MFojZJJSLSaIt19dxfcf8NxMmQy55bn+KIiKzJXyR/39fUUsiGc9fQaQ4dG+bx8Wku0uQ8beWuodPccucDpAJjc0+a0YkcNx0+zs3QiFa/PwZeChwGcPf7zWz/4rvIQopRNNsCuLU/Q3/XopPXi2w4i/1H2AL3RURakrvfk/z9crPLIhvHXUOnee8dJ5jKF4kiZyyb5713TPH2l+5R8NcGDh0bJhUYPZkQM6M3kyKbL3Lw6FBDunu6+3BV98QNNq/q+itGEaPjOSYzKc7rz5AKlQBeBBbp6gkEZrbFzLZV3N9qZluJZ+kUEWlJZrbbzD5lZt80s6Hyrdnlks5029HvMj5dwKM4D55HMD5d4Laj32120WQZHh+fpjs9/+dQTzpkZCzbiKcfNrMfAtzMMmb2GyTdPmXtsvkij56ZZqqBqTlEWtliLX6bgHuYa+27t2KdOk+LSCv7c+B3ibtRvRj4RdRzQepk+Mz0bAJwAEsypw+fmW5uwWRZyukcejJz17SnCyV2bOltxNO/mXgozcXACPAF4Fca8cQbRSlynhzP0d+VYmufWv9kY1ss8PtRd3+kYSUREVk/Pe7+j2ZmST32LjP7J+JgUERkVpPTOZi7/3wjnmijm5wpMpUvsbknzaae9OyFGpGNZLHA77PACxpVkEY5cmKUg0eHGB7LsrMJUzaLSEPkzCwAHjCztwCPAvpHl7rYuaWXR05NgTtm4A6Rw6VbG9JiJGvU5HQO/2JmDwH/B/i0u59pxJNuVO7xGNzxXIFNPWkGuxUAysay3MldOsKRE6PcdPg46bApM3eJSOO8DegF3gq8G/gx4tmJRdbd9S/aFU/uMlOkVHLCwBjsSXP9i5QAvF3s27WVH3j6Ni4/r6+hz+vuu81sH3Ad8FtJaodD7v6XDS3IBlOKnNNTec5OF9jcowTwsnEsFvhdbGbvX2ilu7+1DuWpq4NHh0iH8YxdQMNn7hKRxnD3Y8ndSeLxfSJ1s2/XVt7+0j0cOjbME+PTXKh0DrIC7n4XcJeZ/T7wR8DHAAV+DVCKKhLA96QVAErHWyzwmyae3KVjDI9l2dyTnresgTN3iWx49e5qbWZ/zsKTT7m7v3HdnqwOfv/2b3E2mycMjK5USCYVkEkFdCW3TCokkzIyYUBXKkyWBVXbhYTqutRw+3ZtVaAnK2Zmg8AriVv8nk48zGZfUwu1AVXm/9vUm2awWwGgdKbFAr9T7v6xhpWkAXZu6WV0Ijfb4gcNnblLZENrUFfrv6+x7BLirp8tn4bmr772CFP5tafwigPHgExYHRSWH4ez67tSAemK9dUBZ/kYXbPLagSkyTb6oSSyYvcDfwvc7O5fbXJZNrxiFHFqcobx6QLb+jPzfi+KdILFPtH5hpWiQW7Yv4ubDh8nmy/Skw4bPXOXyIbWiK7W7v7p8n0z2wW8E9gPvAf48Lo8SR39+LMv4KnJGWYKJWaKETPFiHwxIl+auz9TjOutxZQiJ5svkW1wHuh0aOe0RM4PDsMaQehcAJqp2PfcY4TxMdJz23elAk3NLu1ul7srRVaLKZQinjgbNxRs6UvTlWr564Yiy7Jg4OfuL2xkQRrhwJ7t3Az86Ze/y/DpuKvZLx14usb3iTRAo7pam9mzgd8Cng/8IfBmd2+L7L3vf83zGRnLki9Gi24XuSdBYBIYzgaHpXOWz8wLHGuvL68rb1u5TWXAGS3x87RQcgqlIpMz6/iiLCEwZlsiawWTc11kq1ow00FVq2i4YEB6TitoKiBQ66asgZn9ibu/DThsZuf8Z7n7tY0vlVTL5otk80X6ulJs6c2QSelCk7S3DdeGfWDPdl749G08liTWTQUBoxM5+jIpejOhuiqJ1Ekjulqb2d8Ae4H3Ab8KlIDB8v+1u59etydrosCM7nRId7qxV6FLkc8LLmeKEYXZ+/MDx3O3Kc1vwSzMD0hnirUC2WgZQTDkChG5wuLbrbd0aBXBYe3xlpmKlsqaraBVAWnNVs6KIDQdmr6jOsdfJH/f19RSyLJMzRSZminS351ia6+SwEv72nCBX7ViFDGZi5jMFQnM6M2E9HUpCGxV7///vsOHvvIQU/kSfZmQN/3I5bz1J57Z7GLJMjSoq/XVxJO7/Abw68my8j+yA+rXvQZhEHfV7c007jndnUJpfsA52ypZSFo5SxH5op8TQJb3yVfvv0hraXldcYnmzbh1s8QUJaDQkNciMOaN26wcc1k93rJWK+i5AeYSrZxJt1pNFrT+3P2e5O+Xm10WWb7JXJGpmRKbetJsVhJ4aUMrCvzM7Fp3P1yvwjRb5M7kTJHJGQWBrej9/993uOXOBwkMUkHcWnTLnQ8CKPhrA+Wu1gePDjEylq1LkmR3v2zdDiYtwczimUwb3MWqFHlFILhwa2a5++x3npzk2MOnOZsr0J9JsXt7P1v6M3PB5TndbquC02T9YuFm5JArRuSWaAVdb6nA5o+7DMsBp9UMHuNWzoVmpp1/jK50QDqsbCGNl2+U1k0z2w38D+AKoLu83N11kapFuTtnsnkmcgU292Y0A6i0lQUDPzN7VfUi4ANmlgJw98/Us2DNpiCw9XzoKw8lQV/8AzCwuMX2Q195SIFfmziwZ7vG1EpbCAOjJxPSkwmB9KLb3jV0mrse/h6pwHjapm5yhYjvPjXFjc992opSPJRbN+cHmKVzWymrusjWbsE8d8xnddfc8vMsNVlQMXKK+RLZdZhxdrkM5rVszh9vORdwZsJ4vGbl7LO1xmTOb/E8d31XKmh41+nEnwO/C/wx8GLivKP6kdEGSpHPzgC6tS9DX9eG70QndbDeabAW+5R+Evg8MMpcJdQH/BRxl6mODvwqKQhsDVP5EtUX/QNjXaa/FxFZrUPHhimWSpzJliiUItJhQH9XyKFjwysK/Oa1bnbVscBVak0WVA4MZ4PGQmU32/ndbmcK547LnNft9pyW0nj/xXrTOszu30hhYBz9by/m4s09jXrKHnf/RzMzd38EeJeZ/RNxMChtoFCKeHI8R3c6ZGtfplkXEKQD1SMN1mKB3w8ST4F+DLjV3d3MDrj7L67qmTpEdRBYvtLYlQ5nrzhKffRl4nFhlV3qI4+XS3uodwJ3kWZ45PQUE9MFLDCCwChGzthUgWI01eyiLUszJgtyd4rl7rTlgLFwbsB57rjMuW6381sw5wLOuRbPc7vZLjVZUClyuhr7PZ4zswB4wMzeAjwKqFJsQ7lCicfOTNObSbG5N60AUNasHmmwFkvncMzMXgL8V+BOM3s7LDr8YMOJ3JnOl5imBNPx4P7A4u5B3emQ3kxIWjM/rZs3/cjl3HLngxSjiMDioC/yeLm0vgYlcD9Hp49NlubLFyMwZlM8mEHJfMkgYyMzM9KhkQ4D+hr4vJE7hXndYeeCw0IpYktfhk09i3ftXWdvA3qBtwLvBn4MeMNSO5nZR4D/Cxh19+cky7YC/we4DHgY+Dl3H6tHoWVh5RQQPZmQLb1qAZTVq0carEU7JLt7BNxiZp8i7n8uS4jcZ6f9PQWkwyAeJ5JcTdXsaKtXHsenWT3bUyMSuG/0scnSHOnQmClCFDlmUE7HnQlV37eauKdOSFc6ZKBqnZlx+XmNDEPji+zJ3Uni8X3L9VHgfwMfr1j2DuAf3f09ZvaO5PHb16OcsnLT+RLT+WkFgLJq9UiDtayRqO7+KPBzq36WDaxQiihMR4wnLYKpIKA7HWiM4Cq99SeeqUCvTTUogbvGJkvDXbatn5GxKabyc2P8+jIpdmxpbBAh7cPM/pyFe1G5u79xsf3d/aiZXVa1+BXAgeT+x4AjKPBrunIA2NcVdwHtSikAlOWpRxqsRQM/M3sDcCPwrGTRt4D3u/vHF95LFlOMIiZnotkxgn1dKQa6U7oSJB2vEQnc0dhkaYLrrt7JLXc+wHmZFN3pgFwhzgN43dU7m100aV1/X2PZJcRdP1f7g+ACd38cwN0fNzONFWwh5d5gvZkUW/oUAMrS6pEGa7F0Dq8nroB+DbiX+Or5C4A/NDMU/K1d5M5ErsBErjA7dXhfJkVPOlRSUOk4jUjgrrHJ0gz7dm3lRnZz6NgwT4xPc+FgD9ddvXNFM3rKxuLuny7fN7NdwDuB/cQXrj5cz+c2s+uB6wEuueSSej6V1FAeA6gWQFmO9U6DtViL3y8Dr3T3hyuW3WlmrwYOMb9fuaxRKXImc0Umc0XMjO50QG86RU8m1Eyh0hEakcAdNDZZmmPfrq0K9GRFzOzZwG8Bzwf+EHizuxfXcMgnzeyipLXvIuIu7+dw99uA2wD27t2rC2NNUtkCONiTmtcbRqReFvuUDVYFfQC4+8NmNli/IomXZwvNl2AqzitUmXA2EwakQ9P4QGk7jUzgrrHJItKqzOxvgL3A+4BfBUrAYPl73d1Pr+Kwh4lnBH1P8vfv1qWwUlflFsB0GDDQnWKgO62JAKVuFgv8ple5TtZZKfKkYphbFif6DdjUk6a/S1eJRMo0NllE2sDVxN3QfwP49WRZ+de+A4v2gTezTxBP5HKemY0QJ3x/D/BJM3sj8D3gZ9e/2FIvhVLE6ak8Y9kCA90pNvekSSklmKyzxSKGZ5vZ12ssN5aokKT+3J2ZQonRQokxXSUSATQ2WUTag7tftsb9X7PAqh9fy3EX8sTZHA+enOSCgS79zqgzd2d8usBErshgd4rNvRm95rJuFg38GlYKWZPKq0Td6YCedEhPJtSAYdmINDZZRGSd/d19j/I/PneC3kzI9128iat2buaqHZt45gUDCkrqxN05Ww4Ae9Js6tHFfVm7xQK/NPHUwP9cudDMXgQ8VtdSyarUGhvYkwkZ6ErTk1EQKM1TKEXkixHThRLpIGBTb3rpnVZnzWOTzewa4BbiKdU/5O7vqVr/88zlxpoEfsnd719TqUVEWtiDo5MAZPMl/vWh0/zrQ/EQxJ50yPft2MTzdsTBoALB9Re5cyabZ3y6oABQ1myxwO9PiKcXrjadrPupOpRH1lHlTKGpIKAnE8azhWZSqjSkbkqRky9G5AolsoUS+WKE+9zEcZt66hb0wRrHJptZCHwAeAkwAhwzs8Pu/s2KzR4CftTdx8zsZcSz4/3AGsosHeCuodMcOjbM4+PTXKR0DtJh/vBnr+KNL7qcI98+yf3DZ7h/5AyPnckxXShx10OnuSsJBHszIc+5WIFgPZQDwLPThRV1AT1yYpSDR4cYHsuys06zaUv7WCzwu8zdzxnj5+53m9ll9SuS1EMxipjIRUzkwCxPbyakryvOGahKWVYrX4yYKcbBXb4UUSg6xShqZpHWOjZ5H/Cguw8BmNkh4BXAbODn7v9Ssf3XgB2rL650gruGTnPLnQ+QCozB7hSnpma45c4HuJHdCv5k2czsWnc/3OxyLOSiTT385BUX8JNXXADA6HiO+0fOcv/wGf5t+AyPn82RzSsQrLfKLqCbe+MWwIVmeT9yYpSbDh8nHRqbe9KMTuS46fBxbgYFfxvUYoFf9yLreta7INI47j6bPwagKx3Sk45bA7tTrZs8Xletmi9XKDE1UyRXjM5pyWsRax2bfDEwXPF4hMVb894IfK7WCiVJ3jgOHRsmFRg96bhLfU86ZLpQ4tCxYQV+UpOZvap6EfABM0sBuPtnGl+qldk+2M1LrujmJSsIBNU1dP1E7pyeyjORK7KlL1NzhveDR4dIhzabI7A3kyKbL3Lw6JB+P21QiwV+x8zsv7j7n1UuTKYJvqe+xZJGmimUmCmUZh+nw4CudEBXGMZ/U0HTcwbqqlVzRJEzU4yYyhfJzpSa3Zq3HGsdm1zrg14zujWzFxMHfj9Sa72SJG8cj49PM9g9/+u0Ox3wxPjaMx+ZGWE5txtO5My74JLMVrvm55GG+yTweeIk6+V6p494GI0DLR/4VasVCN43cpavD5/hPnUNrZtCKWJ0PMfZdMi2vgzd6bk5HYbHsmyuGl7Rkw4ZGcs2upjSIhYL/N4GfDaZyKAc6O0FMsArl3PwZUySsAf4c+Lp1n/L3d+33H2lfgqliEIpYpK4RbCcM7A7FY8P7E43PhDUVavGiCJnulAimy+RK5QolFo+0Kv2J6xtbPIIsLPi8Q5qBIxm9lzgQ8DL3P3UagoqneOiwR5OTc3QkwkxDDOYzpd42uYeutIhUeREPhe0mRkGmIERT8LVmwkxg8jjKCAMjFRgNfN4uTvuzPbO8OTYpeR58qWImUJEMYpwj6OIFm2h38h+kDjv3jHgVnd3Mzvg7r/Y5HKtm+2D3fzkFd3ndA29r2KM4EJdQ6/asYnnKRBckZlCicfOTNObSbGlL01XKmTnll5GJ3Kzv50ApgsldmzpbWJJpZkWDPzc/Ungh5Kr2s9JFv+Du9+5nAMvc5KE08BbgZ9exb7SIOWcgTOFEmenC5jZbNqI7uRWb7pqVV+5QomJXNz9N2rvH4drHZt8DNhtZpcDjwLXAa+t3MDMLiG+Gv86d//O2oss7SQMLAnKAtKhkU4F/NKBXbzzs//OyckZSpETBkZ/V4rf+7HdXLx5/UdGmMXBZeXj0Jj9gdydDs8ZrOFJQOgOlf/ikTvFyCmWIkpRfD9fjNq9Hmh57n7MzF4C/FfilDNvZ4HeBZ2iukXw5MQM94+c4b7vzbUIqmvo2mXzRbL5In1dKf7zD1/Gu//hW2Tzxdku6IWSc8N+pePeqBZr8QPA3b8EfGkVx17OJAmjwKiZ/YeV7ivNMy9tBBBY/OOnKxXQm4nHC653i6CuWq2v0mzLXpHpfIlS1DG/N9Y0Ntndi2b2FuAO4t4GH3H342b25mT9rcBNwDbgg8nnvOjue9dccmkpYWCkw4B0GJAJA9IpoytVezKsrlRIYAblbphuNfsMN5OZrSi360wx/oFYiuJbeRKnDqorms7dI+AWM/sU8MfNLk+jnT/QxU88+wJ+4tnL7xrakw75vosHuWrnZrUILmFqpsjTt/fzqz+xm0N3DfPY2Wl2aH6EDW/JwG8NVjpJwnrtKw0WVbQIjk8XCMzozYT0dqXoTa/PZDE37N/FTYeP66rVGpQiZyoft+qVg/YOtOaxye5+O3B71bJbK+6/CXjTOpRVmiwdxher0qmAIBlLFwSQCoIV/Zg8eHSIwZ40F26au7bQ7l3Ru1IhNeaKIIrilsNyd9I27RLeUtz9UeDnml2OZluoa2jlZDHThRJ3PTzGXQ+PAWoRXI6rdm7mqp2b6U6HDPak6VNe5w2tnoHfsidJWMu+mjmv9UTuTM4UmUxmDU2HAd3pkEzSKpgJgxUHgwf2bOdm4h9YI2NZXbVapnLS9Gy+SK6wIcb4vI01jk2WzlTZRT2TCmZb89bDRuqKHgRGdxDO605aipxcocRMMttvIek2qu6iizOzNwA3As9KFn0LeL+7f7x5pWodi80aev/IWR49M71ki+Du7f01x8luRLlCPHY/MKO3K6Q/SenV7Mn7pLHqGfgta5KEte6rmfNaX3mymEqpICCTqriF8XiZxSqgA3u2K9BbhHs8A2cuSZqeSyZ32EjWOjZZOoslvQ/61rH3QS07t/Ty8KlJxqeL5EsRmTBgsCfFZdv66/J8rSYMjL6uFH1d85fnixG5YomZQtxCqAlm5pjZ64kvVP0acC/xBe8XAH+YzNSq4K/KYmME5wWC1S2C6ho6T+TOZK7IZK44LwisHEojnaue7/KSkyTUaV9pA8UoopiPyObnLw8DIzAjCIzA4vGDgRnp0GYDRF29i5UDvWy+xHQS7OlHVWwNY5OlDcXj1+KLSKlk8pW4h0FjujT94K6t3PXw6aTOgnwpYnQiz2uu3tg5/MoX9sotg+5ONl9iKl9kpnDuBcEN5peBV7r7wxXL7jSzVwOHAAV+S6geI3hyYmZ2xtD7hivGCC4SCG70FsHKIDAVBPR3pxjoTq1bbwhpPXUL/JYzSYKZXQjcDQwCkZm9DbjC3cdr7VuvskrrKEVOCYdFhqClgoDudDCbeD6T2jgVVHmcXnlyHXWl6hxHToxy8OgQDz01yYWDPVx39U4l/15AYJbMKBx3I292rtGvDp3m/P4ME7m5Fr+B7hRfHTrNW5tWqtZjVm4ZjH96FEvRbPfQXLG0Ubqjlw1WBX0AuPvDZjbYhPK0vfMHunjJFRfMaxFUILh8xSjiTDbP2ekCA90ptvRmNnzraCeqa7vuMiZJeIK4G+ey9hWBuHKanInmjSHsycRjTipbCcvTrrezUuQUSlEc6CX986XzHDkxyk2Hj5MOjcHuNKemZrjlzge4kd0bPvgzsyR1TDwmLxWubHbKRhgey3JefxfnD8xNKuvuHTnGbz2lkh4c5S6iUeRkO3O24VqmV7lOlqlWIFgOAu8fXl7X0I0YCLo749MFpmaKbO7JMNiT0jjADqIOvdL2CqWIwnTE+HThnHXlLl/pMEhm7LMkaTKzSZSDJCdW+S/MJUIuB5BAkgjZZxM0V3KPu0zMTWow9/zlrmflfSrzaDk+bzxedX6tDv/hI4mDR4dIh0ZvJkW+GM3OXHvo2PCGC/xSwVzqhHLA1+o/OpRuZn0ESf7D/q5UnDaoUGIyVyTbmb0bnm1m5+QcJf560nTVdVCra+hKxwhupECwFDmnpmY4O11gsCceA7iRelh1KgV+0tFKUZJzcLG+oyJNVmtWyO50wBPjnX/hvzwBS3xLtWUrvdLNrL/4cxH/2CwHgdmki3uHjA18drMLsNEtGAgOq2topWIUcXoqz+mpfJzXNJlvoTyGV+MB24sCPxGRJqvVYpQrRFw4uGTO+baUScVpFXoyYUdMJ650M/VVGQRC3Msjnpo+mk0s34bSwAXu/s+VC83sRSx/BnRZR4tNFqOuobHyLO1TFcsyqYCB7jQDXam6zZws60eBn4hIk1W2GIVmTBdKFCPnuqt3Lr1zizOz2UCvOx3QnapfWoVmUrqZxinnYCwPqYyi+S2Cq0ljc9fQaQ7dPcxTkzPsbEzg/ifAO2ssn07W/VQ9n1yWtthkMV8fOcvIWO1A8DkXD3LVjnL6iOYHgncNnebQsWEeH5/mojpMHJYvRpyanOFMNs9gd5rBnnRb9tzYKBT4iYg0WWWL0cNPTXJBm8/qGQZGTyakL5OiN9P+LXrS2oJg/myh2Xw8Pf1UvrSsWULvGjrNLXc+QCowNvekGZ3IcdPh49wM9Qz+LnP3c8b4ufvdZnZZvZ5UVq9WIPj1kTP8W9VkMcceHuNYi7QIVn62B7tTdZ04rBQ5Y8msoIM9aTYpAGxJCvxERFpAucVoZCzbll3XymP14kTACvakecrdQkuRMzlTZHKmyMwiMyIfOjZMKrlYUe5Wms0XOXh0qJ6BX/ci6zqzj3eHOX+gix9/9gX8eI0xggu1CHanA77v4k0NaxGc/Wyn45mQGzFxWOQ+mxaiLxPS363k8K1E74SIiKyKmdGdDuhNp+jvbs+JWaRzhYGxKWl5yBcjsvkiuUI8PrByltDHx6cZ7J7/c6gnHdY7HccxM/sv7v5nlQvN7I3APfV8YqmPhSaLuX/4LPePnGFkbJpcIZrXIljvQLDWZ7tRE4e5z114SYcBg91pBro1DrDZFPiJiMiylfNm9mbCjh2vJ50nnoEwA8z/QTqdL3HRYA+npmboyczlh2xAOo63AZ81s59nLtDbC2SAV9bziaUxFgsE7xs+w6Nn6h8Izn6203Of7WZMHFYoRZyamuF0Nj87i3NPOmz6+MeNSIGftJV6D1KW+rpr6DSfuneEJ8ZzjZpAQdZBJhXQ35WiJxO2XPJ0kZUys3gWwu40xVLEm37kcn7n8Dd4ciKHe9xS2N+V4nf+wxV1K4O7Pwn8kJm9GHhOsvgf3P3Ouj2pNFWtQPDrI2e4r44tgtddvZP33nGCJ8dzlCInTMbD/sqBZ9TlHJfi7kzNFJmaKQIkkzSlGOjWeMBGUeAnbeOuodO8944TTOWLRMkg4vfeMcXbX7pHwV8bKA8y70oFjZxAQVapHOz1daWUp2kZjpwY5eDRIYbHsrqo0UZSYUB/d4pUYJjH45Nwo1E/Qd39S8CXGvR00kJqjRFcTiD4nKdt4nk7N3PVzk0864KB5QWCBmbx31ZSKMU5AseyBbqSvIBdqYCuVKhk8XWiwE/axm1Hv8v4dIHAjMAMj2B8usBtR7+rwK8NVA4yb+AECrJCfV0pNvWk6U6rZW+5jpwY5abDx0mHDZ0VUtbJwaNDDPakuXDTXPc31U3SaIsFgpVdQ+9+ZIy7Hzm3RbBWIHjo2DD9XSnO7++aXVbvyV1Ww92T3JxzkzAFZnQlKYB6MuGG/U5a74uKCvykbQyfmSYwZscUmQGRM3ym/oOUZe2aNIGCLCJTcWW1KxWQCQON2VuFg0eHyBdLnJoski9FZJLuSwoc2sPwWJbNPel5y1Q3SbNVB4JPTc5w//AZ7h+JA8HldA197GyWTVWf7UZN7rJWkTvTSW7OsSykgoDuTEBvJkVPOtwQXUPrcVFRgZ+INER5kHl/19zVyAZMoCAVwmAumbq6cK6f7zw5zniuSIARmlEsOaem8hRL480umizDzi29jE7k5k05r7pJWs15/bVbBP+tIn1EdSAYGIxPF2fHaHenAnLFxk/ush6KUcRkLmIyF48PDAMjFQZ0p4Lk3DpvsrGDR4dIhzZbN61HTykFftI2dm7p5ZFTU+COGbhD5HDpVn05t4Prrt7JLXc+wHShRH9gTBdKFErODft3NbtoHa08UUV/d0oTs9RJoeRE7pTccY97I5hBvrR08nBpvhv27+Kmw8fJ5ouzec5UN0mrq90iGLcGlscIRg65YkSumIepeIhfOhVwxUWDHH/s7PLHCLagUuSUohIzhRJnpwsAdKVDulIB6SAgnbK2Dwbr0RthwwV+oxM5vvnYOKcmZzBLBnBb/M9gGOWcw4HN3Tebv86IZwUzm7s/t128bfmYQXkh8ZWXynXVx6H6uCs8zrwy1Chfu7v+Rbt4998fJ1uIcOJz600HXP8ifTm3g327tnIju/nUvSM8OZ5jhybAqJvA4mTUSqbeOKVorh4vX5SS9nBgz3ZuJr66PjKWVd0kbSluEdzOjz87/tyWA8EvfvNJ7h85w0wx/u2UL0bc8c0nueObT65+spgWNVOIA8FK5V4uvZkU3emgrb4P69EbYcMFfl954Cl+7ZP3N7sYTREHgcn9JOiNr0zbbABJRZBbfhyYzQ9MFzoOiwTLyXHmylEVdFc8DiqD2oogNztTZDqpuAAcmC5G3Hr0u/z1se9VlCt+3iB54rmyzw+Kky3nnevs4/L9Bc4Vqi4OLOM4tc61+rVbKIiPn68q4F/gce33Zu61CMpPXj5mxWu2kuPMXYtIXu+q48y+DlWv2X964aU8/fx+vm/HJmR9mMWT5vQkuZE0G1rjhRbXSeUWP70D7eXAnu0K9KSjVAeC5a6h1WMEqyeL6aRAEOJAN1+MODtdwMzoTiaM6UrHY9wrxwoWS1E8gWCLtBLWozfChgv8NrLyj5L4gVetaU+Rw8OnNAC/3bzkigv4s9fvbXYx2loYxK16fRm16jVbOox/KATJhQ93iHAyod4TEWmuWvmPf+0lz1xwsphODgS9YsKYSmFglCq6aaTDgFQYX7ouTyoYJgFhYHMX/g0jCOKJZ+ox2Uw9eiNsuMDvVS/Ywcu/7yIeS2aCjJIxGRB/IMrBUfk+5cd48hdIHkfObMwUsfBxkl0WP07yuNZxKOcWqnGc6vLNe1x9Ll7ef/65zR675nEqypwcZHZ5+XHV88ydX9Xj5FyXe5z4/Zk7zoe/8hAwv+tq+fX9Ty+8dPb1rAxwK8t0zntzzntR9ZonCyu3r37sFfvNe80qHq/kOLNlrH4NK5bPvi41XsNoweMs8Nos8ZmIn2vhz1blezDvs7TEuapFanVSQUBvVxzstVuXlU72zAsGeeipSSZylbN6prn8vP5mF01ENrBy/txUYAx2pzg1NcMtdz7Ajexm366tS44R7PRAsKxU1Te/UIqo6jG6LGEQB4tzv6jmelCVWxFnf3uVfyMRB5LpMM5jmA7jSdjKveR+6Bnn8cKnbyOVTGazVhsu8KtWOXau4o60oI/+y8PgPq8JPooczPiFH7qsaeWSldnUk2ZbRU4hWVw6DOhLxupt1DxGra7cHefCTSlNDiIiLaMyfy4wWz/VyuO30BjB+0fObJgWwbUqVbYIzUoeLxJIlnAKpYhsfuFtzhvoYlCBn2wkOzf38MjpLERVs3puab9piUUW05UO6cvEg9HVOtr6NDmItBIzexiYIP6pWXT3NferP3JilA986UG+N5ad7S7YSgnApbZa+XOXm8dvoUCwnEJCgWB7UuAnbeP6/U/nvXecYCpfJIrilr/BTJrr9z+92UUTWTdP29TTMgPLZfk0OYi0mBe7+1PrcaByEunAqNldUFpXOX9uT0VvkVxhdXn8qgPBU5Mz3LdUi2Aq4DkXx4Hgc3ds4lkXDih/bJMp8JO2sW/XVt7+0j0cOjbME+PTXKirjtKBFPSJSCspJ5HuSoUUS9Gi3QWltVTmz+1OB+QKEcXIue7qnWs+9rYaLYJfTyaKuX/4DMNj0+SK5waCV168ieftjINBtQgurTw5z+hkjku39mlyF9lY9u3aqi8aERGRhTnwBTNz4KC737aWg5WTSFfOf7Hc7oLSXPt2beWaJy7gk/eMMF0o0ZMO+bnv31GX31Hn9XfxY3u282N75rcIzpssphhxzyNj3FMjELxqx2a1CFapnJxnU3ea0YkcNx0+zs2w6uBPgZ+IiIhI5/hhd3/MzLYDXzSzE+5+tLzSzK4Hrge45JJLljxYOYl0V2rt3QWlse4aOs3nv/kkW/sysy1+n//mkzzrwsG6X0RfqEXw/uGKMYIKBBdVOTmPmdGbSZHNFzl4dEiBn4iIiMhG5+6PJX9HzeyzwD7gaMX624DbAPbu3Vs9BeE5yrPWlqIi6dDWtbug1NdKZvWst4VaBOdNFqNAcJ5ak/P0pENGxlafv1qBn7SVWolI1fVTREQEzKwPCNx9Irn/k8DNazlmedbaD3zpQYbHshpf30YeH58mNBgem6FQikiHAVt60y3RTbe6RfDU5MxsMvnKMYIbORC8aLCHR89MMTlTolCK6EqFDPakuGzb6nPEKvCTtnHX0Ol5s3qOZfO8944p3v7SPfoCahN3DZ3mU/eO8MR4jp2a8l5EZL1dAHzW4uTPKeCv3f3zaz3ogT3bef6lWzg1ObPWQ0kD9aVDHj6VJUoeF6OImbMzXLatt6nlqmVbjRbB5QaCV+1IJovpsEDw+Ts38fVHzxAYBAb5UsToRJ7XXL3637wK/KRt3Hb0u4xPFwjMCMzwCManC9x29LsK/NpAeZByVypgc8/6DFIWEZE57j4EXNXsckhryBZKs0FfWZQsb3W1AsGvj5zlvpEz3Pe9jdEi+G/DZ9nam2YqX27xCxjoTvHVodO8dZXHVOAnbWP4zHR81SOZ7t4MiJzhM83vsiBLq8cgZREREantqYnaLbQLLW9l2/q7ePGe7by4xhjB+xZrEXzaIFft3NyWLYKPj0+zpS/D1j4jFQaEgeHuGuMnIq2vHoOURUREpLbiAlP3LLS8nSw0RvD+4apA8HtnuOd7Z4D2axG8aLCHU1Mzs5PzAEwXSuzYsvquugr8pG3s3NLLI6emwB0zcIfI4dKty/sHMDNCM8LQSAVxd9FUYHHLYSJyiNxxB8eJovhxKXIidyIH9w6oMZugXIH1d81VsmutwERERKS20KBU4ydLaOcua3cLjRG8f/gM94+c5Xuns23XInjd1Tu55c4HmC6U6A+MbL5EoeTcsH/Xqo+pwE/axvUv2hVP7jJTpFRywsAY7Elz/Yvm/gHCwEiHAekwIJMKSIdGGBipIG4iXy9R5JSSgLAYxX9LkVMsRbOPi5FjUBGkbuyAsboCmy6svQITERGR2i7d1sdDT01R+evDkuWdbsExgtWBYEWLYFcq4DktFAju27WVG9nNoWPDnJzMccnWvjVPirfhAr8jJ0b54JHv8sjpKaUDaDP7dm3lp696Gn9zzwjZqEQmFfCaq3dy7fOfVpfgbjFBYAQYFa3vy1KKnEIpSm5OvhjNPu505QrsU/eO8OR4jh2a1VNERNqYmWGz9+O/5Wu8ZmDM9SoyO3f78qP4fnlF+U+8bzyhHUlPpLjXUZQ8hzP/grJX9FoC+JUXP53f/btvMJWP8OTQfZmAN+/fRZj0fKrctxh17m+R6jGCp6fySRA4N1nMTK2uoU0OBPft2sq+XVs5b6CLwe70mo+3oQK/IydGuenwccIABrtTnJqa4ZY7H+BGdiv4ayHlFrs4mJvrmvkvDzzF4a8/RtHj1r5i5HzynhH2Xb6tbYKHMDDCIKS7KmIsV+TllsN8MWKmVEoCQ++47qWddTYiIhuP2bkXWsu9XCoDinLAU14/u6wi6KlcVxn4zG07/zlt3n7nlsPxeYGSE5epPNwjqFXGBY5z7jkmw0UadKF5Lbb1ddGTSVGMihSjiFQQ0JNJsX2wu2arXxQ5hSia7bVUKEbkihH5YtRxv0O29mVqdw2tnjW0hVsEV2NDBX4Hjw6RDo3udEihGNGTDpkulDh0bFiBXxNYMsYukwrIhAFd6YCuVLhgq937vvBtxrKF2StdcYtZnvd87lttE/gtJB5/yOy592RCIL6y4+7MFCNmChEzxRL5UlwJtxulcxARaV8DXSn6u1IEVjvok9Zz8OgQgz1pLtzUM7tssdm0g8DoCmp3ZXKPg8FcocR0oUSxlMx9kMyF0O7DWaq7hp6eyscTxYyc4f7huGtodYtgIwLBu4ZOc+jYMKOTOS5t9a6eZnYNcAsQAh9y9/dUrbdk/cuBLPAL7n5vsu5hYAIoAUV337vW8gyPZdnck553/aY7HfDEuNIB1NvcmLtgNtDLpFb2z/HA6ORs0FcWeby8k5nFFyviVsI4GIwiJ1csMZ0vJYN9Wz8QVDoHEZH21Q4tXDJf+XdvpdXOpm1mpMN4HoWBBboclnstlYewxBPjxV1Ic4X2ajXc2pc5p2toecbQ8hjBegeC5QvmqcDY1L0+F8zrFviZWQh8AHgJMAIcM7PD7v7Nis1eBuxObj8A/Gnyt+zF7v7UepVp55ZeRidy87rZ5QoRFw72LLKXrEa6ogWvLxOSWocrILVmplpseScLgjhw6s2k2AbzxgrmChG5Qqnlrr4pnYOIiEjj7NzSy8OnJhmfLpIvRWTCgMGeFJdt66/L88XDWazmhf1y76XpfNxiONNm3UcXDAQXGSO41kCwHhfM69nitw940N2HAMzsEPAKoDLwewXwcY/f+a+Z2WYzu8jdH69HgW7Yv4ubDh8n8iKpwMgV4hkYr7t6Zz2ebsOIW6QCetIhXamQrlSgK4MNlknNb0Gd7R5ajLuHTudLlKqbSxtM6RxEREQa5wd3beWuh08nYxohX4oYncjzmqsbP7ypsvfSFuZ+p0zkikzNFFvuYvVSFgoE709mDl2PFsF6XDCvZ+B3MTBc8XiE+a15C21zMfA48ZjcL5iZAwfd/ba1FujAnu3cDHzwyHf53ukpLtSsnitSvpKTDgNSgZEKA7pS8U39/VtLre6h0/kSkzNFsvliU4JApXMQERFpnK8OnWagK2Q8V6TgcfA32J3iq0OneWuTy1b5O2VbX4bpQjyh3UyxNXstLWW1XUNnZw3dsZk9F80PBNstgXutSKD6XVxsmx9298fMbDvwRTM74e5Hz3kSs+uB6wEuueSSJQt1YM92Xvj0bTx2RuP6ltKVDulOBbP/mI1KlbCQAKg1kq295lNqnp5MSE8mxD0zO1lMeZxgIyrYdknnsIyxyXuAPwdeAPyWu7+v8aUUERFZ3HeeHGcqXyIdBLM5hafyJR54crzZRZsnCIy+rhR9XfHjcmvg1EyxbeYxqFYrEPz6yBn+bXj+ZDH3fu8M9y7QIvgz338xHzjy3bZJ4D4CVPah3AE8ttxt3L38d9TMPkvcdfScwC9pCbwNYO/eve11eaDFZJIgrzcT0p0KW6675rMuHODEExPnJCJ91oUDzSpSW6q8yrYpaQ3MFeLWwKmZ+rYG7tu1lZdceQHb+rvq9hxrscyxyaeBtwI/3fgSioiILE8hmQSh/HvOLJ4cLt/ikyPMaw0EJmeKjE3l2zIALNval+HAs7Zz4Fkr6xq6c0sPkzMlTk7O8PTz+/mlH316y87qeQzYbWaXA48C1wGvrdrmMPCWZPzfDwBn3f1xM+sDAnefSO7/JHBzHcu6IYXJBCH9Xam2GJf39mv28Jufup+J3Fw+moHuFG+/Zk+zi9b2yhXsef1dFJJ0EdOFEtmZUkcndK1hybHJ7j4KjJrZf2hOEUVERJaWSQWzvXrKLX44K55Vvdn6u1L0ZUImZ4pM5IrkCqVmF2nNljtG8MGTU7P77N/ds+ZeUnUL/Ny9aGZvAe4g7jL1EXc/bmZvTtbfCtxOnMrhQeJ0Dr+Y7H4B8Nlk3FgK+Gt3/3y9yrpRhMnMQN2ZMBmbVztXS6s6sGc7f/gzV3Hw6BAjY9mW7SrY7tJhnHajrysF/XFrYDZfYmqm2NZX25ZpOWOTl2Wl3dBFRETW0+7tA5x44ixnp4tEyRi/TT0pdm9vv55SZsZAd5qB7jQzxRJnswUmZ4rNLta6WSwQvH/4DI+cznL15Wufk6Suefzc/Xbi4K5y2a0V9x34lRr7DQFX1bNsG0VXOqQ3HY/tqkxj0e5au5NCZym3Bm7ty5AvRmTzcZ/7dpuKeZmWMzZ5WdQNXUREmqk8q2cYGGmLcx+P50r8YJtPatiVCtk+GLK5GDGRiwPAZs9cvt7KgWBfJsX3TmXJFUt85t4RLhrsbtmuntIEqSCgOxPQm0nR0wITsqynIydG53X1fGpiht/81P384c9cpVa/BonTRmTY3DuXRL48ScxMIWq7WbhqWM7YZBERkZb31aHTbB/InJPHrxVm9VwPmVTAtv4utvZlODtdYCxb6KgL0pUJ3Lf2Zjg9lW/dBO7SGJU59HoyYdt131yJ937+BGPZAmGSSsIdxrIF3vv5Ewr8mmAuifzcsnLOwMmZIvliW3YLXc7YZBERkZY3PJYlU5UnLhMGa8oD14rMjM29Gfq6UpyazJPNd0YX0HZL4C51EJjRlQ7oToVJF7yNk0Nv6KmpJAnp3OxUbs7QU1NL7CmN0pWKLz5s7s1QKEVkZ0pMF0ptk5NnOWOTzexC4G5gEIjM7G3AFe7eWvNji4jIhjbQleI7T04QeTxmoVgqMTI2zTMvaL8xfsuRDgMu3NTNRK7A6al823f/bLcE7rJO0mFAf1cqadHbOIGetLd0GLCpN5iXMmJqptjy3Y+XMTb5CeIuoCIiIi1rIlegMnODAyWPl3eyge40vZkUp6fybX2u9Ujg3l7zuW4g6TBgc2+Gp23uYefWXrb0ZehOmno3qsu39RJ5PLbM3YkiJ/J4ubS+7nTItv4uNlf2DRUREZG6ODmZJ7R4Nk8j/htavLzThYFx/kAXF2/poSfTnsOgrrt6J8XImS6UcHey+eKaE7gr8GsRYWD0d6U4b6CLS7b2snNrL1uTYE9i73jZs9ncm8YCKLljAWzuTfOOlz272UUTERERaTlhYHQlw4O6Up016d9ydKVCLtrUwwWD3aTD9gp79u3ayo0/tpttfV2M5wpsH+jm5muv1Kye7cjM6EoF9GY6f1KW9XJgz3bepzx+IiIiIku6fFsvD56cwqK5BO6RwzPO23g9pfq6UvR1pZjIFTiTLbRNXuJ9u7ayb9dWzhvoYrA7vebjbbjA78iJUT545Ls8cnqKiwZ7uO7qnexrUD6TdBjQnQ7jYC8dEmywqy7r4cCe7Qr0RERERJbwjpc9m9/41P2zee7CwNjctbF7SpWTwOcKJcZzBSZznTED6HJtqMDvyIlRbjp8nDCAwe4Up6ZmuOXOB7iR3XUJ/gKz2cTpvZmw7ZqYRURERKQ9Hdiznde/8FI+9JWHmCqV6AkDXv/CS3UBHZKZ8UM29ZQ4NZknVyg1u0g13TV0mkPHhhmdzHHp1r4193TbUIHfwaNDpEOjOx1SKEb0pEOmCyUOHRtet8AvHcbdN/u6UpqBU6TKkROjHDw6xPBYlp3qqisiIlI3R06M8ql7H+X8gS4uSX7zfureR3nujs367k10pUKetrmHbL7IWLbATAsFgJUJ3Dd1pxmdyK05gfuGaoIaHsvOmxIVoDsd8MT49KqPmQoC+rtT8T9VMinLtv6uDT8Dp0i1cov76ESOzT1zFdiRE6PNLpqIiEjHKTd49GZSswnA06Fx8OhQs4vWcnozKS7e3MPTNvfQ35Vqid/wtRK4r/X921Atfju39DI6kZs3U2auEHHhYM+yj2EWvwE96XhSlkxqQ8XOIqtW+QUEcSWbzRc5eHRIVx5FRETW2fBYls098ycEWWsC8E5X7gJaLEVM5IpM5IoUo+ZMBFOPBO4bKmq5Yf8uCqU4D4YT58UoRs51V+9cdL9MKmBTT5qLNvVw2bZeLtzUzabetII+kRWo1eKuLyAREZH62Lmll+mqrotrTQC+UaTCgC19GXZu7eG8ga6mzNNx0WAPucL8oFMJ3FfgwJ7t3HztlZzf381Ersi2vi5u/LFzJ3Yp59Qrd9/csSXuvtmTUfdNkdXauaWXpyZnGDo5yYknxhk6OclTkzP6AhIREamDG/bvYny6wANPTvCtx8/ywJMTjE8X1pQAfKMxMwa70+zY0sPWvkxD44B6JHDfUF09IQ7+Xvj0bTx2Zm5cX2VOvXITr4isrx/ctZW7Hj5NYBAY5EsRJyfzvHZfY9KpiIiIbDQOYPFvXSx5LCtmZmzuzdCbSfHU5ExDZgHdt2srN7KbQ8eGOTmZ4xLN6rl6mVQwO06vO6WceiL19tWh02wfyDA+XSRfisiEAYM9Kb46dJq3NrtwIiIiHebg0aHZoUplGlu/NplUwNM29zCeK3B6Mk/k9Q2llcB9HXSnQ3UvE2mw4bEs2/q6OK+/e3aZu2uMn4hIi1Mqnvb0wOgE2ZkihcjJhAHnD3TR35XS9+46GOxO05eJc4LXMwm88vjJhqYvn/ZVnlW3PKsnaJC5iLQGfbcsrJyKJx3avFQ8a8klJvV35MQoE7kikTthYBQj57EzObb1p7lsW3+zi9cRwsDYPtDNYHeJsWye6fz6dv9UHj/Z0JQHrr3Nm1V3nQYpi4isVad9t5jZNWb2bTN70MzesdbjHTw6RL5Y4omzOb795ARPnM2RL5aUC67FHTw6xJbeuGugR2CA45ye0uQu6607HXLRpjgHYE9m/eYJqUcePwV+0jaUiLS9lWfV3T7QzdnpAtsHurn52it1xVhEmqqTvlvMLAQ+ALwMuAJ4jZldsZZjfufJcU5N5SmWnNCMYsk5NZXngSfH16PIUifDY1nO6+/iaZt6SIVGyePungNdob5366QyAFyPiSIfH5+mOz0/VFtrGix19ZS2oUSk7e/Anu36whGRltJh3y37gAfdfQjAzA4BrwC+udoDFkrx5BXlSfDMIIqcfEnzQ7ay8vCKwZ40g8nnO5svsn2ge4k9Za260yFP29zD5EyRsak8hdLqEsBfNNjDqamZeTmQlcdPNgwlIhURkfXWYd8tFwPDFY9HkmWrlkkF4BC543g8i6Eny6VlaXhF8/V3pdi5tZftg92r+n+pRx4//ddK21AlJiIi663Dvltq5aaa1zRnZteb2d1mdvfJkyeXPODu7QOcN5AhFRilyEkFxnkDGXZvH1ivMksdaHhF6+jvSrFjSy/nD3SRCpYfeu3btZUbf2w32/q6GM+tz3u44bp6auau9nVgz3ZuJh6PMTKWZYfePxERWaMO+24ZAXZWPN4BPFa5gbvfBtwGsHfv3iX7a96wfxc3HT7OhZtS9KRDpguldg6MNxQNr2gtA0kKiPFcgTPZwrJyACqP3xpoSuL2p0pMRETWWwd9txwDdpvZ5cCjwHXAa9dywA4LjEWaKgiMzb0ZBrvTjGXzjOfingaNsqECv8qZuwB6Mymy+SIHjw6pAhORplJvBBFZK3cvmtlbgDuAEPiIux9f63E7KDAWaQlBYGzr72KwJ83YVJ7Jmfolga+0oQK/Dpu5S0Q6hHojiMh6cffbgdubXQ4RWVo6DNg+2M1gocSpqTwzhfVNAl9tQwV+O7f08tBTk0zkiuRLUZzPpDvF5ef1N7toIrKBHTw6RKFU4tTkXN002JNSb4Q2ceTEKO/53Ld46FR8EXHXeX28/Zo9eu9ERGRZutMhF69DCoilbKhZPX9w11ZOTubJlyICg3wp4uRknh/ctbXZRRORDeyB0QmemshTjJwwMIqR89REngdGJ5pdNFnCkROj/Man7ufBk1O4O+7OA6OT/Oan7ufIidFmF09ERNpIPANoD9v6VzYD6HJtqMDvq0On2T6QIRMGRA6ZMGD7QIavDp1udtFEZAPLFyMwCMwwjMAMLFkuLe3g0SEmZ4qEZoRBkNyMiVw8flxERGQlzIxNPWl2bu1hW18XYVArS8vqbKiunsNjWbb1dXFef/fsMnfXGD8Raap0aEwXIIocMyhP8JUJ16+yl/oYHstSipzQ5t4rMyiWIn23iIjIqpkZm3rTDHSnKK3TzJ8bqsVv55ZepqsGTU4XSuzY0tukEomIwDMvGGRbX4ZUaJTcSYXGtr4Muy8YbHbRZAk7t/QSBkbld7I7pIJA3y0iIrJmQWCkw/UJ2TZU4HfD/l0USk42H+fMyOaLSkIqIk13w/5dZFIhF27q5lkXDHDhpm4yqVB1Uxu4Yf8u+rviq7GlKEpuzkB3Su+fiIi0lA0V+B3Ys52br72S7QPdnJ0usH2gm5uvvVIzr4lIU6lual8H9mznfT9zFc84vw8zw8zYvb2fP/yZq/T+iYhIS9lQY/xASUhFpDWpbmpfeu9ERKQd1LXFz8yuMbNvm9mDZvaOGuvNzN6frP+6mb1gufuKiIiIiIjI8tQt8DOzEPgA8DLgCuA1ZnZF1WYvA3Ynt+uBP13BviIiIiIiIrIM9Wzx2wc86O5D7p4HDgGvqNrmFcDHPfY1YLOZXbTMfUVERERERGQZ6hn4XQwMVzweSZYtZ5vl7CsiIiIiIiLLUM/Ar1bm4ersgwtts5x94wOYXW9md5vZ3SdPnlxhEUVERERERDpfPQO/EWBnxeMdwGPL3GY5+wLg7re5+15333v++eevudAiIiIiIiKdpp6B3zFgt5ldbmYZ4DrgcNU2h4HXJ7N7vhA46+6PL3NfERERERERWYa65fFz96KZvQW4AwiBj7j7cTN7c7L+VuB24OXAg0AW+MXF9q1XWUVERERERDqZudccOteWzOwk8MgyNz8PeKqOxWk2nV970/nNudTd27oft+qmeXR+7U3nN2ej1U2g97+ddfK5gc6vWs36qaMCv5Uws7vdfW+zy1EvOr/2pvPbuDr9tdH5tTed38bW6a9PJ59fJ58b6PyWq55j/ERERERERKQFKPATERERERHpcBs58Lut2QWoM51fe9P5bVyd/tro/Nqbzm9j6/TXp5PPr5PPDXR+y7Jhx/iJiIiIiIhsFBu5xU9ERERERGRD6OjAz8yuMbNvm9mDZvaOGuvNzN6frP+6mb2gGeVcrWWc3wEzO2tm9yW3m5pRztUys4+Y2aiZfWOB9e3+/i11fm37/pnZTjP7kpl9y8yOm9mNNbZp6/dvrVQ/tfXnW3VTm753oPppKaqb2v7zrfqpTd+/htRN7t6RN+LE798FdgEZ4H7giqptXg58DjDghcC/Nrvc63x+B4C/b3ZZ13CO+4EXAN9YYH3bvn/LPL+2ff+Ai4AXJPcHgO900v/fOrw+qp/a+/OtuqlN37uk/KqfFn5tVDe1/+db9VObvn+NqJs6ucVvH/Cguw+5ex44BLyiaptXAB/32NeAzWZ2UaMLukrLOb+25u5HgdOLbNLO799yzq9tufvj7n5vcn8C+BZwcdVmbf3+rZHqpzamuqm9qX5alOqmNqf6qX01om7q5MDvYmC44vEI5754y9mmVS237D9oZveb2efM7MrGFK1h2vn9W662f//M7DLg+cC/Vq3aCO/fQlQ/xdr+872Adn7vlqsj3jvVT+dQ3RTriM/3Atr5/Vuutn//6lU3pdZcstZlNZZVT2G6nG1a1XLKfi9wqbtPmtnLgb8Fdte7YA3Uzu/fcrT9+2dm/cCngbe5+3j16hq7dNL7txjVTx3w+V5EO793y9ER753qp5pUN3XI53sR7fz+LUfbv3/1rJs6ucVvBNhZ8XgH8NgqtmlVS5bd3cfdfTK5fzuQNrPzGlfEumvn929J7f7+mVmauOL6K3f/TI1NOvr9W4Lqpzb/fC+hnd+7JXXCe6f6aUGqmzrg872Edn7/ltTu71+966ZODvyOAbvN7HIzywDXAYertjkMvD6ZIeeFwFl3f7zRBV2lJc/PzC40M0vu7yN+v081vKT1087v35La+f1Lyv1h4Fvu/kcLbNbR798SVD+18ed7Gdr5vVtSu793qp8WpbqpzT/fy9DO79+S2vn9a0Td1LFdPd29aGZvAe4gnsXpI+5+3MzenKy/FbideHacB4Es8IvNKu9KLfP8fgb4JTMrAtPAde7eNs35ZvYJ4tmZzjOzEeB3gTS0//sHyzq/dn7/fhh4HfDvZnZfsuydwCXQGe/fWqh+au/Pt+qm9n3vEqqfFqC6qf0/36qf2vr9q3vdZO3zWoiIiIiIiMhqdHJXTxEREREREUGBn4iIiIiISMdT4CciIiIiItLhFPiJiIiIiIh0OAV+IiIiIiIiHU6BnzScmf2smR03s8jM9lYs32ZmXzKzSTP731X7fN7M7k/2u9XMwmT5fjO718yKZvYzVfu8wcweSG5vqFj+FjN70MzcKpJ6mtlvmtl9ye0bZlYys631eyVEpJWobhKRVqS6SdaLAj+pG4vV+ox9A3gVcLRqeQ74HeA3auzzc+5+FfAc4HzgZ5Pl3wN+AfjrqufeSpzb5QeAfcDvmtmWZPU/Az8BPFK5j7v/obs/z92fB/zfwJfd/fTSZyoi7UR1k4i0ItVNUm8dm8BdmsPMLgM+B3wJ+EHgjJmdDzhxotQ/dvdvJdvO29fdp4CvmNkzqo/r7uPJ3RSQSY6Huz+cHCuq2uWlwBfLFZCZfRG4BviEu/9breev8hrgE8s5ZxFpfaqbRKQVqW6SRlKLn9TDs4CPA28Ciu7+HHf/PuDP13JQM7sDGAUmgE8tsfnFwHDF45Fk2XKep5e4svv0KoopIq1LdZOItCLVTdIQCvykHh5x968BQ8AuM/tfZnYNML7Efoty95cCFwFdwI8tsXmty1K+zKf6KeCf1V1BpOOobhKRVqS6SRpCgZ/UwxSAu48BVwFHgF8BPrTWA7t7DjgMvGKJTUeAnRWPdwCPLfNprkPdFUQ6keomEWlFqpukIRT4Sd0kMz8F7v5p4sHHL1jlcfrN7KLkfgp4OXBiid3uAH7SzLYkg5N/Mlm21HNtAn4U+LvVlFVEWp/qJhFpRaqbpN4U+Ek9XQwcMbP7gI8Sz/iEmb3SzEaIBzH/Q9IHnWTdw8AfAb9gZiNmdgXQBxw2s68D9xP3V7812f7q5Fg/Cxw0s+MASXeDdwPHktvNFQOW35rsswP4uplVXlF7JfCFZMC0iHQm1U0i0opUN0ldmftyu++KiIiIiIhIO1KLn4iIiIiISIdT4CciIiIiItLhFPiJiIiIiIh0OAV+IiIiIiIiHU6Bn4iIiIiISIdT4CciIiIiItLhFPiJiIiIiIh0OAV+IiIiIiIiHe7/B+zH/HCL8wclAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onemillionv3 = datasetv3.data_sc.obs.copy()\n",
+    "onemillionv3_celltype_df = pd.read_csv(\n",
+    "    './1M_v3_20201106_azimuth.tsv',\n",
+    "    sep='\\t', index_col=0\n",
+    ")\n",
+    "onemillionv3 = pd.concat([onemillionv3, onemillionv3_celltype_df], axis=1)\n",
+    "onemillionv3 = onemillionv3[onemillionv3['timepoint']=='UT']\n",
+    "onemillionv3_l1_cellratio_df = onemillionv3.groupby(['assignment', 'predicted.celltype.l1']).size().to_frame()\n",
+    "# display(onemillionv3_l1_cellratio_df.head())\n",
+    "onemillionv3_celltyperatio = onemillionv3.groupby(['assignment', 'predicted.celltype.l2']).size().to_frame()\n",
+    "# display(onemillionv3_celltyperatio.head())\n",
+    "onemillionv3_allcells = onemillionv3['assignment'].value_counts()\n",
+    "\n",
+    "# caluclate the individual CD4T TEM and NAIVE ratio\n",
+    "individual_ratio = pd.DataFrame()\n",
+    "for individual in onemillionv3['assignment'].unique():\n",
+    "    tem_num = onemillionv3_celltyperatio.loc[individual, \"CD4 TEM\"].values[0]\n",
+    "    naive_num = onemillionv3_celltyperatio.loc[individual, \"CD4 Naive\"].values[0]\n",
+    "    cd8t_tem_num = onemillionv3_celltyperatio.loc[individual, \"CD8 TEM\"].values[0]\n",
+    "    tcm_num = onemillionv3_celltyperatio.loc[individual, \"CD4 TCM\"].values[0]\n",
+    "    cd8t_tcm_num = onemillionv3_celltyperatio.loc[individual, \"CD8 TCM\"].values[0]\n",
+    "    cd8t_naive_num = onemillionv3_celltyperatio.loc[individual, \"CD8 Naive\"].values[0]\n",
+    "    cd4t_num = onemillionv3_l1_cellratio_df.loc[individual, 'CD4 T'].values[0]\n",
+    "    cd8t_num = onemillionv3_l1_cellratio_df.loc[individual, 'CD8 T'].values[0]\n",
+    "    all_num = onemillionv3_allcells.loc[individual]\n",
+    "    individual_ratio[individual] = [tem_num,  naive_num, \n",
+    "                                    cd8t_tem_num, cd8t_naive_num,\n",
+    "                                    cd4t_num, cd8t_num,\n",
+    "                                    tcm_num, cd8t_tcm_num, all_num]\n",
+    "\n",
+    "individual_ratio_dfv3 = individual_ratio.T\n",
+    "individual_ratio_dfv3 = individual_ratio_dfv3.rename({0: 'CD4T TEM', 1:'CD4T Naive', \n",
+    "                                                  2: 'CD8T TEM', 3: 'CD8T Naive',\n",
+    "                                                  4: 'CD4T', 5: 'CD8T',\n",
+    "                                                  6: 'CD4T TCM', 7: 'CD8T TCM',\n",
+    "                                                  8: 'all_num'}, \n",
+    "                                                 axis=1)\n",
+    "display(individual_ratio_dfv3.head())\n",
+    "\n",
+    "\n",
+    "common_individuals = list(set(individual_ratio_dfv3.index) & set(gt.columns))\n",
+    "common_individuals_individual_ratio_dfv3 = individual_ratio_dfv3.loc[common_individuals]\n",
+    "common_individuals_individual_ratio_dfv3['gt'] = [float(gt[col].values[0].split(':')[1]) for col in \n",
+    "                                                  common_individuals_individual_ratio_dfv3.index]\n",
+    "common_individuals_individual_ratio_dfv3['chemistry'] = 'v2'\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax1, ax2, ax3 = axes\n",
+    "cd4ydata = (common_individuals_individual_ratio_dfv3['CD4T TEM']) / common_individuals_individual_ratio_dfv3['CD4T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD4 TEM / CD4T')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD4T Naive']) / common_individuals_individual_ratio_dfv3['CD4T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax2)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax2.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax2.set_ylabel('CD4 Naive / CD4T')\n",
+    "ax2.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD4T Naive']) / common_individuals_individual_ratio_dfv3['CD4T TEM']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax3)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax3.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax3.set_ylabel('CD4 Naive / CD4T TEM')\n",
+    "ax3.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax1, ax2, ax3 = axes\n",
+    "cd4ydata = (common_individuals_individual_ratio_dfv3['CD8T TEM']) / common_individuals_individual_ratio_dfv3['CD8T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD8 TEM / CD4T')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD8T Naive']) / common_individuals_individual_ratio_dfv3['CD8T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax2)\n",
+    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax2.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax2.set_ylabel('CD8 Naive / CD8T')\n",
+    "ax2.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD8T Naive']) / common_individuals_individual_ratio_dfv3['CD8T TEM']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax3)\n",
+    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax3.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax3.set_ylabel('CD8 Naive / CD8T TEM')\n",
+    "ax3.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(104, 11) (72, 11) (32, 11)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T TEM</th>\n",
+       "      <th>CD4T Naive</th>\n",
+       "      <th>CD8T TEM</th>\n",
+       "      <th>CD8T Naive</th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>CD4T TCM</th>\n",
+       "      <th>CD8T TCM</th>\n",
+       "      <th>all_num</th>\n",
+       "      <th>gt</th>\n",
+       "      <th>chemistry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1035</th>\n",
+       "      <td>17</td>\n",
+       "      <td>152</td>\n",
+       "      <td>36</td>\n",
+       "      <td>54</td>\n",
+       "      <td>625</td>\n",
+       "      <td>108</td>\n",
+       "      <td>424</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1006</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0960</th>\n",
+       "      <td>36</td>\n",
+       "      <td>89</td>\n",
+       "      <td>113</td>\n",
+       "      <td>13</td>\n",
+       "      <td>586</td>\n",
+       "      <td>143</td>\n",
+       "      <td>374</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1004</th>\n",
+       "      <td>65</td>\n",
+       "      <td>54</td>\n",
+       "      <td>440</td>\n",
+       "      <td>9</td>\n",
+       "      <td>583</td>\n",
+       "      <td>462</td>\n",
+       "      <td>437</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1493</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0918</th>\n",
+       "      <td>19</td>\n",
+       "      <td>34</td>\n",
+       "      <td>119</td>\n",
+       "      <td>7</td>\n",
+       "      <td>249</td>\n",
+       "      <td>136</td>\n",
+       "      <td>168</td>\n",
+       "      <td>14</td>\n",
+       "      <td>598</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0067</th>\n",
+       "      <td>51</td>\n",
+       "      <td>101</td>\n",
+       "      <td>122</td>\n",
+       "      <td>89</td>\n",
+       "      <td>753</td>\n",
+       "      <td>231</td>\n",
+       "      <td>546</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1429</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
+       "LLDeep_1035        17         152        36          54   625   108       424   \n",
+       "LLDeep_0960        36          89       113          13   586   143       374   \n",
+       "LLDeep_1004        65          54       440           9   583   462       437   \n",
+       "LLDeep_0918        19          34       119           7   249   136       168   \n",
+       "LLDeep_0067        51         101       122          89   753   231       546   \n",
+       "\n",
+       "             CD8T TCM  all_num   gt chemistry  \n",
+       "LLDeep_1035        28     1006  1.0        v2  \n",
+       "LLDeep_0960        26     1546  0.0        v2  \n",
+       "LLDeep_1004        16     1493  1.0        v2  \n",
+       "LLDeep_0918        14      598  1.0        v2  \n",
+       "LLDeep_0067        30     1429  1.0        v2  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "concate_v2_v3 = pd.concat([common_individuals_individual_ratio_df,\n",
+    "                           common_individuals_individual_ratio_dfv3],\n",
+    "                         axis=0)\n",
+    "print(concate_v2_v3.shape,common_individuals_individual_ratio_df.shape, common_individuals_individual_ratio_dfv3.shape)\n",
+    "concate_v2_v3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots()\n",
+    "cd4ydata = (concate_v2_v3['CD8T Naive'] + \\\n",
+    "            concate_v2_v3['CD4T Naive']\n",
+    "            ) / (\n",
+    "    concate_v2_v3['CD8T TEM'] + \\\n",
+    "            concate_v2_v3['CD4T TEM']\n",
+    ")\n",
+    "sns.regplot(x=concate_v2_v3['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.spearmanr(concate_v2_v3['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v2 & v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD8+CD4 TEM / CD8+CD4 Naive')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "plt.savefig('TEM_naive_CD4_CD8_v2_v3_rs1131017.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05_coeqtl_interpretation/LDTRAIT.ipynb b/05_coeqtl_interpretation/LDTRAIT.ipynb
new file mode 100644
index 0000000..659b6fa
--- /dev/null
+++ b/05_coeqtl_interpretation/LDTRAIT.ipynb
@@ -0,0 +1,944 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import requests\n",
+    "from tqdm import tqdm\n",
+    "import os\n",
+    "from io import StringIO\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "savedir = Path(\"./annotated_coeqtl_snps/ldtrait\")\n",
+    "\n",
+    "celltypesnps = {}\n",
+    "merged_dict = pd.read_excel('/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/summary/coeQTLs_6majorcelltypes.filtered.xlsx',\n",
+    "                         sheet_name=None)\n",
+    "for celltype in merged_dict.keys():\n",
+    "    celltypesnps[celltype] = list(merged_dict[celltype]['SNP'].unique())\n",
+    "allcelltypes_snps = list(set([ele for l in celltypesnps.values() for ele in l]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "72"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(allcelltypes_snps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 17%|█▋        | 12/72 [05:53<34:19, 34.33s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs62480001\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 18%|█▊        | 13/72 [06:52<39:48, 40.48s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs817352\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 19%|█▉        | 14/72 [07:11<33:43, 34.89s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs80164297\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 24%|██▎       | 17/72 [09:23<37:43, 41.16s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs11772922\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 26%|██▋       | 19/72 [10:43<35:14, 39.89s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs3758833\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 29%|██▉       | 21/72 [11:39<28:28, 33.49s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs11047696\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 31%|███       | 22/72 [12:09<27:15, 32.70s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs9971029\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 32%|███▏      | 23/72 [12:35<24:55, 30.53s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs4949655\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 42%|████▏     | 30/72 [16:56<26:11, 37.41s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs6007595\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 43%|████▎     | 31/72 [17:27<24:21, 35.64s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs7309189\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 44%|████▍     | 32/72 [18:20<27:11, 40.79s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs9657360\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 49%|████▊     | 35/72 [19:43<20:09, 32.70s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs731835\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 53%|█████▎    | 38/72 [26:08<51:53, 91.57s/it]   "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs260503\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 58%|█████▊    | 42/72 [28:04<22:59, 46.00s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs13140099\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 60%|█████▉    | 43/72 [28:34<19:51, 41.10s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs2235910\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 71%|███████   | 51/72 [42:19<40:20, 115.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs1628955\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 74%|███████▎  | 53/72 [43:55<25:05, 79.24s/it] "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs12443580\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 82%|████████▏ | 59/72 [48:18<09:34, 44.16s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs150458741\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 85%|████████▍ | 61/72 [49:39<07:29, 40.88s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs62423804\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 86%|████████▌ | 62/72 [50:16<06:36, 39.69s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs2267989\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 89%|████████▉ | 64/72 [50:56<03:58, 29.82s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs7605964\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 99%|█████████▊| 71/72 [54:21<00:24, 24.54s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no GWAS: rs1261896\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 72/72 [54:53<00:00, 45.75s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# curl -k -H \"Content-Type: application/json\" -X POST -d '{\"snps\": \"rs3\\nrs4\", \"pop\": \"YRI\", \"r2_d\": \"r2\", \"r2_d_threshold\": \"0.1\", \"window\": \"500000\", \"genome_build\": \"grch37\"}' 'https://ldlink.nci.nih.gov/LDlinkRest/ldtrait?token=faketoken123'\n",
+    "# snp = \"rs10276099\"\n",
+    "for snp in tqdm(allcelltypes_snps):\n",
+    "    if os.path.exists(savedir/f'{snp}.tsv'):\n",
+    "        continue\n",
+    "    else:\n",
+    "        params = {\"snps\": snp, \n",
+    "                  \"pop\": \"CEU\", \n",
+    "                  \"r2_d\": \"r2\", \n",
+    "                  \"r2_d_threshold\": \"0.8\", \n",
+    "                  \"window\": \"500000\", \n",
+    "                  \"genome_build\": \"grch37\"}\n",
+    "        r = requests.request(headers={\"Content-Type\": \"application/json\"},\n",
+    "                             method='POST',\n",
+    "                             json=params, \n",
+    "                             url=f'https://ldlink.nci.nih.gov/LDlinkRest/ldtrait?token={token}')\n",
+    "        try:\n",
+    "            if \"No entries in the GWAS Catalog are identified using the LDtrait search criteria.\" in r.text:\n",
+    "                print('no GWAS:', snp)\n",
+    "                continue\n",
+    "            else:\n",
+    "                r_df = pd.read_csv(StringIO(r.text), sep='\\t')\n",
+    "                r_df.to_csv(savedir/f'{snp}.tsv', sep='\\t', index=False)\n",
+    "        except:\n",
+    "            print('failed entry:', snp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 72/72 [00:00<00:00, 298.96it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "allsnps_inld_gwas_df = pd.DataFrame()\n",
+    "for snp in tqdm(allcelltypes_snps):\n",
+    "    if os.path.exists(savedir/f'{snp}.tsv'):\n",
+    "        df = pd.read_csv(savedir/f'{snp}.tsv', sep='\\t')\n",
+    "        if 'error' not in df.iloc[0].values[0]:\n",
+    "            allsnps_inld_gwas_df = pd.concat([allsnps_inld_gwas_df, df],\n",
+    "                                            axis=0)\n",
+    "            \n",
+    "allsnps_inld_gwas_df.to_csv(savedir/'summary.tsv', sep='\\t', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allsnps_inld_gwas_df = pd.read_csv(savedir/'summary.tsv', sep='\\t')\n",
+    "magma_df = pd.read_csv(savedir/'coeqtl_with_gwas_and_magma.tsv', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VARIABLE</th>\n",
+       "      <th>celltype</th>\n",
+       "      <th>SNP</th>\n",
+       "      <th>gene</th>\n",
+       "      <th>TYPE</th>\n",
+       "      <th>NGENES</th>\n",
+       "      <th>BETA</th>\n",
+       "      <th>BETA_STD</th>\n",
+       "      <th>SE</th>\n",
+       "      <th>P</th>\n",
+       "      <th>...</th>\n",
+       "      <th>non_effect_allele</th>\n",
+       "      <th>current_build</th>\n",
+       "      <th>frequency</th>\n",
+       "      <th>sample_size</th>\n",
+       "      <th>zscore</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>effect_size</th>\n",
+       "      <th>standard_error</th>\n",
+       "      <th>imputation_status</th>\n",
+       "      <th>n_cases</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>38</td>\n",
+       "      <td>-0.199320</td>\n",
+       "      <td>-0.008952</td>\n",
+       "      <td>0.12542</td>\n",
+       "      <td>0.943980</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>54612</td>\n",
+       "      <td>0.138937</td>\n",
+       "      <td>0.889500</td>\n",
+       "      <td>0.002200</td>\n",
+       "      <td>0.015600</td>\n",
+       "      <td>original</td>\n",
+       "      <td>17008.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0.201320</td>\n",
+       "      <td>0.009042</td>\n",
+       "      <td>0.12905</td>\n",
+       "      <td>0.059382</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>53293</td>\n",
+       "      <td>1.735682</td>\n",
+       "      <td>0.082620</td>\n",
+       "      <td>0.023902</td>\n",
+       "      <td>0.013700</td>\n",
+       "      <td>original</td>\n",
+       "      <td>19099.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0.163610</td>\n",
+       "      <td>0.007256</td>\n",
+       "      <td>0.12608</td>\n",
+       "      <td>0.097201</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>29344</td>\n",
+       "      <td>-2.348664</td>\n",
+       "      <td>0.018841</td>\n",
+       "      <td>-0.010569</td>\n",
+       "      <td>0.004363</td>\n",
+       "      <td>original</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>38</td>\n",
+       "      <td>-0.010395</td>\n",
+       "      <td>-0.000467</td>\n",
+       "      <td>0.11668</td>\n",
+       "      <td>0.535490</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>15954</td>\n",
+       "      <td>-0.324182</td>\n",
+       "      <td>0.745800</td>\n",
+       "      <td>-0.009950</td>\n",
+       "      <td>0.025700</td>\n",
+       "      <td>original</td>\n",
+       "      <td>7387.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B_rs1131017_RPS26</td>\n",
+       "      <td>B</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>RPS26</td>\n",
+       "      <td>SET</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0.282350</td>\n",
+       "      <td>0.012677</td>\n",
+       "      <td>0.11706</td>\n",
+       "      <td>0.007937</td>\n",
+       "      <td>...</td>\n",
+       "      <td>G</td>\n",
+       "      <td>hg38</td>\n",
+       "      <td>0.580808</td>\n",
+       "      <td>337159</td>\n",
+       "      <td>-1.597883</td>\n",
+       "      <td>0.110069</td>\n",
+       "      <td>-0.000210</td>\n",
+       "      <td>0.000132</td>\n",
+       "      <td>original</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 44 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            VARIABLE celltype        SNP   gene TYPE  NGENES      BETA  \\\n",
+       "0  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      38 -0.199320   \n",
+       "1  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      38  0.201320   \n",
+       "2  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      37  0.163610   \n",
+       "3  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      38 -0.010395   \n",
+       "4  B_rs1131017_RPS26        B  rs1131017  RPS26  SET      38  0.282350   \n",
+       "\n",
+       "   BETA_STD       SE         P  ... non_effect_allele current_build  \\\n",
+       "0 -0.008952  0.12542  0.943980  ...                 G          hg38   \n",
+       "1  0.009042  0.12905  0.059382  ...                 G          hg38   \n",
+       "2  0.007256  0.12608  0.097201  ...                 G          hg38   \n",
+       "3 -0.000467  0.11668  0.535490  ...                 G          hg38   \n",
+       "4  0.012677  0.11706  0.007937  ...                 G          hg38   \n",
+       "\n",
+       "   frequency sample_size    zscore    pvalue effect_size standard_error  \\\n",
+       "0   0.580808       54612  0.138937  0.889500    0.002200       0.015600   \n",
+       "1   0.580808       53293  1.735682  0.082620    0.023902       0.013700   \n",
+       "2   0.580808       29344 -2.348664  0.018841   -0.010569       0.004363   \n",
+       "3   0.580808       15954 -0.324182  0.745800   -0.009950       0.025700   \n",
+       "4   0.580808      337159 -1.597883  0.110069   -0.000210       0.000132   \n",
+       "\n",
+       "   imputation_status  n_cases  \n",
+       "0           original  17008.0  \n",
+       "1           original  19099.0  \n",
+       "2           original      NaN  \n",
+       "3           original   7387.0  \n",
+       "4           original      NaN  \n",
+       "\n",
+       "[5 rows x 44 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "magma_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VARIABLE                                       B_rs1131017_RPS26\n",
+       "celltype                                                       B\n",
+       "SNP                                                    rs1131017\n",
+       "gene                                                       RPS26\n",
+       "TYPE                                                         SET\n",
+       "NGENES                                                        38\n",
+       "BETA                                                    -0.19932\n",
+       "BETA_STD                                               -0.008952\n",
+       "SE                                                       0.12542\n",
+       "P                                                        0.94398\n",
+       "prefix                                  results/current/magma/AD\n",
+       "trait                                                         AD\n",
+       "FDR                                                     0.973479\n",
+       "Tag                                               IGAP_Alzheimer\n",
+       "PUBMED_Paper_Link    http://www.ncbi.nlm.nih.gov/pubmed/24162737\n",
+       "Phenotype                                              Alzheimer\n",
+       "RSID                                                  rs10876864\n",
+       "RSALIAS                                               rs57455456\n",
+       "CHR                                                           12\n",
+       "POS1                                                    56435929\n",
+       "POS2                                                    56401085\n",
+       "DIST                                                      -34844\n",
+       "R2                                                      0.991789\n",
+       "D                                                       0.240643\n",
+       "DPRIME                                                  0.995886\n",
+       "MAJOR                                                          A\n",
+       "MINOR                                                          G\n",
+       "MAF                                                     0.408549\n",
+       "CMMB                                                    0.155229\n",
+       "CM                                                     71.092406\n",
+       "panel_variant_id                          chr12_56007301_G_A_b38\n",
+       "chromosome                                                 chr12\n",
+       "position                                                56007301\n",
+       "effect_allele                                                  A\n",
+       "non_effect_allele                                              G\n",
+       "current_build                                               hg38\n",
+       "frequency                                               0.580808\n",
+       "sample_size                                                54612\n",
+       "zscore                                                  0.138937\n",
+       "pvalue                                                    0.8895\n",
+       "effect_size                                               0.0022\n",
+       "standard_error                                            0.0156\n",
+       "imputation_status                                       original\n",
+       "n_cases                                                  17008.0\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "magma_df.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.041321, 'Inflammatory Bowel Disease'],\n",
+       "       [0.030935, 'Non-cancer illness code, self-reported: psoriasis'],\n",
+       "       [0.0090688,\n",
+       "        'Non-cancer illness code, self-reported: schizophrenia'],\n",
+       "       [0.0042454,\n",
+       "        'Overall breast cancer in Europeans, imputed genotype'],\n",
+       "       [0.032584, 'Diagnoses - main ICD10: G40 Epilepsy'],\n",
+       "       [0.0013766,\n",
+       "        'Estrogen-receptor-negative breast cancer in Europeans, imputed genotype'],\n",
+       "       [0.025212,\n",
+       "        'Non-cancer illness code, self-reported: high cholesterol']],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "magma_df[(magma_df['SNP']=='rs4147638') & (magma_df['P']<0.05)][['P', 'Phenotype']].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Query</th>\n",
+       "      <th>GWAS Trait</th>\n",
+       "      <th>RS Number</th>\n",
+       "      <th>Position (GRCh37)</th>\n",
+       "      <th>Alleles</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>D'</th>\n",
+       "      <th>Risk Allele</th>\n",
+       "      <th>Effect Size (95% CI)</th>\n",
+       "      <th>Beta or OR</th>\n",
+       "      <th>P-value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rs2954654</td>\n",
+       "      <td>Type 2 diabetes</td>\n",
+       "      <td>rs2294120</td>\n",
+       "      <td>chr8:146003567</td>\n",
+       "      <td>A=0.52, G=0.48</td>\n",
+       "      <td>0.846295</td>\n",
+       "      <td>0.957895</td>\n",
+       "      <td>0.455879299759268</td>\n",
+       "      <td>0.04430</td>\n",
+       "      <td>0.029-0.06</td>\n",
+       "      <td>2.000000e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rs4840568</td>\n",
+       "      <td>Albumin-globulin ratio</td>\n",
+       "      <td>rs2409780</td>\n",
+       "      <td>chr8:11337587</td>\n",
+       "      <td>C=0.237, T=0.763</td>\n",
+       "      <td>0.897156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NR</td>\n",
+       "      <td>0.04604</td>\n",
+       "      <td>0.035-0.057</td>\n",
+       "      <td>1.000000e-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rs4840568</td>\n",
+       "      <td>Non-albumin protein levels</td>\n",
+       "      <td>rs2409780</td>\n",
+       "      <td>chr8:11337587</td>\n",
+       "      <td>C=0.237, T=0.763</td>\n",
+       "      <td>0.897156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NR</td>\n",
+       "      <td>0.04456</td>\n",
+       "      <td>0.034-0.055</td>\n",
+       "      <td>1.000000e-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>rs4840568</td>\n",
+       "      <td>Rheumatoid arthritis</td>\n",
+       "      <td>rs2618444</td>\n",
+       "      <td>chr8:11338370</td>\n",
+       "      <td>A=0.763, C=0.237</td>\n",
+       "      <td>0.897156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NR</td>\n",
+       "      <td>0.10050</td>\n",
+       "      <td>0.072-0.129</td>\n",
+       "      <td>7.000000e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>rs4840568</td>\n",
+       "      <td>Systemic lupus erythematosus</td>\n",
+       "      <td>rs2618444</td>\n",
+       "      <td>chr8:11338370</td>\n",
+       "      <td>A=0.763, C=0.237</td>\n",
+       "      <td>0.897156</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NR</td>\n",
+       "      <td>1.36000</td>\n",
+       "      <td>1.22-1.51</td>\n",
+       "      <td>7.000000e-09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Query                    GWAS Trait  RS Number Position (GRCh37)  \\\n",
+       "0  rs2954654               Type 2 diabetes  rs2294120    chr8:146003567   \n",
+       "1  rs4840568        Albumin-globulin ratio  rs2409780     chr8:11337587   \n",
+       "2  rs4840568    Non-albumin protein levels  rs2409780     chr8:11337587   \n",
+       "3  rs4840568          Rheumatoid arthritis  rs2618444     chr8:11338370   \n",
+       "4  rs4840568  Systemic lupus erythematosus  rs2618444     chr8:11338370   \n",
+       "\n",
+       "            Alleles        R2        D'        Risk Allele  \\\n",
+       "0    A=0.52, G=0.48  0.846295  0.957895  0.455879299759268   \n",
+       "1  C=0.237, T=0.763  0.897156  1.000000                 NR   \n",
+       "2  C=0.237, T=0.763  0.897156  1.000000                 NR   \n",
+       "3  A=0.763, C=0.237  0.897156  1.000000                 NR   \n",
+       "4  A=0.763, C=0.237  0.897156  1.000000                 NR   \n",
+       "\n",
+       "   Effect Size (95% CI)   Beta or OR       P-value  \n",
+       "0               0.04430   0.029-0.06  2.000000e-08  \n",
+       "1               0.04604  0.035-0.057  1.000000e-16  \n",
+       "2               0.04456  0.034-0.055  1.000000e-15  \n",
+       "3               0.10050  0.072-0.129  7.000000e-12  \n",
+       "4               1.36000    1.22-1.51  7.000000e-09  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allsnps_inld_gwas_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allsnps_inld_gwas_df.to_excel('./coeqtl_mapping/output/snps_in_ld_with_gwas_catelogue.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05_coeqtl_interpretation/LD_description.R b/05_coeqtl_interpretation/LD_description.R
new file mode 100644
index 0000000..8e1d5e4
--- /dev/null
+++ b/05_coeqtl_interpretation/LD_description.R
@@ -0,0 +1,164 @@
+#install.packages("LDlinkR")
+
+library('LDlinkR')
+
+cells <- c("B", "CD4T", "CD8T", "NK", "DC", "monocyte")
+
+output_file <- c()
+
+# To run this scipt file with LD structure of given population is needed. After all the significant SNPs aremacthed with other SNPs in hight LD. ALternatively, we could also use LD package, which make the mapping of LD as well.  
+outPath <- '/groups/umcg-lld/tmp01/projects/1MCellRNAseq/GRN_reconstruction/ongoing/coeqtl_mapping/output/filtered_results/'
+
+for (cell in cells){
+  name <- paste(outPath, "UT_", cell, '/coeqtls_fullresults.sig.tsv.gz', sep="")
+  tab <- read.table(name, sep='\t', header=T )
+  # SNP <- as.data.frame(tab[,'SNP'])
+  # colnames(SNP) <- "SNP"
+  SNP <- (tab[,'SNP'])
+  output_file <- c(SNP, output_file)
+}
+
+length(output_file)
+
+output_file <- unique(output_file)
+length(output_file)
+
+LD_Score <- LDexpress(output_file[1], 
+                                      pop = "CEU", 
+                                      tissue = "ALL", 
+                                      r2d = "r2", 
+                                      r2d_threshold = 0.8, 
+                                      p_threshold = 0.1, 
+                                      win_size = 500000, 
+                                      token = "d1bfc9a7a30b", 
+                                      file = FALSE
+)
+
+for (i in output_file[63:length(output_file)] ){
+print(i)
+LD_Score_ind <- LDexpress(i, 
+          pop = "CEU", 
+          tissue = "ALL", 
+          r2d = "r2", 
+          r2d_threshold = 0.8, 
+          p_threshold = 0.1, 
+          win_size = 500000, 
+          token = "d1bfc9a7a30b", 
+          file = FALSE
+)
+LD_Score <- rbind(LD_Score_ind,LD_Score)
+}
+
+LD_Score_subset <- subset(LD_Score, select = c('Query',"RS_ID","R2"  ))
+dim(LD_Score_subset)
+LD_Score_subset <- LD_Score_subset[!duplicated(LD_Score_subset$RS_ID),]
+dim(LD_Score_subset)
+
+
+write.table(LD_Score,'/groups/umcg-franke-scrna/tmp01/projects/sc-eqtlgen-consortium-pipeline/ongoing/wg3-QTL-mapping/GRN_downstream_analysis/sign_LD_SNPs_18_12.txt', quote = F, col.names = F, row.names = F, sep='\t')
+
+write.table(LD_Score_subset,'/groups/umcg-franke-scrna/tmp01/projects/sc-eqtlgen-consortium-pipeline/ongoing/wg3-QTL-mapping/GRN_downstream_analysis/sign_LD_SNPs_subset_18_12.txt', quote = F, col.names = F, row.names = F, sep='\t')
+
+######### LDtrait
+
+
+
+LD_Score <- LDtrait(output_file[2], 
+                      pop = "CEU", 
+                      r2d = "r2", 
+                      r2d_threshold = 0.8, 
+                      token = "d1bfc9a7a30b", 
+                      file = FALSE
+)
+LD_Score
+
+for (i in output_file){
+  print(i)
+      LD_Score_ind <- LDtrait(i, 
+                              pop = "CEU", 
+                              r2d = "r2", 
+                              r2d_threshold = 0.8, 
+                              token = "d1bfc9a7a30b", 
+                              file = FALSE
+      )
+      LD_Score <- rbind(LD_Score_ind,LD_Score)
+}
+
+LD_Score_ind <- LDtrait(output_file[1:49], 
+                        pop = "CEU", 
+                        r2d = "r2", 
+                        r2d_threshold = 0.8, 
+                        token = "d1bfc9a7a30b", 
+                        file = FALSE
+)
+
+LD_Score_ind2 <- LDtrait(output_file[50:72], 
+                        pop = "CEU", 
+                        r2d = "r2", 
+                        r2d_threshold = 0.8, 
+                        token = "d1bfc9a7a30b", 
+                        file = FALSE
+)
+LD_Score <- rbind(LD_Score_ind,LD_Score_ind2)
+
+# for (i in output_file){
+#   print(i)
+#   if(i %in% LD_Score$Query){
+#     print('SNP is analyzed')
+#   } else {
+#     
+#   tryCatch({
+#     LD_Score_ind <- LDtrait(i, 
+#                             pop = "CEU", 
+#                             r2d = "r2", 
+#                             r2d_threshold = 0.8, 
+#                             token = "d1bfc9a7a30b", 
+#                             file = FALSE
+#     )
+#     LD_Score <- rbind(LD_Score_ind,LD_Score)
+#   }, error = function(e){
+#     output_file <- output_file[-i]
+#   print(length(output_file))
+#   })
+#   }
+# }
+
+# LD_Score_subset <- subset(LD_Score, select = c('Query',"RS_ID","R2"  ))
+# dim(LD_Score_subset)
+# LD_Score_subset <- LD_Score_subset[!duplicated(LD_Score_subset$RS_ID),]
+# dim(LD_Score_subset)
+
+
+write.table(LD_Score,'/groups/umcg-franke-scrna/tmp01/projects/sc-eqtlgen-consortium-pipeline/ongoing/wg3-QTL-mapping/GRN_downstream_analysis/sign_LD_SNPs_23_01.txt', quote = F, col.names = F, row.names = F, sep='\t')
+
+
+#write.table(output_file,'/groups/umcg-franke-scrna/tmp01/projects/sc-eqtlgen-consortium-pipeline/ongoing/wg3-QTL-mapping/GRN_downstream_analysis/sign_SNPs_17_12.txt', quote = F, col.names = F, row.names = F, sep='\t')
+
+
+
+expand_ld_table <- function(ld){
+  # double the ld table, so we can easily select just from the left or right
+  ld_copy <- ld[, c('CHR_B', 'BP_B', 'SNP_B', 'CHR_A', 'BP_A', 'SNP_A', 'R2')]
+  colnames(ld_copy) <- c('CHR_A', 'BP_A', 'SNP_A', 'CHR_B', 'BP_B', 'SNP_B', 'R2')
+  ld <- rbind(ld, ld_copy)
+  # add each SNP in max LD with itself by copying the unique snps on the left and right
+  ld_left <- ld[, c('CHR_A', 'BP_A', 'SNP_A')]
+  ld_right <- ld[, c('CHR_B', 'BP_B', 'SNP_B')]
+  colnames(ld_right) <- c('CHR_A', 'BP_A', 'SNP_A')
+  ld_left_right <- rbind(ld_left, ld_right)
+  ld_left_right <- unique(ld_left_right)
+  ld_self <- cbind(ld_left_right, ld_left_right)
+  colnames(ld_self) <- c('CHR_A', 'BP_A', 'SNP_A', 'CHR_B', 'BP_B', 'SNP_B')
+  # ld with itself is off course 1
+  ld_self$R2 <- 1
+  # add to existing ld table
+  ld <- rbind(ld, ld_self)
+  return(ld)
+}
+# location of the LD file
+ld_loc <- '/groups/umcg-bios/tmp01/projects/1M_cells_scRNAseq/ongoing/LD_DB/genotypes_eur/EUR.chrAll.phase3_shapeit2_mvncall_integrated_v5b.20130502.genotypes.positions_plus_RSID.plink1.ldwindow10000.r2_075.ld'
+# make the ld table a bit easier to work with
+#ld_loc <- read.table(ld_loc, sep='\t')
+ld <- expand_ld_table(ld_loc)
+# confine the ld table to eQTL snps on the left <- subset here to what SNPs you need the LD of, with the other SNPs
+ld <- ld[ld$SNP_A %in% eqtls$V1, ]
\ No newline at end of file
diff --git a/05_coeqtl_interpretation/MS1_Libraries.r b/05_coeqtl_interpretation/MS1_Libraries.r
new file mode 100644
index 0000000..9018e46
--- /dev/null
+++ b/05_coeqtl_interpretation/MS1_Libraries.r
@@ -0,0 +1,10 @@
+### Libraries for all the project scripts
+library(stringr, quietly = TRUE, verbose = FALSE)
+library(dplyr)
+library(data.table)
+library(tidyverse)
+library('reshape2')
+library(caret)
+library('gprofiler2')
+library('coloc')
+library('biomaRt')
diff --git a/05_coeqtl_interpretation/R1_TRANSFAC_enrichment.ipynb b/05_coeqtl_interpretation/R1_TRANSFAC_enrichment.ipynb
new file mode 100644
index 0000000..9b30961
--- /dev/null
+++ b/05_coeqtl_interpretation/R1_TRANSFAC_enrichment.ipynb
@@ -0,0 +1,3005 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "8839a2ee-257d-4182-8c82-01d012d8f888",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Execute TRANFAC enrichment analysis based on co-eqtl results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26bafe98-a70d-4052-ba5d-fca1b4115633",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "946a1c00-83e8-4260-9093-e79e373c1fe0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "source('MS1_Libraries.r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb04a3b3-e6f5-4458-8b8e-c925813cee89",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b9b799d-d266-4fc8-a49b-ebb550fa64fd",
+   "metadata": {},
+   "source": [
+    "# Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "35c8b11a-e882-4bbb-9706-6c13b9c522f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Path to input data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "c517df12-ff3c-492e-a0a3-53d7cbdb945d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path<-\"\"\n",
+    "outdir<-\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "309ead5e-4bbd-4370-8089-5dfa0c53a194",
+   "metadata": {},
+   "source": [
+    "# Data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "759d077f-409c-4e52-857c-47af7be21134",
+   "metadata": {},
+   "source": [
+    "## Enrichment Data Input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "8dfbd479-4b4a-4abd-828b-c668708fa7e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Exemplary data input load for a cell-type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "4bbb56ca-273c-42dd-857c-be3d358ded78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_type_var = \"CD4T\"\n",
+    "# c(\"CD4T\",\"CD8T\",\"monocyte\",\"NK\",\"B\",\"DC\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "54fb40a9-c9c6-4f88-9ab8-fc98fa60d279",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for(cell_type in cell_type_var){\n",
+    "\n",
+    "  coeqtls <- fread(paste0(path, \"UT_\",cell_type, \n",
+    "                         \"_coeqtls_fullresults_fixed.all.tsv.gz\"))\n",
+    "  coeqtls$gene1<-gsub(\";.*\",\"\",coeqtls$Gene)\n",
+    "  coeqtls$gene2<-gsub(\".*;\",\"\",coeqtls$Gene)\n",
+    "  coeqtls$second_gene<-ifelse(coeqtls$gene1 == coeqtls$eqtlgen, coeqtls$gene2,\n",
+    "                        coeqtls$gene1)\n",
+    "  coeqtls$gene1<-NULL\n",
+    "  coeqtls$gene2<-NULL\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "7e950293-2939-4bd8-905d-729dd8c1a278",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#unique(coeqtls$eqtlgene)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "78dd2696-0f6c-40a8-93c4-0f1d6b3b99a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "372"
+      ],
+      "text/latex": [
+       "372"
+      ],
+      "text/markdown": [
+       "372"
+      ],
+      "text/plain": [
+       "[1] 372"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(coeqtls[(coeqtls$eqtlgene ==  'RPS26') &  (coeqtls$gene2_isSig == TRUE),c('eqtlgene', 'second_gene')])\n",
+    "\n",
+    "# validity check --> 372 significant co-egenes for RPS26"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "1535e95a-ec90-42e9-a5a7-de98dea38ea8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "742"
+      ],
+      "text/latex": [
+       "742"
+      ],
+      "text/markdown": [
+       "742"
+      ],
+      "text/plain": [
+       "[1] 742"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(coeqtls[(coeqtls$eqtlgene ==  'RPS26'),c('eqtlgene', 'second_gene')])\n",
+    "# overall 742 --> those that would not haven been tested"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "599506b1-e79b-4c3a-9c38-b32dd14bf5ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 38</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>snp_genepair</th><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNP</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>⋯</th><th scope=col>multipletestP</th><th scope=col>eqtlgene</th><th scope=col>snp_eqtlgene</th><th scope=col>snp_beta_shape1</th><th scope=col>snp_beta_shape2</th><th scope=col>snp_pvalbeta</th><th scope=col>snp_qval</th><th scope=col>gene2_pthreshold</th><th scope=col>gene2_isSig</th><th scope=col>second_gene</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs11587831_C1orf86;NUDT22</td><td>C1orf86;NUDT22</td><td>1</td><td>2115903</td><td>NA</td><td>C1orf86;NUDT22</td><td>rs11587831</td><td>1</td><td>2110848</td><td>T/G</td><td>⋯</td><td>0.6354470</td><td>C1orf86</td><td>rs11587831_C1orf86</td><td>1.197903</td><td>127.555</td><td>0.5044989</td><td>0.7012273</td><td>4.539067e-05</td><td>FALSE</td><td>NUDT22</td></tr>\n",
+       "\t<tr><td>rs11587831_C1orf86;SDHC  </td><td>C1orf86;SDHC  </td><td>1</td><td>2115903</td><td>NA</td><td>C1orf86;SDHC  </td><td>rs11587831</td><td>1</td><td>2110848</td><td>T/G</td><td>⋯</td><td>0.9144163</td><td>C1orf86</td><td>rs11587831_C1orf86</td><td>1.197903</td><td>127.555</td><td>0.5044989</td><td>0.7012273</td><td>4.539067e-05</td><td>FALSE</td><td>SDHC  </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 38\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " snp\\_genepair & Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNP & SNPChr & SNPPos & SNPAlleles & ⋯ & multipletestP & eqtlgene & snp\\_eqtlgene & snp\\_beta\\_shape1 & snp\\_beta\\_shape2 & snp\\_pvalbeta & snp\\_qval & gene2\\_pthreshold & gene2\\_isSig & second\\_gene\\\\\n",
+       " <chr> & <chr> & <int> & <int> & <lgl> & <chr> & <chr> & <int> & <int> & <chr> & ⋯ & <dbl> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <lgl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t rs11587831\\_C1orf86;NUDT22 & C1orf86;NUDT22 & 1 & 2115903 & NA & C1orf86;NUDT22 & rs11587831 & 1 & 2110848 & T/G & ⋯ & 0.6354470 & C1orf86 & rs11587831\\_C1orf86 & 1.197903 & 127.555 & 0.5044989 & 0.7012273 & 4.539067e-05 & FALSE & NUDT22\\\\\n",
+       "\t rs11587831\\_C1orf86;SDHC   & C1orf86;SDHC   & 1 & 2115903 & NA & C1orf86;SDHC   & rs11587831 & 1 & 2110848 & T/G & ⋯ & 0.9144163 & C1orf86 & rs11587831\\_C1orf86 & 1.197903 & 127.555 & 0.5044989 & 0.7012273 & 4.539067e-05 & FALSE & SDHC  \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 38\n",
+       "\n",
+       "| snp_genepair &lt;chr&gt; | Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNP &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | ⋯ ⋯ | multipletestP &lt;dbl&gt; | eqtlgene &lt;chr&gt; | snp_eqtlgene &lt;chr&gt; | snp_beta_shape1 &lt;dbl&gt; | snp_beta_shape2 &lt;dbl&gt; | snp_pvalbeta &lt;dbl&gt; | snp_qval &lt;dbl&gt; | gene2_pthreshold &lt;dbl&gt; | gene2_isSig &lt;lgl&gt; | second_gene &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| rs11587831_C1orf86;NUDT22 | C1orf86;NUDT22 | 1 | 2115903 | NA | C1orf86;NUDT22 | rs11587831 | 1 | 2110848 | T/G | ⋯ | 0.6354470 | C1orf86 | rs11587831_C1orf86 | 1.197903 | 127.555 | 0.5044989 | 0.7012273 | 4.539067e-05 | FALSE | NUDT22 |\n",
+       "| rs11587831_C1orf86;SDHC   | C1orf86;SDHC   | 1 | 2115903 | NA | C1orf86;SDHC   | rs11587831 | 1 | 2110848 | T/G | ⋯ | 0.9144163 | C1orf86 | rs11587831_C1orf86 | 1.197903 | 127.555 | 0.5044989 | 0.7012273 | 4.539067e-05 | FALSE | SDHC   |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  snp_genepair              Gene           GeneChr GenePos GeneStrand\n",
+       "1 rs11587831_C1orf86;NUDT22 C1orf86;NUDT22 1       2115903 NA        \n",
+       "2 rs11587831_C1orf86;SDHC   C1orf86;SDHC   1       2115903 NA        \n",
+       "  GeneSymbol     SNP        SNPChr SNPPos  SNPAlleles ⋯ multipletestP eqtlgene\n",
+       "1 C1orf86;NUDT22 rs11587831 1      2110848 T/G        ⋯ 0.6354470     C1orf86 \n",
+       "2 C1orf86;SDHC   rs11587831 1      2110848 T/G        ⋯ 0.9144163     C1orf86 \n",
+       "  snp_eqtlgene       snp_beta_shape1 snp_beta_shape2 snp_pvalbeta snp_qval \n",
+       "1 rs11587831_C1orf86 1.197903        127.555         0.5044989    0.7012273\n",
+       "2 rs11587831_C1orf86 1.197903        127.555         0.5044989    0.7012273\n",
+       "  gene2_pthreshold gene2_isSig second_gene\n",
+       "1 4.539067e-05     FALSE       NUDT22     \n",
+       "2 4.539067e-05     FALSE       SDHC       "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coeqtls,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1a2e697-badf-4802-ad22-26a5ac2a9101",
+   "metadata": {},
+   "source": [
+    "## ReMap Results for comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "128f1402-b5bf-411d-b812-5dbed446f4a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Load supplementary table (with ReMap Results to compare):\n",
+    "# \"supptable15.TFenrichment_co-eGenes.xlsx - Sheet1.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "4c21cd7a-c9da-4c2b-8877-bac38c095960",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "old_enrichments = read.csv( paste0(path, \"supptable15.TFenrichment_co-eGenes.xlsx - Sheet1.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "8b28f114-298e-4170-9e98-0644683d93fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "963"
+      ],
+      "text/latex": [
+       "963"
+      ],
+      "text/markdown": [
+       "963"
+      ],
+      "text/plain": [
+       "[1] 963"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(old_enrichments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "f159bb08-15b4-4a74-86a0-dd723fcc83b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>Cell.type</th><th scope=col>eQTL..SNP.eGene.</th><th scope=col>TF</th><th scope=col>TF.is.a.co.eGene.</th><th scope=col>enrichment.p.value</th><th scope=col>X..TF.overlap...co.eGene</th><th scope=col>X..TF.overlap...background</th><th scope=col>X..no.TF.overlap...co.eGene</th><th scope=col>X..background.gene...not.co.eGene</th><th scope=col>enrichment.fdr</th><th scope=col>eQTL.SNP</th><th scope=col>SNP.overlaps.TF.</th><th scope=col>Names.of.overlapping.SNPs</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>CD4T</td><td>rs111454690_HLA-DRB5</td><td>CDK8   </td><td>FALSE</td><td>9.630369e-06</td><td>14</td><td>5</td><td>2778</td><td> 8515</td><td>1.640373e-03</td><td>rs111454690</td><td>FALSE</td><td></td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>CD4T</td><td>rs111454690_HLA-DRB5</td><td>SNRNP70</td><td>FALSE</td><td>1.209254e-09</td><td>11</td><td>8</td><td> 649</td><td>10644</td><td>6.179288e-07</td><td>rs111454690</td><td>FALSE</td><td></td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & Cell.type & eQTL..SNP.eGene. & TF & TF.is.a.co.eGene. & enrichment.p.value & X..TF.overlap...co.eGene & X..TF.overlap...background & X..no.TF.overlap...co.eGene & X..background.gene...not.co.eGene & enrichment.fdr & eQTL.SNP & SNP.overlaps.TF. & Names.of.overlapping.SNPs\\\\\n",
+       "  & <chr> & <chr> & <chr> & <lgl> & <dbl> & <int> & <int> & <int> & <int> & <dbl> & <chr> & <lgl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & CD4T & rs111454690\\_HLA-DRB5 & CDK8    & FALSE & 9.630369e-06 & 14 & 5 & 2778 &  8515 & 1.640373e-03 & rs111454690 & FALSE & \\\\\n",
+       "\t2 & CD4T & rs111454690\\_HLA-DRB5 & SNRNP70 & FALSE & 1.209254e-09 & 11 & 8 &  649 & 10644 & 6.179288e-07 & rs111454690 & FALSE & \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 13\n",
+       "\n",
+       "| <!--/--> | Cell.type &lt;chr&gt; | eQTL..SNP.eGene. &lt;chr&gt; | TF &lt;chr&gt; | TF.is.a.co.eGene. &lt;lgl&gt; | enrichment.p.value &lt;dbl&gt; | X..TF.overlap...co.eGene &lt;int&gt; | X..TF.overlap...background &lt;int&gt; | X..no.TF.overlap...co.eGene &lt;int&gt; | X..background.gene...not.co.eGene &lt;int&gt; | enrichment.fdr &lt;dbl&gt; | eQTL.SNP &lt;chr&gt; | SNP.overlaps.TF. &lt;lgl&gt; | Names.of.overlapping.SNPs &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | CD4T | rs111454690_HLA-DRB5 | CDK8    | FALSE | 9.630369e-06 | 14 | 5 | 2778 |  8515 | 1.640373e-03 | rs111454690 | FALSE | <!----> |\n",
+       "| 2 | CD4T | rs111454690_HLA-DRB5 | SNRNP70 | FALSE | 1.209254e-09 | 11 | 8 |  649 | 10644 | 6.179288e-07 | rs111454690 | FALSE | <!----> |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Cell.type eQTL..SNP.eGene.     TF      TF.is.a.co.eGene. enrichment.p.value\n",
+       "1 CD4T      rs111454690_HLA-DRB5 CDK8    FALSE             9.630369e-06      \n",
+       "2 CD4T      rs111454690_HLA-DRB5 SNRNP70 FALSE             1.209254e-09      \n",
+       "  X..TF.overlap...co.eGene X..TF.overlap...background\n",
+       "1 14                       5                         \n",
+       "2 11                       8                         \n",
+       "  X..no.TF.overlap...co.eGene X..background.gene...not.co.eGene enrichment.fdr\n",
+       "1 2778                         8515                             1.640373e-03  \n",
+       "2  649                        10644                             6.179288e-07  \n",
+       "  eQTL.SNP    SNP.overlaps.TF. Names.of.overlapping.SNPs\n",
+       "1 rs111454690 FALSE                                     \n",
+       "2 rs111454690 FALSE                                     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(old_enrichments,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "c7fc8d4c-f820-4793-b58d-b194e56d6b4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Check out some results of ReMap mentioned in paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "4b439eaa-c15a-4917-9ae8-4df0afd7475a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.0132347204977557"
+      ],
+      "text/latex": [
+       "0.0132347204977557"
+      ],
+      "text/markdown": [
+       "0.0132347204977557"
+      ],
+      "text/plain": [
+       "[1] 0.01323472"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max(old_enrichments$enrichment.p.value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "7899b194-8054-4a2a-b9d7-3e051f7bc380",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.0498313855060291"
+      ],
+      "text/latex": [
+       "0.0498313855060291"
+      ],
+      "text/markdown": [
+       "0.0498313855060291"
+      ],
+      "text/plain": [
+       "[1] 0.04983139"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max(old_enrichments$enrichment.fdr)\n",
+    "# check to use same cut-off for TRANSFAC  --> 0.05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "384f4ce7-13c5-4230-a0d7-5aaa262d1112",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'rs111454690_HLA-DRB5'</li><li>'rs1131017_RPS26'</li><li>'rs4147638_SMDT1'</li><li>'rs7605824_SH3YL1'</li><li>'rs7632486_CMTM8'</li><li>'rs9271520_HLA-DQA2'</li><li>'rs1131017_RPS26_positive'</li><li>'rs1131017_RPS26_negative'</li><li>'rs6708265_PASK'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'rs111454690\\_HLA-DRB5'\n",
+       "\\item 'rs1131017\\_RPS26'\n",
+       "\\item 'rs4147638\\_SMDT1'\n",
+       "\\item 'rs7605824\\_SH3YL1'\n",
+       "\\item 'rs7632486\\_CMTM8'\n",
+       "\\item 'rs9271520\\_HLA-DQA2'\n",
+       "\\item 'rs1131017\\_RPS26\\_positive'\n",
+       "\\item 'rs1131017\\_RPS26\\_negative'\n",
+       "\\item 'rs6708265\\_PASK'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'rs111454690_HLA-DRB5'\n",
+       "2. 'rs1131017_RPS26'\n",
+       "3. 'rs4147638_SMDT1'\n",
+       "4. 'rs7605824_SH3YL1'\n",
+       "5. 'rs7632486_CMTM8'\n",
+       "6. 'rs9271520_HLA-DQA2'\n",
+       "7. 'rs1131017_RPS26_positive'\n",
+       "8. 'rs1131017_RPS26_negative'\n",
+       "9. 'rs6708265_PASK'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"rs111454690_HLA-DRB5\"     \"rs1131017_RPS26\"         \n",
+       "[3] \"rs4147638_SMDT1\"          \"rs7605824_SH3YL1\"        \n",
+       "[5] \"rs7632486_CMTM8\"          \"rs9271520_HLA-DQA2\"      \n",
+       "[7] \"rs1131017_RPS26_positive\" \"rs1131017_RPS26_negative\"\n",
+       "[9] \"rs6708265_PASK\"          "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(old_enrichments[,c( 'eQTL..SNP.eGene.')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "6810f798-9fa5-4aea-9954-b517aa0b49b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "9"
+      ],
+      "text/latex": [
+       "9"
+      ],
+      "text/markdown": [
+       "9"
+      ],
+      "text/plain": [
+       "[1] 9"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(old_enrichments[,c( 'eQTL..SNP.eGene.')]))  # subtract positive and negative case for RPS26 --> yields the 7 mentioned in paper for which there were significant TF enrichments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "528cc4ad-2052-42b3-a6a4-b47bcfaa2bbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'rs1131017_RPS26'</li><li>'rs4147638_SMDT1'</li><li>'rs7605824_SH3YL1'</li><li>'rs9271520_HLA-DQA2'</li><li>'rs1131017_RPS26_positive'</li><li>'rs1131017_RPS26_negative'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'rs1131017\\_RPS26'\n",
+       "\\item 'rs4147638\\_SMDT1'\n",
+       "\\item 'rs7605824\\_SH3YL1'\n",
+       "\\item 'rs9271520\\_HLA-DQA2'\n",
+       "\\item 'rs1131017\\_RPS26\\_positive'\n",
+       "\\item 'rs1131017\\_RPS26\\_negative'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'rs1131017_RPS26'\n",
+       "2. 'rs4147638_SMDT1'\n",
+       "3. 'rs7605824_SH3YL1'\n",
+       "4. 'rs9271520_HLA-DQA2'\n",
+       "5. 'rs1131017_RPS26_positive'\n",
+       "6. 'rs1131017_RPS26_negative'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"rs1131017_RPS26\"          \"rs4147638_SMDT1\"         \n",
+       "[3] \"rs7605824_SH3YL1\"         \"rs9271520_HLA-DQA2\"      \n",
+       "[5] \"rs1131017_RPS26_positive\" \"rs1131017_RPS26_negative\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(old_enrichments[old_enrichments$SNP.overlaps.TF. == TRUE,c('eQTL..SNP.eGene.')])  # results in the 4 pairs mentioned in paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "1e28ceb8-6515-4561-ac51-f1ab0860ad36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## rs1131017–RPS26 examples: RMB39, TCF7, LEF1, KLF6, CD74, MAF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "0a75dc32-fc85-4e53-ad4a-fa630f0460d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 7 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>Cell.type</th><th scope=col>eQTL..SNP.eGene.</th><th scope=col>TF</th><th scope=col>TF.is.a.co.eGene.</th><th scope=col>enrichment.p.value</th><th scope=col>X..TF.overlap...co.eGene</th><th scope=col>X..TF.overlap...background</th><th scope=col>X..no.TF.overlap...co.eGene</th><th scope=col>X..background.gene...not.co.eGene</th><th scope=col>enrichment.fdr</th><th scope=col>eQTL.SNP</th><th scope=col>SNP.overlaps.TF.</th><th scope=col>Names.of.overlapping.SNPs</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>19</th><td>CD4T    </td><td>rs1131017_RPS26</td><td>MAF  </td><td>TRUE</td><td>3.654557e-06</td><td> 92</td><td>280</td><td>1747</td><td>9546</td><td>5.187441e-05</td><td>rs1131017</td><td>TRUE</td><td>rs1131017                     </td></tr>\n",
+       "\t<tr><th scope=row>34</th><td>CD4T    </td><td>rs1131017_RPS26</td><td>RBM39</td><td>TRUE</td><td>2.128100e-06</td><td>244</td><td>128</td><td>6041</td><td>5252</td><td>3.295330e-05</td><td>rs1131017</td><td>TRUE</td><td>rs10876864,rs1131017,rs7297175</td></tr>\n",
+       "\t<tr><th scope=row>50</th><td>CD4T    </td><td>rs1131017_RPS26</td><td>TCF7 </td><td>TRUE</td><td>7.468026e-03</td><td>134</td><td>238</td><td>3379</td><td>7914</td><td>3.052929e-02</td><td>rs1131017</td><td>TRUE</td><td>rs1131017                     </td></tr>\n",
+       "\t<tr><th scope=row>84</th><td>CD4T    </td><td>rs1131017_RPS26</td><td>LEF1 </td><td>TRUE</td><td>4.859147e-05</td><td>153</td><td>219</td><td>3529</td><td>7764</td><td>4.598193e-04</td><td>rs1131017</td><td>TRUE</td><td>rs10876864,rs1131017          </td></tr>\n",
+       "\t<tr><th scope=row>116</th><td>CD4T    </td><td>rs1131017_RPS26</td><td>KLF6 </td><td>TRUE</td><td>1.597304e-03</td><td>139</td><td>233</td><td>3385</td><td>7908</td><td>8.236538e-03</td><td>rs1131017</td><td>TRUE</td><td>rs10876864,rs1131017,rs7297175</td></tr>\n",
+       "\t<tr><th scope=row>119</th><td>CD4T    </td><td>rs1131017_RPS26</td><td>CD74 </td><td>TRUE</td><td>3.954534e-06</td><td>172</td><td>200</td><td>3915</td><td>7378</td><td>5.461532e-05</td><td>rs1131017</td><td>TRUE</td><td>rs1131017                     </td></tr>\n",
+       "\t<tr><th scope=row>730</th><td>monocyte</td><td>rs1131017_RPS26</td><td>CD74 </td><td>TRUE</td><td>7.422301e-03</td><td> 63</td><td> 69</td><td>3526</td><td>6028</td><td>3.134542e-02</td><td>rs1131017</td><td>TRUE</td><td>rs1131017                     </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 7 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & Cell.type & eQTL..SNP.eGene. & TF & TF.is.a.co.eGene. & enrichment.p.value & X..TF.overlap...co.eGene & X..TF.overlap...background & X..no.TF.overlap...co.eGene & X..background.gene...not.co.eGene & enrichment.fdr & eQTL.SNP & SNP.overlaps.TF. & Names.of.overlapping.SNPs\\\\\n",
+       "  & <chr> & <chr> & <chr> & <lgl> & <dbl> & <int> & <int> & <int> & <int> & <dbl> & <chr> & <lgl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t19 & CD4T     & rs1131017\\_RPS26 & MAF   & TRUE & 3.654557e-06 &  92 & 280 & 1747 & 9546 & 5.187441e-05 & rs1131017 & TRUE & rs1131017                     \\\\\n",
+       "\t34 & CD4T     & rs1131017\\_RPS26 & RBM39 & TRUE & 2.128100e-06 & 244 & 128 & 6041 & 5252 & 3.295330e-05 & rs1131017 & TRUE & rs10876864,rs1131017,rs7297175\\\\\n",
+       "\t50 & CD4T     & rs1131017\\_RPS26 & TCF7  & TRUE & 7.468026e-03 & 134 & 238 & 3379 & 7914 & 3.052929e-02 & rs1131017 & TRUE & rs1131017                     \\\\\n",
+       "\t84 & CD4T     & rs1131017\\_RPS26 & LEF1  & TRUE & 4.859147e-05 & 153 & 219 & 3529 & 7764 & 4.598193e-04 & rs1131017 & TRUE & rs10876864,rs1131017          \\\\\n",
+       "\t116 & CD4T     & rs1131017\\_RPS26 & KLF6  & TRUE & 1.597304e-03 & 139 & 233 & 3385 & 7908 & 8.236538e-03 & rs1131017 & TRUE & rs10876864,rs1131017,rs7297175\\\\\n",
+       "\t119 & CD4T     & rs1131017\\_RPS26 & CD74  & TRUE & 3.954534e-06 & 172 & 200 & 3915 & 7378 & 5.461532e-05 & rs1131017 & TRUE & rs1131017                     \\\\\n",
+       "\t730 & monocyte & rs1131017\\_RPS26 & CD74  & TRUE & 7.422301e-03 &  63 &  69 & 3526 & 6028 & 3.134542e-02 & rs1131017 & TRUE & rs1131017                     \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 7 × 13\n",
+       "\n",
+       "| <!--/--> | Cell.type &lt;chr&gt; | eQTL..SNP.eGene. &lt;chr&gt; | TF &lt;chr&gt; | TF.is.a.co.eGene. &lt;lgl&gt; | enrichment.p.value &lt;dbl&gt; | X..TF.overlap...co.eGene &lt;int&gt; | X..TF.overlap...background &lt;int&gt; | X..no.TF.overlap...co.eGene &lt;int&gt; | X..background.gene...not.co.eGene &lt;int&gt; | enrichment.fdr &lt;dbl&gt; | eQTL.SNP &lt;chr&gt; | SNP.overlaps.TF. &lt;lgl&gt; | Names.of.overlapping.SNPs &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 19 | CD4T     | rs1131017_RPS26 | MAF   | TRUE | 3.654557e-06 |  92 | 280 | 1747 | 9546 | 5.187441e-05 | rs1131017 | TRUE | rs1131017                      |\n",
+       "| 34 | CD4T     | rs1131017_RPS26 | RBM39 | TRUE | 2.128100e-06 | 244 | 128 | 6041 | 5252 | 3.295330e-05 | rs1131017 | TRUE | rs10876864,rs1131017,rs7297175 |\n",
+       "| 50 | CD4T     | rs1131017_RPS26 | TCF7  | TRUE | 7.468026e-03 | 134 | 238 | 3379 | 7914 | 3.052929e-02 | rs1131017 | TRUE | rs1131017                      |\n",
+       "| 84 | CD4T     | rs1131017_RPS26 | LEF1  | TRUE | 4.859147e-05 | 153 | 219 | 3529 | 7764 | 4.598193e-04 | rs1131017 | TRUE | rs10876864,rs1131017           |\n",
+       "| 116 | CD4T     | rs1131017_RPS26 | KLF6  | TRUE | 1.597304e-03 | 139 | 233 | 3385 | 7908 | 8.236538e-03 | rs1131017 | TRUE | rs10876864,rs1131017,rs7297175 |\n",
+       "| 119 | CD4T     | rs1131017_RPS26 | CD74  | TRUE | 3.954534e-06 | 172 | 200 | 3915 | 7378 | 5.461532e-05 | rs1131017 | TRUE | rs1131017                      |\n",
+       "| 730 | monocyte | rs1131017_RPS26 | CD74  | TRUE | 7.422301e-03 |  63 |  69 | 3526 | 6028 | 3.134542e-02 | rs1131017 | TRUE | rs1131017                      |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    Cell.type eQTL..SNP.eGene. TF    TF.is.a.co.eGene. enrichment.p.value\n",
+       "19  CD4T      rs1131017_RPS26  MAF   TRUE              3.654557e-06      \n",
+       "34  CD4T      rs1131017_RPS26  RBM39 TRUE              2.128100e-06      \n",
+       "50  CD4T      rs1131017_RPS26  TCF7  TRUE              7.468026e-03      \n",
+       "84  CD4T      rs1131017_RPS26  LEF1  TRUE              4.859147e-05      \n",
+       "116 CD4T      rs1131017_RPS26  KLF6  TRUE              1.597304e-03      \n",
+       "119 CD4T      rs1131017_RPS26  CD74  TRUE              3.954534e-06      \n",
+       "730 monocyte  rs1131017_RPS26  CD74  TRUE              7.422301e-03      \n",
+       "    X..TF.overlap...co.eGene X..TF.overlap...background\n",
+       "19   92                      280                       \n",
+       "34  244                      128                       \n",
+       "50  134                      238                       \n",
+       "84  153                      219                       \n",
+       "116 139                      233                       \n",
+       "119 172                      200                       \n",
+       "730  63                       69                       \n",
+       "    X..no.TF.overlap...co.eGene X..background.gene...not.co.eGene\n",
+       "19  1747                        9546                             \n",
+       "34  6041                        5252                             \n",
+       "50  3379                        7914                             \n",
+       "84  3529                        7764                             \n",
+       "116 3385                        7908                             \n",
+       "119 3915                        7378                             \n",
+       "730 3526                        6028                             \n",
+       "    enrichment.fdr eQTL.SNP  SNP.overlaps.TF. Names.of.overlapping.SNPs     \n",
+       "19  5.187441e-05   rs1131017 TRUE             rs1131017                     \n",
+       "34  3.295330e-05   rs1131017 TRUE             rs10876864,rs1131017,rs7297175\n",
+       "50  3.052929e-02   rs1131017 TRUE             rs1131017                     \n",
+       "84  4.598193e-04   rs1131017 TRUE             rs10876864,rs1131017          \n",
+       "116 8.236538e-03   rs1131017 TRUE             rs10876864,rs1131017,rs7297175\n",
+       "119 5.461532e-05   rs1131017 TRUE             rs1131017                     \n",
+       "730 3.134542e-02   rs1131017 TRUE             rs1131017                     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "old_enrichments[(old_enrichments$eQTL..SNP.eGene. %in% c('rs1131017_RPS26')) & (old_enrichments$TF.is.a.co.eGene. == TRUE)  & ((old_enrichments$SNP.overlaps.TF. == TRUE)),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "973e7d59-4d0d-4aaa-90a6-5ec718d3cdc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MAF and CD74 only negative effect directions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "41107400-6b96-4024-939d-bffa6790c377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TMEM176A nothing found with remap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c479bb0-aeee-4286-8107-70eaebb4968e",
+   "metadata": {},
+   "source": [
+    "# Run TRANSFAC enrichment for all cell-types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "7ee02d44-c7a6-4c44-8957-794c603da722",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Set parameters for function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "5b6b137d-6865-4bea-bf5d-94bdbf71c96b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_val_thres = 0.05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "aea1119e-9ccb-40e4-a43c-ad3022bf4281",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "correction_var = 'fdr'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "52ba5757-2a17-4a25-9975-f73fe9d49888",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Decide on whether to restrict the background set\n",
+    "restrict_background_set = FALSE\n",
+    "\n",
+    "# set to TRUE for adaption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "5fd364b9-2b0d-40a6-aadc-6c32925e8a33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Run enrichments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "id": "33a0acf7-c544-4278-b8e0-60506109d9aa",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC with 58 co-eQTLs\"\n",
+      "[1] \"rs7935082_MS4A7\"\n",
+      "[1] 6054\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs9271520_HLA-DQA2\"\n",
+      "[1] 6054\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T with 500 co-eQTLs\"\n",
+      "[1] \"rs111454690_HLA-DRB5\"\n",
+      "[1] 11300\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs1131017_RPS26\"\n",
+      "[1] 11300\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs2741159_KRT1\"\n",
+      "[1] 11300\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs4147638_SMDT1\"\n",
+      "[1] 11300\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs7605824_SH3YL1\"\n",
+      "[1] 11300\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs7632486_CMTM8\"\n",
+      "[1] 11300\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs9022_CLN8\"\n",
+      "[1] 11300\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs9271520_HLA-DQA2\"\n",
+      "[1] 11300\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n",
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T with 420 co-eQTLs\"\n",
+      "[1] \"rs1131017_RPS26\"\n",
+      "[1] 9579\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs4147638_SMDT1\"\n",
+      "[1] 9579\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs6708265_PASK\"\n",
+      "[1] 9579\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs7605824_SH3YL1\"\n",
+      "[1] 9579\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs9271520_HLA-DQA2\"\n",
+      "[1] 9579\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs9306156_PRMT2\"\n",
+      "[1] 9579\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte with 281 co-eQTLs\"\n",
+      "[1] \"rs111454690_HLA-DRB5\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs1131017_RPS26\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs11577318_CD52\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs3758833_CTSC\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs4782899_DNAAF1\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs5756736_LGALS2\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs7806458_TMEM176A\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs7806458_TMEM176B\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs9271520_HLA-DQA2\"\n",
+      "[1] 9557\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK with 123 co-eQTLs\"\n",
+      "[1] \"rs1131017_RPS26\"\n",
+      "[1] 7271\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs12151742_GNLY\"\n",
+      "[1] 7271\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"rs62480001_MYOM2\"\n",
+      "[1] 7271\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n",
+      "No results to show\n",
+      "Please make sure that the organism is correct or set significant = FALSE\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B with 35 co-eQTLs\"\n",
+      "[1] \"rs1131017_RPS26\"\n",
+      "[1] 1729\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected custom background input, domain scope is set to 'custom'\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "enrichment<-NULL\n",
+    "enrichment_summary<-NULL\n",
+    "coegenes_counts_total<-NULL\n",
+    "for(cell_type in c(\"DC\",\"CD4T\",\"CD8T\",\"monocyte\",\"NK\",\"B\" )){\n",
+    "  # Read in the data\n",
+    "  coeqtls <- fread(paste0(path, \"UT_\",cell_type, \n",
+    "                         \"_coeqtls_fullresults_fixed.all.tsv.gz\"))\n",
+    "  coeqtls$gene1<-gsub(\";.*\",\"\",coeqtls$Gene)\n",
+    "  coeqtls$gene2<-gsub(\".*;\",\"\",coeqtls$Gene)\n",
+    "  coeqtls$second_gene<-ifelse(coeqtls$gene1 == coeqtls$eqtlgen, coeqtls$gene2,\n",
+    "                        coeqtls$gene1)\n",
+    "  coeqtls$gene1<-NULL\n",
+    "  coeqtls$gene2<-NULL\n",
+    "  \n",
+    "  # Take all tested genes as background\n",
+    "  background_genes  <- union(coeqtls$eqtlgen,coeqtls$second_gene)\n",
+    "  \n",
+    "  coeqtls_sign<-coeqtls[coeqtls$gene2_isSig,]\n",
+    "  \n",
+    "  print(paste(cell_type,\"with\",nrow(coeqtls_sign),\"co-eQTLs\"))\n",
+    "  \n",
+    "  # Identify all eQTLs with at least 5 coeGenes\n",
+    "  coegene_count<-coeqtls_sign%>%\n",
+    "    group_by(snp_eqtlgene)%>%\n",
+    "    summarise(count_coeGenes=n())%>%\n",
+    "    filter(count_coeGenes>4)\n",
+    "  \n",
+    "  coegene_count$cell_type<-cell_type\n",
+    "  coegenes_counts_total<-rbind(coegenes_counts_total,\n",
+    "                               coegene_count)\n",
+    "  \n",
+    "  enrichment_found<-0\n",
+    "  #Perform GO enrichemt separately for each eQTL\n",
+    "  for(eqtl in coegene_count$snp_eqtlgene){\n",
+    "      print(eqtl)\n",
+    "      \n",
+    "    # Optional restricted background set\n",
+    "    if(restrict_background_set == TRUE){\n",
+    "        background_genes = unique(c(coeqtls$eqtlgene[coeqtls$snp_eqtlgene ==  eqtl], coeqtls$second_gene[coeqtls$snp_eqtlgene ==  eqtl]))\n",
+    "        }\n",
+    "      print(length(background_genes))\n",
+    "    \n",
+    "    # Run enrichment analysis with background set\n",
+    "    enrich_out <- gost(\n",
+    "                                coeqtls_sign$second_gene[coeqtls_sign$snp_eqtlgene == eqtl],\n",
+    "                                organism = \"hsapiens\",\n",
+    "                                ordered_query = FALSE,\n",
+    "                                multi_query = FALSE,\n",
+    "                                significant = TRUE,\n",
+    "                                exclude_iea = FALSE,\n",
+    "                                measure_underrepresentation = FALSE,\n",
+    "                                evcodes = FALSE,\n",
+    "                                correction_method = correction_var,\n",
+    "                                user_threshold = p_val_thres,\n",
+    "                                custom_bg = background_genes,\n",
+    "                                sources = 'TF'   # only do transfac enrichment\n",
+    "                                )\n",
+    "    \n",
+    "    #if(nrow(enrich_out$result[enrich_out$result$source == 'TF',])>0){\n",
+    "    if(!is.null(enrich_out)){\n",
+    "      # Save if a enrichment was found\n",
+    "      enrichment_found<-enrichment_found+1\n",
+    "      \n",
+    "      # Save result dataframe\n",
+    "      res<-enrich_out$result[enrich_out$result$source == 'TF',]\n",
+    "      res$cell_type<-cell_type\n",
+    "      res$snp_eGene<-eqtl\n",
+    "      enrichment<-rbind(enrichment,\n",
+    "                        res)\n",
+    "    }\n",
+    "\n",
+    "  }\n",
+    "  \n",
+    "  enrichment_summary<-rbind(enrichment_summary,\n",
+    "                            data.frame(cell_type,\n",
+    "                                       n_eqtls_freq=nrow(coegene_count),\n",
+    "                                       n_enrich=enrichment_found,\n",
+    "                                       freq_enrich=enrichment_found/nrow(coegene_count)))\n",
+    "  \n",
+    "    \n",
+    "    \n",
+    "  #Check for CD4T specificallly for RPS26 the positive & negative coeGenes separately\n",
+    "  if(cell_type==\"CD4T\"){\n",
+    "    eqtl<-\"rs1131017_RPS26\"\n",
+    "    \n",
+    "    #Test positive coeGenes (MAF not correctly flipped here)\n",
+    "    enrich_out <-gost(\n",
+    "                        coeqtls_sign$second_gene[coeqtls_sign$snp_eqtlgene == eqtl &\n",
+    "                                                          coeqtls_sign$MetaPZ < 0],\n",
+    "                        organism = \"hsapiens\",\n",
+    "                        ordered_query = FALSE,\n",
+    "                        multi_query = FALSE,\n",
+    "                        significant = TRUE,\n",
+    "                        exclude_iea = FALSE,\n",
+    "                        measure_underrepresentation = FALSE,\n",
+    "                        evcodes = FALSE,\n",
+    "                        correction_method = correction_var,\n",
+    "                        user_threshold = p_val_thres,\n",
+    "                        custom_bg = background_genes,\n",
+    "                        sources = 'TF'   # only do transfac enrichment\n",
+    "                        )\n",
+    "      \n",
+    "      \n",
+    "      \n",
+    "    \n",
+    "    if(!is.null(enrich_out)){\n",
+    "      \n",
+    "      # Save if a enrichment was found\n",
+    "      enrichment_found<-enrichment_found+1\n",
+    "      \n",
+    "      # Save result dataframe\n",
+    "      res<- enrich_out$result[enrich_out$result$source == 'TF',]\n",
+    "      res$cell_type<-cell_type\n",
+    "      res$snp_eGene<-paste0(eqtl,\"_positive\")\n",
+    "      enrichment<-rbind(enrichment,\n",
+    "                        res)\n",
+    "    }\n",
+    "    \n",
+    "    #Test negative coeGenes (MAF not correctly flipped here)\n",
+    "    enrich_out <-gost(\n",
+    "                       coeqtls_sign$second_gene[coeqtls_sign$snp_eqtlgene == eqtl &\n",
+    "                                                          coeqtls_sign$MetaPZ > 0],\n",
+    "                        organism = \"hsapiens\",\n",
+    "                        ordered_query = FALSE,\n",
+    "                        multi_query = FALSE,\n",
+    "                        significant = TRUE,\n",
+    "                        exclude_iea = FALSE,\n",
+    "                        measure_underrepresentation = FALSE,\n",
+    "                        evcodes = FALSE,\n",
+    "                        correction_method = correction_var,\n",
+    "                        user_threshold = p_val_thres,\n",
+    "                        custom_bg = background_genes,\n",
+    "                        sources = 'TF'   # only do transfac enrichment\n",
+    "                        )\n",
+    "    \n",
+    "    if(!is.null(enrich_out)){\n",
+    "      \n",
+    "      # Save if a enrichment was found\n",
+    "      enrichment_found<-enrichment_found+1\n",
+    "      \n",
+    "      # Save result dataframe\n",
+    "      res<-enrich_out$result[enrich_out$result$source == 'TF',]\n",
+    "      res$cell_type<-cell_type\n",
+    "      res$snp_eGene<-paste0(eqtl,\"_negative\")\n",
+    "      enrichment<-rbind(enrichment,\n",
+    "                        res)\n",
+    "    }\n",
+    "  }\n",
+    "  \n",
+    " \n",
+    "  }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "2161ef04-9fdc-4e14-b318-089eb91db546",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Inspect result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "0ae39aef-8d0c-4f59-821e-d1672811783e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 6 × 16</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>query</th><th scope=col>significant</th><th scope=col>p_value</th><th scope=col>term_size</th><th scope=col>query_size</th><th scope=col>intersection_size</th><th scope=col>precision</th><th scope=col>recall</th><th scope=col>term_id</th><th scope=col>source</th><th scope=col>term_name</th><th scope=col>effective_domain_size</th><th scope=col>source_order</th><th scope=col>parents</th><th scope=col>cell_type</th><th scope=col>snp_eGene</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>query_1</td><td>TRUE</td><td>0.049610826</td><td>2342</td><td> 27</td><td> 22</td><td>0.8148148</td><td>0.009393681</td><td>TF:M00665  </td><td>TF</td><td>Factor: Sp3; motif: ASMCTTGGGSRGGG                        </td><td> 5705</td><td>7882</td><td>TF:M00000</td><td>DC  </td><td>rs7935082_MS4A7         </td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>query_1</td><td>TRUE</td><td>0.049610826</td><td>2303</td><td> 27</td><td> 22</td><td>0.8148148</td><td>0.009552757</td><td>TF:M03582  </td><td>TF</td><td>Factor: TWIST; motif: CACCTGG                             </td><td> 5705</td><td>8844</td><td>TF:M00000</td><td>DC  </td><td>rs7935082_MS4A7         </td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>query_1</td><td>TRUE</td><td>0.003978389</td><td>3447</td><td>351</td><td>163</td><td>0.4643875</td><td>0.047287496</td><td>TF:M11438  </td><td>TF</td><td>Factor: SAP-1; motif: NTCGTAAATGCN                        </td><td>10167</td><td>1882</td><td>TF:M00000</td><td>CD4T</td><td>rs1131017_RPS26         </td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>query_1</td><td>TRUE</td><td>0.022537569</td><td>3025</td><td> 20</td><td> 16</td><td>0.8000000</td><td>0.005289256</td><td>TF:M08413  </td><td>TF</td><td>Factor: TEF-3:C/EBPdelta; motif: RGWATGYNRTTRCGYAAY       </td><td>10167</td><td>8434</td><td>TF:M00000</td><td>CD4T</td><td>rs7605824_SH3YL1        </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>query_1</td><td>TRUE</td><td>0.002470867</td><td>3285</td><td>191</td><td> 95</td><td>0.4973822</td><td>0.028919330</td><td>TF:M10785  </td><td>TF</td><td>Factor: hoxa9; motif: RTCGTWANNN                          </td><td>10167</td><td>3774</td><td>TF:M00000</td><td>CD4T</td><td>rs1131017_RPS26_positive</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>query_1</td><td>TRUE</td><td>0.003339438</td><td>1184</td><td>191</td><td> 46</td><td>0.2408377</td><td>0.038851351</td><td>TF:M04696_1</td><td>TF</td><td>Factor: YY1; motif: GCCGCCATNTTGNNNNNGGNCN; match class: 1</td><td>10167</td><td>9013</td><td>TF:M04696</td><td>CD4T</td><td>rs1131017_RPS26_positive</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 16\n",
+       "\\begin{tabular}{r|llllllllllllllll}\n",
+       "  & query & significant & p\\_value & term\\_size & query\\_size & intersection\\_size & precision & recall & term\\_id & source & term\\_name & effective\\_domain\\_size & source\\_order & parents & cell\\_type & snp\\_eGene\\\\\n",
+       "  & <chr> & <lgl> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <int> & <int> & <list> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & query\\_1 & TRUE & 0.049610826 & 2342 &  27 &  22 & 0.8148148 & 0.009393681 & TF:M00665   & TF & Factor: Sp3; motif: ASMCTTGGGSRGGG                         &  5705 & 7882 & TF:M00000 & DC   & rs7935082\\_MS4A7         \\\\\n",
+       "\t2 & query\\_1 & TRUE & 0.049610826 & 2303 &  27 &  22 & 0.8148148 & 0.009552757 & TF:M03582   & TF & Factor: TWIST; motif: CACCTGG                              &  5705 & 8844 & TF:M00000 & DC   & rs7935082\\_MS4A7         \\\\\n",
+       "\t3 & query\\_1 & TRUE & 0.003978389 & 3447 & 351 & 163 & 0.4643875 & 0.047287496 & TF:M11438   & TF & Factor: SAP-1; motif: NTCGTAAATGCN                         & 10167 & 1882 & TF:M00000 & CD4T & rs1131017\\_RPS26         \\\\\n",
+       "\t4 & query\\_1 & TRUE & 0.022537569 & 3025 &  20 &  16 & 0.8000000 & 0.005289256 & TF:M08413   & TF & Factor: TEF-3:C/EBPdelta; motif: RGWATGYNRTTRCGYAAY        & 10167 & 8434 & TF:M00000 & CD4T & rs7605824\\_SH3YL1        \\\\\n",
+       "\t5 & query\\_1 & TRUE & 0.002470867 & 3285 & 191 &  95 & 0.4973822 & 0.028919330 & TF:M10785   & TF & Factor: hoxa9; motif: RTCGTWANNN                           & 10167 & 3774 & TF:M00000 & CD4T & rs1131017\\_RPS26\\_positive\\\\\n",
+       "\t6 & query\\_1 & TRUE & 0.003339438 & 1184 & 191 &  46 & 0.2408377 & 0.038851351 & TF:M04696\\_1 & TF & Factor: YY1; motif: GCCGCCATNTTGNNNNNGGNCN; match class: 1 & 10167 & 9013 & TF:M04696 & CD4T & rs1131017\\_RPS26\\_positive\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 16\n",
+       "\n",
+       "| <!--/--> | query &lt;chr&gt; | significant &lt;lgl&gt; | p_value &lt;dbl&gt; | term_size &lt;int&gt; | query_size &lt;int&gt; | intersection_size &lt;int&gt; | precision &lt;dbl&gt; | recall &lt;dbl&gt; | term_id &lt;chr&gt; | source &lt;chr&gt; | term_name &lt;chr&gt; | effective_domain_size &lt;int&gt; | source_order &lt;int&gt; | parents &lt;list&gt; | cell_type &lt;chr&gt; | snp_eGene &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | query_1 | TRUE | 0.049610826 | 2342 |  27 |  22 | 0.8148148 | 0.009393681 | TF:M00665   | TF | Factor: Sp3; motif: ASMCTTGGGSRGGG                         |  5705 | 7882 | TF:M00000 | DC   | rs7935082_MS4A7          |\n",
+       "| 2 | query_1 | TRUE | 0.049610826 | 2303 |  27 |  22 | 0.8148148 | 0.009552757 | TF:M03582   | TF | Factor: TWIST; motif: CACCTGG                              |  5705 | 8844 | TF:M00000 | DC   | rs7935082_MS4A7          |\n",
+       "| 3 | query_1 | TRUE | 0.003978389 | 3447 | 351 | 163 | 0.4643875 | 0.047287496 | TF:M11438   | TF | Factor: SAP-1; motif: NTCGTAAATGCN                         | 10167 | 1882 | TF:M00000 | CD4T | rs1131017_RPS26          |\n",
+       "| 4 | query_1 | TRUE | 0.022537569 | 3025 |  20 |  16 | 0.8000000 | 0.005289256 | TF:M08413   | TF | Factor: TEF-3:C/EBPdelta; motif: RGWATGYNRTTRCGYAAY        | 10167 | 8434 | TF:M00000 | CD4T | rs7605824_SH3YL1         |\n",
+       "| 5 | query_1 | TRUE | 0.002470867 | 3285 | 191 |  95 | 0.4973822 | 0.028919330 | TF:M10785   | TF | Factor: hoxa9; motif: RTCGTWANNN                           | 10167 | 3774 | TF:M00000 | CD4T | rs1131017_RPS26_positive |\n",
+       "| 6 | query_1 | TRUE | 0.003339438 | 1184 | 191 |  46 | 0.2408377 | 0.038851351 | TF:M04696_1 | TF | Factor: YY1; motif: GCCGCCATNTTGNNNNNGGNCN; match class: 1 | 10167 | 9013 | TF:M04696 | CD4T | rs1131017_RPS26_positive |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  query   significant p_value     term_size query_size intersection_size\n",
+       "1 query_1 TRUE        0.049610826 2342       27         22              \n",
+       "2 query_1 TRUE        0.049610826 2303       27         22              \n",
+       "3 query_1 TRUE        0.003978389 3447      351        163              \n",
+       "4 query_1 TRUE        0.022537569 3025       20         16              \n",
+       "5 query_1 TRUE        0.002470867 3285      191         95              \n",
+       "6 query_1 TRUE        0.003339438 1184      191         46              \n",
+       "  precision recall      term_id     source\n",
+       "1 0.8148148 0.009393681 TF:M00665   TF    \n",
+       "2 0.8148148 0.009552757 TF:M03582   TF    \n",
+       "3 0.4643875 0.047287496 TF:M11438   TF    \n",
+       "4 0.8000000 0.005289256 TF:M08413   TF    \n",
+       "5 0.4973822 0.028919330 TF:M10785   TF    \n",
+       "6 0.2408377 0.038851351 TF:M04696_1 TF    \n",
+       "  term_name                                                 \n",
+       "1 Factor: Sp3; motif: ASMCTTGGGSRGGG                        \n",
+       "2 Factor: TWIST; motif: CACCTGG                             \n",
+       "3 Factor: SAP-1; motif: NTCGTAAATGCN                        \n",
+       "4 Factor: TEF-3:C/EBPdelta; motif: RGWATGYNRTTRCGYAAY       \n",
+       "5 Factor: hoxa9; motif: RTCGTWANNN                          \n",
+       "6 Factor: YY1; motif: GCCGCCATNTTGNNNNNGGNCN; match class: 1\n",
+       "  effective_domain_size source_order parents   cell_type\n",
+       "1  5705                 7882         TF:M00000 DC       \n",
+       "2  5705                 8844         TF:M00000 DC       \n",
+       "3 10167                 1882         TF:M00000 CD4T     \n",
+       "4 10167                 8434         TF:M00000 CD4T     \n",
+       "5 10167                 3774         TF:M00000 CD4T     \n",
+       "6 10167                 9013         TF:M04696 CD4T     \n",
+       "  snp_eGene               \n",
+       "1 rs7935082_MS4A7         \n",
+       "2 rs7935082_MS4A7         \n",
+       "3 rs1131017_RPS26         \n",
+       "4 rs7605824_SH3YL1        \n",
+       "5 rs1131017_RPS26_positive\n",
+       "6 rs1131017_RPS26_positive"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(enrichment)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "id": "724fe97f-5620-4d55-9d2f-154c00f7bcd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 6 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>cell_type</th><th scope=col>n_eqtls_freq</th><th scope=col>n_enrich</th><th scope=col>freq_enrich</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>DC      </td><td>2</td><td>1</td><td>0.5000000</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>CD4T    </td><td>8</td><td>2</td><td>0.2500000</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>CD8T    </td><td>6</td><td>0</td><td>0.0000000</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>monocyte</td><td>9</td><td>1</td><td>0.1111111</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>NK      </td><td>3</td><td>2</td><td>0.6666667</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>B       </td><td>1</td><td>1</td><td>1.0000000</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 4\n",
+       "\\begin{tabular}{r|llll}\n",
+       "  & cell\\_type & n\\_eqtls\\_freq & n\\_enrich & freq\\_enrich\\\\\n",
+       "  & <chr> & <int> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & DC       & 2 & 1 & 0.5000000\\\\\n",
+       "\t2 & CD4T     & 8 & 2 & 0.2500000\\\\\n",
+       "\t3 & CD8T     & 6 & 0 & 0.0000000\\\\\n",
+       "\t4 & monocyte & 9 & 1 & 0.1111111\\\\\n",
+       "\t5 & NK       & 3 & 2 & 0.6666667\\\\\n",
+       "\t6 & B        & 1 & 1 & 1.0000000\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 4\n",
+       "\n",
+       "| <!--/--> | cell_type &lt;chr&gt; | n_eqtls_freq &lt;int&gt; | n_enrich &lt;dbl&gt; | freq_enrich &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| 1 | DC       | 2 | 1 | 0.5000000 |\n",
+       "| 2 | CD4T     | 8 | 2 | 0.2500000 |\n",
+       "| 3 | CD8T     | 6 | 0 | 0.0000000 |\n",
+       "| 4 | monocyte | 9 | 1 | 0.1111111 |\n",
+       "| 5 | NK       | 3 | 2 | 0.6666667 |\n",
+       "| 6 | B        | 1 | 1 | 1.0000000 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  cell_type n_eqtls_freq n_enrich freq_enrich\n",
+       "1 DC        2            1        0.5000000  \n",
+       "2 CD4T      8            2        0.2500000  \n",
+       "3 CD8T      6            0        0.0000000  \n",
+       "4 monocyte  9            1        0.1111111  \n",
+       "5 NK        3            2        0.6666667  \n",
+       "6 B         1            1        1.0000000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(enrichment_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "id": "90e7aea5-2e69-4412-89b7-cbd605fb836d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 6 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>snp_eqtlgene</th><th scope=col>count_coeGenes</th><th scope=col>cell_type</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs7935082_MS4A7     </td><td> 30</td><td>DC  </td></tr>\n",
+       "\t<tr><td>rs9271520_HLA-DQA2  </td><td> 13</td><td>DC  </td></tr>\n",
+       "\t<tr><td>rs111454690_HLA-DRB5</td><td> 19</td><td>CD4T</td></tr>\n",
+       "\t<tr><td>rs1131017_RPS26     </td><td>372</td><td>CD4T</td></tr>\n",
+       "\t<tr><td>rs2741159_KRT1      </td><td>  8</td><td>CD4T</td></tr>\n",
+       "\t<tr><td>rs4147638_SMDT1     </td><td> 19</td><td>CD4T</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A tibble: 6 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " snp\\_eqtlgene & count\\_coeGenes & cell\\_type\\\\\n",
+       " <chr> & <int> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t rs7935082\\_MS4A7      &  30 & DC  \\\\\n",
+       "\t rs9271520\\_HLA-DQA2   &  13 & DC  \\\\\n",
+       "\t rs111454690\\_HLA-DRB5 &  19 & CD4T\\\\\n",
+       "\t rs1131017\\_RPS26      & 372 & CD4T\\\\\n",
+       "\t rs2741159\\_KRT1       &   8 & CD4T\\\\\n",
+       "\t rs4147638\\_SMDT1      &  19 & CD4T\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 6 × 3\n",
+       "\n",
+       "| snp_eqtlgene &lt;chr&gt; | count_coeGenes &lt;int&gt; | cell_type &lt;chr&gt; |\n",
+       "|---|---|---|\n",
+       "| rs7935082_MS4A7      |  30 | DC   |\n",
+       "| rs9271520_HLA-DQA2   |  13 | DC   |\n",
+       "| rs111454690_HLA-DRB5 |  19 | CD4T |\n",
+       "| rs1131017_RPS26      | 372 | CD4T |\n",
+       "| rs2741159_KRT1       |   8 | CD4T |\n",
+       "| rs4147638_SMDT1      |  19 | CD4T |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  snp_eqtlgene         count_coeGenes cell_type\n",
+       "1 rs7935082_MS4A7       30            DC       \n",
+       "2 rs9271520_HLA-DQA2    13            DC       \n",
+       "3 rs111454690_HLA-DRB5  19            CD4T     \n",
+       "4 rs1131017_RPS26      372            CD4T     \n",
+       "5 rs2741159_KRT1         8            CD4T     \n",
+       "6 rs4147638_SMDT1       19            CD4T     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coegenes_counts_total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "a7788afe-58c2-41bf-bd53-bec13a912a46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.0496108257480342"
+      ],
+      "text/latex": [
+       "0.0496108257480342"
+      ],
+      "text/markdown": [
+       "0.0496108257480342"
+      ],
+      "text/plain": [
+       "[1] 0.04961083"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max(enrichment$p_value)  # set to same level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "id": "ba254471-a07d-4b06-9280-970d02582110",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Evaluate amount of enrichments found per cell-type with set p-value threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "id": "6729f307-8da0-4700-9b99-d4cc8f9b2fce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cell_type</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>B       </td><td> 3</td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>54</td></tr>\n",
+       "\t<tr><td>DC      </td><td> 2</td></tr>\n",
+       "\t<tr><td>monocyte</td><td>40</td></tr>\n",
+       "\t<tr><td>NK      </td><td>21</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cell\\_type & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t B        &  3\\\\\n",
+       "\t CD4T     & 54\\\\\n",
+       "\t DC       &  2\\\\\n",
+       "\t monocyte & 40\\\\\n",
+       "\t NK       & 21\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 5 × 2\n",
+       "\n",
+       "| cell_type &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| B        |  3 |\n",
+       "| CD4T     | 54 |\n",
+       "| DC       |  2 |\n",
+       "| monocyte | 40 |\n",
+       "| NK       | 21 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  cell_type n \n",
+       "1 B          3\n",
+       "2 CD4T      54\n",
+       "3 DC         2\n",
+       "4 monocyte  40\n",
+       "5 NK        21"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "enrichment %>% group_by(cell_type) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "id": "7d3dbfdc-2862-4ead-be5a-7645b8a170ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "120"
+      ],
+      "text/latex": [
+       "120"
+      ],
+      "text/markdown": [
+       "120"
+      ],
+      "text/plain": [
+       "[1] 120"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(enrichment)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "id": "8a481a49-f784-45a6-b640-e33cf122469f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Save the enrichment result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "f5e929c8-3fd4-4023-8483-3ef1bb3335d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$parents = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "3db92875-75a3-436f-b26c-e116bb15e0ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.csv(enrichment, paste0(path, \"transfac_results/TRANSFAC_Enrichments.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1c57807-ebfa-4ef6-ad86-4022bcee0fe0",
+   "metadata": {},
+   "source": [
+    "# Compare to previous enrichment results with Remap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "id": "08a4064d-3059-422a-9406-0cac4c75830b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>Cell.type</th><th scope=col>eQTL..SNP.eGene.</th><th scope=col>TF</th><th scope=col>TF.is.a.co.eGene.</th><th scope=col>enrichment.p.value</th><th scope=col>X..TF.overlap...co.eGene</th><th scope=col>X..TF.overlap...background</th><th scope=col>X..no.TF.overlap...co.eGene</th><th scope=col>X..background.gene...not.co.eGene</th><th scope=col>enrichment.fdr</th><th scope=col>eQTL.SNP</th><th scope=col>SNP.overlaps.TF.</th><th scope=col>Names.of.overlapping.SNPs</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>CD4T</td><td>rs111454690_HLA-DRB5</td><td>CDK8   </td><td>FALSE</td><td>9.630369e-06</td><td>14</td><td>5</td><td>2778</td><td> 8515</td><td>1.640373e-03</td><td>rs111454690</td><td>FALSE</td><td></td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>CD4T</td><td>rs111454690_HLA-DRB5</td><td>SNRNP70</td><td>FALSE</td><td>1.209254e-09</td><td>11</td><td>8</td><td> 649</td><td>10644</td><td>6.179288e-07</td><td>rs111454690</td><td>FALSE</td><td></td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & Cell.type & eQTL..SNP.eGene. & TF & TF.is.a.co.eGene. & enrichment.p.value & X..TF.overlap...co.eGene & X..TF.overlap...background & X..no.TF.overlap...co.eGene & X..background.gene...not.co.eGene & enrichment.fdr & eQTL.SNP & SNP.overlaps.TF. & Names.of.overlapping.SNPs\\\\\n",
+       "  & <chr> & <chr> & <chr> & <lgl> & <dbl> & <int> & <int> & <int> & <int> & <dbl> & <chr> & <lgl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & CD4T & rs111454690\\_HLA-DRB5 & CDK8    & FALSE & 9.630369e-06 & 14 & 5 & 2778 &  8515 & 1.640373e-03 & rs111454690 & FALSE & \\\\\n",
+       "\t2 & CD4T & rs111454690\\_HLA-DRB5 & SNRNP70 & FALSE & 1.209254e-09 & 11 & 8 &  649 & 10644 & 6.179288e-07 & rs111454690 & FALSE & \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 13\n",
+       "\n",
+       "| <!--/--> | Cell.type &lt;chr&gt; | eQTL..SNP.eGene. &lt;chr&gt; | TF &lt;chr&gt; | TF.is.a.co.eGene. &lt;lgl&gt; | enrichment.p.value &lt;dbl&gt; | X..TF.overlap...co.eGene &lt;int&gt; | X..TF.overlap...background &lt;int&gt; | X..no.TF.overlap...co.eGene &lt;int&gt; | X..background.gene...not.co.eGene &lt;int&gt; | enrichment.fdr &lt;dbl&gt; | eQTL.SNP &lt;chr&gt; | SNP.overlaps.TF. &lt;lgl&gt; | Names.of.overlapping.SNPs &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | CD4T | rs111454690_HLA-DRB5 | CDK8    | FALSE | 9.630369e-06 | 14 | 5 | 2778 |  8515 | 1.640373e-03 | rs111454690 | FALSE | <!----> |\n",
+       "| 2 | CD4T | rs111454690_HLA-DRB5 | SNRNP70 | FALSE | 1.209254e-09 | 11 | 8 |  649 | 10644 | 6.179288e-07 | rs111454690 | FALSE | <!----> |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Cell.type eQTL..SNP.eGene.     TF      TF.is.a.co.eGene. enrichment.p.value\n",
+       "1 CD4T      rs111454690_HLA-DRB5 CDK8    FALSE             9.630369e-06      \n",
+       "2 CD4T      rs111454690_HLA-DRB5 SNRNP70 FALSE             1.209254e-09      \n",
+       "  X..TF.overlap...co.eGene X..TF.overlap...background\n",
+       "1 14                       5                         \n",
+       "2 11                       8                         \n",
+       "  X..no.TF.overlap...co.eGene X..background.gene...not.co.eGene enrichment.fdr\n",
+       "1 2778                         8515                             1.640373e-03  \n",
+       "2  649                        10644                             6.179288e-07  \n",
+       "  eQTL.SNP    SNP.overlaps.TF. Names.of.overlapping.SNPs\n",
+       "1 rs111454690 FALSE                                     \n",
+       "2 rs111454690 FALSE                                     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(old_enrichments,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "238a1fe5-72cf-4dd4-96b2-c25e18fa41e5",
+   "metadata": {},
+   "source": [
+    "## Compare amount of enrichments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "8058ccda-a072-443d-9b29-66fef671cd7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "amount_enrichments_old = old_enrichments %>% group_by(Cell.type, eQTL..SNP.eGene.) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "id": "baac2391-4638-4e42-923e-912226128e43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 6 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Cell.type</th><th scope=col>eQTL..SNP.eGene.</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>B   </td><td>rs1131017_RPS26         </td><td> 82</td></tr>\n",
+       "\t<tr><td>CD4T</td><td>rs111454690_HLA-DRB5    </td><td> 14</td></tr>\n",
+       "\t<tr><td>CD4T</td><td>rs1131017_RPS26         </td><td>134</td></tr>\n",
+       "\t<tr><td>CD4T</td><td>rs1131017_RPS26_negative</td><td> 93</td></tr>\n",
+       "\t<tr><td>CD4T</td><td>rs1131017_RPS26_positive</td><td>125</td></tr>\n",
+       "\t<tr><td>CD4T</td><td>rs4147638_SMDT1         </td><td> 14</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 6 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " Cell.type & eQTL..SNP.eGene. & n\\\\\n",
+       " <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t B    & rs1131017\\_RPS26          &  82\\\\\n",
+       "\t CD4T & rs111454690\\_HLA-DRB5     &  14\\\\\n",
+       "\t CD4T & rs1131017\\_RPS26          & 134\\\\\n",
+       "\t CD4T & rs1131017\\_RPS26\\_negative &  93\\\\\n",
+       "\t CD4T & rs1131017\\_RPS26\\_positive & 125\\\\\n",
+       "\t CD4T & rs4147638\\_SMDT1          &  14\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 6 × 3\n",
+       "\n",
+       "| Cell.type &lt;chr&gt; | eQTL..SNP.eGene. &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|---|\n",
+       "| B    | rs1131017_RPS26          |  82 |\n",
+       "| CD4T | rs111454690_HLA-DRB5     |  14 |\n",
+       "| CD4T | rs1131017_RPS26          | 134 |\n",
+       "| CD4T | rs1131017_RPS26_negative |  93 |\n",
+       "| CD4T | rs1131017_RPS26_positive | 125 |\n",
+       "| CD4T | rs4147638_SMDT1          |  14 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Cell.type eQTL..SNP.eGene.         n  \n",
+       "1 B         rs1131017_RPS26           82\n",
+       "2 CD4T      rs111454690_HLA-DRB5      14\n",
+       "3 CD4T      rs1131017_RPS26          134\n",
+       "4 CD4T      rs1131017_RPS26_negative  93\n",
+       "5 CD4T      rs1131017_RPS26_positive 125\n",
+       "6 CD4T      rs4147638_SMDT1           14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(amount_enrichments_old)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "id": "6f219e98-8ec6-45fa-b145-7b8086806dd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames(amount_enrichments_old) = c('cell_type', 'snp_eGene', 'ReMap_amount')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "id": "9bcc8ddb-a123-4fe6-b1bb-8e6a5d451519",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transfac_enrichments = enrichment %>% group_by(cell_type, snp_eGene) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "id": "ce779ce7-e46a-466f-be5b-1cab27eadd2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames(transfac_enrichments)= c('cell_type', 'snp_eGene', 'TRANSFAC_amount')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "id": "06ae3596-604f-4ebc-b6e9-66150ffb9559",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overview = merge(amount_enrichments_old, transfac_enrichments, all.x = TRUE, all.y = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "id": "7b372d6d-1e65-4750-9414-6cd3b5294a05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Result of comparisoon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "id": "6754a2a0-cdef-4126-9042-5820a8f65f62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overview[is.na(overview)]= 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "6fc4ba13-519b-4ccd-bf51-967295a82d06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 19 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>cell_type</th><th scope=col>snp_eGene</th><th scope=col>ReMap_amount</th><th scope=col>TRANSFAC_amount</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>5</th><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>125</td><td>51</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>monocyte</td><td>rs1131017_RPS26         </td><td>145</td><td>40</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td>NK      </td><td>rs1131017_RPS26         </td><td>132</td><td>20</td></tr>\n",
+       "\t<tr><th scope=row>1</th><td>B       </td><td>rs1131017_RPS26         </td><td> 82</td><td> 3</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>DC      </td><td>rs7935082_MS4A7         </td><td>  0</td><td> 2</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>CD4T    </td><td>rs1131017_RPS26         </td><td>134</td><td> 1</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>CD4T    </td><td>rs1131017_RPS26_negative</td><td> 93</td><td> 1</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td>CD4T    </td><td>rs7605824_SH3YL1        </td><td> 58</td><td> 1</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td>NK      </td><td>rs12151742_GNLY         </td><td>  0</td><td> 1</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>CD4T    </td><td>rs111454690_HLA-DRB5    </td><td> 14</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>CD4T    </td><td>rs4147638_SMDT1         </td><td> 14</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>CD4T    </td><td>rs7632486_CMTM8         </td><td>  4</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>CD4T    </td><td>rs9271520_HLA-DQA2      </td><td>  5</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td>CD8T    </td><td>rs1131017_RPS26         </td><td> 62</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td>CD8T    </td><td>rs4147638_SMDT1         </td><td> 78</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>CD8T    </td><td>rs6708265_PASK          </td><td>  3</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>CD8T    </td><td>rs7605824_SH3YL1        </td><td>  9</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>monocyte</td><td>rs111454690_HLA-DRB5    </td><td>  1</td><td> 0</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>monocyte</td><td>rs9271520_HLA-DQA2      </td><td>  4</td><td> 0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 19 × 4\n",
+       "\\begin{tabular}{r|llll}\n",
+       "  & cell\\_type & snp\\_eGene & ReMap\\_amount & TRANSFAC\\_amount\\\\\n",
+       "  & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t5 & CD4T     & rs1131017\\_RPS26\\_positive & 125 & 51\\\\\n",
+       "\t16 & monocyte & rs1131017\\_RPS26          & 145 & 40\\\\\n",
+       "\t18 & NK       & rs1131017\\_RPS26          & 132 & 20\\\\\n",
+       "\t1 & B        & rs1131017\\_RPS26          &  82 &  3\\\\\n",
+       "\t14 & DC       & rs7935082\\_MS4A7          &   0 &  2\\\\\n",
+       "\t3 & CD4T     & rs1131017\\_RPS26          & 134 &  1\\\\\n",
+       "\t4 & CD4T     & rs1131017\\_RPS26\\_negative &  93 &  1\\\\\n",
+       "\t7 & CD4T     & rs7605824\\_SH3YL1         &  58 &  1\\\\\n",
+       "\t19 & NK       & rs12151742\\_GNLY          &   0 &  1\\\\\n",
+       "\t2 & CD4T     & rs111454690\\_HLA-DRB5     &  14 &  0\\\\\n",
+       "\t6 & CD4T     & rs4147638\\_SMDT1          &  14 &  0\\\\\n",
+       "\t8 & CD4T     & rs7632486\\_CMTM8          &   4 &  0\\\\\n",
+       "\t9 & CD4T     & rs9271520\\_HLA-DQA2       &   5 &  0\\\\\n",
+       "\t10 & CD8T     & rs1131017\\_RPS26          &  62 &  0\\\\\n",
+       "\t11 & CD8T     & rs4147638\\_SMDT1          &  78 &  0\\\\\n",
+       "\t12 & CD8T     & rs6708265\\_PASK           &   3 &  0\\\\\n",
+       "\t13 & CD8T     & rs7605824\\_SH3YL1         &   9 &  0\\\\\n",
+       "\t15 & monocyte & rs111454690\\_HLA-DRB5     &   1 &  0\\\\\n",
+       "\t17 & monocyte & rs9271520\\_HLA-DQA2       &   4 &  0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 19 × 4\n",
+       "\n",
+       "| <!--/--> | cell_type &lt;chr&gt; | snp_eGene &lt;chr&gt; | ReMap_amount &lt;dbl&gt; | TRANSFAC_amount &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| 5 | CD4T     | rs1131017_RPS26_positive | 125 | 51 |\n",
+       "| 16 | monocyte | rs1131017_RPS26          | 145 | 40 |\n",
+       "| 18 | NK       | rs1131017_RPS26          | 132 | 20 |\n",
+       "| 1 | B        | rs1131017_RPS26          |  82 |  3 |\n",
+       "| 14 | DC       | rs7935082_MS4A7          |   0 |  2 |\n",
+       "| 3 | CD4T     | rs1131017_RPS26          | 134 |  1 |\n",
+       "| 4 | CD4T     | rs1131017_RPS26_negative |  93 |  1 |\n",
+       "| 7 | CD4T     | rs7605824_SH3YL1         |  58 |  1 |\n",
+       "| 19 | NK       | rs12151742_GNLY          |   0 |  1 |\n",
+       "| 2 | CD4T     | rs111454690_HLA-DRB5     |  14 |  0 |\n",
+       "| 6 | CD4T     | rs4147638_SMDT1          |  14 |  0 |\n",
+       "| 8 | CD4T     | rs7632486_CMTM8          |   4 |  0 |\n",
+       "| 9 | CD4T     | rs9271520_HLA-DQA2       |   5 |  0 |\n",
+       "| 10 | CD8T     | rs1131017_RPS26          |  62 |  0 |\n",
+       "| 11 | CD8T     | rs4147638_SMDT1          |  78 |  0 |\n",
+       "| 12 | CD8T     | rs6708265_PASK           |   3 |  0 |\n",
+       "| 13 | CD8T     | rs7605824_SH3YL1         |   9 |  0 |\n",
+       "| 15 | monocyte | rs111454690_HLA-DRB5     |   1 |  0 |\n",
+       "| 17 | monocyte | rs9271520_HLA-DQA2       |   4 |  0 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   cell_type snp_eGene                ReMap_amount TRANSFAC_amount\n",
+       "5  CD4T      rs1131017_RPS26_positive 125          51             \n",
+       "16 monocyte  rs1131017_RPS26          145          40             \n",
+       "18 NK        rs1131017_RPS26          132          20             \n",
+       "1  B         rs1131017_RPS26           82           3             \n",
+       "14 DC        rs7935082_MS4A7            0           2             \n",
+       "3  CD4T      rs1131017_RPS26          134           1             \n",
+       "4  CD4T      rs1131017_RPS26_negative  93           1             \n",
+       "7  CD4T      rs7605824_SH3YL1          58           1             \n",
+       "19 NK        rs12151742_GNLY            0           1             \n",
+       "2  CD4T      rs111454690_HLA-DRB5      14           0             \n",
+       "6  CD4T      rs4147638_SMDT1           14           0             \n",
+       "8  CD4T      rs7632486_CMTM8            4           0             \n",
+       "9  CD4T      rs9271520_HLA-DQA2         5           0             \n",
+       "10 CD8T      rs1131017_RPS26           62           0             \n",
+       "11 CD8T      rs4147638_SMDT1           78           0             \n",
+       "12 CD8T      rs6708265_PASK             3           0             \n",
+       "13 CD8T      rs7605824_SH3YL1           9           0             \n",
+       "15 monocyte  rs111454690_HLA-DRB5       1           0             \n",
+       "17 monocyte  rs9271520_HLA-DQA2         4           0             "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "overview[order(overview$TRANSFAC_amount, decreasing = TRUE),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "id": "c04f7c9b-1e74-401d-bcf4-569919447df6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.csv(overview, paste0(path, \"transfac_results/TRANSFAC_ReMap_comparison.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80ddea9d-1cc5-4ce3-b595-e15d06593b83",
+   "metadata": {},
+   "source": [
+    "## Compare the TFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "id": "8dce9b80-35b0-4f3e-9023-9912c1348657",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Paper: six TFs—RBM39, TCF7, LEF1, KLF6, CD74 and MAF—whose binding sites were enriched in the promoter region of the rs1131017–RPS26\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "id": "fce20036-c368-467e-9f62-07dd7cf63b6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$tf = str_extract(enrichment$term_name, '.*;')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "id": "8ffb3678-a8e1-4648-bcfc-d9fc3d1b30c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$tf = str_replace(enrichment$tf, 'Factor: ', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "id": "9b91aee3-698d-488c-8e7f-d68cc00bdb17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$tf = str_replace(enrichment$tf, ';', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "id": "ea937e43-2fdf-47c3-8f1a-e9919889e5f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$tf = str_replace(enrichment$tf , 'motif.*', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "id": "0855320a-54d6-422c-9991-d2d9329c3fb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$tf = str_replace(enrichment$tf , '-', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "id": "9f0f674c-8690-4e2c-84e2-949048f3e891",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$tf = toupper(enrichment$tf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "id": "891df001-7839-41b9-96f5-6b2521e3b6eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$tf = str_replace(enrichment$tf , ' ', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "id": "2873020c-ae31-4cfb-bb8b-953da1a03c61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enrichment$tf = str_replace(enrichment$tf, 'CETS-1', 'ETS1')\n",
+    "enrichment$tf = str_replace(enrichment$tf, 'C/EBPBETA|C/EBPBETA|C/EBPbeta|C/EBPBETA|GCMA:CEBPB', 'CEBPB')\n",
+    "enrichment$tf = str_replace(enrichment$tf, 'C/EBPDELTA|C/EBPDELTA|TEF3:CEBPD', 'CEBPD')\n",
+    "enrichment$tf = str_replace(enrichment$tf, 'C/EBPGAMMA', 'CEBPG')\n",
+    "enrichment$tf = str_replace(enrichment$tf, 'ELK1:HOXB13', 'ELK1')\n",
+    "enrichment$tf = str_replace(enrichment$tf, 'GTF2IRD1ISOFORM2', 'GTF2I')\n",
+    "enrichment$tf = str_replace(enrichment$tf, 'MEIS1:ELF1', 'ELF1')\n",
+    "enrichment$tf = str_replace(enrichment$tf, 'PU.1', 'SPI1')\n",
+    "enrichment$tf = str_replace(enrichment$tf, 'TEF3:ERG', 'ERG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "id": "f2c5345b-0fdd-40fb-a982-96794b5f6440",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 16</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>query</th><th scope=col>significant</th><th scope=col>p_value</th><th scope=col>term_size</th><th scope=col>query_size</th><th scope=col>intersection_size</th><th scope=col>precision</th><th scope=col>recall</th><th scope=col>term_id</th><th scope=col>source</th><th scope=col>term_name</th><th scope=col>effective_domain_size</th><th scope=col>source_order</th><th scope=col>cell_type</th><th scope=col>snp_eGene</th><th scope=col>tf</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>query_1</td><td>TRUE</td><td>0.04961083</td><td>2342</td><td>27</td><td>22</td><td>0.8148148</td><td>0.009393681</td><td>TF:M00665</td><td>TF</td><td>Factor: Sp3; motif: ASMCTTGGGSRGGG</td><td>5705</td><td>7882</td><td>DC</td><td>rs7935082_MS4A7</td><td>SP3  </td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>query_1</td><td>TRUE</td><td>0.04961083</td><td>2303</td><td>27</td><td>22</td><td>0.8148148</td><td>0.009552757</td><td>TF:M03582</td><td>TF</td><td>Factor: TWIST; motif: CACCTGG     </td><td>5705</td><td>8844</td><td>DC</td><td>rs7935082_MS4A7</td><td>TWIST</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 16\n",
+       "\\begin{tabular}{r|llllllllllllllll}\n",
+       "  & query & significant & p\\_value & term\\_size & query\\_size & intersection\\_size & precision & recall & term\\_id & source & term\\_name & effective\\_domain\\_size & source\\_order & cell\\_type & snp\\_eGene & tf\\\\\n",
+       "  & <chr> & <lgl> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <int> & <int> & <chr> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & query\\_1 & TRUE & 0.04961083 & 2342 & 27 & 22 & 0.8148148 & 0.009393681 & TF:M00665 & TF & Factor: Sp3; motif: ASMCTTGGGSRGGG & 5705 & 7882 & DC & rs7935082\\_MS4A7 & SP3  \\\\\n",
+       "\t2 & query\\_1 & TRUE & 0.04961083 & 2303 & 27 & 22 & 0.8148148 & 0.009552757 & TF:M03582 & TF & Factor: TWIST; motif: CACCTGG      & 5705 & 8844 & DC & rs7935082\\_MS4A7 & TWIST\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 16\n",
+       "\n",
+       "| <!--/--> | query &lt;chr&gt; | significant &lt;lgl&gt; | p_value &lt;dbl&gt; | term_size &lt;int&gt; | query_size &lt;int&gt; | intersection_size &lt;int&gt; | precision &lt;dbl&gt; | recall &lt;dbl&gt; | term_id &lt;chr&gt; | source &lt;chr&gt; | term_name &lt;chr&gt; | effective_domain_size &lt;int&gt; | source_order &lt;int&gt; | cell_type &lt;chr&gt; | snp_eGene &lt;chr&gt; | tf &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | query_1 | TRUE | 0.04961083 | 2342 | 27 | 22 | 0.8148148 | 0.009393681 | TF:M00665 | TF | Factor: Sp3; motif: ASMCTTGGGSRGGG | 5705 | 7882 | DC | rs7935082_MS4A7 | SP3   |\n",
+       "| 2 | query_1 | TRUE | 0.04961083 | 2303 | 27 | 22 | 0.8148148 | 0.009552757 | TF:M03582 | TF | Factor: TWIST; motif: CACCTGG      | 5705 | 8844 | DC | rs7935082_MS4A7 | TWIST |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  query   significant p_value    term_size query_size intersection_size\n",
+       "1 query_1 TRUE        0.04961083 2342      27         22               \n",
+       "2 query_1 TRUE        0.04961083 2303      27         22               \n",
+       "  precision recall      term_id   source term_name                         \n",
+       "1 0.8148148 0.009393681 TF:M00665 TF     Factor: Sp3; motif: ASMCTTGGGSRGGG\n",
+       "2 0.8148148 0.009552757 TF:M03582 TF     Factor: TWIST; motif: CACCTGG     \n",
+       "  effective_domain_size source_order cell_type snp_eGene       tf   \n",
+       "1 5705                  7882         DC        rs7935082_MS4A7 SP3  \n",
+       "2 5705                  8844         DC        rs7935082_MS4A7 TWIST"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(enrichment,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "id": "6ff9d9b7-e3b8-46d7-93ae-8b2f765f381a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames(enrichment) = paste0('TRANSFAC_', colnames(enrichment))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "id": "b940e752-4002-4c52-b066-bb3c2ff83e36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Merge with ReMap REsults"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "id": "60ff3520-ffac-4660-bb4b-9744db63c309",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>Cell.type</th><th scope=col>eQTL..SNP.eGene.</th><th scope=col>TF</th><th scope=col>TF.is.a.co.eGene.</th><th scope=col>enrichment.p.value</th><th scope=col>X..TF.overlap...co.eGene</th><th scope=col>X..TF.overlap...background</th><th scope=col>X..no.TF.overlap...co.eGene</th><th scope=col>X..background.gene...not.co.eGene</th><th scope=col>enrichment.fdr</th><th scope=col>eQTL.SNP</th><th scope=col>SNP.overlaps.TF.</th><th scope=col>Names.of.overlapping.SNPs</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>CD4T</td><td>rs111454690_HLA-DRB5</td><td>CDK8   </td><td>FALSE</td><td>9.630369e-06</td><td>14</td><td>5</td><td>2778</td><td> 8515</td><td>1.640373e-03</td><td>rs111454690</td><td>FALSE</td><td></td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>CD4T</td><td>rs111454690_HLA-DRB5</td><td>SNRNP70</td><td>FALSE</td><td>1.209254e-09</td><td>11</td><td>8</td><td> 649</td><td>10644</td><td>6.179288e-07</td><td>rs111454690</td><td>FALSE</td><td></td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & Cell.type & eQTL..SNP.eGene. & TF & TF.is.a.co.eGene. & enrichment.p.value & X..TF.overlap...co.eGene & X..TF.overlap...background & X..no.TF.overlap...co.eGene & X..background.gene...not.co.eGene & enrichment.fdr & eQTL.SNP & SNP.overlaps.TF. & Names.of.overlapping.SNPs\\\\\n",
+       "  & <chr> & <chr> & <chr> & <lgl> & <dbl> & <int> & <int> & <int> & <int> & <dbl> & <chr> & <lgl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & CD4T & rs111454690\\_HLA-DRB5 & CDK8    & FALSE & 9.630369e-06 & 14 & 5 & 2778 &  8515 & 1.640373e-03 & rs111454690 & FALSE & \\\\\n",
+       "\t2 & CD4T & rs111454690\\_HLA-DRB5 & SNRNP70 & FALSE & 1.209254e-09 & 11 & 8 &  649 & 10644 & 6.179288e-07 & rs111454690 & FALSE & \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 13\n",
+       "\n",
+       "| <!--/--> | Cell.type &lt;chr&gt; | eQTL..SNP.eGene. &lt;chr&gt; | TF &lt;chr&gt; | TF.is.a.co.eGene. &lt;lgl&gt; | enrichment.p.value &lt;dbl&gt; | X..TF.overlap...co.eGene &lt;int&gt; | X..TF.overlap...background &lt;int&gt; | X..no.TF.overlap...co.eGene &lt;int&gt; | X..background.gene...not.co.eGene &lt;int&gt; | enrichment.fdr &lt;dbl&gt; | eQTL.SNP &lt;chr&gt; | SNP.overlaps.TF. &lt;lgl&gt; | Names.of.overlapping.SNPs &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | CD4T | rs111454690_HLA-DRB5 | CDK8    | FALSE | 9.630369e-06 | 14 | 5 | 2778 |  8515 | 1.640373e-03 | rs111454690 | FALSE | <!----> |\n",
+       "| 2 | CD4T | rs111454690_HLA-DRB5 | SNRNP70 | FALSE | 1.209254e-09 | 11 | 8 |  649 | 10644 | 6.179288e-07 | rs111454690 | FALSE | <!----> |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Cell.type eQTL..SNP.eGene.     TF      TF.is.a.co.eGene. enrichment.p.value\n",
+       "1 CD4T      rs111454690_HLA-DRB5 CDK8    FALSE             9.630369e-06      \n",
+       "2 CD4T      rs111454690_HLA-DRB5 SNRNP70 FALSE             1.209254e-09      \n",
+       "  X..TF.overlap...co.eGene X..TF.overlap...background\n",
+       "1 14                       5                         \n",
+       "2 11                       8                         \n",
+       "  X..no.TF.overlap...co.eGene X..background.gene...not.co.eGene enrichment.fdr\n",
+       "1 2778                         8515                             1.640373e-03  \n",
+       "2  649                        10644                             6.179288e-07  \n",
+       "  eQTL.SNP    SNP.overlaps.TF. Names.of.overlapping.SNPs\n",
+       "1 rs111454690 FALSE                                     \n",
+       "2 rs111454690 FALSE                                     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(old_enrichments,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "id": "1e378133-c45d-4ca4-bfe0-b6b89e2dd7f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames(old_enrichments) = paste0('ReMap', colnames(old_enrichments))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "id": "2f103548-f736-4a81-ba6c-af85ac4da9c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined = merge(enrichment, old_enrichments, by.x = c('TRANSFAC_cell_type', 'TRANSFAC_snp_eGene', 'TRANSFAC_tf'), by.y = c('ReMapCell.type', 'ReMapeQTL..SNP.eGene.', 'ReMapTF'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "id": "2b4a65ee-ecf1-4d93-9718-88b5f9f49d20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "31"
+      ],
+      "text/latex": [
+       "31"
+      ],
+      "text/markdown": [
+       "31"
+      ],
+      "text/plain": [
+       "[1] 31"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(combined)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "id": "0ae3f173-266e-4720-bea0-fbd297c726d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'CEBPD'</li><li>'CEBPB'</li><li>'ELK1'</li><li>'FLI1'</li><li>'HOXA9'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'CEBPD'\n",
+       "\\item 'CEBPB'\n",
+       "\\item 'ELK1'\n",
+       "\\item 'FLI1'\n",
+       "\\item 'HOXA9'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'CEBPD'\n",
+       "2. 'CEBPB'\n",
+       "3. 'ELK1'\n",
+       "4. 'FLI1'\n",
+       "5. 'HOXA9'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"CEBPD\" \"CEBPB\" \"ELK1\"  \"FLI1\"  \"HOXA9\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(combined$TRANSFAC_tf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "id": "4924d056-d88a-4dcc-b21d-4f42f2365c10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 31 × 26</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>TRANSFAC_cell_type</th><th scope=col>TRANSFAC_snp_eGene</th><th scope=col>TRANSFAC_tf</th><th scope=col>TRANSFAC_query</th><th scope=col>TRANSFAC_significant</th><th scope=col>TRANSFAC_p_value</th><th scope=col>TRANSFAC_term_size</th><th scope=col>TRANSFAC_query_size</th><th scope=col>TRANSFAC_intersection_size</th><th scope=col>TRANSFAC_precision</th><th scope=col>⋯</th><th scope=col>ReMapTF.is.a.co.eGene.</th><th scope=col>ReMapenrichment.p.value</th><th scope=col>ReMapX..TF.overlap...co.eGene</th><th scope=col>ReMapX..TF.overlap...background</th><th scope=col>ReMapX..no.TF.overlap...co.eGene</th><th scope=col>ReMapX..background.gene...not.co.eGene</th><th scope=col>ReMapenrichment.fdr</th><th scope=col>ReMapeQTL.SNP</th><th scope=col>ReMapSNP.overlaps.TF.</th><th scope=col>ReMapNames.of.overlapping.SNPs</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>B       </td><td>rs1131017_RPS26         </td><td>CEBPD</td><td>query_1</td><td>TRUE</td><td>0.028703406</td><td> 501</td><td> 35</td><td> 23</td><td>0.6571429</td><td>⋯</td><td>FALSE</td><td>3.034184e-06</td><td> 34</td><td>  1</td><td>1096</td><td>  632</td><td>1.107477e-04</td><td>rs1131017</td><td> TRUE</td><td>rs1131017,rs7297175</td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.028742923</td><td>2503</td><td>191</td><td> 70</td><td>0.3664921</td><td>⋯</td><td>FALSE</td><td>2.421193e-05</td><td>159</td><td> 41</td><td>7460</td><td> 3833</td><td>2.877279e-04</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.041618610</td><td>1598</td><td>191</td><td> 49</td><td>0.2565445</td><td>⋯</td><td>FALSE</td><td>2.421193e-05</td><td>159</td><td> 41</td><td>7460</td><td> 3833</td><td>2.877279e-04</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.017941417</td><td>2826</td><td>191</td><td> 78</td><td>0.4083770</td><td>⋯</td><td>FALSE</td><td>2.421193e-05</td><td>159</td><td> 41</td><td>7460</td><td> 3833</td><td>2.877279e-04</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>CEBPD</td><td>query_1</td><td>TRUE</td><td>0.041618610</td><td>1464</td><td>191</td><td> 46</td><td>0.2408377</td><td>⋯</td><td>FALSE</td><td>7.264122e-05</td><td>133</td><td> 67</td><td>5970</td><td> 5323</td><td>7.423933e-04</td><td>rs1131017</td><td> TRUE</td><td>rs1131017,rs7297175</td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>ELK1 </td><td>query_1</td><td>TRUE</td><td>0.007142316</td><td>4467</td><td>191</td><td>113</td><td>0.5916230</td><td>⋯</td><td>FALSE</td><td>7.289311e-04</td><td> 94</td><td>106</td><td>4030</td><td> 7263</td><td>5.173386e-03</td><td>rs1131017</td><td> TRUE</td><td>rs10876864         </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>FLI1 </td><td>query_1</td><td>TRUE</td><td>0.024172382</td><td>2207</td><td>191</td><td> 64</td><td>0.3350785</td><td>⋯</td><td>FALSE</td><td>9.006657e-03</td><td>131</td><td> 69</td><td>6433</td><td> 4860</td><td>4.002088e-02</td><td>rs1131017</td><td> TRUE</td><td>rs1131017          </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>FLI1 </td><td>query_1</td><td>TRUE</td><td>0.047267440</td><td>3060</td><td>191</td><td> 80</td><td>0.4188482</td><td>⋯</td><td>FALSE</td><td>9.006657e-03</td><td>131</td><td> 69</td><td>6433</td><td> 4860</td><td>4.002088e-02</td><td>rs1131017</td><td> TRUE</td><td>rs1131017          </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>FLI1 </td><td>query_1</td><td>TRUE</td><td>0.041618610</td><td>2325</td><td>191</td><td> 65</td><td>0.3403141</td><td>⋯</td><td>FALSE</td><td>9.006657e-03</td><td>131</td><td> 69</td><td>6433</td><td> 4860</td><td>4.002088e-02</td><td>rs1131017</td><td> TRUE</td><td>rs1131017          </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>HOXA9</td><td>query_1</td><td>TRUE</td><td>0.002470867</td><td>3285</td><td>191</td><td> 95</td><td>0.4973822</td><td>⋯</td><td>FALSE</td><td>1.610430e-05</td><td> 20</td><td>180</td><td> 372</td><td>10921</td><td>2.083458e-04</td><td>rs1131017</td><td>FALSE</td><td>                   </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>HOXA9</td><td>query_1</td><td>TRUE</td><td>0.007142316</td><td> 732</td><td>191</td><td> 31</td><td>0.1623037</td><td>⋯</td><td>FALSE</td><td>1.610430e-05</td><td> 20</td><td>180</td><td> 372</td><td>10921</td><td>2.083458e-04</td><td>rs1131017</td><td>FALSE</td><td>                   </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs1131017_RPS26_positive</td><td>HOXA9</td><td>query_1</td><td>TRUE</td><td>0.004188494</td><td>1124</td><td>191</td><td> 43</td><td>0.2251309</td><td>⋯</td><td>FALSE</td><td>1.610430e-05</td><td> 20</td><td>180</td><td> 372</td><td>10921</td><td>2.083458e-04</td><td>rs1131017</td><td>FALSE</td><td>                   </td></tr>\n",
+       "\t<tr><td>CD4T    </td><td>rs7605824_SH3YL1        </td><td>CEBPD</td><td>query_1</td><td>TRUE</td><td>0.022537569</td><td>3025</td><td> 20</td><td> 16</td><td>0.8000000</td><td>⋯</td><td>FALSE</td><td>2.865087e-03</td><td> 17</td><td>  3</td><td>5970</td><td> 5323</td><td>3.327408e-02</td><td>rs7605824</td><td>FALSE</td><td>                   </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.036312693</td><td>3140</td><td>126</td><td> 65</td><td>0.5158730</td><td>⋯</td><td>FALSE</td><td>4.198463e-03</td><td>106</td><td> 26</td><td>6655</td><td> 2899</td><td>1.968270e-02</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.029926625</td><td>1416</td><td>126</td><td> 37</td><td>0.2936508</td><td>⋯</td><td>FALSE</td><td>4.198463e-03</td><td>106</td><td> 26</td><td>6655</td><td> 2899</td><td>1.968270e-02</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.009638736</td><td>2201</td><td>126</td><td> 55</td><td>0.4365079</td><td>⋯</td><td>FALSE</td><td>4.198463e-03</td><td>106</td><td> 26</td><td>6655</td><td> 2899</td><td>1.968270e-02</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.009638736</td><td>2479</td><td>126</td><td> 59</td><td>0.4682540</td><td>⋯</td><td>FALSE</td><td>4.198463e-03</td><td>106</td><td> 26</td><td>6655</td><td> 2899</td><td>1.968270e-02</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.022933498</td><td>1622</td><td>126</td><td> 42</td><td>0.3333333</td><td>⋯</td><td>FALSE</td><td>4.198463e-03</td><td>106</td><td> 26</td><td>6655</td><td> 2899</td><td>1.968270e-02</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>CEBPD</td><td>query_1</td><td>TRUE</td><td>0.049065326</td><td>2296</td><td>126</td><td> 51</td><td>0.4047619</td><td>⋯</td><td>FALSE</td><td>1.611238e-05</td><td> 97</td><td> 35</td><td>5295</td><td> 4259</td><td>1.960339e-04</td><td>rs1131017</td><td> TRUE</td><td>rs1131017,rs7297175</td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>CEBPD</td><td>query_1</td><td>TRUE</td><td>0.026972058</td><td>1316</td><td>126</td><td> 36</td><td>0.2857143</td><td>⋯</td><td>FALSE</td><td>1.611238e-05</td><td> 97</td><td> 35</td><td>5295</td><td> 4259</td><td>1.960339e-04</td><td>rs1131017</td><td> TRUE</td><td>rs1131017,rs7297175</td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>CEBPD</td><td>query_1</td><td>TRUE</td><td>0.028679873</td><td>2564</td><td>126</td><td> 57</td><td>0.4523810</td><td>⋯</td><td>FALSE</td><td>1.611238e-05</td><td> 97</td><td> 35</td><td>5295</td><td> 4259</td><td>1.960339e-04</td><td>rs1131017</td><td> TRUE</td><td>rs1131017,rs7297175</td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>ELK1 </td><td>query_1</td><td>TRUE</td><td>0.028679873</td><td>4261</td><td>126</td><td> 82</td><td>0.6507937</td><td>⋯</td><td>FALSE</td><td>8.317368e-05</td><td> 71</td><td> 61</td><td>3550</td><td> 6004</td><td>8.019198e-04</td><td>rs1131017</td><td> TRUE</td><td>rs10876864         </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>ELK1 </td><td>query_1</td><td>TRUE</td><td>0.049065326</td><td>3932</td><td>126</td><td> 76</td><td>0.6031746</td><td>⋯</td><td>FALSE</td><td>8.317368e-05</td><td> 71</td><td> 61</td><td>3550</td><td> 6004</td><td>8.019198e-04</td><td>rs1131017</td><td> TRUE</td><td>rs10876864         </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>ELK1 </td><td>query_1</td><td>TRUE</td><td>0.044794472</td><td>2973</td><td>126</td><td> 62</td><td>0.4920635</td><td>⋯</td><td>FALSE</td><td>8.317368e-05</td><td> 71</td><td> 61</td><td>3550</td><td> 6004</td><td>8.019198e-04</td><td>rs1131017</td><td> TRUE</td><td>rs10876864         </td></tr>\n",
+       "\t<tr><td>monocyte</td><td>rs1131017_RPS26         </td><td>FLI1 </td><td>query_1</td><td>TRUE</td><td>0.036045611</td><td>1951</td><td>126</td><td> 46</td><td>0.3650794</td><td>⋯</td><td>FALSE</td><td>4.430257e-04</td><td> 97</td><td> 35</td><td>5648</td><td> 3906</td><td>3.144252e-03</td><td>rs1131017</td><td> TRUE</td><td>rs1131017          </td></tr>\n",
+       "\t<tr><td>NK      </td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.006603932</td><td>1894</td><td> 94</td><td> 48</td><td>0.5106383</td><td>⋯</td><td>FALSE</td><td>1.566936e-03</td><td> 80</td><td> 16</td><td>5050</td><td> 2217</td><td>8.428465e-03</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>NK      </td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.031154971</td><td>1076</td><td> 94</td><td> 30</td><td>0.3191489</td><td>⋯</td><td>FALSE</td><td>1.566936e-03</td><td> 80</td><td> 16</td><td>5050</td><td> 2217</td><td>8.428465e-03</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>NK      </td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.016996993</td><td>1249</td><td> 94</td><td> 34</td><td>0.3617021</td><td>⋯</td><td>FALSE</td><td>1.566936e-03</td><td> 80</td><td> 16</td><td>5050</td><td> 2217</td><td>8.428465e-03</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>NK      </td><td>rs1131017_RPS26         </td><td>CEBPB</td><td>query_1</td><td>TRUE</td><td>0.007729787</td><td>1684</td><td> 94</td><td> 43</td><td>0.4574468</td><td>⋯</td><td>FALSE</td><td>1.566936e-03</td><td> 80</td><td> 16</td><td>5050</td><td> 2217</td><td>8.428465e-03</td><td>rs1131017</td><td> TRUE</td><td>rs7297175          </td></tr>\n",
+       "\t<tr><td>NK      </td><td>rs1131017_RPS26         </td><td>CEBPD</td><td>query_1</td><td>TRUE</td><td>0.033634713</td><td> 981</td><td> 94</td><td> 28</td><td>0.2978723</td><td>⋯</td><td>FALSE</td><td>5.531751e-07</td><td> 78</td><td> 18</td><td>4147</td><td> 3120</td><td>8.833515e-06</td><td>rs1131017</td><td> TRUE</td><td>rs1131017,rs7297175</td></tr>\n",
+       "\t<tr><td>NK      </td><td>rs1131017_RPS26         </td><td>CEBPD</td><td>query_1</td><td>TRUE</td><td>0.030378248</td><td>1957</td><td> 94</td><td> 45</td><td>0.4787234</td><td>⋯</td><td>FALSE</td><td>5.531751e-07</td><td> 78</td><td> 18</td><td>4147</td><td> 3120</td><td>8.833515e-06</td><td>rs1131017</td><td> TRUE</td><td>rs1131017,rs7297175</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 31 × 26\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " TRANSFAC\\_cell\\_type & TRANSFAC\\_snp\\_eGene & TRANSFAC\\_tf & TRANSFAC\\_query & TRANSFAC\\_significant & TRANSFAC\\_p\\_value & TRANSFAC\\_term\\_size & TRANSFAC\\_query\\_size & TRANSFAC\\_intersection\\_size & TRANSFAC\\_precision & ⋯ & ReMapTF.is.a.co.eGene. & ReMapenrichment.p.value & ReMapX..TF.overlap...co.eGene & ReMapX..TF.overlap...background & ReMapX..no.TF.overlap...co.eGene & ReMapX..background.gene...not.co.eGene & ReMapenrichment.fdr & ReMapeQTL.SNP & ReMapSNP.overlaps.TF. & ReMapNames.of.overlapping.SNPs\\\\\n",
+       " <chr> & <chr> & <chr> & <chr> & <lgl> & <dbl> & <int> & <int> & <int> & <dbl> & ⋯ & <lgl> & <dbl> & <int> & <int> & <int> & <int> & <dbl> & <chr> & <lgl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t B        & rs1131017\\_RPS26          & CEBPD & query\\_1 & TRUE & 0.028703406 &  501 &  35 &  23 & 0.6571429 & ⋯ & FALSE & 3.034184e-06 &  34 &   1 & 1096 &   632 & 1.107477e-04 & rs1131017 &  TRUE & rs1131017,rs7297175\\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & CEBPB & query\\_1 & TRUE & 0.028742923 & 2503 & 191 &  70 & 0.3664921 & ⋯ & FALSE & 2.421193e-05 & 159 &  41 & 7460 &  3833 & 2.877279e-04 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & CEBPB & query\\_1 & TRUE & 0.041618610 & 1598 & 191 &  49 & 0.2565445 & ⋯ & FALSE & 2.421193e-05 & 159 &  41 & 7460 &  3833 & 2.877279e-04 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & CEBPB & query\\_1 & TRUE & 0.017941417 & 2826 & 191 &  78 & 0.4083770 & ⋯ & FALSE & 2.421193e-05 & 159 &  41 & 7460 &  3833 & 2.877279e-04 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & CEBPD & query\\_1 & TRUE & 0.041618610 & 1464 & 191 &  46 & 0.2408377 & ⋯ & FALSE & 7.264122e-05 & 133 &  67 & 5970 &  5323 & 7.423933e-04 & rs1131017 &  TRUE & rs1131017,rs7297175\\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & ELK1  & query\\_1 & TRUE & 0.007142316 & 4467 & 191 & 113 & 0.5916230 & ⋯ & FALSE & 7.289311e-04 &  94 & 106 & 4030 &  7263 & 5.173386e-03 & rs1131017 &  TRUE & rs10876864         \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & FLI1  & query\\_1 & TRUE & 0.024172382 & 2207 & 191 &  64 & 0.3350785 & ⋯ & FALSE & 9.006657e-03 & 131 &  69 & 6433 &  4860 & 4.002088e-02 & rs1131017 &  TRUE & rs1131017          \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & FLI1  & query\\_1 & TRUE & 0.047267440 & 3060 & 191 &  80 & 0.4188482 & ⋯ & FALSE & 9.006657e-03 & 131 &  69 & 6433 &  4860 & 4.002088e-02 & rs1131017 &  TRUE & rs1131017          \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & FLI1  & query\\_1 & TRUE & 0.041618610 & 2325 & 191 &  65 & 0.3403141 & ⋯ & FALSE & 9.006657e-03 & 131 &  69 & 6433 &  4860 & 4.002088e-02 & rs1131017 &  TRUE & rs1131017          \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & HOXA9 & query\\_1 & TRUE & 0.002470867 & 3285 & 191 &  95 & 0.4973822 & ⋯ & FALSE & 1.610430e-05 &  20 & 180 &  372 & 10921 & 2.083458e-04 & rs1131017 & FALSE &                    \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & HOXA9 & query\\_1 & TRUE & 0.007142316 &  732 & 191 &  31 & 0.1623037 & ⋯ & FALSE & 1.610430e-05 &  20 & 180 &  372 & 10921 & 2.083458e-04 & rs1131017 & FALSE &                    \\\\\n",
+       "\t CD4T     & rs1131017\\_RPS26\\_positive & HOXA9 & query\\_1 & TRUE & 0.004188494 & 1124 & 191 &  43 & 0.2251309 & ⋯ & FALSE & 1.610430e-05 &  20 & 180 &  372 & 10921 & 2.083458e-04 & rs1131017 & FALSE &                    \\\\\n",
+       "\t CD4T     & rs7605824\\_SH3YL1         & CEBPD & query\\_1 & TRUE & 0.022537569 & 3025 &  20 &  16 & 0.8000000 & ⋯ & FALSE & 2.865087e-03 &  17 &   3 & 5970 &  5323 & 3.327408e-02 & rs7605824 & FALSE &                    \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.036312693 & 3140 & 126 &  65 & 0.5158730 & ⋯ & FALSE & 4.198463e-03 & 106 &  26 & 6655 &  2899 & 1.968270e-02 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.029926625 & 1416 & 126 &  37 & 0.2936508 & ⋯ & FALSE & 4.198463e-03 & 106 &  26 & 6655 &  2899 & 1.968270e-02 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.009638736 & 2201 & 126 &  55 & 0.4365079 & ⋯ & FALSE & 4.198463e-03 & 106 &  26 & 6655 &  2899 & 1.968270e-02 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.009638736 & 2479 & 126 &  59 & 0.4682540 & ⋯ & FALSE & 4.198463e-03 & 106 &  26 & 6655 &  2899 & 1.968270e-02 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.022933498 & 1622 & 126 &  42 & 0.3333333 & ⋯ & FALSE & 4.198463e-03 & 106 &  26 & 6655 &  2899 & 1.968270e-02 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & CEBPD & query\\_1 & TRUE & 0.049065326 & 2296 & 126 &  51 & 0.4047619 & ⋯ & FALSE & 1.611238e-05 &  97 &  35 & 5295 &  4259 & 1.960339e-04 & rs1131017 &  TRUE & rs1131017,rs7297175\\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & CEBPD & query\\_1 & TRUE & 0.026972058 & 1316 & 126 &  36 & 0.2857143 & ⋯ & FALSE & 1.611238e-05 &  97 &  35 & 5295 &  4259 & 1.960339e-04 & rs1131017 &  TRUE & rs1131017,rs7297175\\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & CEBPD & query\\_1 & TRUE & 0.028679873 & 2564 & 126 &  57 & 0.4523810 & ⋯ & FALSE & 1.611238e-05 &  97 &  35 & 5295 &  4259 & 1.960339e-04 & rs1131017 &  TRUE & rs1131017,rs7297175\\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & ELK1  & query\\_1 & TRUE & 0.028679873 & 4261 & 126 &  82 & 0.6507937 & ⋯ & FALSE & 8.317368e-05 &  71 &  61 & 3550 &  6004 & 8.019198e-04 & rs1131017 &  TRUE & rs10876864         \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & ELK1  & query\\_1 & TRUE & 0.049065326 & 3932 & 126 &  76 & 0.6031746 & ⋯ & FALSE & 8.317368e-05 &  71 &  61 & 3550 &  6004 & 8.019198e-04 & rs1131017 &  TRUE & rs10876864         \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & ELK1  & query\\_1 & TRUE & 0.044794472 & 2973 & 126 &  62 & 0.4920635 & ⋯ & FALSE & 8.317368e-05 &  71 &  61 & 3550 &  6004 & 8.019198e-04 & rs1131017 &  TRUE & rs10876864         \\\\\n",
+       "\t monocyte & rs1131017\\_RPS26          & FLI1  & query\\_1 & TRUE & 0.036045611 & 1951 & 126 &  46 & 0.3650794 & ⋯ & FALSE & 4.430257e-04 &  97 &  35 & 5648 &  3906 & 3.144252e-03 & rs1131017 &  TRUE & rs1131017          \\\\\n",
+       "\t NK       & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.006603932 & 1894 &  94 &  48 & 0.5106383 & ⋯ & FALSE & 1.566936e-03 &  80 &  16 & 5050 &  2217 & 8.428465e-03 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t NK       & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.031154971 & 1076 &  94 &  30 & 0.3191489 & ⋯ & FALSE & 1.566936e-03 &  80 &  16 & 5050 &  2217 & 8.428465e-03 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t NK       & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.016996993 & 1249 &  94 &  34 & 0.3617021 & ⋯ & FALSE & 1.566936e-03 &  80 &  16 & 5050 &  2217 & 8.428465e-03 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t NK       & rs1131017\\_RPS26          & CEBPB & query\\_1 & TRUE & 0.007729787 & 1684 &  94 &  43 & 0.4574468 & ⋯ & FALSE & 1.566936e-03 &  80 &  16 & 5050 &  2217 & 8.428465e-03 & rs1131017 &  TRUE & rs7297175          \\\\\n",
+       "\t NK       & rs1131017\\_RPS26          & CEBPD & query\\_1 & TRUE & 0.033634713 &  981 &  94 &  28 & 0.2978723 & ⋯ & FALSE & 5.531751e-07 &  78 &  18 & 4147 &  3120 & 8.833515e-06 & rs1131017 &  TRUE & rs1131017,rs7297175\\\\\n",
+       "\t NK       & rs1131017\\_RPS26          & CEBPD & query\\_1 & TRUE & 0.030378248 & 1957 &  94 &  45 & 0.4787234 & ⋯ & FALSE & 5.531751e-07 &  78 &  18 & 4147 &  3120 & 8.833515e-06 & rs1131017 &  TRUE & rs1131017,rs7297175\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 31 × 26\n",
+       "\n",
+       "| TRANSFAC_cell_type &lt;chr&gt; | TRANSFAC_snp_eGene &lt;chr&gt; | TRANSFAC_tf &lt;chr&gt; | TRANSFAC_query &lt;chr&gt; | TRANSFAC_significant &lt;lgl&gt; | TRANSFAC_p_value &lt;dbl&gt; | TRANSFAC_term_size &lt;int&gt; | TRANSFAC_query_size &lt;int&gt; | TRANSFAC_intersection_size &lt;int&gt; | TRANSFAC_precision &lt;dbl&gt; | ⋯ ⋯ | ReMapTF.is.a.co.eGene. &lt;lgl&gt; | ReMapenrichment.p.value &lt;dbl&gt; | ReMapX..TF.overlap...co.eGene &lt;int&gt; | ReMapX..TF.overlap...background &lt;int&gt; | ReMapX..no.TF.overlap...co.eGene &lt;int&gt; | ReMapX..background.gene...not.co.eGene &lt;int&gt; | ReMapenrichment.fdr &lt;dbl&gt; | ReMapeQTL.SNP &lt;chr&gt; | ReMapSNP.overlaps.TF. &lt;lgl&gt; | ReMapNames.of.overlapping.SNPs &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| B        | rs1131017_RPS26          | CEBPD | query_1 | TRUE | 0.028703406 |  501 |  35 |  23 | 0.6571429 | ⋯ | FALSE | 3.034184e-06 |  34 |   1 | 1096 |   632 | 1.107477e-04 | rs1131017 |  TRUE | rs1131017,rs7297175 |\n",
+       "| CD4T     | rs1131017_RPS26_positive | CEBPB | query_1 | TRUE | 0.028742923 | 2503 | 191 |  70 | 0.3664921 | ⋯ | FALSE | 2.421193e-05 | 159 |  41 | 7460 |  3833 | 2.877279e-04 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| CD4T     | rs1131017_RPS26_positive | CEBPB | query_1 | TRUE | 0.041618610 | 1598 | 191 |  49 | 0.2565445 | ⋯ | FALSE | 2.421193e-05 | 159 |  41 | 7460 |  3833 | 2.877279e-04 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| CD4T     | rs1131017_RPS26_positive | CEBPB | query_1 | TRUE | 0.017941417 | 2826 | 191 |  78 | 0.4083770 | ⋯ | FALSE | 2.421193e-05 | 159 |  41 | 7460 |  3833 | 2.877279e-04 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| CD4T     | rs1131017_RPS26_positive | CEBPD | query_1 | TRUE | 0.041618610 | 1464 | 191 |  46 | 0.2408377 | ⋯ | FALSE | 7.264122e-05 | 133 |  67 | 5970 |  5323 | 7.423933e-04 | rs1131017 |  TRUE | rs1131017,rs7297175 |\n",
+       "| CD4T     | rs1131017_RPS26_positive | ELK1  | query_1 | TRUE | 0.007142316 | 4467 | 191 | 113 | 0.5916230 | ⋯ | FALSE | 7.289311e-04 |  94 | 106 | 4030 |  7263 | 5.173386e-03 | rs1131017 |  TRUE | rs10876864          |\n",
+       "| CD4T     | rs1131017_RPS26_positive | FLI1  | query_1 | TRUE | 0.024172382 | 2207 | 191 |  64 | 0.3350785 | ⋯ | FALSE | 9.006657e-03 | 131 |  69 | 6433 |  4860 | 4.002088e-02 | rs1131017 |  TRUE | rs1131017           |\n",
+       "| CD4T     | rs1131017_RPS26_positive | FLI1  | query_1 | TRUE | 0.047267440 | 3060 | 191 |  80 | 0.4188482 | ⋯ | FALSE | 9.006657e-03 | 131 |  69 | 6433 |  4860 | 4.002088e-02 | rs1131017 |  TRUE | rs1131017           |\n",
+       "| CD4T     | rs1131017_RPS26_positive | FLI1  | query_1 | TRUE | 0.041618610 | 2325 | 191 |  65 | 0.3403141 | ⋯ | FALSE | 9.006657e-03 | 131 |  69 | 6433 |  4860 | 4.002088e-02 | rs1131017 |  TRUE | rs1131017           |\n",
+       "| CD4T     | rs1131017_RPS26_positive | HOXA9 | query_1 | TRUE | 0.002470867 | 3285 | 191 |  95 | 0.4973822 | ⋯ | FALSE | 1.610430e-05 |  20 | 180 |  372 | 10921 | 2.083458e-04 | rs1131017 | FALSE | <!----> |\n",
+       "| CD4T     | rs1131017_RPS26_positive | HOXA9 | query_1 | TRUE | 0.007142316 |  732 | 191 |  31 | 0.1623037 | ⋯ | FALSE | 1.610430e-05 |  20 | 180 |  372 | 10921 | 2.083458e-04 | rs1131017 | FALSE | <!----> |\n",
+       "| CD4T     | rs1131017_RPS26_positive | HOXA9 | query_1 | TRUE | 0.004188494 | 1124 | 191 |  43 | 0.2251309 | ⋯ | FALSE | 1.610430e-05 |  20 | 180 |  372 | 10921 | 2.083458e-04 | rs1131017 | FALSE | <!----> |\n",
+       "| CD4T     | rs7605824_SH3YL1         | CEBPD | query_1 | TRUE | 0.022537569 | 3025 |  20 |  16 | 0.8000000 | ⋯ | FALSE | 2.865087e-03 |  17 |   3 | 5970 |  5323 | 3.327408e-02 | rs7605824 | FALSE | <!----> |\n",
+       "| monocyte | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.036312693 | 3140 | 126 |  65 | 0.5158730 | ⋯ | FALSE | 4.198463e-03 | 106 |  26 | 6655 |  2899 | 1.968270e-02 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| monocyte | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.029926625 | 1416 | 126 |  37 | 0.2936508 | ⋯ | FALSE | 4.198463e-03 | 106 |  26 | 6655 |  2899 | 1.968270e-02 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| monocyte | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.009638736 | 2201 | 126 |  55 | 0.4365079 | ⋯ | FALSE | 4.198463e-03 | 106 |  26 | 6655 |  2899 | 1.968270e-02 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| monocyte | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.009638736 | 2479 | 126 |  59 | 0.4682540 | ⋯ | FALSE | 4.198463e-03 | 106 |  26 | 6655 |  2899 | 1.968270e-02 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| monocyte | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.022933498 | 1622 | 126 |  42 | 0.3333333 | ⋯ | FALSE | 4.198463e-03 | 106 |  26 | 6655 |  2899 | 1.968270e-02 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| monocyte | rs1131017_RPS26          | CEBPD | query_1 | TRUE | 0.049065326 | 2296 | 126 |  51 | 0.4047619 | ⋯ | FALSE | 1.611238e-05 |  97 |  35 | 5295 |  4259 | 1.960339e-04 | rs1131017 |  TRUE | rs1131017,rs7297175 |\n",
+       "| monocyte | rs1131017_RPS26          | CEBPD | query_1 | TRUE | 0.026972058 | 1316 | 126 |  36 | 0.2857143 | ⋯ | FALSE | 1.611238e-05 |  97 |  35 | 5295 |  4259 | 1.960339e-04 | rs1131017 |  TRUE | rs1131017,rs7297175 |\n",
+       "| monocyte | rs1131017_RPS26          | CEBPD | query_1 | TRUE | 0.028679873 | 2564 | 126 |  57 | 0.4523810 | ⋯ | FALSE | 1.611238e-05 |  97 |  35 | 5295 |  4259 | 1.960339e-04 | rs1131017 |  TRUE | rs1131017,rs7297175 |\n",
+       "| monocyte | rs1131017_RPS26          | ELK1  | query_1 | TRUE | 0.028679873 | 4261 | 126 |  82 | 0.6507937 | ⋯ | FALSE | 8.317368e-05 |  71 |  61 | 3550 |  6004 | 8.019198e-04 | rs1131017 |  TRUE | rs10876864          |\n",
+       "| monocyte | rs1131017_RPS26          | ELK1  | query_1 | TRUE | 0.049065326 | 3932 | 126 |  76 | 0.6031746 | ⋯ | FALSE | 8.317368e-05 |  71 |  61 | 3550 |  6004 | 8.019198e-04 | rs1131017 |  TRUE | rs10876864          |\n",
+       "| monocyte | rs1131017_RPS26          | ELK1  | query_1 | TRUE | 0.044794472 | 2973 | 126 |  62 | 0.4920635 | ⋯ | FALSE | 8.317368e-05 |  71 |  61 | 3550 |  6004 | 8.019198e-04 | rs1131017 |  TRUE | rs10876864          |\n",
+       "| monocyte | rs1131017_RPS26          | FLI1  | query_1 | TRUE | 0.036045611 | 1951 | 126 |  46 | 0.3650794 | ⋯ | FALSE | 4.430257e-04 |  97 |  35 | 5648 |  3906 | 3.144252e-03 | rs1131017 |  TRUE | rs1131017           |\n",
+       "| NK       | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.006603932 | 1894 |  94 |  48 | 0.5106383 | ⋯ | FALSE | 1.566936e-03 |  80 |  16 | 5050 |  2217 | 8.428465e-03 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| NK       | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.031154971 | 1076 |  94 |  30 | 0.3191489 | ⋯ | FALSE | 1.566936e-03 |  80 |  16 | 5050 |  2217 | 8.428465e-03 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| NK       | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.016996993 | 1249 |  94 |  34 | 0.3617021 | ⋯ | FALSE | 1.566936e-03 |  80 |  16 | 5050 |  2217 | 8.428465e-03 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| NK       | rs1131017_RPS26          | CEBPB | query_1 | TRUE | 0.007729787 | 1684 |  94 |  43 | 0.4574468 | ⋯ | FALSE | 1.566936e-03 |  80 |  16 | 5050 |  2217 | 8.428465e-03 | rs1131017 |  TRUE | rs7297175           |\n",
+       "| NK       | rs1131017_RPS26          | CEBPD | query_1 | TRUE | 0.033634713 |  981 |  94 |  28 | 0.2978723 | ⋯ | FALSE | 5.531751e-07 |  78 |  18 | 4147 |  3120 | 8.833515e-06 | rs1131017 |  TRUE | rs1131017,rs7297175 |\n",
+       "| NK       | rs1131017_RPS26          | CEBPD | query_1 | TRUE | 0.030378248 | 1957 |  94 |  45 | 0.4787234 | ⋯ | FALSE | 5.531751e-07 |  78 |  18 | 4147 |  3120 | 8.833515e-06 | rs1131017 |  TRUE | rs1131017,rs7297175 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   TRANSFAC_cell_type TRANSFAC_snp_eGene       TRANSFAC_tf TRANSFAC_query\n",
+       "1  B                  rs1131017_RPS26          CEBPD       query_1       \n",
+       "2  CD4T               rs1131017_RPS26_positive CEBPB       query_1       \n",
+       "3  CD4T               rs1131017_RPS26_positive CEBPB       query_1       \n",
+       "4  CD4T               rs1131017_RPS26_positive CEBPB       query_1       \n",
+       "5  CD4T               rs1131017_RPS26_positive CEBPD       query_1       \n",
+       "6  CD4T               rs1131017_RPS26_positive ELK1        query_1       \n",
+       "7  CD4T               rs1131017_RPS26_positive FLI1        query_1       \n",
+       "8  CD4T               rs1131017_RPS26_positive FLI1        query_1       \n",
+       "9  CD4T               rs1131017_RPS26_positive FLI1        query_1       \n",
+       "10 CD4T               rs1131017_RPS26_positive HOXA9       query_1       \n",
+       "11 CD4T               rs1131017_RPS26_positive HOXA9       query_1       \n",
+       "12 CD4T               rs1131017_RPS26_positive HOXA9       query_1       \n",
+       "13 CD4T               rs7605824_SH3YL1         CEBPD       query_1       \n",
+       "14 monocyte           rs1131017_RPS26          CEBPB       query_1       \n",
+       "15 monocyte           rs1131017_RPS26          CEBPB       query_1       \n",
+       "16 monocyte           rs1131017_RPS26          CEBPB       query_1       \n",
+       "17 monocyte           rs1131017_RPS26          CEBPB       query_1       \n",
+       "18 monocyte           rs1131017_RPS26          CEBPB       query_1       \n",
+       "19 monocyte           rs1131017_RPS26          CEBPD       query_1       \n",
+       "20 monocyte           rs1131017_RPS26          CEBPD       query_1       \n",
+       "21 monocyte           rs1131017_RPS26          CEBPD       query_1       \n",
+       "22 monocyte           rs1131017_RPS26          ELK1        query_1       \n",
+       "23 monocyte           rs1131017_RPS26          ELK1        query_1       \n",
+       "24 monocyte           rs1131017_RPS26          ELK1        query_1       \n",
+       "25 monocyte           rs1131017_RPS26          FLI1        query_1       \n",
+       "26 NK                 rs1131017_RPS26          CEBPB       query_1       \n",
+       "27 NK                 rs1131017_RPS26          CEBPB       query_1       \n",
+       "28 NK                 rs1131017_RPS26          CEBPB       query_1       \n",
+       "29 NK                 rs1131017_RPS26          CEBPB       query_1       \n",
+       "30 NK                 rs1131017_RPS26          CEBPD       query_1       \n",
+       "31 NK                 rs1131017_RPS26          CEBPD       query_1       \n",
+       "   TRANSFAC_significant TRANSFAC_p_value TRANSFAC_term_size TRANSFAC_query_size\n",
+       "1  TRUE                 0.028703406       501                35                \n",
+       "2  TRUE                 0.028742923      2503               191                \n",
+       "3  TRUE                 0.041618610      1598               191                \n",
+       "4  TRUE                 0.017941417      2826               191                \n",
+       "5  TRUE                 0.041618610      1464               191                \n",
+       "6  TRUE                 0.007142316      4467               191                \n",
+       "7  TRUE                 0.024172382      2207               191                \n",
+       "8  TRUE                 0.047267440      3060               191                \n",
+       "9  TRUE                 0.041618610      2325               191                \n",
+       "10 TRUE                 0.002470867      3285               191                \n",
+       "11 TRUE                 0.007142316       732               191                \n",
+       "12 TRUE                 0.004188494      1124               191                \n",
+       "13 TRUE                 0.022537569      3025                20                \n",
+       "14 TRUE                 0.036312693      3140               126                \n",
+       "15 TRUE                 0.029926625      1416               126                \n",
+       "16 TRUE                 0.009638736      2201               126                \n",
+       "17 TRUE                 0.009638736      2479               126                \n",
+       "18 TRUE                 0.022933498      1622               126                \n",
+       "19 TRUE                 0.049065326      2296               126                \n",
+       "20 TRUE                 0.026972058      1316               126                \n",
+       "21 TRUE                 0.028679873      2564               126                \n",
+       "22 TRUE                 0.028679873      4261               126                \n",
+       "23 TRUE                 0.049065326      3932               126                \n",
+       "24 TRUE                 0.044794472      2973               126                \n",
+       "25 TRUE                 0.036045611      1951               126                \n",
+       "26 TRUE                 0.006603932      1894                94                \n",
+       "27 TRUE                 0.031154971      1076                94                \n",
+       "28 TRUE                 0.016996993      1249                94                \n",
+       "29 TRUE                 0.007729787      1684                94                \n",
+       "30 TRUE                 0.033634713       981                94                \n",
+       "31 TRUE                 0.030378248      1957                94                \n",
+       "   TRANSFAC_intersection_size TRANSFAC_precision ⋯ ReMapTF.is.a.co.eGene.\n",
+       "1   23                        0.6571429          ⋯ FALSE                 \n",
+       "2   70                        0.3664921          ⋯ FALSE                 \n",
+       "3   49                        0.2565445          ⋯ FALSE                 \n",
+       "4   78                        0.4083770          ⋯ FALSE                 \n",
+       "5   46                        0.2408377          ⋯ FALSE                 \n",
+       "6  113                        0.5916230          ⋯ FALSE                 \n",
+       "7   64                        0.3350785          ⋯ FALSE                 \n",
+       "8   80                        0.4188482          ⋯ FALSE                 \n",
+       "9   65                        0.3403141          ⋯ FALSE                 \n",
+       "10  95                        0.4973822          ⋯ FALSE                 \n",
+       "11  31                        0.1623037          ⋯ FALSE                 \n",
+       "12  43                        0.2251309          ⋯ FALSE                 \n",
+       "13  16                        0.8000000          ⋯ FALSE                 \n",
+       "14  65                        0.5158730          ⋯ FALSE                 \n",
+       "15  37                        0.2936508          ⋯ FALSE                 \n",
+       "16  55                        0.4365079          ⋯ FALSE                 \n",
+       "17  59                        0.4682540          ⋯ FALSE                 \n",
+       "18  42                        0.3333333          ⋯ FALSE                 \n",
+       "19  51                        0.4047619          ⋯ FALSE                 \n",
+       "20  36                        0.2857143          ⋯ FALSE                 \n",
+       "21  57                        0.4523810          ⋯ FALSE                 \n",
+       "22  82                        0.6507937          ⋯ FALSE                 \n",
+       "23  76                        0.6031746          ⋯ FALSE                 \n",
+       "24  62                        0.4920635          ⋯ FALSE                 \n",
+       "25  46                        0.3650794          ⋯ FALSE                 \n",
+       "26  48                        0.5106383          ⋯ FALSE                 \n",
+       "27  30                        0.3191489          ⋯ FALSE                 \n",
+       "28  34                        0.3617021          ⋯ FALSE                 \n",
+       "29  43                        0.4574468          ⋯ FALSE                 \n",
+       "30  28                        0.2978723          ⋯ FALSE                 \n",
+       "31  45                        0.4787234          ⋯ FALSE                 \n",
+       "   ReMapenrichment.p.value ReMapX..TF.overlap...co.eGene\n",
+       "1  3.034184e-06             34                          \n",
+       "2  2.421193e-05            159                          \n",
+       "3  2.421193e-05            159                          \n",
+       "4  2.421193e-05            159                          \n",
+       "5  7.264122e-05            133                          \n",
+       "6  7.289311e-04             94                          \n",
+       "7  9.006657e-03            131                          \n",
+       "8  9.006657e-03            131                          \n",
+       "9  9.006657e-03            131                          \n",
+       "10 1.610430e-05             20                          \n",
+       "11 1.610430e-05             20                          \n",
+       "12 1.610430e-05             20                          \n",
+       "13 2.865087e-03             17                          \n",
+       "14 4.198463e-03            106                          \n",
+       "15 4.198463e-03            106                          \n",
+       "16 4.198463e-03            106                          \n",
+       "17 4.198463e-03            106                          \n",
+       "18 4.198463e-03            106                          \n",
+       "19 1.611238e-05             97                          \n",
+       "20 1.611238e-05             97                          \n",
+       "21 1.611238e-05             97                          \n",
+       "22 8.317368e-05             71                          \n",
+       "23 8.317368e-05             71                          \n",
+       "24 8.317368e-05             71                          \n",
+       "25 4.430257e-04             97                          \n",
+       "26 1.566936e-03             80                          \n",
+       "27 1.566936e-03             80                          \n",
+       "28 1.566936e-03             80                          \n",
+       "29 1.566936e-03             80                          \n",
+       "30 5.531751e-07             78                          \n",
+       "31 5.531751e-07             78                          \n",
+       "   ReMapX..TF.overlap...background ReMapX..no.TF.overlap...co.eGene\n",
+       "1    1                             1096                            \n",
+       "2   41                             7460                            \n",
+       "3   41                             7460                            \n",
+       "4   41                             7460                            \n",
+       "5   67                             5970                            \n",
+       "6  106                             4030                            \n",
+       "7   69                             6433                            \n",
+       "8   69                             6433                            \n",
+       "9   69                             6433                            \n",
+       "10 180                              372                            \n",
+       "11 180                              372                            \n",
+       "12 180                              372                            \n",
+       "13   3                             5970                            \n",
+       "14  26                             6655                            \n",
+       "15  26                             6655                            \n",
+       "16  26                             6655                            \n",
+       "17  26                             6655                            \n",
+       "18  26                             6655                            \n",
+       "19  35                             5295                            \n",
+       "20  35                             5295                            \n",
+       "21  35                             5295                            \n",
+       "22  61                             3550                            \n",
+       "23  61                             3550                            \n",
+       "24  61                             3550                            \n",
+       "25  35                             5648                            \n",
+       "26  16                             5050                            \n",
+       "27  16                             5050                            \n",
+       "28  16                             5050                            \n",
+       "29  16                             5050                            \n",
+       "30  18                             4147                            \n",
+       "31  18                             4147                            \n",
+       "   ReMapX..background.gene...not.co.eGene ReMapenrichment.fdr ReMapeQTL.SNP\n",
+       "1    632                                  1.107477e-04        rs1131017    \n",
+       "2   3833                                  2.877279e-04        rs1131017    \n",
+       "3   3833                                  2.877279e-04        rs1131017    \n",
+       "4   3833                                  2.877279e-04        rs1131017    \n",
+       "5   5323                                  7.423933e-04        rs1131017    \n",
+       "6   7263                                  5.173386e-03        rs1131017    \n",
+       "7   4860                                  4.002088e-02        rs1131017    \n",
+       "8   4860                                  4.002088e-02        rs1131017    \n",
+       "9   4860                                  4.002088e-02        rs1131017    \n",
+       "10 10921                                  2.083458e-04        rs1131017    \n",
+       "11 10921                                  2.083458e-04        rs1131017    \n",
+       "12 10921                                  2.083458e-04        rs1131017    \n",
+       "13  5323                                  3.327408e-02        rs7605824    \n",
+       "14  2899                                  1.968270e-02        rs1131017    \n",
+       "15  2899                                  1.968270e-02        rs1131017    \n",
+       "16  2899                                  1.968270e-02        rs1131017    \n",
+       "17  2899                                  1.968270e-02        rs1131017    \n",
+       "18  2899                                  1.968270e-02        rs1131017    \n",
+       "19  4259                                  1.960339e-04        rs1131017    \n",
+       "20  4259                                  1.960339e-04        rs1131017    \n",
+       "21  4259                                  1.960339e-04        rs1131017    \n",
+       "22  6004                                  8.019198e-04        rs1131017    \n",
+       "23  6004                                  8.019198e-04        rs1131017    \n",
+       "24  6004                                  8.019198e-04        rs1131017    \n",
+       "25  3906                                  3.144252e-03        rs1131017    \n",
+       "26  2217                                  8.428465e-03        rs1131017    \n",
+       "27  2217                                  8.428465e-03        rs1131017    \n",
+       "28  2217                                  8.428465e-03        rs1131017    \n",
+       "29  2217                                  8.428465e-03        rs1131017    \n",
+       "30  3120                                  8.833515e-06        rs1131017    \n",
+       "31  3120                                  8.833515e-06        rs1131017    \n",
+       "   ReMapSNP.overlaps.TF. ReMapNames.of.overlapping.SNPs\n",
+       "1   TRUE                 rs1131017,rs7297175           \n",
+       "2   TRUE                 rs7297175                     \n",
+       "3   TRUE                 rs7297175                     \n",
+       "4   TRUE                 rs7297175                     \n",
+       "5   TRUE                 rs1131017,rs7297175           \n",
+       "6   TRUE                 rs10876864                    \n",
+       "7   TRUE                 rs1131017                     \n",
+       "8   TRUE                 rs1131017                     \n",
+       "9   TRUE                 rs1131017                     \n",
+       "10 FALSE                                               \n",
+       "11 FALSE                                               \n",
+       "12 FALSE                                               \n",
+       "13 FALSE                                               \n",
+       "14  TRUE                 rs7297175                     \n",
+       "15  TRUE                 rs7297175                     \n",
+       "16  TRUE                 rs7297175                     \n",
+       "17  TRUE                 rs7297175                     \n",
+       "18  TRUE                 rs7297175                     \n",
+       "19  TRUE                 rs1131017,rs7297175           \n",
+       "20  TRUE                 rs1131017,rs7297175           \n",
+       "21  TRUE                 rs1131017,rs7297175           \n",
+       "22  TRUE                 rs10876864                    \n",
+       "23  TRUE                 rs10876864                    \n",
+       "24  TRUE                 rs10876864                    \n",
+       "25  TRUE                 rs1131017                     \n",
+       "26  TRUE                 rs7297175                     \n",
+       "27  TRUE                 rs7297175                     \n",
+       "28  TRUE                 rs7297175                     \n",
+       "29  TRUE                 rs7297175                     \n",
+       "30  TRUE                 rs1131017,rs7297175           \n",
+       "31  TRUE                 rs1131017,rs7297175           "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "combined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "id": "7ad1c204-f2d5-40ff-8f73-02871ed2a498",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.csv(combined, paste0(path, \"transfac_results/TRANSFAC_ReMap_matches.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23af7863-6d69-4dd0-be60-8689305884dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "4.1.1"
+  },
+  "toc-autonumbering": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/05_coeqtl_interpretation/R2_Coloc.ipynb b/05_coeqtl_interpretation/R2_Coloc.ipynb
new file mode 100644
index 0000000..e0825b2
--- /dev/null
+++ b/05_coeqtl_interpretation/R2_Coloc.ipynb
@@ -0,0 +1,76277 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3cfdb633-0fe4-4885-aecc-4ee2fc1a2fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Run colocalization analysis on output of eqtl and co-eqtl pipeline \n",
+    "### As GWAS input data we use the data from \n",
+    "\n",
+    "### 1) The GTEX Resource: https://zenodo.org/record/3629742#.Y1uTM3ZByUm (contains 114 publicly available GWAS traits, harmonized and imputed to GTEx v8 reference)\n",
+    "\n",
+    "### 2) Additional GWAS input data to test for T1D from: \n",
+    "### Chiou J, Geusz RJ, Okino M, Han JY, Miller M, Melton R, Beebe E,\n",
+    "### Benaglio P, Huang S, Korgaonkar K, Heller S, Kleger A, Preissl S, Gorkin DU,\n",
+    "### Sander M, Gaulton KJ. Cell type-specific genetic risk mechanisms of type 1\n",
+    "### diabetes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc324fd4-05dc-460b-850e-e1143db23937",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "# Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e6756d6-e249-4d48-8ae3-b91ab6376a30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Load the necessary libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4bce754a-b96b-4c84-9cf5-60e9a54b707e",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Attaching package: ‘dplyr’\n",
+      "\n",
+      "\n",
+      "The following objects are masked from ‘package:stats’:\n",
+      "\n",
+      "    filter, lag\n",
+      "\n",
+      "\n",
+      "The following objects are masked from ‘package:base’:\n",
+      "\n",
+      "    intersect, setdiff, setequal, union\n",
+      "\n",
+      "\n",
+      "\n",
+      "Attaching package: ‘data.table’\n",
+      "\n",
+      "\n",
+      "The following objects are masked from ‘package:dplyr’:\n",
+      "\n",
+      "    between, first, last\n",
+      "\n",
+      "\n",
+      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.1 ──\n",
+      "\n",
+      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.6     \u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.2\n",
+      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.7     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
+      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.0     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n",
+      "\n",
+      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdata.table\u001b[39m::\u001b[32mbetween()\u001b[39m masks \u001b[34mdplyr\u001b[39m::between()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m       masks \u001b[34mstats\u001b[39m::filter()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdata.table\u001b[39m::\u001b[32mfirst()\u001b[39m   masks \u001b[34mdplyr\u001b[39m::first()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m          masks \u001b[34mstats\u001b[39m::lag()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdata.table\u001b[39m::\u001b[32mlast()\u001b[39m    masks \u001b[34mdplyr\u001b[39m::last()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mpurrr\u001b[39m::\u001b[32mtranspose()\u001b[39m    masks \u001b[34mdata.table\u001b[39m::transpose()\n",
+      "\n",
+      "\n",
+      "Attaching package: ‘reshape2’\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:tidyr’:\n",
+      "\n",
+      "    smiths\n",
+      "\n",
+      "\n",
+      "The following objects are masked from ‘package:data.table’:\n",
+      "\n",
+      "    dcast, melt\n",
+      "\n",
+      "\n",
+      "Loading required package: lattice\n",
+      "\n",
+      "\n",
+      "Attaching package: ‘caret’\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:purrr’:\n",
+      "\n",
+      "    lift\n",
+      "\n",
+      "\n",
+      "This is a new update to coloc.\n",
+      "\n",
+      "\n",
+      "Attaching package: ‘coloc’\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:caret’:\n",
+      "\n",
+      "    sensitivity\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "source('MS1_Libraries.r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d94372e-0e6d-4dfc-b43f-564c5cd0322a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2ff55a9c-b32c-4075-8860-43f959e1b3e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Define path to stored data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f1b11f33-ed28-444b-8dc6-7b2d6df14520",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"\"\n",
+    "outdir = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "450dcaec-a669-4a0d-9899-19d46e2660dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_data_path = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0104b5db-350f-4435-85da-be5ea1db359c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Define cell-types for which colocalization analysis should be executed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bb521835-3c8b-4298-b6ea-bb81067dee55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_type_var = c(\"CD4T\",\"CD8T\",\"monocyte\",\"NK\",\"B\",\"DC\")\n",
+    "# c(\"CD4T\",\"CD8T\",\"monocyte\",\"NK\",\"B\",\"DC\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d05e2060-86ba-42e8-9306-2b406341b315",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6d1110c1-b170-4689-b643-50209a1db99a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Load the required data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c7a12ef-e9a5-4f80-97b2-ba354e0ebbfa",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## eQTL/ co-eQTL data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3dcf33a-9c6c-4fee-a53f-0989ef9457ff",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### eqtl data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2378dbb5-21cc-441d-b971-592895acdcd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_all_effect = data.frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "896fb460-a23d-4783-aba7-051b8958f82f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for(i in cell_type_var){\n",
+    "    data1 = fread(paste0(path, \"GRN_review/\", i, \"_ciseQTLs_1MB_-AllEffects.txt.gz\"))\n",
+    "    ## add seperate HLA_DQA + SMDT1 data\n",
+    "    data2 = fread(paste0(path, \"coloc_data_HLA_DQA_SMDT1/\", i, \"_ciseQTLs_1MB_HLA_DQA_SMDT1_-AllEffects.txt.gz\"))\n",
+    "    \n",
+    "    data = rbind(data1,data2)\n",
+    "    data$cell_type = paste0(i, '1MB')\n",
+    "    eqtl_all_effect = rbind(data, eqtl_all_effect)\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bd2b32e4-cdbe-4b7b-9854-c669319fccbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 22</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNP</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>SNPEffectAllele</th><th scope=col>⋯</th><th scope=col>MetaPN</th><th scope=col>MetaPZ</th><th scope=col>MetaBeta</th><th scope=col>MetaSE</th><th scope=col>MetaI2</th><th scope=col>NrDatasets</th><th scope=col>DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>cell_type</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>ENSG00000197728</td><td>12</td><td> 56435637</td><td>NA</td><td>RPS26  </td><td>rs12582260</td><td>12</td><td> 55436509</td><td>G/A</td><td>A</td><td>⋯</td><td>171</td><td>0.734837</td><td>0.080962</td><td>0.110177</td><td>0</td><td>4</td><td>0.179883;0.114471;-0.067275;-0.175334</td><td>1.154256;0.957471;-0.365841;-0.819481</td><td>43;72;32;24</td><td>DC1MB</td></tr>\n",
+       "\t<tr><td>ENSG00000026297</td><td> 6</td><td>167342992</td><td>NA</td><td>RNASET2</td><td>rs9365940 </td><td> 6</td><td>166343144</td><td>A/G</td><td>G</td><td>⋯</td><td>171</td><td>0.601634</td><td>0.089124</td><td>0.148136</td><td>0</td><td>4</td><td>0.06959;0.013057;0.034956;0.129805   </td><td>0.443421;0.108859;0.18993;0.60453    </td><td>43;72;32;24</td><td>DC1MB</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 22\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNP & SNPChr & SNPPos & SNPAlleles & SNPEffectAllele & ⋯ & MetaPN & MetaPZ & MetaBeta & MetaSE & MetaI2 & NrDatasets & DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) & DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) & DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) & cell\\_type\\\\\n",
+       " <chr> & <int> & <int> & <lgl> & <chr> & <chr> & <int> & <int> & <chr> & <chr> & ⋯ & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <chr> & <chr> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t ENSG00000197728 & 12 &  56435637 & NA & RPS26   & rs12582260 & 12 &  55436509 & G/A & A & ⋯ & 171 & 0.734837 & 0.080962 & 0.110177 & 0 & 4 & 0.179883;0.114471;-0.067275;-0.175334 & 1.154256;0.957471;-0.365841;-0.819481 & 43;72;32;24 & DC1MB\\\\\n",
+       "\t ENSG00000026297 &  6 & 167342992 & NA & RNASET2 & rs9365940  &  6 & 166343144 & A/G & G & ⋯ & 171 & 0.601634 & 0.089124 & 0.148136 & 0 & 4 & 0.06959;0.013057;0.034956;0.129805    & 0.443421;0.108859;0.18993;0.60453     & 43;72;32;24 & DC1MB\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 22\n",
+       "\n",
+       "| Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNP &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | SNPEffectAllele &lt;chr&gt; | ⋯ ⋯ | MetaPN &lt;int&gt; | MetaPZ &lt;dbl&gt; | MetaBeta &lt;dbl&gt; | MetaSE &lt;dbl&gt; | MetaI2 &lt;dbl&gt; | NrDatasets &lt;int&gt; | DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | cell_type &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| ENSG00000197728 | 12 |  56435637 | NA | RPS26   | rs12582260 | 12 |  55436509 | G/A | A | ⋯ | 171 | 0.734837 | 0.080962 | 0.110177 | 0 | 4 | 0.179883;0.114471;-0.067275;-0.175334 | 1.154256;0.957471;-0.365841;-0.819481 | 43;72;32;24 | DC1MB |\n",
+       "| ENSG00000026297 |  6 | 167342992 | NA | RNASET2 | rs9365940  |  6 | 166343144 | A/G | G | ⋯ | 171 | 0.601634 | 0.089124 | 0.148136 | 0 | 4 | 0.06959;0.013057;0.034956;0.129805    | 0.443421;0.108859;0.18993;0.60453     | 43;72;32;24 | DC1MB |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Gene            GeneChr GenePos   GeneStrand GeneSymbol SNP        SNPChr\n",
+       "1 ENSG00000197728 12       56435637 NA         RPS26      rs12582260 12    \n",
+       "2 ENSG00000026297  6      167342992 NA         RNASET2    rs9365940   6    \n",
+       "  SNPPos    SNPAlleles SNPEffectAllele ⋯ MetaPN MetaPZ   MetaBeta MetaSE  \n",
+       "1  55436509 G/A        A               ⋯ 171    0.734837 0.080962 0.110177\n",
+       "2 166343144 A/G        G               ⋯ 171    0.601634 0.089124 0.148136\n",
+       "  MetaI2 NrDatasets\n",
+       "1 0      4         \n",
+       "2 0      4         \n",
+       "  DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 0.179883;0.114471;-0.067275;-0.175334                               \n",
+       "2 0.06959;0.013057;0.034956;0.129805                                  \n",
+       "  DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 1.154256;0.957471;-0.365841;-0.819481               \n",
+       "2 0.443421;0.108859;0.18993;0.60453                   \n",
+       "  DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) cell_type\n",
+       "1 43;72;32;24                                              DC1MB    \n",
+       "2 43;72;32;24                                              DC1MB    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(eqtl_all_effect,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7f01102d-2452-4dcf-9d8e-54494ee095b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Inspect the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "528fbe24-6082-4a53-a662-73732825b7f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 6 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cell_type</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>B1MB       </td><td>20172</td></tr>\n",
+       "\t<tr><td>CD4T1MB    </td><td>20173</td></tr>\n",
+       "\t<tr><td>CD8T1MB    </td><td>20173</td></tr>\n",
+       "\t<tr><td>DC1MB      </td><td>20167</td></tr>\n",
+       "\t<tr><td>monocyte1MB</td><td>20173</td></tr>\n",
+       "\t<tr><td>NK1MB      </td><td>20173</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 6 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cell\\_type & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t B1MB        & 20172\\\\\n",
+       "\t CD4T1MB     & 20173\\\\\n",
+       "\t CD8T1MB     & 20173\\\\\n",
+       "\t DC1MB       & 20167\\\\\n",
+       "\t monocyte1MB & 20173\\\\\n",
+       "\t NK1MB       & 20173\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 6 × 2\n",
+       "\n",
+       "| cell_type &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| B1MB        | 20172 |\n",
+       "| CD4T1MB     | 20173 |\n",
+       "| CD8T1MB     | 20173 |\n",
+       "| DC1MB       | 20167 |\n",
+       "| monocyte1MB | 20173 |\n",
+       "| NK1MB       | 20173 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  cell_type   n    \n",
+       "1 B1MB        20172\n",
+       "2 CD4T1MB     20173\n",
+       "3 CD8T1MB     20173\n",
+       "4 DC1MB       20167\n",
+       "5 monocyte1MB 20173\n",
+       "6 NK1MB       20173"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eqtl_all_effect %>% group_by(cell_type) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2cd3492f-5c90-4496-8059-4a8121398873",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "20173"
+      ],
+      "text/latex": [
+       "20173"
+      ],
+      "text/markdown": [
+       "20173"
+      ],
+      "text/plain": [
+       "[1] 20173"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(eqtl_all_effect$SNP))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "54fa721e-1e1e-4fdf-bb53-6788c728c97a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#unique(eqtl_all_effect[(eqtl_all_effect$GeneSymbol == 'RPS26'),c('GeneSymbol', 'cell_type', 'MetaPN')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4dc95d9c-99f3-449f-96e6-a7d8f0a91ef0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_all_effect$SNP = paste0(eqtl_all_effect$SNP, '_', eqtl_all_effect$SNPAlleles)  # assuming ordered by 1) reference / 2) effect allele "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "156fb3f8-4234-404a-ae17-69f4d5714b66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'RPS26'</li><li>'RNASET2'</li><li>'TMEM176A'</li><li>'HLA-DQA2'</li><li>'SMDT1'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'RPS26'\n",
+       "\\item 'RNASET2'\n",
+       "\\item 'TMEM176A'\n",
+       "\\item 'HLA-DQA2'\n",
+       "\\item 'SMDT1'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'RPS26'\n",
+       "2. 'RNASET2'\n",
+       "3. 'TMEM176A'\n",
+       "4. 'HLA-DQA2'\n",
+       "5. 'SMDT1'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"RPS26\"    \"RNASET2\"  \"TMEM176A\" \"HLA-DQA2\" \"SMDT1\"   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(eqtl_all_effect$GeneSymbol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dee9629e-3013-4f89-9f4f-fd8398edf678",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e9fdf6d-f7d1-4a7c-a735-cac5d2765b1a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Co-EQTL Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "de6547ab-156f-40f3-a444-8c3e3036df53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_all_effect = data.frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9f46a48f-051a-4403-ba1c-cdc910e80dce",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T\"\n",
+      "[1] \"chr12-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"chr12-batch-2-AllEffects.txt.gz\"\n",
+      "[1] \"chr12-batch-3-AllEffects.txt.gz\"\n",
+      "[1] \"chr12-batch-4-AllEffects.txt.gz\"\n",
+      "[1] \"chr22-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"chr6-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"CD8T\"\n",
+      "[1] \"chr12-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"chr12-batch-2-AllEffects.txt.gz\"\n",
+      "[1] \"chr12-batch-3-AllEffects.txt.gz\"\n",
+      "[1] \"chr22-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"chr6-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"monocyte\"\n",
+      "[1] \"chr12-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"chr12-batch-2-AllEffects.txt.gz\"\n",
+      "[1] \"chr6-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"chr7-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"NK\"\n",
+      "[1] \"chr12-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"B\"\n",
+      "[1] \"chr12-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"DC\"\n",
+      "[1] \"chr12-batch-1-AllEffects.txt.gz\"\n",
+      "[1] \"chr6-batch-1-AllEffects.txt.gz\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "for(cell_type in cell_type_var){\n",
+    "    print(cell_type)\n",
+    "    # for each cell-type query files that should be loaded\n",
+    "    files_to_load = list.files(paste0(path, \"output/\",cell_type, \"/noduplicated/output/\"))\n",
+    "    #print(files_to_load)\n",
+    "    \n",
+    "    # read in the different files per cell-type\n",
+    "    for(i in files_to_load){\n",
+    "        print(i)\n",
+    "        data = fread(paste0(path, \"output/\", cell_type, \"/noduplicated/output/\", i))\n",
+    "        data$batch = i\n",
+    "        \n",
+    "        ## Add the cell-type information\n",
+    "        data$cell_type = cell_type\n",
+    "        \n",
+    "        output_all_effect = rbind(data, output_all_effect)\n",
+    "    }\n",
+    "    \n",
+    "}\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7317efc8-5104-491c-836d-bd58be3a5708",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Inspect the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ce22566f-9ff5-4d35-874f-96646375af85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 22</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNP</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>SNPEffectAllele</th><th scope=col>⋯</th><th scope=col>MetaPN</th><th scope=col>MetaPZ</th><th scope=col>MetaBeta</th><th scope=col>MetaSE</th><th scope=col>NrDatasets</th><th scope=col>DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>batch</th><th scope=col>cell_type</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>CDKN2D;HLA-DQA2 </td><td>6</td><td>32709119</td><td>NA</td><td>CDKN2D;HLA-DQA2 </td><td>rs1144709</td><td>6</td><td>31709349</td><td>C/T</td><td>T</td><td>⋯</td><td>59</td><td> 0.069875</td><td> 0.015741</td><td>0.225274</td><td>2</td><td>-;0.08039;-0.112611;- </td><td>-;0.472978;-0.498951;-</td><td>-;37;22;-</td><td>chr6-batch-1-AllEffects.txt.gz</td><td>DC</td></tr>\n",
+       "\t<tr><td>FAM129C;HLA-DQA2</td><td>6</td><td>32709119</td><td>NA</td><td>FAM129C;HLA-DQA2</td><td>rs1144709</td><td>6</td><td>31709349</td><td>C/T</td><td>T</td><td>⋯</td><td>59</td><td>-0.271313</td><td>-0.062011</td><td>0.228560</td><td>2</td><td>-;-0.084633;0.052223;-</td><td>-;-0.505195;0.224816;-</td><td>-;38;21;-</td><td>chr6-batch-1-AllEffects.txt.gz</td><td>DC</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 22\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNP & SNPChr & SNPPos & SNPAlleles & SNPEffectAllele & ⋯ & MetaPN & MetaPZ & MetaBeta & MetaSE & NrDatasets & DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) & DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) & DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) & batch & cell\\_type\\\\\n",
+       " <chr> & <int> & <int> & <lgl> & <chr> & <chr> & <int> & <int> & <chr> & <chr> & ⋯ & <int> & <dbl> & <dbl> & <dbl> & <int> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t CDKN2D;HLA-DQA2  & 6 & 32709119 & NA & CDKN2D;HLA-DQA2  & rs1144709 & 6 & 31709349 & C/T & T & ⋯ & 59 &  0.069875 &  0.015741 & 0.225274 & 2 & -;0.08039;-0.112611;-  & -;0.472978;-0.498951;- & -;37;22;- & chr6-batch-1-AllEffects.txt.gz & DC\\\\\n",
+       "\t FAM129C;HLA-DQA2 & 6 & 32709119 & NA & FAM129C;HLA-DQA2 & rs1144709 & 6 & 31709349 & C/T & T & ⋯ & 59 & -0.271313 & -0.062011 & 0.228560 & 2 & -;-0.084633;0.052223;- & -;-0.505195;0.224816;- & -;38;21;- & chr6-batch-1-AllEffects.txt.gz & DC\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 22\n",
+       "\n",
+       "| Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNP &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | SNPEffectAllele &lt;chr&gt; | ⋯ ⋯ | MetaPN &lt;int&gt; | MetaPZ &lt;dbl&gt; | MetaBeta &lt;dbl&gt; | MetaSE &lt;dbl&gt; | NrDatasets &lt;int&gt; | DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | batch &lt;chr&gt; | cell_type &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| CDKN2D;HLA-DQA2  | 6 | 32709119 | NA | CDKN2D;HLA-DQA2  | rs1144709 | 6 | 31709349 | C/T | T | ⋯ | 59 |  0.069875 |  0.015741 | 0.225274 | 2 | -;0.08039;-0.112611;-  | -;0.472978;-0.498951;- | -;37;22;- | chr6-batch-1-AllEffects.txt.gz | DC |\n",
+       "| FAM129C;HLA-DQA2 | 6 | 32709119 | NA | FAM129C;HLA-DQA2 | rs1144709 | 6 | 31709349 | C/T | T | ⋯ | 59 | -0.271313 | -0.062011 | 0.228560 | 2 | -;-0.084633;0.052223;- | -;-0.505195;0.224816;- | -;38;21;- | chr6-batch-1-AllEffects.txt.gz | DC |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Gene             GeneChr GenePos  GeneStrand GeneSymbol       SNP      \n",
+       "1 CDKN2D;HLA-DQA2  6       32709119 NA         CDKN2D;HLA-DQA2  rs1144709\n",
+       "2 FAM129C;HLA-DQA2 6       32709119 NA         FAM129C;HLA-DQA2 rs1144709\n",
+       "  SNPChr SNPPos   SNPAlleles SNPEffectAllele ⋯ MetaPN MetaPZ    MetaBeta \n",
+       "1 6      31709349 C/T        T               ⋯ 59      0.069875  0.015741\n",
+       "2 6      31709349 C/T        T               ⋯ 59     -0.271313 -0.062011\n",
+       "  MetaSE   NrDatasets\n",
+       "1 0.225274 2         \n",
+       "2 0.228560 2         \n",
+       "  DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -;0.08039;-0.112611;-                                               \n",
+       "2 -;-0.084633;0.052223;-                                              \n",
+       "  DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -;0.472978;-0.498951;-                              \n",
+       "2 -;-0.505195;0.224816;-                              \n",
+       "  DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -;37;22;-                                               \n",
+       "2 -;38;21;-                                               \n",
+       "  batch                          cell_type\n",
+       "1 chr6-batch-1-AllEffects.txt.gz DC       \n",
+       "2 chr6-batch-1-AllEffects.txt.gz DC       "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(output_all_effect,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ec8d11de-369a-4a33-9114-be8d68ce3ce5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'T'</li><li>'G'</li><li>'A'</li><li>'C'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'T'\n",
+       "\\item 'G'\n",
+       "\\item 'A'\n",
+       "\\item 'C'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'T'\n",
+       "2. 'G'\n",
+       "3. 'A'\n",
+       "4. 'C'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"T\" \"G\" \"A\" \"C\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(output_all_effect$SNPEffectAllele)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e5eb30eb-a888-4e34-a0f2-adc7c2ebd75b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'DC'</li><li>'B'</li><li>'NK'</li><li>'monocyte'</li><li>'CD8T'</li><li>'CD4T'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'DC'\n",
+       "\\item 'B'\n",
+       "\\item 'NK'\n",
+       "\\item 'monocyte'\n",
+       "\\item 'CD8T'\n",
+       "\\item 'CD4T'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'DC'\n",
+       "2. 'B'\n",
+       "3. 'NK'\n",
+       "4. 'monocyte'\n",
+       "5. 'CD8T'\n",
+       "6. 'CD4T'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"DC\"       \"B\"        \"NK\"       \"monocyte\" \"CD8T\"     \"CD4T\"    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(output_all_effect$cell_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "70e0d362-7144-4d2b-bbae-715e30588cbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>6</li><li>12</li><li>7</li><li>22</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 6\n",
+       "\\item 12\n",
+       "\\item 7\n",
+       "\\item 22\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 6\n",
+       "2. 12\n",
+       "3. 7\n",
+       "4. 22\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  6 12  7 22"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(output_all_effect$GeneChr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f7443a38-f285-4dd1-aefe-514bd4beaac5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract eGene - based on chromosome and matches \n",
+    "\n",
+    "# Match with chromosome\n",
+    "output_all_effect$eGene[output_all_effect$GeneChr == 12] = str_extract(output_all_effect$Gene[output_all_effect$GeneChr == 12], 'RPS26' )\n",
+    "output_all_effect$eGene[output_all_effect$GeneChr == 6] = str_extract(output_all_effect$Gene[output_all_effect$GeneChr == 6], 'HLA-DQA2|RNASET2' )\n",
+    "output_all_effect$eGene[output_all_effect$GeneChr == 22] = str_extract(output_all_effect$Gene[output_all_effect$GeneChr == 22], 'SMDT1' )\n",
+    "output_all_effect$eGene[output_all_effect$GeneChr == 7] = str_extract(output_all_effect$Gene[output_all_effect$GeneChr == 7], 'TMEM176A' )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "442e0b24-ea36-48ad-9bee-14dc50444a6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'HLA-DQA2'</li><li>'RPS26'</li><li>'TMEM176A'</li><li>'RNASET2'</li><li>'SMDT1'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'HLA-DQA2'\n",
+       "\\item 'RPS26'\n",
+       "\\item 'TMEM176A'\n",
+       "\\item 'RNASET2'\n",
+       "\\item 'SMDT1'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'HLA-DQA2'\n",
+       "2. 'RPS26'\n",
+       "3. 'TMEM176A'\n",
+       "4. 'RNASET2'\n",
+       "5. 'SMDT1'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"HLA-DQA2\" \"RPS26\"    \"TMEM176A\" \"RNASET2\"  \"SMDT1\"   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(output_all_effect$eGene)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9d8219f6-9bc2-45c2-8492-eb3805bf6e24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#output_all_effect[is.na(output_all_effect$eGene) ,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "93e61558-78c1-4b44-a96d-d5b3b7eaf013",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in cbind(parts$left, ellip_h, parts$right, deparse.level = 0L):\n",
+      "“number of rows of result is not a multiple of vector length (arg 2)”\n",
+      "Warning message in cbind(parts$left, ellip_h, parts$right, deparse.level = 0L):\n",
+      "“number of rows of result is not a multiple of vector length (arg 2)”\n",
+      "Warning message in cbind(parts$left, ellip_h, parts$right, deparse.level = 0L):\n",
+      "“number of rows of result is not a multiple of vector length (arg 2)”\n",
+      "Warning message in cbind(parts$left, ellip_h, parts$right, deparse.level = 0L):\n",
+      "“number of rows of result is not a multiple of vector length (arg 2)”\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 0 × 23</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNP</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>SNPEffectAllele</th><th scope=col>⋯</th><th scope=col>MetaPZ</th><th scope=col>MetaBeta</th><th scope=col>MetaSE</th><th scope=col>NrDatasets</th><th scope=col>DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>batch</th><th scope=col>cell_type</th><th scope=col>eGene</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 0 × 23\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNP & SNPChr & SNPPos & SNPAlleles & SNPEffectAllele & ⋯ & MetaPZ & MetaBeta & MetaSE & NrDatasets & DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) & DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) & DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) & batch & cell\\_type & eGene\\\\\n",
+       " <chr> & <int> & <int> & <lgl> & <chr> & <chr> & <int> & <int> & <chr> & <chr> & ⋯ & <dbl> & <dbl> & <dbl> & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 0 × 23\n",
+       "\n",
+       "| Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNP &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | SNPEffectAllele &lt;chr&gt; | ⋯ ⋯ | MetaPZ &lt;dbl&gt; | MetaBeta &lt;dbl&gt; | MetaSE &lt;dbl&gt; | NrDatasets &lt;int&gt; | DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | batch &lt;chr&gt; | cell_type &lt;chr&gt; | eGene &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     Gene GeneChr GenePos GeneStrand GeneSymbol SNP SNPChr SNPPos SNPAlleles\n",
+       "     SNPEffectAllele ⋯ MetaPZ MetaBeta MetaSE NrDatasets\n",
+       "     DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "     DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "     DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) batch cell_type\n",
+       "     eGene"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_all_effect[is.na(output_all_effect$eGene),]   # check whether everything could be matched"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d678822f-2057-427b-8936-d86c0472ced9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "887b0f03-e247-4a4b-80eb-2d452edfaabf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "7"
+      ],
+      "text/latex": [
+       "7"
+      ],
+      "text/markdown": [
+       "7"
+      ],
+      "text/plain": [
+       "[1] 7"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(output_all_effect$batch))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "9165d3b1-1278-4842-bece-c91c974d353d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "604"
+      ],
+      "text/latex": [
+       "604"
+      ],
+      "text/markdown": [
+       "604"
+      ],
+      "text/plain": [
+       "[1] 604"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(output_all_effect$Gene))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "164fea7a-f29a-4b1e-8471-e43b7efb6257",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Check out the duplicates values from different batches and remove"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "764089ce-89d3-4279-b280-edc53f5926ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 4 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Gene</th><th scope=col>SNP</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>22:42911257</td><td>2</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>rs1001586  </td><td>2</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>rs1001587  </td><td>2</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>rs1003619  </td><td>2</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 4 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " Gene & SNP & n\\\\\n",
+       " <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t RPS26;SMDT1 & 22:42911257 & 2\\\\\n",
+       "\t RPS26;SMDT1 & rs1001586   & 2\\\\\n",
+       "\t RPS26;SMDT1 & rs1001587   & 2\\\\\n",
+       "\t RPS26;SMDT1 & rs1003619   & 2\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 4 × 3\n",
+       "\n",
+       "| Gene &lt;chr&gt; | SNP &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|---|\n",
+       "| RPS26;SMDT1 | 22:42911257 | 2 |\n",
+       "| RPS26;SMDT1 | rs1001586   | 2 |\n",
+       "| RPS26;SMDT1 | rs1001587   | 2 |\n",
+       "| RPS26;SMDT1 | rs1003619   | 2 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Gene        SNP         n\n",
+       "1 RPS26;SMDT1 22:42911257 2\n",
+       "2 RPS26;SMDT1 rs1001586   2\n",
+       "3 RPS26;SMDT1 rs1001587   2\n",
+       "4 RPS26;SMDT1 rs1003619   2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(output_all_effect[output_all_effect$GeneSymbol == 'RPS26;SMDT1',] %>% group_by(Gene, SNP) %>% count() %>% filter(n>=2),4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9e9a32fa-8020-4c2a-ac74-1c5e55f302fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 7 × 18</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNP</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>SNPEffectAllele</th><th scope=col>SNPEffectAlleleFreq</th><th scope=col>MetaP</th><th scope=col>MetaPN</th><th scope=col>MetaPZ</th><th scope=col>MetaBeta</th><th scope=col>MetaSE</th><th scope=col>batch</th><th scope=col>cell_type</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26;SMDT1</td><td>rs10047532</td><td>12</td><td>55618447</td><td>A/T</td><td>T</td><td>0.627907</td><td>0.4738383</td><td>172</td><td>-0.716248</td><td>-0.079774</td><td>0.111378</td><td>chr12-batch-3-AllEffects.txt.gz</td><td>CD8T</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26;SMDT1</td><td>rs10047532</td><td>12</td><td>55618447</td><td>A/T</td><td>T</td><td>0.627907</td><td>0.4738383</td><td>172</td><td>-0.716248</td><td>-0.079774</td><td>0.111378</td><td>chr12-batch-2-AllEffects.txt.gz</td><td>CD8T</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26;SMDT1</td><td>rs10047532</td><td>12</td><td>55618447</td><td>A/T</td><td>T</td><td>0.627907</td><td>0.4738383</td><td>172</td><td>-0.716248</td><td>-0.079774</td><td>0.111378</td><td>chr12-batch-1-AllEffects.txt.gz</td><td>CD8T</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26;SMDT1</td><td>rs10047532</td><td>12</td><td>55618447</td><td>A/T</td><td>T</td><td>0.630058</td><td>0.8095858</td><td>173</td><td>-0.240960</td><td>-0.026827</td><td>0.111335</td><td>chr12-batch-4-AllEffects.txt.gz</td><td>CD4T</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26;SMDT1</td><td>rs10047532</td><td>12</td><td>55618447</td><td>A/T</td><td>T</td><td>0.630058</td><td>0.8095858</td><td>173</td><td>-0.240960</td><td>-0.026827</td><td>0.111335</td><td>chr12-batch-3-AllEffects.txt.gz</td><td>CD4T</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26;SMDT1</td><td>rs10047532</td><td>12</td><td>55618447</td><td>A/T</td><td>T</td><td>0.630058</td><td>0.8095858</td><td>173</td><td>-0.240960</td><td>-0.026827</td><td>0.111335</td><td>chr12-batch-2-AllEffects.txt.gz</td><td>CD4T</td></tr>\n",
+       "\t<tr><td>RPS26;SMDT1</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26;SMDT1</td><td>rs10047532</td><td>12</td><td>55618447</td><td>A/T</td><td>T</td><td>0.630058</td><td>0.8095858</td><td>173</td><td>-0.240960</td><td>-0.026827</td><td>0.111335</td><td>chr12-batch-1-AllEffects.txt.gz</td><td>CD4T</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 7 × 18\n",
+       "\\begin{tabular}{llllllllllllllllll}\n",
+       " Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNP & SNPChr & SNPPos & SNPAlleles & SNPEffectAllele & SNPEffectAlleleFreq & MetaP & MetaPN & MetaPZ & MetaBeta & MetaSE & batch & cell\\_type\\\\\n",
+       " <chr> & <int> & <int> & <lgl> & <chr> & <chr> & <int> & <int> & <chr> & <chr> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t RPS26;SMDT1 & 12 & 56435637 & NA & RPS26;SMDT1 & rs10047532 & 12 & 55618447 & A/T & T & 0.627907 & 0.4738383 & 172 & -0.716248 & -0.079774 & 0.111378 & chr12-batch-3-AllEffects.txt.gz & CD8T\\\\\n",
+       "\t RPS26;SMDT1 & 12 & 56435637 & NA & RPS26;SMDT1 & rs10047532 & 12 & 55618447 & A/T & T & 0.627907 & 0.4738383 & 172 & -0.716248 & -0.079774 & 0.111378 & chr12-batch-2-AllEffects.txt.gz & CD8T\\\\\n",
+       "\t RPS26;SMDT1 & 12 & 56435637 & NA & RPS26;SMDT1 & rs10047532 & 12 & 55618447 & A/T & T & 0.627907 & 0.4738383 & 172 & -0.716248 & -0.079774 & 0.111378 & chr12-batch-1-AllEffects.txt.gz & CD8T\\\\\n",
+       "\t RPS26;SMDT1 & 12 & 56435637 & NA & RPS26;SMDT1 & rs10047532 & 12 & 55618447 & A/T & T & 0.630058 & 0.8095858 & 173 & -0.240960 & -0.026827 & 0.111335 & chr12-batch-4-AllEffects.txt.gz & CD4T\\\\\n",
+       "\t RPS26;SMDT1 & 12 & 56435637 & NA & RPS26;SMDT1 & rs10047532 & 12 & 55618447 & A/T & T & 0.630058 & 0.8095858 & 173 & -0.240960 & -0.026827 & 0.111335 & chr12-batch-3-AllEffects.txt.gz & CD4T\\\\\n",
+       "\t RPS26;SMDT1 & 12 & 56435637 & NA & RPS26;SMDT1 & rs10047532 & 12 & 55618447 & A/T & T & 0.630058 & 0.8095858 & 173 & -0.240960 & -0.026827 & 0.111335 & chr12-batch-2-AllEffects.txt.gz & CD4T\\\\\n",
+       "\t RPS26;SMDT1 & 12 & 56435637 & NA & RPS26;SMDT1 & rs10047532 & 12 & 55618447 & A/T & T & 0.630058 & 0.8095858 & 173 & -0.240960 & -0.026827 & 0.111335 & chr12-batch-1-AllEffects.txt.gz & CD4T\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 7 × 18\n",
+       "\n",
+       "| Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNP &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | SNPEffectAllele &lt;chr&gt; | SNPEffectAlleleFreq &lt;dbl&gt; | MetaP &lt;dbl&gt; | MetaPN &lt;int&gt; | MetaPZ &lt;dbl&gt; | MetaBeta &lt;dbl&gt; | MetaSE &lt;dbl&gt; | batch &lt;chr&gt; | cell_type &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| RPS26;SMDT1 | 12 | 56435637 | NA | RPS26;SMDT1 | rs10047532 | 12 | 55618447 | A/T | T | 0.627907 | 0.4738383 | 172 | -0.716248 | -0.079774 | 0.111378 | chr12-batch-3-AllEffects.txt.gz | CD8T |\n",
+       "| RPS26;SMDT1 | 12 | 56435637 | NA | RPS26;SMDT1 | rs10047532 | 12 | 55618447 | A/T | T | 0.627907 | 0.4738383 | 172 | -0.716248 | -0.079774 | 0.111378 | chr12-batch-2-AllEffects.txt.gz | CD8T |\n",
+       "| RPS26;SMDT1 | 12 | 56435637 | NA | RPS26;SMDT1 | rs10047532 | 12 | 55618447 | A/T | T | 0.627907 | 0.4738383 | 172 | -0.716248 | -0.079774 | 0.111378 | chr12-batch-1-AllEffects.txt.gz | CD8T |\n",
+       "| RPS26;SMDT1 | 12 | 56435637 | NA | RPS26;SMDT1 | rs10047532 | 12 | 55618447 | A/T | T | 0.630058 | 0.8095858 | 173 | -0.240960 | -0.026827 | 0.111335 | chr12-batch-4-AllEffects.txt.gz | CD4T |\n",
+       "| RPS26;SMDT1 | 12 | 56435637 | NA | RPS26;SMDT1 | rs10047532 | 12 | 55618447 | A/T | T | 0.630058 | 0.8095858 | 173 | -0.240960 | -0.026827 | 0.111335 | chr12-batch-3-AllEffects.txt.gz | CD4T |\n",
+       "| RPS26;SMDT1 | 12 | 56435637 | NA | RPS26;SMDT1 | rs10047532 | 12 | 55618447 | A/T | T | 0.630058 | 0.8095858 | 173 | -0.240960 | -0.026827 | 0.111335 | chr12-batch-2-AllEffects.txt.gz | CD4T |\n",
+       "| RPS26;SMDT1 | 12 | 56435637 | NA | RPS26;SMDT1 | rs10047532 | 12 | 55618447 | A/T | T | 0.630058 | 0.8095858 | 173 | -0.240960 | -0.026827 | 0.111335 | chr12-batch-1-AllEffects.txt.gz | CD4T |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Gene        GeneChr GenePos  GeneStrand GeneSymbol  SNP        SNPChr\n",
+       "1 RPS26;SMDT1 12      56435637 NA         RPS26;SMDT1 rs10047532 12    \n",
+       "2 RPS26;SMDT1 12      56435637 NA         RPS26;SMDT1 rs10047532 12    \n",
+       "3 RPS26;SMDT1 12      56435637 NA         RPS26;SMDT1 rs10047532 12    \n",
+       "4 RPS26;SMDT1 12      56435637 NA         RPS26;SMDT1 rs10047532 12    \n",
+       "5 RPS26;SMDT1 12      56435637 NA         RPS26;SMDT1 rs10047532 12    \n",
+       "6 RPS26;SMDT1 12      56435637 NA         RPS26;SMDT1 rs10047532 12    \n",
+       "7 RPS26;SMDT1 12      56435637 NA         RPS26;SMDT1 rs10047532 12    \n",
+       "  SNPPos   SNPAlleles SNPEffectAllele SNPEffectAlleleFreq MetaP     MetaPN\n",
+       "1 55618447 A/T        T               0.627907            0.4738383 172   \n",
+       "2 55618447 A/T        T               0.627907            0.4738383 172   \n",
+       "3 55618447 A/T        T               0.627907            0.4738383 172   \n",
+       "4 55618447 A/T        T               0.630058            0.8095858 173   \n",
+       "5 55618447 A/T        T               0.630058            0.8095858 173   \n",
+       "6 55618447 A/T        T               0.630058            0.8095858 173   \n",
+       "7 55618447 A/T        T               0.630058            0.8095858 173   \n",
+       "  MetaPZ    MetaBeta  MetaSE   batch                           cell_type\n",
+       "1 -0.716248 -0.079774 0.111378 chr12-batch-3-AllEffects.txt.gz CD8T     \n",
+       "2 -0.716248 -0.079774 0.111378 chr12-batch-2-AllEffects.txt.gz CD8T     \n",
+       "3 -0.716248 -0.079774 0.111378 chr12-batch-1-AllEffects.txt.gz CD8T     \n",
+       "4 -0.240960 -0.026827 0.111335 chr12-batch-4-AllEffects.txt.gz CD4T     \n",
+       "5 -0.240960 -0.026827 0.111335 chr12-batch-3-AllEffects.txt.gz CD4T     \n",
+       "6 -0.240960 -0.026827 0.111335 chr12-batch-2-AllEffects.txt.gz CD4T     \n",
+       "7 -0.240960 -0.026827 0.111335 chr12-batch-1-AllEffects.txt.gz CD4T     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_all_effect[(output_all_effect$GeneSymbol == 'RPS26;SMDT1') &(output_all_effect$SNP == 'rs10047532') ,c(1:16, 21:22)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "5672972f-aa31-4c88-8687-3014ade8a015",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "6484420"
+      ],
+      "text/latex": [
+       "6484420"
+      ],
+      "text/markdown": [
+       "6484420"
+      ],
+      "text/plain": [
+       "[1] 6484420"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(output_all_effect)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "2eff66ab-a2d9-4f2a-8a1a-7fbbd8ab6f9c",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 21 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>eGene</th><th scope=col>batch</th><th scope=col>cell_type</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>HLA-DQA2</td><td>chr6-batch-1-AllEffects.txt.gz </td><td>CD4T    </td><td>116816</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>chr6-batch-1-AllEffects.txt.gz </td><td>CD8T    </td><td> 50641</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>chr6-batch-1-AllEffects.txt.gz </td><td>DC      </td><td> 80196</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>chr6-batch-1-AllEffects.txt.gz </td><td>monocyte</td><td>119521</td></tr>\n",
+       "\t<tr><td>RNASET2 </td><td>chr6-batch-1-AllEffects.txt.gz </td><td>CD4T    </td><td> 16152</td></tr>\n",
+       "\t<tr><td>RNASET2 </td><td>chr6-batch-1-AllEffects.txt.gz </td><td>monocyte</td><td>  7198</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>B       </td><td> 74127</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>CD4T    </td><td>789252</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>CD8T    </td><td>621313</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>DC      </td><td>  6195</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>monocyte</td><td>280221</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>NK      </td><td>202989</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-2-AllEffects.txt.gz</td><td>CD4T    </td><td>789252</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-2-AllEffects.txt.gz</td><td>CD8T    </td><td>621313</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-2-AllEffects.txt.gz</td><td>monocyte</td><td>280221</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-3-AllEffects.txt.gz</td><td>CD4T    </td><td>789252</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-3-AllEffects.txt.gz</td><td>CD8T    </td><td>621313</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-4-AllEffects.txt.gz</td><td>CD4T    </td><td>789252</td></tr>\n",
+       "\t<tr><td>SMDT1   </td><td>chr22-batch-1-AllEffects.txt.gz</td><td>CD4T    </td><td> 59540</td></tr>\n",
+       "\t<tr><td>SMDT1   </td><td>chr22-batch-1-AllEffects.txt.gz</td><td>CD8T    </td><td>130916</td></tr>\n",
+       "\t<tr><td>TMEM176A</td><td>chr7-batch-1-AllEffects.txt.gz </td><td>monocyte</td><td> 38740</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 21 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " eGene & batch & cell\\_type & n\\\\\n",
+       " <chr> & <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t HLA-DQA2 & chr6-batch-1-AllEffects.txt.gz  & CD4T     & 116816\\\\\n",
+       "\t HLA-DQA2 & chr6-batch-1-AllEffects.txt.gz  & CD8T     &  50641\\\\\n",
+       "\t HLA-DQA2 & chr6-batch-1-AllEffects.txt.gz  & DC       &  80196\\\\\n",
+       "\t HLA-DQA2 & chr6-batch-1-AllEffects.txt.gz  & monocyte & 119521\\\\\n",
+       "\t RNASET2  & chr6-batch-1-AllEffects.txt.gz  & CD4T     &  16152\\\\\n",
+       "\t RNASET2  & chr6-batch-1-AllEffects.txt.gz  & monocyte &   7198\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & B        &  74127\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & CD4T     & 789252\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & CD8T     & 621313\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & DC       &   6195\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & monocyte & 280221\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & NK       & 202989\\\\\n",
+       "\t RPS26    & chr12-batch-2-AllEffects.txt.gz & CD4T     & 789252\\\\\n",
+       "\t RPS26    & chr12-batch-2-AllEffects.txt.gz & CD8T     & 621313\\\\\n",
+       "\t RPS26    & chr12-batch-2-AllEffects.txt.gz & monocyte & 280221\\\\\n",
+       "\t RPS26    & chr12-batch-3-AllEffects.txt.gz & CD4T     & 789252\\\\\n",
+       "\t RPS26    & chr12-batch-3-AllEffects.txt.gz & CD8T     & 621313\\\\\n",
+       "\t RPS26    & chr12-batch-4-AllEffects.txt.gz & CD4T     & 789252\\\\\n",
+       "\t SMDT1    & chr22-batch-1-AllEffects.txt.gz & CD4T     &  59540\\\\\n",
+       "\t SMDT1    & chr22-batch-1-AllEffects.txt.gz & CD8T     & 130916\\\\\n",
+       "\t TMEM176A & chr7-batch-1-AllEffects.txt.gz  & monocyte &  38740\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 21 × 4\n",
+       "\n",
+       "| eGene &lt;chr&gt; | batch &lt;chr&gt; | cell_type &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|---|---|\n",
+       "| HLA-DQA2 | chr6-batch-1-AllEffects.txt.gz  | CD4T     | 116816 |\n",
+       "| HLA-DQA2 | chr6-batch-1-AllEffects.txt.gz  | CD8T     |  50641 |\n",
+       "| HLA-DQA2 | chr6-batch-1-AllEffects.txt.gz  | DC       |  80196 |\n",
+       "| HLA-DQA2 | chr6-batch-1-AllEffects.txt.gz  | monocyte | 119521 |\n",
+       "| RNASET2  | chr6-batch-1-AllEffects.txt.gz  | CD4T     |  16152 |\n",
+       "| RNASET2  | chr6-batch-1-AllEffects.txt.gz  | monocyte |   7198 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | B        |  74127 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | CD4T     | 789252 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | CD8T     | 621313 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | DC       |   6195 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | monocyte | 280221 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | NK       | 202989 |\n",
+       "| RPS26    | chr12-batch-2-AllEffects.txt.gz | CD4T     | 789252 |\n",
+       "| RPS26    | chr12-batch-2-AllEffects.txt.gz | CD8T     | 621313 |\n",
+       "| RPS26    | chr12-batch-2-AllEffects.txt.gz | monocyte | 280221 |\n",
+       "| RPS26    | chr12-batch-3-AllEffects.txt.gz | CD4T     | 789252 |\n",
+       "| RPS26    | chr12-batch-3-AllEffects.txt.gz | CD8T     | 621313 |\n",
+       "| RPS26    | chr12-batch-4-AllEffects.txt.gz | CD4T     | 789252 |\n",
+       "| SMDT1    | chr22-batch-1-AllEffects.txt.gz | CD4T     |  59540 |\n",
+       "| SMDT1    | chr22-batch-1-AllEffects.txt.gz | CD8T     | 130916 |\n",
+       "| TMEM176A | chr7-batch-1-AllEffects.txt.gz  | monocyte |  38740 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   eGene    batch                           cell_type n     \n",
+       "1  HLA-DQA2 chr6-batch-1-AllEffects.txt.gz  CD4T      116816\n",
+       "2  HLA-DQA2 chr6-batch-1-AllEffects.txt.gz  CD8T       50641\n",
+       "3  HLA-DQA2 chr6-batch-1-AllEffects.txt.gz  DC         80196\n",
+       "4  HLA-DQA2 chr6-batch-1-AllEffects.txt.gz  monocyte  119521\n",
+       "5  RNASET2  chr6-batch-1-AllEffects.txt.gz  CD4T       16152\n",
+       "6  RNASET2  chr6-batch-1-AllEffects.txt.gz  monocyte    7198\n",
+       "7  RPS26    chr12-batch-1-AllEffects.txt.gz B          74127\n",
+       "8  RPS26    chr12-batch-1-AllEffects.txt.gz CD4T      789252\n",
+       "9  RPS26    chr12-batch-1-AllEffects.txt.gz CD8T      621313\n",
+       "10 RPS26    chr12-batch-1-AllEffects.txt.gz DC          6195\n",
+       "11 RPS26    chr12-batch-1-AllEffects.txt.gz monocyte  280221\n",
+       "12 RPS26    chr12-batch-1-AllEffects.txt.gz NK        202989\n",
+       "13 RPS26    chr12-batch-2-AllEffects.txt.gz CD4T      789252\n",
+       "14 RPS26    chr12-batch-2-AllEffects.txt.gz CD8T      621313\n",
+       "15 RPS26    chr12-batch-2-AllEffects.txt.gz monocyte  280221\n",
+       "16 RPS26    chr12-batch-3-AllEffects.txt.gz CD4T      789252\n",
+       "17 RPS26    chr12-batch-3-AllEffects.txt.gz CD8T      621313\n",
+       "18 RPS26    chr12-batch-4-AllEffects.txt.gz CD4T      789252\n",
+       "19 SMDT1    chr22-batch-1-AllEffects.txt.gz CD4T       59540\n",
+       "20 SMDT1    chr22-batch-1-AllEffects.txt.gz CD8T      130916\n",
+       "21 TMEM176A chr7-batch-1-AllEffects.txt.gz  monocyte   38740"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_all_effect %>% group_by(eGene, batch, cell_type) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "e01d104d-b652-4050-aabf-6e8d9457d806",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 21 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>eGene</th><th scope=col>batch</th><th scope=col>cell_type</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>HLA-DQA2</td><td>chr6-batch-1-AllEffects.txt.gz </td><td>CD4T    </td><td> 16</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>chr6-batch-1-AllEffects.txt.gz </td><td>CD8T    </td><td>  7</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>chr6-batch-1-AllEffects.txt.gz </td><td>DC      </td><td> 13</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>chr6-batch-1-AllEffects.txt.gz </td><td>monocyte</td><td> 17</td></tr>\n",
+       "\t<tr><td>RNASET2 </td><td>chr6-batch-1-AllEffects.txt.gz </td><td>CD4T    </td><td>  4</td></tr>\n",
+       "\t<tr><td>RNASET2 </td><td>chr6-batch-1-AllEffects.txt.gz </td><td>monocyte</td><td>  1</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>B       </td><td> 35</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>CD4T    </td><td>372</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>CD8T    </td><td>293</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>DC      </td><td>  3</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>monocyte</td><td>132</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-1-AllEffects.txt.gz</td><td>NK      </td><td> 96</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-2-AllEffects.txt.gz</td><td>CD4T    </td><td>372</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-2-AllEffects.txt.gz</td><td>CD8T    </td><td>293</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-2-AllEffects.txt.gz</td><td>monocyte</td><td>132</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-3-AllEffects.txt.gz</td><td>CD4T    </td><td>372</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-3-AllEffects.txt.gz</td><td>CD8T    </td><td>293</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>chr12-batch-4-AllEffects.txt.gz</td><td>CD4T    </td><td>372</td></tr>\n",
+       "\t<tr><td>SMDT1   </td><td>chr22-batch-1-AllEffects.txt.gz</td><td>CD4T    </td><td> 20</td></tr>\n",
+       "\t<tr><td>SMDT1   </td><td>chr22-batch-1-AllEffects.txt.gz</td><td>CD8T    </td><td> 44</td></tr>\n",
+       "\t<tr><td>TMEM176A</td><td>chr7-batch-1-AllEffects.txt.gz </td><td>monocyte</td><td> 11</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 21 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " eGene & batch & cell\\_type & n\\\\\n",
+       " <chr> & <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t HLA-DQA2 & chr6-batch-1-AllEffects.txt.gz  & CD4T     &  16\\\\\n",
+       "\t HLA-DQA2 & chr6-batch-1-AllEffects.txt.gz  & CD8T     &   7\\\\\n",
+       "\t HLA-DQA2 & chr6-batch-1-AllEffects.txt.gz  & DC       &  13\\\\\n",
+       "\t HLA-DQA2 & chr6-batch-1-AllEffects.txt.gz  & monocyte &  17\\\\\n",
+       "\t RNASET2  & chr6-batch-1-AllEffects.txt.gz  & CD4T     &   4\\\\\n",
+       "\t RNASET2  & chr6-batch-1-AllEffects.txt.gz  & monocyte &   1\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & B        &  35\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & CD4T     & 372\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & CD8T     & 293\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & DC       &   3\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & monocyte & 132\\\\\n",
+       "\t RPS26    & chr12-batch-1-AllEffects.txt.gz & NK       &  96\\\\\n",
+       "\t RPS26    & chr12-batch-2-AllEffects.txt.gz & CD4T     & 372\\\\\n",
+       "\t RPS26    & chr12-batch-2-AllEffects.txt.gz & CD8T     & 293\\\\\n",
+       "\t RPS26    & chr12-batch-2-AllEffects.txt.gz & monocyte & 132\\\\\n",
+       "\t RPS26    & chr12-batch-3-AllEffects.txt.gz & CD4T     & 372\\\\\n",
+       "\t RPS26    & chr12-batch-3-AllEffects.txt.gz & CD8T     & 293\\\\\n",
+       "\t RPS26    & chr12-batch-4-AllEffects.txt.gz & CD4T     & 372\\\\\n",
+       "\t SMDT1    & chr22-batch-1-AllEffects.txt.gz & CD4T     &  20\\\\\n",
+       "\t SMDT1    & chr22-batch-1-AllEffects.txt.gz & CD8T     &  44\\\\\n",
+       "\t TMEM176A & chr7-batch-1-AllEffects.txt.gz  & monocyte &  11\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 21 × 4\n",
+       "\n",
+       "| eGene &lt;chr&gt; | batch &lt;chr&gt; | cell_type &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|---|---|\n",
+       "| HLA-DQA2 | chr6-batch-1-AllEffects.txt.gz  | CD4T     |  16 |\n",
+       "| HLA-DQA2 | chr6-batch-1-AllEffects.txt.gz  | CD8T     |   7 |\n",
+       "| HLA-DQA2 | chr6-batch-1-AllEffects.txt.gz  | DC       |  13 |\n",
+       "| HLA-DQA2 | chr6-batch-1-AllEffects.txt.gz  | monocyte |  17 |\n",
+       "| RNASET2  | chr6-batch-1-AllEffects.txt.gz  | CD4T     |   4 |\n",
+       "| RNASET2  | chr6-batch-1-AllEffects.txt.gz  | monocyte |   1 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | B        |  35 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | CD4T     | 372 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | CD8T     | 293 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | DC       |   3 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | monocyte | 132 |\n",
+       "| RPS26    | chr12-batch-1-AllEffects.txt.gz | NK       |  96 |\n",
+       "| RPS26    | chr12-batch-2-AllEffects.txt.gz | CD4T     | 372 |\n",
+       "| RPS26    | chr12-batch-2-AllEffects.txt.gz | CD8T     | 293 |\n",
+       "| RPS26    | chr12-batch-2-AllEffects.txt.gz | monocyte | 132 |\n",
+       "| RPS26    | chr12-batch-3-AllEffects.txt.gz | CD4T     | 372 |\n",
+       "| RPS26    | chr12-batch-3-AllEffects.txt.gz | CD8T     | 293 |\n",
+       "| RPS26    | chr12-batch-4-AllEffects.txt.gz | CD4T     | 372 |\n",
+       "| SMDT1    | chr22-batch-1-AllEffects.txt.gz | CD4T     |  20 |\n",
+       "| SMDT1    | chr22-batch-1-AllEffects.txt.gz | CD8T     |  44 |\n",
+       "| TMEM176A | chr7-batch-1-AllEffects.txt.gz  | monocyte |  11 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   eGene    batch                           cell_type n  \n",
+       "1  HLA-DQA2 chr6-batch-1-AllEffects.txt.gz  CD4T       16\n",
+       "2  HLA-DQA2 chr6-batch-1-AllEffects.txt.gz  CD8T        7\n",
+       "3  HLA-DQA2 chr6-batch-1-AllEffects.txt.gz  DC         13\n",
+       "4  HLA-DQA2 chr6-batch-1-AllEffects.txt.gz  monocyte   17\n",
+       "5  RNASET2  chr6-batch-1-AllEffects.txt.gz  CD4T        4\n",
+       "6  RNASET2  chr6-batch-1-AllEffects.txt.gz  monocyte    1\n",
+       "7  RPS26    chr12-batch-1-AllEffects.txt.gz B          35\n",
+       "8  RPS26    chr12-batch-1-AllEffects.txt.gz CD4T      372\n",
+       "9  RPS26    chr12-batch-1-AllEffects.txt.gz CD8T      293\n",
+       "10 RPS26    chr12-batch-1-AllEffects.txt.gz DC          3\n",
+       "11 RPS26    chr12-batch-1-AllEffects.txt.gz monocyte  132\n",
+       "12 RPS26    chr12-batch-1-AllEffects.txt.gz NK         96\n",
+       "13 RPS26    chr12-batch-2-AllEffects.txt.gz CD4T      372\n",
+       "14 RPS26    chr12-batch-2-AllEffects.txt.gz CD8T      293\n",
+       "15 RPS26    chr12-batch-2-AllEffects.txt.gz monocyte  132\n",
+       "16 RPS26    chr12-batch-3-AllEffects.txt.gz CD4T      372\n",
+       "17 RPS26    chr12-batch-3-AllEffects.txt.gz CD8T      293\n",
+       "18 RPS26    chr12-batch-4-AllEffects.txt.gz CD4T      372\n",
+       "19 SMDT1    chr22-batch-1-AllEffects.txt.gz CD4T       20\n",
+       "20 SMDT1    chr22-batch-1-AllEffects.txt.gz CD8T       44\n",
+       "21 TMEM176A chr7-batch-1-AllEffects.txt.gz  monocyte   11"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Inspect amount of co-eGenes\n",
+    "unique(output_all_effect[,c('Gene', 'eGene', 'batch', 'cell_type')]) %>% group_by(eGene, batch, cell_type) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "5533662b-f3d6-4d04-8a4f-a99f4d7fbaf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#output_all_effect[,c(21)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "26f15d48-8809-407b-a53f-a8fd311b01dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_all_effect = unique(output_all_effect[,-c(21)]) # remove duplicate entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "6492e4b4-4e23-4865-a588-2618c08182cd",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 15 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>eGene</th><th scope=col>cell_type</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>HLA-DQA2</td><td>CD4T    </td><td>116816</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>CD8T    </td><td> 50641</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>DC      </td><td> 80196</td></tr>\n",
+       "\t<tr><td>HLA-DQA2</td><td>monocyte</td><td>119521</td></tr>\n",
+       "\t<tr><td>RNASET2 </td><td>CD4T    </td><td> 16152</td></tr>\n",
+       "\t<tr><td>RNASET2 </td><td>monocyte</td><td>  7198</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>B       </td><td> 74127</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>CD4T    </td><td>789252</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>CD8T    </td><td>621313</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>DC      </td><td>  6195</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>monocyte</td><td>280221</td></tr>\n",
+       "\t<tr><td>RPS26   </td><td>NK      </td><td>202989</td></tr>\n",
+       "\t<tr><td>SMDT1   </td><td>CD4T    </td><td> 59540</td></tr>\n",
+       "\t<tr><td>SMDT1   </td><td>CD8T    </td><td>130916</td></tr>\n",
+       "\t<tr><td>TMEM176A</td><td>monocyte</td><td> 38740</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 15 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " eGene & cell\\_type & n\\\\\n",
+       " <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t HLA-DQA2 & CD4T     & 116816\\\\\n",
+       "\t HLA-DQA2 & CD8T     &  50641\\\\\n",
+       "\t HLA-DQA2 & DC       &  80196\\\\\n",
+       "\t HLA-DQA2 & monocyte & 119521\\\\\n",
+       "\t RNASET2  & CD4T     &  16152\\\\\n",
+       "\t RNASET2  & monocyte &   7198\\\\\n",
+       "\t RPS26    & B        &  74127\\\\\n",
+       "\t RPS26    & CD4T     & 789252\\\\\n",
+       "\t RPS26    & CD8T     & 621313\\\\\n",
+       "\t RPS26    & DC       &   6195\\\\\n",
+       "\t RPS26    & monocyte & 280221\\\\\n",
+       "\t RPS26    & NK       & 202989\\\\\n",
+       "\t SMDT1    & CD4T     &  59540\\\\\n",
+       "\t SMDT1    & CD8T     & 130916\\\\\n",
+       "\t TMEM176A & monocyte &  38740\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 15 × 3\n",
+       "\n",
+       "| eGene &lt;chr&gt; | cell_type &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|---|\n",
+       "| HLA-DQA2 | CD4T     | 116816 |\n",
+       "| HLA-DQA2 | CD8T     |  50641 |\n",
+       "| HLA-DQA2 | DC       |  80196 |\n",
+       "| HLA-DQA2 | monocyte | 119521 |\n",
+       "| RNASET2  | CD4T     |  16152 |\n",
+       "| RNASET2  | monocyte |   7198 |\n",
+       "| RPS26    | B        |  74127 |\n",
+       "| RPS26    | CD4T     | 789252 |\n",
+       "| RPS26    | CD8T     | 621313 |\n",
+       "| RPS26    | DC       |   6195 |\n",
+       "| RPS26    | monocyte | 280221 |\n",
+       "| RPS26    | NK       | 202989 |\n",
+       "| SMDT1    | CD4T     |  59540 |\n",
+       "| SMDT1    | CD8T     | 130916 |\n",
+       "| TMEM176A | monocyte |  38740 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   eGene    cell_type n     \n",
+       "1  HLA-DQA2 CD4T      116816\n",
+       "2  HLA-DQA2 CD8T       50641\n",
+       "3  HLA-DQA2 DC         80196\n",
+       "4  HLA-DQA2 monocyte  119521\n",
+       "5  RNASET2  CD4T       16152\n",
+       "6  RNASET2  monocyte    7198\n",
+       "7  RPS26    B          74127\n",
+       "8  RPS26    CD4T      789252\n",
+       "9  RPS26    CD8T      621313\n",
+       "10 RPS26    DC          6195\n",
+       "11 RPS26    monocyte  280221\n",
+       "12 RPS26    NK        202989\n",
+       "13 SMDT1    CD4T       59540\n",
+       "14 SMDT1    CD8T      130916\n",
+       "15 TMEM176A monocyte   38740"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_all_effect %>% group_by(eGene, cell_type) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "f8e386d3-c4d7-44a3-b7c8-305b49eedd75",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 0 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Gene</th><th scope=col>SNP</th><th scope=col>cell_type</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 0 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " Gene & SNP & cell\\_type & n\\\\\n",
+       " <chr> & <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 0 × 4\n",
+       "\n",
+       "| Gene &lt;chr&gt; | SNP &lt;chr&gt; | cell_type &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     Gene SNP cell_type n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output_all_effect %>% group_by(Gene, SNP, cell_type) %>% count() %>% filter(n>=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "768a4df2-0b38-4d59-9573-cc5cfe4cff71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Adjust Gene Symbol Format for better extraction and tests later on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "cc34f225-afa8-49c7-9d5d-37e02066517e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_all_effect$GeneSymbol = paste0(output_all_effect$eGene, '___', output_all_effect$GeneSymbol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "9517168f-a87d-49dc-acba-9f53417f5854",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_all_effect$GeneSymbol = str_replace(output_all_effect$GeneSymbol, ';', '__')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "c3ce084d-222f-4287-9b85-1f01486f9c46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_all_effect$ident = paste0(output_all_effect$cell_type, '_', output_all_effect$GeneSymbol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "63c6c906-8f2b-44b3-b9dd-c9842b7cea14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Adjust SNP name to only map onto matching ref and effect allele"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "e3033e05-dff4-446f-8264-876b83f0a832",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'Gene'</li><li>'GeneChr'</li><li>'GenePos'</li><li>'GeneStrand'</li><li>'GeneSymbol'</li><li>'SNP'</li><li>'SNPChr'</li><li>'SNPPos'</li><li>'SNPAlleles'</li><li>'SNPEffectAllele'</li><li>'SNPEffectAlleleFreq'</li><li>'MetaP'</li><li>'MetaPN'</li><li>'MetaPZ'</li><li>'MetaBeta'</li><li>'MetaSE'</li><li>'NrDatasets'</li><li>'DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)'</li><li>'DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)'</li><li>'DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)'</li><li>'cell_type'</li><li>'eGene'</li><li>'ident'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'Gene'\n",
+       "\\item 'GeneChr'\n",
+       "\\item 'GenePos'\n",
+       "\\item 'GeneStrand'\n",
+       "\\item 'GeneSymbol'\n",
+       "\\item 'SNP'\n",
+       "\\item 'SNPChr'\n",
+       "\\item 'SNPPos'\n",
+       "\\item 'SNPAlleles'\n",
+       "\\item 'SNPEffectAllele'\n",
+       "\\item 'SNPEffectAlleleFreq'\n",
+       "\\item 'MetaP'\n",
+       "\\item 'MetaPN'\n",
+       "\\item 'MetaPZ'\n",
+       "\\item 'MetaBeta'\n",
+       "\\item 'MetaSE'\n",
+       "\\item 'NrDatasets'\n",
+       "\\item 'DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)'\n",
+       "\\item 'DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)'\n",
+       "\\item 'DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)'\n",
+       "\\item 'cell\\_type'\n",
+       "\\item 'eGene'\n",
+       "\\item 'ident'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'Gene'\n",
+       "2. 'GeneChr'\n",
+       "3. 'GenePos'\n",
+       "4. 'GeneStrand'\n",
+       "5. 'GeneSymbol'\n",
+       "6. 'SNP'\n",
+       "7. 'SNPChr'\n",
+       "8. 'SNPPos'\n",
+       "9. 'SNPAlleles'\n",
+       "10. 'SNPEffectAllele'\n",
+       "11. 'SNPEffectAlleleFreq'\n",
+       "12. 'MetaP'\n",
+       "13. 'MetaPN'\n",
+       "14. 'MetaPZ'\n",
+       "15. 'MetaBeta'\n",
+       "16. 'MetaSE'\n",
+       "17. 'NrDatasets'\n",
+       "18. 'DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)'\n",
+       "19. 'DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)'\n",
+       "20. 'DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)'\n",
+       "21. 'cell_type'\n",
+       "22. 'eGene'\n",
+       "23. 'ident'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       " [1] \"Gene\"                                                                \n",
+       " [2] \"GeneChr\"                                                             \n",
+       " [3] \"GenePos\"                                                             \n",
+       " [4] \"GeneStrand\"                                                          \n",
+       " [5] \"GeneSymbol\"                                                          \n",
+       " [6] \"SNP\"                                                                 \n",
+       " [7] \"SNPChr\"                                                              \n",
+       " [8] \"SNPPos\"                                                              \n",
+       " [9] \"SNPAlleles\"                                                          \n",
+       "[10] \"SNPEffectAllele\"                                                     \n",
+       "[11] \"SNPEffectAlleleFreq\"                                                 \n",
+       "[12] \"MetaP\"                                                               \n",
+       "[13] \"MetaPN\"                                                              \n",
+       "[14] \"MetaPZ\"                                                              \n",
+       "[15] \"MetaBeta\"                                                            \n",
+       "[16] \"MetaSE\"                                                              \n",
+       "[17] \"NrDatasets\"                                                          \n",
+       "[18] \"DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)\"\n",
+       "[19] \"DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)\"                \n",
+       "[20] \"DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)\"            \n",
+       "[21] \"cell_type\"                                                           \n",
+       "[22] \"eGene\"                                                               \n",
+       "[23] \"ident\"                                                               "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colnames(output_all_effect)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "a23eea21-fb9f-49a1-aa64-29129efbb60a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_all_effect$SNP = paste0(output_all_effect$SNP, '_', output_all_effect$SNPAlleles)  # assuming ordered by 1) reference / 2) effect allele "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c1a9eadc-628f-4884-aa76-4b72e1ddc9f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 23</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNP</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>SNPEffectAllele</th><th scope=col>⋯</th><th scope=col>MetaPZ</th><th scope=col>MetaBeta</th><th scope=col>MetaSE</th><th scope=col>NrDatasets</th><th scope=col>DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>cell_type</th><th scope=col>eGene</th><th scope=col>ident</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>CDKN2D;HLA-DQA2 </td><td>6</td><td>32709119</td><td>NA</td><td>HLA-DQA2___CDKN2D__HLA-DQA2 </td><td>rs1144709_C/T</td><td>6</td><td>31709349</td><td>C/T</td><td>T</td><td>⋯</td><td> 0.069875</td><td> 0.015741</td><td>0.225274</td><td>2</td><td>-;0.08039;-0.112611;- </td><td>-;0.472978;-0.498951;-</td><td>-;37;22;-</td><td>DC</td><td>HLA-DQA2</td><td>DC_HLA-DQA2___CDKN2D__HLA-DQA2 </td></tr>\n",
+       "\t<tr><td>FAM129C;HLA-DQA2</td><td>6</td><td>32709119</td><td>NA</td><td>HLA-DQA2___FAM129C__HLA-DQA2</td><td>rs1144709_C/T</td><td>6</td><td>31709349</td><td>C/T</td><td>T</td><td>⋯</td><td>-0.271313</td><td>-0.062011</td><td>0.228560</td><td>2</td><td>-;-0.084633;0.052223;-</td><td>-;-0.505195;0.224816;-</td><td>-;38;21;-</td><td>DC</td><td>HLA-DQA2</td><td>DC_HLA-DQA2___FAM129C__HLA-DQA2</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 23\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNP & SNPChr & SNPPos & SNPAlleles & SNPEffectAllele & ⋯ & MetaPZ & MetaBeta & MetaSE & NrDatasets & DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) & DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) & DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) & cell\\_type & eGene & ident\\\\\n",
+       " <chr> & <int> & <int> & <lgl> & <chr> & <chr> & <int> & <int> & <chr> & <chr> & ⋯ & <dbl> & <dbl> & <dbl> & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t CDKN2D;HLA-DQA2  & 6 & 32709119 & NA & HLA-DQA2\\_\\_\\_CDKN2D\\_\\_HLA-DQA2  & rs1144709\\_C/T & 6 & 31709349 & C/T & T & ⋯ &  0.069875 &  0.015741 & 0.225274 & 2 & -;0.08039;-0.112611;-  & -;0.472978;-0.498951;- & -;37;22;- & DC & HLA-DQA2 & DC\\_HLA-DQA2\\_\\_\\_CDKN2D\\_\\_HLA-DQA2 \\\\\n",
+       "\t FAM129C;HLA-DQA2 & 6 & 32709119 & NA & HLA-DQA2\\_\\_\\_FAM129C\\_\\_HLA-DQA2 & rs1144709\\_C/T & 6 & 31709349 & C/T & T & ⋯ & -0.271313 & -0.062011 & 0.228560 & 2 & -;-0.084633;0.052223;- & -;-0.505195;0.224816;- & -;38;21;- & DC & HLA-DQA2 & DC\\_HLA-DQA2\\_\\_\\_FAM129C\\_\\_HLA-DQA2\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 23\n",
+       "\n",
+       "| Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNP &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | SNPEffectAllele &lt;chr&gt; | ⋯ ⋯ | MetaPZ &lt;dbl&gt; | MetaBeta &lt;dbl&gt; | MetaSE &lt;dbl&gt; | NrDatasets &lt;int&gt; | DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | cell_type &lt;chr&gt; | eGene &lt;chr&gt; | ident &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| CDKN2D;HLA-DQA2  | 6 | 32709119 | NA | HLA-DQA2___CDKN2D__HLA-DQA2  | rs1144709_C/T | 6 | 31709349 | C/T | T | ⋯ |  0.069875 |  0.015741 | 0.225274 | 2 | -;0.08039;-0.112611;-  | -;0.472978;-0.498951;- | -;37;22;- | DC | HLA-DQA2 | DC_HLA-DQA2___CDKN2D__HLA-DQA2  |\n",
+       "| FAM129C;HLA-DQA2 | 6 | 32709119 | NA | HLA-DQA2___FAM129C__HLA-DQA2 | rs1144709_C/T | 6 | 31709349 | C/T | T | ⋯ | -0.271313 | -0.062011 | 0.228560 | 2 | -;-0.084633;0.052223;- | -;-0.505195;0.224816;- | -;38;21;- | DC | HLA-DQA2 | DC_HLA-DQA2___FAM129C__HLA-DQA2 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Gene             GeneChr GenePos  GeneStrand GeneSymbol                  \n",
+       "1 CDKN2D;HLA-DQA2  6       32709119 NA         HLA-DQA2___CDKN2D__HLA-DQA2 \n",
+       "2 FAM129C;HLA-DQA2 6       32709119 NA         HLA-DQA2___FAM129C__HLA-DQA2\n",
+       "  SNP           SNPChr SNPPos   SNPAlleles SNPEffectAllele ⋯ MetaPZ   \n",
+       "1 rs1144709_C/T 6      31709349 C/T        T               ⋯  0.069875\n",
+       "2 rs1144709_C/T 6      31709349 C/T        T               ⋯ -0.271313\n",
+       "  MetaBeta  MetaSE   NrDatasets\n",
+       "1  0.015741 0.225274 2         \n",
+       "2 -0.062011 0.228560 2         \n",
+       "  DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -;0.08039;-0.112611;-                                               \n",
+       "2 -;-0.084633;0.052223;-                                              \n",
+       "  DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -;0.472978;-0.498951;-                              \n",
+       "2 -;-0.505195;0.224816;-                              \n",
+       "  DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) cell_type eGene   \n",
+       "1 -;37;22;-                                                DC        HLA-DQA2\n",
+       "2 -;38;21;-                                                DC        HLA-DQA2\n",
+       "  ident                          \n",
+       "1 DC_HLA-DQA2___CDKN2D__HLA-DQA2 \n",
+       "2 DC_HLA-DQA2___FAM129C__HLA-DQA2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(output_all_effect,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f9f2db5-8ef3-4c76-948f-c7a8581f06f3",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## GWAS summary statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10a6c2ae-fae1-422b-9cd4-e5868e87a982",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### GTEX data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "48c257a1-1aa8-4592-bc55-4e5e90ca10bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Get metadata to choose the GWAS traits that should be tested from GTEX"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "befecfe2-e3f9-4afe-91f7-935ed8ad720a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata = fread(paste0(gwas_data_path, \"public_data/gtex_gwas_data/gwas_metadata.txt\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "35aadb35-f2e3-4029-993f-a555842235f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "114"
+      ],
+      "text/latex": [
+       "114"
+      ],
+      "text/markdown": [
+       "114"
+      ],
+      "text/plain": [
+       "[1] 114"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(metadata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "3e3a406d-7437-462b-b134-533b27abfc31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 29</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>GTEx_GWAS</th><th scope=col>Tag</th><th scope=col>PUBMED_Paper_Link</th><th scope=col>Pheno_File</th><th scope=col>Source_File</th><th scope=col>Portal</th><th scope=col>Consortium</th><th scope=col>Link</th><th scope=col>Notes</th><th scope=col>Header</th><th scope=col>⋯</th><th scope=col>Declared_Effect_Allele</th><th scope=col>Genome_Reference</th><th scope=col>Binary</th><th scope=col>Cases</th><th scope=col>abbreviation</th><th scope=col>new_abbreviation</th><th scope=col>new_Phenotype</th><th scope=col>Category</th><th scope=col>Deflation</th><th scope=col>color</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Yes</td><td>ADIPOGen_Adiponectin              </td><td>http://www.ncbi.nlm.nih.gov/pubmed/22479202 </td><td>adipogen.discovery.eur_.meta_.public.release.part1_.txt</td><td>adipogen.discovery.eur_.meta_.public.release.part1_.txt</td><td>http://www.mcgill.ca/genepi/adipogen-consortium       </td><td>ADIPOGen        </td><td>http://www.mcgill.ca/genepi/files/genepi/adipogen.discovery.eur_.meta_.public.release.part1_.txt                       </td><td>NA</td><td>NA                                                                                                                                                                           </td><td>⋯</td><td>NA</td><td>b36/hg18</td><td>0</td><td>NA</td><td>ANT</td><td>ADPN</td><td>Adiponectin     </td><td>Cardiometabolic</td><td>1</td><td>#004000</td></tr>\n",
+       "\t<tr><td>Yes</td><td>Astle_et_al_2016_Eosinophil_counts</td><td>https://www.ncbi.nlm.nih.gov/pubmed/27863252</td><td>eo_build37_172275_20161212.tsv.gz                      </td><td>eo_build37_172275_20161212.tsv.gz                      </td><td>http://www.ebi.ac.uk/gwas/downloads/summary-statistics</td><td>Astle_et_al_2016</td><td>ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/AstleWJ_27863252_GCST004606/eo_build37_172275_20161212.tsv.gz</td><td>NA</td><td>VARIANT        ID        CHR        BP        REF        ALT        ALT_MINOR        DIRECTION        EFFECT        SE        P        MLOG10P        ALT_FREQ        MA_FREQ</td><td>⋯</td><td>NA</td><td>b37/hg19</td><td>0</td><td>NA</td><td>BEC</td><td>EC  </td><td>Eosinophil_Count</td><td>Blood          </td><td>0</td><td>#C00000</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 29\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " GTEx\\_GWAS & Tag & PUBMED\\_Paper\\_Link & Pheno\\_File & Source\\_File & Portal & Consortium & Link & Notes & Header & ⋯ & Declared\\_Effect\\_Allele & Genome\\_Reference & Binary & Cases & abbreviation & new\\_abbreviation & new\\_Phenotype & Category & Deflation & color\\\\\n",
+       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & ⋯ & <chr> & <chr> & <int> & <int> & <chr> & <chr> & <chr> & <chr> & <int> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t Yes & ADIPOGen\\_Adiponectin               & http://www.ncbi.nlm.nih.gov/pubmed/22479202  & adipogen.discovery.eur\\_.meta\\_.public.release.part1\\_.txt & adipogen.discovery.eur\\_.meta\\_.public.release.part1\\_.txt & http://www.mcgill.ca/genepi/adipogen-consortium        & ADIPOGen         & http://www.mcgill.ca/genepi/files/genepi/adipogen.discovery.eur\\_.meta\\_.public.release.part1\\_.txt                        & NA & NA                                                                                                                                                                            & ⋯ & NA & b36/hg18 & 0 & NA & ANT & ADPN & Adiponectin      & Cardiometabolic & 1 & \\#004000\\\\\n",
+       "\t Yes & Astle\\_et\\_al\\_2016\\_Eosinophil\\_counts & https://www.ncbi.nlm.nih.gov/pubmed/27863252 & eo\\_build37\\_172275\\_20161212.tsv.gz                       & eo\\_build37\\_172275\\_20161212.tsv.gz                       & http://www.ebi.ac.uk/gwas/downloads/summary-statistics & Astle\\_et\\_al\\_2016 & ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary\\_statistics/AstleWJ\\_27863252\\_GCST004606/eo\\_build37\\_172275\\_20161212.tsv.gz & NA & VARIANT        ID        CHR        BP        REF        ALT        ALT\\_MINOR        DIRECTION        EFFECT        SE        P        MLOG10P        ALT\\_FREQ        MA\\_FREQ & ⋯ & NA & b37/hg19 & 0 & NA & BEC & EC   & Eosinophil\\_Count & Blood           & 0 & \\#C00000\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 29\n",
+       "\n",
+       "| GTEx_GWAS &lt;chr&gt; | Tag &lt;chr&gt; | PUBMED_Paper_Link &lt;chr&gt; | Pheno_File &lt;chr&gt; | Source_File &lt;chr&gt; | Portal &lt;chr&gt; | Consortium &lt;chr&gt; | Link &lt;chr&gt; | Notes &lt;chr&gt; | Header &lt;chr&gt; | ⋯ ⋯ | Declared_Effect_Allele &lt;chr&gt; | Genome_Reference &lt;chr&gt; | Binary &lt;int&gt; | Cases &lt;int&gt; | abbreviation &lt;chr&gt; | new_abbreviation &lt;chr&gt; | new_Phenotype &lt;chr&gt; | Category &lt;chr&gt; | Deflation &lt;int&gt; | color &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| Yes | ADIPOGen_Adiponectin               | http://www.ncbi.nlm.nih.gov/pubmed/22479202  | adipogen.discovery.eur_.meta_.public.release.part1_.txt | adipogen.discovery.eur_.meta_.public.release.part1_.txt | http://www.mcgill.ca/genepi/adipogen-consortium        | ADIPOGen         | http://www.mcgill.ca/genepi/files/genepi/adipogen.discovery.eur_.meta_.public.release.part1_.txt                        | NA | NA                                                                                                                                                                            | ⋯ | NA | b36/hg18 | 0 | NA | ANT | ADPN | Adiponectin      | Cardiometabolic | 1 | #004000 |\n",
+       "| Yes | Astle_et_al_2016_Eosinophil_counts | https://www.ncbi.nlm.nih.gov/pubmed/27863252 | eo_build37_172275_20161212.tsv.gz                       | eo_build37_172275_20161212.tsv.gz                       | http://www.ebi.ac.uk/gwas/downloads/summary-statistics | Astle_et_al_2016 | ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/AstleWJ_27863252_GCST004606/eo_build37_172275_20161212.tsv.gz | NA | VARIANT        ID        CHR        BP        REF        ALT        ALT_MINOR        DIRECTION        EFFECT        SE        P        MLOG10P        ALT_FREQ        MA_FREQ | ⋯ | NA | b37/hg19 | 0 | NA | BEC | EC   | Eosinophil_Count | Blood           | 0 | #C00000 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  GTEx_GWAS Tag                               \n",
+       "1 Yes       ADIPOGen_Adiponectin              \n",
+       "2 Yes       Astle_et_al_2016_Eosinophil_counts\n",
+       "  PUBMED_Paper_Link                           \n",
+       "1 http://www.ncbi.nlm.nih.gov/pubmed/22479202 \n",
+       "2 https://www.ncbi.nlm.nih.gov/pubmed/27863252\n",
+       "  Pheno_File                                             \n",
+       "1 adipogen.discovery.eur_.meta_.public.release.part1_.txt\n",
+       "2 eo_build37_172275_20161212.tsv.gz                      \n",
+       "  Source_File                                            \n",
+       "1 adipogen.discovery.eur_.meta_.public.release.part1_.txt\n",
+       "2 eo_build37_172275_20161212.tsv.gz                      \n",
+       "  Portal                                                 Consortium      \n",
+       "1 http://www.mcgill.ca/genepi/adipogen-consortium        ADIPOGen        \n",
+       "2 http://www.ebi.ac.uk/gwas/downloads/summary-statistics Astle_et_al_2016\n",
+       "  Link                                                                                                                   \n",
+       "1 http://www.mcgill.ca/genepi/files/genepi/adipogen.discovery.eur_.meta_.public.release.part1_.txt                       \n",
+       "2 ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/AstleWJ_27863252_GCST004606/eo_build37_172275_20161212.tsv.gz\n",
+       "  Notes\n",
+       "1 NA   \n",
+       "2 NA   \n",
+       "  Header                                                                                                                                                                       \n",
+       "1 NA                                                                                                                                                                           \n",
+       "2 VARIANT        ID        CHR        BP        REF        ALT        ALT_MINOR        DIRECTION        EFFECT        SE        P        MLOG10P        ALT_FREQ        MA_FREQ\n",
+       "  ⋯ Declared_Effect_Allele Genome_Reference Binary Cases abbreviation\n",
+       "1 ⋯ NA                     b36/hg18         0      NA    ANT         \n",
+       "2 ⋯ NA                     b37/hg19         0      NA    BEC         \n",
+       "  new_abbreviation new_Phenotype    Category        Deflation color  \n",
+       "1 ADPN             Adiponectin      Cardiometabolic 1         #004000\n",
+       "2 EC               Eosinophil_Count Blood           0         #C00000"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(metadata[order(metadata$Tag, decreasing =FALSE),],2)  # File Overview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "73c3070f-c072-4e65-98e2-7c53356a6d50",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 15 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Phenotype</th><th scope=col>Tag</th><th scope=col>PUBMED_Paper_Link</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Blood clot in the lung               </td><td>UKB_6152_7_diagnosed_by_doctor_Blood_clot_in_the_lung              </td><td>http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202</td></tr>\n",
+       "\t<tr><td>Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Asthma                               </td><td>UKB_6152_8_diagnosed_by_doctor_Asthma                              </td><td>http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202</td></tr>\n",
+       "\t<tr><td>Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Hayfever, allergic rhinitis or eczema</td><td>UKB_6152_9_diagnosed_by_doctor_Hayfever_allergic_rhinitis_or_eczema</td><td>http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202</td></tr>\n",
+       "\t<tr><td>Myeloid white cell count                                                                                                            </td><td>Astle_et_al_2016_Myeloid_white_cell_count                          </td><td>https://www.ncbi.nlm.nih.gov/pubmed/27863252            </td></tr>\n",
+       "\t<tr><td>Red blood cell count                                                                                                                </td><td>Astle_et_al_2016_Red_blood_cell_count                              </td><td>https://www.ncbi.nlm.nih.gov/pubmed/27863252            </td></tr>\n",
+       "\t<tr><td>White blood cell count                                                                                                              </td><td>Astle_et_al_2016_White_blood_cell_count                            </td><td>https://www.ncbi.nlm.nih.gov/pubmed/27863252            </td></tr>\n",
+       "\t<tr><td>Type 2 Diabetes                                                                                                                     </td><td>DIAGRAM_T2D_TRANS_ETHNIC                                           </td><td>http://www.ncbi.nlm.nih.gov/pubmed/22885922             </td></tr>\n",
+       "\t<tr><td>Asthma                                                                                                                              </td><td>GABRIEL_Asthma                                                     </td><td>http://www.ncbi.nlm.nih.gov/pubmed/17611496             </td></tr>\n",
+       "\t<tr><td>Crohn's Disease                                                                                                                     </td><td>IBD.EUR.Crohns_Disease                                             </td><td>http://www.ncbi.nlm.nih.gov/pubmed/26192919             </td></tr>\n",
+       "\t<tr><td>Inflammatory Bowel Disease                                                                                                          </td><td>IBD.EUR.Inflammatory_Bowel_Disease                                 </td><td>http://www.ncbi.nlm.nih.gov/pubmed/26192919             </td></tr>\n",
+       "\t<tr><td>Multiple Sclerosis                                                                                                                  </td><td>IMMUNOBASE_Multiple_sclerosis_hg19                                 </td><td>http://www.ncbi.nlm.nih.gov/pubmed/21833088             </td></tr>\n",
+       "\t<tr><td>Rheumatoid Arthritis                                                                                                                </td><td>RA_OKADA_TRANS_ETHNIC                                              </td><td>http://www.ncbi.nlm.nih.gov/pubmed/24390342             </td></tr>\n",
+       "\t<tr><td>Asthma                                                                                                                              </td><td>TAGC_Asthma_EUR                                                    </td><td>https://www.ncbi.nlm.nih.gov/pubmed/29273806            </td></tr>\n",
+       "\t<tr><td>Systolic Blood Pressure                                                                                                             </td><td>ICBP_SystolicPressure                                              </td><td>https://www.ncbi.nlm.nih.gov/pubmed/21909115            </td></tr>\n",
+       "\t<tr><td>Diastolic Blood Pressure                                                                                                            </td><td>ICBP_DiastolicPressure                                             </td><td>https://www.ncbi.nlm.nih.gov/pubmed/21909115            </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 15 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " Phenotype & Tag & PUBMED\\_Paper\\_Link\\\\\n",
+       " <chr> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Blood clot in the lung                & UKB\\_6152\\_7\\_diagnosed\\_by\\_doctor\\_Blood\\_clot\\_in\\_the\\_lung               & http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202\\\\\n",
+       "\t Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Asthma                                & UKB\\_6152\\_8\\_diagnosed\\_by\\_doctor\\_Asthma                               & http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202\\\\\n",
+       "\t Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Hayfever, allergic rhinitis or eczema & UKB\\_6152\\_9\\_diagnosed\\_by\\_doctor\\_Hayfever\\_allergic\\_rhinitis\\_or\\_eczema & http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202\\\\\n",
+       "\t Myeloid white cell count                                                                                                             & Astle\\_et\\_al\\_2016\\_Myeloid\\_white\\_cell\\_count                           & https://www.ncbi.nlm.nih.gov/pubmed/27863252            \\\\\n",
+       "\t Red blood cell count                                                                                                                 & Astle\\_et\\_al\\_2016\\_Red\\_blood\\_cell\\_count                               & https://www.ncbi.nlm.nih.gov/pubmed/27863252            \\\\\n",
+       "\t White blood cell count                                                                                                               & Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count                             & https://www.ncbi.nlm.nih.gov/pubmed/27863252            \\\\\n",
+       "\t Type 2 Diabetes                                                                                                                      & DIAGRAM\\_T2D\\_TRANS\\_ETHNIC                                            & http://www.ncbi.nlm.nih.gov/pubmed/22885922             \\\\\n",
+       "\t Asthma                                                                                                                               & GABRIEL\\_Asthma                                                      & http://www.ncbi.nlm.nih.gov/pubmed/17611496             \\\\\n",
+       "\t Crohn's Disease                                                                                                                      & IBD.EUR.Crohns\\_Disease                                              & http://www.ncbi.nlm.nih.gov/pubmed/26192919             \\\\\n",
+       "\t Inflammatory Bowel Disease                                                                                                           & IBD.EUR.Inflammatory\\_Bowel\\_Disease                                  & http://www.ncbi.nlm.nih.gov/pubmed/26192919             \\\\\n",
+       "\t Multiple Sclerosis                                                                                                                   & IMMUNOBASE\\_Multiple\\_sclerosis\\_hg19                                  & http://www.ncbi.nlm.nih.gov/pubmed/21833088             \\\\\n",
+       "\t Rheumatoid Arthritis                                                                                                                 & RA\\_OKADA\\_TRANS\\_ETHNIC                                               & http://www.ncbi.nlm.nih.gov/pubmed/24390342             \\\\\n",
+       "\t Asthma                                                                                                                               & TAGC\\_Asthma\\_EUR                                                     & https://www.ncbi.nlm.nih.gov/pubmed/29273806            \\\\\n",
+       "\t Systolic Blood Pressure                                                                                                              & ICBP\\_SystolicPressure                                               & https://www.ncbi.nlm.nih.gov/pubmed/21909115            \\\\\n",
+       "\t Diastolic Blood Pressure                                                                                                             & ICBP\\_DiastolicPressure                                              & https://www.ncbi.nlm.nih.gov/pubmed/21909115            \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 15 × 3\n",
+       "\n",
+       "| Phenotype &lt;chr&gt; | Tag &lt;chr&gt; | PUBMED_Paper_Link &lt;chr&gt; |\n",
+       "|---|---|---|\n",
+       "| Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Blood clot in the lung                | UKB_6152_7_diagnosed_by_doctor_Blood_clot_in_the_lung               | http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202 |\n",
+       "| Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Asthma                                | UKB_6152_8_diagnosed_by_doctor_Asthma                               | http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202 |\n",
+       "| Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Hayfever, allergic rhinitis or eczema | UKB_6152_9_diagnosed_by_doctor_Hayfever_allergic_rhinitis_or_eczema | http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202 |\n",
+       "| Myeloid white cell count                                                                                                             | Astle_et_al_2016_Myeloid_white_cell_count                           | https://www.ncbi.nlm.nih.gov/pubmed/27863252             |\n",
+       "| Red blood cell count                                                                                                                 | Astle_et_al_2016_Red_blood_cell_count                               | https://www.ncbi.nlm.nih.gov/pubmed/27863252             |\n",
+       "| White blood cell count                                                                                                               | Astle_et_al_2016_White_blood_cell_count                             | https://www.ncbi.nlm.nih.gov/pubmed/27863252             |\n",
+       "| Type 2 Diabetes                                                                                                                      | DIAGRAM_T2D_TRANS_ETHNIC                                            | http://www.ncbi.nlm.nih.gov/pubmed/22885922              |\n",
+       "| Asthma                                                                                                                               | GABRIEL_Asthma                                                      | http://www.ncbi.nlm.nih.gov/pubmed/17611496              |\n",
+       "| Crohn's Disease                                                                                                                      | IBD.EUR.Crohns_Disease                                              | http://www.ncbi.nlm.nih.gov/pubmed/26192919              |\n",
+       "| Inflammatory Bowel Disease                                                                                                           | IBD.EUR.Inflammatory_Bowel_Disease                                  | http://www.ncbi.nlm.nih.gov/pubmed/26192919              |\n",
+       "| Multiple Sclerosis                                                                                                                   | IMMUNOBASE_Multiple_sclerosis_hg19                                  | http://www.ncbi.nlm.nih.gov/pubmed/21833088              |\n",
+       "| Rheumatoid Arthritis                                                                                                                 | RA_OKADA_TRANS_ETHNIC                                               | http://www.ncbi.nlm.nih.gov/pubmed/24390342              |\n",
+       "| Asthma                                                                                                                               | TAGC_Asthma_EUR                                                     | https://www.ncbi.nlm.nih.gov/pubmed/29273806             |\n",
+       "| Systolic Blood Pressure                                                                                                              | ICBP_SystolicPressure                                               | https://www.ncbi.nlm.nih.gov/pubmed/21909115             |\n",
+       "| Diastolic Blood Pressure                                                                                                             | ICBP_DiastolicPressure                                              | https://www.ncbi.nlm.nih.gov/pubmed/21909115             |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   Phenotype                                                                                                                           \n",
+       "1  Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Blood clot in the lung               \n",
+       "2  Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Asthma                               \n",
+       "3  Blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor: Hayfever, allergic rhinitis or eczema\n",
+       "4  Myeloid white cell count                                                                                                            \n",
+       "5  Red blood cell count                                                                                                                \n",
+       "6  White blood cell count                                                                                                              \n",
+       "7  Type 2 Diabetes                                                                                                                     \n",
+       "8  Asthma                                                                                                                              \n",
+       "9  Crohn's Disease                                                                                                                     \n",
+       "10 Inflammatory Bowel Disease                                                                                                          \n",
+       "11 Multiple Sclerosis                                                                                                                  \n",
+       "12 Rheumatoid Arthritis                                                                                                                \n",
+       "13 Asthma                                                                                                                              \n",
+       "14 Systolic Blood Pressure                                                                                                             \n",
+       "15 Diastolic Blood Pressure                                                                                                            \n",
+       "   Tag                                                                \n",
+       "1  UKB_6152_7_diagnosed_by_doctor_Blood_clot_in_the_lung              \n",
+       "2  UKB_6152_8_diagnosed_by_doctor_Asthma                              \n",
+       "3  UKB_6152_9_diagnosed_by_doctor_Hayfever_allergic_rhinitis_or_eczema\n",
+       "4  Astle_et_al_2016_Myeloid_white_cell_count                          \n",
+       "5  Astle_et_al_2016_Red_blood_cell_count                              \n",
+       "6  Astle_et_al_2016_White_blood_cell_count                            \n",
+       "7  DIAGRAM_T2D_TRANS_ETHNIC                                           \n",
+       "8  GABRIEL_Asthma                                                     \n",
+       "9  IBD.EUR.Crohns_Disease                                             \n",
+       "10 IBD.EUR.Inflammatory_Bowel_Disease                                 \n",
+       "11 IMMUNOBASE_Multiple_sclerosis_hg19                                 \n",
+       "12 RA_OKADA_TRANS_ETHNIC                                              \n",
+       "13 TAGC_Asthma_EUR                                                    \n",
+       "14 ICBP_SystolicPressure                                              \n",
+       "15 ICBP_DiastolicPressure                                             \n",
+       "   PUBMED_Paper_Link                                       \n",
+       "1  http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202\n",
+       "2  http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202\n",
+       "3  http://biobank.ctsu.ox.ac.uk/showcase/field.cgi?id=41202\n",
+       "4  https://www.ncbi.nlm.nih.gov/pubmed/27863252            \n",
+       "5  https://www.ncbi.nlm.nih.gov/pubmed/27863252            \n",
+       "6  https://www.ncbi.nlm.nih.gov/pubmed/27863252            \n",
+       "7  http://www.ncbi.nlm.nih.gov/pubmed/22885922             \n",
+       "8  http://www.ncbi.nlm.nih.gov/pubmed/17611496             \n",
+       "9  http://www.ncbi.nlm.nih.gov/pubmed/26192919             \n",
+       "10 http://www.ncbi.nlm.nih.gov/pubmed/26192919             \n",
+       "11 http://www.ncbi.nlm.nih.gov/pubmed/21833088             \n",
+       "12 http://www.ncbi.nlm.nih.gov/pubmed/24390342             \n",
+       "13 https://www.ncbi.nlm.nih.gov/pubmed/29273806            \n",
+       "14 https://www.ncbi.nlm.nih.gov/pubmed/21909115            \n",
+       "15 https://www.ncbi.nlm.nih.gov/pubmed/21909115            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Extract potentially relevant traits\n",
+    "tail(unique(metadata[!is.na(str_extract(metadata$Phenotype, 'Diabetes|diabetes|rheomatoid|Rheomatoid|arthritis|Arthritis|Blood|blood|crohn|Crohn|Sclerosis|sclerosis|Fever|fever|Asthma|asthma|bowel|Bowel|white|White')),c('Phenotype', 'Tag', 'PUBMED_Paper_Link')]), 15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "e335acf0-f3ef-46f1-b2b0-29f35153134c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Load data based on selected trait tags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "46395cc3-3506-46bb-940e-554a2c8ac613",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Select tags or take all tags - which GWAS to look into\n",
+    "tag = c('RA_OKADA_TRANS_ETHNIC', 'TAGC_Asthma_EUR', 'IMMUNOBASE_Multiple_sclerosis_hg19', 'IBD.EUR.Inflammatory_Bowel_Disease', 'IBD.EUR.Crohns_Disease', 'Astle_et_al_2016_White_blood_cell_count')  # rheomatoid arthritis tag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "1e1573eb-37cf-4c39-bf54-a06d12c9a81b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'RA_OKADA_TRANS_ETHNIC'</li><li>'TAGC_Asthma_EUR'</li><li>'IMMUNOBASE_Multiple_sclerosis_hg19'</li><li>'IBD.EUR.Inflammatory_Bowel_Disease'</li><li>'IBD.EUR.Crohns_Disease'</li><li>'Astle_et_al_2016_White_blood_cell_count'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'RA\\_OKADA\\_TRANS\\_ETHNIC'\n",
+       "\\item 'TAGC\\_Asthma\\_EUR'\n",
+       "\\item 'IMMUNOBASE\\_Multiple\\_sclerosis\\_hg19'\n",
+       "\\item 'IBD.EUR.Inflammatory\\_Bowel\\_Disease'\n",
+       "\\item 'IBD.EUR.Crohns\\_Disease'\n",
+       "\\item 'Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'RA_OKADA_TRANS_ETHNIC'\n",
+       "2. 'TAGC_Asthma_EUR'\n",
+       "3. 'IMMUNOBASE_Multiple_sclerosis_hg19'\n",
+       "4. 'IBD.EUR.Inflammatory_Bowel_Disease'\n",
+       "5. 'IBD.EUR.Crohns_Disease'\n",
+       "6. 'Astle_et_al_2016_White_blood_cell_count'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"RA_OKADA_TRANS_ETHNIC\"                  \n",
+       "[2] \"TAGC_Asthma_EUR\"                        \n",
+       "[3] \"IMMUNOBASE_Multiple_sclerosis_hg19\"     \n",
+       "[4] \"IBD.EUR.Inflammatory_Bowel_Disease\"     \n",
+       "[5] \"IBD.EUR.Crohns_Disease\"                 \n",
+       "[6] \"Astle_et_al_2016_White_blood_cell_count\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(tag)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "76f80b20-6440-459a-9fb1-6ae0b9fc6f74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_gtex = data.frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "65fb2716-9d64-4124-931b-f0903909e946",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"RA_OKADA_TRANS_ETHNIC\"                  \n",
+      "[2] \"TAGC_Asthma_EUR\"                        \n",
+      "[3] \"IMMUNOBASE_Multiple_sclerosis_hg19\"     \n",
+      "[4] \"IBD.EUR.Inflammatory_Bowel_Disease\"     \n",
+      "[5] \"IBD.EUR.Crohns_Disease\"                 \n",
+      "[6] \"Astle_et_al_2016_White_blood_cell_count\"\n",
+      "[1] \"RA_OKADA_TRANS_ETHNIC\"                  \n",
+      "[2] \"TAGC_Asthma_EUR\"                        \n",
+      "[3] \"IMMUNOBASE_Multiple_sclerosis_hg19\"     \n",
+      "[4] \"IBD.EUR.Inflammatory_Bowel_Disease\"     \n",
+      "[5] \"IBD.EUR.Crohns_Disease\"                 \n",
+      "[6] \"Astle_et_al_2016_White_blood_cell_count\"\n",
+      "[1] \"RA_OKADA_TRANS_ETHNIC\"                  \n",
+      "[2] \"TAGC_Asthma_EUR\"                        \n",
+      "[3] \"IMMUNOBASE_Multiple_sclerosis_hg19\"     \n",
+      "[4] \"IBD.EUR.Inflammatory_Bowel_Disease\"     \n",
+      "[5] \"IBD.EUR.Crohns_Disease\"                 \n",
+      "[6] \"Astle_et_al_2016_White_blood_cell_count\"\n",
+      "[1] \"RA_OKADA_TRANS_ETHNIC\"                  \n",
+      "[2] \"TAGC_Asthma_EUR\"                        \n",
+      "[3] \"IMMUNOBASE_Multiple_sclerosis_hg19\"     \n",
+      "[4] \"IBD.EUR.Inflammatory_Bowel_Disease\"     \n",
+      "[5] \"IBD.EUR.Crohns_Disease\"                 \n",
+      "[6] \"Astle_et_al_2016_White_blood_cell_count\"\n",
+      "[1] \"RA_OKADA_TRANS_ETHNIC\"                  \n",
+      "[2] \"TAGC_Asthma_EUR\"                        \n",
+      "[3] \"IMMUNOBASE_Multiple_sclerosis_hg19\"     \n",
+      "[4] \"IBD.EUR.Inflammatory_Bowel_Disease\"     \n",
+      "[5] \"IBD.EUR.Crohns_Disease\"                 \n",
+      "[6] \"Astle_et_al_2016_White_blood_cell_count\"\n",
+      "[1] \"RA_OKADA_TRANS_ETHNIC\"                  \n",
+      "[2] \"TAGC_Asthma_EUR\"                        \n",
+      "[3] \"IMMUNOBASE_Multiple_sclerosis_hg19\"     \n",
+      "[4] \"IBD.EUR.Inflammatory_Bowel_Disease\"     \n",
+      "[5] \"IBD.EUR.Crohns_Disease\"                 \n",
+      "[6] \"Astle_et_al_2016_White_blood_cell_count\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# GWAS for selected tags\n",
+    "for(i in tag){\n",
+    "    print(tag)\n",
+    "    data = fread(paste0(gwas_data_path, \"public_data/gtex_gwas_data/imputed_gwas_hg38_1.1/imputed_\", i, \".txt.gz\"))\n",
+    "    data$tag = i\n",
+    "    gwas_gtex = rbind(data, gwas_gtex)\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "79ff14ab-c98a-4a4f-945a-452a1c16cafb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Inspect loaded data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "fc876108-c448-4983-8414-c8b62a4378db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'Astle_et_al_2016_White_blood_cell_count'</li><li>'IBD.EUR.Crohns_Disease'</li><li>'IBD.EUR.Inflammatory_Bowel_Disease'</li><li>'IMMUNOBASE_Multiple_sclerosis_hg19'</li><li>'TAGC_Asthma_EUR'</li><li>'RA_OKADA_TRANS_ETHNIC'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count'\n",
+       "\\item 'IBD.EUR.Crohns\\_Disease'\n",
+       "\\item 'IBD.EUR.Inflammatory\\_Bowel\\_Disease'\n",
+       "\\item 'IMMUNOBASE\\_Multiple\\_sclerosis\\_hg19'\n",
+       "\\item 'TAGC\\_Asthma\\_EUR'\n",
+       "\\item 'RA\\_OKADA\\_TRANS\\_ETHNIC'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'Astle_et_al_2016_White_blood_cell_count'\n",
+       "2. 'IBD.EUR.Crohns_Disease'\n",
+       "3. 'IBD.EUR.Inflammatory_Bowel_Disease'\n",
+       "4. 'IMMUNOBASE_Multiple_sclerosis_hg19'\n",
+       "5. 'TAGC_Asthma_EUR'\n",
+       "6. 'RA_OKADA_TRANS_ETHNIC'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"Astle_et_al_2016_White_blood_cell_count\"\n",
+       "[2] \"IBD.EUR.Crohns_Disease\"                 \n",
+       "[3] \"IBD.EUR.Inflammatory_Bowel_Disease\"     \n",
+       "[4] \"IMMUNOBASE_Multiple_sclerosis_hg19\"     \n",
+       "[5] \"TAGC_Asthma_EUR\"                        \n",
+       "[6] \"RA_OKADA_TRANS_ETHNIC\"                  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(gwas_gtex$tag)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "c98046ee-6c60-417d-8d6e-7230e56dc161",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 16</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>variant_id</th><th scope=col>panel_variant_id</th><th scope=col>chromosome</th><th scope=col>position</th><th scope=col>effect_allele</th><th scope=col>non_effect_allele</th><th scope=col>current_build</th><th scope=col>frequency</th><th scope=col>sample_size</th><th scope=col>zscore</th><th scope=col>pvalue</th><th scope=col>effect_size</th><th scope=col>standard_error</th><th scope=col>imputation_status</th><th scope=col>n_cases</th><th scope=col>tag</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs554008981</td><td>chr1_13550_G_A_b38</td><td>chr1</td><td>13550</td><td>A</td><td>G</td><td>hg38</td><td>0.01731602</td><td>173480</td><td>1.2054297</td><td>0.2280375</td><td>NA</td><td>NA</td><td>imputed</td><td>NA</td><td>Astle_et_al_2016_White_blood_cell_count</td></tr>\n",
+       "\t<tr><td>rs201055865</td><td>chr1_14671_G_C_b38</td><td>chr1</td><td>14671</td><td>C</td><td>G</td><td>hg38</td><td>0.01298701</td><td>173480</td><td>0.2324989</td><td>0.8161506</td><td>NA</td><td>NA</td><td>imputed</td><td>NA</td><td>Astle_et_al_2016_White_blood_cell_count</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 16\n",
+       "\\begin{tabular}{llllllllllllllll}\n",
+       " variant\\_id & panel\\_variant\\_id & chromosome & position & effect\\_allele & non\\_effect\\_allele & current\\_build & frequency & sample\\_size & zscore & pvalue & effect\\_size & standard\\_error & imputation\\_status & n\\_cases & tag\\\\\n",
+       " <chr> & <chr> & <chr> & <int> & <chr> & <chr> & <chr> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t rs554008981 & chr1\\_13550\\_G\\_A\\_b38 & chr1 & 13550 & A & G & hg38 & 0.01731602 & 173480 & 1.2054297 & 0.2280375 & NA & NA & imputed & NA & Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count\\\\\n",
+       "\t rs201055865 & chr1\\_14671\\_G\\_C\\_b38 & chr1 & 14671 & C & G & hg38 & 0.01298701 & 173480 & 0.2324989 & 0.8161506 & NA & NA & imputed & NA & Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 16\n",
+       "\n",
+       "| variant_id &lt;chr&gt; | panel_variant_id &lt;chr&gt; | chromosome &lt;chr&gt; | position &lt;int&gt; | effect_allele &lt;chr&gt; | non_effect_allele &lt;chr&gt; | current_build &lt;chr&gt; | frequency &lt;dbl&gt; | sample_size &lt;int&gt; | zscore &lt;dbl&gt; | pvalue &lt;dbl&gt; | effect_size &lt;dbl&gt; | standard_error &lt;dbl&gt; | imputation_status &lt;chr&gt; | n_cases &lt;dbl&gt; | tag &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| rs554008981 | chr1_13550_G_A_b38 | chr1 | 13550 | A | G | hg38 | 0.01731602 | 173480 | 1.2054297 | 0.2280375 | NA | NA | imputed | NA | Astle_et_al_2016_White_blood_cell_count |\n",
+       "| rs201055865 | chr1_14671_G_C_b38 | chr1 | 14671 | C | G | hg38 | 0.01298701 | 173480 | 0.2324989 | 0.8161506 | NA | NA | imputed | NA | Astle_et_al_2016_White_blood_cell_count |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  variant_id  panel_variant_id   chromosome position effect_allele\n",
+       "1 rs554008981 chr1_13550_G_A_b38 chr1       13550    A            \n",
+       "2 rs201055865 chr1_14671_G_C_b38 chr1       14671    C            \n",
+       "  non_effect_allele current_build frequency  sample_size zscore    pvalue   \n",
+       "1 G                 hg38          0.01731602 173480      1.2054297 0.2280375\n",
+       "2 G                 hg38          0.01298701 173480      0.2324989 0.8161506\n",
+       "  effect_size standard_error imputation_status n_cases\n",
+       "1 NA          NA             imputed           NA     \n",
+       "2 NA          NA             imputed           NA     \n",
+       "  tag                                    \n",
+       "1 Astle_et_al_2016_White_blood_cell_count\n",
+       "2 Astle_et_al_2016_White_blood_cell_count"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(gwas_gtex,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "142fc0d1-74ee-409f-a5eb-c528ed408bc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1"
+      ],
+      "text/latex": [
+       "1"
+      ],
+      "text/markdown": [
+       "1"
+      ],
+      "text/plain": [
+       "[1] 1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether also non-sign. results included\n",
+    "max(gwas_gtex$pvalue)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "c7b5304c-f168-43ec-a856-060ac755e72b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 6 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>effect_allele</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>A </td><td>13915259</td></tr>\n",
+       "\t<tr><td>T </td><td>13893494</td></tr>\n",
+       "\t<tr><td>C </td><td>11989998</td></tr>\n",
+       "\t<tr><td>G </td><td>11810065</td></tr>\n",
+       "\t<tr><td>AT</td><td>  132012</td></tr>\n",
+       "\t<tr><td>TA</td><td>  117094</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 6 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " effect\\_allele & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t A  & 13915259\\\\\n",
+       "\t T  & 13893494\\\\\n",
+       "\t C  & 11989998\\\\\n",
+       "\t G  & 11810065\\\\\n",
+       "\t AT &   132012\\\\\n",
+       "\t TA &   117094\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 6 × 2\n",
+       "\n",
+       "| effect_allele &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| A  | 13915259 |\n",
+       "| T  | 13893494 |\n",
+       "| C  | 11989998 |\n",
+       "| G  | 11810065 |\n",
+       "| AT |   132012 |\n",
+       "| TA |   117094 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  effect_allele n       \n",
+       "1 A             13915259\n",
+       "2 T             13893494\n",
+       "3 C             11989998\n",
+       "4 G             11810065\n",
+       "5 AT              132012\n",
+       "6 TA              117094"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## check effect allele reporting\n",
+    "head(gwas_gtex %>% group_by(effect_allele) %>% count() %>% arrange(desc(n)), 6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "25764286-f953-485e-bfb1-af6f3896e804",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#unique(gwas_gtex$effect_allele)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "fd8d8db3-c616-4740-86d2-bc3fcad9d173",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add phenotype info and sample size etc. from metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "77e35774-84ee-4d08-929d-d9f17b3f5141",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 16</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>variant_id</th><th scope=col>panel_variant_id</th><th scope=col>chromosome</th><th scope=col>position</th><th scope=col>effect_allele</th><th scope=col>non_effect_allele</th><th scope=col>current_build</th><th scope=col>frequency</th><th scope=col>sample_size</th><th scope=col>zscore</th><th scope=col>pvalue</th><th scope=col>effect_size</th><th scope=col>standard_error</th><th scope=col>imputation_status</th><th scope=col>n_cases</th><th scope=col>tag</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs554008981</td><td>chr1_13550_G_A_b38</td><td>chr1</td><td>13550</td><td>A</td><td>G</td><td>hg38</td><td>0.01731602</td><td>173480</td><td>1.2054297</td><td>0.2280375</td><td>NA</td><td>NA</td><td>imputed</td><td>NA</td><td>Astle_et_al_2016_White_blood_cell_count</td></tr>\n",
+       "\t<tr><td>rs201055865</td><td>chr1_14671_G_C_b38</td><td>chr1</td><td>14671</td><td>C</td><td>G</td><td>hg38</td><td>0.01298701</td><td>173480</td><td>0.2324989</td><td>0.8161506</td><td>NA</td><td>NA</td><td>imputed</td><td>NA</td><td>Astle_et_al_2016_White_blood_cell_count</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 16\n",
+       "\\begin{tabular}{llllllllllllllll}\n",
+       " variant\\_id & panel\\_variant\\_id & chromosome & position & effect\\_allele & non\\_effect\\_allele & current\\_build & frequency & sample\\_size & zscore & pvalue & effect\\_size & standard\\_error & imputation\\_status & n\\_cases & tag\\\\\n",
+       " <chr> & <chr> & <chr> & <int> & <chr> & <chr> & <chr> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t rs554008981 & chr1\\_13550\\_G\\_A\\_b38 & chr1 & 13550 & A & G & hg38 & 0.01731602 & 173480 & 1.2054297 & 0.2280375 & NA & NA & imputed & NA & Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count\\\\\n",
+       "\t rs201055865 & chr1\\_14671\\_G\\_C\\_b38 & chr1 & 14671 & C & G & hg38 & 0.01298701 & 173480 & 0.2324989 & 0.8161506 & NA & NA & imputed & NA & Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 16\n",
+       "\n",
+       "| variant_id &lt;chr&gt; | panel_variant_id &lt;chr&gt; | chromosome &lt;chr&gt; | position &lt;int&gt; | effect_allele &lt;chr&gt; | non_effect_allele &lt;chr&gt; | current_build &lt;chr&gt; | frequency &lt;dbl&gt; | sample_size &lt;int&gt; | zscore &lt;dbl&gt; | pvalue &lt;dbl&gt; | effect_size &lt;dbl&gt; | standard_error &lt;dbl&gt; | imputation_status &lt;chr&gt; | n_cases &lt;dbl&gt; | tag &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| rs554008981 | chr1_13550_G_A_b38 | chr1 | 13550 | A | G | hg38 | 0.01731602 | 173480 | 1.2054297 | 0.2280375 | NA | NA | imputed | NA | Astle_et_al_2016_White_blood_cell_count |\n",
+       "| rs201055865 | chr1_14671_G_C_b38 | chr1 | 14671 | C | G | hg38 | 0.01298701 | 173480 | 0.2324989 | 0.8161506 | NA | NA | imputed | NA | Astle_et_al_2016_White_blood_cell_count |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  variant_id  panel_variant_id   chromosome position effect_allele\n",
+       "1 rs554008981 chr1_13550_G_A_b38 chr1       13550    A            \n",
+       "2 rs201055865 chr1_14671_G_C_b38 chr1       14671    C            \n",
+       "  non_effect_allele current_build frequency  sample_size zscore    pvalue   \n",
+       "1 G                 hg38          0.01731602 173480      1.2054297 0.2280375\n",
+       "2 G                 hg38          0.01298701 173480      0.2324989 0.8161506\n",
+       "  effect_size standard_error imputation_status n_cases\n",
+       "1 NA          NA             imputed           NA     \n",
+       "2 NA          NA             imputed           NA     \n",
+       "  tag                                    \n",
+       "1 Astle_et_al_2016_White_blood_cell_count\n",
+       "2 Astle_et_al_2016_White_blood_cell_count"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(gwas_gtex,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "96e09072-54fc-4b4e-b53a-ccfa47810121",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_gtex = merge(gwas_gtex, metadata[,c('Tag', 'Phenotype', 'Sample_Size', 'Cases')], by.x = 'tag', by.y = 'Tag')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "a26e5fc7-f959-4130-9881-cfdd25967cdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 19</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>tag</th><th scope=col>variant_id</th><th scope=col>panel_variant_id</th><th scope=col>chromosome</th><th scope=col>position</th><th scope=col>effect_allele</th><th scope=col>non_effect_allele</th><th scope=col>current_build</th><th scope=col>frequency</th><th scope=col>sample_size</th><th scope=col>zscore</th><th scope=col>pvalue</th><th scope=col>effect_size</th><th scope=col>standard_error</th><th scope=col>imputation_status</th><th scope=col>n_cases</th><th scope=col>Phenotype</th><th scope=col>Sample_Size</th><th scope=col>Cases</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Astle_et_al_2016_White_blood_cell_count</td><td>rs554008981</td><td>chr1_13550_G_A_b38</td><td>chr1</td><td>13550</td><td>A</td><td>G</td><td>hg38</td><td>0.01731602</td><td>173480</td><td>1.2054297</td><td>0.2280375</td><td>NA</td><td>NA</td><td>imputed</td><td>NA</td><td>White blood cell count</td><td>173480</td><td>NA</td></tr>\n",
+       "\t<tr><td>Astle_et_al_2016_White_blood_cell_count</td><td>rs201055865</td><td>chr1_14671_G_C_b38</td><td>chr1</td><td>14671</td><td>C</td><td>G</td><td>hg38</td><td>0.01298701</td><td>173480</td><td>0.2324989</td><td>0.8161506</td><td>NA</td><td>NA</td><td>imputed</td><td>NA</td><td>White blood cell count</td><td>173480</td><td>NA</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 19\n",
+       "\\begin{tabular}{lllllllllllllllllll}\n",
+       " tag & variant\\_id & panel\\_variant\\_id & chromosome & position & effect\\_allele & non\\_effect\\_allele & current\\_build & frequency & sample\\_size & zscore & pvalue & effect\\_size & standard\\_error & imputation\\_status & n\\_cases & Phenotype & Sample\\_Size & Cases\\\\\n",
+       " <chr> & <chr> & <chr> & <chr> & <int> & <chr> & <chr> & <chr> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <chr> & <int> & <int>\\\\\n",
+       "\\hline\n",
+       "\t Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count & rs554008981 & chr1\\_13550\\_G\\_A\\_b38 & chr1 & 13550 & A & G & hg38 & 0.01731602 & 173480 & 1.2054297 & 0.2280375 & NA & NA & imputed & NA & White blood cell count & 173480 & NA\\\\\n",
+       "\t Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count & rs201055865 & chr1\\_14671\\_G\\_C\\_b38 & chr1 & 14671 & C & G & hg38 & 0.01298701 & 173480 & 0.2324989 & 0.8161506 & NA & NA & imputed & NA & White blood cell count & 173480 & NA\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 19\n",
+       "\n",
+       "| tag &lt;chr&gt; | variant_id &lt;chr&gt; | panel_variant_id &lt;chr&gt; | chromosome &lt;chr&gt; | position &lt;int&gt; | effect_allele &lt;chr&gt; | non_effect_allele &lt;chr&gt; | current_build &lt;chr&gt; | frequency &lt;dbl&gt; | sample_size &lt;int&gt; | zscore &lt;dbl&gt; | pvalue &lt;dbl&gt; | effect_size &lt;dbl&gt; | standard_error &lt;dbl&gt; | imputation_status &lt;chr&gt; | n_cases &lt;dbl&gt; | Phenotype &lt;chr&gt; | Sample_Size &lt;int&gt; | Cases &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| Astle_et_al_2016_White_blood_cell_count | rs554008981 | chr1_13550_G_A_b38 | chr1 | 13550 | A | G | hg38 | 0.01731602 | 173480 | 1.2054297 | 0.2280375 | NA | NA | imputed | NA | White blood cell count | 173480 | NA |\n",
+       "| Astle_et_al_2016_White_blood_cell_count | rs201055865 | chr1_14671_G_C_b38 | chr1 | 14671 | C | G | hg38 | 0.01298701 | 173480 | 0.2324989 | 0.8161506 | NA | NA | imputed | NA | White blood cell count | 173480 | NA |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  tag                                     variant_id  panel_variant_id  \n",
+       "1 Astle_et_al_2016_White_blood_cell_count rs554008981 chr1_13550_G_A_b38\n",
+       "2 Astle_et_al_2016_White_blood_cell_count rs201055865 chr1_14671_G_C_b38\n",
+       "  chromosome position effect_allele non_effect_allele current_build frequency \n",
+       "1 chr1       13550    A             G                 hg38          0.01731602\n",
+       "2 chr1       14671    C             G                 hg38          0.01298701\n",
+       "  sample_size zscore    pvalue    effect_size standard_error imputation_status\n",
+       "1 173480      1.2054297 0.2280375 NA          NA             imputed          \n",
+       "2 173480      0.2324989 0.8161506 NA          NA             imputed          \n",
+       "  n_cases Phenotype              Sample_Size Cases\n",
+       "1 NA      White blood cell count 173480      NA   \n",
+       "2 NA      White blood cell count 173480      NA   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(gwas_gtex, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "4045e4fa-091c-4455-9813-df6468451d82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Reduce to columns also in other gwas dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "595ed055-cb42-410d-869b-eb9bc32cea94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_var = c('tag','position', 'non_effect_allele', 'frequency', 'pvalue','effect_size', 'Phenotype','Sample_Size',\n",
+    "             'variant_id', 'sample_size', 'standard_error', 'effect_allele')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "3c8d5a87-32d7-4889-b816-1a877dcb2d07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_gtex = gwas_gtex[,c('tag','position', 'non_effect_allele', 'frequency', 'pvalue','effect_size', 'Phenotype','Sample_Size',\n",
+    "             'variant_id', 'sample_size', 'standard_error', 'effect_allele')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "e0ef8f10-fad8-4c09-ab95-f1fcc777f787",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#gwas_gtex[is.na(gwas_gtex$variant_id),]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7840027-dcfc-48a1-a0f4-720939add529",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Additional GWAS input (load T1D GWAS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "631f4da5-a455-4f95-9ef5-b7b4afbd2c47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Load other T1D GWAS database to add to gtex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "a9b89bfd-8968-4388-84d6-dae3d841643f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition = fread(paste0(path, \"additional_gwas/GCST90014023_buildGRCh38.tsv.gz\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "c79a6548-9bd0-4e27-8392-48cf6b2a102a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 10</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>variant_id</th><th scope=col>p_value</th><th scope=col>chromosome</th><th scope=col>base_pair_location</th><th scope=col>effect_allele</th><th scope=col>other_allele</th><th scope=col>effect_allele_frequency</th><th scope=col>beta</th><th scope=col>standard_error</th><th scope=col>sample_size</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs367896724</td><td>2.84e-01</td><td>1</td><td>10177</td><td>AC</td><td>A</td><td>0.398</td><td>0.059058</td><td>0.055112</td><td>363495</td></tr>\n",
+       "\t<tr><td>rs555500075</td><td>7.31e-01</td><td>1</td><td>10352</td><td>TA</td><td>T</td><td>0.393</td><td>0.019496</td><td>0.056730</td><td>363495</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 10\n",
+       "\\begin{tabular}{llllllllll}\n",
+       " variant\\_id & p\\_value & chromosome & base\\_pair\\_location & effect\\_allele & other\\_allele & effect\\_allele\\_frequency & beta & standard\\_error & sample\\_size\\\\\n",
+       " <chr> & <chr> & <chr> & <int> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
+       "\\hline\n",
+       "\t rs367896724 & 2.84e-01 & 1 & 10177 & AC & A & 0.398 & 0.059058 & 0.055112 & 363495\\\\\n",
+       "\t rs555500075 & 7.31e-01 & 1 & 10352 & TA & T & 0.393 & 0.019496 & 0.056730 & 363495\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 10\n",
+       "\n",
+       "| variant_id &lt;chr&gt; | p_value &lt;chr&gt; | chromosome &lt;chr&gt; | base_pair_location &lt;int&gt; | effect_allele &lt;chr&gt; | other_allele &lt;chr&gt; | effect_allele_frequency &lt;dbl&gt; | beta &lt;dbl&gt; | standard_error &lt;dbl&gt; | sample_size &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| rs367896724 | 2.84e-01 | 1 | 10177 | AC | A | 0.398 | 0.059058 | 0.055112 | 363495 |\n",
+       "| rs555500075 | 7.31e-01 | 1 | 10352 | TA | T | 0.393 | 0.019496 | 0.056730 | 363495 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  variant_id  p_value  chromosome base_pair_location effect_allele other_allele\n",
+       "1 rs367896724 2.84e-01 1          10177              AC            A           \n",
+       "2 rs555500075 7.31e-01 1          10352              TA            T           \n",
+       "  effect_allele_frequency beta     standard_error sample_size\n",
+       "1 0.398                   0.059058 0.055112       363495     \n",
+       "2 0.393                   0.019496 0.056730       363495     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(gwas_addition,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "53ac048e-f99c-4dde-b2b4-95d93f2a434a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Add columns and rename to adjust to other dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "d56cbefb-48ba-41df-a77e-c63d42176500",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition$tag = 'chiou_type_1_diabetes_study'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "8068703a-3174-4fba-aadd-fbd1347216ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition$position = gwas_addition$base_pair_location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "a2010569-80c8-4e7e-96ed-354d1779685a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition$non_effect_allele = gwas_addition$other_allele"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "f1c2a40a-7c6a-4fe1-9808-20e478154049",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition$frequency = gwas_addition$effect_allele_frequency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "e3838258-c6cd-439d-bbbd-5cc3ed2f0afe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sort(unique(gwas_addition$sample_size))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "c6cb4ab0-ef62-452a-8598-674dfd6612c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 5 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>variant_id</th><th scope=col>p_value</th><th scope=col>chromosome</th><th scope=col>base_pair_location</th><th scope=col>effect_allele</th><th scope=col>other_allele</th><th scope=col>effect_allele_frequency</th><th scope=col>beta</th><th scope=col>standard_error</th><th scope=col>sample_size</th><th scope=col>tag</th><th scope=col>position</th><th scope=col>non_effect_allele</th><th scope=col>frequency</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs1157621571</td><td>1.69e-01</td><td>1</td><td>  778705</td><td>CGCCCTTGTG</td><td>C</td><td>0.004720</td><td>-0.672700</td><td>0.489300</td><td>115043</td><td>chiou_type_1_diabetes_study</td><td>  778705</td><td>C</td><td>0.004720</td></tr>\n",
+       "\t<tr><td>rs1157621571</td><td>3.68e-01</td><td>1</td><td>  778705</td><td>CGCCCTTGCG</td><td>C</td><td>0.013100</td><td>-0.223200</td><td>0.248100</td><td>115043</td><td>chiou_type_1_diabetes_study</td><td>  778705</td><td>C</td><td>0.013100</td></tr>\n",
+       "\t<tr><td>rs111497730 </td><td>2.97e-01</td><td>1</td><td>  987345</td><td>A         </td><td>G</td><td>0.001130</td><td> 0.434325</td><td>0.416662</td><td>379804</td><td>chiou_type_1_diabetes_study</td><td>  987345</td><td>G</td><td>0.001130</td></tr>\n",
+       "\t<tr><td>rs111497730 </td><td>9.80e-01</td><td>1</td><td>  987345</td><td>C         </td><td>G</td><td>0.000082</td><td> 0.092465</td><td>3.691368</td><td>363495</td><td>chiou_type_1_diabetes_study</td><td>  987345</td><td>G</td><td>0.000082</td></tr>\n",
+       "\t<tr><td>rs10751776  </td><td>2.67e-08</td><td>1</td><td>24970252</td><td>C         </td><td>A</td><td>0.510000</td><td> 0.078145</td><td>0.014050</td><td>520580</td><td>chiou_type_1_diabetes_study</td><td>24970252</td><td>A</td><td>0.510000</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 5 × 14\n",
+       "\\begin{tabular}{llllllllllllll}\n",
+       " variant\\_id & p\\_value & chromosome & base\\_pair\\_location & effect\\_allele & other\\_allele & effect\\_allele\\_frequency & beta & standard\\_error & sample\\_size & tag & position & non\\_effect\\_allele & frequency\\\\\n",
+       " <chr> & <chr> & <chr> & <int> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <int> & <chr> & <int> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t rs1157621571 & 1.69e-01 & 1 &   778705 & CGCCCTTGTG & C & 0.004720 & -0.672700 & 0.489300 & 115043 & chiou\\_type\\_1\\_diabetes\\_study &   778705 & C & 0.004720\\\\\n",
+       "\t rs1157621571 & 3.68e-01 & 1 &   778705 & CGCCCTTGCG & C & 0.013100 & -0.223200 & 0.248100 & 115043 & chiou\\_type\\_1\\_diabetes\\_study &   778705 & C & 0.013100\\\\\n",
+       "\t rs111497730  & 2.97e-01 & 1 &   987345 & A          & G & 0.001130 &  0.434325 & 0.416662 & 379804 & chiou\\_type\\_1\\_diabetes\\_study &   987345 & G & 0.001130\\\\\n",
+       "\t rs111497730  & 9.80e-01 & 1 &   987345 & C          & G & 0.000082 &  0.092465 & 3.691368 & 363495 & chiou\\_type\\_1\\_diabetes\\_study &   987345 & G & 0.000082\\\\\n",
+       "\t rs10751776   & 2.67e-08 & 1 & 24970252 & C          & A & 0.510000 &  0.078145 & 0.014050 & 520580 & chiou\\_type\\_1\\_diabetes\\_study & 24970252 & A & 0.510000\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 5 × 14\n",
+       "\n",
+       "| variant_id &lt;chr&gt; | p_value &lt;chr&gt; | chromosome &lt;chr&gt; | base_pair_location &lt;int&gt; | effect_allele &lt;chr&gt; | other_allele &lt;chr&gt; | effect_allele_frequency &lt;dbl&gt; | beta &lt;dbl&gt; | standard_error &lt;dbl&gt; | sample_size &lt;int&gt; | tag &lt;chr&gt; | position &lt;int&gt; | non_effect_allele &lt;chr&gt; | frequency &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| rs1157621571 | 1.69e-01 | 1 |   778705 | CGCCCTTGTG | C | 0.004720 | -0.672700 | 0.489300 | 115043 | chiou_type_1_diabetes_study |   778705 | C | 0.004720 |\n",
+       "| rs1157621571 | 3.68e-01 | 1 |   778705 | CGCCCTTGCG | C | 0.013100 | -0.223200 | 0.248100 | 115043 | chiou_type_1_diabetes_study |   778705 | C | 0.013100 |\n",
+       "| rs111497730  | 2.97e-01 | 1 |   987345 | A          | G | 0.001130 |  0.434325 | 0.416662 | 379804 | chiou_type_1_diabetes_study |   987345 | G | 0.001130 |\n",
+       "| rs111497730  | 9.80e-01 | 1 |   987345 | C          | G | 0.000082 |  0.092465 | 3.691368 | 363495 | chiou_type_1_diabetes_study |   987345 | G | 0.000082 |\n",
+       "| rs10751776   | 2.67e-08 | 1 | 24970252 | C          | A | 0.510000 |  0.078145 | 0.014050 | 520580 | chiou_type_1_diabetes_study | 24970252 | A | 0.510000 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  variant_id   p_value  chromosome base_pair_location effect_allele\n",
+       "1 rs1157621571 1.69e-01 1            778705           CGCCCTTGTG   \n",
+       "2 rs1157621571 3.68e-01 1            778705           CGCCCTTGCG   \n",
+       "3 rs111497730  2.97e-01 1            987345           A            \n",
+       "4 rs111497730  9.80e-01 1            987345           C            \n",
+       "5 rs10751776   2.67e-08 1          24970252           C            \n",
+       "  other_allele effect_allele_frequency beta      standard_error sample_size\n",
+       "1 C            0.004720                -0.672700 0.489300       115043     \n",
+       "2 C            0.013100                -0.223200 0.248100       115043     \n",
+       "3 G            0.001130                 0.434325 0.416662       379804     \n",
+       "4 G            0.000082                 0.092465 3.691368       363495     \n",
+       "5 A            0.510000                 0.078145 0.014050       520580     \n",
+       "  tag                         position non_effect_allele frequency\n",
+       "1 chiou_type_1_diabetes_study   778705 C                 0.004720 \n",
+       "2 chiou_type_1_diabetes_study   778705 C                 0.013100 \n",
+       "3 chiou_type_1_diabetes_study   987345 G                 0.001130 \n",
+       "4 chiou_type_1_diabetes_study   987345 G                 0.000082 \n",
+       "5 chiou_type_1_diabetes_study 24970252 A                 0.510000 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gwas_addition[gwas_addition$variant_id %in% c( 'rs111497730', 'rs1157621571', 'rs10751776'),]\n",
+    "# duplicate variant entries with different effect alleles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "3fd0971f-5716-41b3-83fa-f4ec92f54598",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 6 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>effect_allele</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>A </td><td>17225304</td></tr>\n",
+       "\t<tr><td>T </td><td>17186648</td></tr>\n",
+       "\t<tr><td>C </td><td>13153907</td></tr>\n",
+       "\t<tr><td>G </td><td>12880964</td></tr>\n",
+       "\t<tr><td>AT</td><td>  180595</td></tr>\n",
+       "\t<tr><td>TA</td><td>  157712</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 6 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " effect\\_allele & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t A  & 17225304\\\\\n",
+       "\t T  & 17186648\\\\\n",
+       "\t C  & 13153907\\\\\n",
+       "\t G  & 12880964\\\\\n",
+       "\t AT &   180595\\\\\n",
+       "\t TA &   157712\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 6 × 2\n",
+       "\n",
+       "| effect_allele &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| A  | 17225304 |\n",
+       "| T  | 17186648 |\n",
+       "| C  | 13153907 |\n",
+       "| G  | 12880964 |\n",
+       "| AT |   180595 |\n",
+       "| TA |   157712 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  effect_allele n       \n",
+       "1 A             17225304\n",
+       "2 T             17186648\n",
+       "3 C             13153907\n",
+       "4 G             12880964\n",
+       "5 AT              180595\n",
+       "6 TA              157712"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### check out effect allele reporting\n",
+    "head(gwas_addition %>% group_by(effect_allele) %>% count() %>% arrange(desc(n)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "797023d7-dd6c-45b3-9847-66a16af5ed73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition$pvalue = gwas_addition$p_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "66c445e3-7099-4526-8295-dd7109b4e759",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition$effect_size = gwas_addition$beta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "577e881d-807d-4574-b8bc-9ed70533fa32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition$Phenotype = 'Type_1_Diabetes'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "2c51c54c-a4f3-4f5d-99fe-da34884debcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_addition$Sample_Size = gwas_addition$sample_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "b01da779-4a86-44c0-a46d-3bc2dd257c43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Filter on relevant columns\n",
+    "gwas_addition = gwas_addition[,c('tag','position', 'non_effect_allele', 'frequency', 'pvalue','effect_size', 'Phenotype','Sample_Size',\n",
+    "             'variant_id', 'sample_size', 'standard_error', 'effect_allele')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3cf13c8-8527-4d9b-bf53-aa2357ed1c76",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "f983f6ba-758e-443f-b434-1e34678f9ff3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "62115237"
+      ],
+      "text/latex": [
+       "62115237"
+      ],
+      "text/markdown": [
+       "62115237"
+      ],
+      "text/plain": [
+       "[1] 62115237"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(gwas_addition)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "6a9d1428-d872-4bb4-9dee-770283e29e04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "60344051"
+      ],
+      "text/latex": [
+       "60344051"
+      ],
+      "text/markdown": [
+       "60344051"
+      ],
+      "text/plain": [
+       "[1] 60344051"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(gwas_addition$variant_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "885eb37c-7dea-4607-9c8e-a845715f534c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "###  Combine to dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "eafe1878-a8c4-4e84-b44a-ebac6e9cdabd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_gtex = rbind(gwas_gtex, gwas_addition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85046bcf-c9ad-451f-8a51-7057c9842c30",
+   "metadata": {},
+   "source": [
+    "## Adjust format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "5c96544f-7654-4c1f-a370-365299cadf96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_gtex$variant_id = paste0(gwas_gtex$variant_id, '_', gwas_gtex$non_effect_allele, '/', gwas_gtex$effect_allele)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "01322a6f-e35d-490f-abb2-3a0459930590",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>tag</th><th scope=col>position</th><th scope=col>non_effect_allele</th><th scope=col>frequency</th><th scope=col>pvalue</th><th scope=col>effect_size</th><th scope=col>Phenotype</th><th scope=col>Sample_Size</th><th scope=col>variant_id</th><th scope=col>sample_size</th><th scope=col>standard_error</th><th scope=col>effect_allele</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Astle_et_al_2016_White_blood_cell_count</td><td>13550</td><td>G</td><td>0.01731602</td><td>0.228037473787046</td><td>NA</td><td>White blood cell count</td><td>173480</td><td>rs554008981_G/A</td><td>173480</td><td>NA</td><td>A</td></tr>\n",
+       "\t<tr><td>Astle_et_al_2016_White_blood_cell_count</td><td>14671</td><td>G</td><td>0.01298701</td><td>0.816150563702573</td><td>NA</td><td>White blood cell count</td><td>173480</td><td>rs201055865_G/C</td><td>173480</td><td>NA</td><td>C</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 12\n",
+       "\\begin{tabular}{llllllllllll}\n",
+       " tag & position & non\\_effect\\_allele & frequency & pvalue & effect\\_size & Phenotype & Sample\\_Size & variant\\_id & sample\\_size & standard\\_error & effect\\_allele\\\\\n",
+       " <chr> & <int> & <chr> & <dbl> & <chr> & <dbl> & <chr> & <int> & <chr> & <int> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count & 13550 & G & 0.01731602 & 0.228037473787046 & NA & White blood cell count & 173480 & rs554008981\\_G/A & 173480 & NA & A\\\\\n",
+       "\t Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count & 14671 & G & 0.01298701 & 0.816150563702573 & NA & White blood cell count & 173480 & rs201055865\\_G/C & 173480 & NA & C\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 12\n",
+       "\n",
+       "| tag &lt;chr&gt; | position &lt;int&gt; | non_effect_allele &lt;chr&gt; | frequency &lt;dbl&gt; | pvalue &lt;chr&gt; | effect_size &lt;dbl&gt; | Phenotype &lt;chr&gt; | Sample_Size &lt;int&gt; | variant_id &lt;chr&gt; | sample_size &lt;int&gt; | standard_error &lt;dbl&gt; | effect_allele &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| Astle_et_al_2016_White_blood_cell_count | 13550 | G | 0.01731602 | 0.228037473787046 | NA | White blood cell count | 173480 | rs554008981_G/A | 173480 | NA | A |\n",
+       "| Astle_et_al_2016_White_blood_cell_count | 14671 | G | 0.01298701 | 0.816150563702573 | NA | White blood cell count | 173480 | rs201055865_G/C | 173480 | NA | C |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  tag                                     position non_effect_allele frequency \n",
+       "1 Astle_et_al_2016_White_blood_cell_count 13550    G                 0.01731602\n",
+       "2 Astle_et_al_2016_White_blood_cell_count 14671    G                 0.01298701\n",
+       "  pvalue            effect_size Phenotype              Sample_Size\n",
+       "1 0.228037473787046 NA          White blood cell count 173480     \n",
+       "2 0.816150563702573 NA          White blood cell count 173480     \n",
+       "  variant_id      sample_size standard_error effect_allele\n",
+       "1 rs554008981_G/A 173480      NA             A            \n",
+       "2 rs201055865_G/C 173480      NA             C            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(gwas_gtex,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28625564-b2e8-4a91-936e-71527147b9f7",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Adaption of positions (map for different reference genomes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "2a923770-3f7f-4617-88a5-ec4e6262a4c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# align positions given in eqtl/co-eqtl data and GWAS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "fd3c4740-0115-47bf-9ea5-a018c38dabfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "positions_map = unique(gwas_gtex[,c('variant_id', 'position')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "de4b95ae-11a3-4347-a528-77035f1c88d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>variant_id</th><th scope=col>position</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs554008981_G/A</td><td>13550</td></tr>\n",
+       "\t<tr><td>rs201055865_G/C</td><td>14671</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " variant\\_id & position\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t rs554008981\\_G/A & 13550\\\\\n",
+       "\t rs201055865\\_G/C & 14671\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 2\n",
+       "\n",
+       "| variant_id &lt;chr&gt; | position &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| rs554008981_G/A | 13550 |\n",
+       "| rs201055865_G/C | 14671 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  variant_id      position\n",
+       "1 rs554008981_G/A 13550   \n",
+       "2 rs201055865_G/C 14671   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(positions_map,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "fda68fdc-2da6-4c5a-8059-f5bc92c5b578",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## relevant snps from eqtl and co-eqtl data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "ad0d117a-8a8a-4dc6-b309-600b9060c19c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snps = unique(c(unique(output_all_effect$SNP), unique(eqtl_all_effect$SNP)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "96606eea-c7fd-4e19-87f3-995a5cdc41f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'rs1144709_C/T'</li><li>'rs3131380_A/G'</li><li>'rs1144708_C/T'</li><li>'rs2293861_C/T'</li><li>'rs2075788_T/G'</li><li>'rs3132445_G/A'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'rs1144709\\_C/T'\n",
+       "\\item 'rs3131380\\_A/G'\n",
+       "\\item 'rs1144708\\_C/T'\n",
+       "\\item 'rs2293861\\_C/T'\n",
+       "\\item 'rs2075788\\_T/G'\n",
+       "\\item 'rs3132445\\_G/A'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'rs1144709_C/T'\n",
+       "2. 'rs3131380_A/G'\n",
+       "3. 'rs1144708_C/T'\n",
+       "4. 'rs2293861_C/T'\n",
+       "5. 'rs2075788_T/G'\n",
+       "6. 'rs3132445_G/A'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"rs1144709_C/T\" \"rs3131380_A/G\" \"rs1144708_C/T\" \"rs2293861_C/T\"\n",
+       "[5] \"rs2075788_T/G\" \"rs3132445_G/A\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(snps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "201eafc3-953c-4075-b24e-e1abfeec6255",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "20527"
+      ],
+      "text/latex": [
+       "20527"
+      ],
+      "text/markdown": [
+       "20527"
+      ],
+      "text/plain": [
+       "[1] 20527"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(snps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "6795059c-8681-4d66-b284-dd987289e5b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Reduce position_mapping to relevant snps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "2395effd-fd1c-4d25-ac68-e712ab3b5fa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "positions_map = positions_map[positions_map$variant_id %in% snps,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "b13d2c55-b00f-442a-b785-3ea21f475c35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "15349"
+      ],
+      "text/latex": [
+       "15349"
+      ],
+      "text/markdown": [
+       "15349"
+      ],
+      "text/plain": [
+       "[1] 15349"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(positions_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "92c7a402-b46e-46f3-b36c-0c35b35f186b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Check for uniqueness per variant of position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "6957d036-4155-4cc5-ab66-2da5b78473d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 0 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>variant_id</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 0 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " variant\\_id & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 0 × 2\n",
+       "\n",
+       "| variant_id &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     variant_id n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "positions_map %>% group_by(variant_id) %>% count() %>% filter(n > 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "e94f30d0-2c3e-4329-a255-11fbd26a61c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### map positions to eqtl and coeqtl data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "bc7f69be-9a44-492c-b3a5-c45bfe67be8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_all_effect = merge(eqtl_all_effect, positions_map, by.x = 'SNP', by.y = 'variant_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "471b72e7-d0ff-46a8-867b-ad655fdc3183",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_all_effect = merge(output_all_effect, positions_map, by.x = 'SNP', by.y = 'variant_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "e3ac9ce1-bf13-4943-9082-bcd364f48116",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_all_effect$SNPPos = eqtl_all_effect$position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "a4efa4b8-45eb-4ec5-a87f-a2b264eea305",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_all_effect$SNPPos = output_all_effect$position"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a43203c0-5ae1-4658-a9be-0ae4292031b9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Colocalization for eQTLS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d48a6eb2-2c29-4489-ab4c-d7e3102ea634",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Prepare data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b833ac8-39a8-4267-b7f5-5d04a4a03d0c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### EQTL input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "a02a254e-5445-4f74-9afc-3403662db04c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 23</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>SNP</th><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>SNPEffectAllele</th><th scope=col>⋯</th><th scope=col>MetaPZ</th><th scope=col>MetaBeta</th><th scope=col>MetaSE</th><th scope=col>MetaI2</th><th scope=col>NrDatasets</th><th scope=col>DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>cell_type</th><th scope=col>position</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs1001116_C/T</td><td>ENSG00000002933</td><td>7</td><td>150497491</td><td>NA</td><td>TMEM176A</td><td>7</td><td>151743278</td><td>C/T</td><td>T</td><td>⋯</td><td> 0.065366</td><td> 0.007623</td><td>0.116622</td><td>0</td><td>4</td><td>-0.026173;-0.036659;-0.002511;0.2012 </td><td>-0.166602;-0.305718;-0.013637;0.942746</td><td>43;72;32;24</td><td>DC1MB</td><td>151743278</td></tr>\n",
+       "\t<tr><td>rs1001116_C/T</td><td>ENSG00000002933</td><td>7</td><td>150497491</td><td>NA</td><td>TMEM176A</td><td>7</td><td>151743278</td><td>C/T</td><td>T</td><td>⋯</td><td>-0.334464</td><td>-0.038844</td><td>0.116137</td><td>0</td><td>4</td><td>-0.14377;0.113441;-0.080077;-0.165963</td><td>-0.931054;0.948804;-0.435662;-0.775045</td><td>44;72;32;24</td><td>B1MB </td><td>151743278</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 23\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " SNP & Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNPChr & SNPPos & SNPAlleles & SNPEffectAllele & ⋯ & MetaPZ & MetaBeta & MetaSE & MetaI2 & NrDatasets & DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) & DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) & DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) & cell\\_type & position\\\\\n",
+       " <chr> & <chr> & <int> & <int> & <lgl> & <chr> & <int> & <int> & <chr> & <chr> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <chr> & <chr> & <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t rs1001116\\_C/T & ENSG00000002933 & 7 & 150497491 & NA & TMEM176A & 7 & 151743278 & C/T & T & ⋯ &  0.065366 &  0.007623 & 0.116622 & 0 & 4 & -0.026173;-0.036659;-0.002511;0.2012  & -0.166602;-0.305718;-0.013637;0.942746 & 43;72;32;24 & DC1MB & 151743278\\\\\n",
+       "\t rs1001116\\_C/T & ENSG00000002933 & 7 & 150497491 & NA & TMEM176A & 7 & 151743278 & C/T & T & ⋯ & -0.334464 & -0.038844 & 0.116137 & 0 & 4 & -0.14377;0.113441;-0.080077;-0.165963 & -0.931054;0.948804;-0.435662;-0.775045 & 44;72;32;24 & B1MB  & 151743278\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 23\n",
+       "\n",
+       "| SNP &lt;chr&gt; | Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | SNPEffectAllele &lt;chr&gt; | ⋯ ⋯ | MetaPZ &lt;dbl&gt; | MetaBeta &lt;dbl&gt; | MetaSE &lt;dbl&gt; | MetaI2 &lt;dbl&gt; | NrDatasets &lt;int&gt; | DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | cell_type &lt;chr&gt; | position &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| rs1001116_C/T | ENSG00000002933 | 7 | 150497491 | NA | TMEM176A | 7 | 151743278 | C/T | T | ⋯ |  0.065366 |  0.007623 | 0.116622 | 0 | 4 | -0.026173;-0.036659;-0.002511;0.2012  | -0.166602;-0.305718;-0.013637;0.942746 | 43;72;32;24 | DC1MB | 151743278 |\n",
+       "| rs1001116_C/T | ENSG00000002933 | 7 | 150497491 | NA | TMEM176A | 7 | 151743278 | C/T | T | ⋯ | -0.334464 | -0.038844 | 0.116137 | 0 | 4 | -0.14377;0.113441;-0.080077;-0.165963 | -0.931054;0.948804;-0.435662;-0.775045 | 44;72;32;24 | B1MB  | 151743278 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  SNP           Gene            GeneChr GenePos   GeneStrand GeneSymbol SNPChr\n",
+       "1 rs1001116_C/T ENSG00000002933 7       150497491 NA         TMEM176A   7     \n",
+       "2 rs1001116_C/T ENSG00000002933 7       150497491 NA         TMEM176A   7     \n",
+       "  SNPPos    SNPAlleles SNPEffectAllele ⋯ MetaPZ    MetaBeta  MetaSE   MetaI2\n",
+       "1 151743278 C/T        T               ⋯  0.065366  0.007623 0.116622 0     \n",
+       "2 151743278 C/T        T               ⋯ -0.334464 -0.038844 0.116137 0     \n",
+       "  NrDatasets\n",
+       "1 4         \n",
+       "2 4         \n",
+       "  DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -0.026173;-0.036659;-0.002511;0.2012                                \n",
+       "2 -0.14377;0.113441;-0.080077;-0.165963                               \n",
+       "  DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -0.166602;-0.305718;-0.013637;0.942746              \n",
+       "2 -0.931054;0.948804;-0.435662;-0.775045              \n",
+       "  DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) cell_type position \n",
+       "1 43;72;32;24                                              DC1MB     151743278\n",
+       "2 44;72;32;24                                              B1MB      151743278"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(eqtl_all_effect,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "3d629c05-be8a-40ef-bef2-23a917036d3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "5"
+      ],
+      "text/latex": [
+       "5"
+      ],
+      "text/markdown": [
+       "5"
+      ],
+      "text/plain": [
+       "[1] 5"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(eqtl_all_effect$Gene))   # 3 Genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "d4b97124-510f-422f-9ba4-13ed003ca869",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_all_effect$ident = paste0(eqtl_all_effect$cell_type, '_', eqtl_all_effect$GeneSymbol)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "971474e0-b036-454b-bbbe-0cb67e9b2096",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "30"
+      ],
+      "text/latex": [
+       "30"
+      ],
+      "text/markdown": [
+       "30"
+      ],
+      "text/plain": [
+       "[1] 30"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(eqtl_all_effect$ident))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "b8720e1c-bddf-4955-b48d-4b7f0a980566",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_input_eqtl = list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "1b5708eb-06d3-4dbc-a037-6becd9d43b51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for( i in unique(eqtl_all_effect$ident)){\n",
+    "    \n",
+    "    D1 = eqtl_all_effect[eqtl_all_effect$ident == i,]\n",
+    "    \n",
+    "    \n",
+    "    ## Prepare input vectors\n",
+    "\n",
+    "    # Beta\n",
+    "    beta_eqtl = D1$MetaBeta\n",
+    "    names(beta_eqtl) = D1$SNP\n",
+    "\n",
+    "    # Varbeta \n",
+    "    varbeta_eqtl =  (D1$MetaSE)^2\n",
+    "    names(varbeta_eqtl) = D1$SNP\n",
+    "\n",
+    "    # MAF - not needed? when setting sdY = 1\n",
+    "    MAF_eqtl = D1$SNPEffectAlleleFreq\n",
+    "    names(MAF_eqtl) = D1$SNP\n",
+    "\n",
+    "    # Position \n",
+    "    position_eqtl = D1$SNPPos\n",
+    "    names(position_eqtl) = D1$SNP\n",
+    "\n",
+    "    # SNP\n",
+    "    snp_eqtl = D1$SNP\n",
+    "    names(snp_eqtl) =  D1$SNP\n",
+    "\n",
+    "    # Pvalues\n",
+    "    pvalues_eqtl =  D1$MetaP\n",
+    "    names(pvalues_eqtl) = D1$SNP\n",
+    "    \n",
+    "    # Sample_size\n",
+    "    sample_size_eqtl = D1$MetaPN  # TBD\n",
+    "    \n",
+    "    \n",
+    "    ### Format as input list for colocalization\n",
+    "\n",
+    "    D1_list = list(beta = beta_eqtl, # regression coefficient\n",
+    "               varbeta = varbeta_eqtl,  # variance/ standard deviation of beta?\n",
+    "               N = sample_size_eqtl, # number of samples in dataset 1\n",
+    "               #sdY =1,   # population standard deviation of the trait, if quantitative trait\n",
+    "                       # if unknown will be approximated based on beta, varbeta, N, MAF\n",
+    "                       # can be set to 1, if the trait was standardized to have variance 1\n",
+    "               type = 'quant',  # quant or cc to denote quantitative or case-control\n",
+    "               MAF = MAF_eqtl,   # minor allele frequency of the variants\n",
+    "               snp = snp_eqtl,  # character vector of SNP ids\n",
+    "               position = position_eqtl,\n",
+    "               pvalues = pvalues_eqtl\n",
+    "              )\n",
+    "    data_input_eqtl[[i]] = D1_list\n",
+    "    }\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "dc0f0087-a3da-48a5-81da-09a412240cf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'DC1MB_TMEM176A'</li><li>'B1MB_TMEM176A'</li><li>'NK1MB_TMEM176A'</li><li>'monocyte1MB_TMEM176A'</li><li>'CD8T1MB_TMEM176A'</li><li>'CD4T1MB_TMEM176A'</li><li>'DC1MB_SMDT1'</li><li>'B1MB_SMDT1'</li><li>'NK1MB_SMDT1'</li><li>'monocyte1MB_SMDT1'</li><li>'CD8T1MB_SMDT1'</li><li>'CD4T1MB_SMDT1'</li><li>'DC1MB_HLA-DQA2'</li><li>'B1MB_HLA-DQA2'</li><li>'NK1MB_HLA-DQA2'</li><li>'monocyte1MB_HLA-DQA2'</li><li>'CD8T1MB_HLA-DQA2'</li><li>'CD4T1MB_HLA-DQA2'</li><li>'DC1MB_RNASET2'</li><li>'B1MB_RNASET2'</li><li>'NK1MB_RNASET2'</li><li>'monocyte1MB_RNASET2'</li><li>'CD8T1MB_RNASET2'</li><li>'CD4T1MB_RNASET2'</li><li>'DC1MB_RPS26'</li><li>'B1MB_RPS26'</li><li>'NK1MB_RPS26'</li><li>'monocyte1MB_RPS26'</li><li>'CD8T1MB_RPS26'</li><li>'CD4T1MB_RPS26'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'DC1MB\\_TMEM176A'\n",
+       "\\item 'B1MB\\_TMEM176A'\n",
+       "\\item 'NK1MB\\_TMEM176A'\n",
+       "\\item 'monocyte1MB\\_TMEM176A'\n",
+       "\\item 'CD8T1MB\\_TMEM176A'\n",
+       "\\item 'CD4T1MB\\_TMEM176A'\n",
+       "\\item 'DC1MB\\_SMDT1'\n",
+       "\\item 'B1MB\\_SMDT1'\n",
+       "\\item 'NK1MB\\_SMDT1'\n",
+       "\\item 'monocyte1MB\\_SMDT1'\n",
+       "\\item 'CD8T1MB\\_SMDT1'\n",
+       "\\item 'CD4T1MB\\_SMDT1'\n",
+       "\\item 'DC1MB\\_HLA-DQA2'\n",
+       "\\item 'B1MB\\_HLA-DQA2'\n",
+       "\\item 'NK1MB\\_HLA-DQA2'\n",
+       "\\item 'monocyte1MB\\_HLA-DQA2'\n",
+       "\\item 'CD8T1MB\\_HLA-DQA2'\n",
+       "\\item 'CD4T1MB\\_HLA-DQA2'\n",
+       "\\item 'DC1MB\\_RNASET2'\n",
+       "\\item 'B1MB\\_RNASET2'\n",
+       "\\item 'NK1MB\\_RNASET2'\n",
+       "\\item 'monocyte1MB\\_RNASET2'\n",
+       "\\item 'CD8T1MB\\_RNASET2'\n",
+       "\\item 'CD4T1MB\\_RNASET2'\n",
+       "\\item 'DC1MB\\_RPS26'\n",
+       "\\item 'B1MB\\_RPS26'\n",
+       "\\item 'NK1MB\\_RPS26'\n",
+       "\\item 'monocyte1MB\\_RPS26'\n",
+       "\\item 'CD8T1MB\\_RPS26'\n",
+       "\\item 'CD4T1MB\\_RPS26'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'DC1MB_TMEM176A'\n",
+       "2. 'B1MB_TMEM176A'\n",
+       "3. 'NK1MB_TMEM176A'\n",
+       "4. 'monocyte1MB_TMEM176A'\n",
+       "5. 'CD8T1MB_TMEM176A'\n",
+       "6. 'CD4T1MB_TMEM176A'\n",
+       "7. 'DC1MB_SMDT1'\n",
+       "8. 'B1MB_SMDT1'\n",
+       "9. 'NK1MB_SMDT1'\n",
+       "10. 'monocyte1MB_SMDT1'\n",
+       "11. 'CD8T1MB_SMDT1'\n",
+       "12. 'CD4T1MB_SMDT1'\n",
+       "13. 'DC1MB_HLA-DQA2'\n",
+       "14. 'B1MB_HLA-DQA2'\n",
+       "15. 'NK1MB_HLA-DQA2'\n",
+       "16. 'monocyte1MB_HLA-DQA2'\n",
+       "17. 'CD8T1MB_HLA-DQA2'\n",
+       "18. 'CD4T1MB_HLA-DQA2'\n",
+       "19. 'DC1MB_RNASET2'\n",
+       "20. 'B1MB_RNASET2'\n",
+       "21. 'NK1MB_RNASET2'\n",
+       "22. 'monocyte1MB_RNASET2'\n",
+       "23. 'CD8T1MB_RNASET2'\n",
+       "24. 'CD4T1MB_RNASET2'\n",
+       "25. 'DC1MB_RPS26'\n",
+       "26. 'B1MB_RPS26'\n",
+       "27. 'NK1MB_RPS26'\n",
+       "28. 'monocyte1MB_RPS26'\n",
+       "29. 'CD8T1MB_RPS26'\n",
+       "30. 'CD4T1MB_RPS26'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       " [1] \"DC1MB_TMEM176A\"       \"B1MB_TMEM176A\"        \"NK1MB_TMEM176A\"      \n",
+       " [4] \"monocyte1MB_TMEM176A\" \"CD8T1MB_TMEM176A\"     \"CD4T1MB_TMEM176A\"    \n",
+       " [7] \"DC1MB_SMDT1\"          \"B1MB_SMDT1\"           \"NK1MB_SMDT1\"         \n",
+       "[10] \"monocyte1MB_SMDT1\"    \"CD8T1MB_SMDT1\"        \"CD4T1MB_SMDT1\"       \n",
+       "[13] \"DC1MB_HLA-DQA2\"       \"B1MB_HLA-DQA2\"        \"NK1MB_HLA-DQA2\"      \n",
+       "[16] \"monocyte1MB_HLA-DQA2\" \"CD8T1MB_HLA-DQA2\"     \"CD4T1MB_HLA-DQA2\"    \n",
+       "[19] \"DC1MB_RNASET2\"        \"B1MB_RNASET2\"         \"NK1MB_RNASET2\"       \n",
+       "[22] \"monocyte1MB_RNASET2\"  \"CD8T1MB_RNASET2\"      \"CD4T1MB_RNASET2\"     \n",
+       "[25] \"DC1MB_RPS26\"          \"B1MB_RPS26\"           \"NK1MB_RPS26\"         \n",
+       "[28] \"monocyte1MB_RPS26\"    \"CD8T1MB_RPS26\"        \"CD4T1MB_RPS26\"       "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "names(data_input_eqtl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "199dab4a-3bb1-4c25-86a5-ab71b76ee125",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "List of 8\n",
+      " $ beta    : Named num [1:2992] 0.00762 0.02939 0.02411 -0.06009 0.28133 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2992] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ varbeta : Named num [1:2992] 0.0136 0.0132 0.0128 0.0135 0.0304 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2992] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ N       : int [1:2992] 171 171 171 171 147 171 171 171 171 171 ...\n",
+      " $ type    : chr \"quant\"\n",
+      " $ MAF     : Named num [1:2992] 0.313 0.333 0.351 0.316 0.126 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2992] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ snp     : Named chr [1:2992] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2992] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ position: Named int [1:2992] 151743278 151744884 151150662 149826188 151677209 150656640 150846560 150846633 149819792 150814835 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2992] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ pvalues : Named num [1:2992] 0.948 0.798 0.831 0.605 0.107 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2992] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "str(data_input_eqtl[[1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "1e601277-1751-4a84-b43d-eb62da4a6359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Check validity of constructed dataset for coloc input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "c2879899-1cd2-42d0-b8ad-579a44a30a74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "9.71881330455308e-11"
+      ],
+      "text/latex": [
+       "9.71881330455308e-11"
+      ],
+      "text/markdown": [
+       "9.71881330455308e-11"
+      ],
+      "text/plain": [
+       "[1] 9.718813e-11"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "min(eqtl_all_effect$MetaP[eqtl_all_effect$ident == 'DC1MB_TMEM176A'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "7745eac3-4979-46ab-9607-5d902a13e81f",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.7189e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 0.0046418\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 0.004909\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 0.0067582\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 0.0037104\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5268e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.4595e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3059e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 0.011178\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2246e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 0.00063663\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.899e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    }
+   ],
+   "source": [
+    "for( i  in names(data_input_eqtl)){\n",
+    "    print(i)\n",
+    "    check_dataset(data_input_eqtl[[i]], warn.minp = 1e-70)\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74107ad4-fe37-43d3-8a84-6e2ea898daa7",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### GWAS input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "d0596321-5720-4fc6-8ac2-0723f82502f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas = gwas_gtex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "ff419dbb-0bf3-407d-ac38-a0801dee7348",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>tag</th><th scope=col>position</th><th scope=col>non_effect_allele</th><th scope=col>frequency</th><th scope=col>pvalue</th><th scope=col>effect_size</th><th scope=col>Phenotype</th><th scope=col>Sample_Size</th><th scope=col>variant_id</th><th scope=col>sample_size</th><th scope=col>standard_error</th><th scope=col>effect_allele</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Astle_et_al_2016_White_blood_cell_count</td><td>13550</td><td>G</td><td>0.01731602</td><td>0.228037473787046</td><td>NA</td><td>White blood cell count</td><td>173480</td><td>rs554008981_G/A</td><td>173480</td><td>NA</td><td>A</td></tr>\n",
+       "\t<tr><td>Astle_et_al_2016_White_blood_cell_count</td><td>14671</td><td>G</td><td>0.01298701</td><td>0.816150563702573</td><td>NA</td><td>White blood cell count</td><td>173480</td><td>rs201055865_G/C</td><td>173480</td><td>NA</td><td>C</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 12\n",
+       "\\begin{tabular}{llllllllllll}\n",
+       " tag & position & non\\_effect\\_allele & frequency & pvalue & effect\\_size & Phenotype & Sample\\_Size & variant\\_id & sample\\_size & standard\\_error & effect\\_allele\\\\\n",
+       " <chr> & <int> & <chr> & <dbl> & <chr> & <dbl> & <chr> & <int> & <chr> & <int> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count & 13550 & G & 0.01731602 & 0.228037473787046 & NA & White blood cell count & 173480 & rs554008981\\_G/A & 173480 & NA & A\\\\\n",
+       "\t Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count & 14671 & G & 0.01298701 & 0.816150563702573 & NA & White blood cell count & 173480 & rs201055865\\_G/C & 173480 & NA & C\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 12\n",
+       "\n",
+       "| tag &lt;chr&gt; | position &lt;int&gt; | non_effect_allele &lt;chr&gt; | frequency &lt;dbl&gt; | pvalue &lt;chr&gt; | effect_size &lt;dbl&gt; | Phenotype &lt;chr&gt; | Sample_Size &lt;int&gt; | variant_id &lt;chr&gt; | sample_size &lt;int&gt; | standard_error &lt;dbl&gt; | effect_allele &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| Astle_et_al_2016_White_blood_cell_count | 13550 | G | 0.01731602 | 0.228037473787046 | NA | White blood cell count | 173480 | rs554008981_G/A | 173480 | NA | A |\n",
+       "| Astle_et_al_2016_White_blood_cell_count | 14671 | G | 0.01298701 | 0.816150563702573 | NA | White blood cell count | 173480 | rs201055865_G/C | 173480 | NA | C |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  tag                                     position non_effect_allele frequency \n",
+       "1 Astle_et_al_2016_White_blood_cell_count 13550    G                 0.01731602\n",
+       "2 Astle_et_al_2016_White_blood_cell_count 14671    G                 0.01298701\n",
+       "  pvalue            effect_size Phenotype              Sample_Size\n",
+       "1 0.228037473787046 NA          White blood cell count 173480     \n",
+       "2 0.816150563702573 NA          White blood cell count 173480     \n",
+       "  variant_id      sample_size standard_error effect_allele\n",
+       "1 rs554008981_G/A 173480      NA             A            \n",
+       "2 rs201055865_G/C 173480      NA             C            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(gwas,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "8f756246-5807-40bb-b05c-7276650c415a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Compare variants and pre-filter on variants in EQTL data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "a5fc255e-89e7-4aa1-890b-3e979729d1b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_input = gwas[gwas$variant_id %in%  unique(eqtl_all_effect$SNP),] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "45ba3c74-311f-469f-978f-82f236e16f16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "15158"
+      ],
+      "text/latex": [
+       "15158"
+      ],
+      "text/markdown": [
+       "15158"
+      ],
+      "text/plain": [
+       "[1] 15158"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(gwas_input$variant_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "699e09b6-585b-482f-91f9-8ded84238b34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 7 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Phenotype</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Asthma                    </td><td> 8843303</td></tr>\n",
+       "\t<tr><td>Crohn's Disease           </td><td> 8860907</td></tr>\n",
+       "\t<tr><td>Inflammatory Bowel Disease</td><td> 8858738</td></tr>\n",
+       "\t<tr><td>Multiple Sclerosis        </td><td> 8867478</td></tr>\n",
+       "\t<tr><td>Rheumatoid Arthritis      </td><td> 8857562</td></tr>\n",
+       "\t<tr><td>Type_1_Diabetes           </td><td>62115237</td></tr>\n",
+       "\t<tr><td>White blood cell count    </td><td> 8871979</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 7 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " Phenotype & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t Asthma                     &  8843303\\\\\n",
+       "\t Crohn's Disease            &  8860907\\\\\n",
+       "\t Inflammatory Bowel Disease &  8858738\\\\\n",
+       "\t Multiple Sclerosis         &  8867478\\\\\n",
+       "\t Rheumatoid Arthritis       &  8857562\\\\\n",
+       "\t Type\\_1\\_Diabetes            & 62115237\\\\\n",
+       "\t White blood cell count     &  8871979\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 7 × 2\n",
+       "\n",
+       "| Phenotype &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| Asthma                     |  8843303 |\n",
+       "| Crohn's Disease            |  8860907 |\n",
+       "| Inflammatory Bowel Disease |  8858738 |\n",
+       "| Multiple Sclerosis         |  8867478 |\n",
+       "| Rheumatoid Arthritis       |  8857562 |\n",
+       "| Type_1_Diabetes            | 62115237 |\n",
+       "| White blood cell count     |  8871979 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Phenotype                  n       \n",
+       "1 Asthma                      8843303\n",
+       "2 Crohn's Disease             8860907\n",
+       "3 Inflammatory Bowel Disease  8858738\n",
+       "4 Multiple Sclerosis          8867478\n",
+       "5 Rheumatoid Arthritis        8857562\n",
+       "6 Type_1_Diabetes            62115237\n",
+       "7 White blood cell count      8871979"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gwas %>% group_by(Phenotype) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "a33c6d60-872e-4cf1-828f-2e5cc9ead620",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Remove NA values in case there are some in the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "aa4375cd-db85-472e-ba5a-3616ed3694ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_input = gwas_input[!is.na(gwas_input$effect_size),]\n",
+    "gwas_input = gwas_input[!is.na(gwas_input$standard_error),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "c1274221-9f0e-4a5f-8c43-88e70637e971",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "15155"
+      ],
+      "text/latex": [
+       "15155"
+      ],
+      "text/markdown": [
+       "15155"
+      ],
+      "text/plain": [
+       "[1] 15155"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(gwas_input$variant_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "92259ea2-b6c8-49bc-9539-dbdf9ae605a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Prepare GWAS Input per trait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "49f1c915-548c-475f-946a-2a03af6caa47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_input_list = list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "19943e62-5b63-4a24-80e6-d47a87335615",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#length(input$position)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "0f5b69f0-3093-4d60-818b-83c0aece3167",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for(i in unique(gwas_input$Phenotype)){\n",
+    "    input = gwas_input[gwas_input$Phenotype == i,]\n",
+    "    \n",
+    "    ## Prepare GWAS Input\n",
+    "    # Beta\n",
+    "    beta_gwas =  input$effect_size\n",
+    "    names(beta_gwas) = input$variant_id\n",
+    "\n",
+    "    # Varbeta \n",
+    "    varbeta_gwas =  ( input$standard_error)^2\n",
+    "    names(varbeta_gwas) = input$variant_id\n",
+    "\n",
+    "    # MAF \n",
+    "    MAF_gwas = input$frequency\n",
+    "    names(MAF_gwas) = input$variant_id\n",
+    "\n",
+    "    # Position \n",
+    "    position_gwas = input$position\n",
+    "    names(position_gwas) = input$variant_id\n",
+    "\n",
+    "    # SNP\n",
+    "    snp_gwas = input$variant_id\n",
+    "    names(snp_gwas) = input$variant_id\n",
+    "    \n",
+    "    ### Input Parameters - check with available data\n",
+    "\n",
+    "    GWAS_list = list(\n",
+    "               beta = beta_gwas, # regression coefficient\n",
+    "               varbeta =varbeta_gwas,  # variance/ standard deviation of beta?\n",
+    "               #N = sample_size_gwas, # number of samples in dataset 1\n",
+    "               #sdY =   # population standard deviation of the trait, if quantitative trait\n",
+    "                       # if unknown will be approximated based on beta, varbeta, N, MAF\n",
+    "               type = 'cc',  # quant or cc to denote quantitative or case-control\n",
+    "               #MAF = MAF_gwas,   # minor allele frequency of the variants\n",
+    "               # LD =  needed?\n",
+    "               snp = snp_gwas,  # character vector of SNP ids\n",
+    "               position = position_gwas)\n",
+    "    \n",
+    "    gwas_input_list[[i]] = GWAS_list\n",
+    "    \n",
+    "    }\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "509e689e-9b79-43c5-9b21-748d5d28c8a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "List of 5\n",
+      " $ beta    : Named num [1:8682] -0.067465 -0.018547 -0.041826 -0.076376 0.000736 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:8682] \"rs9270493_T/C\" \"rs9270505_A/G\" \"rs9273238_A/G\" \"rs3021302_T/C\" ...\n",
+      " $ varbeta : Named num [1:8682] 1.83e-05 1.35e-05 1.65e-05 2.32e-05 1.35e-05 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:8682] \"rs9270493_T/C\" \"rs9270505_A/G\" \"rs9273238_A/G\" \"rs3021302_T/C\" ...\n",
+      " $ type    : chr \"cc\"\n",
+      " $ snp     : Named chr [1:8682] \"rs9270493_T/C\" \"rs9270505_A/G\" \"rs9273238_A/G\" \"rs3021302_T/C\" ...\n",
+      "  ..- attr(*, \"names\")= chr [1:8682] \"rs9270493_T/C\" \"rs9270505_A/G\" \"rs9273238_A/G\" \"rs3021302_T/C\" ...\n",
+      " $ position: Named int [1:8682] 32591333 32591439 32646220 32655373 32655653 32660495 32660630 32660643 32660651 32660921 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:8682] \"rs9270493_T/C\" \"rs9270505_A/G\" \"rs9273238_A/G\" \"rs3021302_T/C\" ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "str(gwas_input_list[[1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "bc281452-7ec0-4704-ada2-928c9321518e",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"White blood cell count\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(gwas_input_list[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Crohn's Disease\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(gwas_input_list[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.025e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Inflammatory Bowel Disease\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(gwas_input_list[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"Asthma\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(gwas_input_list[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Type_1_Diabetes\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "for(i in names(gwas_input_list)){\n",
+    "    print(i)\n",
+    "    check_dataset(gwas_input_list[[i]],  warn.minp = 1e-70)\n",
+    "    }\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcb4d4e1-916f-42f2-afdf-9686c2cb7faa",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Run Coloc Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15782951-2152-4385-b799-340888e5da29",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Visualize a concrete eQTL and GWAS trait example and save data for further investigation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "5bb1509e-c609-400a-a91e-e353b979fec1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Choose Phenotype to visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "619a6f06-3087-4107-b0e2-c398f4821896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = 'Type_1_Diabetes'\n",
+    "#i = unique(gwas_input$Phenotype)[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "6a84b77c-801b-47d2-87f6-8d087e79116f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'Type_1_Diabetes'"
+      ],
+      "text/latex": [
+       "'Type\\_1\\_Diabetes'"
+      ],
+      "text/markdown": [
+       "'Type_1_Diabetes'"
+      ],
+      "text/plain": [
+       "[1] \"Type_1_Diabetes\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "4a226f3d-53d5-4757-aafc-3bc2bd7fb41a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Choose eQTL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "0351f8ae-bcc7-4c14-ba46-6f965d86a335",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_var = 'CD4T1MB_RPS26'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "a7200673-9ba8-40a2-a952-abfa45bfc678",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Plot the SNP significance values for the matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "56dc908f-440b-45df-b01d-79903f849b9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Type_1_Diabetes\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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dJoNION6nQ6T4YBgNGj2AHwa+PHjx/B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\nQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# GWAS\n",
+    "print(i)\n",
+    "plot_dataset(lapply(gwas_input_list[[i]], function(x){\n",
+    "    x[unique(eqtl_all_effect$SNP[eqtl_all_effect$ident == eqtl_var])]}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "d3db5dfe-9564-4ee3-83cb-73d3d26cc6df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+0Xj364bHrKmqzpM6aMSRsSFazT2MzN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\nAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# coeQTL\n",
+    "print(eqtl_var)\n",
+    "plot_dataset(data_input_eqtl[[eqtl_var]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "a6725532-a9bf-4807-ae71-4fc8919c7078",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Make combined p-value plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "e6ad562b-367d-4a80-a0ab-661cc859ee91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input = gwas_input[gwas_input$Phenotype == i,] # GWAS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "87d80cd1-7bfa-4e1d-9073-b1087f01c8c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_eqtl = eqtl_all_effect[eqtl_all_effect$ident == eqtl_var,] # Co-EQTL Input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "2348335d-a774-451a-99c2-ef8ab9b65382",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_data = merge(input_eqtl[,c('SNP', 'MetaBeta', 'MetaP')], input[,c('variant_id', 'pvalue', 'effect_size')], by.x = 'SNP', by.y = 'variant_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "abb88e62-13c8-4bd3-9149-7db933abab06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1341"
+      ],
+      "text/latex": [
+       "1341"
+      ],
+      "text/markdown": [
+       "1341"
+      ],
+      "text/plain": [
+       "[1] 1341"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(plot_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "05ce330b-4d02-42e6-a89e-a21413cbc8a4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gAAAEBIINgBAAAACAkEOwAAAAAhgWAHAAAAICQQ7AAAAACEBIIdAAAAgJBAsAMA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Lc3JyKrJUAAAAr0D/il30zm92REn1dh/4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\nmR3/1ObgaQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "Plot with title “Type_1_Diabetes_( CD4T1MB_RPS26)”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(-log10(plot_data$MetaP), -log10(as.numeric(plot_data$pvalue)), xlab = '-log10(pval_eqtl)', ylab = '-log10(pval_gwas)', main = paste0( i,'_',  '( ', eqtl_var, ')'), cex = 0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26a60720-a28b-494a-b199-3fa15b2d54c0",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Run for all cell-types + genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "4015c678-0f7d-40f4-a475-fa94c96fc0f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Execute colocalization analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "83d4a8c5-6ae7-4119-87ba-2ba00d5a0dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#str(gwas_input_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "0fd62bac-0426-473d-8296-5c0c76c7bb5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#head(str(data_input_eqtl))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "0946f702-8660-4d67-b405-5b9de0a0bed2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_result_summary = data.frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "0be5ef22-96c6-40c6-bc9e-c777af459168",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_result_detail = data.frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "640961fc-8540-4daa-93bd-0b1d996a13be",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.18e-04  1.44e-05  9.81e-01  1.73e-02  5.24e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0524%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0046418\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.804000  0.014200  0.178000  0.003150  0.000143 \n",
+      "[1] \"PP abf for shared variant: 0.0143%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.004909\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.795000  0.014000  0.187000  0.003300  0.000137 \n",
+      "[1] \"PP abf for shared variant: 0.0137%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.92e-34  5.14e-36  9.82e-01  1.73e-02  5.26e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0526%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0067582\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.799000  0.014100  0.184000  0.003240  0.000123 \n",
+      "[1] \"PP abf for shared variant: 0.0123%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0037104\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.822000  0.014500  0.160000  0.002830  0.000112 \n",
+      "[1] \"PP abf for shared variant: 0.0112%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.88e-03  8.07e-05  9.74e-01  1.33e-02  6.49e-03 \n",
+      "[1] \"PP abf for shared variant: 0.649%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.68e-07  9.17e-09  9.79e-01  1.34e-02  7.18e-03 \n",
+      "[1] \"PP abf for shared variant: 0.718%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.17e-04  9.85e-06  9.79e-01  1.34e-02  6.97e-03 \n",
+      "[1] \"PP abf for shared variant: 0.697%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.30e-10  5.90e-12  9.79e-01  1.34e-02  7.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.724%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.06e-18  5.58e-20  9.79e-01  1.34e-02  7.41e-03 \n",
+      "[1] \"PP abf for shared variant: 0.741%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.46e-31  1.16e-32  9.79e-01  1.34e-02  7.43e-03 \n",
+      "[1] \"PP abf for shared variant: 0.743%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-58  6.15e-09  2.96e-50  1.00e+00  2.59e-08 \n",
+      "[1] \"PP abf for shared variant: 2.59e-06%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-58  5.43e-09  2.96e-50  1.00e+00  6.40e-09 \n",
+      "[1] \"PP abf for shared variant: 6.4e-07%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-50  8.05e-01  5.31e-51  1.79e-01  1.56e-02 \n",
+      "[1] \"PP abf for shared variant: 1.56%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-57  8.94e-08  2.96e-50  1.00e+00  3.38e-07 \n",
+      "[1] \"PP abf for shared variant: 3.38e-05%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-54  4.98e-05  2.96e-50  1.00e+00  3.87e-06 \n",
+      "[1] \"PP abf for shared variant: 0.000387%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.36e-51  2.15e-01  2.26e-50  7.63e-01  2.16e-02 \n",
+      "[1] \"PP abf for shared variant: 2.16%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.3059e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.563000  0.009020  0.421000  0.006750  0.000195 \n",
+      "[1] \"PP abf for shared variant: 0.0195%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.011178\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.800000  0.012800  0.184000  0.002960  0.000107 \n",
+      "[1] \"PP abf for shared variant: 0.0107%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2246e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.224000  0.003580  0.760000  0.012200  0.000352 \n",
+      "[1] \"PP abf for shared variant: 0.0352%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00063663\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.757000  0.012100  0.228000  0.003650  0.000159 \n",
+      "[1] \"PP abf for shared variant: 0.0159%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.07e-04  8.14e-06  9.83e-01  1.58e-02  4.60e-04 \n",
+      "[1] \"PP abf for shared variant: 0.046%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-14  4.51e-16  9.84e-01  1.58e-02  4.02e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0402%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-44  6.83e-46  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-47  7.72e-49  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.00e-42  2.33e-43  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.06e-47  4.22e-48  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.83e-46  3.65e-47  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.39e-44  2.51e-45  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000752  0.000080  0.902000  0.096000  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0046418\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.734000  0.078100  0.170000  0.018000  0.000727 \n",
+      "[1] \"PP abf for shared variant: 0.0727%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.004909\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.726000  0.077200  0.178000  0.018900  0.000731 \n",
+      "[1] \"PP abf for shared variant: 0.0731%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.68e-34  2.85e-35  9.03e-01  9.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0067582\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.72900   0.07760   0.17400   0.01850   0.00072 \n",
+      "[1] \"PP abf for shared variant: 0.072%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0037104\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.75100   0.07990   0.15200   0.01620   0.00069 \n",
+      "[1] \"PP abf for shared variant: 0.069%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000460  0.000147  0.757000  0.242000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.14e-09  1.32e-09  7.57e-01  2.42e-01  1.00e-03 \n",
+      "[1] \"PP abf for shared variant: 0.1%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.63e-05  1.16e-05  7.57e-01  2.42e-01  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.89e-12  1.56e-12  7.57e-01  2.42e-01  1.00e-03 \n",
+      "[1] \"PP abf for shared variant: 0.1%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-20  5.86e-21  7.57e-01  2.42e-01  1.00e-03 \n",
+      "[1] \"PP abf for shared variant: 0.1%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.57e-34  2.42e-34  7.57e-01  2.42e-01  9.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0988%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.80e-37  8.78e-33  4.32e-05  1.00e+00  6.66e-08 \n",
+      "[1] \"PP abf for shared variant: 6.66e-06%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-29  2.88e-25  4.32e-05  1.00e+00  6.76e-08 \n",
+      "[1] \"PP abf for shared variant: 6.76e-06%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-11  2.55e-07  4.32e-05  1.00e+00  8.63e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00863%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.86e-31  1.35e-26  4.32e-05  1.00e+00  6.72e-08 \n",
+      "[1] \"PP abf for shared variant: 6.72e-06%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-16  2.83e-12  4.32e-05  1.00e+00  4.03e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0403%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-10  2.60e-06  4.32e-05  1.00e+00  9.24e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00924%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.3059e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.41e-08  5.11e-01  4.14e-08  3.90e-01  9.88e-02 \n",
+      "[1] \"PP abf for shared variant: 9.88%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.011178\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.45e-08  7.99e-01  2.00e-08  1.89e-01  1.22e-02 \n",
+      "[1] \"PP abf for shared variant: 1.22%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2246e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-08  1.47e-01  5.35e-08  5.02e-01  3.52e-01 \n",
+      "[1] \"PP abf for shared variant: 35.2%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00063663\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.96e-08  7.53e-01  2.45e-08  2.32e-01  1.55e-02 \n",
+      "[1] \"PP abf for shared variant: 1.55%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-11  2.37e-04  4.87e-08  4.54e-01  5.45e-01 \n",
+      "[1] \"PP abf for shared variant: 54.5%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.16e-22  2.99e-15  1.10e-08  9.54e-02  9.05e-01 \n",
+      "[1] \"PP abf for shared variant: 90.5%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-44  7.10e-46  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-47  8.02e-49  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.99e-42  2.42e-43  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.03e-47  4.39e-48  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.81e-46  3.79e-47  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.38e-44  2.61e-45  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.56e-04  7.63e-05  9.07e-01  9.15e-02  8.29e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0829%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0046418\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.737000  0.074400  0.170000  0.017200  0.000721 \n",
+      "[1] \"PP abf for shared variant: 0.0721%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.004909\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.730000  0.073600  0.178000  0.018000  0.000704 \n",
+      "[1] \"PP abf for shared variant: 0.0704%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-34  2.72e-35  9.08e-01  9.15e-02  8.37e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0837%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0067582\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.733000  0.073900  0.175000  0.017600  0.000679 \n",
+      "[1] \"PP abf for shared variant: 0.0679%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0037104\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.755000  0.076100  0.153000  0.015400  0.000604 \n",
+      "[1] \"PP abf for shared variant: 0.0604%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.68e-04  7.09e-05  8.88e-01  1.11e-01  7.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0792%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.05e-09  6.31e-10  8.88e-01  1.11e-01  7.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0784%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.45e-05  5.56e-06  8.88e-01  1.11e-01  7.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0785%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.88e-12  7.35e-13  8.88e-01  1.11e-01  7.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0787%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-20  2.73e-21  8.88e-01  1.11e-01  7.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0788%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.89e-34  1.11e-34  8.88e-01  1.11e-01  7.94e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0794%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.47e-54  8.12e-33  7.37e-22  9.24e-01  7.58e-02 \n",
+      "[1] \"PP abf for shared variant: 7.58%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.13e-46  2.67e-25  7.38e-22  9.26e-01  7.37e-02 \n",
+      "[1] \"PP abf for shared variant: 7.37%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-28  2.50e-07  7.82e-22  9.82e-01  1.80e-02 \n",
+      "[1] \"PP abf for shared variant: 1.8%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.79e-48  1.23e-26  7.23e-22  9.07e-01  9.33e-02 \n",
+      "[1] \"PP abf for shared variant: 9.33%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-33  2.65e-12  7.46e-22  9.36e-01  6.40e-02 \n",
+      "[1] \"PP abf for shared variant: 6.4%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-27  2.29e-06  6.99e-22  8.77e-01  1.23e-01 \n",
+      "[1] \"PP abf for shared variant: 12.3%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.3059e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.51e-06  5.03e-01  4.21e-06  3.83e-01  1.14e-01 \n",
+      "[1] \"PP abf for shared variant: 11.4%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.011178\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.79e-06  8.02e-01  2.08e-06  1.90e-01  7.80e-03 \n",
+      "[1] \"PP abf for shared variant: 0.78%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2246e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-06  1.42e-01  5.36e-06  4.85e-01  3.73e-01 \n",
+      "[1] \"PP abf for shared variant: 37.3%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00063663\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.19e-06  7.47e-01  2.52e-06  2.30e-01  2.28e-02 \n",
+      "[1] \"PP abf for shared variant: 2.28%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-09  3.26e-04  6.91e-06  6.27e-01  3.72e-01 \n",
+      "[1] \"PP abf for shared variant: 37.2%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-20  2.24e-15  8.59e-07  6.91e-02  9.31e-01 \n",
+      "[1] \"PP abf for shared variant: 93.1%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-44  2.63e-46  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-47  2.98e-49  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.14e-42  8.98e-44  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.31e-47  1.63e-48  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.05e-46  1.41e-47  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.54e-44  9.69e-46  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.16e-03  3.21e-05  9.87e-01  3.45e-03  8.48e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0848%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0046418\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.87e-01  3.46e-03  9.62e-03  3.35e-05  2.83e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00283%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.004909\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.86e-01  3.46e-03  1.03e-02  3.57e-05  3.14e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00314%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.95e-33  1.39e-35  9.96e-01  3.49e-03  8.51e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0851%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0067582\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.86e-01  3.46e-03  1.02e-02  3.53e-05  4.00e-05 \n",
+      "[1] \"PP abf for shared variant: 0.004%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0037104\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.88e-01  3.47e-03  8.88e-03  3.09e-05  2.69e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00269%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-02  8.15e-05  9.81e-01  7.58e-03  1.10e-03 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-07  8.08e-10  9.91e-01  7.66e-03  1.10e-03 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.23e-04  6.37e-06  9.90e-01  7.66e-03  1.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-10  1.01e-12  9.91e-01  7.66e-03  1.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.25e-19  4.06e-21  9.91e-01  7.66e-03  1.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.82e-32  2.96e-34  9.91e-01  7.66e-03  1.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.86e-75  1.39e-06  3.51e-71  7.74e-07  1.00e+00 \n",
+      "[1] \"PP abf for shared variant: 100%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.08e-75  8.80e-07  3.51e-71  1.26e-06  1.00e+00 \n",
+      "[1] \"PP abf for shared variant: 100%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-69  8.95e-01  2.21e-71  5.31e-03  9.94e-02 \n",
+      "[1] \"PP abf for shared variant: 9.94%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-75  4.13e-07  3.51e-71  5.01e-08  1.00e+00 \n",
+      "[1] \"PP abf for shared variant: 100%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.16e-72  2.04e-03  3.54e-71  1.17e-04  9.98e-01 \n",
+      "[1] \"PP abf for shared variant: 99.8%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.42e-70  2.12e-01  3.25e-71  1.41e-03  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.3059e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.52e-01  4.16e-03  4.36e-02  1.90e-04  7.87e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00787%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.011178\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.82e-01  4.30e-03  1.33e-02  5.78e-05  3.68e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00368%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2246e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.955000  0.004180  0.040900  0.000178  0.000069 \n",
+      "[1] \"PP abf for shared variant: 0.0069%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00063663\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.75e-01  4.27e-03  2.02e-02  8.79e-05  4.55e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00455%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.72e-01  4.25e-03  2.37e-02  1.03e-04  5.04e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00504%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.71e-03  3.81e-05  9.86e-01  4.30e-03  1.05e-03 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-37  1.81e-40  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.23e-41  4.42e-44  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.74e-40  1.06e-42  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-39  2.34e-42  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-41  1.49e-44  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.53e-39  3.46e-42  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000730  0.000103  0.875000  0.123000  0.001350 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0046418\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.748000  0.105000  0.128000  0.017900  0.000878 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.004909\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.741000  0.104000  0.135000  0.019000  0.000992 \n",
+      "[1] \"PP abf for shared variant: 0.0992%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-34  3.66e-35  8.76e-01  1.23e-01  1.34e-03 \n",
+      "[1] \"PP abf for shared variant: 0.134%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0067582\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.745000  0.105000  0.131000  0.018400  0.000809 \n",
+      "[1] \"PP abf for shared variant: 0.0809%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0037104\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.762000  0.107000  0.114000  0.016000  0.000853 \n",
+      "[1] \"PP abf for shared variant: 0.0853%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003430  0.000298  0.915000  0.079400  0.001580 \n",
+      "[1] \"PP abf for shared variant: 0.158%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.74e-08  3.24e-09  9.19e-01  7.97e-02  1.63e-03 \n",
+      "[1] \"PP abf for shared variant: 0.163%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-04  2.37e-05  9.18e-01  7.97e-02  1.59e-03 \n",
+      "[1] \"PP abf for shared variant: 0.159%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.45e-11  3.86e-12  9.19e-01  7.97e-02  1.63e-03 \n",
+      "[1] \"PP abf for shared variant: 0.163%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-19  1.48e-20  9.19e-01  7.97e-02  1.63e-03 \n",
+      "[1] \"PP abf for shared variant: 0.163%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-32  1.08e-33  9.19e-01  7.97e-02  1.64e-03 \n",
+      "[1] \"PP abf for shared variant: 0.164%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "7.47e-276  2.96e-33 8.45e-244  3.28e-01  6.72e-01 \n",
+      "[1] \"PP abf for shared variant: 67.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.53e-268  1.00e-25 8.62e-244  3.35e-01  6.65e-01 \n",
+      "[1] \"PP abf for shared variant: 66.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.28e-247  5.08e-05 1.68e-243  6.63e-01  3.37e-01 \n",
+      "[1] \"PP abf for shared variant: 33.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.25e-269  4.96e-27 8.57e-244  3.33e-01  6.67e-01 \n",
+      "[1] \"PP abf for shared variant: 66.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "6.42e-255  2.54e-12 1.97e-243  7.77e-01  2.23e-01 \n",
+      "[1] \"PP abf for shared variant: 22.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "5.51e-249  2.18e-06 1.66e-243  6.55e-01  3.45e-01 \n",
+      "[1] \"PP abf for shared variant: 34.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.3059e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.11e-24  5.76e-01  4.25e-24  4.01e-01  2.29e-02 \n",
+      "[1] \"PP abf for shared variant: 2.29%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.011178\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.92e-24  8.42e-01  1.61e-24  1.51e-01  6.39e-03 \n",
+      "[1] \"PP abf for shared variant: 0.639%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2246e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-24  2.23e-01  8.00e-24  7.54e-01  2.27e-02 \n",
+      "[1] \"PP abf for shared variant: 2.27%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00063663\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.52e-24  8.04e-01  2.01e-24  1.90e-01  5.84e-03 \n",
+      "[1] \"PP abf for shared variant: 0.584%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.47e-27  5.16e-04  1.06e-23  9.99e-01  1.09e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00109%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-37  2.86e-14  1.06e-23  1.00e+00  1.03e-14 \n",
+      "[1] \"PP abf for shared variant: 1.03e-12%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.81e-49  3.90e-45  5.08e-05  2.47e-01  7.53e-01 \n",
+      "[1] \"PP abf for shared variant: 75.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.86e-52  4.43e-48  5.11e-05  2.48e-01  7.52e-01 \n",
+      "[1] \"PP abf for shared variant: 75.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-46  1.32e-42  5.06e-05  2.45e-01  7.55e-01 \n",
+      "[1] \"PP abf for shared variant: 75.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.79e-51  2.39e-47  5.05e-05  2.45e-01  7.55e-01 \n",
+      "[1] \"PP abf for shared variant: 75.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.17e-50  2.08e-46  5.09e-05  2.47e-01  7.53e-01 \n",
+      "[1] \"PP abf for shared variant: 75.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-48  1.43e-44  5.08e-05  2.46e-01  7.54e-01 \n",
+      "[1] \"PP abf for shared variant: 75.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001950  0.000344  0.846000  0.149000  0.001840 \n",
+      "[1] \"PP abf for shared variant: 0.184%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0046418\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.79300   0.14000   0.05670   0.01000   0.00091 \n",
+      "[1] \"PP abf for shared variant: 0.091%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.004909\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.790000  0.139000  0.059300  0.010400  0.000961 \n",
+      "[1] \"PP abf for shared variant: 0.0961%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.25e-34  1.46e-34  8.48e-01  1.50e-01  1.84e-03 \n",
+      "[1] \"PP abf for shared variant: 0.184%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0067582\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.78900   0.13900   0.05990   0.01060   0.00117 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0037104\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.79800   0.14100   0.05070   0.00894   0.00132 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-03  5.21e-05  9.79e-01  1.67e-02  6.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0696%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-08  4.42e-10  9.82e-01  1.68e-02  7.15e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0715%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-04  3.89e-06  9.82e-01  1.68e-02  7.07e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0707%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-11  4.97e-13  9.82e-01  1.68e-02  7.42e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0742%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-19  1.80e-21  9.82e-01  1.68e-02  7.58e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0758%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.22e-33  5.51e-35  9.82e-01  1.68e-02  8.46e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0846%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-25  4.03e-15  1.37e-11  3.41e-01  6.59e-01 \n",
+      "[1] \"PP abf for shared variant: 65.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-25  3.07e-15  4.05e-13  2.65e-04  1.00e+00 \n",
+      "[1] \"PP abf for shared variant: 100%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-16  3.27e-06  3.94e-13  1.46e-06  1.00e+00 \n",
+      "[1] \"PP abf for shared variant: 100%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-26  2.84e-16  1.38e-12  2.53e-02  9.75e-01 \n",
+      "[1] \"PP abf for shared variant: 97.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.28e-24  1.59e-13  3.94e-13  4.76e-09  1.00e+00 \n",
+      "[1] \"PP abf for shared variant: 100%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-18  4.65e-08  3.95e-13  4.38e-06  1.00e+00 \n",
+      "[1] \"PP abf for shared variant: 100%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.3059e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.72100   0.01480   0.25800   0.00526   0.00146 \n",
+      "[1] \"PP abf for shared variant: 0.146%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.011178\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.892000  0.018300  0.088200  0.001810  0.000173 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2246e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.39400   0.00807   0.58500   0.01200   0.00146 \n",
+      "[1] \"PP abf for shared variant: 0.146%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00063663\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.867000  0.017800  0.112000  0.002290  0.000391 \n",
+      "[1] \"PP abf for shared variant: 0.0391%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-03  2.82e-05  9.77e-01  2.00e-02  2.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.206%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.79e-13  2.00e-14  9.78e-01  2.00e-02  2.07e-03 \n",
+      "[1] \"PP abf for shared variant: 0.207%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-44  4.19e-45  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-47  4.70e-48  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-42  1.41e-42  3.32e-01  1.25e-01  5.43e-01 \n",
+      "[1] \"PP abf for shared variant: 54.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.65e-47  2.61e-47  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.69e-46  2.23e-46  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-44  1.54e-44  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.52e-04  7.94e-05  9.03e-01  9.53e-02  8.32e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0832%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0046418\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.713000  0.075300  0.191000  0.020100  0.000677 \n",
+      "[1] \"PP abf for shared variant: 0.0677%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.004909\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.705000  0.074400  0.199000  0.021000  0.000738 \n",
+      "[1] \"PP abf for shared variant: 0.0738%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.68e-34  2.83e-35  9.04e-01  9.53e-02  8.24e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0824%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0067582\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.708000  0.074700  0.196000  0.020700  0.000682 \n",
+      "[1] \"PP abf for shared variant: 0.0682%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T1MB_TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0037104\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.733000  0.077300  0.171000  0.018100  0.000635 \n",
+      "[1] \"PP abf for shared variant: 0.0635%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000254  0.000247  0.506000  0.492000  0.002080 \n",
+      "[1] \"PP abf for shared variant: 0.208%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.17e-09  2.11e-09  5.06e-01  4.92e-01  2.10e-03 \n",
+      "[1] \"PP abf for shared variant: 0.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-05  1.88e-05  5.06e-01  4.92e-01  2.09e-03 \n",
+      "[1] \"PP abf for shared variant: 0.209%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.49e-12  2.42e-12  5.06e-01  4.92e-01  2.10e-03 \n",
+      "[1] \"PP abf for shared variant: 0.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.07e-21  8.82e-21  5.06e-01  4.92e-01  2.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.211%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T1MB_SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.15e-34  3.06e-34  5.06e-01  4.92e-01  2.05e-03 \n",
+      "[1] \"PP abf for shared variant: 0.205%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.00e+00  4.16e-33  0.00e+00  1.00e+00  2.37e-26 \n",
+      "[1] \"PP abf for shared variant: 2.37e-24%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.00e+00  1.35e-25  0.00e+00  1.00e+00  5.35e-19 \n",
+      "[1] \"PP abf for shared variant: 5.35e-17%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.00e+00  5.38e-07  0.00e+00  1.00e+00  1.25e-07 \n",
+      "[1] \"PP abf for shared variant: 1.25e-05%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.00e+00  6.80e-27  0.00e+00  1.00e+00  1.31e-21 \n",
+      "[1] \"PP abf for shared variant: 1.31e-19%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.00e+00  1.71e-14  0.00e+00  1.00e+00  1.28e-08 \n",
+      "[1] \"PP abf for shared variant: 1.28e-06%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T1MB_HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.00e+00  2.13e-06  0.00e+00  1.00e+00  4.42e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0442%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.3059e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.47000   0.06140   0.41300   0.05400   0.00148 \n",
+      "[1] \"PP abf for shared variant: 0.148%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.011178\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.692000  0.090500  0.191000  0.025000  0.000885 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2246e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.17500   0.02290   0.70600   0.09230   0.00354 \n",
+      "[1] \"PP abf for shared variant: 0.354%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00063663\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n",
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.652000  0.085300  0.232000  0.030300  0.000999 \n",
+      "[1] \"PP abf for shared variant: 0.0999%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-04  4.83e-05  8.78e-01  1.15e-01  6.77e-03 \n",
+      "[1] \"PP abf for shared variant: 0.677%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T1MB_RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-14  3.24e-15  8.82e-01  1.15e-01  2.52e-03 \n",
+      "[1] \"PP abf for shared variant: 0.252%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "8.79e-101  1.03e-44  8.54e-57  1.00e+00  4.14e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0414%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.19e-103  1.39e-47  8.54e-57  1.00e+00  1.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0122%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-98  3.47e-42  8.54e-57  1.00e+00  3.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0365%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "6.84e-103  8.01e-47  8.54e-57  1.00e+00  1.54e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0154%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "4.48e-102  5.25e-46  8.54e-57  1.00e+00  4.13e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0413%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T1MB_RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in sdY.est(d$varbeta, d$MAF, d$N):\n",
+      "“estimating sdY from maf and varbeta, please directly supply sdY if known”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "3.64e-100  4.27e-44  8.54e-57  1.00e+00  2.32e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0232%\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "for(i in names(gwas_input_list)){\n",
+    "    for(ident in names(data_input_eqtl)){\n",
+    "        \n",
+    "        print(i)\n",
+    "        print(ident)\n",
+    "        \n",
+    "        colocalization_result = coloc.abf(\n",
+    "            dataset1=gwas_input_list[[i]], # GWAS\n",
+    "            dataset2=data_input_eqtl[[ident]],  # eQTL\n",
+    "            p1 = 1e-04, p2 = 1e-04, p12 = 1e-06) # Parameters\n",
+    "\n",
+    "        result_summary = data.frame(parameter = names(colocalization_result$summary), value = colocalization_result$summary, trait = i, identifier = ident)\n",
+    "        coloc_result_summary = rbind(coloc_result_summary, result_summary)\n",
+    "\n",
+    "        result_detail = colocalization_result$results\n",
+    "        result_detail$trait = i\n",
+    "        result_detail$identifier = ident\n",
+    "        coloc_result_detail = rbind(coloc_result_detail, result_detail)\n",
+    "    }\n",
+    "    }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "b4bf6de5-4153-4343-88c5-d8b6bad40a02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##\n",
+    "# HO: locus is not associated with any of the traits\n",
+    "# H1:  locus is only significant in the GWAS \n",
+    "# H2:  the locus is only a significant eQTL \n",
+    "# H3: locus is associated with both traits due to two independent signals\n",
+    "# H4:  locus is associated with both traits due to a single colocalizing SNP\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "31f724f3-f101-4fab-98e6-67aea4fb713b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_result_summary_wide = coloc_result_summary %>% dcast(trait + identifier~ parameter, value.var = 'value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "5cc7f38f-532b-4387-9d62-d32f62349977",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 7 × 8</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>nsnps</th><th scope=col>PP.H0.abf</th><th scope=col>PP.H1.abf</th><th scope=col>PP.H2.abf</th><th scope=col>PP.H3.abf</th><th scope=col>PP.H4.abf</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>8</th><td>Asthma                    </td><td>CD4T1MB_RPS26</td><td> 381</td><td> 3.927482e-44</td><td>1.542417e-44</td><td>3.377647e-01</td><td>0.127299005</td><td>0.5349363254</td></tr>\n",
+       "\t<tr><th scope=row>38</th><td>Crohn's Disease           </td><td>CD4T1MB_RPS26</td><td>1112</td><td> 5.377536e-44</td><td>2.610708e-45</td><td>9.525417e-01</td><td>0.046232133</td><td>0.0012261225</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>Inflammatory Bowel Disease</td><td>CD4T1MB_RPS26</td><td>1110</td><td> 5.543671e-44</td><td>9.686722e-46</td><td>9.819699e-01</td><td>0.017149625</td><td>0.0008804495</td></tr>\n",
+       "\t<tr><th scope=row>98</th><td>Multiple Sclerosis        </td><td>CD4T1MB_RPS26</td><td>  58</td><td> 2.528911e-39</td><td>3.462667e-42</td><td>9.977730e-01</td><td>0.001357488</td><td>0.0008695298</td></tr>\n",
+       "\t<tr><th scope=row>128</th><td>Rheumatoid Arthritis      </td><td>CD4T1MB_RPS26</td><td> 882</td><td> 2.866338e-48</td><td>1.432885e-44</td><td>5.077245e-05</td><td>0.246275259</td><td>0.7536739683</td></tr>\n",
+       "\t<tr><th scope=row>158</th><td>Type_1_Diabetes           </td><td>CD4T1MB_RPS26</td><td>1341</td><td>3.644262e-100</td><td>4.267404e-44</td><td>8.537799e-57</td><td>0.999767580</td><td>0.0002324201</td></tr>\n",
+       "\t<tr><th scope=row>188</th><td>White blood cell count    </td><td>CD4T1MB_RPS26</td><td>1078</td><td> 5.393380e-44</td><td>2.511048e-45</td><td>9.553483e-01</td><td>0.044477333</td><td>0.0001743428</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 7 × 8\n",
+       "\\begin{tabular}{r|llllllll}\n",
+       "  & trait & identifier & nsnps & PP.H0.abf & PP.H1.abf & PP.H2.abf & PP.H3.abf & PP.H4.abf\\\\\n",
+       "  & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t8 & Asthma                     & CD4T1MB\\_RPS26 &  381 &  3.927482e-44 & 1.542417e-44 & 3.377647e-01 & 0.127299005 & 0.5349363254\\\\\n",
+       "\t38 & Crohn's Disease            & CD4T1MB\\_RPS26 & 1112 &  5.377536e-44 & 2.610708e-45 & 9.525417e-01 & 0.046232133 & 0.0012261225\\\\\n",
+       "\t68 & Inflammatory Bowel Disease & CD4T1MB\\_RPS26 & 1110 &  5.543671e-44 & 9.686722e-46 & 9.819699e-01 & 0.017149625 & 0.0008804495\\\\\n",
+       "\t98 & Multiple Sclerosis         & CD4T1MB\\_RPS26 &   58 &  2.528911e-39 & 3.462667e-42 & 9.977730e-01 & 0.001357488 & 0.0008695298\\\\\n",
+       "\t128 & Rheumatoid Arthritis       & CD4T1MB\\_RPS26 &  882 &  2.866338e-48 & 1.432885e-44 & 5.077245e-05 & 0.246275259 & 0.7536739683\\\\\n",
+       "\t158 & Type\\_1\\_Diabetes            & CD4T1MB\\_RPS26 & 1341 & 3.644262e-100 & 4.267404e-44 & 8.537799e-57 & 0.999767580 & 0.0002324201\\\\\n",
+       "\t188 & White blood cell count     & CD4T1MB\\_RPS26 & 1078 &  5.393380e-44 & 2.511048e-45 & 9.553483e-01 & 0.044477333 & 0.0001743428\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 7 × 8\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | nsnps &lt;dbl&gt; | PP.H0.abf &lt;dbl&gt; | PP.H1.abf &lt;dbl&gt; | PP.H2.abf &lt;dbl&gt; | PP.H3.abf &lt;dbl&gt; | PP.H4.abf &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 8 | Asthma                     | CD4T1MB_RPS26 |  381 |  3.927482e-44 | 1.542417e-44 | 3.377647e-01 | 0.127299005 | 0.5349363254 |\n",
+       "| 38 | Crohn's Disease            | CD4T1MB_RPS26 | 1112 |  5.377536e-44 | 2.610708e-45 | 9.525417e-01 | 0.046232133 | 0.0012261225 |\n",
+       "| 68 | Inflammatory Bowel Disease | CD4T1MB_RPS26 | 1110 |  5.543671e-44 | 9.686722e-46 | 9.819699e-01 | 0.017149625 | 0.0008804495 |\n",
+       "| 98 | Multiple Sclerosis         | CD4T1MB_RPS26 |   58 |  2.528911e-39 | 3.462667e-42 | 9.977730e-01 | 0.001357488 | 0.0008695298 |\n",
+       "| 128 | Rheumatoid Arthritis       | CD4T1MB_RPS26 |  882 |  2.866338e-48 | 1.432885e-44 | 5.077245e-05 | 0.246275259 | 0.7536739683 |\n",
+       "| 158 | Type_1_Diabetes            | CD4T1MB_RPS26 | 1341 | 3.644262e-100 | 4.267404e-44 | 8.537799e-57 | 0.999767580 | 0.0002324201 |\n",
+       "| 188 | White blood cell count     | CD4T1MB_RPS26 | 1078 |  5.393380e-44 | 2.511048e-45 | 9.553483e-01 | 0.044477333 | 0.0001743428 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    trait                      identifier    nsnps PP.H0.abf     PP.H1.abf   \n",
+       "8   Asthma                     CD4T1MB_RPS26  381   3.927482e-44 1.542417e-44\n",
+       "38  Crohn's Disease            CD4T1MB_RPS26 1112   5.377536e-44 2.610708e-45\n",
+       "68  Inflammatory Bowel Disease CD4T1MB_RPS26 1110   5.543671e-44 9.686722e-46\n",
+       "98  Multiple Sclerosis         CD4T1MB_RPS26   58   2.528911e-39 3.462667e-42\n",
+       "128 Rheumatoid Arthritis       CD4T1MB_RPS26  882   2.866338e-48 1.432885e-44\n",
+       "158 Type_1_Diabetes            CD4T1MB_RPS26 1341  3.644262e-100 4.267404e-44\n",
+       "188 White blood cell count     CD4T1MB_RPS26 1078   5.393380e-44 2.511048e-45\n",
+       "    PP.H2.abf    PP.H3.abf   PP.H4.abf   \n",
+       "8   3.377647e-01 0.127299005 0.5349363254\n",
+       "38  9.525417e-01 0.046232133 0.0012261225\n",
+       "68  9.819699e-01 0.017149625 0.0008804495\n",
+       "98  9.977730e-01 0.001357488 0.0008695298\n",
+       "128 5.077245e-05 0.246275259 0.7536739683\n",
+       "158 8.537799e-57 0.999767580 0.0002324201\n",
+       "188 9.553483e-01 0.044477333 0.0001743428"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coloc_result_summary_wide[coloc_result_summary_wide$identifier == 'CD4T1MB_RPS26',] # check whether we get same result for CD4T- RPS26 signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "cf43ca04-e857-49ea-bc92-0bc42c260d21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Save the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "aea90368-d79e-4bd6-89fe-0ac8551e584e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#write.csv(coloc_result_summary, paste0(path, \"/colocalization_results/\", \"EQTL_summary.csv\"))\n",
+    "#write.table(coloc_result_summary, file = paste0(path, \"/colocalization_results/\", \"EQTL_summary.csv\"), append =TRUE, sep = \",\", row.names = FALSE, col.names =FALSE)\n",
+    "\n",
+    "write.table(coloc_result_summary, file = paste0(path, \"/colocalization_results/\", \"EQTL_summary_update.csv\"), append =FALSE, sep = \",\", row.names = FALSE, col.names =TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "de723637-f2b7-40ad-bf4c-8584a17e1147",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#write.csv(coloc_result_detail,  paste0(path, \"/colocalization_results/\", \"EQTL_detail.csv\"))\n",
+    "#write.table(coloc_result_detail, file = paste0(path, \"/colocalization_results/\", \"EQTL_detail.csv\"), append =TRUE, sep = \",\", row.names = FALSE, col.names = FALSE)\n",
+    "\n",
+    "write.table(coloc_result_detail, file = paste0(path, \"/colocalization_results/\", \"EQTL_detail_update.csv\"), append =FALSE, sep = \",\", row.names = FALSE, col.names = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07b27ab0-855f-40d1-b810-f2d0e71d4ebb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Colocalization for co-eQTLS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ce117bd-b13b-494f-a008-49422ad25cda",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601b36f9-a8b6-4248-a18f-9ccaf4f62228",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Co-EQTL input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "8d39fe8b-7e1c-4bfe-8bf1-1ea64f1231a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 24</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>SNP</th><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>SNPEffectAllele</th><th scope=col>⋯</th><th scope=col>MetaBeta</th><th scope=col>MetaSE</th><th scope=col>NrDatasets</th><th scope=col>DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2)</th><th scope=col>cell_type</th><th scope=col>eGene</th><th scope=col>ident</th><th scope=col>position</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs1001116_C/T</td><td>CAPG;TMEM176A </td><td>7</td><td>150497491</td><td>NA</td><td>TMEM176A___CAPG__TMEM176A </td><td>7</td><td>151743278</td><td>C/T</td><td>T</td><td>⋯</td><td> 0.079829</td><td>0.121971</td><td>4</td><td>-0.075203;0.149838;-0.214833;0.318816 </td><td>-0.422702;1.210238;-1.180776;1.518437</td><td>34;67;32;24</td><td>monocyte</td><td>TMEM176A</td><td>monocyte_TMEM176A___CAPG__TMEM176A </td><td>151743278</td></tr>\n",
+       "\t<tr><td>rs1001116_C/T</td><td>PTAFR;TMEM176A</td><td>7</td><td>150497491</td><td>NA</td><td>TMEM176A___PTAFR__TMEM176A</td><td>7</td><td>151743278</td><td>C/T</td><td>T</td><td>⋯</td><td>-0.100600</td><td>0.121873</td><td>4</td><td>0.018313;-0.015859;-0.229843;-0.120209</td><td>0.102794;-0.127373;-1.265483;-0.5595 </td><td>34;67;32;24</td><td>monocyte</td><td>TMEM176A</td><td>monocyte_TMEM176A___PTAFR__TMEM176A</td><td>151743278</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 24\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " SNP & Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNPChr & SNPPos & SNPAlleles & SNPEffectAllele & ⋯ & MetaBeta & MetaSE & NrDatasets & DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) & DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) & DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) & cell\\_type & eGene & ident & position\\\\\n",
+       " <chr> & <chr> & <int> & <int> & <lgl> & <chr> & <int> & <int> & <chr> & <chr> & ⋯ & <dbl> & <dbl> & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t rs1001116\\_C/T & CAPG;TMEM176A  & 7 & 150497491 & NA & TMEM176A\\_\\_\\_CAPG\\_\\_TMEM176A  & 7 & 151743278 & C/T & T & ⋯ &  0.079829 & 0.121971 & 4 & -0.075203;0.149838;-0.214833;0.318816  & -0.422702;1.210238;-1.180776;1.518437 & 34;67;32;24 & monocyte & TMEM176A & monocyte\\_TMEM176A\\_\\_\\_CAPG\\_\\_TMEM176A  & 151743278\\\\\n",
+       "\t rs1001116\\_C/T & PTAFR;TMEM176A & 7 & 150497491 & NA & TMEM176A\\_\\_\\_PTAFR\\_\\_TMEM176A & 7 & 151743278 & C/T & T & ⋯ & -0.100600 & 0.121873 & 4 & 0.018313;-0.015859;-0.229843;-0.120209 & 0.102794;-0.127373;-1.265483;-0.5595  & 34;67;32;24 & monocyte & TMEM176A & monocyte\\_TMEM176A\\_\\_\\_PTAFR\\_\\_TMEM176A & 151743278\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 24\n",
+       "\n",
+       "| SNP &lt;chr&gt; | Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | SNPEffectAllele &lt;chr&gt; | ⋯ ⋯ | MetaBeta &lt;dbl&gt; | MetaSE &lt;dbl&gt; | NrDatasets &lt;int&gt; | DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) &lt;chr&gt; | cell_type &lt;chr&gt; | eGene &lt;chr&gt; | ident &lt;chr&gt; | position &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| rs1001116_C/T | CAPG;TMEM176A  | 7 | 150497491 | NA | TMEM176A___CAPG__TMEM176A  | 7 | 151743278 | C/T | T | ⋯ |  0.079829 | 0.121971 | 4 | -0.075203;0.149838;-0.214833;0.318816  | -0.422702;1.210238;-1.180776;1.518437 | 34;67;32;24 | monocyte | TMEM176A | monocyte_TMEM176A___CAPG__TMEM176A  | 151743278 |\n",
+       "| rs1001116_C/T | PTAFR;TMEM176A | 7 | 150497491 | NA | TMEM176A___PTAFR__TMEM176A | 7 | 151743278 | C/T | T | ⋯ | -0.100600 | 0.121873 | 4 | 0.018313;-0.015859;-0.229843;-0.120209 | 0.102794;-0.127373;-1.265483;-0.5595  | 34;67;32;24 | monocyte | TMEM176A | monocyte_TMEM176A___PTAFR__TMEM176A | 151743278 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  SNP           Gene           GeneChr GenePos   GeneStrand\n",
+       "1 rs1001116_C/T CAPG;TMEM176A  7       150497491 NA        \n",
+       "2 rs1001116_C/T PTAFR;TMEM176A 7       150497491 NA        \n",
+       "  GeneSymbol                 SNPChr SNPPos    SNPAlleles SNPEffectAllele ⋯\n",
+       "1 TMEM176A___CAPG__TMEM176A  7      151743278 C/T        T               ⋯\n",
+       "2 TMEM176A___PTAFR__TMEM176A 7      151743278 C/T        T               ⋯\n",
+       "  MetaBeta  MetaSE   NrDatasets\n",
+       "1  0.079829 0.121971 4         \n",
+       "2 -0.100600 0.121873 4         \n",
+       "  DatasetCorrelationCoefficients(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -0.075203;0.149838;-0.214833;0.318816                               \n",
+       "2 0.018313;-0.015859;-0.229843;-0.120209                              \n",
+       "  DatasetZScores(ng;onemillionv2;onemillionv3;stemiv2)\n",
+       "1 -0.422702;1.210238;-1.180776;1.518437               \n",
+       "2 0.102794;-0.127373;-1.265483;-0.5595                \n",
+       "  DatasetSampleSizes(ng;onemillionv2;onemillionv3;stemiv2) cell_type eGene   \n",
+       "1 34;67;32;24                                              monocyte  TMEM176A\n",
+       "2 34;67;32;24                                              monocyte  TMEM176A\n",
+       "  ident                               position \n",
+       "1 monocyte_TMEM176A___CAPG__TMEM176A  151743278\n",
+       "2 monocyte_TMEM176A___PTAFR__TMEM176A 151743278"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(output_all_effect,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "72350ed2-7925-41ad-9ab3-1a22db5600c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_input_coeqtl = list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "6fcdb173-51e9-4f71-a0ca-d70725bbe618",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      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+     ]
+    }
+   ],
+   "source": [
+    "for( i in unique(output_all_effect$ident)){\n",
+    "    \n",
+    "    D1 = output_all_effect[output_all_effect$ident == i,]\n",
+    "    cat(i)\n",
+    "    cat(\"\")\n",
+    "    cat(nrow(D1))\n",
+    "    flush.console()\n",
+    "\n",
+    "    \n",
+    "    ## Prepare input vectors\n",
+    "\n",
+    "    # Beta\n",
+    "    beta_eqtl = D1$MetaBeta\n",
+    "    names(beta_eqtl) = D1$SNP\n",
+    "\n",
+    "    # Varbeta \n",
+    "    varbeta_eqtl =  (D1$MetaSE)^2\n",
+    "    names(varbeta_eqtl) = D1$SNP\n",
+    "\n",
+    "    # MAF  - not needed\n",
+    "    #MAF_eqtl = D1$SNPEffectAlleleFreq\n",
+    "    #names(MAF_eqtl) = D1$SNP\n",
+    "\n",
+    "    # Position \n",
+    "    position_eqtl = D1$SNPPos\n",
+    "    names(position_eqtl) = D1$SNP\n",
+    "\n",
+    "    # SNP\n",
+    "    snp_eqtl = D1$SNP\n",
+    "    names(snp_eqtl) =  D1$SNP\n",
+    "\n",
+    "    # Pvalues\n",
+    "    pvalues_eqtl =  D1$MetaP\n",
+    "    names(pvalues_eqtl) = D1$SNP\n",
+    "    \n",
+    "    \n",
+    "    ### Format as input list for colocalization\n",
+    "\n",
+    "    D1_list = list(beta = beta_eqtl, # regression coefficient\n",
+    "               varbeta = varbeta_eqtl,  # variance/ standard deviation of beta?\n",
+    "               #N = sample_size_eqtl, # number of samples in dataset 1\n",
+    "               sdY =1,   # population standard deviation of the trait, if quantitative trait\n",
+    "                       # if unknown will be approximated based on beta, varbeta, N, MAF\n",
+    "                       # can be set to 1, if the trait was standardized to have variance 1\n",
+    "               type = 'quant',  # quant or cc to denote quantitative or case-control\n",
+    "               #MAF = MAF_eqtl,   # minor allele frequency of the variants\n",
+    "               # LD =  needed?\n",
+    "               snp = snp_eqtl,  # character vector of SNP ids\n",
+    "               position = position_eqtl,\n",
+    "               pvalues = pvalues_eqtl\n",
+    "              )\n",
+    "    data_input_coeqtl[[i]] = D1_list\n",
+    "    }\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "52a382a8-e828-4fac-b638-d2c1589d0ebf",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "List of 7\n",
+      " $ beta    : Named num [1:2990] 0.0798 0.0956 0.0246 -0.1784 0.057 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2990] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ varbeta : Named num [1:2990] 0.0149 0.0143 0.0137 0.0144 0.0337 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2990] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ sdY     : num 1\n",
+      " $ type    : chr \"quant\"\n",
+      " $ snp     : Named chr [1:2990] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2990] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ position: Named int [1:2990] 151743278 151744884 151150662 149826188 151677209 150656640 150846560 150846633 149819792 150814835 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2990] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n",
+      " $ pvalues : Named num [1:2990] 0.513 0.425 0.834 0.137 0.756 ...\n",
+      "  ..- attr(*, \"names\")= chr [1:2990] \"rs1001116_C/T\" \"rs1001117_G/A\" \"rs1001760_A/G\" \"rs1004200_A/G\" ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "str(data_input_coeqtl[[1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "70b07df3-d05d-4dbc-987c-ce7d4dbd27f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Check validity of input data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "1d9b4495-f3cb-4f58-9089-ccd8b8413b79",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___CAPG__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3126e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___PTAFR__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.51e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___MNDA__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.5916e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___RNASE6__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.0913e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___TMEM176A__TSPO\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.4308e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___TMEM176A__VMO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5549e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___S100A9__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.5296e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___QPCT__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.8504e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___BLVRB__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1205e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___LYZ__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2197e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_TMEM176A___CLEC4A__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL36__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.9771e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL5__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.4351e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL7__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.542e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL32__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.466e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___EEF1A1__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.2654e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL38__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3136e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL35A__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.5856e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL3__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6861e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS4X__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7338e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS3A__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.1192e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS15A__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2841e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS8__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.1299e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS25__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.6863e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS12__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.439e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___NKG7__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.6053e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___B2M__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.4346e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL15__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.4898e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___PFN1__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.8685e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS28__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.797e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL13A__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8379e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___GZMH__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.4648e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___LTB__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.146e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL39__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3898e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS14__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5694e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL13__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0258e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS23__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7211e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS29__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.8491e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL22__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.0306e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL9__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.5234e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL12__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.624e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL18__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0996e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 0.00027483\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___MAL__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2271e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___PRF1__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0438e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS13__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.1839e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS6__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.714e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPS18__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7346e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL21__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1662e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___SMDT1__TMSB4X\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4439e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL14__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.6327e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL11__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.9188e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL34__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.8118e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL10A__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.5183e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_SMDT1___RPL30__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5944e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL3__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.3589e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS25__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5487e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL13A__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.6882e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS13__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9433e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS4X__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.8205e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS18__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.7853e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL31__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2754e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS15__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.0919e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___ACTB__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2599e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL36__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2591e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL35A__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.4759e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS12__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2585e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL11__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.9237e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL14__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.1292e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL10__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8366e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS3A__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.934e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 0.0032661\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___CD48__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3621e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPL7__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0745e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_SMDT1___RPS27__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3074e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___CST3__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6842e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4402e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6671e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___CLIC3__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6852e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__PTPRCAP\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3026e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___CDKN2D__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5969e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.0931e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.1257e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.2498e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MAP1A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2736e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___FAM129C__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.2114e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MT-CO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1338e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3171e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___CST3__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.568e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.4835e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___CD74__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.6066e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.5222e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6723e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7523e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DMA__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.6043e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS23\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.9636e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1092e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.606e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___EEF1A1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.7871e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2401e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL10\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.6513e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DMB__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.0082e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRB1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.3211e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6324e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.7109e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RNASET2___HLA-DRB5__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.115e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_HLA-DQA2___CCL5__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0099e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_HLA-DQA2___CD74__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.8036e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.763e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__NKG7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.6836e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPL34\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.4533e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.0477e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_HLA-DQA2___CMC1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9636e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS14\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.946e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3044e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.2797e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1617e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.4701e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___CD52__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.1462e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS13\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.9209e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A10\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3433e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__SH3BGRL3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2688e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___EEF1B2__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.6729e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL13\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.0795e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___B2M__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.3005e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___GAPDH__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6996e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL32\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.7203e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.6142e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A4\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2579e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RNASET2___ITGB1__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.7852e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RNASET2___CRIP1__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.3182e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RNASET2___B2M__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5524e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RNASET2___ALOX5AP__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.0253e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_RPS26___RPL13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.4874e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"DC_RPS26___RPL21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.6982e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1593e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__UBE2J1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.0878e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.8367e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8633e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1042e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.1644e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL30__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0169e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.0557e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1352e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL32__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.9479e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3267e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.2349e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPLP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3875e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7757e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__RPS6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.969e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___EEF1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.2492e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2778e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0623e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__RPS3A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.9434e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__RPS5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.1792e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL41__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.2827e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL34__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6536e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1408e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.791e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1436e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1123e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL35A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.4516e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2489e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.4825e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__RPS9\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.1995e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1034e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__RPS27\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0967e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS26__RPS27A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.0103e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1639e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"B_RPS26___RPS10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.4055e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL18A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__UBA52\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9873e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL36__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.2665e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___GNB2L1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1076e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL35A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL13A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL28__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.8252e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4178e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS27A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8421e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPLP0__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.325e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL35__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1007e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___ARPC2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.0471e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL8__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1883e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL32__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS4X\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7055e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.419e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___EEF1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS15A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL7A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.6714e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__TOMM7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6965e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL37__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.234e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1991e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS3A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL30__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS27\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS16__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.7789e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPLP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2807e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS24__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___FAU__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8904e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS14__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___GLTSCR2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.1598e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.4326e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL24__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6754e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0968e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2563e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL14__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL10A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7373e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL34__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL15__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.917e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL38__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2433e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___GPR183__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.0676e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL41__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.0058e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL31__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6141e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__TPT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.0455e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPLP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5827e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.9702e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4099e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5173e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL37A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0993e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS15__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0395e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___KLRC1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9075e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.4275e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___B2M__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.3814e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS9\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___MALAT1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9089e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___ACTB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.9089e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8351e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4043e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL29__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0076e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7026e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___EEF1B2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.7114e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8859e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL36A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8405e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS20__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.0908e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.574e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS26__RPS5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.02e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2336e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"NK_RPS26___RPL27A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___NRGN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7437e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EEF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.4621e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.7611e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EIF3L__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.3485e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS15A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL35A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9696e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL37A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EEF1B2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3916e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL30__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__VCAN\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.0193e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__UQCRH\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.9605e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__SLC7A7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.2351e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EPB41L3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.859e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__S100A11\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3529e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL32__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___HNRNPA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.4422e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___QARS__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1543e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8385e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL24__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.2547e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS9\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL35__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8781e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__TPT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___CSTA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4315e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3141e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EIF3K__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.9301e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS27\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EIF3H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2896e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___ERP29__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5663e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__TNFAIP2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2465e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__VIM\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.0947e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL27A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EEF1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL37__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL8__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8144e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL7A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS27A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.0862e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.5874e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___HLA-DRA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.7195e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL15__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__SLC25A6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1687e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1173e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL22__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.5779e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___FAU__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5435e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL36__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5515e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___HLA-DPA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4467e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS3A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS20__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.7495e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___PABPC1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3725e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___CST3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7382e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EMP3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.2616e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___GNLY__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0351e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS24__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EIF3M__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.7748e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.4586e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___AP1S2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1328e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPLP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__SEC11A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3851e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL36A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5431e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPLP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL28__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3305e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS15__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.5394e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL41__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___ATP5G2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1345e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3936e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__SLC25A5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.8025e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL18A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.2302e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPLP0__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.933e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EVI2B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.886e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___CD48__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3518e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___EIF3E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2162e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___GAPDH__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1023e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS4X\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___LGALS2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.912e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___CYBA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.4619e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL29__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS16__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2402e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___GPX1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2329e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___LTA4H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RNASE6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2068e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___FTH1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5593e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.1949e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___DRAM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1829e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___IL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6926e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___ATP5A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.4193e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL38__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.4709e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL13A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7467e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__RPS6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___IPO7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2873e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___GNB2L1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL34__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2966e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__UBA52\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL14__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.4809e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___CRTAP__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5648e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___H3F3B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.9122e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL10A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___CD74__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.9394e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS14__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___C6orf48__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.443e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___GPR183__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3884e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPL31__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"monocyte_RPS26___RPS26__TKT\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6203e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPLP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SCML1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.512e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ACTN1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8558e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS16__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__ZFAND1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4561e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.9631e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF3L__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7485e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EFHD2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.149e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SELL\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___B2M__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___APBA2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3058e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EEF1G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.3542e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FAIM3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.465e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___APOBEC3C__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1694e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HLA-DRA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.548e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TPT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___C11orf1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8471e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___LCP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.1464e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL31__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GZMM__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.8914e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CFL1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3855e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RSL1D1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8408e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TXN\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1256e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CTSW__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6044e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CD99__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2167e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FLT3LG__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5912e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NKG7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.837e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__UQCRB\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2818e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3964e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CREM__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3111e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__S100A4\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1381e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RGS10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5768e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL22__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS9\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___LDHB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ATP1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.0977e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.4403e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SYNE1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.8506e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.137e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPLP0__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.4612e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MYL6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PDE3B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.099e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MT-CO1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.2099e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2449e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___LTB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PTPN7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.0346e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL29__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PFN1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___IER2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1556e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL8__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.805e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL37A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS4X\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CMC1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6291e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SAT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.4219e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6129e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___AKNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.4233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9277e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS14__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NELL2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5801e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FLNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.2951e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___C12orf75__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.42e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL32__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HLA-C__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___METRNL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4496e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PFDN5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.0295e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CAMK4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.9693e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___BHLHE40__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1054e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___IFITM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2604e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SLA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1484e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CD8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.1948e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NGFRAP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2462e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS20__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TUBA4A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8361e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__UBA52\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8001e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RCAN3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9651e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PPP2R5C__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3399e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL28__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__S100A6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___DNAJB6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.4144e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RAP1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.077e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SH3BGRL3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5836e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PABPC1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.231e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FBL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.8077e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CCDC104__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CCL5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GAPDH__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NPM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8054e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL41__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MT-CO2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5892e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TESPA1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8387e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__S100A10\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.4291e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PSMA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4155e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PLEK__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7657e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SUB1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7951e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL27A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.4281e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___KLRD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4604e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MYC__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.6317e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RGS1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.062e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___KLF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.391e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SLC25A6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3651e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HNRNPA2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ARAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3907e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HLA-A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__UBB\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2924e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.1082e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GNB2L1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__UBC\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.7981e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CD74__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.0411e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TGFB1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.839e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL7A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL18A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___LYPD3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.2696e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TMSB10\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7568e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CLIC1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.5234e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___C12orf57__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7538e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TMEM243\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1526e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ID2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.0728e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL10A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CCR7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PPA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4071e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EEF1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___COX7C__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.5433e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NFKBIA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.944e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NDFIP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.6595e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS15A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GZMA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.4252e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ABHD14B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.2272e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL35__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TPI1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2111e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GIMAP7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1009e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FAU__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SC5D\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.7243e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPLP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL34__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.8358e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL24__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SH3YL1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8286e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.9814e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SRP14\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.906e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0298e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HMGB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.7036e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NOSIP__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.5202e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL36__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL35A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MYL12B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GNLY__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8365e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MIR142__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1648e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF3K__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.5308e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL13A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SRSF3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7372e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GLTSCR2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.5262e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PTP4A2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.9215e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.9666e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPSAP58\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1243e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___C6orf48__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CCDC57__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.9666e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ITGB2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7619e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF2A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8915e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.4185e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ARF6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.5214e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CD81__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.3139e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TMEM123\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1846e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ALKBH7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0315e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___LDHA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2712e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PIK3IP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.151e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FOXP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.1991e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CCL4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.654e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NEAT1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2048e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___KLRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9856e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5042e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__ZFAS1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4349e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___C1orf21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1023e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___H3F3B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CALM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1052e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HOPX__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.7747e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CD55__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.4084e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS15__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7845e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SRSF2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.2025e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EEF1B2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.507e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL38__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PTMA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.1327e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL36A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GNG2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2555e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TIGIT\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1444e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF3H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.145e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ACTB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___C1QBP__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9922e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CD27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.689e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___KLRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.1218e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___MAL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___REL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.691e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ARPC2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FTL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.7677e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CYBA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.133e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SEPT7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.6793e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TCF7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.966e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__S100A11\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.0148e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___FCGR3A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4314e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PSMB9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.645e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___LEF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PTPRC__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SRSF7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3453e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6448e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ANXA2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0625e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___LGALS1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.9042e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5519e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___DOK2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.062e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CRIP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.4254e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CALR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9449e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TTC38\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1223e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___C1orf228__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1403e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___DUSP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7349e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF4B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TRABD2A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0288e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS27A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PASK__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5327e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___OAZ1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS3A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___OXNAD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1359e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TOMM7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4281e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SRGN\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___HLA-E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1541e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TYROBP\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.981e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__YBX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1331e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CST7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.025e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___AIF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.1062e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___IL7R__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.4995e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RHOH__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.1452e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8276e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___DBI__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0352e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL37__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___PRKCQ-AS1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SNHG8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.5389e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___POMP__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.9872e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL30__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RAB8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0817e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___GZMH__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7802e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF3E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1259e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS24__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL14__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6131e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EIF4A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8946e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___APOBEC3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.2129e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RP11-291B21.2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.409e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__RPS27\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__SERF2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__TMSB4X\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___EEF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3126e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL15__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPL4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___RPS26__S1PR5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1943e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD8T_RPS26___CD48__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8536e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TMSB10\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7937e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CHCHD2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.3173e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EMP3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2218e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FMNL1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.7187e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS27A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LEF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3125e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HERPUD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.267e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ANXA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.2192e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SOD2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5135e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MYL6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GNB2L1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ATP1B3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0508e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SRSF5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.1719e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS14__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL28__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EML4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5207e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SCML1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7087e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MCL1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.4819e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NOG__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.7003e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PRMT2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.2646e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9486e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.5146e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TMSB4X\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FAM129A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.906e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__S100A4\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2936e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPLP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ALOX5AP__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3504e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TSHZ2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9454e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TIGIT\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3573e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ARHGDIB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.7204e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FAU__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1701e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___JUND__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.279e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SH3YL1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.4835e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS27\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___C12orf75__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7218e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CYBA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF18\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.0096e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6857e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL12__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PTPRC__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD55__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5944e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CREM__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.5722e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__VMP1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.6668e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HMGB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3581e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL31__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___C1orf228__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.163e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GALM__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0575e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TRABD2A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.2691e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF2A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6052e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ANXA5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7131e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___IDS__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.055e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ARID5B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6325e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___IMPDH2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.024e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TPT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__ST13\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0809e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CXCR3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.5567e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9158e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL37A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SPOCK2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.8428e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___C15orf48__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.226e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SNRPF\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1448e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___H3F3B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FAM134B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.6427e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ISG20__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.9172e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CFL1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NUCB2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1362e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ALKBH7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.1794e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LINC00493__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.1855e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL32__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__VIM\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.4615e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SNHG8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CDC42__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5049e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF1B\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.2924e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NELL2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.1011e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ACTN4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7123e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___IKZF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.2081e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LDHA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2503e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL41__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS16__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RP11-138A9.1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2073e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NAMPT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8087e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__ZFAS1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0064e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CALM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.9897e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF3E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-ND2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.8901e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL8__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD52__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.4538e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF3L__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___H3F3A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.2196e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ADTRP__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT2A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1117e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SNRPD2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8359e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__ZFP36\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.5316e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.624e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___DYNLL1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.1282e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SAMSN1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9213e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LMNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.4915e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS4X\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RUNX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.355e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RGS1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3102e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ERGIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.423e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SELL\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TYMP\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.9655e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.687e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS15A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__UQCRB\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.334e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SRGN\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-ND4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.3822e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ABHD14B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6804e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ATP5E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1607e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPSAP58\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL24__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MAL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ATP2B4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.7512e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ARPC1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.29e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PDCD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.2067e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS15__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SAT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HLA-E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TCF7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.7474e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PIK3IP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.9797e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LGALS3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.3924e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MIAT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7934e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SUB1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CCR7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3646e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL14__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL18A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RNF19A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5478e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-CO3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.7912e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EEF1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF3H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FAS__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.7693e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPLP0__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GYPC__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.9635e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL30__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL34__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TPM4\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1269e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LDHB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___AIF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5405e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL35A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ITGB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3293e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TXN\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.785e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FTH1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4687e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___COX7C__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.9501e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HLA-A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LCP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1393e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__UBB\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4426e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL29__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ARPC2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TMEM123\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.8648e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PPP1R15A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.4985e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___IL32__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.6007e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL37__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TOMM20\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.6235e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF3F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8529e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ERP29__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4196e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___KLF6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.5548e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GIMAP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9335e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TGFBR2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3747e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RNF213__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6662e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___C19orf53__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.3495e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SERF2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MIR4435-1HG__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MZT2B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.218e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___AK5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.6605e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NDFIP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.5858e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HNRNPA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL7A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SRSF7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3122e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL22__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___C1QBP__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4357e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CXCR6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.4185e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ARPC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.0449e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MRPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PPA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.7452e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EBPL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.7052e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FTL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3893e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__UXT\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LSM5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2201e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___KMT2E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6569e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-CO2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TAGLN2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.2808e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CDCA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4164e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EEF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.345e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EPB41L4A-AS1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1501e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FLNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TATDN1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.1792e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HLA-DPA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.443e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___C12orf57__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___BTG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.8696e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___C8orf59__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.964e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD58__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7975e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-CO1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPLP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.5612e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___AKAP13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.7772e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF4B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.4604e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.6237e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS24__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.487e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ANXA2R__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8144e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___IL8__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.918e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LINC00152__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FOXP3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.8638e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RGS10__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___B2M__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___KLRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2737e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL36A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL35__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___DAP3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4062e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___C6orf48__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SVIP\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.6639e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HLA-C__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL10A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PRKCQ-AS1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5814e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GIMAP7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4524e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ENTPD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.3605e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___DUSP4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__YWHAB\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.391e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CCR6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.7975e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-ND1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2726e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PFN1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ADAM19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2414e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CLDND1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.189e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PFDN5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.2756e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FBL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD37__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.5931e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___APEX1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.8871e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD74__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS20__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LETMD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.1067e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GK__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1383e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NOSIP__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1729e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___AHNAK__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.3968e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SLC7A5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.2535e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GLTSCR2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HLA-DRA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6925e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS3A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__S100A6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-ATP6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8034e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__S100A11\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CCL5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.9659e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RILPL2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.7165e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SSR2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3313e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF4\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.2807e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__UBC\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__S100A10\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MAF__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.5193e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___COMMD6__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.891e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS11__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.8659e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NSMCE1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.6391e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TGFB1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.659e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PRDX1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5827e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS9\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FAM46C__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5817e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RORA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.9068e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.9571e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD44__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.0649e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS4Y1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.3618e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LGALS1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.5113e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___COX7A2L__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.6756e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL15__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HADHA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8011e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SATB1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.8858e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__UGP2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.6699e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SBDS\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4438e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SYNE2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4397e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TMA7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1543e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NEAT1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NR3C1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.8583e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CCT8__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.65e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TNFAIP3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.263e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SH2D2A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7729e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NPM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CLNS1A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.0466e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RSL1D1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ATP6V0E1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.2832e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL27A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___DUSP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.4405e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL13A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__ZFP36L2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1642e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF3D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7672e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RP11-138A9.2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.3794e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___APRT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.9849e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.5144e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ANP32B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4591e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PPP2R5C__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.7186e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF3M__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.2314e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CMPK1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.5804e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7491e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GIMAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___COTL1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.4135e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF2S3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HSP90AA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1807e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MT-CYB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 7.3179e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HSPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.1825e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CRIP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TXK\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.3442e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL36__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___GAPDH__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ANXA2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8732e-15\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CLIC1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.627e-14\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD99__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.9832e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___LYRM4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.6923e-07\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EEF1B2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ACTB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EZR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.1347e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ATP5A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8326e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ATP5O__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EIF3K__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.1189e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPL38__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SUCLG2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 4.0155e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD3E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.3999e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___NSA2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CST7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.0229e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___HIGD2A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 6.6022e-08\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___EEF1G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___IGBP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.7011e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___OAZ1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___MYH9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__UBA52\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___ATP2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 5.3714e-11\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__RPS6\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RBM39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.8267e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.8565e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SH3BGRL3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___COX4I1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 3.0583e-10\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___PMAIP1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 9.9735e-09\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__TOMM7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SNHG7\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 8.206e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___FHIT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___RPS26__SRSF2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.4873e-13\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    }
+   ],
+   "source": [
+    "for( i  in names(data_input_coeqtl)){\n",
+    "    print(i)\n",
+    "    check_dataset(data_input_coeqtl[[i]], warn.minp = 1e-70)\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "377709c3-72e8-4616-add2-52cb97dfdae9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### GWAS input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "8cadaeec-80d3-4eed-a455-1604b16395d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas = gwas_gtex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "74900a53-29fc-41ad-9704-307172077b0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>tag</th><th scope=col>position</th><th scope=col>non_effect_allele</th><th scope=col>frequency</th><th scope=col>pvalue</th><th scope=col>effect_size</th><th scope=col>Phenotype</th><th scope=col>Sample_Size</th><th scope=col>variant_id</th><th scope=col>sample_size</th><th scope=col>standard_error</th><th scope=col>effect_allele</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Astle_et_al_2016_White_blood_cell_count</td><td>13550</td><td>G</td><td>0.01731602</td><td>0.228037473787046</td><td>NA</td><td>White blood cell count</td><td>173480</td><td>rs554008981_G/A</td><td>173480</td><td>NA</td><td>A</td></tr>\n",
+       "\t<tr><td>Astle_et_al_2016_White_blood_cell_count</td><td>14671</td><td>G</td><td>0.01298701</td><td>0.816150563702573</td><td>NA</td><td>White blood cell count</td><td>173480</td><td>rs201055865_G/C</td><td>173480</td><td>NA</td><td>C</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 12\n",
+       "\\begin{tabular}{llllllllllll}\n",
+       " tag & position & non\\_effect\\_allele & frequency & pvalue & effect\\_size & Phenotype & Sample\\_Size & variant\\_id & sample\\_size & standard\\_error & effect\\_allele\\\\\n",
+       " <chr> & <int> & <chr> & <dbl> & <chr> & <dbl> & <chr> & <int> & <chr> & <int> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count & 13550 & G & 0.01731602 & 0.228037473787046 & NA & White blood cell count & 173480 & rs554008981\\_G/A & 173480 & NA & A\\\\\n",
+       "\t Astle\\_et\\_al\\_2016\\_White\\_blood\\_cell\\_count & 14671 & G & 0.01298701 & 0.816150563702573 & NA & White blood cell count & 173480 & rs201055865\\_G/C & 173480 & NA & C\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 12\n",
+       "\n",
+       "| tag &lt;chr&gt; | position &lt;int&gt; | non_effect_allele &lt;chr&gt; | frequency &lt;dbl&gt; | pvalue &lt;chr&gt; | effect_size &lt;dbl&gt; | Phenotype &lt;chr&gt; | Sample_Size &lt;int&gt; | variant_id &lt;chr&gt; | sample_size &lt;int&gt; | standard_error &lt;dbl&gt; | effect_allele &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| Astle_et_al_2016_White_blood_cell_count | 13550 | G | 0.01731602 | 0.228037473787046 | NA | White blood cell count | 173480 | rs554008981_G/A | 173480 | NA | A |\n",
+       "| Astle_et_al_2016_White_blood_cell_count | 14671 | G | 0.01298701 | 0.816150563702573 | NA | White blood cell count | 173480 | rs201055865_G/C | 173480 | NA | C |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  tag                                     position non_effect_allele frequency \n",
+       "1 Astle_et_al_2016_White_blood_cell_count 13550    G                 0.01731602\n",
+       "2 Astle_et_al_2016_White_blood_cell_count 14671    G                 0.01298701\n",
+       "  pvalue            effect_size Phenotype              Sample_Size\n",
+       "1 0.228037473787046 NA          White blood cell count 173480     \n",
+       "2 0.816150563702573 NA          White blood cell count 173480     \n",
+       "  variant_id      sample_size standard_error effect_allele\n",
+       "1 rs554008981_G/A 173480      NA             A            \n",
+       "2 rs201055865_G/C 173480      NA             C            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(gwas,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "4716b285-ac0a-46b3-acd2-3f07ed14813c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Compare variants and pre-filter on variants in coEQTL data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "dbcdaf2e-1cde-4eb9-9b48-302d09a5e53c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_input = gwas[gwas$variant_id %in%  unique(output_all_effect$SNP),] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "abdcc242-6be2-4c8c-92b5-4c6cd9a06451",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "15346"
+      ],
+      "text/latex": [
+       "15346"
+      ],
+      "text/markdown": [
+       "15346"
+      ],
+      "text/plain": [
+       "[1] 15346"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(gwas_input$variant_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "3ba5ceed-b6ef-40ba-97b6-f987dea42583",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 7 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>Phenotype</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>Asthma                    </td><td> 8843303</td></tr>\n",
+       "\t<tr><td>Crohn's Disease           </td><td> 8860907</td></tr>\n",
+       "\t<tr><td>Inflammatory Bowel Disease</td><td> 8858738</td></tr>\n",
+       "\t<tr><td>Multiple Sclerosis        </td><td> 8867478</td></tr>\n",
+       "\t<tr><td>Rheumatoid Arthritis      </td><td> 8857562</td></tr>\n",
+       "\t<tr><td>Type_1_Diabetes           </td><td>62115237</td></tr>\n",
+       "\t<tr><td>White blood cell count    </td><td> 8871979</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 7 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " Phenotype & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t Asthma                     &  8843303\\\\\n",
+       "\t Crohn's Disease            &  8860907\\\\\n",
+       "\t Inflammatory Bowel Disease &  8858738\\\\\n",
+       "\t Multiple Sclerosis         &  8867478\\\\\n",
+       "\t Rheumatoid Arthritis       &  8857562\\\\\n",
+       "\t Type\\_1\\_Diabetes            & 62115237\\\\\n",
+       "\t White blood cell count     &  8871979\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 7 × 2\n",
+       "\n",
+       "| Phenotype &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| Asthma                     |  8843303 |\n",
+       "| Crohn's Disease            |  8860907 |\n",
+       "| Inflammatory Bowel Disease |  8858738 |\n",
+       "| Multiple Sclerosis         |  8867478 |\n",
+       "| Rheumatoid Arthritis       |  8857562 |\n",
+       "| Type_1_Diabetes            | 62115237 |\n",
+       "| White blood cell count     |  8871979 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Phenotype                  n       \n",
+       "1 Asthma                      8843303\n",
+       "2 Crohn's Disease             8860907\n",
+       "3 Inflammatory Bowel Disease  8858738\n",
+       "4 Multiple Sclerosis          8867478\n",
+       "5 Rheumatoid Arthritis        8857562\n",
+       "6 Type_1_Diabetes            62115237\n",
+       "7 White blood cell count      8871979"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gwas %>% group_by(Phenotype) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "fab9f20f-d504-4567-bb9c-f8ff115594a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Remove NA values in case there are some"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "be726af0-36b5-4ba2-b124-25427848965b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_input = gwas_input[!is.na(gwas_input$effect_size),]\n",
+    "gwas_input = gwas_input[!is.na(gwas_input$standard_error),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "48b55843-d1dd-4f84-a021-44973c85dd2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "15343"
+      ],
+      "text/latex": [
+       "15343"
+      ],
+      "text/markdown": [
+       "15343"
+      ],
+      "text/plain": [
+       "[1] 15343"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(gwas_input$variant_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "c72cbd85-3436-446d-b1ba-69e796c04425",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "58534"
+      ],
+      "text/latex": [
+       "58534"
+      ],
+      "text/markdown": [
+       "58534"
+      ],
+      "text/plain": [
+       "[1] 58534"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(gwas_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "aa5f7675-33fc-466d-9bf7-bb679fc122b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Prepare GWAS Input per trait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "23f91313-9598-41f2-bfd9-a6e451fc5e41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gwas_input_list = list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "fde6b930-24a5-4a00-8750-a606590df679",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for(i in unique(gwas_input$Phenotype)){\n",
+    "    input = gwas_input[gwas_input$Phenotype == i,]\n",
+    "    \n",
+    "    ## Prepare GWAS Input\n",
+    "    # Beta\n",
+    "    beta_gwas =  input$effect_size\n",
+    "    names(beta_gwas) = input$variant_id\n",
+    "\n",
+    "    # Varbeta \n",
+    "    varbeta_gwas =  ( input$standard_error)^2\n",
+    "    names(varbeta_gwas) = input$variant_id\n",
+    "\n",
+    "    # MAF - not needed\n",
+    "    #MAF_gwas = input$frequency\n",
+    "    #names(MAF_gwas) = input$variant_id\n",
+    "\n",
+    "    # Position \n",
+    "    position_gwas = input$position\n",
+    "    names(position_gwas) = input$variant_id\n",
+    "\n",
+    "    # SNP\n",
+    "    snp_gwas = input$variant_id\n",
+    "    names(snp_gwas) = input$variant_id\n",
+    "\n",
+    "    \n",
+    "    ### Input Parameters - check with available data\n",
+    "\n",
+    "    GWAS_list = list(\n",
+    "               beta = beta_gwas, # regression coefficient\n",
+    "               varbeta =varbeta_gwas,  # variance/ standard deviation of beta?\n",
+    "               #N =  sample_size_gwas, # number of samples in dataset 1\n",
+    "               #sdY =   # population standard deviation of the trait, if quantitative trait\n",
+    "                       # if unknown will be approximated based on beta, varbeta, N, MAF\n",
+    "               type = 'cc',  # quant or cc to denote quantitative or case-control\n",
+    "               #  MAF = MAF_gwas,   # minor allele frequency of the variants\n",
+    "               # LD =  needed?\n",
+    "               snp = snp_gwas,  # character vector of SNP ids\n",
+    "               position = position_gwas)\n",
+    "    \n",
+    "    gwas_input_list[[i]] = GWAS_list\n",
+    "    \n",
+    "    }\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "626e2a63-0f8f-4b96-9f6a-e7d6ec4be081",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"White blood cell count\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(gwas_input_list[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Crohn's Disease\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(gwas_input_list[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: 1.025e-12\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Inflammatory Bowel Disease\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(gwas_input_list[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"Asthma\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(gwas_input_list[[i]], warn.minp = 1e-70):\n",
+      "“minimum p value is: < 2.22e-16\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Type_1_Diabetes\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "for(i in names(gwas_input_list)){\n",
+    "    print(i)\n",
+    "    check_dataset(gwas_input_list[[i]], warn.minp = 1e-70)\n",
+    "    }\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b121c830-3310-4912-ad82-bb5805090b16",
+   "metadata": {},
+   "source": [
+    "## Run Coloc Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cf94a2a-f46c-436e-91f5-0ab2527c512b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Visualize a concrete co-egene and GWAS trait example and save data for further investigation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "cb6c3827-5bdc-4315-bb67-513a781c61c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Choose Phenotype to visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "43bc467e-a622-4cca-8629-d36a59177581",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = 'Type_1_Diabetes'\n",
+    "#i = unique(gwas_input$Phenotype)[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "e1b37f88-e4a9-497e-a35b-3b151cf1a96e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'Type_1_Diabetes'"
+      ],
+      "text/latex": [
+       "'Type\\_1\\_Diabetes'"
+      ],
+      "text/markdown": [
+       "'Type_1_Diabetes'"
+      ],
+      "text/plain": [
+       "[1] \"Type_1_Diabetes\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "9ee48e01-0c11-40bb-97c3-bc8434677efa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Choose co-egene to visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "5389727f-479e-40bc-a0ba-5bbd37707306",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "co_egene_var = 'CD4T_RPS26___CD48__RPS26'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "3f15c780-590b-41b7-b295-b361b2036aff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Plot the SNP significance values for the matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "f822c402-35c6-4c11-a565-9661f0267b39",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"Type_1_Diabetes\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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dJoNION6nQ6T4YBgNGj2AHwa+PHjx/B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\nQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# GWAS\n",
+    "print(i)\n",
+    "plot_dataset(lapply(gwas_input_list[[i]], function(x){\n",
+    "    x[unique(output_all_effect$SNP[output_all_effect$ident == co_egene_var])]}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "73ab0379-5bad-493c-aefd-90602502e070",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'CD4T_RPS26___CD48__RPS26'"
+      ],
+      "text/latex": [
+       "'CD4T\\_RPS26\\_\\_\\_CD48\\_\\_RPS26'"
+      ],
+      "text/markdown": [
+       "'CD4T_RPS26___CD48__RPS26'"
+      ],
+      "text/plain": [
+       "[1] \"CD4T_RPS26___CD48__RPS26\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "co_egene_var"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "1a36ca5a-505b-4580-a3d8-42a5845d3d9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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RBieHi4qqqqq6srhFUBwGwQ7ABEouHh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\nRCCwAwAAAJAIBHYAAAAAEvH/A8g/L8519pEBAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# coeQTL\n",
+    "print(co_egene_var)\n",
+    "plot_dataset(data_input_coeqtl[[co_egene_var]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "cb6a61f0-beaa-47f5-9e1f-ef3b084f5f4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Make combined p-value plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "de926728-9f29-4d03-8c47-82f61dcadc73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input = gwas_input[gwas_input$Phenotype == i,] # GWAS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "id": "6f766f43-9b74-4bd6-ac8d-f8e42db0552e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_coeqtl = output_all_effect[output_all_effect$ident == co_egene_var,] # Co-EQTL Input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "637d3983-e2a2-40d0-80d0-e9f785b9dc0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_data = merge(input_coeqtl[,c('SNP', 'MetaBeta', 'MetaP')], input[,c('variant_id', 'pvalue', 'effect_size')], by.x = 'SNP', by.y = 'variant_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "ea5a0ee6-f131-4c91-a8dd-752e31db49f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1341"
+      ],
+      "text/latex": [
+       "1341"
+      ],
+      "text/markdown": [
+       "1341"
+      ],
+      "text/plain": [
+       "[1] 1341"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(plot_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "838d67ad-1437-488d-8ca9-3210c226bf71",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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K0LAOgPXbEAAHzDZrNXrFixefPmwsJCQsjy5ctv376dnZ2tra39/v37oUOHUl0g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\nIf4fbymF3Vna8+IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "Plot with title “Type_1_Diabetes_( CD4T_RPS26___CD48__RPS26)”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(-log10(plot_data$MetaP), -log10(as.numeric(plot_data$pvalue)), xlab = '-log10(pval_eqtl)', ylab = '-log10(pval_gwas)', main = paste0( i,'_',  '( ', co_egene_var, ')'), cex = 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "id": "e87aa6ca-0ceb-43e7-b9c1-81631a4fe718",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Save the used data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "f78910dd-11b2-4658-9626-1367194152ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames(input) = paste0('gwas_', colnames(input))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "1fdf4a68-37a3-46a4-9909-e8c622282a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_data = merge(input_coeqtl, input, by.x = 'SNP', by.y = 'gwas_variant_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "6a437b34-8c74-4c9e-8b71-af494017b24d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.table: 2 × 35</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>SNP</th><th scope=col>Gene</th><th scope=col>GeneChr</th><th scope=col>GenePos</th><th scope=col>GeneStrand</th><th scope=col>GeneSymbol</th><th scope=col>SNPChr</th><th scope=col>SNPPos</th><th scope=col>SNPAlleles</th><th scope=col>SNPEffectAllele</th><th scope=col>⋯</th><th scope=col>gwas_position</th><th scope=col>gwas_non_effect_allele</th><th scope=col>gwas_frequency</th><th scope=col>gwas_pvalue</th><th scope=col>gwas_effect_size</th><th scope=col>gwas_Phenotype</th><th scope=col>gwas_Sample_Size</th><th scope=col>gwas_sample_size</th><th scope=col>gwas_standard_error</th><th scope=col>gwas_effect_allele</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>rs10128982_A/C</td><td>CD48;RPS26</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26___CD48__RPS26</td><td>12</td><td>55895415</td><td>A/C</td><td>C</td><td>⋯</td><td>55895415</td><td>A</td><td>0.201</td><td>1.32e-01</td><td>-0.029132</td><td>Type_1_Diabetes</td><td>405537</td><td>405537</td><td>0.019325</td><td>C</td></tr>\n",
+       "\t<tr><td>rs1020848_G/C </td><td>CD48;RPS26</td><td>12</td><td>56435637</td><td>NA</td><td>RPS26___CD48__RPS26</td><td>12</td><td>56669675</td><td>G/C</td><td>C</td><td>⋯</td><td>56669675</td><td>G</td><td>0.672</td><td>7.26e-02</td><td>-0.027293</td><td>Type_1_Diabetes</td><td>520580</td><td>520580</td><td>0.015202</td><td>C</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.table: 2 × 35\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " SNP & Gene & GeneChr & GenePos & GeneStrand & GeneSymbol & SNPChr & SNPPos & SNPAlleles & SNPEffectAllele & ⋯ & gwas\\_position & gwas\\_non\\_effect\\_allele & gwas\\_frequency & gwas\\_pvalue & gwas\\_effect\\_size & gwas\\_Phenotype & gwas\\_Sample\\_Size & gwas\\_sample\\_size & gwas\\_standard\\_error & gwas\\_effect\\_allele\\\\\n",
+       " <chr> & <chr> & <int> & <int> & <lgl> & <chr> & <int> & <int> & <chr> & <chr> & ⋯ & <int> & <chr> & <dbl> & <chr> & <dbl> & <chr> & <int> & <int> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t rs10128982\\_A/C & CD48;RPS26 & 12 & 56435637 & NA & RPS26\\_\\_\\_CD48\\_\\_RPS26 & 12 & 55895415 & A/C & C & ⋯ & 55895415 & A & 0.201 & 1.32e-01 & -0.029132 & Type\\_1\\_Diabetes & 405537 & 405537 & 0.019325 & C\\\\\n",
+       "\t rs1020848\\_G/C  & CD48;RPS26 & 12 & 56435637 & NA & RPS26\\_\\_\\_CD48\\_\\_RPS26 & 12 & 56669675 & G/C & C & ⋯ & 56669675 & G & 0.672 & 7.26e-02 & -0.027293 & Type\\_1\\_Diabetes & 520580 & 520580 & 0.015202 & C\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.table: 2 × 35\n",
+       "\n",
+       "| SNP &lt;chr&gt; | Gene &lt;chr&gt; | GeneChr &lt;int&gt; | GenePos &lt;int&gt; | GeneStrand &lt;lgl&gt; | GeneSymbol &lt;chr&gt; | SNPChr &lt;int&gt; | SNPPos &lt;int&gt; | SNPAlleles &lt;chr&gt; | SNPEffectAllele &lt;chr&gt; | ⋯ ⋯ | gwas_position &lt;int&gt; | gwas_non_effect_allele &lt;chr&gt; | gwas_frequency &lt;dbl&gt; | gwas_pvalue &lt;chr&gt; | gwas_effect_size &lt;dbl&gt; | gwas_Phenotype &lt;chr&gt; | gwas_Sample_Size &lt;int&gt; | gwas_sample_size &lt;int&gt; | gwas_standard_error &lt;dbl&gt; | gwas_effect_allele &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| rs10128982_A/C | CD48;RPS26 | 12 | 56435637 | NA | RPS26___CD48__RPS26 | 12 | 55895415 | A/C | C | ⋯ | 55895415 | A | 0.201 | 1.32e-01 | -0.029132 | Type_1_Diabetes | 405537 | 405537 | 0.019325 | C |\n",
+       "| rs1020848_G/C  | CD48;RPS26 | 12 | 56435637 | NA | RPS26___CD48__RPS26 | 12 | 56669675 | G/C | C | ⋯ | 56669675 | G | 0.672 | 7.26e-02 | -0.027293 | Type_1_Diabetes | 520580 | 520580 | 0.015202 | C |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  SNP            Gene       GeneChr GenePos  GeneStrand GeneSymbol         \n",
+       "1 rs10128982_A/C CD48;RPS26 12      56435637 NA         RPS26___CD48__RPS26\n",
+       "2 rs1020848_G/C  CD48;RPS26 12      56435637 NA         RPS26___CD48__RPS26\n",
+       "  SNPChr SNPPos   SNPAlleles SNPEffectAllele ⋯ gwas_position\n",
+       "1 12     55895415 A/C        C               ⋯ 55895415     \n",
+       "2 12     56669675 G/C        C               ⋯ 56669675     \n",
+       "  gwas_non_effect_allele gwas_frequency gwas_pvalue gwas_effect_size\n",
+       "1 A                      0.201          1.32e-01    -0.029132       \n",
+       "2 G                      0.672          7.26e-02    -0.027293       \n",
+       "  gwas_Phenotype  gwas_Sample_Size gwas_sample_size gwas_standard_error\n",
+       "1 Type_1_Diabetes 405537           405537           0.019325           \n",
+       "2 Type_1_Diabetes 520580           520580           0.015202           \n",
+       "  gwas_effect_allele\n",
+       "1 C                 \n",
+       "2 C                 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(save_data,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "cf0a3db2-f1dc-4d61-867b-374bc73297af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1341"
+      ],
+      "text/latex": [
+       "1341"
+      ],
+      "text/markdown": [
+       "1341"
+      ],
+      "text/plain": [
+       "[1] 1341"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(save_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "f99deed4-a2d6-4476-88cb-2dbab6fe8a90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.table(save_data, file = paste0(path, \"/colocalization_results/\", \"COEQTL_example_input_\", i, \".csv\"), append =FALSE, sep = \",\", row.names = FALSE, col.names = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "799ec02d-42a5-40f0-85b6-5937a0f8beb2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7844b5ce-dd7b-4829-816d-49a60fe75e83",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Run for all cell-types + genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "1c4ab74c-52b6-4eda-ad71-a4af86209fe4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Execute colocalization analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "id": "a4d2d946-4103-46db-b393-9b611a50e06a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_result_summary = data.frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "id": "298af684-fbeb-400b-8065-5680ae2e73b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_result_detail = data.frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "25836456-ad19-4974-9a21-b45639c96a5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_detail = FALSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "5a35eae8-58ec-4c05-b593-aedced3701a1",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___CAPG__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.026100  0.000460  0.956000  0.016900  0.000609 \n",
+      "[1] \"PP abf for shared variant: 0.0609%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___PTAFR__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07630   0.00135   0.90600   0.01600   0.00047 \n",
+      "[1] \"PP abf for shared variant: 0.047%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___MNDA__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.5916e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.513000  0.009040  0.470000  0.008280  0.000273 \n",
+      "[1] \"PP abf for shared variant: 0.0273%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___RNASE6__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.18200   0.00320   0.80100   0.01410   0.00045 \n",
+      "[1] \"PP abf for shared variant: 0.045%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__TSPO\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.233000  0.004120  0.749000  0.013200  0.000513 \n",
+      "[1] \"PP abf for shared variant: 0.0513%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__VMO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5549e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.514000  0.009020  0.469000  0.008230  0.000286 \n",
+      "[1] \"PP abf for shared variant: 0.0286%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___S100A9__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.232000  0.004090  0.750000  0.013200  0.000446 \n",
+      "[1] \"PP abf for shared variant: 0.0446%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___QPCT__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.8504e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.449000  0.007920  0.533000  0.009390  0.000383 \n",
+      "[1] \"PP abf for shared variant: 0.0383%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___BLVRB__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1205e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.37500   0.00661   0.60700   0.01070   0.00042 \n",
+      "[1] \"PP abf for shared variant: 0.042%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___LYZ__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.014600  0.000257  0.968000  0.017100  0.000552 \n",
+      "[1] \"PP abf for shared variant: 0.0552%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_TMEM176A___CLEC4A__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53400   0.00941   0.44900   0.00791   0.00027 \n",
+      "[1] \"PP abf for shared variant: 0.027%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.062900  0.000864  0.918000  0.012500  0.006000 \n",
+      "[1] \"PP abf for shared variant: 0.6%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL5__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.62600   0.00860   0.35800   0.00491   0.00163 \n",
+      "[1] \"PP abf for shared variant: 0.163%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.30000   0.00413   0.68200   0.00933   0.00421 \n",
+      "[1] \"PP abf for shared variant: 0.421%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL32__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.056500  0.000777  0.923000  0.012600  0.006570 \n",
+      "[1] \"PP abf for shared variant: 0.657%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___EEF1A1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.58000   0.00796   0.40500   0.00554   0.00201 \n",
+      "[1] \"PP abf for shared variant: 0.201%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL38__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.25100   0.00345   0.73200   0.01000   0.00312 \n",
+      "[1] \"PP abf for shared variant: 0.312%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.19100   0.00262   0.79000   0.01080   0.00585 \n",
+      "[1] \"PP abf for shared variant: 0.585%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.45400   0.00624   0.53000   0.00725   0.00259 \n",
+      "[1] \"PP abf for shared variant: 0.259%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.50800   0.00697   0.47700   0.00652   0.00226 \n",
+      "[1] \"PP abf for shared variant: 0.226%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53800   0.00739   0.44700   0.00612   0.00167 \n",
+      "[1] \"PP abf for shared variant: 0.167%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS15A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.37800   0.00520   0.60400   0.00826   0.00378 \n",
+      "[1] \"PP abf for shared variant: 0.378%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS8__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.45500   0.00626   0.52900   0.00724   0.00252 \n",
+      "[1] \"PP abf for shared variant: 0.252%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53100   0.00729   0.45400   0.00622   0.00186 \n",
+      "[1] \"PP abf for shared variant: 0.186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.066700  0.000916  0.914000  0.012500  0.005610 \n",
+      "[1] \"PP abf for shared variant: 0.561%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___NKG7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.44700   0.00614   0.53700   0.00735   0.00276 \n",
+      "[1] \"PP abf for shared variant: 0.276%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___B2M__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.17000   0.00233   0.81200   0.01110   0.00512 \n",
+      "[1] \"PP abf for shared variant: 0.512%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.17600   0.00242   0.80500   0.01100   0.00596 \n",
+      "[1] \"PP abf for shared variant: 0.596%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___PFN1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13900   0.00192   0.84200   0.01150   0.00513 \n",
+      "[1] \"PP abf for shared variant: 0.513%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS28__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.34400   0.00472   0.64000   0.00876   0.00286 \n",
+      "[1] \"PP abf for shared variant: 0.286%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.31900   0.00439   0.66300   0.00906   0.00442 \n",
+      "[1] \"PP abf for shared variant: 0.442%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___GZMH__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14700   0.00202   0.83400   0.01140   0.00538 \n",
+      "[1] \"PP abf for shared variant: 0.538%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___LTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.19000   0.00261   0.79100   0.01080   0.00592 \n",
+      "[1] \"PP abf for shared variant: 0.592%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL39__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.35500   0.00487   0.62800   0.00859   0.00380 \n",
+      "[1] \"PP abf for shared variant: 0.38%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.38300   0.00526   0.60000   0.00821   0.00314 \n",
+      "[1] \"PP abf for shared variant: 0.314%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.35000   0.00481   0.63300   0.00865   0.00392 \n",
+      "[1] \"PP abf for shared variant: 0.392%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS23__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09140   0.00126   0.88900   0.01210   0.00622 \n",
+      "[1] \"PP abf for shared variant: 0.622%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS29__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26600   0.00366   0.71600   0.00979   0.00414 \n",
+      "[1] \"PP abf for shared variant: 0.414%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL22__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.65600   0.00901   0.33000   0.00451   0.00134 \n",
+      "[1] \"PP abf for shared variant: 0.134%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL9__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.35900   0.00493   0.62400   0.00853   0.00381 \n",
+      "[1] \"PP abf for shared variant: 0.381%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53200   0.00731   0.45200   0.00619   0.00226 \n",
+      "[1] \"PP abf for shared variant: 0.226%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.50800   0.00697   0.47600   0.00651   0.00267 \n",
+      "[1] \"PP abf for shared variant: 0.267%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00027483\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.793000  0.010900  0.193000  0.002660  0.000165 \n",
+      "[1] \"PP abf for shared variant: 0.0165%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___MAL__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.36300   0.00462   0.62100   0.00786   0.00354 \n",
+      "[1] \"PP abf for shared variant: 0.354%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___PRF1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.48400   0.00665   0.50100   0.00686   0.00173 \n",
+      "[1] \"PP abf for shared variant: 0.173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.033300  0.000457  0.947000  0.012900  0.006530 \n",
+      "[1] \"PP abf for shared variant: 0.653%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS6__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.33100   0.00454   0.65200   0.00892   0.00381 \n",
+      "[1] \"PP abf for shared variant: 0.381%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.034000  0.000467  0.946000  0.012900  0.006450 \n",
+      "[1] \"PP abf for shared variant: 0.645%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL21__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13100   0.00180   0.85000   0.01160   0.00582 \n",
+      "[1] \"PP abf for shared variant: 0.582%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___SMDT1__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.38e-03  6.02e-05  9.75e-01  1.33e-02  7.04e-03 \n",
+      "[1] \"PP abf for shared variant: 0.704%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.17500   0.00241   0.80600   0.01100   0.00546 \n",
+      "[1] \"PP abf for shared variant: 0.546%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.014700  0.000202  0.965000  0.013200  0.007110 \n",
+      "[1] \"PP abf for shared variant: 0.711%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL34__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13200   0.00182   0.84900   0.01160   0.00543 \n",
+      "[1] \"PP abf for shared variant: 0.543%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL10A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.27900   0.00384   0.70400   0.00963   0.00341 \n",
+      "[1] \"PP abf for shared variant: 0.341%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_SMDT1___RPL30__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.10700   0.00146   0.87400   0.01190   0.00589 \n",
+      "[1] \"PP abf for shared variant: 0.589%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.035200  0.000483  0.947000  0.013000  0.003970 \n",
+      "[1] \"PP abf for shared variant: 0.397%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53300   0.00732   0.45200   0.00619   0.00172 \n",
+      "[1] \"PP abf for shared variant: 0.172%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16200   0.00223   0.82000   0.01120   0.00447 \n",
+      "[1] \"PP abf for shared variant: 0.447%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08930   0.00123   0.89100   0.01220   0.00602 \n",
+      "[1] \"PP abf for shared variant: 0.602%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.65000   0.00893   0.33500   0.00459   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.51900   0.00713   0.46600   0.00639   0.00140 \n",
+      "[1] \"PP abf for shared variant: 0.14%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL31__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.62400   0.00857   0.36100   0.00495   0.00141 \n",
+      "[1] \"PP abf for shared variant: 0.141%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.44900   0.00617   0.53500   0.00733   0.00244 \n",
+      "[1] \"PP abf for shared variant: 0.244%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___ACTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16400   0.00226   0.81800   0.01120   0.00409 \n",
+      "[1] \"PP abf for shared variant: 0.409%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.60200   0.00827   0.38300   0.00524   0.00182 \n",
+      "[1] \"PP abf for shared variant: 0.182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012800  0.000176  0.967000  0.013200  0.006900 \n",
+      "[1] \"PP abf for shared variant: 0.69%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.65000   0.00892   0.33500   0.00459   0.00145 \n",
+      "[1] \"PP abf for shared variant: 0.145%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.42300   0.00581   0.56000   0.00767   0.00296 \n",
+      "[1] \"PP abf for shared variant: 0.296%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.46700   0.00642   0.51600   0.00707   0.00279 \n",
+      "[1] \"PP abf for shared variant: 0.279%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL10__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07600   0.00104   0.90800   0.01240   0.00295 \n",
+      "[1] \"PP abf for shared variant: 0.295%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.19200   0.00264   0.79200   0.01090   0.00187 \n",
+      "[1] \"PP abf for shared variant: 0.187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0032661\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.791000  0.010900  0.195000  0.002680  0.000161 \n",
+      "[1] \"PP abf for shared variant: 0.0161%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___CD48__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.58400   0.00802   0.40100   0.00549   0.00175 \n",
+      "[1] \"PP abf for shared variant: 0.175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.51200   0.00703   0.47200   0.00646   0.00252 \n",
+      "[1] \"PP abf for shared variant: 0.252%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_SMDT1___RPS27__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.67300   0.00925   0.31200   0.00427   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.13e-50  9.43e-01  3.49e-51  4.03e-02  1.65e-02 \n",
+      "[1] \"PP abf for shared variant: 1.65%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.07e-50  9.36e-01  3.58e-51  4.13e-02  2.30e-02 \n",
+      "[1] \"PP abf for shared variant: 2.3%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.07e-50  9.36e-01  3.61e-51  4.16e-02  2.24e-02 \n",
+      "[1] \"PP abf for shared variant: 2.24%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___CLIC3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.79e-50  9.44e-01  1.35e-51  4.57e-02  1.02e-02 \n",
+      "[1] \"PP abf for shared variant: 1.02%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__PTPRCAP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.12e-50  9.42e-01  3.48e-51  4.02e-02  1.76e-02 \n",
+      "[1] \"PP abf for shared variant: 1.76%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___CDKN2D__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.5969e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-50  9.28e-01  1.59e-51  5.35e-02  1.87e-02 \n",
+      "[1] \"PP abf for shared variant: 1.87%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0931e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.12e-50  9.42e-01  4.18e-51  4.84e-02  9.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.93%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.01e-50  9.29e-01  3.72e-51  4.29e-02  2.79e-02 \n",
+      "[1] \"PP abf for shared variant: 2.79%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.01e-50  9.29e-01  3.81e-51  4.39e-02  2.75e-02 \n",
+      "[1] \"PP abf for shared variant: 2.75%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MAP1A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.80e-50  9.45e-01  1.24e-51  4.17e-02  1.30e-02 \n",
+      "[1] \"PP abf for shared variant: 1.3%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___FAM129C__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.79e-50  9.44e-01  1.33e-51  4.49e-02  1.06e-02 \n",
+      "[1] \"PP abf for shared variant: 1.06%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MT-CO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1338e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.11e-50  9.41e-01  3.79e-51  4.38e-02  1.50e-02 \n",
+      "[1] \"PP abf for shared variant: 1.5%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.94e-50  9.21e-01  4.02e-51  4.63e-02  3.28e-02 \n",
+      "[1] \"PP abf for shared variant: 3.28%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-50  9.41e-01  1.31e-51  4.42e-02  1.48e-02 \n",
+      "[1] \"PP abf for shared variant: 1.48%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-50  8.69e-01  3.01e-51  1.01e-01  2.98e-02 \n",
+      "[1] \"PP abf for shared variant: 2.98%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-50  9.22e-01  1.79e-51  6.05e-02  1.76e-02 \n",
+      "[1] \"PP abf for shared variant: 1.76%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.71e-50  9.16e-01  1.96e-51  6.59e-02  1.78e-02 \n",
+      "[1] \"PP abf for shared variant: 1.78%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-50  8.95e-01  1.88e-51  6.31e-02  4.18e-02 \n",
+      "[1] \"PP abf for shared variant: 4.18%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-50  9.03e-01  2.20e-51  7.41e-02  2.34e-02 \n",
+      "[1] \"PP abf for shared variant: 2.34%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMA__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-50  9.21e-01  1.87e-51  6.30e-02  1.62e-02 \n",
+      "[1] \"PP abf for shared variant: 1.62%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS23\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-50  9.00e-01  1.87e-51  6.28e-02  3.70e-02 \n",
+      "[1] \"PP abf for shared variant: 3.7%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-50  9.15e-01  2.13e-51  7.19e-02  1.36e-02 \n",
+      "[1] \"PP abf for shared variant: 1.36%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-50  9.39e-01  1.50e-51  5.08e-02  9.98e-03 \n",
+      "[1] \"PP abf for shared variant: 0.998%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___EEF1A1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-50  7.66e-01  2.76e-51  9.20e-02  1.42e-01 \n",
+      "[1] \"PP abf for shared variant: 14.2%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-50  9.23e-01  1.45e-51  4.86e-02  2.83e-02 \n",
+      "[1] \"PP abf for shared variant: 2.83%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-50  9.02e-01  2.33e-51  7.85e-02  1.99e-02 \n",
+      "[1] \"PP abf for shared variant: 1.99%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMB__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-50  9.02e-01  2.57e-51  8.68e-02  1.08e-02 \n",
+      "[1] \"PP abf for shared variant: 1.08%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-50  8.99e-01  2.54e-51  8.56e-02  1.59e-02 \n",
+      "[1] \"PP abf for shared variant: 1.59%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-50  9.11e-01  2.18e-51  7.36e-02  1.51e-02 \n",
+      "[1] \"PP abf for shared variant: 1.51%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.79e-50  9.42e-01  1.41e-51  4.76e-02  1.02e-02 \n",
+      "[1] \"PP abf for shared variant: 1.02%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RNASET2___HLA-DRB5__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-50  9.85e-01  2.06e-52  6.88e-03  8.13e-03 \n",
+      "[1] \"PP abf for shared variant: 0.813%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_HLA-DQA2___CCL5__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-50  8.99e-01  2.61e-51  8.80e-02  1.33e-02 \n",
+      "[1] \"PP abf for shared variant: 1.33%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-50  8.91e-01  1.88e-51  6.33e-02  4.54e-02 \n",
+      "[1] \"PP abf for shared variant: 4.54%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.80e-50  9.46e-01  1.34e-51  4.51e-02  9.04e-03 \n",
+      "[1] \"PP abf for shared variant: 0.904%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__NKG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-50  8.86e-01  2.77e-51  9.34e-02  2.08e-02 \n",
+      "[1] \"PP abf for shared variant: 2.08%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPL34\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-50  9.23e-01  1.76e-51  5.93e-02  1.73e-02 \n",
+      "[1] \"PP abf for shared variant: 1.73%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-50  9.38e-01  1.29e-51  4.35e-02  1.85e-02 \n",
+      "[1] \"PP abf for shared variant: 1.85%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_HLA-DQA2___CMC1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-50  8.93e-01  2.30e-51  7.74e-02  2.98e-02 \n",
+      "[1] \"PP abf for shared variant: 2.98%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-50  9.29e-01  1.87e-51  6.31e-02  7.76e-03 \n",
+      "[1] \"PP abf for shared variant: 0.776%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-50  9.34e-01  1.72e-51  5.81e-02  7.60e-03 \n",
+      "[1] \"PP abf for shared variant: 0.76%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.80e-50  9.46e-01  1.37e-51  4.61e-02  7.88e-03 \n",
+      "[1] \"PP abf for shared variant: 0.788%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-50  9.09e-01  2.46e-51  8.30e-02  7.69e-03 \n",
+      "[1] \"PP abf for shared variant: 0.769%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-50  9.27e-01  1.93e-51  6.52e-02  7.75e-03 \n",
+      "[1] \"PP abf for shared variant: 0.775%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___CD52__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-50  9.39e-01  1.57e-51  5.31e-02  7.82e-03 \n",
+      "[1] \"PP abf for shared variant: 0.782%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-50  9.33e-01  1.75e-51  5.90e-02  8.05e-03 \n",
+      "[1] \"PP abf for shared variant: 0.805%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-50  9.34e-01  1.70e-51  5.75e-02  7.96e-03 \n",
+      "[1] \"PP abf for shared variant: 0.796%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-50  8.94e-01  2.87e-51  9.70e-02  8.66e-03 \n",
+      "[1] \"PP abf for shared variant: 0.866%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___EEF1B2__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-50  9.38e-01  1.58e-51  5.32e-02  8.43e-03 \n",
+      "[1] \"PP abf for shared variant: 0.843%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-50  9.34e-01  1.73e-51  5.86e-02  7.92e-03 \n",
+      "[1] \"PP abf for shared variant: 0.792%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___B2M__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-50  9.38e-01  1.61e-51  5.45e-02  7.73e-03 \n",
+      "[1] \"PP abf for shared variant: 0.773%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___GAPDH__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-50  9.31e-01  1.78e-51  6.02e-02  8.62e-03 \n",
+      "[1] \"PP abf for shared variant: 0.862%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL32\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-50  9.12e-01  2.37e-51  7.99e-02  7.72e-03 \n",
+      "[1] \"PP abf for shared variant: 0.772%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-50  9.37e-01  1.63e-51  5.50e-02  8.00e-03 \n",
+      "[1] \"PP abf for shared variant: 0.8%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-50  9.31e-01  1.78e-51  6.00e-02  8.83e-03 \n",
+      "[1] \"PP abf for shared variant: 0.883%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RNASET2___ITGB1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008400  0.000135  0.975000  0.015600  0.000482 \n",
+      "[1] \"PP abf for shared variant: 0.0482%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RNASET2___CRIP1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.087000  0.001390  0.897000  0.014400  0.000422 \n",
+      "[1] \"PP abf for shared variant: 0.0422%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RNASET2___B2M__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.039000  0.000625  0.945000  0.015100  0.000446 \n",
+      "[1] \"PP abf for shared variant: 0.0446%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RNASET2___ALOX5AP__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.098100  0.001570  0.886000  0.014200  0.000419 \n",
+      "[1] \"PP abf for shared variant: 0.0419%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0253e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.698000  0.003970  0.296000  0.001680  0.000111 \n",
+      "[1] \"PP abf for shared variant: 0.0111%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.564000  0.003210  0.431000  0.002450  0.000122 \n",
+      "[1] \"PP abf for shared variant: 0.0122%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"DC_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.623000  0.003540  0.372000  0.002110  0.000102 \n",
+      "[1] \"PP abf for shared variant: 0.0102%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.282000  0.013100  0.673000  0.031200  0.000304 \n",
+      "[1] \"PP abf for shared variant: 0.0304%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__UBE2J1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.0878e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.607000  0.028100  0.349000  0.016200  0.000317 \n",
+      "[1] \"PP abf for shared variant: 0.0317%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.098300  0.004560  0.857000  0.039800  0.000211 \n",
+      "[1] \"PP abf for shared variant: 0.0211%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.552000  0.025600  0.404000  0.018700  0.000308 \n",
+      "[1] \"PP abf for shared variant: 0.0308%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1042e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.537000  0.024900  0.418000  0.019400  0.000365 \n",
+      "[1] \"PP abf for shared variant: 0.0365%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.1644e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.731000  0.033900  0.224000  0.010400  0.000359 \n",
+      "[1] \"PP abf for shared variant: 0.0359%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.00e-04  3.25e-05  9.55e-01  4.43e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.0557e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.674000  0.031300  0.281000  0.013000  0.000345 \n",
+      "[1] \"PP abf for shared variant: 0.0345%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009500  0.000441  0.946000  0.043900  0.000189 \n",
+      "[1] \"PP abf for shared variant: 0.0189%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.171000  0.007960  0.784000  0.036400  0.000222 \n",
+      "[1] \"PP abf for shared variant: 0.0222%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004750  0.000221  0.951000  0.044100  0.000182 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.552000  0.025600  0.403000  0.018700  0.000325 \n",
+      "[1] \"PP abf for shared variant: 0.0325%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.402000  0.018700  0.554000  0.025700  0.000276 \n",
+      "[1] \"PP abf for shared variant: 0.0276%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.7757e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.574000  0.026700  0.381000  0.017700  0.000334 \n",
+      "[1] \"PP abf for shared variant: 0.0334%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.112000  0.005220  0.843000  0.039100  0.000222 \n",
+      "[1] \"PP abf for shared variant: 0.0222%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.470000  0.021800  0.485000  0.022500  0.000294 \n",
+      "[1] \"PP abf for shared variant: 0.0294%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2778e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.562000  0.026100  0.393000  0.018200  0.000321 \n",
+      "[1] \"PP abf for shared variant: 0.0321%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0623e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.733000  0.034000  0.223000  0.010300  0.000353 \n",
+      "[1] \"PP abf for shared variant: 0.0353%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.039500  0.001840  0.916000  0.042500  0.000193 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.077600  0.003600  0.878000  0.040800  0.000214 \n",
+      "[1] \"PP abf for shared variant: 0.0214%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.253000  0.011700  0.703000  0.032600  0.000259 \n",
+      "[1] \"PP abf for shared variant: 0.0259%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.65e-05  3.55e-06  9.55e-01  4.44e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1408e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.748000  0.034700  0.207000  0.009630  0.000339 \n",
+      "[1] \"PP abf for shared variant: 0.0339%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.791e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5490    0.0255    0.4060    0.0188    0.0003 \n",
+      "[1] \"PP abf for shared variant: 0.03%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1436e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.697000  0.032400  0.258000  0.012000  0.000341 \n",
+      "[1] \"PP abf for shared variant: 0.0341%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1123e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.781000  0.036300  0.175000  0.008110  0.000363 \n",
+      "[1] \"PP abf for shared variant: 0.0363%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.204000  0.009480  0.751000  0.034900  0.000227 \n",
+      "[1] \"PP abf for shared variant: 0.0227%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.199000  0.009230  0.757000  0.035100  0.000249 \n",
+      "[1] \"PP abf for shared variant: 0.0249%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-05  7.93e-07  9.55e-01  4.44e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.177000  0.008200  0.779000  0.036200  0.000223 \n",
+      "[1] \"PP abf for shared variant: 0.0223%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.027800  0.001290  0.928000  0.043100  0.000184 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-05  1.45e-06  9.55e-01  4.44e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.157000  0.007300  0.798000  0.037100  0.000228 \n",
+      "[1] \"PP abf for shared variant: 0.0228%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1639e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.743000  0.034500  0.212000  0.009860  0.000349 \n",
+      "[1] \"PP abf for shared variant: 0.0349%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"B_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.065500  0.003040  0.890000  0.041300  0.000198 \n",
+      "[1] \"PP abf for shared variant: 0.0198%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-08  8.23e-10  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-12  8.75e-14  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.095200  0.004430  0.860000  0.040000  0.000211 \n",
+      "[1] \"PP abf for shared variant: 0.0211%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-08  1.49e-09  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-06  8.53e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.34e-05  3.41e-06  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.22e-17  1.50e-18  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.02e-11  3.73e-12  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.96e-12  2.31e-13  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.83e-11  3.18e-12  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-05  5.42e-07  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-03  6.18e-05  9.54e-01  4.44e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-11  7.85e-13  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.30e-15  1.53e-16  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03210   0.00150   0.92300   0.04300   0.00019 \n",
+      "[1] \"PP abf for shared variant: 0.019%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.070100  0.003260  0.885000  0.041200  0.000195 \n",
+      "[1] \"PP abf for shared variant: 0.0195%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-10  1.03e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-06  5.75e-08  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.011300  0.000527  0.944000  0.043900  0.000181 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.91e-07  2.28e-08  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-17  4.92e-19  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-11  1.61e-12  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.20e-11  1.96e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__SPON2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013700  0.000637  0.942000  0.043800  0.000245 \n",
+      "[1] \"PP abf for shared variant: 0.0245%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-05  9.72e-07  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-19  7.50e-21  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.42e-13  1.59e-14  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.63e-12  3.08e-13  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.55e-13  2.12e-14  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-06  6.78e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.231000  0.010700  0.725000  0.033700  0.000249 \n",
+      "[1] \"PP abf for shared variant: 0.0249%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-05  5.54e-07  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1991e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.487000  0.022600  0.469000  0.021800  0.000315 \n",
+      "[1] \"PP abf for shared variant: 0.0315%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-09  1.29e-10  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-12  1.70e-13  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-09  9.10e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-12  4.70e-14  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-15  5.90e-17  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.10e-18  1.44e-19  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-04  1.91e-05  9.55e-01  4.44e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.81e-10  2.70e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-04  4.76e-06  9.55e-01  4.44e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.38e-10  2.04e-11  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-13  9.44e-15  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-08  4.66e-10  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-15  9.32e-17  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-13  5.07e-15  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.30e-04  4.33e-05  9.54e-01  4.44e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.86e-13  1.79e-14  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.41400   0.01930   0.54100   0.02520   0.00027 \n",
+      "[1] \"PP abf for shared variant: 0.027%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.34e-07  1.55e-08  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.022800  0.001060  0.933000  0.043400  0.000181 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-08  1.48e-09  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-04  1.15e-05  9.55e-01  4.44e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.63e-18  3.55e-19  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-07  9.88e-09  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-09  9.57e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-06  8.85e-08  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-10  1.59e-11  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-15  7.98e-17  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-06  1.02e-07  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-05  1.04e-06  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.331000  0.015400  0.624000  0.029000  0.000262 \n",
+      "[1] \"PP abf for shared variant: 0.0262%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.09e-16  2.37e-17  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010100  0.000471  0.945000  0.044000  0.000180 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-08  7.01e-10  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-06  1.54e-07  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.42e-05  2.52e-06  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-05  9.68e-07  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.40e-07  2.98e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4099e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.446000  0.020700  0.509000  0.023700  0.000289 \n",
+      "[1] \"PP abf for shared variant: 0.0289%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5173e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.791000  0.036800  0.164000  0.007650  0.000345 \n",
+      "[1] \"PP abf for shared variant: 0.0345%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-03  5.43e-05  9.54e-01  4.44e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.46e-07  3.94e-08  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.98e-13  2.32e-14  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___KLRC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.100000  0.004650  0.855000  0.039800  0.000214 \n",
+      "[1] \"PP abf for shared variant: 0.0214%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.4275e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.698000  0.032500  0.257000  0.011900  0.000471 \n",
+      "[1] \"PP abf for shared variant: 0.0471%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-06  6.99e-08  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-08  5.30e-10  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___MALAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019300  0.000899  0.936000  0.043600  0.000218 \n",
+      "[1] \"PP abf for shared variant: 0.0218%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003080  0.000144  0.952000  0.044300  0.000177 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.96e-11  2.77e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8351e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.658000  0.030600  0.297000  0.013800  0.000344 \n",
+      "[1] \"PP abf for shared variant: 0.0344%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.64e-07  3.09e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.10e-05  3.30e-06  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___FGFBP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.322000  0.015000  0.633000  0.029500  0.000279 \n",
+      "[1] \"PP abf for shared variant: 0.0279%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009330  0.000434  0.946000  0.044000  0.000182 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.78e-04  2.69e-05  9.55e-01  4.44e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-03  4.67e-05  9.54e-01  4.44e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.047900  0.002230  0.907000  0.042200  0.000199 \n",
+      "[1] \"PP abf for shared variant: 0.0199%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.574e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.745000  0.034600  0.210000  0.009760  0.000339 \n",
+      "[1] \"PP abf for shared variant: 0.0339%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.10e-06  3.77e-07  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.89e-15  3.67e-16  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2336e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.646000  0.030100  0.309000  0.014400  0.000334 \n",
+      "[1] \"PP abf for shared variant: 0.0334%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.52e-10  3.50e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"NK_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.76e-11  2.68e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___NRGN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7437e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53600   0.02500   0.41900   0.01950   0.00034 \n",
+      "[1] \"PP abf for shared variant: 0.034%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.289000  0.013500  0.666000  0.031000  0.000291 \n",
+      "[1] \"PP abf for shared variant: 0.0291%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001630  0.000076  0.954000  0.044400  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-06  8.38e-08  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.33e-16  2.02e-17  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-06  5.07e-08  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-09  7.95e-11  9.55e-01  4.45e-02  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015500  0.000723  0.940000  0.043800  0.000188 \n",
+      "[1] \"PP abf for shared variant: 0.0188%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.01e-09  4.20e-10  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-14  5.19e-16  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VCAN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.466000  0.021700  0.489000  0.022800  0.000283 \n",
+      "[1] \"PP abf for shared variant: 0.0283%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UQCRH\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-05  7.21e-07  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC7A7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.014100  0.000656  0.941000  0.043800  0.000189 \n",
+      "[1] \"PP abf for shared variant: 0.0189%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EPB41L3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.25600   0.01190   0.70000   0.03260   0.00025 \n",
+      "[1] \"PP abf for shared variant: 0.025%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007640  0.000355  0.948000  0.044100  0.000180 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.32e-14  2.01e-15  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015300  0.000712  0.940000  0.043800  0.000188 \n",
+      "[1] \"PP abf for shared variant: 0.0188%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___QARS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.284000  0.013200  0.671000  0.031300  0.000302 \n",
+      "[1] \"PP abf for shared variant: 0.0302%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-06  2.02e-07  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.16e-15  2.40e-16  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.82e-06  3.64e-07  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-15  1.37e-16  9.55e-01  4.45e-02  1.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-08  6.61e-10  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-09  8.68e-11  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.78e-12  2.69e-13  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.021200  0.000987  0.934000  0.043500  0.000196 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.39e-10  4.37e-11  9.55e-01  4.45e-02  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___CSTA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.69e-04  4.05e-05  9.54e-01  4.44e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.49e-07  1.16e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.141000  0.006550  0.815000  0.037900  0.000261 \n",
+      "[1] \"PP abf for shared variant: 0.0261%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-12  7.39e-14  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002540  0.000118  0.953000  0.044400  0.000178 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.56e-15  3.06e-16  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.350000  0.016300  0.605000  0.028200  0.000277 \n",
+      "[1] \"PP abf for shared variant: 0.0277%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TNFAIP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.340000  0.015800  0.615000  0.028600  0.000401 \n",
+      "[1] \"PP abf for shared variant: 0.0401%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016300  0.000760  0.939000  0.043700  0.000189 \n",
+      "[1] \"PP abf for shared variant: 0.0189%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.68e-12  3.57e-13  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-20  2.70e-21  9.55e-01  4.45e-02  1.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-11  9.21e-13  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-05  6.63e-07  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-10  1.80e-11  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.83e-09  2.25e-10  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-04  4.97e-06  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001870  0.000087  0.953000  0.044400  0.000180 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.15e-04  2.86e-05  9.55e-01  4.44e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-13  9.80e-15  9.55e-01  4.45e-02  1.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-15  4.87e-17  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.95e-09  4.17e-10  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-04  9.52e-06  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.29e-11  2.93e-12  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1173e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.370000  0.017200  0.586000  0.027300  0.000267 \n",
+      "[1] \"PP abf for shared variant: 0.0267%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.27e-07  2.92e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.52e-10  1.18e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-07  6.99e-09  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003240  0.000151  0.952000  0.044300  0.000181 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.73e-09  3.60e-10  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.98e-06  2.32e-07  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.024700  0.001150  0.931000  0.043300  0.000206 \n",
+      "[1] \"PP abf for shared variant: 0.0206%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-11  6.05e-13  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.73e-11  3.60e-12  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.36e-05  2.96e-06  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.13e-04  1.92e-05  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___CST3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7382e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.576000  0.026800  0.379000  0.017600  0.000378 \n",
+      "[1] \"PP abf for shared variant: 0.0378%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.036400  0.001700  0.919000  0.042800  0.000196 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.270000  0.012600  0.685000  0.031900  0.000269 \n",
+      "[1] \"PP abf for shared variant: 0.0269%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-15  1.36e-16  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.31900   0.01480   0.63700   0.02960   0.00025 \n",
+      "[1] \"PP abf for shared variant: 0.025%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-03  5.35e-05  9.54e-01  4.44e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___AP1S2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26500   0.01240   0.69000   0.03210   0.00025 \n",
+      "[1] \"PP abf for shared variant: 0.025%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-10  8.26e-12  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.95e-09  3.24e-10  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SEC11A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015400  0.000719  0.940000  0.043800  0.000207 \n",
+      "[1] \"PP abf for shared variant: 0.0207%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-04  1.72e-05  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.34e-11  3.88e-12  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-08  7.33e-10  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.73e-11  3.60e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-07  1.28e-08  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.65e-07  2.16e-08  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-07  7.66e-09  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___ATP5G2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009720  0.000453  0.946000  0.044000  0.000182 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.83e-06  4.58e-07  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.76e-09  3.15e-10  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016600  0.000774  0.939000  0.043700  0.000187 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.34e-13  2.95e-14  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.45e-16  3.93e-17  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-17  6.72e-19  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-06  6.55e-08  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.42e-07  1.13e-08  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-16  1.13e-17  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-04  9.61e-06  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EVI2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.369000  0.017200  0.586000  0.027300  0.000261 \n",
+      "[1] \"PP abf for shared variant: 0.0261%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.050600  0.002360  0.905000  0.042100  0.000199 \n",
+      "[1] \"PP abf for shared variant: 0.0199%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.025700  0.001200  0.930000  0.043300  0.000191 \n",
+      "[1] \"PP abf for shared variant: 0.0191%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.41e-04  4.38e-05  9.54e-01  4.44e-02  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-12  5.19e-14  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___LGALS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.219000  0.010200  0.736000  0.034300  0.000233 \n",
+      "[1] \"PP abf for shared variant: 0.0233%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.325000  0.015100  0.630000  0.029300  0.000314 \n",
+      "[1] \"PP abf for shared variant: 0.0314%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.57e-11  2.59e-12  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-14  6.91e-16  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.56e-11  3.52e-12  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.71e-06  1.26e-07  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___GPX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.041500  0.001930  0.914000  0.042500  0.000201 \n",
+      "[1] \"PP abf for shared variant: 0.0201%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___LTA4H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.538000  0.025000  0.418000  0.019400  0.000346 \n",
+      "[1] \"PP abf for shared variant: 0.0346%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RNASE6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.262000  0.012200  0.694000  0.032300  0.000296 \n",
+      "[1] \"PP abf for shared variant: 0.0296%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.103000  0.004780  0.853000  0.039700  0.000209 \n",
+      "[1] \"PP abf for shared variant: 0.0209%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___BTF3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010900  0.000507  0.944000  0.044000  0.000183 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___DRAM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1829e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.549000  0.025600  0.406000  0.018900  0.000353 \n",
+      "[1] \"PP abf for shared variant: 0.0353%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___IL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001100  0.000051  0.954000  0.044400  0.000177 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008720  0.000406  0.947000  0.044100  0.000219 \n",
+      "[1] \"PP abf for shared variant: 0.0219%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.17e-07  3.34e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.16e-11  4.26e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-13  6.74e-15  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.314000  0.014600  0.641000  0.029900  0.000257 \n",
+      "[1] \"PP abf for shared variant: 0.0257%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-14  6.70e-16  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.63e-12  4.49e-13  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___IPO7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.093000  0.004330  0.862000  0.040100  0.000215 \n",
+      "[1] \"PP abf for shared variant: 0.0215%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-07  7.30e-09  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.91e-12  3.22e-13  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.00e-13  1.86e-14  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2966e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.505000  0.023500  0.451000  0.021000  0.000343 \n",
+      "[1] \"PP abf for shared variant: 0.0343%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-08  1.66e-09  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-05  5.50e-07  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___CRTAP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015300  0.000711  0.940000  0.043800  0.000188 \n",
+      "[1] \"PP abf for shared variant: 0.0188%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.395000  0.018400  0.560000  0.026100  0.000387 \n",
+      "[1] \"PP abf for shared variant: 0.0387%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.82e-09  1.78e-10  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002350  0.000109  0.953000  0.044400  0.000180 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-09  7.30e-11  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.348000  0.016200  0.607000  0.028300  0.000287 \n",
+      "[1] \"PP abf for shared variant: 0.0287%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.050100  0.002330  0.905000  0.042100  0.000193 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.90e-10  2.75e-11  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-11  9.66e-13  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TKT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-04  1.05e-05  9.55e-01  4.45e-02  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.39e-15  1.58e-16  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.127000  0.005910  0.828000  0.038500  0.000221 \n",
+      "[1] \"PP abf for shared variant: 0.0221%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ACTN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.68e-04  4.04e-05  9.55e-01  4.44e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.33e-15  3.88e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAND1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4561e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.410000  0.019100  0.545000  0.025400  0.000315 \n",
+      "[1] \"PP abf for shared variant: 0.0315%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.09e-15  1.90e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PRF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013600  0.000633  0.942000  0.043800  0.000186 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-05  1.41e-06  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EFHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.048300  0.002250  0.907000  0.042200  0.000194 \n",
+      "[1] \"PP abf for shared variant: 0.0194%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-10  7.03e-12  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.35e-16  3.42e-17  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.28e-14  2.92e-15  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.00e-13  2.33e-14  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___APBA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.092500  0.004290  0.863000  0.040000  0.000203 \n",
+      "[1] \"PP abf for shared variant: 0.0203%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.31e-04  2.01e-05  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FAIM3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.070800  0.003300  0.885000  0.041200  0.000197 \n",
+      "[1] \"PP abf for shared variant: 0.0197%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53100   0.02470   0.42400   0.01970   0.00039 \n",
+      "[1] \"PP abf for shared variant: 0.039%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.353000  0.016400  0.602000  0.028000  0.000292 \n",
+      "[1] \"PP abf for shared variant: 0.0292%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016100  0.000751  0.939000  0.043700  0.000185 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-14  1.27e-15  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___C11orf1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.8471e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.644000  0.030000  0.312000  0.014500  0.000327 \n",
+      "[1] \"PP abf for shared variant: 0.0327%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.80e-04  2.24e-05  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-17  1.23e-18  9.55e-01  4.44e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.30e-17  2.00e-18  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GZMM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.400000  0.018600  0.555000  0.025800  0.000266 \n",
+      "[1] \"PP abf for shared variant: 0.0266%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.32e-06  1.55e-07  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.63e-04  3.55e-05  9.55e-01  4.44e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.292000  0.013600  0.663000  0.030900  0.000269 \n",
+      "[1] \"PP abf for shared variant: 0.0269%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CTSW__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.250000  0.011600  0.706000  0.032800  0.000296 \n",
+      "[1] \"PP abf for shared variant: 0.0296%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-05  1.25e-06  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-18  6.09e-20  9.55e-01  4.45e-02  1.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FLT3LG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-04  1.48e-05  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NKG7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.48e-05  1.62e-06  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051400  0.002390  0.904000  0.042100  0.000196 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.3964e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.613000  0.028600  0.342000  0.015900  0.000433 \n",
+      "[1] \"PP abf for shared variant: 0.0433%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.337000  0.015700  0.618000  0.028800  0.000305 \n",
+      "[1] \"PP abf for shared variant: 0.0305%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-04  7.97e-06  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-06  1.67e-07  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-09  1.07e-10  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-12  1.00e-13  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.78e-14  3.16e-15  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ATP1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 9.0977e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.726000  0.033800  0.229000  0.010600  0.000352 \n",
+      "[1] \"PP abf for shared variant: 0.0352%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.220000  0.010300  0.735000  0.034200  0.000233 \n",
+      "[1] \"PP abf for shared variant: 0.0233%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SYNE1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.165000  0.007680  0.790000  0.036800  0.000227 \n",
+      "[1] \"PP abf for shared variant: 0.0227%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.137e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.631000  0.029400  0.324000  0.015100  0.000315 \n",
+      "[1] \"PP abf for shared variant: 0.0315%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-06  8.36e-08  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-10  9.25e-12  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PDE3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-04  6.19e-06  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-19  5.12e-21  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.63e-05  2.62e-06  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZEB2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01200   0.00056   0.94300   0.04390   0.00018 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___LTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-08  1.39e-09  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PTPN7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.368000  0.017100  0.587000  0.027300  0.000294 \n",
+      "[1] \"PP abf for shared variant: 0.0294%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-12  5.53e-14  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-10  7.71e-12  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___IER2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1556e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.68300   0.03180   0.27200   0.01270   0.00035 \n",
+      "[1] \"PP abf for shared variant: 0.035%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.51e-05  3.50e-06  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-09  5.80e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.96e-20  1.38e-21  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.64e-15  2.62e-16  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CMC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-03  4.73e-05  9.54e-01  4.44e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-04  5.74e-06  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.91e-13  1.82e-14  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GZMB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03000   0.00140   0.92500   0.04310   0.00019 \n",
+      "[1] \"PP abf for shared variant: 0.019%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___AKNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.67300   0.03130   0.28300   0.01320   0.00041 \n",
+      "[1] \"PP abf for shared variant: 0.041%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9277e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.568000  0.026500  0.387000  0.018000  0.000346 \n",
+      "[1] \"PP abf for shared variant: 0.0346%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.15e-20  2.40e-21  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-08  1.31e-09  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.77e-04  1.75e-05  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.038200  0.001780  0.917000  0.042700  0.000192 \n",
+      "[1] \"PP abf for shared variant: 0.0192%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.040700  0.001890  0.915000  0.042600  0.000193 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-16  9.26e-18  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-11  1.20e-12  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-14  4.73e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___METRNL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4496e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.725000  0.033600  0.231000  0.010700  0.000412 \n",
+      "[1] \"PP abf for shared variant: 0.0412%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.029700  0.001380  0.926000  0.043100  0.000186 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CAMK4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.91e-07  1.82e-08  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___BHLHE40__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015200  0.000709  0.940000  0.043800  0.000196 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___IFITM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2604e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.576000  0.026800  0.379000  0.017700  0.000397 \n",
+      "[1] \"PP abf for shared variant: 0.0397%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.086300  0.004020  0.869000  0.040500  0.000197 \n",
+      "[1] \"PP abf for shared variant: 0.0197%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CD8B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-04  1.11e-05  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-18  7.73e-20  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NGFRAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.069600  0.003230  0.886000  0.041100  0.000197 \n",
+      "[1] \"PP abf for shared variant: 0.0197%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.79e-14  3.63e-15  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TUBA4A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.062100  0.002890  0.893000  0.041600  0.000196 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-05  1.67e-06  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-19  1.02e-20  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RCAN3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.14e-06  3.79e-07  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.74e-13  2.21e-14  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.75e-04  1.75e-05  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.34e-11  3.42e-12  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.65e-08  4.03e-09  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___DNAJB6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.098600  0.004590  0.857000  0.039900  0.000203 \n",
+      "[1] \"PP abf for shared variant: 0.0203%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RAP1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.077e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.750000  0.034900  0.205000  0.009530  0.000402 \n",
+      "[1] \"PP abf for shared variant: 0.0402%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-05  9.48e-07  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002770  0.000129  0.953000  0.044300  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008300  0.000387  0.947000  0.044100  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CCDC104__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.646000  0.030000  0.309000  0.014300  0.000335 \n",
+      "[1] \"PP abf for shared variant: 0.0335%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.72e-09  4.06e-10  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-08  5.82e-10  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.92e-06  2.75e-07  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.59e-18  2.13e-19  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.053600  0.002500  0.902000  0.042000  0.000213 \n",
+      "[1] \"PP abf for shared variant: 0.0213%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TESPA1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.021700  0.001010  0.934000  0.043300  0.000189 \n",
+      "[1] \"PP abf for shared variant: 0.0189%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.011000  0.000513  0.944000  0.044000  0.000189 \n",
+      "[1] \"PP abf for shared variant: 0.0189%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PSMA7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.339000  0.015800  0.616000  0.028700  0.000275 \n",
+      "[1] \"PP abf for shared variant: 0.0275%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PLEK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.308000  0.014300  0.648000  0.030200  0.000263 \n",
+      "[1] \"PP abf for shared variant: 0.0263%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.50e-04  1.63e-05  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.48e-16  3.95e-17  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.4281e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.540000  0.025100  0.416000  0.019300  0.000298 \n",
+      "[1] \"PP abf for shared variant: 0.0298%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___KLRD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006960  0.000324  0.948000  0.044200  0.000181 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MYC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007060  0.000329  0.948000  0.044100  0.000178 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.31100   0.01450   0.64400   0.03000   0.00026 \n",
+      "[1] \"PP abf for shared variant: 0.026%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___KLF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.391e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.538000  0.025000  0.417000  0.019400  0.000345 \n",
+      "[1] \"PP abf for shared variant: 0.0345%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14900   0.00696   0.80600   0.03750   0.00027 \n",
+      "[1] \"PP abf for shared variant: 0.027%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HNRNPA2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.586000  0.027300  0.369000  0.017200  0.000316 \n",
+      "[1] \"PP abf for shared variant: 0.0316%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ARAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3907e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.77400   0.03600   0.18100   0.00844   0.00036 \n",
+      "[1] \"PP abf for shared variant: 0.036%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-16  1.24e-17  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-05  8.08e-07  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.80e-05  2.70e-06  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-12  4.78e-14  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.10e-06  3.77e-07  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-05  5.24e-07  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.198000  0.009220  0.757000  0.035300  0.000236 \n",
+      "[1] \"PP abf for shared variant: 0.0236%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-09  1.04e-10  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-18  1.41e-19  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___LYPD3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.370000  0.017200  0.585000  0.027200  0.000275 \n",
+      "[1] \"PP abf for shared variant: 0.0275%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-04  6.82e-06  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21600   0.01010   0.73900   0.03440   0.00026 \n",
+      "[1] \"PP abf for shared variant: 0.026%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.088800  0.004130  0.867000  0.040300  0.000204 \n",
+      "[1] \"PP abf for shared variant: 0.0204%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM243\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-03  9.71e-05  9.53e-01  4.44e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.16e-15  1.93e-16  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ID2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.185000  0.008600  0.770000  0.035900  0.000251 \n",
+      "[1] \"PP abf for shared variant: 0.0251%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.29e-15  2.00e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.34e-10  2.95e-11  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004370  0.000204  0.951000  0.044300  0.000194 \n",
+      "[1] \"PP abf for shared variant: 0.0194%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.25e-25  1.98e-26  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.374000  0.017400  0.581000  0.027000  0.000339 \n",
+      "[1] \"PP abf for shared variant: 0.0339%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NFKBIA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 7.944e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.664000  0.030900  0.291000  0.013500  0.000363 \n",
+      "[1] \"PP abf for shared variant: 0.0363%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.032000  0.001490  0.923000  0.043000  0.000217 \n",
+      "[1] \"PP abf for shared variant: 0.0217%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.67e-17  4.04e-18  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.91e-15  8.87e-17  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-09  5.65e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GZMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-07  1.61e-08  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.331000  0.015400  0.624000  0.029100  0.000261 \n",
+      "[1] \"PP abf for shared variant: 0.0261%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-10  8.23e-12  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPI1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.441000  0.020500  0.515000  0.024000  0.000337 \n",
+      "[1] \"PP abf for shared variant: 0.0337%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.12e-19  4.24e-20  9.55e-01  4.44e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.144000  0.006720  0.811000  0.037800  0.000211 \n",
+      "[1] \"PP abf for shared variant: 0.0211%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.80e-10  2.24e-11  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-15  1.00e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SC5D\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.431000  0.020100  0.524000  0.024400  0.000426 \n",
+      "[1] \"PP abf for shared variant: 0.0426%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-10  8.47e-12  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-17  1.46e-18  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RIC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-03  8.12e-05  9.54e-01  4.44e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.46e-11  2.08e-12  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-05  1.28e-06  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.9814e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.665000  0.031000  0.290000  0.013500  0.000428 \n",
+      "[1] \"PP abf for shared variant: 0.0428%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRP14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-04  5.65e-06  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0298e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.611000  0.028400  0.345000  0.016000  0.000375 \n",
+      "[1] \"PP abf for shared variant: 0.0375%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.90e-04  3.21e-05  9.55e-01  4.44e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-06  4.70e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.00e-16  1.40e-17  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-18  4.98e-20  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-19  1.03e-20  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MYL12B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.546000  0.025400  0.409000  0.019000  0.000335 \n",
+      "[1] \"PP abf for shared variant: 0.0335%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.028900  0.001340  0.926000  0.043100  0.000193 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MIR142__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1648e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.745000  0.034700  0.210000  0.009780  0.000351 \n",
+      "[1] \"PP abf for shared variant: 0.0351%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007650  0.000356  0.948000  0.044100  0.000191 \n",
+      "[1] \"PP abf for shared variant: 0.0191%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-17  1.07e-18  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.300000  0.014000  0.655000  0.030500  0.000245 \n",
+      "[1] \"PP abf for shared variant: 0.0245%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-05  4.92e-07  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PTP4A2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.414000  0.019300  0.542000  0.025200  0.000336 \n",
+      "[1] \"PP abf for shared variant: 0.0336%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.9666e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.70000   0.03260   0.25500   0.01190   0.00038 \n",
+      "[1] \"PP abf for shared variant: 0.038%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-04  4.73e-06  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.60e-08  2.14e-09  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.98e-25  1.85e-26  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CCDC57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-06  1.04e-07  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ITGB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.450000  0.021000  0.505000  0.023500  0.000289 \n",
+      "[1] \"PP abf for shared variant: 0.0289%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.088300  0.004110  0.867000  0.040400  0.000237 \n",
+      "[1] \"PP abf for shared variant: 0.0237%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4185e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.68100   0.03170   0.27400   0.01270   0.00034 \n",
+      "[1] \"PP abf for shared variant: 0.034%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ARF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.144000  0.006710  0.811000  0.037800  0.000218 \n",
+      "[1] \"PP abf for shared variant: 0.0218%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CD81__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.25000   0.01160   0.70500   0.03280   0.00029 \n",
+      "[1] \"PP abf for shared variant: 0.029%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004220  0.000196  0.951000  0.044300  0.000178 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004230  0.000197  0.951000  0.044300  0.000178 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-03  4.69e-05  9.54e-01  4.44e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-05  1.38e-06  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FOXP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.03e-04  3.74e-05  9.55e-01  4.44e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CCL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.05e-05  4.21e-06  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012800  0.000595  0.943000  0.043900  0.000188 \n",
+      "[1] \"PP abf for shared variant: 0.0188%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___KLRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9856e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.611000  0.028400  0.345000  0.016000  0.000324 \n",
+      "[1] \"PP abf for shared variant: 0.0324%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5042e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.726000  0.033800  0.230000  0.010700  0.000348 \n",
+      "[1] \"PP abf for shared variant: 0.0348%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26700   0.01240   0.68800   0.03200   0.00024 \n",
+      "[1] \"PP abf for shared variant: 0.024%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-15  9.25e-17  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___C1orf21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1023e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.591000  0.027500  0.365000  0.017000  0.000314 \n",
+      "[1] \"PP abf for shared variant: 0.0314%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-10  1.22e-11  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CALM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.115000  0.005350  0.840000  0.039100  0.000209 \n",
+      "[1] \"PP abf for shared variant: 0.0209%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HOPX__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.348000  0.016200  0.607000  0.028200  0.000413 \n",
+      "[1] \"PP abf for shared variant: 0.0413%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.035800  0.001670  0.919000  0.042800  0.000193 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.37e-15  2.04e-16  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.16e-04  4.27e-05  9.54e-01  4.44e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-16  1.61e-17  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008870  0.000413  0.946000  0.044100  0.000198 \n",
+      "[1] \"PP abf for shared variant: 0.0198%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.56e-11  2.12e-12  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.507e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.71500   0.03330   0.24000   0.01120   0.00036 \n",
+      "[1] \"PP abf for shared variant: 0.036%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-17  6.79e-19  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-13  5.03e-15  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PTMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002950  0.000137  0.952000  0.044300  0.000177 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-10  8.27e-12  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GNG2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.238000  0.011100  0.717000  0.033400  0.000236 \n",
+      "[1] \"PP abf for shared variant: 0.0236%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.023400  0.001090  0.932000  0.043400  0.000184 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.14e-07  3.32e-08  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-10  1.78e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.55e-04  4.45e-05  9.54e-01  4.44e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CD27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.689e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.589000  0.027400  0.366000  0.017000  0.000322 \n",
+      "[1] \"PP abf for shared variant: 0.0322%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002170  0.000101  0.953000  0.044400  0.000177 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-09  6.87e-11  9.56e-01  4.43e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.00e-16  3.26e-17  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___REL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.691e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.583000  0.027100  0.372000  0.017300  0.000304 \n",
+      "[1] \"PP abf for shared variant: 0.0304%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-11  9.32e-13  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.412000  0.019200  0.543000  0.025300  0.000316 \n",
+      "[1] \"PP abf for shared variant: 0.0316%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-08  7.27e-10  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.16e-13  1.47e-14  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-07  2.28e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SEPT7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.133000  0.006190  0.822000  0.038300  0.000222 \n",
+      "[1] \"PP abf for shared variant: 0.0222%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.145000  0.006730  0.811000  0.037700  0.000236 \n",
+      "[1] \"PP abf for shared variant: 0.0236%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.466000  0.021700  0.489000  0.022800  0.000322 \n",
+      "[1] \"PP abf for shared variant: 0.0322%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.87e-09  2.27e-10  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___FCGR3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.082100  0.003820  0.873000  0.040600  0.000278 \n",
+      "[1] \"PP abf for shared variant: 0.0278%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PSMB9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.645e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.670000  0.031200  0.286000  0.013300  0.000422 \n",
+      "[1] \"PP abf for shared variant: 0.0422%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.33e-09  1.08e-10  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.64e-08  2.16e-09  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.374000  0.017400  0.582000  0.027100  0.000319 \n",
+      "[1] \"PP abf for shared variant: 0.0319%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-03  4.69e-05  9.54e-01  4.44e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.53e-16  3.97e-17  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-13  1.60e-14  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.086700  0.004040  0.869000  0.040400  0.000212 \n",
+      "[1] \"PP abf for shared variant: 0.0212%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.075000  0.003490  0.880000  0.041000  0.000197 \n",
+      "[1] \"PP abf for shared variant: 0.0197%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-14  1.62e-15  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5519e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.523000  0.024400  0.432000  0.020100  0.000326 \n",
+      "[1] \"PP abf for shared variant: 0.0326%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.61e-11  3.08e-12  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___DOK2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.350000  0.016300  0.606000  0.028200  0.000298 \n",
+      "[1] \"PP abf for shared variant: 0.0298%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.14e-06  1.93e-07  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CALR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9449e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.518000  0.024100  0.437000  0.020300  0.000301 \n",
+      "[1] \"PP abf for shared variant: 0.0301%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TTC38\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1223e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.528000  0.024600  0.427000  0.019900  0.000341 \n",
+      "[1] \"PP abf for shared variant: 0.0341%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.026200  0.001220  0.929000  0.043300  0.000191 \n",
+      "[1] \"PP abf for shared variant: 0.0191%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.005280  0.000246  0.950000  0.044200  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.77e-09  1.76e-10  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-09  5.70e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.04e-06  2.35e-07  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.69e-16  2.65e-17  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PASK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.15e-07  2.39e-08  9.56e-01  4.43e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-10  6.60e-12  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.09e-17  3.30e-18  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___OXNAD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1359e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.590000  0.027500  0.365000  0.017000  0.000504 \n",
+      "[1] \"PP abf for shared variant: 0.0504%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.043500  0.002020  0.912000  0.042500  0.000192 \n",
+      "[1] \"PP abf for shared variant: 0.0192%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.23e-16  1.50e-17  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-03  6.37e-05  9.54e-01  4.44e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TYROBP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007550  0.000352  0.948000  0.044100  0.000188 \n",
+      "[1] \"PP abf for shared variant: 0.0188%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YBX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1331e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.755000  0.035200  0.200000  0.009320  0.000353 \n",
+      "[1] \"PP abf for shared variant: 0.0353%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.17e-07  3.81e-08  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-04  1.38e-05  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___IL7R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-03  8.03e-05  9.54e-01  4.44e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RHOH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.027900  0.001300  0.927000  0.043200  0.000193 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.81e-16  1.77e-17  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-18  2.28e-19  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016800  0.000782  0.939000  0.043700  0.000185 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___DBI__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008050  0.000375  0.947000  0.044100  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-11  8.01e-13  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-09  9.31e-11  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.24e-07  1.97e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___POMP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.453000  0.021100  0.502000  0.023400  0.000297 \n",
+      "[1] \"PP abf for shared variant: 0.0297%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.82e-15  3.64e-16  9.55e-01  4.44e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RAB8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0817e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.653000  0.030400  0.302000  0.014100  0.000348 \n",
+      "[1] \"PP abf for shared variant: 0.0348%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___GZMH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.137000  0.006360  0.819000  0.038100  0.000249 \n",
+      "[1] \"PP abf for shared variant: 0.0249%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.10e-05  4.24e-06  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-10  1.31e-11  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-19  7.04e-21  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-17  1.20e-18  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___ABLIM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004290  0.000199  0.951000  0.044100  0.000178 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EIF4A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8946e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.582000  0.027100  0.373000  0.017400  0.000366 \n",
+      "[1] \"PP abf for shared variant: 0.0366%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-04  1.52e-05  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RP11-291B21.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-05  8.68e-07  9.55e-01  4.43e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-19  1.49e-20  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.63e-18  1.69e-19  9.55e-01  4.45e-02  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.24e-09  1.51e-10  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.27e-10  2.45e-11  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.79e-18  2.23e-19  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012100  0.000564  0.943000  0.043900  0.000181 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.82e-14  2.24e-15  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-11  6.11e-13  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S1PR5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1943e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.772000  0.004360  0.222000  0.001250  0.000131 \n",
+      "[1] \"PP abf for shared variant: 0.0131%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD8T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.61e-05  3.08e-06  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.056800  0.002640  0.899000  0.041800  0.000194 \n",
+      "[1] \"PP abf for shared variant: 0.0194%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CHCHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.147000  0.006820  0.809000  0.037700  0.000247 \n",
+      "[1] \"PP abf for shared variant: 0.0247%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.19e-04  4.28e-05  9.54e-01  4.44e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FMNL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.228000  0.010600  0.727000  0.033800  0.000275 \n",
+      "[1] \"PP abf for shared variant: 0.0275%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.89e-24  8.79e-26  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.96e-07  2.78e-08  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HERPUD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.267e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.480000  0.022300  0.476000  0.022100  0.000329 \n",
+      "[1] \"PP abf for shared variant: 0.0329%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ANXA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-05  7.57e-07  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SOD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016800  0.000783  0.939000  0.043700  0.000186 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-17  1.04e-18  9.55e-01  4.45e-02  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-13  6.12e-15  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ATP1B3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.233000  0.010900  0.722000  0.033600  0.000233 \n",
+      "[1] \"PP abf for shared variant: 0.0233%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.426000  0.019800  0.529000  0.024600  0.000308 \n",
+      "[1] \"PP abf for shared variant: 0.0308%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-25  5.01e-27  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-11  1.20e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EML4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.84e-04  1.79e-05  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002530  0.000118  0.953000  0.044400  0.000199 \n",
+      "[1] \"PP abf for shared variant: 0.0199%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MCL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-06  1.03e-07  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NOG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.080100  0.003710  0.875000  0.040500  0.000215 \n",
+      "[1] \"PP abf for shared variant: 0.0215%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PRMT2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-03  6.41e-05  9.54e-01  4.44e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.59e-05  1.21e-06  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.398000  0.018500  0.557000  0.025900  0.000312 \n",
+      "[1] \"PP abf for shared variant: 0.0312%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-11  6.15e-13  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FAM129A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-06  4.69e-08  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.91e-15  8.91e-17  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.2936e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.550000  0.025600  0.405000  0.018900  0.000297 \n",
+      "[1] \"PP abf for shared variant: 0.0297%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-25  1.22e-26  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ALOX5AP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003690  0.000172  0.952000  0.044300  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TSHZ2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-03  5.61e-05  9.54e-01  4.44e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.14e-06  4.26e-07  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ARHGDIB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.39e-05  1.11e-06  9.55e-01  4.45e-02  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-10  1.04e-11  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-20  6.16e-22  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-28  7.13e-30  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.90e-28  4.61e-29  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YBX1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-05  1.04e-06  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.46e-25  3.94e-26  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___JUND__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.279e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.351000  0.016300  0.605000  0.028100  0.000279 \n",
+      "[1] \"PP abf for shared variant: 0.0279%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.19e-07  1.95e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-26  8.77e-28  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010700  0.000500  0.944000  0.044000  0.000563 \n",
+      "[1] \"PP abf for shared variant: 0.0563%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.65e-11  2.63e-12  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF18\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015900  0.000741  0.939000  0.043700  0.000184 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MYO1F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003760  0.000175  0.952000  0.044300  0.000181 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.63e-24  3.09e-25  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.11e-09  3.31e-10  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.430000  0.020000  0.525000  0.024500  0.000284 \n",
+      "[1] \"PP abf for shared variant: 0.0284%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.24e-25  4.30e-26  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000258  0.000012  0.955000  0.044500  0.000175 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VMP1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.220000  0.010300  0.735000  0.034200  0.000239 \n",
+      "[1] \"PP abf for shared variant: 0.0239%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.31e-06  3.40e-07  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.12e-25  1.92e-26  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.329000  0.015300  0.627000  0.029200  0.000252 \n",
+      "[1] \"PP abf for shared variant: 0.0252%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GALM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.249000  0.011600  0.707000  0.032900  0.000234 \n",
+      "[1] \"PP abf for shared variant: 0.0234%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.053400  0.002490  0.902000  0.042000  0.000195 \n",
+      "[1] \"PP abf for shared variant: 0.0195%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001780  0.000083  0.954000  0.044400  0.000184 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-14  1.08e-15  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.49e-16  1.62e-17  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ANXA5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019300  0.000897  0.936000  0.043600  0.000180 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___IDS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.31e-04  3.87e-05  9.55e-01  4.44e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ARID5B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.110000  0.005130  0.845000  0.039300  0.000233 \n",
+      "[1] \"PP abf for shared variant: 0.0233%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___IMPDH2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.87e-06  4.60e-07  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.38e-14  2.97e-15  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ST13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.00e-07  4.19e-08  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CXCR3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008900  0.000414  0.946000  0.044100  0.000212 \n",
+      "[1] \"PP abf for shared variant: 0.0212%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-06  7.70e-08  9.55e-01  4.45e-02  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.47e-15  1.15e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SPOCK2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.031100  0.001450  0.924000  0.043000  0.000193 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___C15orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.164000  0.007620  0.791000  0.036700  0.000224 \n",
+      "[1] \"PP abf for shared variant: 0.0224%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPF\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1448e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.613000  0.028500  0.343000  0.015900  0.000402 \n",
+      "[1] \"PP abf for shared variant: 0.0402%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-14  6.11e-16  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FAM134B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.210000  0.009780  0.745000  0.034700  0.000265 \n",
+      "[1] \"PP abf for shared variant: 0.0265%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ISG20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007460  0.000347  0.948000  0.044100  0.000183 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.50e-10  2.10e-11  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NUCB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.084400  0.003930  0.871000  0.040500  0.000208 \n",
+      "[1] \"PP abf for shared variant: 0.0208%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002460  0.000114  0.953000  0.044400  0.000184 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LINC00493__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.114000  0.005300  0.841000  0.039200  0.000219 \n",
+      "[1] \"PP abf for shared variant: 0.0219%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-25  4.98e-27  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006230  0.000290  0.949000  0.044200  0.000185 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-11  4.79e-13  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CDC42__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007920  0.000369  0.947000  0.044100  0.000180 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF1B\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.035700  0.001660  0.920000  0.042800  0.000219 \n",
+      "[1] \"PP abf for shared variant: 0.0219%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051100  0.002380  0.904000  0.042100  0.000187 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-18  7.35e-20  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ACTN4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002280  0.000106  0.953000  0.044400  0.000178 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___IKZF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.12900   0.00600   0.82600   0.03850   0.00021 \n",
+      "[1] \"PP abf for shared variant: 0.021%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.304000  0.014100  0.652000  0.030300  0.000253 \n",
+      "[1] \"PP abf for shared variant: 0.0253%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.56e-17  1.19e-18  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.72e-09  4.52e-10  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.411000  0.019100  0.544000  0.025300  0.000283 \n",
+      "[1] \"PP abf for shared variant: 0.0283%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NAMPT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8087e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.455000  0.021200  0.500000  0.023300  0.000335 \n",
+      "[1] \"PP abf for shared variant: 0.0335%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-05  9.53e-07  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CALM2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.10e-05  3.77e-06  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-15  6.98e-17  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-ND2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.183000  0.008520  0.772000  0.035900  0.000291 \n",
+      "[1] \"PP abf for shared variant: 0.0291%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.42e-15  3.92e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD52__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.07e-04  2.36e-05  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.73e-08  4.53e-09  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___H3F3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.24e-05  1.97e-06  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ADTRP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.18e-08  1.48e-09  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.160000  0.007430  0.796000  0.037000  0.000283 \n",
+      "[1] \"PP abf for shared variant: 0.0283%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.055700  0.002600  0.900000  0.041900  0.000211 \n",
+      "[1] \"PP abf for shared variant: 0.0211%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.42e-05  4.39e-06  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-05  1.23e-06  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___DYNLL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001700  0.000079  0.954000  0.044400  0.000182 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAMSN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.46e-05  1.61e-06  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LMNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-09  6.68e-11  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-ND5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-07  8.08e-09  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-21  4.77e-23  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RUNX3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.131000  0.006090  0.824000  0.038400  0.000252 \n",
+      "[1] \"PP abf for shared variant: 0.0252%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.84e-18  1.79e-19  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-05  5.00e-07  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ERGIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.423e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.448000  0.020900  0.507000  0.023600  0.000305 \n",
+      "[1] \"PP abf for shared variant: 0.0305%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.88e-08  1.34e-09  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TYMP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.044300  0.002060  0.911000  0.042400  0.000205 \n",
+      "[1] \"PP abf for shared variant: 0.0205%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.131000  0.006100  0.824000  0.038400  0.000227 \n",
+      "[1] \"PP abf for shared variant: 0.0227%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-22  8.11e-24  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-04  1.39e-05  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.37e-23  3.43e-24  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.48e-21  3.48e-22  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-ND4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.348000  0.016200  0.607000  0.028300  0.000318 \n",
+      "[1] \"PP abf for shared variant: 0.0318%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.41e-04  2.52e-05  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ATP5E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004020  0.000187  0.951000  0.044300  0.000183 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-10  4.93e-12  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.57e-13  3.06e-14  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.91e-22  3.68e-23  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.32e-09  1.54e-10  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ATP2B4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.045000  0.002090  0.910000  0.042400  0.000194 \n",
+      "[1] \"PP abf for shared variant: 0.0194%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ARPC1B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.525000  0.024400  0.430000  0.020000  0.000389 \n",
+      "[1] \"PP abf for shared variant: 0.0389%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PDCD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003970  0.000184  0.952000  0.044100  0.000180 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.96e-21  4.64e-22  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-26  8.28e-28  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.92e-22  1.36e-23  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.59e-13  1.67e-14  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.25e-08  1.51e-09  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.85e-05  3.19e-06  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.44e-04  1.14e-05  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LGALS3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.237000  0.011100  0.718000  0.033400  0.000237 \n",
+      "[1] \"PP abf for shared variant: 0.0237%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MIAT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.017800  0.000831  0.937000  0.043600  0.000186 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.29e-21  2.00e-22  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.02e-08  1.87e-09  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.020600  0.000961  0.935000  0.043500  0.000192 \n",
+      "[1] \"PP abf for shared variant: 0.0192%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.30e-17  1.07e-18  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.58e-23  2.13e-24  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RNF19A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-03  9.38e-05  9.53e-01  4.44e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-CO3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.11e-07  1.91e-08  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-24  6.78e-26  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-11  7.93e-13  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FAS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.200000  0.009300  0.756000  0.035200  0.000231 \n",
+      "[1] \"PP abf for shared variant: 0.0231%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.85e-10  4.12e-11  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.15e-10  3.79e-11  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GYPC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.64e-06  4.02e-07  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-22  7.58e-24  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-24  7.96e-26  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPM4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.028800  0.001340  0.927000  0.043100  0.000194 \n",
+      "[1] \"PP abf for shared variant: 0.0194%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.61e-12  4.01e-13  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.09e-05  3.77e-06  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.35e-23  2.02e-24  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ITGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-07  1.66e-08  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.60e-07  2.14e-08  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.027400  0.001270  0.928000  0.043200  0.000188 \n",
+      "[1] \"PP abf for shared variant: 0.0188%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-26  9.49e-28  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.111000  0.005150  0.845000  0.039300  0.000209 \n",
+      "[1] \"PP abf for shared variant: 0.0209%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.54e-18  2.58e-19  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001630  0.000076  0.954000  0.044400  0.000184 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-03  7.75e-05  9.54e-01  4.44e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.01e-23  1.87e-24  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.91e-17  4.61e-18  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.067900  0.003160  0.887000  0.041300  0.000204 \n",
+      "[1] \"PP abf for shared variant: 0.0204%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PPP1R15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.459000  0.021400  0.496000  0.023100  0.000284 \n",
+      "[1] \"PP abf for shared variant: 0.0284%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___IL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.12e-04  3.78e-05  9.55e-01  4.44e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.35e-26  2.49e-27  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.05e-16  1.88e-17  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM20\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.420000  0.019600  0.535000  0.024900  0.000356 \n",
+      "[1] \"PP abf for shared variant: 0.0356%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF3F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.32e-04  4.34e-05  9.54e-01  4.44e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004240  0.000197  0.951000  0.044300  0.000205 \n",
+      "[1] \"PP abf for shared variant: 0.0205%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___KLF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-05  7.44e-07  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GIMAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.42e-04  3.92e-05  9.54e-01  4.44e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFBR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.063000  0.002930  0.892000  0.041500  0.000206 \n",
+      "[1] \"PP abf for shared variant: 0.0206%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RNF213__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.318000  0.014800  0.637000  0.029700  0.000251 \n",
+      "[1] \"PP abf for shared variant: 0.0251%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___C19orf53__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.50400   0.02340   0.45100   0.02100   0.00046 \n",
+      "[1] \"PP abf for shared variant: 0.046%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-10  4.78e-12  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-15  1.70e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MIR4435-1HG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.43e-10  2.53e-11  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-15  8.12e-17  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MZT2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.99e-04  2.32e-05  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___AK5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.087700  0.004080  0.868000  0.040400  0.000269 \n",
+      "[1] \"PP abf for shared variant: 0.0269%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.267000  0.012500  0.688000  0.032000  0.000428 \n",
+      "[1] \"PP abf for shared variant: 0.0428%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-08  8.32e-10  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-20  1.03e-21  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-04  1.24e-05  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.00e-19  3.73e-20  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.027900  0.001300  0.927000  0.043200  0.000187 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CXCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.351000  0.016300  0.605000  0.028100  0.000277 \n",
+      "[1] \"PP abf for shared variant: 0.0277%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ARPC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.124000  0.005790  0.831000  0.038700  0.000209 \n",
+      "[1] \"PP abf for shared variant: 0.0209%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MRPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.486000  0.022600  0.469000  0.021800  0.000304 \n",
+      "[1] \"PP abf for shared variant: 0.0304%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-15  8.47e-17  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-06  9.74e-08  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EBPL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.40900   0.01900   0.54600   0.02540   0.00029 \n",
+      "[1] \"PP abf for shared variant: 0.029%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.213000  0.009920  0.742000  0.034600  0.000235 \n",
+      "[1] \"PP abf for shared variant: 0.0235%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UXT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-08  7.26e-10  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LSM5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.71e-04  1.73e-05  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___KMT2E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.6569e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.537000  0.025000  0.418000  0.019500  0.000389 \n",
+      "[1] \"PP abf for shared variant: 0.0389%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-07  7.90e-09  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TAGLN2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.492000  0.022900  0.463000  0.021600  0.000294 \n",
+      "[1] \"PP abf for shared variant: 0.0294%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CDCA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4164e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.558000  0.026000  0.397000  0.018500  0.000384 \n",
+      "[1] \"PP abf for shared variant: 0.0384%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-06  7.31e-08  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EPB41L4A-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-06  8.84e-08  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.64e-08  3.09e-09  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TATDN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.028300  0.001320  0.927000  0.043200  0.000182 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.066600  0.003100  0.889000  0.041400  0.000199 \n",
+      "[1] \"PP abf for shared variant: 0.0199%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-13  1.52e-14  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-23  9.20e-25  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-20  8.41e-22  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___BTG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.15e-04  1.93e-05  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___C8orf59__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.032500  0.001510  0.923000  0.043000  0.000197 \n",
+      "[1] \"PP abf for shared variant: 0.0197%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD58__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007440  0.000346  0.948000  0.044100  0.000195 \n",
+      "[1] \"PP abf for shared variant: 0.0195%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-17  7.21e-19  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.57e-06  2.59e-07  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___AKAP13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03510   0.00163   0.92000   0.04280   0.00020 \n",
+      "[1] \"PP abf for shared variant: 0.02%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.73e-07  1.74e-08  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___DDX5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-05  1.66e-06  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.029400  0.001370  0.926000  0.043100  0.000189 \n",
+      "[1] \"PP abf for shared variant: 0.0189%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ANXA2R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.285000  0.013300  0.670000  0.031200  0.000244 \n",
+      "[1] \"PP abf for shared variant: 0.0244%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___IL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.275000  0.012800  0.681000  0.031700  0.000281 \n",
+      "[1] \"PP abf for shared variant: 0.0281%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LINC00152__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-08  7.57e-10  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FOXP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.045600  0.002110  0.910000  0.042000  0.000204 \n",
+      "[1] \"PP abf for shared variant: 0.0204%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.45e-09  4.40e-10  9.55e-01  4.45e-02  1.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-21  6.77e-23  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.97e-05  4.64e-06  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-16  4.96e-18  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-14  6.80e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___DAP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.41e-04  3.45e-05  9.55e-01  4.44e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-11  6.86e-13  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SVIP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.404000  0.018800  0.551000  0.025700  0.000295 \n",
+      "[1] \"PP abf for shared variant: 0.0295%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-15  1.17e-16  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.59e-27  1.67e-28  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-18  8.94e-20  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-07  1.03e-08  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03240   0.00151   0.92300   0.04300   0.00019 \n",
+      "[1] \"PP abf for shared variant: 0.019%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ENTPD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.145000  0.006710  0.811000  0.037600  0.000215 \n",
+      "[1] \"PP abf for shared variant: 0.0215%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___DUSP4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-10  1.58e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-14  9.55e-16  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002280  0.000106  0.953000  0.044400  0.000180 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.186000  0.008680  0.769000  0.035800  0.000241 \n",
+      "[1] \"PP abf for shared variant: 0.0241%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-ND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-03  8.99e-05  9.53e-01  4.44e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.95e-17  4.17e-18  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ADAM19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.28800   0.01340   0.66600   0.03100   0.00173 \n",
+      "[1] \"PP abf for shared variant: 0.173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CLDND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.298000  0.013900  0.658000  0.030600  0.000275 \n",
+      "[1] \"PP abf for shared variant: 0.0275%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.27e-07  4.32e-08  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-09  4.87e-11  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.224000  0.010400  0.731000  0.034100  0.000266 \n",
+      "[1] \"PP abf for shared variant: 0.0266%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___APEX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.321000  0.014900  0.634000  0.029500  0.000268 \n",
+      "[1] \"PP abf for shared variant: 0.0268%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.56e-08  1.19e-09  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.69e-22  4.05e-23  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LETMD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-03  5.65e-05  9.54e-01  4.44e-02  1.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0192%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002530  0.000117  0.953000  0.044200  0.000181 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.29e-07  3.86e-08  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___AHNAK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012800  0.000596  0.942000  0.043900  0.000664 \n",
+      "[1] \"PP abf for shared variant: 0.0664%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SLC7A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.495000  0.023000  0.460000  0.021400  0.000466 \n",
+      "[1] \"PP abf for shared variant: 0.0466%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.76e-10  3.15e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-20  8.48e-22  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000666  0.000031  0.955000  0.044400  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.51e-28  3.50e-29  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.88e-13  2.74e-14  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.70e-25  1.72e-26  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-ATP6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.73e-05  1.74e-06  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-18  1.27e-19  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.08e-06  1.44e-07  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RILPL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.220000  0.010200  0.735000  0.034200  0.000245 \n",
+      "[1] \"PP abf for shared variant: 0.0245%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SSR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-05  1.66e-06  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003360  0.000156  0.952000  0.044300  0.000177 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.75e-09  2.21e-10  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.16e-14  3.80e-15  9.55e-01  4.45e-02  1.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MAF__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-05  5.17e-07  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-10  1.49e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___COMMD6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.84e-04  2.25e-05  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.50e-07  3.96e-08  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NSMCE1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004790  0.000223  0.951000  0.044300  0.000178 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.02e-05  2.34e-06  9.55e-01  4.45e-02  2.08e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0208%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PRDX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.081600  0.003800  0.874000  0.040700  0.000198 \n",
+      "[1] \"PP abf for shared variant: 0.0198%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.39e-20  2.97e-21  9.55e-01  4.45e-02  1.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0173%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FAM46C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.027900  0.001300  0.927000  0.043200  0.000184 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.39e-22  3.90e-23  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-21  7.18e-23  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.74e-27  3.14e-28  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-23  6.47e-25  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RORA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.384000  0.017900  0.571000  0.026600  0.000279 \n",
+      "[1] \"PP abf for shared variant: 0.0279%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-04  1.55e-05  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.40e-19  1.58e-20  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD44__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-05  7.60e-07  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4Y1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.445000  0.020700  0.510000  0.023700  0.000419 \n",
+      "[1] \"PP abf for shared variant: 0.0419%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-05  7.86e-07  9.55e-01  4.45e-02  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___COX7A2L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051600  0.002400  0.904000  0.042100  0.000196 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-21  1.10e-22  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HADHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.082800  0.003850  0.873000  0.040600  0.000201 \n",
+      "[1] \"PP abf for shared variant: 0.0201%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SATB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.181000  0.008420  0.774000  0.036100  0.000232 \n",
+      "[1] \"PP abf for shared variant: 0.0232%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UGP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.035800  0.001670  0.920000  0.042800  0.000186 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SBDS\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.258000  0.012000  0.698000  0.032500  0.000274 \n",
+      "[1] \"PP abf for shared variant: 0.0274%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SYNE2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.005550  0.000258  0.950000  0.044200  0.000187 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMA7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0710    0.0033    0.8840    0.0412    0.0002 \n",
+      "[1] \"PP abf for shared variant: 0.02%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-09  6.85e-11  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NR3C1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.050500  0.002350  0.905000  0.042100  0.000191 \n",
+      "[1] \"PP abf for shared variant: 0.0191%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.59e-27  3.07e-28  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CCT8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.020700  0.000961  0.935000  0.043500  0.000217 \n",
+      "[1] \"PP abf for shared variant: 0.0217%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFAIP3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.047100  0.002190  0.908000  0.042300  0.000191 \n",
+      "[1] \"PP abf for shared variant: 0.0191%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH2D2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.384000  0.017800  0.571000  0.026500  0.000295 \n",
+      "[1] \"PP abf for shared variant: 0.0295%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.84e-11  1.79e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CLNS1A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004890  0.000228  0.950000  0.044200  0.000182 \n",
+      "[1] \"PP abf for shared variant: 0.0182%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-08  1.06e-09  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ATP6V0E1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.045700  0.002130  0.910000  0.042300  0.000192 \n",
+      "[1] \"PP abf for shared variant: 0.0192%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-26  5.91e-28  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.416000  0.019400  0.539000  0.025100  0.000273 \n",
+      "[1] \"PP abf for shared variant: 0.0273%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-27  7.76e-29  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36L2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.052100  0.002430  0.903000  0.042100  0.000193 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF3D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-04  1.05e-05  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.086600  0.004030  0.869000  0.040400  0.000207 \n",
+      "[1] \"PP abf for shared variant: 0.0207%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.11e-19  1.92e-20  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___APRT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007260  0.000338  0.948000  0.044100  0.000234 \n",
+      "[1] \"PP abf for shared variant: 0.0234%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FYN__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003890  0.000181  0.951000  0.044300  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ANP32B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.081200  0.003780  0.874000  0.040700  0.000245 \n",
+      "[1] \"PP abf for shared variant: 0.0245%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.037300  0.001730  0.918000  0.042700  0.000187 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.70e-04  2.66e-05  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-27  1.04e-28  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CMPK1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.62e-05  2.15e-06  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAZ\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.142000  0.006610  0.813000  0.037900  0.000278 \n",
+      "[1] \"PP abf for shared variant: 0.0278%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GIMAP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-08  1.97e-09  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___COTL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.26e-06  2.45e-07  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF2S3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.93e-11  3.69e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HSP90AA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1807e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.474000  0.022100  0.481000  0.022400  0.000289 \n",
+      "[1] \"PP abf for shared variant: 0.0289%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MT-CYB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-04  8.41e-06  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HSPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.27800   0.01300   0.67700   0.03150   0.00024 \n",
+      "[1] \"PP abf for shared variant: 0.024%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.46e-10  2.08e-11  9.55e-01  4.45e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-09  1.37e-10  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.91e-18  4.62e-19  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXK\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.20e-05  1.96e-06  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-16  9.86e-18  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.59e-14  2.60e-15  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-06  8.50e-08  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.61e-05  1.68e-06  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.063800  0.002970  0.892000  0.041500  0.000196 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___LYRM4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.263000  0.012300  0.692000  0.032200  0.000258 \n",
+      "[1] \"PP abf for shared variant: 0.0258%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.13e-20  9.90e-22  9.55e-01  4.45e-02  1.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0186%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.44e-20  4.39e-21  9.55e-01  4.45e-02  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-10  1.41e-11  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EZR__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002670  0.000124  0.953000  0.044400  0.000183 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.29e-04  4.33e-05  9.54e-01  4.44e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ATP5O__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-08  5.64e-10  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.079100  0.003680  0.876000  0.040800  0.000206 \n",
+      "[1] \"PP abf for shared variant: 0.0206%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-19  4.68e-21  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUCLG2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-04  1.34e-05  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CD3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.050000  0.002330  0.905000  0.042100  0.000217 \n",
+      "[1] \"PP abf for shared variant: 0.0217%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-20  1.41e-21  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___NSA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-07  5.52e-09  9.55e-01  4.45e-02  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.055400  0.002580  0.900000  0.041900  0.000195 \n",
+      "[1] \"PP abf for shared variant: 0.0195%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___HIGD2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.143000  0.006640  0.813000  0.037800  0.000285 \n",
+      "[1] \"PP abf for shared variant: 0.0285%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-09  1.41e-10  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___IGBP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003380  0.000157  0.952000  0.044300  0.000189 \n",
+      "[1] \"PP abf for shared variant: 0.0189%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-19  1.36e-20  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___MYH9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.581000  0.027000  0.374000  0.017400  0.000473 \n",
+      "[1] \"PP abf for shared variant: 0.0473%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.54e-08  3.04e-09  9.55e-01  4.45e-02  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___ATP2B1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-03  5.41e-05  9.54e-01  4.44e-02  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-28  6.58e-30  9.55e-01  4.45e-02  1.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0187%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RBM39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006150  0.000286  0.949000  0.044200  0.000196 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___CCNG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002620  0.000122  0.953000  0.044400  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.77e-16  1.76e-17  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___COX4I1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.005870  0.000274  0.949000  0.044200  0.000179 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___PMAIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051800  0.002410  0.904000  0.042100  0.000194 \n",
+      "[1] \"PP abf for shared variant: 0.0194%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-11  1.76e-12  9.55e-01  4.45e-02  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-07  2.23e-08  9.55e-01  4.45e-02  1.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0181%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___FHIT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-10  1.72e-11  9.55e-01  4.45e-02  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"White blood cell count\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-05  9.74e-07  9.55e-01  4.45e-02  1.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___CAPG__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02350   0.00250   0.87900   0.09350   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___PTAFR__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06970   0.00741   0.83300   0.08850   0.00157 \n",
+      "[1] \"PP abf for shared variant: 0.157%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___MNDA__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.5916e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.46600   0.04960   0.43700   0.04640   0.00111 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___RNASE6__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16600   0.01770   0.73700   0.07830   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__TSPO\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2130    0.0227    0.6900    0.0733    0.0012 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__VMO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5549e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.46700   0.04920   0.43600   0.04590   0.00141 \n",
+      "[1] \"PP abf for shared variant: 0.141%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___S100A9__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21200   0.02250   0.69100   0.07340   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___QPCT__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.8504e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.40700   0.04320   0.49600   0.05260   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___BLVRB__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1205e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.34100   0.03620   0.56200   0.05970   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___LYZ__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01340   0.00142   0.88900   0.09450   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_TMEM176A___CLEC4A__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.486000  0.051700  0.417000  0.044300  0.000946 \n",
+      "[1] \"PP abf for shared variant: 0.0946%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002860  0.000913  0.754000  0.241000  0.001030 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL5__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08740   0.02800   0.66900   0.21400   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04190   0.01340   0.71500   0.22900   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL32__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00344   0.00110   0.75400   0.24100   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___EEF1A1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.10000   0.03200   0.65700   0.21000   0.00113 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL38__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01900   0.00608   0.73800   0.23600   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02540   0.00812   0.73200   0.23400   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04230   0.01350   0.71500   0.22800   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09530   0.03050   0.66200   0.21100   0.00114 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09650   0.03090   0.66000   0.21100   0.00113 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS15A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02960   0.00947   0.72700   0.23300   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS8__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0337    0.0108    0.7230    0.2310    0.0011 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09340   0.02990   0.66300   0.21200   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001490  0.000475  0.755000  0.242000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___NKG7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05050   0.01610   0.70600   0.22600   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___B2M__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01030   0.00330   0.74700   0.23900   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02100   0.00672   0.73600   0.23500   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___PFN1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00699   0.00223   0.75000   0.24000   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS28__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02410   0.00769   0.73300   0.23400   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02830   0.00906   0.72900   0.23300   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___GZMH__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00704   0.00225   0.75000   0.24000   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___LTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01330   0.00426   0.74400   0.23800   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL39__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02010   0.00642   0.73700   0.23600   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02730   0.00873   0.73000   0.23300   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01760   0.00562   0.73900   0.23600   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS23__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00817   0.00261   0.74900   0.23900   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS29__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01220   0.00389   0.74500   0.23800   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL22__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07830   0.02500   0.67900   0.21700   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL9__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03290   0.01050   0.72400   0.23100   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07550   0.02410   0.68100   0.21800   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06270   0.02000   0.69400   0.22200   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00027483\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.59100   0.18900   0.16500   0.05280   0.00167 \n",
+      "[1] \"PP abf for shared variant: 0.167%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___MAL__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03600   0.01140   0.72200   0.23000   0.00108 \n",
+      "[1] \"PP abf for shared variant: 0.108%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___PRF1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09590   0.03060   0.66100   0.21100   0.00113 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001430  0.000458  0.756000  0.242000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS6__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01650   0.00527   0.74000   0.23700   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001670  0.000534  0.755000  0.241000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL21__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01070   0.00341   0.74600   0.23900   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___SMDT1__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000395  0.000126  0.757000  0.242000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01720   0.00549   0.74000   0.23600   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001870  0.000596  0.755000  0.241000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL34__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00681   0.00218   0.75000   0.24000   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL10A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01060   0.00338   0.74600   0.23900   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL30__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01490   0.00478   0.74200   0.23700   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002380  0.000762  0.755000  0.241000  0.001080 \n",
+      "[1] \"PP abf for shared variant: 0.108%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07870   0.02520   0.67800   0.21700   0.00111 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00895   0.00286   0.74800   0.23900   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00594   0.00190   0.75100   0.24000   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.15600   0.04990   0.60100   0.19200   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13200   0.04210   0.62500   0.20000   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL31__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.12400   0.03980   0.63200   0.20200   0.00114 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0565    0.0181    0.7000    0.2240    0.0011 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___ACTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00641   0.00205   0.75100   0.24000   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04630   0.01480   0.71100   0.22700   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000714  0.000228  0.756000  0.242000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.15200   0.04870   0.60500   0.19300   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03410   0.01090   0.72300   0.23100   0.00108 \n",
+      "[1] \"PP abf for shared variant: 0.108%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03830   0.01220   0.71900   0.23000   0.00115 \n",
+      "[1] \"PP abf for shared variant: 0.115%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL10__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01460   0.00467   0.74200   0.23700   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05080   0.01620   0.70600   0.22600   0.00142 \n",
+      "[1] \"PP abf for shared variant: 0.142%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0032661\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.58300   0.18600   0.17400   0.05550   0.00179 \n",
+      "[1] \"PP abf for shared variant: 0.179%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___CD48__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05080   0.01620   0.70600   0.22600   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08550   0.02730   0.67100   0.21500   0.00113 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS27__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14800   0.04720   0.60900   0.19500   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-05  3.65e-01  2.74e-05  6.32e-01  3.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.324%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-05  2.68e-01  3.16e-05  7.30e-01  2.35e-03 \n",
+      "[1] \"PP abf for shared variant: 0.235%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.11e-06  1.87e-01  3.51e-05  8.11e-01  1.61e-03 \n",
+      "[1] \"PP abf for shared variant: 0.161%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___CLIC3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-05  3.58e-01  2.79e-05  6.38e-01  3.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.33%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__PTPRCAP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.34e-05  5.35e-01  2.01e-05  4.60e-01  4.63e-03 \n",
+      "[1] \"PP abf for shared variant: 0.463%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___CDKN2D__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.5969e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-05  4.72e-01  2.27e-05  5.24e-01  4.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.406%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0931e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-05  6.11e-01  1.66e-05  3.83e-01  5.58e-03 \n",
+      "[1] \"PP abf for shared variant: 0.558%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-05  4.57e-01  2.33e-05  5.39e-01  3.80e-03 \n",
+      "[1] \"PP abf for shared variant: 0.38%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.41e-06  1.48e-01  3.68e-05  8.50e-01  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MAP1A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-05  5.18e-01  2.09e-05  4.78e-01  4.51e-03 \n",
+      "[1] \"PP abf for shared variant: 0.451%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___FAM129C__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-05  3.34e-01  2.90e-05  6.63e-01  2.79e-03 \n",
+      "[1] \"PP abf for shared variant: 0.279%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MT-CO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1338e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-05  5.33e-01  2.00e-05  4.62e-01  4.49e-03 \n",
+      "[1] \"PP abf for shared variant: 0.449%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.26e-06  1.68e-01  3.59e-05  8.31e-01  1.45e-03 \n",
+      "[1] \"PP abf for shared variant: 0.145%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-06  2.59e-02  4.21e-05  9.74e-01  2.00e-04 \n",
+      "[1] \"PP abf for shared variant: 0.02%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-08  3.78e-04  4.32e-05  1.00e+00  4.48e-06 \n",
+      "[1] \"PP abf for shared variant: 0.000448%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-08  3.95e-04  4.32e-05  1.00e+00  4.47e-06 \n",
+      "[1] \"PP abf for shared variant: 0.000447%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-07  4.48e-03  4.30e-05  9.95e-01  3.47e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00347%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.49e-08  8.07e-04  4.32e-05  9.99e-01  7.45e-06 \n",
+      "[1] \"PP abf for shared variant: 0.000745%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-08  4.12e-04  4.32e-05  1.00e+00  3.33e-06 \n",
+      "[1] \"PP abf for shared variant: 0.000333%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMA__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.65e-08  1.77e-03  4.32e-05  9.98e-01  1.40e-05 \n",
+      "[1] \"PP abf for shared variant: 0.0014%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS23\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-06  4.59e-02  4.12e-05  9.54e-01  4.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0459%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.41e-06  1.25e-01  3.78e-05  8.74e-01  9.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0996%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.68e-06  1.78e-01  3.55e-05  8.21e-01  1.42e-03 \n",
+      "[1] \"PP abf for shared variant: 0.142%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___EEF1A1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.09e-07  1.18e-02  4.27e-05  9.88e-01  1.02e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.22e-07  1.90e-02  4.24e-05  9.81e-01  1.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0179%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.74e-06  8.65e-02  3.95e-05  9.13e-01  7.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0785%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMB__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.47e-06  5.72e-02  4.07e-05  9.42e-01  4.35e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0435%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-07  4.25e-03  4.30e-05  9.96e-01  4.11e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00411%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-07  3.08e-03  4.31e-05  9.97e-01  4.52e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00452%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.77e-06  1.80e-01  3.54e-05  8.19e-01  1.62e-03 \n",
+      "[1] \"PP abf for shared variant: 0.162%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RNASET2___HLA-DRB5__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.33e-06  1.50e-01  3.01e-05  8.49e-01  1.18e-03 \n",
+      "[1] \"PP abf for shared variant: 0.118%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___CCL5__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-06  6.71e-02  4.03e-05  9.32e-01  5.71e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0571%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-05  2.52e-01  3.23e-05  7.46e-01  2.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.211%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.56e-06  1.98e-01  3.46e-05  8.00e-01  1.66e-03 \n",
+      "[1] \"PP abf for shared variant: 0.166%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__NKG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-06  4.24e-02  4.14e-05  9.57e-01  4.25e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0425%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPL34\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.56e-06  1.05e-01  3.87e-05  8.94e-01  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.36e-06  1.47e-01  3.69e-05  8.52e-01  1.17e-03 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___CMC1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.36e-06  1.01e-01  3.89e-05  8.98e-01  1.04e-03 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-06  5.00e-02  4.11e-05  9.49e-01  7.12e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0712%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-06  4.97e-02  4.11e-05  9.50e-01  4.30e-04 \n",
+      "[1] \"PP abf for shared variant: 0.043%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.27e-06  1.91e-01  3.49e-05  8.07e-01  1.69e-03 \n",
+      "[1] \"PP abf for shared variant: 0.169%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.77e-06  1.80e-01  3.54e-05  8.19e-01  1.66e-03 \n",
+      "[1] \"PP abf for shared variant: 0.166%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-05  2.37e-01  3.29e-05  7.60e-01  2.41e-03 \n",
+      "[1] \"PP abf for shared variant: 0.241%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___CD52__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-06  5.00e-02  4.10e-05  9.48e-01  2.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.206%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-05  2.53e-01  3.22e-05  7.44e-01  2.80e-03 \n",
+      "[1] \"PP abf for shared variant: 0.28%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-06  4.54e-02  4.13e-05  9.54e-01  3.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0392%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.25e-06  7.51e-02  4.00e-05  9.24e-01  8.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___EEF1B2__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-06  9.08e-02  3.93e-05  9.08e-01  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.06e-06  1.17e-01  3.81e-05  8.81e-01  1.60e-03 \n",
+      "[1] \"PP abf for shared variant: 0.16%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___B2M__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-06  5.29e-02  4.09e-05  9.46e-01  9.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.097%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___GAPDH__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.44e-06  2.18e-01  3.37e-05  7.79e-01  2.79e-03 \n",
+      "[1] \"PP abf for shared variant: 0.279%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL32\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.38e-06  1.01e-01  3.88e-05  8.97e-01  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-05  3.47e-01  2.81e-05  6.50e-01  2.72e-03 \n",
+      "[1] \"PP abf for shared variant: 0.272%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-06  3.69e-02  4.16e-05  9.63e-01  3.72e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0372%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RNASET2___ITGB1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.93e-10  4.65e-03  5.76e-08  5.40e-01  4.56e-01 \n",
+      "[1] \"PP abf for shared variant: 45.6%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RNASET2___CRIP1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.58e-09  4.33e-02  4.74e-08  4.42e-01  5.14e-01 \n",
+      "[1] \"PP abf for shared variant: 51.4%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RNASET2___B2M__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-09  1.90e-02  4.89e-08  4.57e-01  5.24e-01 \n",
+      "[1] \"PP abf for shared variant: 52.4%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RNASET2___ALOX5AP__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.88e-09  5.55e-02  5.33e-08  4.99e-01  4.45e-01 \n",
+      "[1] \"PP abf for shared variant: 44.5%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0253e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.669000  0.031900  0.285000  0.013600  0.000569 \n",
+      "[1] \"PP abf for shared variant: 0.0569%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.539000  0.025700  0.414000  0.019800  0.000756 \n",
+      "[1] \"PP abf for shared variant: 0.0756%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"DC_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.596000  0.028500  0.357000  0.017000  0.000672 \n",
+      "[1] \"PP abf for shared variant: 0.0672%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2810    0.0136    0.6710    0.0325    0.0010 \n",
+      "[1] \"PP abf for shared variant: 0.1%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__UBE2J1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.0878e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.604000  0.029100  0.350000  0.016900  0.000732 \n",
+      "[1] \"PP abf for shared variant: 0.0732%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09800   0.00474   0.85500   0.04140   0.00115 \n",
+      "[1] \"PP abf for shared variant: 0.115%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.549000  0.026600  0.404000  0.019500  0.000693 \n",
+      "[1] \"PP abf for shared variant: 0.0693%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1042e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.535000  0.025900  0.418000  0.020200  0.000739 \n",
+      "[1] \"PP abf for shared variant: 0.0739%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.1644e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.728000  0.035200  0.226000  0.010900  0.000615 \n",
+      "[1] \"PP abf for shared variant: 0.0615%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.98e-04  3.38e-05  9.52e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.0557e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.671000  0.032500  0.282000  0.013700  0.000702 \n",
+      "[1] \"PP abf for shared variant: 0.0702%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009470  0.000458  0.943000  0.045600  0.001220 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.17100   0.00827   0.78200   0.03780   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004740  0.000229  0.948000  0.045900  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.550000  0.026600  0.403000  0.019500  0.000762 \n",
+      "[1] \"PP abf for shared variant: 0.0762%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.400000  0.019400  0.553000  0.026700  0.000913 \n",
+      "[1] \"PP abf for shared variant: 0.0913%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.7757e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.572000  0.027700  0.381000  0.018400  0.000702 \n",
+      "[1] \"PP abf for shared variant: 0.0702%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11200   0.00542   0.84100   0.04070   0.00115 \n",
+      "[1] \"PP abf for shared variant: 0.115%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.468000  0.022700  0.485000  0.023500  0.000807 \n",
+      "[1] \"PP abf for shared variant: 0.0807%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2778e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.560000  0.027100  0.393000  0.019000  0.000694 \n",
+      "[1] \"PP abf for shared variant: 0.0694%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0623e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.729000  0.035300  0.225000  0.010900  0.000662 \n",
+      "[1] \"PP abf for shared variant: 0.0662%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03940   0.00191   0.91300   0.04420   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07730   0.00374   0.87500   0.04240   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.25200   0.01220   0.70100   0.03390   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.63e-05  3.69e-06  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1408e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.744000  0.036000  0.209000  0.010100  0.000534 \n",
+      "[1] \"PP abf for shared variant: 0.0534%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.791e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.54700   0.02650   0.40600   0.01960   0.00068 \n",
+      "[1] \"PP abf for shared variant: 0.068%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1436e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.694000  0.033600  0.260000  0.012600  0.000561 \n",
+      "[1] \"PP abf for shared variant: 0.0561%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1123e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.777000  0.037600  0.176000  0.008510  0.000439 \n",
+      "[1] \"PP abf for shared variant: 0.0439%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.20300   0.00985   0.74900   0.03630   0.00108 \n",
+      "[1] \"PP abf for shared variant: 0.108%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.19800   0.00958   0.75500   0.03650   0.00101 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-05  8.23e-07  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.17600   0.00852   0.77700   0.03760   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02770   0.00134   0.92500   0.04470   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-05  1.51e-06  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.15700   0.00758   0.79600   0.03850   0.00114 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1639e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.74000   0.03580   0.21400   0.01030   0.00048 \n",
+      "[1] \"PP abf for shared variant: 0.048%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"B_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06530   0.00316   0.88700   0.04290   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-08  8.54e-10  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-12  9.08e-14  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09490   0.00459   0.85800   0.04150   0.00120 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-08  1.55e-09  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-06  8.85e-08  9.53e-01  4.61e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.32e-05  3.54e-06  9.53e-01  4.61e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-17  1.55e-18  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.99e-11  3.87e-12  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.95e-12  2.40e-13  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.81e-11  3.30e-12  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-05  5.62e-07  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-03  6.42e-05  9.51e-01  4.60e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-11  8.15e-13  9.53e-01  4.61e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.29e-15  1.59e-16  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03200   0.00155   0.92100   0.04460   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06990   0.00338   0.88300   0.04270   0.00120 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.20e-10  1.06e-11  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-06  5.97e-08  9.53e-01  4.61e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.011300  0.000547  0.941000  0.045600  0.001290 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.89e-07  2.37e-08  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-17  5.10e-19  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.46e-11  1.67e-12  9.53e-01  4.61e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.19e-11  2.03e-12  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__SPON2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013600  0.000661  0.939000  0.045400  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-05  1.01e-06  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-19  7.78e-21  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-13  1.65e-14  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.61e-12  3.20e-13  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.54e-13  2.20e-14  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-06  7.03e-08  9.53e-01  4.61e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.23000   0.01110   0.72300   0.03500   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-05  5.75e-07  9.53e-01  4.61e-02  1.37e-03 \n",
+      "[1] \"PP abf for shared variant: 0.137%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1991e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.485000  0.023500  0.468000  0.022700  0.000974 \n",
+      "[1] \"PP abf for shared variant: 0.0974%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-09  1.34e-10  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.64e-12  1.76e-13  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-09  9.44e-11  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-12  4.88e-14  9.53e-01  4.61e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-15  6.12e-17  9.53e-01  4.61e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-18  1.50e-19  9.53e-01  4.61e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.08e-04  1.98e-05  9.52e-01  4.61e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-10  2.80e-11  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-04  4.94e-06  9.52e-01  4.61e-02  1.33e-03 \n",
+      "[1] \"PP abf for shared variant: 0.133%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.37e-10  2.12e-11  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.02e-13  9.79e-15  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.97e-09  4.83e-10  9.53e-01  4.61e-02  1.36e-03 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-15  9.67e-17  9.53e-01  4.61e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-13  5.26e-15  9.53e-01  4.61e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.27e-04  4.49e-05  9.52e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-13  1.86e-14  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.412000  0.020000  0.541000  0.026200  0.000897 \n",
+      "[1] \"PP abf for shared variant: 0.0897%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-07  1.61e-08  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02280   0.00110   0.93000   0.04500   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.18e-08  1.54e-09  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-04  1.19e-05  9.52e-01  4.61e-02  1.36e-03 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.60e-18  3.68e-19  9.53e-01  4.61e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-07  1.03e-08  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-09  9.93e-11  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-06  9.19e-08  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.40e-10  1.65e-11  9.53e-01  4.61e-02  1.40e-03 \n",
+      "[1] \"PP abf for shared variant: 0.14%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-15  8.28e-17  9.53e-01  4.61e-02  1.33e-03 \n",
+      "[1] \"PP abf for shared variant: 0.133%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-06  1.06e-07  9.53e-01  4.61e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-05  1.08e-06  9.53e-01  4.61e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.330000  0.016000  0.623000  0.030100  0.000967 \n",
+      "[1] \"PP abf for shared variant: 0.0967%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.08e-16  2.46e-17  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010100  0.000489  0.943000  0.045600  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-08  7.27e-10  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.30e-06  1.60e-07  9.53e-01  4.61e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.41e-05  2.62e-06  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.07e-05  1.00e-06  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.38e-07  3.09e-08  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4099e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.444000  0.021500  0.509000  0.024600  0.000924 \n",
+      "[1] \"PP abf for shared variant: 0.0924%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5173e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.788000  0.038100  0.166000  0.008020  0.000456 \n",
+      "[1] \"PP abf for shared variant: 0.0456%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-03  5.64e-05  9.51e-01  4.60e-02  1.38e-03 \n",
+      "[1] \"PP abf for shared variant: 0.138%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.44e-07  4.09e-08  9.53e-01  4.61e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.97e-13  2.40e-14  9.53e-01  4.61e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___KLRC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09970   0.00483   0.85300   0.04130   0.00131 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.4275e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.695000  0.033700  0.258000  0.012500  0.000672 \n",
+      "[1] \"PP abf for shared variant: 0.0672%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-06  7.25e-08  9.53e-01  4.61e-02  1.35e-03 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-08  5.50e-10  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___MALAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019300  0.000933  0.933000  0.045200  0.001230 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003080  0.000149  0.950000  0.046000  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.94e-11  2.88e-12  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8351e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.655000  0.031700  0.298000  0.014400  0.000767 \n",
+      "[1] \"PP abf for shared variant: 0.0767%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.62e-07  3.21e-08  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.07e-05  3.43e-06  9.53e-01  4.61e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___FGFBP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.32100   0.01550   0.63200   0.03060   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009310  0.000451  0.943000  0.045700  0.001340 \n",
+      "[1] \"PP abf for shared variant: 0.134%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.77e-04  2.79e-05  9.52e-01  4.61e-02  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-03  4.84e-05  9.52e-01  4.61e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04770   0.00231   0.90500   0.04380   0.00138 \n",
+      "[1] \"PP abf for shared variant: 0.138%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.574e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.743000  0.034000  0.212000  0.009720  0.000551 \n",
+      "[1] \"PP abf for shared variant: 0.0551%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.08e-06  3.91e-07  9.53e-01  4.61e-02  1.34e-03 \n",
+      "[1] \"PP abf for shared variant: 0.134%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.87e-15  3.81e-16  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2336e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.644000  0.031200  0.310000  0.015000  0.000595 \n",
+      "[1] \"PP abf for shared variant: 0.0595%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.50e-10  3.63e-11  9.53e-01  4.61e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"NK_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.74e-11  2.78e-12  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___NRGN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7437e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53400   0.02590   0.41900   0.02030   0.00085 \n",
+      "[1] \"PP abf for shared variant: 0.085%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.288000  0.014000  0.665000  0.032300  0.000973 \n",
+      "[1] \"PP abf for shared variant: 0.0973%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001630  0.000079  0.951000  0.046200  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-06  8.72e-08  9.52e-01  4.62e-02  1.36e-03 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.32e-16  2.10e-17  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-06  5.28e-08  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-09  8.26e-11  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015500  0.000752  0.937000  0.045500  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.99e-09  4.36e-10  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-14  5.40e-16  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VCAN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.464000  0.022500  0.489000  0.023700  0.000818 \n",
+      "[1] \"PP abf for shared variant: 0.0818%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UQCRH\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-05  7.50e-07  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC7A7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.014100  0.000682  0.938000  0.045500  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EPB41L3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.25500   0.01230   0.69800   0.03380   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00761   0.00037   0.94500   0.04590   0.00130 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.31e-14  2.09e-15  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01530   0.00074   0.93700   0.04550   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___QARS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.28300   0.01370   0.67000   0.03250   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.32e-06  2.10e-07  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.14e-15  2.50e-16  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.80e-06  3.78e-07  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.93e-15  1.42e-16  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-08  6.87e-10  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-09  9.02e-11  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.76e-12  2.80e-13  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02110   0.00103   0.93100   0.04520   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.36e-10  4.55e-11  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___CSTA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.66e-04  4.21e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-07  1.20e-08  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14000   0.00681   0.81200   0.03940   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-12  7.68e-14  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002540  0.000123  0.950000  0.046100  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.54e-15  3.18e-16  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.34900   0.01690   0.60400   0.02930   0.00091 \n",
+      "[1] \"PP abf for shared variant: 0.091%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TNFAIP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.33900   0.01640   0.61300   0.02980   0.00182 \n",
+      "[1] \"PP abf for shared variant: 0.182%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01630   0.00079   0.93600   0.04540   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.66e-12  3.72e-13  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.77e-20  2.80e-21  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-11  9.57e-13  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-05  6.90e-07  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.86e-10  1.87e-11  9.52e-01  4.62e-02  1.33e-03 \n",
+      "[1] \"PP abf for shared variant: 0.133%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.81e-09  2.34e-10  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-04  5.17e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-03  9.04e-05  9.51e-01  4.61e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.13e-04  2.98e-05  9.52e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-13  1.02e-14  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-15  5.07e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.93e-09  4.33e-10  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-04  9.90e-06  9.52e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.27e-11  3.05e-12  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1173e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.368000  0.017900  0.585000  0.028400  0.000858 \n",
+      "[1] \"PP abf for shared variant: 0.0858%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.25e-07  3.03e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.52e-10  1.22e-11  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-07  7.27e-09  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003230  0.000157  0.949000  0.046100  0.001310 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.70e-09  3.74e-10  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.96e-06  2.41e-07  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02460   0.00119   0.92800   0.04500   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-11  6.29e-13  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.70e-11  3.74e-12  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.34e-05  3.08e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000412  0.000020  0.952000  0.046200  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___CST3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7382e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.573000  0.027800  0.380000  0.018400  0.000846 \n",
+      "[1] \"PP abf for shared variant: 0.0846%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03630   0.00176   0.91600   0.04450   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26900   0.01310   0.68400   0.03320   0.00111 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-15  1.41e-16  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.318000  0.015400  0.635000  0.030800  0.000998 \n",
+      "[1] \"PP abf for shared variant: 0.0998%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-03  5.56e-05  9.51e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___AP1S2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.264000  0.012800  0.689000  0.033400  0.000986 \n",
+      "[1] \"PP abf for shared variant: 0.0986%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-10  8.59e-12  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.93e-09  3.37e-10  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SEC11A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015400  0.000747  0.937000  0.045500  0.001230 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-04  1.79e-05  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.32e-11  4.04e-12  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-08  7.62e-10  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.71e-11  3.74e-12  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-07  1.33e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.64e-07  2.25e-08  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-07  7.97e-09  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___ATP5G2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009690  0.000471  0.943000  0.045800  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.80e-06  4.76e-07  9.52e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.74e-09  3.27e-10  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016600  0.000805  0.936000  0.045400  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.32e-13  3.07e-14  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.42e-16  4.09e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-17  6.99e-19  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-06  6.81e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.41e-07  1.17e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.42e-16  1.18e-17  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-04  9.99e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EVI2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.368000  0.017900  0.585000  0.028400  0.000899 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05040   0.00245   0.90200   0.04380   0.00127 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02570   0.00125   0.92700   0.04500   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.38e-04  4.55e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-12  5.40e-14  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___LGALS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21900   0.01060   0.73400   0.03560   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.324000  0.015700  0.629000  0.030500  0.000975 \n",
+      "[1] \"PP abf for shared variant: 0.0975%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-11  2.69e-12  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-14  7.18e-16  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.54e-11  3.66e-12  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-06  1.31e-07  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___GPX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04140   0.00201   0.91100   0.04420   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___LTA4H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.535000  0.026000  0.418000  0.020300  0.000785 \n",
+      "[1] \"PP abf for shared variant: 0.0785%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RNASE6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26100   0.01270   0.69200   0.03360   0.00111 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.10200   0.00497   0.85000   0.04130   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___BTF3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010900  0.000528  0.942000  0.045700  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___DRAM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1829e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.547000  0.026600  0.406000  0.019700  0.000801 \n",
+      "[1] \"PP abf for shared variant: 0.0801%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___IL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-03  5.29e-05  9.52e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008690  0.000422  0.944000  0.045800  0.001410 \n",
+      "[1] \"PP abf for shared variant: 0.141%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.15e-07  3.47e-08  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.13e-11  4.43e-12  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-13  7.01e-15  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.313000  0.015200  0.640000  0.031100  0.000949 \n",
+      "[1] \"PP abf for shared variant: 0.0949%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-14  6.97e-16  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.61e-12  4.66e-13  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___IPO7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09270   0.00450   0.86000   0.04170   0.00141 \n",
+      "[1] \"PP abf for shared variant: 0.141%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-07  7.59e-09  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.89e-12  3.34e-13  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.99e-13  1.94e-14  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2966e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.50300   0.02440   0.45000   0.02180   0.00111 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.55e-08  1.72e-09  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-05  5.72e-07  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___CRTAP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015200  0.000739  0.937000  0.045500  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.394000  0.019100  0.559000  0.027100  0.000896 \n",
+      "[1] \"PP abf for shared variant: 0.0896%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.80e-09  1.85e-10  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002340  0.000114  0.950000  0.046100  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-09  7.59e-11  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.34700   0.01680   0.60600   0.02940   0.00094 \n",
+      "[1] \"PP abf for shared variant: 0.094%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04990   0.00242   0.90300   0.04380   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.88e-10  2.86e-11  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.07e-11  1.00e-12  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TKT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-04  1.09e-05  9.52e-01  4.62e-02  1.37e-03 \n",
+      "[1] \"PP abf for shared variant: 0.137%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-15  1.64e-16  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.12700   0.00613   0.82600   0.03990   0.00114 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ACTN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.65e-04  4.19e-05  9.52e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.30e-15  4.03e-16  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAND1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4561e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.408000  0.019800  0.545000  0.026400  0.000829 \n",
+      "[1] \"PP abf for shared variant: 0.0829%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.08e-15  1.98e-16  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PRF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013500  0.000658  0.939000  0.045600  0.001290 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-05  1.47e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EFHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04810   0.00234   0.90400   0.04390   0.00132 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-10  7.31e-12  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.33e-16  3.56e-17  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.26e-14  3.04e-15  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.98e-13  2.42e-14  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___APBA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09240   0.00420   0.86300   0.03920   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.30e-04  2.09e-05  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FAIM3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07060   0.00343   0.88200   0.04280   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.52900   0.02570   0.42400   0.02060   0.00079 \n",
+      "[1] \"PP abf for shared variant: 0.079%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.351000  0.017100  0.601000  0.029200  0.000983 \n",
+      "[1] \"PP abf for shared variant: 0.0983%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016100  0.000781  0.936000  0.045400  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-14  1.32e-15  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___C11orf1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.8471e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.641000  0.031100  0.312000  0.015100  0.000659 \n",
+      "[1] \"PP abf for shared variant: 0.0659%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.79e-04  2.32e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-17  1.27e-18  9.53e-01  4.61e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-17  2.08e-18  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GZMM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.399000  0.019400  0.554000  0.026900  0.000929 \n",
+      "[1] \"PP abf for shared variant: 0.0929%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-06  1.61e-07  9.52e-01  4.62e-02  1.38e-03 \n",
+      "[1] \"PP abf for shared variant: 0.138%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.60e-04  3.69e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.29100   0.01410   0.66100   0.03210   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CTSW__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.24900   0.01210   0.70400   0.03410   0.00144 \n",
+      "[1] \"PP abf for shared variant: 0.144%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.68e-05  1.30e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-18  6.33e-20  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FLT3LG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.18e-04  1.54e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NKG7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-05  1.69e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05130   0.00249   0.90100   0.04370   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.3964e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.611000  0.029700  0.342000  0.016600  0.000775 \n",
+      "[1] \"PP abf for shared variant: 0.0775%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.336000  0.016300  0.617000  0.029900  0.000961 \n",
+      "[1] \"PP abf for shared variant: 0.0961%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-04  8.29e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-06  1.73e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-09  1.11e-10  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-12  1.04e-13  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.76e-14  3.28e-15  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ATP1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 9.0977e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.724000  0.035100  0.230000  0.011100  0.000549 \n",
+      "[1] \"PP abf for shared variant: 0.0549%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21900   0.01070   0.73300   0.03560   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SYNE1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16400   0.00798   0.78800   0.03830   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.137e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.628000  0.030500  0.325000  0.015800  0.000646 \n",
+      "[1] \"PP abf for shared variant: 0.0646%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-06  8.69e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-10  9.62e-12  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PDE3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-04  6.43e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-19  5.32e-21  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.61e-05  2.72e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZEB2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012000  0.000582  0.941000  0.045600  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___LTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.98e-08  1.45e-09  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PTPN7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.367000  0.017800  0.586000  0.028400  0.000953 \n",
+      "[1] \"PP abf for shared variant: 0.0953%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-12  5.75e-14  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-10  8.02e-12  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___IER2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1556e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.680000  0.033000  0.273000  0.013200  0.000672 \n",
+      "[1] \"PP abf for shared variant: 0.0672%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.49e-05  3.64e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-09  6.03e-11  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.95e-20  1.43e-21  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.62e-15  2.73e-16  9.52e-01  4.62e-02  1.38e-03 \n",
+      "[1] \"PP abf for shared variant: 0.138%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CMC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-03  4.92e-05  9.51e-01  4.62e-02  1.36e-03 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-04  5.97e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.90e-13  1.89e-14  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GZMB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02990   0.00145   0.92300   0.04480   0.00132 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___AKNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.670000  0.032500  0.283000  0.013800  0.000558 \n",
+      "[1] \"PP abf for shared variant: 0.0558%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9277e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.565000  0.027500  0.388000  0.018800  0.000727 \n",
+      "[1] \"PP abf for shared variant: 0.0727%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.14e-20  2.49e-21  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-08  1.36e-09  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-04  1.82e-05  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03810   0.00185   0.91400   0.04440   0.00129 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04060   0.00197   0.91200   0.04430   0.00130 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-16  9.61e-18  9.53e-01  4.61e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-11  1.25e-12  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-14  4.92e-16  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___METRNL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4496e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.72300   0.03320   0.23300   0.01070   0.00068 \n",
+      "[1] \"PP abf for shared variant: 0.068%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02960   0.00144   0.92300   0.04480   0.00135 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CAMK4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.90e-07  1.89e-08  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___BHLHE40__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015200  0.000737  0.937000  0.045500  0.001360 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___IFITM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2604e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.573000  0.027800  0.380000  0.018400  0.000747 \n",
+      "[1] \"PP abf for shared variant: 0.0747%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08600   0.00418   0.86600   0.04210   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CD8B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.37e-04  1.15e-05  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-18  8.03e-20  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NGFRAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06950   0.00318   0.88600   0.04050   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.76e-14  3.77e-15  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TUBA4A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06190   0.00301   0.89100   0.04320   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-05  1.73e-06  9.52e-01  4.62e-02  1.35e-03 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-19  1.06e-20  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RCAN3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.11e-06  3.94e-07  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.73e-13  2.30e-14  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.74e-04  1.82e-05  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.32e-11  3.55e-12  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.62e-08  4.19e-09  9.52e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___DNAJB6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09830   0.00477   0.85400   0.04150   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RAP1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.077e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.747000  0.036300  0.206000  0.010000  0.000552 \n",
+      "[1] \"PP abf for shared variant: 0.0552%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-05  9.85e-07  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002760  0.000134  0.950000  0.046100  0.001290 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008280  0.000402  0.944000  0.045800  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CCDC104__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.64500   0.02950   0.31100   0.01420   0.00059 \n",
+      "[1] \"PP abf for shared variant: 0.059%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.69e-09  4.22e-10  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-08  6.05e-10  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.90e-06  2.86e-07  9.52e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.57e-18  2.22e-19  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05340   0.00259   0.89900   0.04360   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TESPA1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.021700  0.000983  0.934000  0.042300  0.001300 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.011000  0.000533  0.942000  0.045700  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PSMA7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.338000  0.016400  0.615000  0.029800  0.000961 \n",
+      "[1] \"PP abf for shared variant: 0.0961%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PLEK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.30700   0.01490   0.64600   0.03140   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000349  0.000017  0.952000  0.046200  0.001290 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.45e-16  4.10e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.4281e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.537000  0.026100  0.416000  0.020200  0.000756 \n",
+      "[1] \"PP abf for shared variant: 0.0756%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___KLRD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006940  0.000337  0.946000  0.045900  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MYC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007040  0.000342  0.945000  0.045900  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.310000  0.015000  0.643000  0.031200  0.000971 \n",
+      "[1] \"PP abf for shared variant: 0.0971%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___KLF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.391e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53500   0.02600   0.41700   0.02030   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14900   0.00723   0.80400   0.03900   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HNRNPA2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.584000  0.028300  0.369000  0.017900  0.000752 \n",
+      "[1] \"PP abf for shared variant: 0.0752%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ARAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3907e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.77100   0.03740   0.18300   0.00886   0.00050 \n",
+      "[1] \"PP abf for shared variant: 0.05%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-16  1.29e-17  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.73e-05  8.40e-07  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.78e-05  2.81e-06  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-12  4.97e-14  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.08e-06  3.92e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-05  5.45e-07  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.19700   0.00958   0.75500   0.03670   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-09  1.08e-10  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-18  1.47e-19  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___LYPD3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.369000  0.017800  0.584000  0.028200  0.000902 \n",
+      "[1] \"PP abf for shared variant: 0.0902%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-04  7.09e-06  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21500   0.01040   0.73700   0.03580   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0885    0.0043    0.8640    0.0419    0.0012 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM243\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002080  0.000101  0.950000  0.046100  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.15e-15  2.01e-16  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ID2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.18400   0.00894   0.76800   0.03730   0.00128 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-15  2.08e-16  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.32e-10  3.07e-11  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004360  0.000212  0.948000  0.046000  0.001340 \n",
+      "[1] \"PP abf for shared variant: 0.134%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.24e-25  2.05e-26  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.373000  0.018100  0.580000  0.028100  0.000951 \n",
+      "[1] \"PP abf for shared variant: 0.0951%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NFKBIA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 7.944e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.662000  0.032100  0.292000  0.014100  0.000685 \n",
+      "[1] \"PP abf for shared variant: 0.0685%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03190   0.00155   0.92100   0.04470   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.65e-17  4.20e-18  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-15  9.22e-17  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-09  5.87e-11  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GZMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-07  1.67e-08  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.330000  0.016000  0.623000  0.030200  0.000956 \n",
+      "[1] \"PP abf for shared variant: 0.0956%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-10  8.55e-12  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPI1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.43900   0.02130   0.51400   0.02490   0.00133 \n",
+      "[1] \"PP abf for shared variant: 0.133%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.09e-19  4.40e-20  9.53e-01  4.61e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14400   0.00699   0.80900   0.03930   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.79e-10  2.33e-11  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-15  1.04e-16  9.53e-01  4.61e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SC5D\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.430000  0.020900  0.523000  0.025400  0.000862 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-10  8.81e-12  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-17  1.51e-18  9.53e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RIC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-03  8.44e-05  9.51e-01  4.61e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.45e-11  2.16e-12  9.52e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-05  1.33e-06  9.53e-01  4.61e-02  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.9814e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.663000  0.032200  0.291000  0.014100  0.000669 \n",
+      "[1] \"PP abf for shared variant: 0.0669%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRP14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-04  5.87e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0298e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.608000  0.029500  0.345000  0.016700  0.000696 \n",
+      "[1] \"PP abf for shared variant: 0.0696%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.88e-04  3.34e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-06  4.89e-08  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-16  1.45e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-18  5.18e-20  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-19  1.07e-20  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MYL12B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.54400   0.02640   0.40900   0.01980   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02880   0.00140   0.92400   0.04480   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MIR142__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1648e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.742000  0.036000  0.212000  0.010300  0.000469 \n",
+      "[1] \"PP abf for shared variant: 0.0469%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007630  0.000371  0.945000  0.045900  0.001320 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.30e-17  1.12e-18  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.299000  0.014500  0.654000  0.031700  0.000964 \n",
+      "[1] \"PP abf for shared variant: 0.0964%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-05  5.11e-07  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PTP4A2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.412000  0.020000  0.541000  0.026200  0.000977 \n",
+      "[1] \"PP abf for shared variant: 0.0977%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.9666e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.697000  0.033800  0.256000  0.012400  0.000595 \n",
+      "[1] \"PP abf for shared variant: 0.0595%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-04  4.92e-06  9.52e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.59e-08  2.23e-09  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.97e-25  1.93e-26  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CCDC57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-06  1.08e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ITGB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.448000  0.021800  0.505000  0.024500  0.000824 \n",
+      "[1] \"PP abf for shared variant: 0.0824%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08800   0.00427   0.86500   0.04200   0.00120 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4185e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.679000  0.032900  0.274000  0.013300  0.000647 \n",
+      "[1] \"PP abf for shared variant: 0.0647%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ARF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14400   0.00697   0.80900   0.03930   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CD81__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.24900   0.01210   0.70400   0.03410   0.00113 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004210  0.000204  0.948000  0.046000  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004210  0.000205  0.948000  0.046000  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-03  4.88e-05  9.51e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.96e-05  1.44e-06  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FOXP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.01e-04  3.89e-05  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CCL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.02e-05  4.38e-06  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012700  0.000619  0.940000  0.045600  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___KLRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9856e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.608000  0.029500  0.345000  0.016700  0.000769 \n",
+      "[1] \"PP abf for shared variant: 0.0769%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5042e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.722000  0.035100  0.231000  0.011200  0.000528 \n",
+      "[1] \"PP abf for shared variant: 0.0528%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26600   0.01290   0.68700   0.03330   0.00101 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-15  9.62e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___C1orf21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1023e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.588000  0.028600  0.365000  0.017700  0.000721 \n",
+      "[1] \"PP abf for shared variant: 0.0721%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-10  1.27e-11  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CALM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11500   0.00556   0.83800   0.04070   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HOPX__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3470    0.0169    0.6060    0.0294    0.0011 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03570   0.00173   0.91700   0.04450   0.00127 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.36e-15  2.12e-16  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.14e-04  4.44e-05  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-16  1.67e-17  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008840  0.000429  0.944000  0.045800  0.001320 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.55e-11  2.21e-12  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.507e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.712000  0.034500  0.241000  0.011700  0.000696 \n",
+      "[1] \"PP abf for shared variant: 0.0696%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-17  7.05e-19  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-13  5.23e-15  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PTMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002940  0.000143  0.950000  0.046100  0.001290 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-10  8.60e-12  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GNG2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.23700   0.01150   0.71600   0.03470   0.00108 \n",
+      "[1] \"PP abf for shared variant: 0.108%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02330   0.00113   0.92900   0.04510   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.12e-07  3.46e-08  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.81e-10  1.85e-11  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.52e-04  4.62e-05  9.52e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CD27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.689e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.587000  0.028500  0.366000  0.017800  0.000734 \n",
+      "[1] \"PP abf for shared variant: 0.0734%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002170  0.000105  0.950000  0.046100  0.001290 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-09  6.71e-11  9.55e-01  4.33e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.98e-16  3.38e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___REL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.691e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.580000  0.028200  0.373000  0.018100  0.000672 \n",
+      "[1] \"PP abf for shared variant: 0.0672%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-11  9.69e-13  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.410000  0.019900  0.543000  0.026300  0.000968 \n",
+      "[1] \"PP abf for shared variant: 0.0968%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-08  7.56e-10  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.15e-13  1.53e-14  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.89e-07  2.37e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SEPT7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13300   0.00643   0.82000   0.03980   0.00130 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14400   0.00699   0.80800   0.03920   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.46400   0.02250   0.48900   0.02370   0.00111 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.86e-09  2.36e-10  9.52e-01  4.62e-02  1.35e-03 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___FCGR3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08180   0.00397   0.87000   0.04220   0.00230 \n",
+      "[1] \"PP abf for shared variant: 0.23%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PSMB9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.645e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.66600   0.03230   0.28700   0.01390   0.00139 \n",
+      "[1] \"PP abf for shared variant: 0.139%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-09  1.13e-10  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.63e-08  2.25e-09  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.37200   0.01810   0.58100   0.02820   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-03  4.88e-05  9.51e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.50e-16  4.13e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.43e-13  1.66e-14  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08640   0.00420   0.86600   0.04200   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07480   0.00363   0.87800   0.04260   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.46e-14  1.68e-15  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5519e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.521000  0.025300  0.432000  0.020900  0.000785 \n",
+      "[1] \"PP abf for shared variant: 0.0785%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.59e-11  3.20e-12  9.52e-01  4.62e-02  1.39e-03 \n",
+      "[1] \"PP abf for shared variant: 0.139%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___DOK2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.34800   0.01690   0.60400   0.02930   0.00111 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.13e-06  2.00e-07  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CALR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9449e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.516000  0.025000  0.437000  0.021200  0.000793 \n",
+      "[1] \"PP abf for shared variant: 0.0793%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TTC38\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1223e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.52600   0.02550   0.42700   0.02070   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02610   0.00127   0.92600   0.04490   0.00129 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.005270  0.000256  0.947000  0.046000  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-09  1.83e-10  9.52e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-09  5.92e-11  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.03e-06  2.44e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.67e-16  2.75e-17  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PASK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.15e-07  2.33e-08  9.55e-01  4.33e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-10  6.86e-12  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.07e-17  3.42e-18  9.53e-01  4.61e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___OXNAD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1359e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.58700   0.02850   0.36500   0.01770   0.00154 \n",
+      "[1] \"PP abf for shared variant: 0.154%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04340   0.00210   0.90900   0.04410   0.00127 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.22e-16  1.56e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-03  6.63e-05  9.51e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TYROBP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007530  0.000365  0.945000  0.045900  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YBX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1331e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.752000  0.036500  0.201000  0.009770  0.000547 \n",
+      "[1] \"PP abf for shared variant: 0.0547%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.15e-07  3.96e-08  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.96e-04  1.44e-05  9.52e-01  4.62e-02  1.37e-03 \n",
+      "[1] \"PP abf for shared variant: 0.137%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___IL7R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-03  8.34e-05  9.51e-01  4.61e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RHOH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02790   0.00135   0.92500   0.04490   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.80e-16  1.85e-17  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.88e-18  2.37e-19  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016700  0.000812  0.936000  0.045400  0.001320 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___DBI__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00803   0.00039   0.94400   0.04580   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-11  8.33e-13  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-09  9.68e-11  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-07  2.05e-08  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___POMP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.451000  0.021900  0.502000  0.024300  0.000796 \n",
+      "[1] \"PP abf for shared variant: 0.0796%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.80e-15  3.77e-16  9.53e-01  4.60e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RAB8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0817e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.651000  0.031600  0.302000  0.014700  0.000634 \n",
+      "[1] \"PP abf for shared variant: 0.0634%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___GZMH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13600   0.00661   0.81600   0.03960   0.00136 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.07e-05  4.40e-06  9.52e-01  4.62e-02  1.37e-03 \n",
+      "[1] \"PP abf for shared variant: 0.137%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.80e-10  1.36e-11  9.52e-01  4.62e-02  1.37e-03 \n",
+      "[1] \"PP abf for shared variant: 0.137%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-19  7.32e-21  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-17  1.25e-18  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___ABLIM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004280  0.000196  0.951000  0.043600  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF4A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8946e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.57900   0.02810   0.37300   0.01810   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.25e-04  1.58e-05  9.52e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RP11-291B21.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-05  8.57e-07  9.55e-01  4.38e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-19  1.55e-20  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.62e-18  1.76e-19  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.23e-09  1.57e-10  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.25e-10  2.55e-11  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-18  2.32e-19  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012100  0.000586  0.940000  0.045600  0.001290 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.80e-14  2.33e-15  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-11  6.35e-13  9.52e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S1PR5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1943e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.740000  0.033800  0.215000  0.009830  0.000759 \n",
+      "[1] \"PP abf for shared variant: 0.0759%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD8T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.59e-05  3.20e-06  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05660   0.00275   0.89600   0.04350   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CHCHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14600   0.00709   0.80700   0.03910   0.00114 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.17e-04  4.45e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FMNL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.22700   0.01100   0.72500   0.03520   0.00114 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-24  9.13e-26  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.95e-07  2.89e-08  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HERPUD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.267e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.478000  0.023200  0.475000  0.023100  0.000776 \n",
+      "[1] \"PP abf for shared variant: 0.0776%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ANXA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-05  7.87e-07  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SOD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016800  0.000814  0.936000  0.045400  0.001230 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-17  1.08e-18  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-13  6.36e-15  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP1B3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.23300   0.01130   0.72000   0.03490   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.424000  0.020600  0.529000  0.025700  0.000828 \n",
+      "[1] \"PP abf for shared variant: 0.0828%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-25  5.21e-27  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.56e-11  1.24e-12  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EML4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-04  1.86e-05  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002530  0.000123  0.950000  0.046100  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MCL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-06  1.08e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NOG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08010   0.00362   0.87600   0.03960   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PRMT2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-03  6.66e-05  9.51e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-05  1.25e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.396000  0.019200  0.556000  0.027000  0.000899 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-11  6.40e-13  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FAM129A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-06  4.88e-08  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.91e-15  9.26e-17  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.2936e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.547000  0.026600  0.406000  0.019700  0.000695 \n",
+      "[1] \"PP abf for shared variant: 0.0695%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-25  1.27e-26  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ALOX5AP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003680  0.000179  0.949000  0.046100  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TSHZ2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-03  5.83e-05  9.51e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.11e-06  4.42e-07  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ARHGDIB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-05  1.16e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-10  1.08e-11  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-20  6.40e-22  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-28  7.41e-30  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.87e-28  4.79e-29  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YBX1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-05  1.08e-06  9.52e-01  4.62e-02  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.43e-25  4.09e-26  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___JUND__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.279e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.34900   0.01690   0.60400   0.02930   0.00147 \n",
+      "[1] \"PP abf for shared variant: 0.147%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.17e-07  2.03e-08  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-26  9.12e-28  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01070   0.00052   0.94200   0.04570   0.00135 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.63e-11  2.73e-12  9.52e-01  4.62e-02  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF18\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01590   0.00077   0.93700   0.04550   0.00127 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MYO1F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003750  0.000182  0.949000  0.046000  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.61e-24  3.21e-25  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.08e-09  3.44e-10  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.428000  0.020800  0.525000  0.025500  0.000856 \n",
+      "[1] \"PP abf for shared variant: 0.0856%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.21e-25  4.47e-26  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-04  1.25e-05  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VMP1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21900   0.01070   0.73300   0.03560   0.00108 \n",
+      "[1] \"PP abf for shared variant: 0.108%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.28e-06  3.54e-07  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.11e-25  1.99e-26  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.32700   0.01590   0.62500   0.03030   0.00097 \n",
+      "[1] \"PP abf for shared variant: 0.097%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GALM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.24800   0.01200   0.70500   0.03420   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05320   0.00258   0.89900   0.04360   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-03  8.62e-05  9.51e-01  4.61e-02  1.22e-03 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-14  1.12e-15  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.48e-16  1.69e-17  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ANXA5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019200  0.000933  0.933000  0.045300  0.001230 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___IDS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.28e-04  4.02e-05  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ARID5B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11000   0.00533   0.84300   0.04090   0.00115 \n",
+      "[1] \"PP abf for shared variant: 0.115%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___IMPDH2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.85e-06  4.78e-07  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.36e-14  3.09e-15  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ST13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.97e-07  4.36e-08  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CXCR3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00888   0.00043   0.94400   0.04570   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-06  8.01e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-15  1.20e-16  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SPOCK2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03100   0.00151   0.92200   0.04470   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___C15orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16400   0.00752   0.79100   0.03620   0.00110 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPF\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1448e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.61000   0.02960   0.34300   0.01660   0.00101 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-14  6.35e-16  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FAM134B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.20900   0.01020   0.74300   0.03610   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ISG20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007440  0.000361  0.945000  0.045900  0.001350 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.49e-10  2.18e-11  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NUCB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08410   0.00409   0.86800   0.04210   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002450  0.000119  0.950000  0.046100  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LINC00493__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11400   0.00551   0.83900   0.04070   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-25  5.17e-27  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006220  0.000302  0.946000  0.045900  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-11  4.98e-13  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CDC42__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007890  0.000383  0.945000  0.045800  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF1B\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03560   0.00173   0.91700   0.04450   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05090   0.00247   0.90200   0.04380   0.00120 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-18  7.65e-20  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ACTN4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00228   0.00011   0.95000   0.04610   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___IKZF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.12800   0.00624   0.82400   0.04000   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.30300   0.01470   0.65000   0.03160   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.55e-17  1.24e-18  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.69e-09  4.70e-10  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.409000  0.019900  0.544000  0.026400  0.000847 \n",
+      "[1] \"PP abf for shared variant: 0.0847%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NAMPT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8087e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.45000   0.02190   0.50200   0.02440   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-05  9.90e-07  9.52e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CALM2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.08e-05  3.92e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-15  7.25e-17  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ND2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.18200   0.00886   0.77000   0.03740   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.40e-15  4.08e-16  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD52__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.06e-04  2.46e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.70e-08  4.71e-09  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___H3F3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-05  2.05e-06  9.52e-01  4.62e-02  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ADTRP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-08  1.54e-09  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.15900   0.00773   0.79300   0.03850   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05560   0.00270   0.89700   0.04350   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.40e-05  4.56e-06  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-05  1.28e-06  9.52e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___DYNLL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-03  8.21e-05  9.51e-01  4.61e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAMSN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-05  1.67e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LMNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.43e-09  6.95e-11  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ND5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.73e-07  8.40e-09  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-21  4.96e-23  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RUNX3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13000   0.00633   0.82200   0.03990   0.00120 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-18  1.86e-19  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-05  5.19e-07  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ERGIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.423e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.446000  0.021700  0.506000  0.024600  0.000818 \n",
+      "[1] \"PP abf for shared variant: 0.0818%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-08  1.40e-09  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TYMP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04420   0.00215   0.90800   0.04410   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13100   0.00634   0.82200   0.03990   0.00115 \n",
+      "[1] \"PP abf for shared variant: 0.115%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-22  8.43e-24  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.98e-04  1.45e-05  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.35e-23  3.57e-24  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.46e-21  3.62e-22  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ND4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.347000  0.016800  0.606000  0.029400  0.000913 \n",
+      "[1] \"PP abf for shared variant: 0.0913%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.39e-04  2.62e-05  9.52e-01  4.62e-02  1.37e-03 \n",
+      "[1] \"PP abf for shared variant: 0.137%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP5E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004010  0.000195  0.948000  0.046000  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-10  5.13e-12  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.55e-13  3.18e-14  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.89e-22  3.83e-23  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-09  1.60e-10  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP2B4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04480   0.00218   0.90800   0.04410   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ARPC1B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.52300   0.02540   0.43000   0.02090   0.00084 \n",
+      "[1] \"PP abf for shared variant: 0.084%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PDCD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003970  0.000181  0.951000  0.043300  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.93e-21  4.82e-22  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-26  8.61e-28  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.92e-22  1.41e-23  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-13  1.74e-14  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.24e-08  1.57e-09  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.83e-05  3.32e-06  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-04  1.18e-05  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LGALS3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.23700   0.01150   0.71600   0.03480   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MIAT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.017800  0.000864  0.935000  0.045400  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-21  2.08e-22  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.01e-08  1.95e-09  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.020600  0.000999  0.932000  0.045200  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.30e-17  1.11e-18  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.57e-23  2.22e-24  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RNF19A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-03  9.75e-05  9.50e-01  4.61e-02  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-CO3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-07  1.99e-08  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-24  7.05e-26  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-11  8.24e-13  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FAS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.19900   0.00966   0.75400   0.03660   0.00110 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.82e-10  4.28e-11  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.12e-10  3.94e-11  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GYPC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.62e-06  4.18e-07  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-22  7.88e-24  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-24  8.27e-26  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPM4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02870   0.00139   0.92400   0.04480   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.59e-12  4.17e-13  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.06e-05  3.91e-06  9.52e-01  4.62e-02  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.33e-23  2.10e-24  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ITGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-07  1.73e-08  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.59e-07  2.23e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02730   0.00132   0.92500   0.04490   0.00126 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-26  9.87e-28  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11000   0.00535   0.84200   0.04090   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.53e-18  2.68e-19  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001630  0.000079  0.951000  0.046200  0.001260 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-03  8.06e-05  9.51e-01  4.61e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.00e-23  1.94e-24  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.88e-17  4.80e-18  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06770   0.00329   0.88500   0.04290   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PPP1R15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.457000  0.022200  0.496000  0.024100  0.000836 \n",
+      "[1] \"PP abf for shared variant: 0.0836%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___IL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.10e-04  3.93e-05  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.33e-26  2.59e-27  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.03e-16  1.96e-17  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM20\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.419000  0.020300  0.534000  0.025900  0.000883 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.29e-04  4.51e-05  9.52e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004230  0.000205  0.948000  0.046000  0.001200 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___KLF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-05  7.74e-07  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GIMAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.40e-04  4.08e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFBR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06280   0.00305   0.89000   0.04320   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RNF213__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.317000  0.015400  0.636000  0.030900  0.000929 \n",
+      "[1] \"PP abf for shared variant: 0.0929%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___C19orf53__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.502000  0.024400  0.451000  0.021900  0.000819 \n",
+      "[1] \"PP abf for shared variant: 0.0819%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-10  4.97e-12  9.52e-01  4.62e-02  1.33e-03 \n",
+      "[1] \"PP abf for shared variant: 0.133%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-15  1.77e-16  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MIR4435-1HG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.42e-10  2.63e-11  9.52e-01  4.62e-02  1.36e-03 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-15  8.44e-17  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MZT2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.98e-04  2.42e-05  9.52e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___AK5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08740   0.00424   0.86500   0.04200   0.00115 \n",
+      "[1] \"PP abf for shared variant: 0.115%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26700   0.01290   0.68600   0.03330   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-08  8.65e-10  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-20  1.07e-21  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-04  1.29e-05  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.98e-19  3.87e-20  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02790   0.00135   0.92500   0.04490   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CXCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.349000  0.016800  0.604000  0.029000  0.000916 \n",
+      "[1] \"PP abf for shared variant: 0.0916%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ARPC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.12400   0.00602   0.82900   0.04020   0.00119 \n",
+      "[1] \"PP abf for shared variant: 0.119%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MRPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.48400   0.02350   0.46900   0.02280   0.00078 \n",
+      "[1] \"PP abf for shared variant: 0.078%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-15  8.80e-17  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-06  1.01e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EBPL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.40800   0.01980   0.54500   0.02650   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21200   0.01030   0.74000   0.03590   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UXT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-08  7.55e-10  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LSM5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.70e-04  1.79e-05  9.52e-01  4.62e-02  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___KMT2E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.6569e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53400   0.02590   0.41900   0.02030   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-07  8.22e-09  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TAGLN2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.490000  0.023800  0.463000  0.022500  0.000812 \n",
+      "[1] \"PP abf for shared variant: 0.0812%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CDCA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4164e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.556000  0.027000  0.397000  0.019200  0.000723 \n",
+      "[1] \"PP abf for shared variant: 0.0723%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-06  7.60e-08  9.53e-01  4.62e-02  1.22e-03 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EPB41L4A-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.89e-06  9.19e-08  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.62e-08  3.21e-09  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TATDN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02820   0.00137   0.92400   0.04490   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06640   0.00322   0.88600   0.04300   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.25e-13  1.58e-14  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-23  9.56e-25  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-20  8.74e-22  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___BTG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.13e-04  2.01e-05  9.52e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___C8orf59__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03240   0.00157   0.92000   0.04470   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD58__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00742   0.00036   0.94500   0.04590   0.00132 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-17  7.50e-19  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-06  2.69e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___AKAP13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03500   0.00170   0.91700   0.04450   0.00135 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.72e-07  1.80e-08  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___DDX5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.55e-05  1.72e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02930   0.00142   0.92300   0.04480   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ANXA2R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.28400   0.01380   0.66900   0.03250   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___IL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.27400   0.01330   0.67900   0.03300   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LINC00152__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-08  7.87e-10  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FOXP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04550   0.00218   0.90700   0.04350   0.00145 \n",
+      "[1] \"PP abf for shared variant: 0.145%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.42e-09  4.57e-10  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-21  7.04e-23  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.94e-05  4.83e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-16  5.15e-18  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-14  7.07e-16  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___DAP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.39e-04  3.59e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-11  7.13e-13  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SVIP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.40200   0.01950   0.55000   0.02670   0.00086 \n",
+      "[1] \"PP abf for shared variant: 0.086%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.50e-15  1.21e-16  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-27  1.74e-28  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.91e-18  9.29e-20  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-07  1.07e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03230   0.00157   0.92000   0.04470   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ENTPD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14400   0.00679   0.81000   0.03800   0.00115 \n",
+      "[1] \"PP abf for shared variant: 0.115%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___DUSP4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.37e-10  1.64e-11  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-14  9.93e-16  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00228   0.00011   0.95000   0.04610   0.00130 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.18600   0.00902   0.76700   0.03720   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-03  9.35e-05  9.51e-01  4.61e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.92e-17  4.33e-18  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ADAM19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.28700   0.01390   0.66600   0.03230   0.00112 \n",
+      "[1] \"PP abf for shared variant: 0.112%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CLDND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.297000  0.014400  0.656000  0.031800  0.000988 \n",
+      "[1] \"PP abf for shared variant: 0.0988%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.24e-07  4.49e-08  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-09  5.07e-11  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.22300   0.01080   0.73000   0.03540   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___APEX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.320000  0.015500  0.633000  0.030700  0.000959 \n",
+      "[1] \"PP abf for shared variant: 0.0959%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.55e-08  1.24e-09  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.66e-22  4.21e-23  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LETMD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-03  5.87e-05  9.51e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002530  0.000116  0.952000  0.043600  0.001300 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.27e-07  4.01e-08  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___AHNAK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01280   0.00062   0.94000   0.04560   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SLC7A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.493000  0.023900  0.460000  0.022300  0.000766 \n",
+      "[1] \"PP abf for shared variant: 0.0766%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.74e-10  3.27e-11  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-20  8.81e-22  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.64e-04  3.23e-05  9.52e-01  4.62e-02  1.37e-03 \n",
+      "[1] \"PP abf for shared variant: 0.137%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.49e-28  3.63e-29  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.86e-13  2.84e-14  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-25  1.79e-26  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ATP6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.72e-05  1.80e-06  9.52e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-18  1.32e-19  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-06  1.49e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RILPL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21900   0.01060   0.73400   0.03560   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SSR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-05  1.73e-06  9.52e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003350  0.000163  0.949000  0.046100  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.74e-09  2.30e-10  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.14e-14  3.95e-15  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MAF__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-05  5.38e-07  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.18e-10  1.55e-11  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___COMMD6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.82e-04  2.34e-05  9.52e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.48e-07  4.11e-08  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NSMCE1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004770  0.000232  0.948000  0.046000  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.01e-05  2.43e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PRDX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08140   0.00395   0.87100   0.04230   0.00120 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.37e-20  3.09e-21  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FAM46C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02780   0.00135   0.92500   0.04490   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.36e-22  4.06e-23  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-21  7.46e-23  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.72e-27  3.26e-28  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-23  6.73e-25  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RORA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.38200   0.01850   0.57100   0.02770   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.32e-04  1.61e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.39e-19  1.64e-20  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD44__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-05  7.90e-07  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4Y1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.44300   0.02150   0.51000   0.02470   0.00084 \n",
+      "[1] \"PP abf for shared variant: 0.084%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-05  8.17e-07  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___COX7A2L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0514    0.0025    0.9010    0.0437    0.0012 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.35e-21  1.14e-22  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HADHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08250   0.00401   0.87000   0.04220   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SATB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.18000   0.00875   0.77200   0.03750   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UGP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03570   0.00173   0.91700   0.04450   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SBDS\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.25700   0.01250   0.69600   0.03380   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SYNE2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.005530  0.000269  0.947000  0.046000  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMA7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07080   0.00343   0.88200   0.04280   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-09  7.13e-11  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NR3C1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05040   0.00244   0.90200   0.04380   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.57e-27  3.19e-28  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CCT8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02060   0.00100   0.93200   0.04520   0.00123 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFAIP3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04700   0.00228   0.90600   0.04400   0.00120 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH2D2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.38400   0.01740   0.57200   0.02600   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-11  1.86e-12  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CLNS1A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004870  0.000237  0.948000  0.046000  0.001230 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-08  1.10e-09  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP6V0E1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04550   0.00221   0.90700   0.04400   0.00118 \n",
+      "[1] \"PP abf for shared variant: 0.118%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-26  6.14e-28  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.41500   0.02010   0.53800   0.02610   0.00117 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-27  8.07e-29  9.53e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36L2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05200   0.00252   0.90100   0.04370   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000226  0.000011  0.952000  0.046200  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08630   0.00419   0.86600   0.04200   0.00118 \n",
+      "[1] \"PP abf for shared variant: 0.118%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-19  1.99e-20  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___APRT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007240  0.000351  0.945000  0.045900  0.001450 \n",
+      "[1] \"PP abf for shared variant: 0.145%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FYN__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003880  0.000188  0.949000  0.046000  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ANP32B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08090   0.00393   0.87200   0.04230   0.00127 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03710   0.00180   0.91500   0.04440   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.69e-04  2.76e-05  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-27  1.08e-28  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CMPK1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.60e-05  2.24e-06  9.52e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAZ\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14200   0.00687   0.81100   0.03940   0.00129 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GIMAP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.22e-08  2.05e-09  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___COTL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.24e-06  2.54e-07  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF2S3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.91e-11  3.84e-12  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HSP90AA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1807e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.472000  0.022900  0.480000  0.023300  0.000793 \n",
+      "[1] \"PP abf for shared variant: 0.0793%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-CYB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-04  8.74e-06  9.52e-01  4.62e-02  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HSPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.277000  0.013500  0.676000  0.032800  0.000992 \n",
+      "[1] \"PP abf for shared variant: 0.0992%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.45e-10  2.16e-11  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.93e-09  1.42e-10  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.88e-18  4.80e-19  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXK\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.19e-05  2.03e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-16  1.03e-17  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.57e-14  2.71e-15  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-06  8.84e-08  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.60e-05  1.75e-06  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06360   0.00309   0.88900   0.04310   0.00120 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___LYRM4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.262000  0.012700  0.691000  0.033500  0.000983 \n",
+      "[1] \"PP abf for shared variant: 0.0983%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-20  1.03e-21  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.41e-20  4.57e-21  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-10  1.47e-11  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EZR__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002660  0.000129  0.950000  0.046100  0.001270 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000926  0.000045  0.952000  0.046200  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP5O__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-08  5.86e-10  9.52e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07890   0.00383   0.87400   0.04240   0.00118 \n",
+      "[1] \"PP abf for shared variant: 0.118%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-19  4.86e-21  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUCLG2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.86e-04  1.39e-05  9.52e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CD3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04990   0.00242   0.90300   0.04380   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-20  1.46e-21  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___NSA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-07  5.74e-09  9.53e-01  4.62e-02  1.24e-03 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05520   0.00268   0.89700   0.04360   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___HIGD2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14200   0.00690   0.81000   0.03930   0.00116 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-09  1.47e-10  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___IGBP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003370  0.000163  0.949000  0.046100  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-19  1.41e-20  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___MYH9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.579000  0.028100  0.374000  0.018200  0.000819 \n",
+      "[1] \"PP abf for shared variant: 0.0819%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.52e-08  3.16e-09  9.53e-01  4.62e-02  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP2B1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-03  5.62e-05  9.51e-01  4.62e-02  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-28  6.84e-30  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RBM39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006130  0.000298  0.946000  0.045900  0.001240 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___CCNG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002610  0.000127  0.950000  0.046100  0.001280 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-16  1.83e-17  9.53e-01  4.62e-02  1.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___COX4I1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.005860  0.000284  0.947000  0.045900  0.001250 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___PMAIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05160   0.00251   0.90100   0.04370   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.78e-11  1.83e-12  9.52e-01  4.62e-02  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.77e-07  2.31e-08  9.52e-01  4.62e-02  1.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___FHIT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-10  1.78e-11  9.53e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Crohn's Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-05  1.01e-06  9.52e-01  4.62e-02  1.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.126%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___CAPG__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02360   0.00238   0.88400   0.08910   0.00084 \n",
+      "[1] \"PP abf for shared variant: 0.084%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___PTAFR__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.070000  0.007060  0.838000  0.084500  0.000917 \n",
+      "[1] \"PP abf for shared variant: 0.0917%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___MNDA__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.5916e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.468000  0.047200  0.439000  0.044300  0.000796 \n",
+      "[1] \"PP abf for shared variant: 0.0796%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___RNASE6__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.167000  0.016800  0.741000  0.074700  0.000826 \n",
+      "[1] \"PP abf for shared variant: 0.0826%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__TSPO\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.213000  0.021500  0.695000  0.070100  0.000862 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__VMO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5549e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.468000  0.046800  0.440000  0.044000  0.000792 \n",
+      "[1] \"PP abf for shared variant: 0.0792%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___S100A9__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.212000  0.021300  0.696000  0.070200  0.000879 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___QPCT__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.8504e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.409000  0.041200  0.499000  0.050200  0.000776 \n",
+      "[1] \"PP abf for shared variant: 0.0776%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___BLVRB__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1205e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.33900   0.03420   0.56900   0.05730   0.00097 \n",
+      "[1] \"PP abf for shared variant: 0.097%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___LYZ__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013200  0.001330  0.894000  0.090200  0.000851 \n",
+      "[1] \"PP abf for shared variant: 0.0851%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_TMEM176A___CLEC4A__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.488000  0.049200  0.420000  0.042300  0.000751 \n",
+      "[1] \"PP abf for shared variant: 0.0751%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003500  0.000437  0.885000  0.111000  0.000791 \n",
+      "[1] \"PP abf for shared variant: 0.0791%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL5__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.108000  0.013500  0.780000  0.097500  0.000834 \n",
+      "[1] \"PP abf for shared variant: 0.0834%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051300  0.006410  0.837000  0.105000  0.000795 \n",
+      "[1] \"PP abf for shared variant: 0.0795%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL32__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004220  0.000527  0.884000  0.110000  0.000787 \n",
+      "[1] \"PP abf for shared variant: 0.0787%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___EEF1A1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.12200   0.01520   0.76600   0.09570   0.00079 \n",
+      "[1] \"PP abf for shared variant: 0.079%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL38__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.023300  0.002920  0.865000  0.108000  0.000805 \n",
+      "[1] \"PP abf for shared variant: 0.0805%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.031000  0.003870  0.857000  0.107000  0.000795 \n",
+      "[1] \"PP abf for shared variant: 0.0795%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.052200  0.006520  0.836000  0.104000  0.000809 \n",
+      "[1] \"PP abf for shared variant: 0.0809%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.116000  0.014500  0.773000  0.096500  0.000834 \n",
+      "[1] \"PP abf for shared variant: 0.0834%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.118000  0.014800  0.770000  0.096200  0.000865 \n",
+      "[1] \"PP abf for shared variant: 0.0865%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS15A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.036100  0.004510  0.852000  0.106000  0.000789 \n",
+      "[1] \"PP abf for shared variant: 0.0789%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS8__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.041500  0.005180  0.847000  0.106000  0.000805 \n",
+      "[1] \"PP abf for shared variant: 0.0805%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.115000  0.014400  0.773000  0.096600  0.000807 \n",
+      "[1] \"PP abf for shared variant: 0.0807%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001830  0.000229  0.886000  0.111000  0.000784 \n",
+      "[1] \"PP abf for shared variant: 0.0784%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___NKG7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.062400  0.007790  0.826000  0.103000  0.000806 \n",
+      "[1] \"PP abf for shared variant: 0.0806%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___B2M__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012700  0.001590  0.876000  0.109000  0.000788 \n",
+      "[1] \"PP abf for shared variant: 0.0788%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.025700  0.003210  0.863000  0.108000  0.000805 \n",
+      "[1] \"PP abf for shared variant: 0.0805%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___PFN1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008660  0.001080  0.880000  0.110000  0.000786 \n",
+      "[1] \"PP abf for shared variant: 0.0786%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS28__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.029900  0.003740  0.858000  0.107000  0.000816 \n",
+      "[1] \"PP abf for shared variant: 0.0816%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.034600  0.004330  0.854000  0.107000  0.000788 \n",
+      "[1] \"PP abf for shared variant: 0.0788%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___GZMH__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008760  0.001090  0.879000  0.110000  0.000788 \n",
+      "[1] \"PP abf for shared variant: 0.0788%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___LTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016400  0.002050  0.872000  0.109000  0.000794 \n",
+      "[1] \"PP abf for shared variant: 0.0794%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL39__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02470   0.00309   0.86400   0.10800   0.00079 \n",
+      "[1] \"PP abf for shared variant: 0.079%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.033900  0.004240  0.854000  0.107000  0.000786 \n",
+      "[1] \"PP abf for shared variant: 0.0786%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.021700  0.002710  0.867000  0.108000  0.000787 \n",
+      "[1] \"PP abf for shared variant: 0.0787%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS23__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010000  0.001250  0.878000  0.110000  0.000792 \n",
+      "[1] \"PP abf for shared variant: 0.0792%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS29__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.014900  0.001860  0.873000  0.109000  0.000793 \n",
+      "[1] \"PP abf for shared variant: 0.0793%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL22__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.096900  0.012100  0.791000  0.098900  0.000789 \n",
+      "[1] \"PP abf for shared variant: 0.0789%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL9__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04060   0.00507   0.84800   0.10600   0.00080 \n",
+      "[1] \"PP abf for shared variant: 0.08%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.092300  0.011500  0.796000  0.099400  0.000827 \n",
+      "[1] \"PP abf for shared variant: 0.0827%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.076300  0.009530  0.812000  0.101000  0.000802 \n",
+      "[1] \"PP abf for shared variant: 0.0802%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00027483\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.694000  0.086800  0.194000  0.024200  0.000845 \n",
+      "[1] \"PP abf for shared variant: 0.0845%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___MAL__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.044700  0.005540  0.844000  0.105000  0.000804 \n",
+      "[1] \"PP abf for shared variant: 0.0804%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___PRF1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11700   0.01470   0.77100   0.09630   0.00082 \n",
+      "[1] \"PP abf for shared variant: 0.082%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001770  0.000221  0.886000  0.111000  0.000785 \n",
+      "[1] \"PP abf for shared variant: 0.0785%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS6__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.020200  0.002530  0.868000  0.108000  0.000796 \n",
+      "[1] \"PP abf for shared variant: 0.0796%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002040  0.000254  0.886000  0.111000  0.000790 \n",
+      "[1] \"PP abf for shared variant: 0.079%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL21__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013100  0.001630  0.875000  0.109000  0.000791 \n",
+      "[1] \"PP abf for shared variant: 0.0791%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___SMDT1__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.81e-04  6.01e-05  8.88e-01  1.11e-01  7.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.079%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.020900  0.002620  0.867000  0.108000  0.000792 \n",
+      "[1] \"PP abf for shared variant: 0.0792%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002260  0.000282  0.886000  0.111000  0.000794 \n",
+      "[1] \"PP abf for shared variant: 0.0794%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL34__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008440  0.001050  0.880000  0.110000  0.000801 \n",
+      "[1] \"PP abf for shared variant: 0.0801%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL10A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013100  0.001640  0.875000  0.109000  0.000796 \n",
+      "[1] \"PP abf for shared variant: 0.0796%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_SMDT1___RPL30__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.018200  0.002280  0.870000  0.109000  0.000806 \n",
+      "[1] \"PP abf for shared variant: 0.0806%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002900  0.000362  0.885000  0.111000  0.000838 \n",
+      "[1] \"PP abf for shared variant: 0.0838%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.095600  0.011900  0.793000  0.099000  0.000823 \n",
+      "[1] \"PP abf for shared variant: 0.0823%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010900  0.001370  0.877000  0.110000  0.000793 \n",
+      "[1] \"PP abf for shared variant: 0.0793%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007240  0.000905  0.881000  0.110000  0.000790 \n",
+      "[1] \"PP abf for shared variant: 0.079%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.188000  0.023500  0.700000  0.087400  0.000813 \n",
+      "[1] \"PP abf for shared variant: 0.0813%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.159000  0.019800  0.730000  0.091100  0.000895 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL31__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.151000  0.018900  0.737000  0.092100  0.000795 \n",
+      "[1] \"PP abf for shared variant: 0.0795%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06910   0.00863   0.81900   0.10200   0.00088 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___ACTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007790  0.000974  0.880000  0.110000  0.000798 \n",
+      "[1] \"PP abf for shared variant: 0.0798%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.056400  0.007050  0.832000  0.104000  0.000792 \n",
+      "[1] \"PP abf for shared variant: 0.0792%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000869  0.000109  0.887000  0.111000  0.000788 \n",
+      "[1] \"PP abf for shared variant: 0.0788%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.184000  0.023000  0.704000  0.088000  0.000782 \n",
+      "[1] \"PP abf for shared variant: 0.0782%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.041800  0.005220  0.846000  0.106000  0.000791 \n",
+      "[1] \"PP abf for shared variant: 0.0791%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.047100  0.005880  0.841000  0.105000  0.000786 \n",
+      "[1] \"PP abf for shared variant: 0.0786%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL10__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.017700  0.002210  0.870000  0.109000  0.000984 \n",
+      "[1] \"PP abf for shared variant: 0.0984%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06120   0.00765   0.82700   0.10300   0.00117 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0032661\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.685000  0.085600  0.203000  0.025300  0.000841 \n",
+      "[1] \"PP abf for shared variant: 0.0841%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___CD48__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06240   0.00779   0.82600   0.10300   0.00080 \n",
+      "[1] \"PP abf for shared variant: 0.08%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.104000  0.013000  0.784000  0.098000  0.000819 \n",
+      "[1] \"PP abf for shared variant: 0.0819%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_SMDT1___RPS27__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.179000  0.022300  0.709000  0.088600  0.000794 \n",
+      "[1] \"PP abf for shared variant: 0.0794%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-22  3.54e-01  5.56e-22  6.13e-01  3.30e-02 \n",
+      "[1] \"PP abf for shared variant: 3.3%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-22  2.60e-01  6.43e-22  7.10e-01  3.00e-02 \n",
+      "[1] \"PP abf for shared variant: 3%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-22  1.81e-01  7.08e-22  7.82e-01  3.78e-02 \n",
+      "[1] \"PP abf for shared variant: 3.78%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___CLIC3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-22  2.93e-01  4.72e-22  5.19e-01  1.88e-01 \n",
+      "[1] \"PP abf for shared variant: 18.8%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__PTPRCAP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.57e-22  5.04e-01  3.93e-22  4.33e-01  6.27e-02 \n",
+      "[1] \"PP abf for shared variant: 6.27%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___CDKN2D__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.5969e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.18e-22  4.61e-01  4.65e-22  5.13e-01  2.59e-02 \n",
+      "[1] \"PP abf for shared variant: 2.59%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0931e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.17e-22  5.70e-01  3.25e-22  3.58e-01  7.19e-02 \n",
+      "[1] \"PP abf for shared variant: 7.19%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.94e-22  4.35e-01  4.64e-22  5.12e-01  5.28e-02 \n",
+      "[1] \"PP abf for shared variant: 5.28%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-22  1.45e-01  7.53e-22  8.32e-01  2.33e-02 \n",
+      "[1] \"PP abf for shared variant: 2.33%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MAP1A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.54e-22  5.01e-01  4.19e-22  4.62e-01  3.68e-02 \n",
+      "[1] \"PP abf for shared variant: 3.68%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___FAM129C__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.79e-22  3.08e-01  5.55e-22  6.12e-01  7.94e-02 \n",
+      "[1] \"PP abf for shared variant: 7.94%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MT-CO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1338e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.60e-22  5.08e-01  3.99e-22  4.40e-01  5.23e-02 \n",
+      "[1] \"PP abf for shared variant: 5.23%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-22  1.61e-01  7.21e-22  7.96e-01  4.36e-02 \n",
+      "[1] \"PP abf for shared variant: 4.36%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-23  1.92e-02  5.76e-22  7.20e-01  2.61e-01 \n",
+      "[1] \"PP abf for shared variant: 26.1%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-25  3.34e-04  7.04e-22  8.82e-01  1.17e-01 \n",
+      "[1] \"PP abf for shared variant: 11.7%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.86e-25  3.59e-04  7.24e-22  9.08e-01  9.19e-02 \n",
+      "[1] \"PP abf for shared variant: 9.19%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.24e-24  4.07e-03  7.19e-22  9.02e-01  9.37e-02 \n",
+      "[1] \"PP abf for shared variant: 9.37%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-25  7.27e-04  7.17e-22  9.00e-01  9.97e-02 \n",
+      "[1] \"PP abf for shared variant: 9.97%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.80e-25  3.52e-04  6.80e-22  8.52e-01  1.48e-01 \n",
+      "[1] \"PP abf for shared variant: 14.8%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMA__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-24  1.30e-03  5.86e-22  7.33e-01  2.66e-01 \n",
+      "[1] \"PP abf for shared variant: 26.6%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS23\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-23  4.04e-02  6.68e-22  8.37e-01  1.22e-01 \n",
+      "[1] \"PP abf for shared variant: 12.2%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.57e-23  1.08e-01  5.99e-22  7.51e-01  1.41e-01 \n",
+      "[1] \"PP abf for shared variant: 14.1%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.28e-22  1.61e-01  5.91e-22  7.41e-01  9.84e-02 \n",
+      "[1] \"PP abf for shared variant: 9.84%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___EEF1A1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.73e-24  1.10e-02  7.33e-22  9.20e-01  6.89e-02 \n",
+      "[1] \"PP abf for shared variant: 6.89%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-23  1.62e-02  6.64e-22  8.33e-01  1.51e-01 \n",
+      "[1] \"PP abf for shared variant: 15.1%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.46e-23  6.86e-02  5.76e-22  7.22e-01  2.10e-01 \n",
+      "[1] \"PP abf for shared variant: 21%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMB__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.82e-23  4.79e-02  6.29e-22  7.88e-01  1.64e-01 \n",
+      "[1] \"PP abf for shared variant: 16.4%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.16e-24  3.97e-03  7.40e-22  9.29e-01  6.68e-02 \n",
+      "[1] \"PP abf for shared variant: 6.68%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-24  2.69e-03  6.93e-22  8.69e-01  1.28e-01 \n",
+      "[1] \"PP abf for shared variant: 12.8%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-22  1.50e-01  5.46e-22  6.83e-01  1.66e-01 \n",
+      "[1] \"PP abf for shared variant: 16.6%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RNASET2___HLA-DRB5__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-22  1.50e-01  6.76e-22  8.49e-01  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___CCL5__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.06e-23  6.35e-02  7.02e-22  8.82e-01  5.49e-02 \n",
+      "[1] \"PP abf for shared variant: 5.49%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-22  2.35e-01  5.55e-22  6.96e-01  6.90e-02 \n",
+      "[1] \"PP abf for shared variant: 6.9%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-22  1.76e-01  5.67e-22  7.11e-01  1.13e-01 \n",
+      "[1] \"PP abf for shared variant: 11.3%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__NKG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-23  4.01e-02  7.22e-22  9.06e-01  5.36e-02 \n",
+      "[1] \"PP abf for shared variant: 5.36%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPL34\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.03e-23  1.01e-01  6.81e-22  8.54e-01  4.47e-02 \n",
+      "[1] \"PP abf for shared variant: 4.47%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-22  1.41e-01  6.51e-22  8.18e-01  4.11e-02 \n",
+      "[1] \"PP abf for shared variant: 4.11%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_HLA-DQA2___CMC1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.64e-23  9.60e-02  6.81e-22  8.55e-01  4.94e-02 \n",
+      "[1] \"PP abf for shared variant: 4.94%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-23  4.31e-02  6.53e-22  8.18e-01  1.38e-01 \n",
+      "[1] \"PP abf for shared variant: 13.8%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.74e-23  4.70e-02  7.16e-22  8.99e-01  5.40e-02 \n",
+      "[1] \"PP abf for shared variant: 5.4%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-22  1.70e-01  5.73e-22  7.18e-01  1.12e-01 \n",
+      "[1] \"PP abf for shared variant: 11.2%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-22  1.58e-01  5.74e-22  7.19e-01  1.23e-01 \n",
+      "[1] \"PP abf for shared variant: 12.3%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-22  2.22e-01  5.65e-22  7.09e-01  6.88e-02 \n",
+      "[1] \"PP abf for shared variant: 6.88%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___CD52__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.63e-23  4.56e-02  6.88e-22  8.63e-01  9.15e-02 \n",
+      "[1] \"PP abf for shared variant: 9.15%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-22  2.24e-01  5.23e-22  6.55e-01  1.21e-01 \n",
+      "[1] \"PP abf for shared variant: 12.1%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.52e-23  4.42e-02  7.40e-22  9.30e-01  2.61e-02 \n",
+      "[1] \"PP abf for shared variant: 2.61%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.49e-23  6.90e-02  6.77e-22  8.49e-01  8.22e-02 \n",
+      "[1] \"PP abf for shared variant: 8.22%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___EEF1B2__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.37e-23  8.00e-02  6.37e-22  7.99e-01  1.21e-01 \n",
+      "[1] \"PP abf for shared variant: 12.1%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.02e-23  1.01e-01  6.04e-22  7.58e-01  1.42e-01 \n",
+      "[1] \"PP abf for shared variant: 14.2%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___B2M__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.80e-23  4.78e-02  6.80e-22  8.53e-01  9.97e-02 \n",
+      "[1] \"PP abf for shared variant: 9.97%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___GAPDH__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-22  1.92e-01  5.47e-22  6.86e-01  1.22e-01 \n",
+      "[1] \"PP abf for shared variant: 12.2%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL32\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.73e-23  8.45e-02  5.96e-22  7.47e-01  1.69e-01 \n",
+      "[1] \"PP abf for shared variant: 16.9%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.53e-22  3.18e-01  4.75e-22  5.96e-01  8.54e-02 \n",
+      "[1] \"PP abf for shared variant: 8.54%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-23  3.15e-02  6.56e-22  8.22e-01  1.46e-01 \n",
+      "[1] \"PP abf for shared variant: 14.6%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RNASET2___ITGB1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-08  5.29e-03  6.77e-06  6.14e-01  3.80e-01 \n",
+      "[1] \"PP abf for shared variant: 38%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RNASET2___CRIP1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.44e-07  4.96e-02  5.62e-06  5.09e-01  4.42e-01 \n",
+      "[1] \"PP abf for shared variant: 44.2%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RNASET2___B2M__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.52e-07  2.30e-02  6.11e-06  5.54e-01  4.23e-01 \n",
+      "[1] \"PP abf for shared variant: 42.3%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RNASET2___ALOX5AP__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.61e-07  7.86e-02  7.81e-06  7.11e-01  2.11e-01 \n",
+      "[1] \"PP abf for shared variant: 21.1%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0253e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.689000  0.011500  0.294000  0.004910  0.000358 \n",
+      "[1] \"PP abf for shared variant: 0.0358%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.556000  0.009300  0.427000  0.007130  0.000452 \n",
+      "[1] \"PP abf for shared variant: 0.0452%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"DC_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.615000  0.010300  0.368000  0.006150  0.000389 \n",
+      "[1] \"PP abf for shared variant: 0.0389%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.290000  0.005050  0.692000  0.012000  0.000725 \n",
+      "[1] \"PP abf for shared variant: 0.0725%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__UBE2J1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.0878e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.622000  0.010800  0.360000  0.006250  0.000379 \n",
+      "[1] \"PP abf for shared variant: 0.0379%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.101000  0.001760  0.881000  0.015300  0.000811 \n",
+      "[1] \"PP abf for shared variant: 0.0811%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.566000  0.009850  0.416000  0.007230  0.000421 \n",
+      "[1] \"PP abf for shared variant: 0.0421%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1042e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.551000  0.009590  0.431000  0.007490  0.000448 \n",
+      "[1] \"PP abf for shared variant: 0.0448%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.1644e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.750000  0.013000  0.233000  0.004040  0.000279 \n",
+      "[1] \"PP abf for shared variant: 0.0279%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.19e-04  1.25e-05  9.81e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.0557e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.692000  0.012000  0.291000  0.005060  0.000373 \n",
+      "[1] \"PP abf for shared variant: 0.0373%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009770  0.000170  0.972000  0.016900  0.000876 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.176000  0.003060  0.806000  0.014000  0.000744 \n",
+      "[1] \"PP abf for shared variant: 0.0744%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004890  0.000085  0.977000  0.017000  0.000885 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.567000  0.009850  0.416000  0.007230  0.000421 \n",
+      "[1] \"PP abf for shared variant: 0.0421%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.413000  0.007180  0.570000  0.009900  0.000599 \n",
+      "[1] \"PP abf for shared variant: 0.0599%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.7757e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.590000  0.010300  0.393000  0.006830  0.000419 \n",
+      "[1] \"PP abf for shared variant: 0.0419%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.116000  0.002010  0.867000  0.015100  0.000817 \n",
+      "[1] \"PP abf for shared variant: 0.0817%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.483000  0.008400  0.500000  0.008680  0.000491 \n",
+      "[1] \"PP abf for shared variant: 0.0491%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2778e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.577000  0.010000  0.405000  0.007040  0.000425 \n",
+      "[1] \"PP abf for shared variant: 0.0425%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0623e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.75100   0.01310   0.23100   0.00402   0.00029 \n",
+      "[1] \"PP abf for shared variant: 0.029%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.040600  0.000707  0.941000  0.016400  0.000853 \n",
+      "[1] \"PP abf for shared variant: 0.0853%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.079700  0.001390  0.902000  0.015700  0.000852 \n",
+      "[1] \"PP abf for shared variant: 0.0852%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.260000  0.004510  0.723000  0.012600  0.000755 \n",
+      "[1] \"PP abf for shared variant: 0.0755%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.86e-05  1.37e-06  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1408e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.767000  0.013300  0.216000  0.003750  0.000266 \n",
+      "[1] \"PP abf for shared variant: 0.0266%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.791e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.564000  0.009810  0.418000  0.007270  0.000419 \n",
+      "[1] \"PP abf for shared variant: 0.0419%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1436e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.715000  0.012400  0.267000  0.004650  0.000306 \n",
+      "[1] \"PP abf for shared variant: 0.0306%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1123e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.801000  0.013900  0.181000  0.003150  0.000224 \n",
+      "[1] \"PP abf for shared variant: 0.0224%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.210000  0.003650  0.772000  0.013400  0.000755 \n",
+      "[1] \"PP abf for shared variant: 0.0755%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.204000  0.003550  0.778000  0.013500  0.000706 \n",
+      "[1] \"PP abf for shared variant: 0.0706%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-05  3.05e-07  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.182000  0.003160  0.801000  0.013900  0.000753 \n",
+      "[1] \"PP abf for shared variant: 0.0753%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.028600  0.000497  0.953000  0.016600  0.000866 \n",
+      "[1] \"PP abf for shared variant: 0.0866%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.22e-05  5.60e-07  9.82e-01  1.71e-02  8.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.162000  0.002810  0.821000  0.014300  0.000791 \n",
+      "[1] \"PP abf for shared variant: 0.0791%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1639e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.763000  0.013300  0.220000  0.003830  0.000249 \n",
+      "[1] \"PP abf for shared variant: 0.0249%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"B_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.067300  0.001170  0.915000  0.015900  0.000847 \n",
+      "[1] \"PP abf for shared variant: 0.0847%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-08  3.14e-10  9.82e-01  1.70e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-12  3.35e-14  9.82e-01  1.70e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.097800  0.001690  0.884000  0.015300  0.000847 \n",
+      "[1] \"PP abf for shared variant: 0.0847%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.30e-08  5.71e-10  9.82e-01  1.70e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-06  3.26e-08  9.82e-01  1.70e-02  9.13e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0913%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.54e-05  1.30e-06  9.82e-01  1.70e-02  9.53e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0953%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-17  5.72e-19  9.82e-01  1.70e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.24e-11  1.43e-12  9.82e-01  1.70e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.10e-12  8.83e-14  9.82e-01  1.70e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.02e-11  1.22e-12  9.82e-01  1.70e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-05  2.07e-07  9.82e-01  1.70e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-03  2.36e-05  9.81e-01  1.70e-02  8.97e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0897%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-11  3.00e-13  9.82e-01  1.70e-02  9.48e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0948%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.39e-15  5.86e-17  9.82e-01  1.70e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.033000  0.000572  0.949000  0.016400  0.000863 \n",
+      "[1] \"PP abf for shared variant: 0.0863%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07210   0.00125   0.91000   0.01570   0.00086 \n",
+      "[1] \"PP abf for shared variant: 0.086%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-10  3.92e-12  9.82e-01  1.70e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-06  2.20e-08  9.82e-01  1.70e-02  9.45e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0945%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.011700  0.000202  0.970000  0.016800  0.000958 \n",
+      "[1] \"PP abf for shared variant: 0.0958%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.04e-07  8.73e-09  9.82e-01  1.70e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-17  1.88e-19  9.82e-01  1.70e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-11  6.17e-13  9.82e-01  1.70e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.32e-11  7.48e-13  9.82e-01  1.70e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__SPON2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.014100  0.000243  0.968000  0.016700  0.000938 \n",
+      "[1] \"PP abf for shared variant: 0.0938%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-05  3.71e-07  9.82e-01  1.70e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-19  2.86e-21  9.82e-01  1.70e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.52e-13  6.08e-15  9.82e-01  1.70e-02  8.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.81e-12  1.18e-13  9.82e-01  1.70e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.68e-13  8.10e-15  9.82e-01  1.70e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-06  2.59e-08  9.82e-01  1.70e-02  9.16e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0916%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.237000  0.004100  0.745000  0.012900  0.000768 \n",
+      "[1] \"PP abf for shared variant: 0.0768%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-05  2.12e-07  9.82e-01  1.70e-02  1.01e-03 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1991e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.500000  0.008650  0.483000  0.008350  0.000531 \n",
+      "[1] \"PP abf for shared variant: 0.0531%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.86e-09  4.94e-11  9.82e-01  1.70e-02  8.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-12  6.50e-14  9.82e-01  1.70e-02  8.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-09  3.48e-11  9.82e-01  1.70e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-12  1.80e-14  9.82e-01  1.70e-02  9.48e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0948%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-15  2.25e-17  9.82e-01  1.70e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.18e-18  5.51e-20  9.82e-01  1.70e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.21e-04  7.29e-06  9.82e-01  1.70e-02  9.57e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0957%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.97e-10  1.03e-11  9.82e-01  1.70e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-04  1.82e-06  9.82e-01  1.70e-02  9.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0965%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.51e-10  7.80e-12  9.82e-01  1.70e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-13  3.61e-15  9.82e-01  1.70e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-08  1.78e-10  9.82e-01  1.70e-02  9.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.099%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-15  3.56e-17  9.82e-01  1.70e-02  9.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0976%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-13  1.94e-15  9.82e-01  1.70e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-04  1.65e-05  9.81e-01  1.70e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.97e-13  6.86e-15  9.82e-01  1.70e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.425000  0.007360  0.557000  0.009630  0.000564 \n",
+      "[1] \"PP abf for shared variant: 0.0564%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.43e-07  5.93e-09  9.82e-01  1.70e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.023500  0.000406  0.959000  0.016600  0.000870 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.28e-08  5.67e-10  9.82e-01  1.70e-02  9.00e-04 \n",
+      "[1] \"PP abf for shared variant: 0.09%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.53e-04  4.38e-06  9.82e-01  1.70e-02  1.07e-03 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.84e-18  1.36e-19  9.82e-01  1.70e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-07  3.78e-09  9.82e-01  1.70e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-09  3.66e-11  9.82e-01  1.70e-02  8.94e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0894%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-06  3.38e-08  9.82e-01  1.70e-02  8.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.51e-10  6.07e-12  9.82e-01  1.70e-02  1.03e-03 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-15  3.05e-17  9.82e-01  1.70e-02  9.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0968%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-06  3.89e-08  9.82e-01  1.70e-02  1.01e-03 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-05  3.96e-07  9.82e-01  1.70e-02  9.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0947%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.340000  0.005890  0.642000  0.011100  0.000622 \n",
+      "[1] \"PP abf for shared variant: 0.0622%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.23e-16  9.06e-18  9.82e-01  1.70e-02  8.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0892%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010400  0.000180  0.972000  0.016800  0.000879 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-08  2.68e-10  9.82e-01  1.70e-02  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.40e-06  5.88e-08  9.82e-01  1.70e-02  9.21e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0921%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.57e-05  9.64e-07  9.82e-01  1.70e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-05  3.70e-07  9.82e-01  1.70e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.58e-07  1.14e-08  9.82e-01  1.70e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4099e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.458000  0.007920  0.525000  0.009070  0.000533 \n",
+      "[1] \"PP abf for shared variant: 0.0533%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5173e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.812000  0.014000  0.171000  0.002950  0.000213 \n",
+      "[1] \"PP abf for shared variant: 0.0213%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-03  2.08e-05  9.81e-01  1.70e-02  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.70e-07  1.51e-08  9.82e-01  1.70e-02  8.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.12e-13  8.86e-15  9.82e-01  1.70e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___KLRC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.103000  0.001780  0.879000  0.015200  0.000936 \n",
+      "[1] \"PP abf for shared variant: 0.0936%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.4275e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.717000  0.012400  0.266000  0.004590  0.000365 \n",
+      "[1] \"PP abf for shared variant: 0.0365%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-06  2.67e-08  9.82e-01  1.70e-02  9.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0979%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-08  2.03e-10  9.82e-01  1.70e-02  8.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0893%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___MALAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019900  0.000344  0.962000  0.016600  0.000897 \n",
+      "[1] \"PP abf for shared variant: 0.0897%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-03  5.49e-05  9.79e-01  1.69e-02  9.21e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0921%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.13e-11  1.06e-12  9.82e-01  1.70e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8351e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.675000  0.011700  0.307000  0.005310  0.000344 \n",
+      "[1] \"PP abf for shared variant: 0.0344%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.83e-07  1.18e-08  9.82e-01  1.70e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.29e-05  1.26e-06  9.82e-01  1.70e-02  9.52e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0952%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___FGFBP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.331000  0.005720  0.652000  0.011300  0.000741 \n",
+      "[1] \"PP abf for shared variant: 0.0741%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009590  0.000166  0.972000  0.016800  0.000992 \n",
+      "[1] \"PP abf for shared variant: 0.0992%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.95e-04  1.03e-05  9.81e-01  1.70e-02  9.58e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0958%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-03  1.78e-05  9.81e-01  1.70e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.049200  0.000851  0.933000  0.016100  0.000958 \n",
+      "[1] \"PP abf for shared variant: 0.0958%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.574e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.765000  0.012600  0.218000  0.003610  0.000249 \n",
+      "[1] \"PP abf for shared variant: 0.0249%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.33e-06  1.44e-07  9.82e-01  1.70e-02  1.01e-03 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.11e-15  1.40e-16  9.82e-01  1.70e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2336e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.663000  0.011500  0.319000  0.005520  0.000347 \n",
+      "[1] \"PP abf for shared variant: 0.0347%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.73e-10  1.34e-11  9.82e-01  1.70e-02  9.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0918%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"NK_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.92e-11  1.02e-12  9.82e-01  1.70e-02  9.08e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0908%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___NRGN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7437e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.550000  0.009620  0.432000  0.007540  0.000504 \n",
+      "[1] \"PP abf for shared variant: 0.0504%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.297000  0.005190  0.685000  0.012000  0.000649 \n",
+      "[1] \"PP abf for shared variant: 0.0649%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-03  2.93e-05  9.80e-01  1.71e-02  9.20e-04 \n",
+      "[1] \"PP abf for shared variant: 0.092%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.85e-06  3.23e-08  9.82e-01  1.71e-02  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.46e-16  7.78e-18  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-06  1.96e-08  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-09  3.07e-11  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016000  0.000279  0.966000  0.016900  0.000913 \n",
+      "[1] \"PP abf for shared variant: 0.0913%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.26e-09  1.62e-10  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-14  2.00e-16  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VCAN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.478000  0.008360  0.504000  0.008800  0.000507 \n",
+      "[1] \"PP abf for shared variant: 0.0507%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UQCRH\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-05  2.78e-07  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC7A7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.014500  0.000253  0.967000  0.016900  0.000902 \n",
+      "[1] \"PP abf for shared variant: 0.0902%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EPB41L3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.263000  0.004550  0.720000  0.012400  0.000696 \n",
+      "[1] \"PP abf for shared variant: 0.0696%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007850  0.000137  0.974000  0.017000  0.000953 \n",
+      "[1] \"PP abf for shared variant: 0.0953%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.44e-14  7.77e-16  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015700  0.000275  0.966000  0.016900  0.000867 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___QARS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.292000  0.005090  0.691000  0.012100  0.000707 \n",
+      "[1] \"PP abf for shared variant: 0.0707%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.46e-06  7.79e-08  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.30e-15  9.26e-17  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.04e-06  1.40e-07  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-15  5.28e-17  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-08  2.55e-10  9.82e-01  1.71e-02  9.38e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0938%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-09  3.35e-11  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.94e-12  1.04e-13  9.82e-01  1.71e-02  8.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0875%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.021800  0.000381  0.960000  0.016800  0.000869 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.65e-10  1.69e-11  9.82e-01  1.71e-02  8.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0875%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___CSTA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.93e-04  1.56e-05  9.81e-01  1.71e-02  9.12e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0912%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.56e-07  4.47e-09  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.145000  0.002530  0.837000  0.014600  0.000787 \n",
+      "[1] \"PP abf for shared variant: 0.0787%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-12  2.85e-14  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-03  4.57e-05  9.79e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.75e-15  1.18e-16  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.359000  0.006280  0.623000  0.010900  0.000611 \n",
+      "[1] \"PP abf for shared variant: 0.0611%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TNFAIP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.349000  0.006100  0.633000  0.011000  0.000752 \n",
+      "[1] \"PP abf for shared variant: 0.0752%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016800  0.000293  0.965000  0.016900  0.000879 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.89e-12  1.38e-13  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.95e-20  1.04e-21  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-11  3.55e-13  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-05  2.56e-07  9.82e-01  1.71e-02  9.15e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0915%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.98e-10  6.96e-12  9.82e-01  1.71e-02  9.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0965%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.96e-09  8.67e-11  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-04  1.92e-06  9.82e-01  1.71e-02  8.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0892%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-03  3.35e-05  9.80e-01  1.71e-02  9.10e-04 \n",
+      "[1] \"PP abf for shared variant: 0.091%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000632  0.000011  0.981000  0.017100  0.000885 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-13  3.78e-15  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-15  1.88e-17  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.20e-09  1.61e-10  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-04  3.67e-06  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.47e-11  1.13e-12  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1173e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.380000  0.006630  0.603000  0.010500  0.000585 \n",
+      "[1] \"PP abf for shared variant: 0.0585%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.44e-07  1.13e-08  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.59e-10  4.53e-12  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-07  2.70e-09  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-03  5.81e-05  9.79e-01  1.71e-02  9.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.095%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.94e-09  1.39e-10  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.11e-06  8.94e-08  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.025400  0.000443  0.957000  0.016700  0.000875 \n",
+      "[1] \"PP abf for shared variant: 0.0875%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.34e-11  2.33e-13  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.94e-11  1.39e-12  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.54e-05  1.14e-06  9.82e-01  1.71e-02  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.25e-04  7.42e-06  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___CST3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7382e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.591000  0.010300  0.391000  0.006830  0.000435 \n",
+      "[1] \"PP abf for shared variant: 0.0435%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.037400  0.000654  0.945000  0.016500  0.000871 \n",
+      "[1] \"PP abf for shared variant: 0.0871%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.277000  0.004840  0.705000  0.012300  0.000699 \n",
+      "[1] \"PP abf for shared variant: 0.0699%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-15  5.23e-17  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.327000  0.005720  0.655000  0.011400  0.000638 \n",
+      "[1] \"PP abf for shared variant: 0.0638%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-03  2.06e-05  9.81e-01  1.71e-02  9.07e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0907%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___AP1S2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.272000  0.004760  0.710000  0.012400  0.000667 \n",
+      "[1] \"PP abf for shared variant: 0.0667%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-10  3.19e-12  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.15e-09  1.25e-10  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SEC11A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015900  0.000277  0.966000  0.016900  0.000888 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-04  6.62e-06  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.57e-11  1.50e-12  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-08  2.83e-10  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.95e-11  1.39e-12  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.83e-07  4.95e-09  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-07  8.35e-09  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-07  2.96e-09  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___ATP5G2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009990  0.000175  0.972000  0.017000  0.000880 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-05  1.77e-07  9.82e-01  1.71e-02  9.53e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0953%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.95e-09  1.21e-10  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.017100  0.000299  0.965000  0.016900  0.000879 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.52e-13  1.14e-14  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.68e-16  1.52e-17  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-17  2.59e-19  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-06  2.53e-08  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.49e-07  4.35e-09  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.50e-16  4.37e-18  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-04  3.71e-06  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EVI2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.379000  0.006630  0.603000  0.010500  0.000588 \n",
+      "[1] \"PP abf for shared variant: 0.0588%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.052000  0.000909  0.930000  0.016200  0.000964 \n",
+      "[1] \"PP abf for shared variant: 0.0964%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.026500  0.000462  0.956000  0.016700  0.000864 \n",
+      "[1] \"PP abf for shared variant: 0.0864%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.67e-04  1.69e-05  9.81e-01  1.71e-02  8.94e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0894%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-12  2.00e-14  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___LGALS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.225000  0.003940  0.757000  0.013200  0.000717 \n",
+      "[1] \"PP abf for shared variant: 0.0717%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.334000  0.005830  0.648000  0.011300  0.000648 \n",
+      "[1] \"PP abf for shared variant: 0.0648%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.72e-11  1.00e-12  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-14  2.66e-16  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.77e-11  1.36e-12  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.79e-06  4.87e-08  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___GPX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.042700  0.000746  0.939000  0.016400  0.000893 \n",
+      "[1] \"PP abf for shared variant: 0.0893%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___LTA4H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.551000  0.009630  0.431000  0.007530  0.000485 \n",
+      "[1] \"PP abf for shared variant: 0.0485%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RNASE6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26900   0.00469   0.71300   0.01250   0.00085 \n",
+      "[1] \"PP abf for shared variant: 0.085%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.106000  0.001840  0.876000  0.015300  0.000809 \n",
+      "[1] \"PP abf for shared variant: 0.0809%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___BTF3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.011200  0.000196  0.971000  0.017000  0.000881 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___DRAM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1829e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.564000  0.009860  0.418000  0.007310  0.000436 \n",
+      "[1] \"PP abf for shared variant: 0.0436%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___IL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-03  1.95e-05  9.81e-01  1.70e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008960  0.000157  0.973000  0.017000  0.001310 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.37e-07  1.29e-08  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.41e-11  1.64e-12  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-13  2.60e-15  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.322000  0.005630  0.660000  0.011500  0.000638 \n",
+      "[1] \"PP abf for shared variant: 0.0638%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-14  2.59e-16  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.90e-12  1.73e-13  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___IPO7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.095600  0.001670  0.886000  0.015500  0.000932 \n",
+      "[1] \"PP abf for shared variant: 0.0932%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-07  2.82e-09  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.10e-12  1.24e-13  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.11e-13  7.19e-15  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2966e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.518000  0.009060  0.464000  0.008100  0.000563 \n",
+      "[1] \"PP abf for shared variant: 0.0563%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-08  6.39e-10  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-05  2.12e-07  9.82e-01  1.71e-02  9.03e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0903%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___CRTAP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015700  0.000274  0.966000  0.016900  0.000876 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.40600   0.00709   0.57600   0.01010   0.00063 \n",
+      "[1] \"PP abf for shared variant: 0.063%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.92e-09  6.85e-11  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.41e-03  4.21e-05  9.80e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-09  2.82e-11  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.357000  0.006250  0.625000  0.010900  0.000595 \n",
+      "[1] \"PP abf for shared variant: 0.0595%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051500  0.000899  0.931000  0.016300  0.000844 \n",
+      "[1] \"PP abf for shared variant: 0.0844%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.07e-10  1.06e-11  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.13e-11  3.73e-13  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TKT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-04  4.05e-06  9.82e-01  1.71e-02  1.09e-03 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.48e-15  6.07e-17  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.131000  0.002250  0.852000  0.014700  0.000793 \n",
+      "[1] \"PP abf for shared variant: 0.0793%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ACTN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.92e-04  1.54e-05  9.81e-01  1.70e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.56e-15  1.50e-16  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAND1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4561e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.421000  0.007360  0.561000  0.009800  0.000551 \n",
+      "[1] \"PP abf for shared variant: 0.0551%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.20e-15  7.34e-17  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PRF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.014000  0.000244  0.968000  0.016900  0.000907 \n",
+      "[1] \"PP abf for shared variant: 0.0907%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-05  5.44e-07  9.82e-01  1.71e-02  8.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0892%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EFHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.049600  0.000867  0.932000  0.016300  0.000885 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-10  2.71e-12  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.56e-16  1.32e-17  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.45e-14  1.13e-15  9.82e-01  1.71e-02  8.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.14e-13  8.98e-15  9.82e-01  1.71e-02  8.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___APBA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.095100  0.001570  0.888000  0.014600  0.000836 \n",
+      "[1] \"PP abf for shared variant: 0.0836%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.43e-04  7.75e-06  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FAIM3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.072800  0.001270  0.909000  0.015900  0.000901 \n",
+      "[1] \"PP abf for shared variant: 0.0901%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.545000  0.009530  0.437000  0.007630  0.000494 \n",
+      "[1] \"PP abf for shared variant: 0.0494%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.362000  0.006330  0.620000  0.010800  0.000627 \n",
+      "[1] \"PP abf for shared variant: 0.0627%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016600  0.000290  0.965000  0.016900  0.000895 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-14  4.91e-16  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___C11orf1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.8471e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.661000  0.011500  0.322000  0.005620  0.000357 \n",
+      "[1] \"PP abf for shared variant: 0.0357%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.94e-04  8.62e-06  9.81e-01  1.71e-02  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.71e-17  4.72e-19  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.42e-17  7.72e-19  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GZMM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.411000  0.007180  0.571000  0.009970  0.000587 \n",
+      "[1] \"PP abf for shared variant: 0.0587%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-06  5.96e-08  9.82e-01  1.71e-02  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.84e-04  1.37e-05  9.81e-01  1.71e-02  9.05e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0905%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.300000  0.005250  0.682000  0.011900  0.000705 \n",
+      "[1] \"PP abf for shared variant: 0.0705%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CTSW__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.256000  0.004480  0.726000  0.012700  0.000756 \n",
+      "[1] \"PP abf for shared variant: 0.0756%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-05  4.84e-07  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.34e-18  2.35e-20  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FLT3LG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.28e-04  5.72e-06  9.82e-01  1.71e-02  9.02e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0902%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NKG7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-05  6.25e-07  9.82e-01  1.71e-02  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.052800  0.000923  0.929000  0.016200  0.000907 \n",
+      "[1] \"PP abf for shared variant: 0.0907%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.3964e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.630000  0.011000  0.353000  0.006160  0.000457 \n",
+      "[1] \"PP abf for shared variant: 0.0457%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.346000  0.006050  0.636000  0.011100  0.000673 \n",
+      "[1] \"PP abf for shared variant: 0.0673%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-04  3.08e-06  9.82e-01  1.71e-02  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-06  6.43e-08  9.82e-01  1.71e-02  8.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0893%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-09  4.12e-11  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-12  3.88e-14  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.97e-14  1.22e-15  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ATP1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 9.0977e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.746000  0.013000  0.237000  0.004130  0.000278 \n",
+      "[1] \"PP abf for shared variant: 0.0278%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.226000  0.003950  0.756000  0.013200  0.000733 \n",
+      "[1] \"PP abf for shared variant: 0.0733%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SYNE1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16900   0.00296   0.81300   0.01420   0.00081 \n",
+      "[1] \"PP abf for shared variant: 0.081%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.137e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.648000  0.011300  0.335000  0.005850  0.000366 \n",
+      "[1] \"PP abf for shared variant: 0.0366%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.85e-06  3.22e-08  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-10  3.57e-12  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PDE3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-04  2.39e-06  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-19  1.97e-21  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.78e-05  1.01e-06  9.82e-01  1.71e-02  8.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0893%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZEB2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012400  0.000216  0.970000  0.016900  0.000917 \n",
+      "[1] \"PP abf for shared variant: 0.0917%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___LTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-08  5.36e-10  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PTPN7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.378000  0.006610  0.604000  0.010500  0.000645 \n",
+      "[1] \"PP abf for shared variant: 0.0645%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-12  2.13e-14  9.82e-01  1.71e-02  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-10  2.98e-12  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___IER2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1556e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.701000  0.012300  0.281000  0.004910  0.000335 \n",
+      "[1] \"PP abf for shared variant: 0.0335%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.72e-05  1.35e-06  9.82e-01  1.71e-02  8.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.28e-09  2.24e-11  9.82e-01  1.71e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.04e-20  5.32e-22  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-15  1.01e-16  9.82e-01  1.71e-02  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CMC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-03  1.82e-05  9.81e-01  1.71e-02  9.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0999%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-04  2.22e-06  9.82e-01  1.71e-02  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.02e-13  7.03e-15  9.82e-01  1.71e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GZMB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.030800  0.000539  0.951000  0.016600  0.000886 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___AKNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.690000  0.012100  0.292000  0.005100  0.000338 \n",
+      "[1] \"PP abf for shared variant: 0.0338%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9277e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.583000  0.010200  0.399000  0.006970  0.000447 \n",
+      "[1] \"PP abf for shared variant: 0.0447%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.30e-20  9.26e-22  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.89e-08  5.05e-10  9.82e-01  1.71e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-04  6.77e-06  9.82e-01  1.71e-02  9.25e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0925%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.039300  0.000686  0.943000  0.016500  0.000976 \n",
+      "[1] \"PP abf for shared variant: 0.0976%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.041800  0.000731  0.940000  0.016400  0.000940 \n",
+      "[1] \"PP abf for shared variant: 0.094%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-16  3.56e-18  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-11  4.64e-13  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-14  1.82e-16  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___METRNL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4496e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.744000  0.012400  0.239000  0.003990  0.000314 \n",
+      "[1] \"PP abf for shared variant: 0.0314%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.030500  0.000533  0.951000  0.016600  0.001000 \n",
+      "[1] \"PP abf for shared variant: 0.1%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CAMK4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.02e-07  7.03e-09  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___BHLHE40__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.015600  0.000273  0.966000  0.016900  0.000957 \n",
+      "[1] \"PP abf for shared variant: 0.0957%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___IFITM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2604e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.591000  0.010300  0.391000  0.006830  0.000497 \n",
+      "[1] \"PP abf for shared variant: 0.0497%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08870   0.00155   0.89300   0.01560   0.00090 \n",
+      "[1] \"PP abf for shared variant: 0.09%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CD8B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.44e-04  4.27e-06  9.82e-01  1.71e-02  9.23e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0923%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-18  2.98e-20  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NGFRAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.071600  0.001180  0.911000  0.015000  0.000857 \n",
+      "[1] \"PP abf for shared variant: 0.0857%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.00e-14  1.40e-15  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TUBA4A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.063900  0.001120  0.918000  0.016000  0.000897 \n",
+      "[1] \"PP abf for shared variant: 0.0897%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-05  6.43e-07  9.82e-01  1.71e-02  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-19  3.93e-21  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RCAN3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.37e-06  1.46e-07  9.82e-01  1.71e-02  8.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.88e-13  8.52e-15  9.82e-01  1.71e-02  8.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.86e-04  6.74e-06  9.82e-01  1.71e-02  9.26e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0926%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.54e-11  1.32e-12  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.89e-08  1.55e-09  9.82e-01  1.71e-02  9.36e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0936%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___DNAJB6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.101000  0.001770  0.881000  0.015400  0.000936 \n",
+      "[1] \"PP abf for shared variant: 0.0936%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RAP1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.077e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.770000  0.013500  0.213000  0.003710  0.000271 \n",
+      "[1] \"PP abf for shared variant: 0.0271%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-05  3.66e-07  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.85e-03  4.97e-05  9.79e-01  1.71e-02  9.12e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0912%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008530  0.000149  0.973000  0.017000  0.000889 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CCDC104__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.664000  0.011000  0.320000  0.005290  0.000341 \n",
+      "[1] \"PP abf for shared variant: 0.0341%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.96e-09  1.57e-10  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-08  2.25e-10  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.08e-06  1.06e-07  9.82e-01  1.71e-02  9.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0943%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.71e-18  8.24e-20  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.055100  0.000963  0.927000  0.016200  0.000891 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TESPA1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.022300  0.000366  0.961000  0.015700  0.000938 \n",
+      "[1] \"PP abf for shared variant: 0.0938%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.011300  0.000198  0.971000  0.017000  0.000881 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PSMA7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.349000  0.006090  0.634000  0.011100  0.000614 \n",
+      "[1] \"PP abf for shared variant: 0.0614%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PLEK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.316000  0.005520  0.666000  0.011600  0.000676 \n",
+      "[1] \"PP abf for shared variant: 0.0676%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.60e-04  6.29e-06  9.82e-01  1.71e-02  9.16e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0916%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.71e-16  1.52e-17  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.4281e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.554000  0.009680  0.428000  0.007480  0.000451 \n",
+      "[1] \"PP abf for shared variant: 0.0451%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___KLRD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007160  0.000125  0.975000  0.017000  0.000890 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MYC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007250  0.000127  0.975000  0.017000  0.000877 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.319000  0.005580  0.663000  0.011600  0.000663 \n",
+      "[1] \"PP abf for shared variant: 0.0663%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___KLF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.391e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.552000  0.009640  0.430000  0.007520  0.000472 \n",
+      "[1] \"PP abf for shared variant: 0.0472%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.154000  0.002680  0.829000  0.014500  0.000746 \n",
+      "[1] \"PP abf for shared variant: 0.0746%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HNRNPA2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.602000  0.010500  0.381000  0.006650  0.000423 \n",
+      "[1] \"PP abf for shared variant: 0.0423%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ARAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3907e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.794000  0.013900  0.188000  0.003290  0.000242 \n",
+      "[1] \"PP abf for shared variant: 0.0242%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-16  4.79e-18  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-05  3.12e-07  9.82e-01  1.71e-02  9.57e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0957%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.96e-05  1.04e-06  9.82e-01  1.71e-02  9.28e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0928%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-12  1.84e-14  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.33e-06  1.46e-07  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-05  2.02e-07  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.204000  0.003560  0.779000  0.013600  0.000733 \n",
+      "[1] \"PP abf for shared variant: 0.0733%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-09  4.00e-11  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-18  5.45e-20  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___LYPD3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.380000  0.006540  0.602000  0.010400  0.000623 \n",
+      "[1] \"PP abf for shared variant: 0.0623%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-04  2.63e-06  9.82e-01  1.71e-02  9.09e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0909%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.222000  0.003880  0.760000  0.013300  0.000744 \n",
+      "[1] \"PP abf for shared variant: 0.0744%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.091300  0.001590  0.891000  0.015600  0.000859 \n",
+      "[1] \"PP abf for shared variant: 0.0859%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM243\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-03  3.75e-05  9.80e-01  1.71e-02  9.06e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0906%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.27e-15  7.46e-17  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ID2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.190000  0.003320  0.792000  0.013800  0.000849 \n",
+      "[1] \"PP abf for shared variant: 0.0849%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.41e-15  7.70e-17  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.51e-10  1.14e-11  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.49e-03  7.85e-05  9.77e-01  1.71e-02  9.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0999%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.37e-25  7.61e-27  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.384000  0.006720  0.598000  0.010400  0.000657 \n",
+      "[1] \"PP abf for shared variant: 0.0657%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NFKBIA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 7.944e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.682000  0.011900  0.300000  0.005250  0.000374 \n",
+      "[1] \"PP abf for shared variant: 0.0374%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.032900  0.000575  0.949000  0.016600  0.000923 \n",
+      "[1] \"PP abf for shared variant: 0.0923%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.92e-17  1.56e-18  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-15  3.42e-17  9.82e-01  1.71e-02  9.26e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0926%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-09  2.18e-11  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GZMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.55e-07  6.20e-09  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.340000  0.005950  0.642000  0.011200  0.000641 \n",
+      "[1] \"PP abf for shared variant: 0.0641%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-10  3.17e-12  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPI1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.453000  0.007910  0.530000  0.009250  0.000585 \n",
+      "[1] \"PP abf for shared variant: 0.0585%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.38e-19  1.63e-20  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.148000  0.002590  0.834000  0.014600  0.000797 \n",
+      "[1] \"PP abf for shared variant: 0.0797%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.94e-10  8.63e-12  9.82e-01  1.71e-02  8.97e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0897%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-15  3.85e-17  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SC5D\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.443000  0.007740  0.539000  0.009420  0.000644 \n",
+      "[1] \"PP abf for shared variant: 0.0644%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-10  3.27e-12  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.22e-17  5.62e-19  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RIC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-03  3.13e-05  9.80e-01  1.71e-02  9.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0919%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.59e-11  8.01e-13  9.82e-01  1.71e-02  9.46e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0946%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.83e-05  4.90e-07  9.82e-01  1.70e-02  9.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0949%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.9814e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.683000  0.011900  0.299000  0.005230  0.000368 \n",
+      "[1] \"PP abf for shared variant: 0.0368%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRP14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-04  2.18e-06  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0298e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.627000  0.010900  0.356000  0.006200  0.000387 \n",
+      "[1] \"PP abf for shared variant: 0.0387%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.09e-04  1.24e-05  9.81e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-06  1.81e-08  9.82e-01  1.71e-02  9.08e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0908%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.08e-16  5.39e-18  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-18  1.92e-20  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-19  3.98e-21  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MYL12B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.561000  0.009800  0.422000  0.007360  0.000498 \n",
+      "[1] \"PP abf for shared variant: 0.0498%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.029700  0.000518  0.952000  0.016600  0.000867 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MIR142__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1648e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.765000  0.013400  0.218000  0.003810  0.000267 \n",
+      "[1] \"PP abf for shared variant: 0.0267%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007870  0.000137  0.974000  0.017000  0.000981 \n",
+      "[1] \"PP abf for shared variant: 0.0981%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.37e-17  4.14e-19  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.308000  0.005390  0.674000  0.011800  0.000635 \n",
+      "[1] \"PP abf for shared variant: 0.0635%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-05  1.90e-07  9.82e-01  1.71e-02  9.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0918%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PTP4A2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.425000  0.007420  0.557000  0.009730  0.000603 \n",
+      "[1] \"PP abf for shared variant: 0.0603%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.9666e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.719000  0.012600  0.264000  0.004610  0.000298 \n",
+      "[1] \"PP abf for shared variant: 0.0298%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-04  1.83e-06  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.73e-08  8.26e-10  9.82e-01  1.71e-02  8.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.09e-25  7.14e-27  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CCDC57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-06  4.00e-08  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ITGB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.462000  0.008080  0.520000  0.009080  0.000507 \n",
+      "[1] \"PP abf for shared variant: 0.0507%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.090700  0.001580  0.891000  0.015600  0.000873 \n",
+      "[1] \"PP abf for shared variant: 0.0873%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4185e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.700000  0.012200  0.283000  0.004940  0.000351 \n",
+      "[1] \"PP abf for shared variant: 0.0351%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ARF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.148000  0.002590  0.834000  0.014600  0.000768 \n",
+      "[1] \"PP abf for shared variant: 0.0768%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CD81__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.25700   0.00449   0.72500   0.01270   0.00079 \n",
+      "[1] \"PP abf for shared variant: 0.079%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-03  7.58e-05  9.78e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-03  7.59e-05  9.78e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-03  1.81e-05  9.81e-01  1.71e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-05  5.33e-07  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FOXP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.26e-04  1.44e-05  9.81e-01  1.71e-02  9.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.095%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CCL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.30e-05  1.62e-06  9.82e-01  1.71e-02  9.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0927%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013100  0.000230  0.969000  0.016900  0.000927 \n",
+      "[1] \"PP abf for shared variant: 0.0927%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___KLRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9856e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.627000  0.010900  0.356000  0.006200  0.000402 \n",
+      "[1] \"PP abf for shared variant: 0.0402%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5042e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.745000  0.013000  0.238000  0.004150  0.000282 \n",
+      "[1] \"PP abf for shared variant: 0.0282%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.274000  0.004790  0.708000  0.012400  0.000689 \n",
+      "[1] \"PP abf for shared variant: 0.0689%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-15  3.57e-17  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___C1orf21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1023e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.606000  0.010600  0.376000  0.006570  0.000407 \n",
+      "[1] \"PP abf for shared variant: 0.0407%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-10  4.70e-12  9.82e-01  1.71e-02  8.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CALM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.118000  0.002060  0.864000  0.015100  0.000914 \n",
+      "[1] \"PP abf for shared variant: 0.0914%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HOPX__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.358000  0.006250  0.624000  0.010900  0.000747 \n",
+      "[1] \"PP abf for shared variant: 0.0747%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.036800  0.000643  0.945000  0.016500  0.000909 \n",
+      "[1] \"PP abf for shared variant: 0.0909%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.49e-15  7.85e-17  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.42e-04  1.65e-05  9.81e-01  1.71e-02  9.25e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0925%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.55e-16  6.18e-18  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009110  0.000159  0.973000  0.017000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.69e-11  8.19e-13  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.507e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.734000  0.012800  0.249000  0.004340  0.000321 \n",
+      "[1] \"PP abf for shared variant: 0.0321%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-17  2.61e-19  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-13  1.94e-15  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PTMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003030  0.000053  0.979000  0.017100  0.000910 \n",
+      "[1] \"PP abf for shared variant: 0.091%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-10  3.19e-12  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GNG2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.244000  0.004270  0.738000  0.012900  0.000734 \n",
+      "[1] \"PP abf for shared variant: 0.0734%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.024000  0.000420  0.958000  0.016700  0.000902 \n",
+      "[1] \"PP abf for shared variant: 0.0902%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.34e-07  1.28e-08  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-10  6.87e-12  9.82e-01  1.71e-02  8.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.82e-04  1.72e-05  9.81e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CD27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.689e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.605000  0.010600  0.378000  0.006590  0.000431 \n",
+      "[1] \"PP abf for shared variant: 0.0431%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002230  0.000039  0.980000  0.017100  0.000927 \n",
+      "[1] \"PP abf for shared variant: 0.0927%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-09  2.49e-11  9.83e-01  1.60e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.19e-16  1.25e-17  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___REL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.691e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.598000  0.010400  0.384000  0.006710  0.000465 \n",
+      "[1] \"PP abf for shared variant: 0.0465%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-11  3.60e-13  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.423000  0.007390  0.559000  0.009770  0.000601 \n",
+      "[1] \"PP abf for shared variant: 0.0601%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-08  2.80e-10  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.25e-13  5.68e-15  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.04e-07  8.81e-09  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SEPT7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13700   0.00239   0.84500   0.01480   0.00082 \n",
+      "[1] \"PP abf for shared variant: 0.082%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.149000  0.002590  0.834000  0.014600  0.000838 \n",
+      "[1] \"PP abf for shared variant: 0.0838%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.478000  0.008360  0.504000  0.008800  0.000526 \n",
+      "[1] \"PP abf for shared variant: 0.0526%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.01e-09  8.75e-11  9.82e-01  1.71e-02  9.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0986%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___FCGR3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08440   0.00147   0.89700   0.01570   0.00097 \n",
+      "[1] \"PP abf for shared variant: 0.097%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PSMB9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.645e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.687000  0.012000  0.296000  0.005160  0.000422 \n",
+      "[1] \"PP abf for shared variant: 0.0422%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.39e-09  4.18e-11  9.82e-01  1.71e-02  8.94e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0894%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.77e-08  8.34e-10  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.384000  0.006700  0.598000  0.010500  0.000693 \n",
+      "[1] \"PP abf for shared variant: 0.0693%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-03  1.81e-05  9.81e-01  1.71e-02  9.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0959%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.77e-16  1.53e-17  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-13  6.17e-15  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.089100  0.001560  0.893000  0.015600  0.000916 \n",
+      "[1] \"PP abf for shared variant: 0.0916%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.077100  0.001350  0.905000  0.015800  0.000834 \n",
+      "[1] \"PP abf for shared variant: 0.0834%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-14  6.24e-16  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5519e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.538000  0.009390  0.445000  0.007770  0.000464 \n",
+      "[1] \"PP abf for shared variant: 0.0464%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.79e-11  1.19e-12  9.82e-01  1.71e-02  1.05e-03 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___DOK2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.35900   0.00628   0.62300   0.01090   0.00064 \n",
+      "[1] \"PP abf for shared variant: 0.064%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.25e-06  7.43e-08  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CALR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9449e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.532000  0.009290  0.451000  0.007870  0.000499 \n",
+      "[1] \"PP abf for shared variant: 0.0499%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TTC38\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1223e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.542000  0.009470  0.440000  0.007670  0.000521 \n",
+      "[1] \"PP abf for shared variant: 0.0521%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.026900  0.000470  0.955000  0.016700  0.000927 \n",
+      "[1] \"PP abf for shared variant: 0.0927%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.43e-03  9.49e-05  9.77e-01  1.71e-02  9.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0918%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.88e-09  6.78e-11  9.82e-01  1.71e-02  9.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0981%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-09  2.20e-11  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.19e-06  9.05e-08  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.85e-16  1.02e-17  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PASK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.30e-07  8.65e-09  9.83e-01  1.60e-02  9.08e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0908%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-10  2.55e-12  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.29e-17  1.27e-18  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___OXNAD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1359e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.606000  0.010600  0.377000  0.006580  0.000504 \n",
+      "[1] \"PP abf for shared variant: 0.0504%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.044700  0.000781  0.937000  0.016400  0.000921 \n",
+      "[1] \"PP abf for shared variant: 0.0921%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.32e-16  5.80e-18  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-03  2.46e-05  9.81e-01  1.71e-02  8.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TYROBP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007760  0.000136  0.974000  0.017000  0.000893 \n",
+      "[1] \"PP abf for shared variant: 0.0893%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YBX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1331e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.77500   0.01350   0.20700   0.00362   0.00026 \n",
+      "[1] \"PP abf for shared variant: 0.026%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.40e-07  1.47e-08  9.82e-01  1.71e-02  9.00e-04 \n",
+      "[1] \"PP abf for shared variant: 0.09%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.06e-04  5.34e-06  9.82e-01  1.71e-02  1.01e-03 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___IL7R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001770  0.000031  0.980000  0.017100  0.000887 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RHOH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.028700  0.000502  0.953000  0.016600  0.000917 \n",
+      "[1] \"PP abf for shared variant: 0.0917%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.92e-16  6.85e-18  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.03e-18  8.79e-20  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.017300  0.000301  0.965000  0.016800  0.000914 \n",
+      "[1] \"PP abf for shared variant: 0.0914%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___DBI__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008280  0.000145  0.974000  0.017000  0.000906 \n",
+      "[1] \"PP abf for shared variant: 0.0906%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-11  3.09e-13  9.82e-01  1.71e-02  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-09  3.59e-11  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.36e-07  7.61e-09  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___POMP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.46500   0.00813   0.51700   0.00903   0.00051 \n",
+      "[1] \"PP abf for shared variant: 0.051%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.04e-15  1.40e-16  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RAB8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0817e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.671000  0.011700  0.312000  0.005440  0.000339 \n",
+      "[1] \"PP abf for shared variant: 0.0339%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___GZMH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.140000  0.002450  0.842000  0.014700  0.000816 \n",
+      "[1] \"PP abf for shared variant: 0.0816%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.35e-05  1.63e-06  9.82e-01  1.71e-02  1.01e-03 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.89e-10  5.04e-12  9.82e-01  1.71e-02  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-19  2.72e-21  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-17  4.64e-19  9.82e-01  1.71e-02  8.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___ABLIM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.41e-03  7.34e-05  9.78e-01  1.63e-02  9.34e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0934%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EIF4A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8946e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.59700   0.01040   0.38500   0.00672   0.00046 \n",
+      "[1] \"PP abf for shared variant: 0.046%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.35e-04  5.85e-06  9.82e-01  1.71e-02  9.40e-04 \n",
+      "[1] \"PP abf for shared variant: 0.094%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RP11-291B21.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-05  3.20e-07  9.83e-01  1.64e-02  8.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.29e-19  5.75e-21  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.73e-18  6.52e-20  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-09  5.83e-11  9.82e-01  1.71e-02  9.05e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0905%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.41e-10  9.46e-12  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.92e-18  8.60e-20  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012400  0.000218  0.969000  0.016900  0.000932 \n",
+      "[1] \"PP abf for shared variant: 0.0932%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.95e-14  8.65e-16  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-11  2.36e-13  9.82e-01  1.71e-02  9.40e-04 \n",
+      "[1] \"PP abf for shared variant: 0.094%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S1PR5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1943e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.76200   0.01260   0.22200   0.00366   0.00030 \n",
+      "[1] \"PP abf for shared variant: 0.03%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD8T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.79e-05  1.19e-06  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.058400  0.001020  0.924000  0.016100  0.000912 \n",
+      "[1] \"PP abf for shared variant: 0.0912%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CHCHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.151000  0.002630  0.831000  0.014500  0.000846 \n",
+      "[1] \"PP abf for shared variant: 0.0846%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.45e-04  1.65e-05  9.81e-01  1.71e-02  8.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FMNL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.234000  0.004100  0.748000  0.013100  0.000769 \n",
+      "[1] \"PP abf for shared variant: 0.0769%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-24  3.39e-26  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.13e-07  1.07e-08  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HERPUD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.267e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.493000  0.008610  0.490000  0.008550  0.000533 \n",
+      "[1] \"PP abf for shared variant: 0.0533%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ANXA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-05  2.92e-07  9.82e-01  1.71e-02  8.94e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0894%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SOD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.017300  0.000302  0.965000  0.016800  0.000880 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-17  4.00e-19  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-13  2.36e-15  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP1B3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.24000   0.00419   0.74200   0.01300   0.00071 \n",
+      "[1] \"PP abf for shared variant: 0.071%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.437000  0.007640  0.545000  0.009520  0.000533 \n",
+      "[1] \"PP abf for shared variant: 0.0533%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-25  1.93e-27  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-11  4.62e-13  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EML4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.94e-04  6.89e-06  9.82e-01  1.71e-02  9.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0943%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-03  4.55e-05  9.79e-01  1.71e-02  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MCL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-06  3.99e-08  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NOG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.082400  0.001360  0.901000  0.014800  0.000828 \n",
+      "[1] \"PP abf for shared variant: 0.0828%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PRMT2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-03  2.47e-05  9.80e-01  1.71e-02  9.39e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0939%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-05  4.65e-07  9.82e-01  1.71e-02  8.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0893%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.409000  0.007120  0.574000  0.009990  0.000584 \n",
+      "[1] \"PP abf for shared variant: 0.0584%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-11  2.37e-13  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FAM129A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-06  1.81e-08  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-15  3.44e-17  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.2936e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.564000  0.009860  0.418000  0.007300  0.000426 \n",
+      "[1] \"PP abf for shared variant: 0.0426%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-25  4.70e-27  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ALOX5AP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-03  6.63e-05  9.78e-01  1.71e-02  9.01e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0901%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TSHZ2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-03  2.16e-05  9.81e-01  1.71e-02  9.09e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0909%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.39e-06  1.64e-07  9.82e-01  1.71e-02  9.26e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0926%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ARHGDIB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-05  4.30e-07  9.82e-01  1.71e-02  8.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0896%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-10  4.01e-12  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-20  2.37e-22  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-28  2.75e-30  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-27  1.78e-29  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YBX1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.30e-05  4.02e-07  9.82e-01  1.71e-02  9.60e-04 \n",
+      "[1] \"PP abf for shared variant: 0.096%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.69e-25  1.52e-26  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___JUND__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.279e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.36000   0.00629   0.62200   0.01090   0.00062 \n",
+      "[1] \"PP abf for shared variant: 0.062%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.30e-07  7.52e-09  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-26  3.38e-28  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.011000  0.000193  0.971000  0.016900  0.001320 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.81e-11  1.01e-12  9.82e-01  1.71e-02  9.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0959%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF18\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.016400  0.000286  0.966000  0.016900  0.000903 \n",
+      "[1] \"PP abf for shared variant: 0.0903%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MYO1F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.86e-03  6.75e-05  9.78e-01  1.71e-02  8.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0892%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.82e-24  1.19e-25  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.30e-09  1.28e-10  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.441000  0.007710  0.541000  0.009450  0.000555 \n",
+      "[1] \"PP abf for shared variant: 0.0555%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.49e-25  1.66e-26  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-04  4.63e-06  9.82e-01  1.71e-02  9.01e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0901%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VMP1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.226000  0.003950  0.756000  0.013200  0.000713 \n",
+      "[1] \"PP abf for shared variant: 0.0713%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.51e-06  1.31e-07  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-25  7.39e-27  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.338000  0.005900  0.645000  0.011300  0.000617 \n",
+      "[1] \"PP abf for shared variant: 0.0617%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GALM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.255000  0.004460  0.727000  0.012700  0.000747 \n",
+      "[1] \"PP abf for shared variant: 0.0747%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.054900  0.000959  0.927000  0.016200  0.000846 \n",
+      "[1] \"PP abf for shared variant: 0.0846%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001830  0.000032  0.980000  0.017100  0.000881 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.39e-14  4.17e-16  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-16  6.26e-18  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ANXA5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019800  0.000346  0.962000  0.016800  0.000872 \n",
+      "[1] \"PP abf for shared variant: 0.0872%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___IDS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.54e-04  1.49e-05  9.81e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ARID5B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.113000  0.001980  0.869000  0.015200  0.000822 \n",
+      "[1] \"PP abf for shared variant: 0.0822%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___IMPDH2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-05  1.77e-07  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.56e-14  1.15e-15  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ST13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.25e-07  1.62e-08  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CXCR3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009150  0.000159  0.973000  0.016900  0.000907 \n",
+      "[1] \"PP abf for shared variant: 0.0907%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-06  2.97e-08  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.54e-15  4.44e-17  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SPOCK2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.032000  0.000559  0.950000  0.016600  0.000879 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___C15orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.169000  0.002810  0.814000  0.013500  0.000766 \n",
+      "[1] \"PP abf for shared variant: 0.0766%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPF\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1448e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.629000  0.011000  0.353000  0.006170  0.000422 \n",
+      "[1] \"PP abf for shared variant: 0.0422%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-14  2.36e-16  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FAM134B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21600   0.00377   0.76600   0.01340   0.00073 \n",
+      "[1] \"PP abf for shared variant: 0.073%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ISG20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007670  0.000134  0.974000  0.017000  0.000976 \n",
+      "[1] \"PP abf for shared variant: 0.0976%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.63e-10  8.08e-12  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NUCB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.086700  0.001520  0.895000  0.015600  0.000867 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.52e-03  4.41e-05  9.79e-01  1.71e-02  9.12e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0912%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LINC00493__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.117000  0.002040  0.865000  0.015100  0.000871 \n",
+      "[1] \"PP abf for shared variant: 0.0871%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-25  1.92e-27  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006410  0.000112  0.976000  0.017000  0.000913 \n",
+      "[1] \"PP abf for shared variant: 0.0913%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-11  1.85e-13  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CDC42__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008140  0.000142  0.974000  0.017000  0.000898 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF1B\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.036700  0.000641  0.945000  0.016500  0.000947 \n",
+      "[1] \"PP abf for shared variant: 0.0947%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.052500  0.000918  0.929000  0.016200  0.000845 \n",
+      "[1] \"PP abf for shared variant: 0.0845%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-18  2.84e-20  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ACTN4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002350  0.000041  0.980000  0.017100  0.000891 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___IKZF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.132000  0.002310  0.850000  0.014800  0.000782 \n",
+      "[1] \"PP abf for shared variant: 0.0782%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.312000  0.005450  0.670000  0.011700  0.000689 \n",
+      "[1] \"PP abf for shared variant: 0.0689%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-17  4.59e-19  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.99e-09  1.75e-10  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.422000  0.007370  0.560000  0.009790  0.000559 \n",
+      "[1] \"PP abf for shared variant: 0.0559%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NAMPT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8087e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.464000  0.008120  0.518000  0.009040  0.000704 \n",
+      "[1] \"PP abf for shared variant: 0.0704%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-05  3.67e-07  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CALM2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.33e-05  1.46e-06  9.82e-01  1.71e-02  8.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0892%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-15  2.69e-17  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ND2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.188000  0.003290  0.794000  0.013900  0.000769 \n",
+      "[1] \"PP abf for shared variant: 0.0769%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.66e-15  1.51e-16  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD52__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.21e-04  9.11e-06  9.81e-01  1.71e-02  8.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0896%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-07  1.75e-09  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___H3F3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.36e-05  7.61e-07  9.82e-01  1.71e-02  9.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.095%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ADTRP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.27e-08  5.71e-10  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16400   0.00287   0.81800   0.01430   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.057300  0.001000  0.925000  0.016100  0.000853 \n",
+      "[1] \"PP abf for shared variant: 0.0853%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.69e-05  1.69e-06  9.82e-01  1.71e-02  9.14e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0914%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.71e-05  4.74e-07  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___DYNLL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-03  3.05e-05  9.80e-01  1.71e-02  9.04e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0904%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAMSN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.55e-05  6.21e-07  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LMNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-09  2.58e-11  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ND5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-07  3.12e-09  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-21  1.84e-23  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RUNX3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.134000  0.002350  0.848000  0.014800  0.000835 \n",
+      "[1] \"PP abf for shared variant: 0.0835%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.94e-18  6.89e-20  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-05  1.93e-07  9.82e-01  1.71e-02  9.25e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0925%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ERGIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.423e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.460000  0.008040  0.522000  0.009120  0.000535 \n",
+      "[1] \"PP abf for shared variant: 0.0535%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.96e-08  5.18e-10  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TYMP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.045600  0.000796  0.936000  0.016400  0.000857 \n",
+      "[1] \"PP abf for shared variant: 0.0857%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.135000  0.002350  0.847000  0.014800  0.000833 \n",
+      "[1] \"PP abf for shared variant: 0.0833%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-22  3.12e-24  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-04  5.36e-06  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.58e-23  1.32e-24  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.69e-21  1.34e-22  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ND4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.357000  0.006250  0.625000  0.010900  0.000634 \n",
+      "[1] \"PP abf for shared variant: 0.0634%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.56e-04  9.71e-06  9.81e-01  1.71e-02  1.01e-03 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP5E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.14e-03  7.23e-05  9.78e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-10  1.90e-12  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.75e-13  1.18e-14  9.82e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.13e-22  1.42e-23  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-09  5.95e-11  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP2B4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.046200  0.000807  0.936000  0.016300  0.000891 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ARPC1B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.539000  0.009420  0.443000  0.007740  0.000507 \n",
+      "[1] \"PP abf for shared variant: 0.0507%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PDCD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004080  0.000067  0.979000  0.016100  0.000882 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-20  1.79e-22  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-26  3.19e-28  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.01e-22  5.24e-24  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-13  6.45e-15  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.34e-08  5.84e-10  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.05e-05  1.23e-06  9.82e-01  1.71e-02  9.24e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0924%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-04  4.38e-06  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LGALS3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.244000  0.004260  0.738000  0.012900  0.000689 \n",
+      "[1] \"PP abf for shared variant: 0.0689%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MIAT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.018300  0.000321  0.964000  0.016800  0.000923 \n",
+      "[1] \"PP abf for shared variant: 0.0923%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.41e-21  7.71e-23  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.13e-08  7.22e-10  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.021200  0.000371  0.961000  0.016800  0.000904 \n",
+      "[1] \"PP abf for shared variant: 0.0904%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.37e-17  4.13e-19  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.71e-23  8.22e-25  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RNF19A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.07e-03  3.62e-05  9.80e-01  1.71e-02  9.56e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0956%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-CO3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-07  7.39e-09  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-24  2.61e-26  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-11  3.06e-13  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FAS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.205000  0.003590  0.777000  0.013600  0.000737 \n",
+      "[1] \"PP abf for shared variant: 0.0737%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.09e-10  1.59e-11  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.37e-10  1.46e-11  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GYPC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.88e-06  1.55e-07  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-22  2.92e-24  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-24  3.07e-26  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPM4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.029600  0.000516  0.952000  0.016600  0.000867 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.85e-12  1.55e-13  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.31e-05  1.45e-06  9.82e-01  1.71e-02  9.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0943%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.47e-23  7.80e-25  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ITGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-07  6.42e-09  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.73e-07  8.26e-09  9.82e-01  1.71e-02  8.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0892%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.028100  0.000491  0.954000  0.016700  0.000902 \n",
+      "[1] \"PP abf for shared variant: 0.0902%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-26  3.66e-28  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11400   0.00198   0.86800   0.01520   0.00091 \n",
+      "[1] \"PP abf for shared variant: 0.091%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.70e-18  9.96e-20  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-03  2.93e-05  9.80e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-03  2.99e-05  9.80e-01  1.71e-02  9.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0919%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.12e-23  7.20e-25  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-16  1.78e-18  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.069800  0.001220  0.912000  0.015900  0.000834 \n",
+      "[1] \"PP abf for shared variant: 0.0834%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PPP1R15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.471000  0.008230  0.511000  0.008930  0.000526 \n",
+      "[1] \"PP abf for shared variant: 0.0526%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___IL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.35e-04  1.46e-05  9.81e-01  1.71e-02  9.06e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0906%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.50e-26  9.60e-28  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.16e-16  7.27e-18  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM20\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.432000  0.007540  0.551000  0.009610  0.000573 \n",
+      "[1] \"PP abf for shared variant: 0.0573%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.58e-04  1.67e-05  9.81e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.36e-03  7.62e-05  9.78e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___KLF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-05  2.87e-07  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GIMAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.66e-04  1.51e-05  9.81e-01  1.71e-02  9.02e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0902%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFBR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.064800  0.001130  0.917000  0.016000  0.000872 \n",
+      "[1] \"PP abf for shared variant: 0.0872%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RNF213__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.327000  0.005710  0.656000  0.011500  0.000635 \n",
+      "[1] \"PP abf for shared variant: 0.0635%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___C19orf53__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.51700   0.00904   0.46500   0.00812   0.00048 \n",
+      "[1] \"PP abf for shared variant: 0.048%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-10  1.84e-12  9.82e-01  1.71e-02  9.55e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0955%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-15  6.57e-17  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MIR4435-1HG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.58e-10  9.76e-12  9.82e-01  1.71e-02  9.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.099%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-15  3.13e-17  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MZT2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.13e-04  8.97e-06  9.81e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___AK5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.090100  0.001570  0.892000  0.015600  0.000856 \n",
+      "[1] \"PP abf for shared variant: 0.0856%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.275000  0.004800  0.707000  0.012300  0.000844 \n",
+      "[1] \"PP abf for shared variant: 0.0844%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-08  3.21e-10  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-20  3.98e-22  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-04  4.78e-06  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.22e-19  1.44e-20  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.028700  0.000502  0.953000  0.016600  0.000885 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CXCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.360000  0.006140  0.622000  0.010600  0.000627 \n",
+      "[1] \"PP abf for shared variant: 0.0627%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ARPC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.128000  0.002230  0.854000  0.014900  0.000807 \n",
+      "[1] \"PP abf for shared variant: 0.0807%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MRPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.499000  0.008720  0.483000  0.008440  0.000484 \n",
+      "[1] \"PP abf for shared variant: 0.0484%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-15  3.27e-17  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-06  3.76e-08  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EBPL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.42000   0.00734   0.56200   0.00982   0.00058 \n",
+      "[1] \"PP abf for shared variant: 0.058%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.219000  0.003820  0.763000  0.013300  0.000928 \n",
+      "[1] \"PP abf for shared variant: 0.0928%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UXT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-08  2.80e-10  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LSM5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.81e-04  6.66e-06  9.81e-01  1.71e-02  9.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.098%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___KMT2E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.6569e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.551000  0.009630  0.431000  0.007530  0.000504 \n",
+      "[1] \"PP abf for shared variant: 0.0504%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-07  3.05e-09  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TAGLN2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.505000  0.008820  0.477000  0.008340  0.000503 \n",
+      "[1] \"PP abf for shared variant: 0.0503%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CDCA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4164e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.573000  0.009980  0.409000  0.007120  0.000476 \n",
+      "[1] \"PP abf for shared variant: 0.0476%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-06  2.82e-08  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EPB41L4A-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-06  3.41e-08  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.82e-08  1.19e-09  9.82e-01  1.71e-02  8.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0893%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TATDN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.029100  0.000508  0.953000  0.016600  0.000862 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.068500  0.001200  0.914000  0.016000  0.000849 \n",
+      "[1] \"PP abf for shared variant: 0.0849%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.35e-13  5.85e-15  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-23  3.55e-25  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-20  3.24e-22  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___BTG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.26e-04  7.44e-06  9.82e-01  1.71e-02  9.02e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0902%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___C8orf59__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.033400  0.000584  0.949000  0.016600  0.000860 \n",
+      "[1] \"PP abf for shared variant: 0.086%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD58__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.007640  0.000134  0.974000  0.017000  0.000987 \n",
+      "[1] \"PP abf for shared variant: 0.0987%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-17  2.78e-19  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.72e-06  9.99e-08  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___AKAP13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.036100  0.000630  0.946000  0.016500  0.000986 \n",
+      "[1] \"PP abf for shared variant: 0.0986%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-07  6.69e-09  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___DDX5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-05  6.39e-07  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.030200  0.000529  0.952000  0.016600  0.000869 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ANXA2R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.293000  0.005110  0.690000  0.012000  0.000692 \n",
+      "[1] \"PP abf for shared variant: 0.0692%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___IL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.282000  0.004930  0.700000  0.012200  0.000665 \n",
+      "[1] \"PP abf for shared variant: 0.0665%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LINC00152__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-08  2.92e-10  9.82e-01  1.71e-02  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FOXP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.046900  0.000803  0.935000  0.016000  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.71e-09  1.70e-10  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-21  2.61e-23  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-04  1.79e-06  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-16  1.91e-18  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-14  2.62e-16  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___DAP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.62e-04  1.33e-05  9.81e-01  1.71e-02  8.94e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0894%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-11  2.65e-13  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SVIP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.415000  0.007250  0.567000  0.009910  0.000544 \n",
+      "[1] \"PP abf for shared variant: 0.0544%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-15  4.50e-17  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-27  6.44e-29  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-18  3.45e-20  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-07  3.99e-09  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.033300  0.000581  0.949000  0.016600  0.000868 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ENTPD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14900   0.00249   0.83400   0.01390   0.00081 \n",
+      "[1] \"PP abf for shared variant: 0.081%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___DUSP4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.48e-10  6.08e-12  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-14  3.68e-16  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002350  0.000041  0.980000  0.017100  0.000944 \n",
+      "[1] \"PP abf for shared variant: 0.0944%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.191000  0.003350  0.791000  0.013800  0.000741 \n",
+      "[1] \"PP abf for shared variant: 0.0741%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-03  3.47e-05  9.80e-01  1.71e-02  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.20e-17  1.61e-18  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ADAM19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.296000  0.005170  0.686000  0.012000  0.000788 \n",
+      "[1] \"PP abf for shared variant: 0.0788%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CLDND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.306000  0.005340  0.676000  0.011800  0.000646 \n",
+      "[1] \"PP abf for shared variant: 0.0646%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.53e-07  1.67e-08  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-09  1.88e-11  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.230000  0.004020  0.752000  0.013100  0.000704 \n",
+      "[1] \"PP abf for shared variant: 0.0704%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___APEX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.330000  0.005760  0.653000  0.011400  0.000649 \n",
+      "[1] \"PP abf for shared variant: 0.0649%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-08  4.59e-10  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.93e-22  1.56e-23  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LETMD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-03  2.18e-05  9.81e-01  1.71e-02  8.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-03  4.33e-05  9.80e-01  1.63e-02  9.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0949%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.52e-07  1.49e-08  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___AHNAK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01320   0.00023   0.96900   0.01690   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SLC7A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.508000  0.008880  0.474000  0.008280  0.000547 \n",
+      "[1] \"PP abf for shared variant: 0.0547%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.95e-10  1.21e-11  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-20  3.27e-22  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000685  0.000012  0.981000  0.017100  0.001020 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.72e-28  1.35e-29  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.04e-13  1.06e-14  9.82e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.80e-25  6.64e-27  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-ATP6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-05  6.69e-07  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-18  4.91e-20  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-06  5.54e-08  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RILPL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.226000  0.003950  0.756000  0.013200  0.000708 \n",
+      "[1] \"PP abf for shared variant: 0.0708%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SSR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-05  6.41e-07  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-03  6.04e-05  9.79e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.89e-09  8.54e-11  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.39e-14  1.47e-15  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MAF__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-05  2.00e-07  9.82e-01  1.71e-02  8.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0889%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.28e-10  5.73e-12  9.82e-01  1.71e-02  8.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0896%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___COMMD6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.97e-04  8.69e-06  9.81e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.74e-07  1.53e-08  9.82e-01  1.71e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NSMCE1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.92e-03  8.59e-05  9.77e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.16e-05  9.02e-07  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PRDX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.083900  0.001470  0.898000  0.015700  0.000851 \n",
+      "[1] \"PP abf for shared variant: 0.0851%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.56e-20  1.15e-21  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FAM46C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.028700  0.000501  0.953000  0.016600  0.000874 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.62e-22  1.50e-23  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-21  2.77e-23  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.93e-27  1.21e-28  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.43e-23  2.50e-25  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RORA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.394000  0.006880  0.589000  0.010300  0.000582 \n",
+      "[1] \"PP abf for shared variant: 0.0582%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.42e-04  5.98e-06  9.82e-01  1.71e-02  8.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0898%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.49e-19  6.10e-21  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD44__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-05  2.93e-07  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4Y1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.457000  0.007990  0.525000  0.009170  0.000593 \n",
+      "[1] \"PP abf for shared variant: 0.0593%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-05  3.03e-07  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___COX7A2L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.053000  0.000927  0.929000  0.016200  0.000850 \n",
+      "[1] \"PP abf for shared variant: 0.085%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-21  4.24e-23  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HADHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.085100  0.001490  0.897000  0.015700  0.000812 \n",
+      "[1] \"PP abf for shared variant: 0.0812%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SATB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.186000  0.003250  0.796000  0.013900  0.000747 \n",
+      "[1] \"PP abf for shared variant: 0.0747%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UGP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.036800  0.000642  0.945000  0.016500  0.000862 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SBDS\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.265000  0.004620  0.718000  0.012500  0.000723 \n",
+      "[1] \"PP abf for shared variant: 0.0723%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SYNE2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.70e-03  9.96e-05  9.76e-01  1.70e-02  9.31e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0931%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMA7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.072900  0.001270  0.909000  0.015900  0.000908 \n",
+      "[1] \"PP abf for shared variant: 0.0908%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-09  2.64e-11  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NR3C1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051900  0.000907  0.930000  0.016200  0.000856 \n",
+      "[1] \"PP abf for shared variant: 0.0856%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.77e-27  1.18e-28  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CCT8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.021200  0.000371  0.961000  0.016800  0.000883 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFAIP3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.048400  0.000846  0.934000  0.016300  0.000842 \n",
+      "[1] \"PP abf for shared variant: 0.0842%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH2D2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.395000  0.006510  0.588000  0.009710  0.000573 \n",
+      "[1] \"PP abf for shared variant: 0.0573%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.94e-11  6.89e-13  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CLNS1A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.02e-03  8.78e-05  9.77e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.34e-08  4.09e-10  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP6V0E1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.047000  0.000820  0.935000  0.016300  0.000844 \n",
+      "[1] \"PP abf for shared variant: 0.0844%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-26  2.28e-28  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.428000  0.007470  0.555000  0.009690  0.000552 \n",
+      "[1] \"PP abf for shared variant: 0.0552%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-27  2.99e-29  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36L2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.053600  0.000936  0.928000  0.016200  0.000858 \n",
+      "[1] \"PP abf for shared variant: 0.0858%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.33e-04  4.07e-06  9.82e-01  1.71e-02  9.09e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0909%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.089000  0.001550  0.893000  0.015600  0.000851 \n",
+      "[1] \"PP abf for shared variant: 0.0851%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-19  7.39e-21  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___APRT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00746   0.00013   0.97400   0.01700   0.00093 \n",
+      "[1] \"PP abf for shared variant: 0.093%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FYN__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.00e-03  6.98e-05  9.78e-01  1.71e-02  9.11e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0911%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ANP32B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.083400  0.001460  0.898000  0.015700  0.000986 \n",
+      "[1] \"PP abf for shared variant: 0.0986%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.038300  0.000669  0.944000  0.016500  0.000853 \n",
+      "[1] \"PP abf for shared variant: 0.0853%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.86e-04  1.02e-05  9.81e-01  1.71e-02  8.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-27  4.00e-29  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CMPK1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.75e-05  8.29e-07  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAZ\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.146000  0.002550  0.836000  0.014600  0.000832 \n",
+      "[1] \"PP abf for shared variant: 0.0832%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GIMAP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.35e-08  7.59e-10  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___COTL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.40e-06  9.44e-08  9.82e-01  1.71e-02  8.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF2S3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.15e-11  1.42e-12  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HSP90AA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1807e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.487000  0.008510  0.495000  0.008650  0.000497 \n",
+      "[1] \"PP abf for shared variant: 0.0497%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MT-CYB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-04  3.24e-06  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HSPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.286000  0.004990  0.696000  0.012200  0.000653 \n",
+      "[1] \"PP abf for shared variant: 0.0653%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.58e-10  8.01e-12  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-09  5.28e-11  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-17  1.78e-19  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXK\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.32e-05  7.55e-07  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-16  3.80e-18  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.75e-14  1.00e-15  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-06  3.28e-08  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.71e-05  6.48e-07  9.82e-01  1.71e-02  8.88e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06560   0.00115   0.91600   0.01600   0.00084 \n",
+      "[1] \"PP abf for shared variant: 0.084%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___LYRM4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.270000  0.004720  0.712000  0.012400  0.000672 \n",
+      "[1] \"PP abf for shared variant: 0.0672%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-20  3.82e-22  9.82e-01  1.71e-02  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.70e-20  1.69e-21  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-10  5.44e-12  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EZR__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002750  0.000048  0.979000  0.017100  0.000897 \n",
+      "[1] \"PP abf for shared variant: 0.0897%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.55e-04  1.67e-05  9.81e-01  1.71e-02  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP5O__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-08  2.18e-10  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08130   0.00142   0.90100   0.01570   0.00082 \n",
+      "[1] \"PP abf for shared variant: 0.082%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-19  1.80e-21  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUCLG2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.95e-04  5.16e-06  9.82e-01  1.71e-02  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CD3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051400  0.000898  0.931000  0.016300  0.000850 \n",
+      "[1] \"PP abf for shared variant: 0.085%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-20  5.43e-22  9.82e-01  1.71e-02  8.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0883%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___NSA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-07  2.13e-09  9.82e-01  1.71e-02  8.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0881%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.056900  0.000994  0.925000  0.016200  0.000890 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___HIGD2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.147000  0.002560  0.835000  0.014600  0.000845 \n",
+      "[1] \"PP abf for shared variant: 0.0845%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-09  5.45e-11  9.82e-01  1.71e-02  8.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0885%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___IGBP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-03  6.06e-05  9.78e-01  1.71e-02  9.32e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0932%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-19  5.23e-21  9.82e-01  1.71e-02  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___MYH9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.597000  0.010400  0.386000  0.006740  0.000476 \n",
+      "[1] \"PP abf for shared variant: 0.0476%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.72e-08  1.17e-09  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___ATP2B1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-03  2.09e-05  9.81e-01  1.71e-02  9.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0947%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-28  2.54e-30  9.82e-01  1.71e-02  8.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0873%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RBM39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006320  0.000110  0.976000  0.017000  0.000912 \n",
+      "[1] \"PP abf for shared variant: 0.0912%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___CCNG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-03  4.71e-05  9.79e-01  1.71e-02  9.25e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0925%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.88e-16  6.77e-18  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___COX4I1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006040  0.000106  0.976000  0.017000  0.000884 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___PMAIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.053200  0.000930  0.929000  0.016200  0.000855 \n",
+      "[1] \"PP abf for shared variant: 0.0855%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.90e-11  6.81e-13  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.91e-07  8.58e-09  9.82e-01  1.71e-02  9.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0919%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___FHIT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-10  6.62e-12  9.82e-01  1.71e-02  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Inflammatory Bowel Disease\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-05  3.76e-07  9.82e-01  1.71e-02  8.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0886%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___CAPG__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.246000  0.000863  0.750000  0.002630  0.000661 \n",
+      "[1] \"PP abf for shared variant: 0.0661%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___PTAFR__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.506000  0.001780  0.489000  0.001710  0.000813 \n",
+      "[1] \"PP abf for shared variant: 0.0813%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___MNDA__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.5916e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.935000  0.003280  0.061700  0.000215  0.000126 \n",
+      "[1] \"PP abf for shared variant: 0.0126%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___RNASE6__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.728000  0.002550  0.269000  0.000940  0.000265 \n",
+      "[1] \"PP abf for shared variant: 0.0265%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__TSPO\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.804000  0.002820  0.192000  0.000671  0.000212 \n",
+      "[1] \"PP abf for shared variant: 0.0212%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__VMO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5549e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.941000  0.003280  0.055700  0.000193  0.000146 \n",
+      "[1] \"PP abf for shared variant: 0.0146%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___S100A9__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.836000  0.002940  0.160000  0.000559  0.000204 \n",
+      "[1] \"PP abf for shared variant: 0.0204%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___QPCT__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.8504e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.30e-01  3.24e-03  6.64e-02  2.31e-04  8.66e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00866%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___BLVRB__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1205e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.903000  0.003170  0.093600  0.000327  0.000125 \n",
+      "[1] \"PP abf for shared variant: 0.0125%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___LYZ__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.156000  0.000549  0.839000  0.002940  0.000736 \n",
+      "[1] \"PP abf for shared variant: 0.0736%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_TMEM176A___CLEC4A__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.24e-01  3.24e-03  7.24e-02  2.53e-04  9.19e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00919%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.067500  0.000522  0.924000  0.007140  0.001030 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL5__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.693000  0.005370  0.299000  0.002310  0.000378 \n",
+      "[1] \"PP abf for shared variant: 0.0378%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53500   0.00414   0.45700   0.00353   0.00055 \n",
+      "[1] \"PP abf for shared variant: 0.055%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL32__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.078600  0.000609  0.913000  0.007050  0.001030 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___EEF1A1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.740000  0.005730  0.252000  0.001950  0.000348 \n",
+      "[1] \"PP abf for shared variant: 0.0348%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL38__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.326000  0.002520  0.666000  0.005140  0.000793 \n",
+      "[1] \"PP abf for shared variant: 0.0793%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.415000  0.003220  0.576000  0.004450  0.000675 \n",
+      "[1] \"PP abf for shared variant: 0.0675%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.511000  0.003950  0.481000  0.003720  0.000634 \n",
+      "[1] \"PP abf for shared variant: 0.0634%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.743000  0.005750  0.249000  0.001920  0.000333 \n",
+      "[1] \"PP abf for shared variant: 0.0333%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.725000  0.005610  0.267000  0.002070  0.000338 \n",
+      "[1] \"PP abf for shared variant: 0.0338%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS15A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.442000  0.003420  0.550000  0.004250  0.000639 \n",
+      "[1] \"PP abf for shared variant: 0.0639%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS8__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.46100   0.00357   0.53100   0.00410   0.00064 \n",
+      "[1] \"PP abf for shared variant: 0.064%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.71300   0.00552   0.27900   0.00216   0.00036 \n",
+      "[1] \"PP abf for shared variant: 0.036%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.034500  0.000267  0.957000  0.007400  0.001060 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___NKG7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.56100   0.00434   0.43100   0.00333   0.00051 \n",
+      "[1] \"PP abf for shared variant: 0.051%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___B2M__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.203000  0.001570  0.788000  0.006090  0.000887 \n",
+      "[1] \"PP abf for shared variant: 0.0887%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.355000  0.002750  0.637000  0.004920  0.000739 \n",
+      "[1] \"PP abf for shared variant: 0.0739%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___PFN1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.137000  0.001060  0.854000  0.006600  0.000961 \n",
+      "[1] \"PP abf for shared variant: 0.0961%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS28__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.365000  0.002820  0.627000  0.004840  0.000738 \n",
+      "[1] \"PP abf for shared variant: 0.0738%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.428000  0.003310  0.564000  0.004360  0.000674 \n",
+      "[1] \"PP abf for shared variant: 0.0674%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___GZMH__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.148000  0.001150  0.843000  0.006520  0.000955 \n",
+      "[1] \"PP abf for shared variant: 0.0955%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___LTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.242000  0.001870  0.750000  0.005800  0.000857 \n",
+      "[1] \"PP abf for shared variant: 0.0857%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL39__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.332000  0.002570  0.659000  0.005100  0.000768 \n",
+      "[1] \"PP abf for shared variant: 0.0768%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.396000  0.003070  0.595000  0.004600  0.000686 \n",
+      "[1] \"PP abf for shared variant: 0.0686%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.301000  0.002330  0.691000  0.005340  0.000788 \n",
+      "[1] \"PP abf for shared variant: 0.0788%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS23__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.178000  0.001380  0.813000  0.006290  0.000927 \n",
+      "[1] \"PP abf for shared variant: 0.0927%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS29__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.229000  0.001770  0.763000  0.005890  0.000882 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL22__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.662000  0.005120  0.330000  0.002550  0.000409 \n",
+      "[1] \"PP abf for shared variant: 0.0409%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL9__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.453000  0.003500  0.539000  0.004170  0.000641 \n",
+      "[1] \"PP abf for shared variant: 0.0641%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.673000  0.005210  0.319000  0.002460  0.000416 \n",
+      "[1] \"PP abf for shared variant: 0.0416%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.636000  0.004920  0.356000  0.002750  0.000437 \n",
+      "[1] \"PP abf for shared variant: 0.0437%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00027483\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.82e-01  7.60e-03  1.05e-02  8.05e-05  7.42e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00742%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___MAL__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.45900   0.00355   0.53300   0.00411   0.00070 \n",
+      "[1] \"PP abf for shared variant: 0.07%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___PRF1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.732000  0.005670  0.260000  0.002010  0.000348 \n",
+      "[1] \"PP abf for shared variant: 0.0348%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.033900  0.000262  0.957000  0.007400  0.001060 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS6__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.292000  0.002260  0.700000  0.005410  0.000797 \n",
+      "[1] \"PP abf for shared variant: 0.0797%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.040500  0.000314  0.951000  0.007350  0.001070 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL21__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.215000  0.001670  0.776000  0.006000  0.000879 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___SMDT1__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-02  8.28e-05  9.81e-01  7.58e-03  1.09e-03 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.321000  0.002480  0.670000  0.005180  0.000869 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051300  0.000397  0.940000  0.007270  0.001050 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL34__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.136000  0.001050  0.855000  0.006610  0.000958 \n",
+      "[1] \"PP abf for shared variant: 0.0958%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL10A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.200000  0.001550  0.791000  0.006110  0.000894 \n",
+      "[1] \"PP abf for shared variant: 0.0894%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_SMDT1___RPL30__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.299000  0.002310  0.692000  0.005350  0.000793 \n",
+      "[1] \"PP abf for shared variant: 0.0793%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.062400  0.000483  0.929000  0.007180  0.001060 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.690000  0.005340  0.302000  0.002330  0.000376 \n",
+      "[1] \"PP abf for shared variant: 0.0376%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.194000  0.001500  0.797000  0.006160  0.000899 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13700   0.00106   0.85500   0.00660   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.842000  0.006510  0.151000  0.001160  0.000224 \n",
+      "[1] \"PP abf for shared variant: 0.0224%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.805000  0.006230  0.187000  0.001450  0.000294 \n",
+      "[1] \"PP abf for shared variant: 0.0294%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL31__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.787000  0.006090  0.205000  0.001580  0.000309 \n",
+      "[1] \"PP abf for shared variant: 0.0309%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.619000  0.004790  0.372000  0.002880  0.000476 \n",
+      "[1] \"PP abf for shared variant: 0.0476%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___ACTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.188000  0.001460  0.803000  0.006210  0.000904 \n",
+      "[1] \"PP abf for shared variant: 0.0904%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.549000  0.004250  0.442000  0.003420  0.000538 \n",
+      "[1] \"PP abf for shared variant: 0.0538%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019600  0.000152  0.972000  0.007510  0.001080 \n",
+      "[1] \"PP abf for shared variant: 0.108%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.828000  0.006410  0.164000  0.001260  0.000229 \n",
+      "[1] \"PP abf for shared variant: 0.0229%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.485000  0.003750  0.507000  0.003920  0.000598 \n",
+      "[1] \"PP abf for shared variant: 0.0598%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.491000  0.003800  0.501000  0.003870  0.000589 \n",
+      "[1] \"PP abf for shared variant: 0.0589%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL10__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.286000  0.002220  0.705000  0.005450  0.000834 \n",
+      "[1] \"PP abf for shared variant: 0.0834%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.625000  0.004840  0.367000  0.002830  0.000471 \n",
+      "[1] \"PP abf for shared variant: 0.0471%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0032661\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.80e-01  7.59e-03  1.18e-02  9.04e-05  6.85e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00685%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___CD48__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.569000  0.004400  0.423000  0.003270  0.000541 \n",
+      "[1] \"PP abf for shared variant: 0.0541%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.72200   0.00559   0.27000   0.00209   0.00038 \n",
+      "[1] \"PP abf for shared variant: 0.038%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_SMDT1___RPS27__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.832000  0.006440  0.160000  0.001240  0.000307 \n",
+      "[1] \"PP abf for shared variant: 0.0307%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.40e-69  9.69e-01  1.61e-71  4.31e-03  2.67e-02 \n",
+      "[1] \"PP abf for shared variant: 2.67%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.36e-69  9.59e-01  1.60e-71  4.21e-03  3.65e-02 \n",
+      "[1] \"PP abf for shared variant: 3.65%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-69  9.44e-01  1.71e-71  4.37e-03  5.12e-02 \n",
+      "[1] \"PP abf for shared variant: 5.12%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___CLIC3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-69  9.45e-01  1.79e-71  4.60e-03  5.00e-02 \n",
+      "[1] \"PP abf for shared variant: 5%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__PTPRCAP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.34e-69  9.53e-01  1.48e-71  3.78e-03  4.32e-02 \n",
+      "[1] \"PP abf for shared variant: 4.32%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___CDKN2D__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.5969e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-69  8.92e-01  2.30e-71  5.54e-03  1.03e-01 \n",
+      "[1] \"PP abf for shared variant: 10.3%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0931e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.37e-69  9.62e-01  1.78e-71  4.74e-03  3.30e-02 \n",
+      "[1] \"PP abf for shared variant: 3.3%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-69  8.93e-01  1.68e-71  3.75e-03  1.03e-01 \n",
+      "[1] \"PP abf for shared variant: 10.3%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.34e-69  9.52e-01  1.72e-71  4.48e-03  4.38e-02 \n",
+      "[1] \"PP abf for shared variant: 4.38%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MAP1A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-69  9.11e-01  1.83e-71  4.37e-03  8.46e-02 \n",
+      "[1] \"PP abf for shared variant: 8.46%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___FAM129C__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-69  8.97e-01  1.77e-71  4.07e-03  9.88e-02 \n",
+      "[1] \"PP abf for shared variant: 9.88%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MT-CO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1338e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-69  8.53e-01  1.94e-71  4.10e-03  1.43e-01 \n",
+      "[1] \"PP abf for shared variant: 14.3%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-69  9.11e-01  1.81e-71  4.33e-03  8.43e-02 \n",
+      "[1] \"PP abf for shared variant: 8.43%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.89e-69  8.26e-01  1.96e-71  3.89e-03  1.70e-01 \n",
+      "[1] \"PP abf for shared variant: 17%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.09e-70  2.02e-01  3.62e-71  2.38e-03  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-69  8.97e-01  2.19e-71  5.28e-03  9.82e-02 \n",
+      "[1] \"PP abf for shared variant: 9.82%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-69  6.39e-01  7.33e-70  2.07e-01  1.54e-01 \n",
+      "[1] \"PP abf for shared variant: 15.4%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-69  6.40e-01  2.87e-71  4.65e-03  3.55e-01 \n",
+      "[1] \"PP abf for shared variant: 35.5%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.89e-69  5.40e-01  3.38e-71  5.10e-03  4.55e-01 \n",
+      "[1] \"PP abf for shared variant: 45.5%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMA__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-69  8.53e-01  3.00e-71  7.17e-03  1.39e-01 \n",
+      "[1] \"PP abf for shared variant: 13.9%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS23\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.49e-69  7.11e-01  3.54e-71  7.28e-03  2.82e-01 \n",
+      "[1] \"PP abf for shared variant: 28.2%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-69  9.74e-01  1.96e-71  5.39e-03  2.07e-02 \n",
+      "[1] \"PP abf for shared variant: 2.07%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-69  8.97e-01  2.25e-71  5.43e-03  9.77e-02 \n",
+      "[1] \"PP abf for shared variant: 9.77%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___EEF1A1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-69  7.42e-01  2.39e-71  4.28e-03  2.54e-01 \n",
+      "[1] \"PP abf for shared variant: 25.4%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.29e-69  9.38e-01  8.59e-71  2.41e-02  3.82e-02 \n",
+      "[1] \"PP abf for shared variant: 3.82%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.34e-69  9.54e-01  4.21e-71  1.17e-02  3.46e-02 \n",
+      "[1] \"PP abf for shared variant: 3.46%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMB__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-69  9.64e-01  2.90e-71  8.00e-03  2.78e-02 \n",
+      "[1] \"PP abf for shared variant: 2.78%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.84e-69  8.09e-01  5.39e-71  1.36e-02  1.78e-01 \n",
+      "[1] \"PP abf for shared variant: 17.8%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-69  9.14e-01  3.11e-71  8.09e-03  7.77e-02 \n",
+      "[1] \"PP abf for shared variant: 7.77%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.37e-69  9.62e-01  6.57e-71  1.85e-02  1.98e-02 \n",
+      "[1] \"PP abf for shared variant: 1.98%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RNASET2___HLA-DRB5__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-69  9.81e-01  1.15e-71  3.11e-03  1.60e-02 \n",
+      "[1] \"PP abf for shared variant: 1.6%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_HLA-DQA2___CCL5__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-69  8.48e-01  3.19e-71  7.65e-03  1.45e-01 \n",
+      "[1] \"PP abf for shared variant: 14.5%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.42e-69  9.75e-01  2.60e-71  7.24e-03  1.78e-02 \n",
+      "[1] \"PP abf for shared variant: 1.78%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-69  9.09e-01  2.03e-71  4.94e-03  8.61e-02 \n",
+      "[1] \"PP abf for shared variant: 8.61%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__NKG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-69  8.88e-01  2.05e-71  4.78e-03  1.07e-01 \n",
+      "[1] \"PP abf for shared variant: 10.7%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPL34\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.22e-69  9.18e-01  2.09e-71  5.20e-03  7.65e-02 \n",
+      "[1] \"PP abf for shared variant: 7.65%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-69  8.30e-01  1.88e-71  3.70e-03  1.67e-01 \n",
+      "[1] \"PP abf for shared variant: 16.7%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_HLA-DQA2___CMC1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.37e-69  9.62e-01  2.01e-71  5.41e-03  3.25e-02 \n",
+      "[1] \"PP abf for shared variant: 3.25%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.54e-69  7.25e-01  2.43e-71  4.22e-03  2.70e-01 \n",
+      "[1] \"PP abf for shared variant: 27%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-70  7.83e-02  3.44e-71  6.01e-04  9.21e-01 \n",
+      "[1] \"PP abf for shared variant: 92.1%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.16e-69  9.00e-01  1.77e-71  4.08e-03  9.58e-02 \n",
+      "[1] \"PP abf for shared variant: 9.58%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.32e-69  9.46e-01  1.98e-71  5.16e-03  4.90e-02 \n",
+      "[1] \"PP abf for shared variant: 4.9%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-69  7.53e-01  2.68e-71  5.22e-03  2.42e-01 \n",
+      "[1] \"PP abf for shared variant: 24.2%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___CD52__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.30e-69  9.43e-01  2.28e-71  5.98e-03  5.14e-02 \n",
+      "[1] \"PP abf for shared variant: 5.14%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.79e-69  7.95e-01  2.15e-71  4.12e-03  2.01e-01 \n",
+      "[1] \"PP abf for shared variant: 20.1%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-69  8.87e-01  2.66e-71  6.52e-03  1.06e-01 \n",
+      "[1] \"PP abf for shared variant: 10.6%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.93e-69  8.35e-01  3.02e-71  7.03e-03  1.58e-01 \n",
+      "[1] \"PP abf for shared variant: 15.8%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___EEF1B2__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-69  8.91e-01  1.96e-71  4.55e-03  1.04e-01 \n",
+      "[1] \"PP abf for shared variant: 10.4%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-69  8.90e-01  2.47e-71  6.01e-03  1.04e-01 \n",
+      "[1] \"PP abf for shared variant: 10.4%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___B2M__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-69  9.12e-01  2.37e-71  5.94e-03  8.24e-02 \n",
+      "[1] \"PP abf for shared variant: 8.24%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___GAPDH__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-69  9.13e-01  2.40e-71  6.05e-03  8.10e-02 \n",
+      "[1] \"PP abf for shared variant: 8.1%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL32\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.88e-69  8.21e-01  2.72e-71  6.04e-03  1.73e-01 \n",
+      "[1] \"PP abf for shared variant: 17.3%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.71e-69  7.73e-01  2.18e-71  4.00e-03  2.23e-01 \n",
+      "[1] \"PP abf for shared variant: 22.3%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-69  8.93e-01  2.07e-71  4.89e-03  1.02e-01 \n",
+      "[1] \"PP abf for shared variant: 10.2%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RNASET2___ITGB1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.34e-01  4.08e-03  6.19e-02  2.70e-04  9.48e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00948%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RNASET2___CRIP1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.73e-01  4.26e-03  2.28e-02  9.95e-05  4.69e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00469%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RNASET2___B2M__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.65e-01  4.22e-03  3.07e-02  1.34e-04  6.41e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00641%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RNASET2___ALOX5AP__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.63e-01  4.21e-03  3.25e-02  1.42e-04  5.98e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00598%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0253e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.70e-01  1.30e-03  2.83e-02  3.76e-05  4.43e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00443%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.886000  0.001190  0.112000  0.000150  0.000118 \n",
+      "[1] \"PP abf for shared variant: 0.0118%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"DC_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.884000  0.001190  0.114000  0.000152  0.000118 \n",
+      "[1] \"PP abf for shared variant: 0.0118%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.781000  0.001070  0.217000  0.000295  0.000234 \n",
+      "[1] \"PP abf for shared variant: 0.0234%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__UBE2J1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.0878e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.68e-01  1.32e-03  3.09e-02  4.19e-05  3.87e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00387%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.594000  0.000814  0.404000  0.000549  0.000386 \n",
+      "[1] \"PP abf for shared variant: 0.0386%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.30e-01  1.27e-03  6.90e-02  9.37e-05  7.44e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00744%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1042e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.65e-01  1.32e-03  3.38e-02  4.57e-05  5.39e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00539%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.1644e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.70e-01  1.33e-03  2.83e-02  3.84e-05  4.22e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00422%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-02  1.76e-05  9.85e-01  1.34e-03  8.62e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.0557e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.69e-01  1.33e-03  3.00e-02  4.04e-05  6.07e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00607%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.165000  0.000226  0.833000  0.001130  0.000730 \n",
+      "[1] \"PP abf for shared variant: 0.073%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.631000  0.000863  0.368000  0.000500  0.000349 \n",
+      "[1] \"PP abf for shared variant: 0.0349%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.21e-02  7.13e-05  9.46e-01  1.29e-03  8.46e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0846%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.38e-01  1.28e-03  6.06e-02  8.23e-05  7.63e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00763%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.797000  0.001090  0.202000  0.000274  0.000198 \n",
+      "[1] \"PP abf for shared variant: 0.0198%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.7757e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.39e-01  1.29e-03  5.90e-02  7.99e-05  9.79e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00979%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.434000  0.000594  0.565000  0.000768  0.000511 \n",
+      "[1] \"PP abf for shared variant: 0.0511%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.896000  0.001230  0.102000  0.000139  0.000109 \n",
+      "[1] \"PP abf for shared variant: 0.0109%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2778e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.20e-01  1.26e-03  7.86e-02  1.07e-04  7.97e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00797%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0623e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.59e-01  1.31e-03  3.91e-02  5.30e-05  5.55e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00555%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.316000  0.000432  0.682000  0.000928  0.000642 \n",
+      "[1] \"PP abf for shared variant: 0.0642%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.691000  0.000947  0.307000  0.000413  0.000644 \n",
+      "[1] \"PP abf for shared variant: 0.0644%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.624000  0.000854  0.375000  0.000509  0.000364 \n",
+      "[1] \"PP abf for shared variant: 0.0364%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-03  2.52e-06  9.96e-01  1.35e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1408e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.74e-01  1.33e-03  2.45e-02  3.33e-05  3.48e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00348%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.791e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.913000  0.001250  0.085100  0.000116  0.000096 \n",
+      "[1] \"PP abf for shared variant: 0.0096%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1436e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.940000  0.001290  0.058200  0.000079  0.000070 \n",
+      "[1] \"PP abf for shared variant: 0.007%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1123e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.78e-01  1.34e-03  2.01e-02  2.73e-05  2.73e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00273%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.680000  0.000931  0.318000  0.000433  0.000296 \n",
+      "[1] \"PP abf for shared variant: 0.0296%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.756000  0.001040  0.242000  0.000329  0.000267 \n",
+      "[1] \"PP abf for shared variant: 0.0267%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-03  2.44e-06  9.96e-01  1.36e-03  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.710000  0.000972  0.289000  0.000393  0.000271 \n",
+      "[1] \"PP abf for shared variant: 0.0271%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.148000  0.000202  0.850000  0.001160  0.000746 \n",
+      "[1] \"PP abf for shared variant: 0.0746%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.26e-04  8.57e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.537000  0.000735  0.461000  0.000627  0.000416 \n",
+      "[1] \"PP abf for shared variant: 0.0416%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1639e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.48e-01  1.30e-03  5.08e-02  6.89e-05  6.14e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00614%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"B_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.372000  0.000509  0.626000  0.000852  0.000588 \n",
+      "[1] \"PP abf for shared variant: 0.0588%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.28e-07  1.75e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-11  3.58e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.312000  0.000427  0.686000  0.000933  0.000612 \n",
+      "[1] \"PP abf for shared variant: 0.0612%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-07  2.64e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.97e-05  1.09e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.95e-04  6.78e-07  9.97e-01  1.36e-03  8.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0878%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.96e-16  6.79e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.15e-10  5.69e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-10  2.31e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-10  2.36e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.35e-04  7.32e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-02  1.65e-05  9.86e-01  1.34e-03  8.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0865%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-10  1.41e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-14  1.78e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.396000  0.000542  0.602000  0.000819  0.000539 \n",
+      "[1] \"PP abf for shared variant: 0.0539%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.331000  0.000453  0.667000  0.000907  0.000591 \n",
+      "[1] \"PP abf for shared variant: 0.0591%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-09  5.02e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.29e-06  1.13e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.11e-02  9.73e-05  9.27e-01  1.26e-03  8.16e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0816%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.08e-06  4.22e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-17  2.93e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.24e-11  7.17e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.58e-10  6.27e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__SPON2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.147000  0.000201  0.851000  0.001150  0.001080 \n",
+      "[1] \"PP abf for shared variant: 0.108%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.83e-04  3.88e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.37e-18  5.99e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-12  2.26e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.69e-11  9.16e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-12  1.79e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-05  4.53e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.605000  0.000828  0.393000  0.000535  0.000375 \n",
+      "[1] \"PP abf for shared variant: 0.0375%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.65e-05  1.32e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1991e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.33e-01  1.28e-03  6.53e-02  8.82e-05  1.15e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0115%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-08  1.41e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.74e-11  5.12e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-08  2.72e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-10  2.64e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.86e-14  5.28e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.83e-17  6.61e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.98e-03  1.37e-05  9.88e-01  1.34e-03  8.62e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.45e-08  3.35e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.06e-04  6.93e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.22e-08  1.13e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-12  2.90e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.71e-08  6.45e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.81e-14  5.22e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.36e-12  5.97e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-03  2.94e-06  9.96e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.32e-13  8.65e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.873000  0.001200  0.125000  0.000170  0.000131 \n",
+      "[1] \"PP abf for shared variant: 0.0131%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-05  2.00e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.411000  0.000562  0.587000  0.000799  0.000532 \n",
+      "[1] \"PP abf for shared variant: 0.0532%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-06  2.70e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.095000  0.000130  0.903000  0.001230  0.000814 \n",
+      "[1] \"PP abf for shared variant: 0.0814%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-16  5.61e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.27e-07  5.85e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-08  4.46e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.84e-06  1.07e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.87e-09  1.08e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.96e-15  1.09e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.29e-05  7.24e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.82e-05  1.07e-07  9.98e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.82900   0.00114   0.16900   0.00023   0.00018 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.55e-15  3.49e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.137000  0.000187  0.861000  0.001170  0.000755 \n",
+      "[1] \"PP abf for shared variant: 0.0755%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.15e-08  7.06e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-05  2.69e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-04  2.02e-07  9.98e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.99e-04  1.23e-06  9.97e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.86e-06  8.02e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4099e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.30e-01  1.27e-03  6.87e-02  9.32e-05  8.50e-05 \n",
+      "[1] \"PP abf for shared variant: 0.0085%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5173e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.85e-01  1.35e-03  1.37e-02  1.86e-05  2.20e-05 \n",
+      "[1] \"PP abf for shared variant: 0.0022%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.017500  0.000024  0.980000  0.001330  0.000859 \n",
+      "[1] \"PP abf for shared variant: 0.0859%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-06  5.62e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.11e-11  1.25e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___KLRC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.596000  0.000817  0.402000  0.000546  0.000408 \n",
+      "[1] \"PP abf for shared variant: 0.0408%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.4275e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.73e-01  1.33e-03  2.53e-02  3.41e-05  5.19e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00519%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.06e-05  9.67e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.99e-08  5.47e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___MALAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.489000  0.000669  0.509000  0.000689  0.000810 \n",
+      "[1] \"PP abf for shared variant: 0.081%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.56e-02  7.62e-05  9.42e-01  1.28e-03  8.42e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0842%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.06e-09  4.19e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8351e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.59e-01  1.31e-03  3.92e-02  5.30e-05  6.18e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00618%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.55e-05  3.49e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.47e-04  3.39e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___FGFBP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.864000  0.001180  0.134000  0.000181  0.000239 \n",
+      "[1] \"PP abf for shared variant: 0.0239%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.077900  0.000107  0.920000  0.001250  0.000806 \n",
+      "[1] \"PP abf for shared variant: 0.0806%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002920  0.000004  0.995000  0.001350  0.000867 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-03  2.88e-06  9.96e-01  1.35e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.429000  0.000588  0.569000  0.000773  0.000572 \n",
+      "[1] \"PP abf for shared variant: 0.0572%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.574e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.84e-01  1.27e-03  1.47e-02  1.87e-05  2.57e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00257%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-04  2.98e-07  9.98e-01  1.36e-03  8.71e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0871%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-14  1.87e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2336e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.73e-01  1.33e-03  2.54e-02  3.44e-05  3.65e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00365%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-08  2.87e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"NK_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.77e-10  1.20e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___NRGN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7437e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.878000  0.001200  0.120000  0.000163  0.000199 \n",
+      "[1] \"PP abf for shared variant: 0.0199%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.717000  0.000982  0.281000  0.000382  0.000260 \n",
+      "[1] \"PP abf for shared variant: 0.026%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.71e-03  1.19e-05  9.89e-01  1.35e-03  8.66e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0866%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-05  1.55e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.08e-16  1.11e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.15e-06  8.42e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-09  2.94e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.46e-02  6.11e-05  9.53e-01  1.30e-03  8.37e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0837%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-08  1.74e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.68e-14  6.41e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VCAN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.77000   0.00105   0.22800   0.00031   0.00022 \n",
+      "[1] \"PP abf for shared variant: 0.022%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UQCRH\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.93e-05  4.01e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC7A7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-02  4.26e-05  9.67e-01  1.32e-03  8.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0849%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EPB41L3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.833000  0.001140  0.165000  0.000225  0.000178 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.076000  0.000104  0.922000  0.001250  0.000813 \n",
+      "[1] \"PP abf for shared variant: 0.0813%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.36e-14  8.71e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.169000  0.000231  0.829000  0.001130  0.000730 \n",
+      "[1] \"PP abf for shared variant: 0.073%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___QARS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.784000  0.001070  0.214000  0.000291  0.000214 \n",
+      "[1] \"PP abf for shared variant: 0.0214%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-05  3.98e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.43e-15  1.29e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.91e-05  1.08e-07  9.98e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-15  4.46e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-08  4.21e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-09  2.88e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.81e-12  9.32e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.167000  0.000229  0.831000  0.001130  0.000728 \n",
+      "[1] \"PP abf for shared variant: 0.0728%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-09  1.64e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___CSTA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-03  2.30e-06  9.96e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.98e-06  4.08e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.331000  0.000454  0.667000  0.000907  0.000586 \n",
+      "[1] \"PP abf for shared variant: 0.0586%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-12  3.39e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.84e-02  3.89e-05  9.69e-01  1.32e-03  8.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.085%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.89e-15  1.22e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.709000  0.000971  0.289000  0.000393  0.000296 \n",
+      "[1] \"PP abf for shared variant: 0.0296%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TNFAIP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.735000  0.001010  0.263000  0.000357  0.000391 \n",
+      "[1] \"PP abf for shared variant: 0.0391%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-02  4.56e-05  9.64e-01  1.31e-03  8.45e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0845%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.15e-11  1.25e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.40e-20  8.77e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.50e-11  6.16e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.83e-05  6.61e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-09  2.22e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.82e-09  9.34e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-04  2.23e-07  9.98e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.94e-03  9.50e-06  9.91e-01  1.35e-03  8.64e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0864%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-03  5.14e-06  9.94e-01  1.35e-03  8.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.34e-13  3.21e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-15  1.73e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-07  1.62e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.43e-04  8.80e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-10  2.98e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1173e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.769000  0.001050  0.230000  0.000312  0.000252 \n",
+      "[1] \"PP abf for shared variant: 0.0252%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.43e-06  1.02e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.24e-09  4.44e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-07  3.02e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009490  0.000013  0.988000  0.001340  0.000862 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-08  2.68e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.29e-05  4.51e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.082000  0.000112  0.916000  0.001250  0.000803 \n",
+      "[1] \"PP abf for shared variant: 0.0803%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.56e-11  7.62e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-10  6.54e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.55e-04  4.86e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.14e-04  1.11e-06  9.97e-01  1.36e-03  8.71e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0871%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___CST3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7382e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.894000  0.001220  0.105000  0.000142  0.000116 \n",
+      "[1] \"PP abf for shared variant: 0.0116%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.151000  0.000207  0.847000  0.001150  0.000765 \n",
+      "[1] \"PP abf for shared variant: 0.0765%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.838000  0.001150  0.160000  0.000217  0.000216 \n",
+      "[1] \"PP abf for shared variant: 0.0216%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-15  5.86e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.895000  0.001230  0.104000  0.000141  0.000117 \n",
+      "[1] \"PP abf for shared variant: 0.0117%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-02  1.62e-05  9.86e-01  1.34e-03  8.60e-04 \n",
+      "[1] \"PP abf for shared variant: 0.086%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___AP1S2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.611000  0.000837  0.387000  0.000526  0.000362 \n",
+      "[1] \"PP abf for shared variant: 0.0362%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.76e-10  7.89e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-08  4.90e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SEC11A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.128000  0.000175  0.870000  0.001180  0.000776 \n",
+      "[1] \"PP abf for shared variant: 0.0776%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-03  3.61e-06  9.95e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.60e-10  4.94e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-08  4.72e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.94e-10  1.36e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.17e-07  5.70e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-06  1.64e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.83e-07  6.62e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___ATP5G2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-02  7.59e-05  9.42e-01  1.28e-03  8.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0827%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-05  3.62e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-08  3.65e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-02  4.46e-05  9.65e-01  1.31e-03  8.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0843%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.39e-13  1.29e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-15  1.37e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.01e-17  1.10e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.26e-06  8.57e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.93e-07  4.02e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.33e-16  5.93e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-04  5.27e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EVI2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.842000  0.001150  0.156000  0.000212  0.000152 \n",
+      "[1] \"PP abf for shared variant: 0.0152%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.426000  0.000583  0.572000  0.000777  0.000587 \n",
+      "[1] \"PP abf for shared variant: 0.0587%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.154000  0.000211  0.844000  0.001150  0.000776 \n",
+      "[1] \"PP abf for shared variant: 0.0776%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-03  1.91e-06  9.96e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.41e-12  3.30e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___LGALS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.447000  0.000612  0.551000  0.000750  0.000492 \n",
+      "[1] \"PP abf for shared variant: 0.0492%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.634000  0.000868  0.365000  0.000495  0.000383 \n",
+      "[1] \"PP abf for shared variant: 0.0383%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.82e-11  1.07e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-14  2.43e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.07e-10  2.84e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-05  2.12e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___GPX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.139000  0.000190  0.859000  0.001170  0.000763 \n",
+      "[1] \"PP abf for shared variant: 0.0763%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___LTA4H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.34e-01  1.28e-03  6.49e-02  8.80e-05  7.94e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00794%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RNASE6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.693000  0.000949  0.305000  0.000414  0.000372 \n",
+      "[1] \"PP abf for shared variant: 0.0372%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.577000  0.000790  0.422000  0.000573  0.000432 \n",
+      "[1] \"PP abf for shared variant: 0.0432%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___BTF3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.093400  0.000128  0.904000  0.001230  0.000794 \n",
+      "[1] \"PP abf for shared variant: 0.0794%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___DRAM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1829e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.12e-01  1.25e-03  8.67e-02  1.18e-04  9.31e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00931%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___IL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-02  1.84e-05  9.84e-01  1.34e-03  8.62e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.050400  0.000069  0.947000  0.001290  0.000907 \n",
+      "[1] \"PP abf for shared variant: 0.0907%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-05  1.92e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-09  1.45e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.94e-13  9.51e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.689000  0.000943  0.310000  0.000421  0.000300 \n",
+      "[1] \"PP abf for shared variant: 0.03%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.49e-14  7.52e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-11  1.89e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___IPO7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.395000  0.000541  0.603000  0.000820  0.000591 \n",
+      "[1] \"PP abf for shared variant: 0.0591%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.84e-07  3.89e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.02e-10  8.25e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.42e-12  8.79e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2966e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.885000  0.001210  0.114000  0.000154  0.000159 \n",
+      "[1] \"PP abf for shared variant: 0.0159%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-07  1.43e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.27e-05  7.21e-08  9.98e-01  1.36e-03  8.72e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0872%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___CRTAP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.84e-02  6.62e-05  9.49e-01  1.29e-03  8.31e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0831%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.836000  0.001140  0.162000  0.000220  0.000218 \n",
+      "[1] \"PP abf for shared variant: 0.0218%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.30e-09  5.89e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.66e-03  1.05e-05  9.90e-01  1.35e-03  8.63e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0863%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-08  1.92e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.846000  0.001160  0.153000  0.000207  0.000150 \n",
+      "[1] \"PP abf for shared variant: 0.015%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.447000  0.000612  0.551000  0.000750  0.000491 \n",
+      "[1] \"PP abf for shared variant: 0.0491%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.73e-10  1.20e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-11  4.07e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TKT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.44e-03  3.34e-06  9.95e-01  1.35e-03  9.08e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0908%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-14  1.90e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.701000  0.000951  0.298000  0.000401  0.000293 \n",
+      "[1] \"PP abf for shared variant: 0.0293%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ACTN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-02  2.93e-05  9.76e-01  1.33e-03  8.54e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0854%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.88e-13  3.95e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAND1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4561e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.896000  0.001230  0.103000  0.000140  0.000106 \n",
+      "[1] \"PP abf for shared variant: 0.0106%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.36e-14  7.34e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PRF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.66e-02  6.38e-05  9.51e-01  1.29e-03  8.36e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0836%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.50e-04  4.80e-07  9.97e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EFHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.372000  0.000510  0.626000  0.000851  0.000607 \n",
+      "[1] \"PP abf for shared variant: 0.0607%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-08  4.15e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-15  2.57e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.59e-13  1.04e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-12  2.56e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___APBA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.732000  0.000925  0.266000  0.000334  0.000248 \n",
+      "[1] \"PP abf for shared variant: 0.0248%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-03  1.58e-06  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FAIM3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.376000  0.000515  0.622000  0.000846  0.000562 \n",
+      "[1] \"PP abf for shared variant: 0.0562%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.15e-01  1.25e-03  8.33e-02  1.13e-04  9.41e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00941%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.884000  0.001210  0.114000  0.000155  0.000123 \n",
+      "[1] \"PP abf for shared variant: 0.0123%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.224000  0.000306  0.774000  0.001050  0.000765 \n",
+      "[1] \"PP abf for shared variant: 0.0765%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.14e-13  8.40e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___C11orf1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.8471e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.71e-01  1.33e-03  2.76e-02  3.74e-05  3.87e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00387%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.63e-03  7.71e-06  9.92e-01  1.35e-03  8.66e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0866%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-16  3.16e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-16  2.24e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GZMM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.777000  0.001060  0.222000  0.000302  0.000220 \n",
+      "[1] \"PP abf for shared variant: 0.022%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-05  5.94e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-03  2.83e-06  9.96e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.631000  0.000864  0.367000  0.000499  0.000338 \n",
+      "[1] \"PP abf for shared variant: 0.0338%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CTSW__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.646000  0.000885  0.352000  0.000478  0.000404 \n",
+      "[1] \"PP abf for shared variant: 0.0404%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-04  5.01e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-18  1.99e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FLT3LG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.08e-03  1.24e-05  9.89e-01  1.35e-03  8.63e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0863%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NKG7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.01e-04  8.23e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.272000  0.000373  0.726000  0.000987  0.000671 \n",
+      "[1] \"PP abf for shared variant: 0.0671%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.3964e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.51e-01  1.30e-03  4.72e-02  6.39e-05  6.37e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00637%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.816000  0.001120  0.182000  0.000248  0.000184 \n",
+      "[1] \"PP abf for shared variant: 0.0184%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-03  1.77e-06  9.96e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-05  1.51e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.65e-08  1.05e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-12  5.04e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-12  2.10e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ATP1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 9.0977e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.46e-01  1.29e-03  5.28e-02  7.16e-05  6.97e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00697%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.764000  0.001050  0.234000  0.000318  0.000231 \n",
+      "[1] \"PP abf for shared variant: 0.0231%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SYNE1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.413000  0.000566  0.585000  0.000795  0.000576 \n",
+      "[1] \"PP abf for shared variant: 0.0576%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.137e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.70e-01  1.33e-03  2.82e-02  3.81e-05  4.11e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00411%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.35e-06  5.96e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.85e-09  8.02e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PDE3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-03  2.36e-06  9.96e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-19  2.18e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-04  3.97e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZEB2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.082300  0.000113  0.916000  0.001250  0.000803 \n",
+      "[1] \"PP abf for shared variant: 0.0803%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___LTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-06  1.91e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PTPN7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.876000  0.001200  0.122000  0.000166  0.000130 \n",
+      "[1] \"PP abf for shared variant: 0.013%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-12  4.24e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.54e-10  6.22e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___IER2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1556e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.60e-01  1.31e-03  3.83e-02  5.18e-05  5.79e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00579%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-04  2.74e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-08  2.41e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.87e-20  9.40e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.80e-14  1.34e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CMC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-02  1.87e-05  9.84e-01  1.34e-03  8.60e-04 \n",
+      "[1] \"PP abf for shared variant: 0.086%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-03  4.75e-06  9.94e-01  1.35e-03  8.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-12  2.99e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GZMB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.275000  0.000377  0.723000  0.000983  0.000681 \n",
+      "[1] \"PP abf for shared variant: 0.0681%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___AKNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.73e-01  1.33e-03  2.56e-02  3.48e-05  3.31e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00331%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9277e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.17e-01  1.26e-03  8.15e-02  1.11e-04  8.85e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00885%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-18  4.88e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.39e-07  3.27e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-03  2.41e-06  9.96e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.232000  0.000318  0.766000  0.001040  0.000677 \n",
+      "[1] \"PP abf for shared variant: 0.0677%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.258000  0.000353  0.740000  0.001010  0.000665 \n",
+      "[1] \"PP abf for shared variant: 0.0665%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-15  2.17e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-09  1.72e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-12  1.66e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___METRNL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4496e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.59e-01  1.24e-03  3.98e-02  5.07e-05  7.19e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00719%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.337000  0.000462  0.661000  0.000899  0.000588 \n",
+      "[1] \"PP abf for shared variant: 0.0588%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CAMK4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-06  2.43e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___BHLHE40__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.097300  0.000133  0.901000  0.001220  0.000831 \n",
+      "[1] \"PP abf for shared variant: 0.0831%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___IFITM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2604e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.914000  0.001250  0.084200  0.000113  0.000251 \n",
+      "[1] \"PP abf for shared variant: 0.0251%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.527000  0.000721  0.472000  0.000642  0.000425 \n",
+      "[1] \"PP abf for shared variant: 0.0425%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CD8B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-03  2.65e-06  9.96e-01  1.35e-03  8.72e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0872%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-17  2.08e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NGFRAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.608000  0.000779  0.390000  0.000496  0.000391 \n",
+      "[1] \"PP abf for shared variant: 0.0391%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-12  2.21e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TUBA4A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.460000  0.000630  0.538000  0.000732  0.000483 \n",
+      "[1] \"PP abf for shared variant: 0.0483%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-04  5.03e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.39e-19  4.64e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RCAN3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.96e-05  5.42e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.84e-12  5.25e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-03  2.24e-06  9.96e-01  1.36e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-10  3.58e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-07  3.26e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___DNAJB6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.567000  0.000777  0.431000  0.000586  0.000395 \n",
+      "[1] \"PP abf for shared variant: 0.0395%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RAP1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.077e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.58e-01  1.31e-03  4.10e-02  5.55e-05  6.75e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00675%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-04  4.55e-07  9.97e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-02  1.93e-05  9.84e-01  1.34e-03  8.64e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0864%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.67e-02  6.39e-05  9.51e-01  1.29e-03  8.31e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0831%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CCDC104__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.53e-01  1.22e-03  4.59e-02  5.82e-05  5.23e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00523%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.05e-07  1.10e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.58e-06  9.00e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-05  3.16e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.63e-18  1.04e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.287000  0.000393  0.711000  0.000967  0.000663 \n",
+      "[1] \"PP abf for shared variant: 0.0663%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TESPA1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.092000  0.000116  0.906000  0.001140  0.000803 \n",
+      "[1] \"PP abf for shared variant: 0.0803%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.369000  0.000505  0.629000  0.000855  0.000622 \n",
+      "[1] \"PP abf for shared variant: 0.0622%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PSMA7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.868000  0.001190  0.130000  0.000177  0.000139 \n",
+      "[1] \"PP abf for shared variant: 0.0139%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PLEK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.826000  0.001130  0.172000  0.000234  0.000188 \n",
+      "[1] \"PP abf for shared variant: 0.0188%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-03  1.82e-06  9.96e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-15  3.03e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.4281e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.863000  0.001180  0.136000  0.000184  0.000143 \n",
+      "[1] \"PP abf for shared variant: 0.0143%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___KLRD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-02  4.62e-05  9.64e-01  1.31e-03  8.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0847%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MYC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.37e-02  7.35e-05  9.44e-01  1.28e-03  8.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0827%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.756000  0.001040  0.242000  0.000330  0.000226 \n",
+      "[1] \"PP abf for shared variant: 0.0226%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___KLF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.391e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-01  1.31e-03  4.26e-02  5.74e-05  8.87e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00887%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.21e-01  1.26e-03  7.78e-02  1.06e-04  8.07e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00807%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HNRNPA2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.45e-01  1.29e-03  5.38e-02  7.30e-05  6.99e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00699%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ARAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3907e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.77e-01  1.34e-03  2.12e-02  2.87e-05  3.89e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00389%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.17e-15  2.98e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.22e-05  7.14e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-04  1.70e-07  9.98e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.84e-12  3.89e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.22e-05  5.77e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-04  3.65e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.603000  0.000826  0.395000  0.000537  0.000356 \n",
+      "[1] \"PP abf for shared variant: 0.0356%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.26e-09  5.83e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.70e-18  6.44e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___LYPD3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.877000  0.001180  0.122000  0.000162  0.000123 \n",
+      "[1] \"PP abf for shared variant: 0.0123%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-04  5.01e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.747000  0.001020  0.251000  0.000341  0.000292 \n",
+      "[1] \"PP abf for shared variant: 0.0292%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.220000  0.000301  0.778000  0.001060  0.000685 \n",
+      "[1] \"PP abf for shared variant: 0.0685%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM243\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-02  2.29e-05  9.81e-01  1.33e-03  8.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-14  2.60e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ID2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.552000  0.000756  0.446000  0.000606  0.000441 \n",
+      "[1] \"PP abf for shared variant: 0.0441%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.99e-14  6.83e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-08  1.48e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.45e-02  7.46e-05  9.43e-01  1.28e-03  8.40e-04 \n",
+      "[1] \"PP abf for shared variant: 0.084%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.04e-25  1.10e-27  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.880000  0.001210  0.118000  0.000160  0.000127 \n",
+      "[1] \"PP abf for shared variant: 0.0127%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NFKBIA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 7.944e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.915000  0.001250  0.083000  0.000111  0.000305 \n",
+      "[1] \"PP abf for shared variant: 0.0305%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.286000  0.000392  0.712000  0.000968  0.000645 \n",
+      "[1] \"PP abf for shared variant: 0.0645%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-16  1.92e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-14  2.23e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-08  1.66e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GZMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.19e-07  9.85e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.679000  0.000929  0.320000  0.000435  0.000290 \n",
+      "[1] \"PP abf for shared variant: 0.029%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-10  4.06e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPI1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.854000  0.001170  0.145000  0.000196  0.000215 \n",
+      "[1] \"PP abf for shared variant: 0.0215%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-17  2.81e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.722000  0.000989  0.276000  0.000376  0.000268 \n",
+      "[1] \"PP abf for shared variant: 0.0268%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.53e-10  1.03e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.82e-14  3.87e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SC5D\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.910000  0.001250  0.088000  0.000117  0.000338 \n",
+      "[1] \"PP abf for shared variant: 0.0338%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-09  1.42e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.59e-17  4.91e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RIC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-02  2.99e-05  9.76e-01  1.33e-03  8.53e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0853%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-10  2.04e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.58e-04  7.64e-07  9.97e-01  1.36e-03  8.71e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0871%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.9814e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.62e-01  1.32e-03  3.62e-02  4.89e-05  6.87e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00687%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRP14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.22e-03  5.78e-06  9.94e-01  1.35e-03  8.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0298e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.24e-01  1.25e-03  7.43e-02  9.99e-05  8.62e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00862%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.24e-02  4.43e-05  9.65e-01  1.31e-03  8.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0849%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-05  3.23e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.98e-15  6.81e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-18  1.68e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-18  1.92e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MYL12B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.924000  0.001260  0.074800  0.000101  0.000112 \n",
+      "[1] \"PP abf for shared variant: 0.0112%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.102000  0.000139  0.896000  0.001220  0.000784 \n",
+      "[1] \"PP abf for shared variant: 0.0784%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MIR142__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1648e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.78e-01  1.34e-03  2.03e-02  2.75e-05  2.89e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00289%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.29e-02  7.24e-05  9.45e-01  1.28e-03  9.52e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0952%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.97e-17  6.80e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.585000  0.000801  0.413000  0.000562  0.000369 \n",
+      "[1] \"PP abf for shared variant: 0.0369%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-05  3.77e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PTP4A2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.856000  0.001170  0.143000  0.000194  0.000161 \n",
+      "[1] \"PP abf for shared variant: 0.0161%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.9666e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.61e-01  1.32e-03  3.76e-02  5.09e-05  5.16e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00516%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.39e-04  8.75e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.13e-07  5.65e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-22  2.66e-25  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CCDC57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-04  1.76e-07  9.98e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ITGB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.39e-01  1.29e-03  5.95e-02  8.09e-05  6.32e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00632%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.412000  0.000564  0.587000  0.000798  0.000524 \n",
+      "[1] \"PP abf for shared variant: 0.0524%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4185e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.58e-01  1.31e-03  4.05e-02  5.49e-05  5.95e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00595%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ARF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.427000  0.000584  0.572000  0.000778  0.000507 \n",
+      "[1] \"PP abf for shared variant: 0.0507%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CD81__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.827000  0.001130  0.172000  0.000233  0.000174 \n",
+      "[1] \"PP abf for shared variant: 0.0174%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-02  3.64e-05  9.71e-01  1.32e-03  8.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0849%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.95e-02  4.04e-05  9.68e-01  1.32e-03  8.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0847%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-02  1.82e-05  9.84e-01  1.34e-03  8.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0859%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-03  2.63e-06  9.96e-01  1.35e-03  8.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0876%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FOXP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-02  4.26e-05  9.67e-01  1.31e-03  9.07e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0907%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CCL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.36e-04  7.33e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.092900  0.000127  0.905000  0.001230  0.000796 \n",
+      "[1] \"PP abf for shared variant: 0.0796%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___KLRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9856e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.41e-01  1.29e-03  5.72e-02  7.74e-05  9.80e-05 \n",
+      "[1] \"PP abf for shared variant: 0.0098%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5042e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.78e-01  1.34e-03  2.05e-02  2.78e-05  3.03e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00303%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.702000  0.000962  0.296000  0.000402  0.000277 \n",
+      "[1] \"PP abf for shared variant: 0.0277%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-14  3.58e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___C1orf21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1023e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.45e-01  1.29e-03  5.32e-02  7.21e-05  7.24e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00724%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.11e-09  9.74e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CALM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.626000  0.000857  0.372000  0.000506  0.000352 \n",
+      "[1] \"PP abf for shared variant: 0.0352%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HOPX__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.80000   0.00110   0.19700   0.00026   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.236000  0.000323  0.762000  0.001040  0.000685 \n",
+      "[1] \"PP abf for shared variant: 0.0685%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.61e-15  9.05e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.52e-03  4.83e-06  9.94e-01  1.35e-03  8.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.08e-15  6.96e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.058400  0.000080  0.939000  0.001280  0.000855 \n",
+      "[1] \"PP abf for shared variant: 0.0855%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.13e-09  9.77e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.507e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.78e-01  1.34e-03  2.03e-02  2.73e-05  4.76e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00476%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-17  3.69e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.43e-13  6.06e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PTMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-02  3.97e-05  9.69e-01  1.32e-03  8.48e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0848%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-09  4.38e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GNG2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.717000  0.000981  0.282000  0.000383  0.000263 \n",
+      "[1] \"PP abf for shared variant: 0.0263%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.244000  0.000335  0.753000  0.001020  0.000671 \n",
+      "[1] \"PP abf for shared variant: 0.0671%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-05  3.85e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.80e-09  3.83e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-02  2.31e-05  9.81e-01  1.33e-03  8.56e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0856%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CD27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.689e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.914000  0.001250  0.084200  0.000114  0.000107 \n",
+      "[1] \"PP abf for shared variant: 0.0107%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.08e-02  5.59e-05  9.57e-01  1.30e-03  8.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0847%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-08  1.45e-11  9.98e-01  1.25e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-15  5.03e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___REL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.691e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.59e-01  1.31e-03  3.97e-02  5.38e-05  5.15e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00515%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-10  3.26e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.835000  0.001140  0.163000  0.000222  0.000163 \n",
+      "[1] \"PP abf for shared variant: 0.0163%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.78e-08  7.92e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-11  1.62e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.91e-06  1.08e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SEPT7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.516000  0.000706  0.482000  0.000656  0.000480 \n",
+      "[1] \"PP abf for shared variant: 0.048%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.611000  0.000837  0.387000  0.000526  0.000393 \n",
+      "[1] \"PP abf for shared variant: 0.0393%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.913000  0.001250  0.085800  0.000116  0.000108 \n",
+      "[1] \"PP abf for shared variant: 0.0108%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.94e-08  9.51e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___FCGR3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.322000  0.000441  0.676000  0.000915  0.001000 \n",
+      "[1] \"PP abf for shared variant: 0.1%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PSMB9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.645e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.49e-01  1.30e-03  4.91e-02  6.58e-05  1.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0143%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.05e-08  6.91e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.78e-07  5.18e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.885000  0.001210  0.113000  0.000154  0.000142 \n",
+      "[1] \"PP abf for shared variant: 0.0142%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-02  1.44e-05  9.87e-01  1.34e-03  8.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0873%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.50e-15  1.30e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-12  1.51e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.645000  0.000883  0.354000  0.000481  0.000327 \n",
+      "[1] \"PP abf for shared variant: 0.0327%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.407000  0.000557  0.591000  0.000804  0.000544 \n",
+      "[1] \"PP abf for shared variant: 0.0544%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-13  2.39e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5519e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-01  1.31e-03  4.26e-02  5.77e-05  5.98e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00598%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-09  2.99e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___DOK2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.820000  0.001120  0.178000  0.000242  0.000194 \n",
+      "[1] \"PP abf for shared variant: 0.0194%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.51e-04  4.81e-07  9.97e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CALR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9449e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.883000  0.001210  0.115000  0.000157  0.000118 \n",
+      "[1] \"PP abf for shared variant: 0.0118%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TTC38\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1223e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.886000  0.001210  0.112000  0.000152  0.000158 \n",
+      "[1] \"PP abf for shared variant: 0.0158%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.128000  0.000176  0.870000  0.001180  0.000771 \n",
+      "[1] \"PP abf for shared variant: 0.0771%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.112000  0.000153  0.886000  0.001210  0.000785 \n",
+      "[1] \"PP abf for shared variant: 0.0785%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.98e-08  4.08e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-08  1.53e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-05  2.10e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.90e-16  1.22e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PASK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-05  1.95e-08  9.98e-01  1.25e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.43e-09  8.81e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-16  1.84e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___OXNAD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1359e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.60e-01  1.32e-03  3.79e-02  4.98e-05  2.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0219%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.371000  0.000507  0.628000  0.000854  0.000556 \n",
+      "[1] \"PP abf for shared variant: 0.0556%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.82e-13  9.34e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.59e-03  4.91e-06  9.94e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TYROBP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.033600  0.000046  0.964000  0.001310  0.000855 \n",
+      "[1] \"PP abf for shared variant: 0.0855%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YBX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1331e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.71e-01  1.33e-03  2.71e-02  3.66e-05  5.36e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00536%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-05  3.18e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.41e-03  1.01e-05  9.90e-01  1.35e-03  8.66e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0866%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___IL7R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.40e-03  1.15e-05  9.89e-01  1.35e-03  8.63e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0863%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RHOH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.417000  0.000571  0.581000  0.000790  0.000542 \n",
+      "[1] \"PP abf for shared variant: 0.0542%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.61e-16  7.68e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.68e-17  6.41e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.079400  0.000109  0.918000  0.001250  0.000824 \n",
+      "[1] \"PP abf for shared variant: 0.0824%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___DBI__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.079000  0.000108  0.919000  0.001250  0.000805 \n",
+      "[1] \"PP abf for shared variant: 0.0805%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-11  3.13e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.85e-07  2.53e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-05  1.46e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___POMP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.829000  0.001140  0.169000  0.000230  0.000157 \n",
+      "[1] \"PP abf for shared variant: 0.0157%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-13  1.77e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RAB8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0817e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.77e-01  1.34e-03  2.20e-02  2.98e-05  3.59e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00359%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___GZMH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.566000  0.000774  0.433000  0.000588  0.000458 \n",
+      "[1] \"PP abf for shared variant: 0.0458%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-03  2.23e-06  9.96e-01  1.36e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-09  2.32e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.68e-17  7.78e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-16  4.17e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___ABLIM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.92e-02  3.74e-05  9.69e-01  1.23e-03  8.46e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0846%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EIF4A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8946e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.40e-01  1.29e-03  5.82e-02  7.87e-05  1.02e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0102%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-03  4.39e-06  9.95e-01  1.35e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RP11-291B21.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.39e-05  5.62e-08  9.98e-01  1.27e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-18  2.55e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.69e-18  6.41e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-08  2.87e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-08  1.74e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.74e-17  6.49e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.109000  0.000149  0.889000  0.001210  0.000784 \n",
+      "[1] \"PP abf for shared variant: 0.0784%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.05e-13  8.29e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.76e-11  6.52e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S1PR5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1943e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.73e-01  1.25e-03  2.58e-02  3.24e-05  6.67e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00667%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD8T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.024900  0.000034  0.973000  0.001320  0.000850 \n",
+      "[1] \"PP abf for shared variant: 0.085%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.310000  0.000424  0.688000  0.000936  0.000623 \n",
+      "[1] \"PP abf for shared variant: 0.0623%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CHCHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.655000  0.000897  0.343000  0.000466  0.000372 \n",
+      "[1] \"PP abf for shared variant: 0.0372%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.78e-03  5.17e-06  9.94e-01  1.35e-03  8.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FMNL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.705000  0.000965  0.293000  0.000397  0.000519 \n",
+      "[1] \"PP abf for shared variant: 0.0519%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-23  2.87e-26  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-06  2.88e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HERPUD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.267e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.862000  0.001180  0.136000  0.000185  0.000149 \n",
+      "[1] \"PP abf for shared variant: 0.0149%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ANXA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.96e-05  5.43e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SOD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.221000  0.000302  0.777000  0.001060  0.000701 \n",
+      "[1] \"PP abf for shared variant: 0.0701%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-17  4.02e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-11  5.86e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ATP1B3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.882000  0.001210  0.116000  0.000158  0.000120 \n",
+      "[1] \"PP abf for shared variant: 0.012%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-01  1.31e-03  4.27e-02  5.78e-05  6.82e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00682%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-24  1.77e-27  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.79e-10  1.07e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EML4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.29e-03  7.24e-06  9.92e-01  1.35e-03  8.71e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0871%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-02  1.68e-05  9.85e-01  1.34e-03  9.24e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0924%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MCL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-05  2.84e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NOG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.297000  0.000378  0.701000  0.000887  0.000624 \n",
+      "[1] \"PP abf for shared variant: 0.0624%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PRMT2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-02  1.47e-05  9.87e-01  1.34e-03  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.95e-05  9.52e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.856000  0.001160  0.142000  0.000192  0.000142 \n",
+      "[1] \"PP abf for shared variant: 0.0142%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-11  4.88e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FAM129A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.99e-06  8.20e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.87e-15  9.40e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.2936e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.894000  0.001220  0.105000  0.000142  0.000107 \n",
+      "[1] \"PP abf for shared variant: 0.0107%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-24  1.85e-27  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ALOX5AP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-02  2.65e-05  9.78e-01  1.33e-03  8.58e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0858%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TSHZ2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.85e-03  3.90e-06  9.95e-01  1.35e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.85e-05  2.53e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ARHGDIB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-05  4.28e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.78e-08  5.18e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-17  3.52e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.07e-26  8.31e-29  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.88e-26  8.06e-29  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YBX1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.63e-05  7.71e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.67e-24  1.05e-26  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___JUND__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.279e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.851000  0.001170  0.147000  0.000200  0.000154 \n",
+      "[1] \"PP abf for shared variant: 0.0154%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.78e-06  9.28e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-25  2.19e-28  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.094000  0.000129  0.902000  0.001210  0.002850 \n",
+      "[1] \"PP abf for shared variant: 0.285%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.30e-10  4.51e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF18\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.67e-02  7.77e-05  9.41e-01  1.28e-03  8.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0827%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MYO1F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-02  1.38e-05  9.88e-01  1.34e-03  8.62e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.89e-19  2.58e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-07  6.71e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.858000  0.001170  0.141000  0.000191  0.000160 \n",
+      "[1] \"PP abf for shared variant: 0.016%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-22  2.14e-25  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-03  4.67e-06  9.94e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VMP1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.710000  0.000973  0.288000  0.000392  0.000278 \n",
+      "[1] \"PP abf for shared variant: 0.0278%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-05  2.01e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.96e-24  1.23e-26  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.755000  0.001030  0.243000  0.000331  0.000228 \n",
+      "[1] \"PP abf for shared variant: 0.0228%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GALM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.691000  0.000946  0.307000  0.000418  0.000285 \n",
+      "[1] \"PP abf for shared variant: 0.0285%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.120000  0.000165  0.878000  0.001190  0.000770 \n",
+      "[1] \"PP abf for shared variant: 0.077%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.86e-02  3.91e-05  9.69e-01  1.32e-03  9.12e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0912%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.28e-12  9.97e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.69e-15  1.05e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ANXA5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.430000  0.000589  0.568000  0.000772  0.000513 \n",
+      "[1] \"PP abf for shared variant: 0.0513%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___IDS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.54e-03  6.22e-06  9.93e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ARID5B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.382000  0.000523  0.616000  0.000834  0.000875 \n",
+      "[1] \"PP abf for shared variant: 0.0875%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___IMPDH2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-04  2.21e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.16e-12  1.12e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ST13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-05  2.88e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CXCR3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-02  3.36e-05  9.73e-01  1.31e-03  1.10e-03 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-06  2.87e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.17e-12  2.97e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SPOCK2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.183000  0.000251  0.815000  0.001110  0.000750 \n",
+      "[1] \"PP abf for shared variant: 0.075%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___C15orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.689000  0.000886  0.310000  0.000396  0.000288 \n",
+      "[1] \"PP abf for shared variant: 0.0288%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPF\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1448e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.916000  0.001250  0.083000  0.000112  0.000149 \n",
+      "[1] \"PP abf for shared variant: 0.0149%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.09e-14  5.60e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FAM134B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.568000  0.000778  0.430000  0.000585  0.000440 \n",
+      "[1] \"PP abf for shared variant: 0.044%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ISG20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.15e-02  9.79e-05  9.26e-01  1.26e-03  8.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0822%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.59e-10  1.18e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NUCB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.169000  0.000231  0.829000  0.001130  0.000735 \n",
+      "[1] \"PP abf for shared variant: 0.0735%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-02  3.03e-05  9.76e-01  1.33e-03  8.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0895%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LINC00493__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.557000  0.000763  0.441000  0.000600  0.000411 \n",
+      "[1] \"PP abf for shared variant: 0.0411%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.94e-22  8.13e-25  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-02  3.08e-05  9.75e-01  1.33e-03  8.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0859%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-11  5.28e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CDC42__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.142000  0.000195  0.856000  0.001160  0.000775 \n",
+      "[1] \"PP abf for shared variant: 0.0775%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF1B\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.238000  0.000326  0.760000  0.001030  0.000699 \n",
+      "[1] \"PP abf for shared variant: 0.0699%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.452000  0.000619  0.546000  0.000743  0.000494 \n",
+      "[1] \"PP abf for shared variant: 0.0494%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.79e-16  9.30e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ACTN4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010200  0.000014  0.988000  0.001340  0.000862 \n",
+      "[1] \"PP abf for shared variant: 0.0862%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___IKZF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.573000  0.000784  0.425000  0.000578  0.000410 \n",
+      "[1] \"PP abf for shared variant: 0.041%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.871000  0.001190  0.127000  0.000173  0.000137 \n",
+      "[1] \"PP abf for shared variant: 0.0137%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-16  2.93e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-07  2.49e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.873000  0.001190  0.126000  0.000171  0.000133 \n",
+      "[1] \"PP abf for shared variant: 0.0133%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NAMPT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8087e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.911000  0.001250  0.087600  0.000118  0.000151 \n",
+      "[1] \"PP abf for shared variant: 0.0151%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.36e-05  8.71e-08  9.98e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CALM2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.09e-04  8.34e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-13  1.84e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-ND2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.843000  0.001150  0.155000  0.000211  0.000171 \n",
+      "[1] \"PP abf for shared variant: 0.0171%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.59e-12  3.55e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD52__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-03  1.57e-06  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.32e-07  8.65e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___H3F3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-04  1.39e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ADTRP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.19e-07  8.48e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.551000  0.000755  0.447000  0.000607  0.000468 \n",
+      "[1] \"PP abf for shared variant: 0.0468%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.362000  0.000496  0.635000  0.000863  0.000730 \n",
+      "[1] \"PP abf for shared variant: 0.073%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-03  1.39e-06  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-03  2.01e-06  9.96e-01  1.36e-03  8.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0884%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___DYNLL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-03  4.99e-06  9.94e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAMSN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-04  1.73e-07  9.98e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LMNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-08  1.42e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-ND5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-05  1.57e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.32e-20  5.92e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RUNX3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.421000  0.000577  0.577000  0.000784  0.000531 \n",
+      "[1] \"PP abf for shared variant: 0.0531%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.40e-17  6.03e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-05  3.58e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ERGIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.423e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.46e-01  1.30e-03  5.24e-02  7.09e-05  8.76e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00876%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.94e-07  6.77e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TYMP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.243000  0.000332  0.755000  0.001030  0.000670 \n",
+      "[1] \"PP abf for shared variant: 0.067%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.413000  0.000566  0.585000  0.000795  0.000531 \n",
+      "[1] \"PP abf for shared variant: 0.0531%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-19  1.43e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.19e-03  8.48e-06  9.92e-01  1.35e-03  8.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0865%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.81e-22  7.95e-25  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.10e-20  6.99e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-ND4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.789000  0.001080  0.209000  0.000284  0.000203 \n",
+      "[1] \"PP abf for shared variant: 0.0203%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-03  5.02e-06  9.94e-01  1.35e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ATP5E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.72e-03  1.06e-05  9.90e-01  1.35e-03  8.66e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0866%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-08  2.84e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-12  2.26e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-20  4.20e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.28e-08  1.75e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ATP2B4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.137000  0.000187  0.861000  0.001170  0.000755 \n",
+      "[1] \"PP abf for shared variant: 0.0755%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ARPC1B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.870000  0.001190  0.128000  0.000174  0.000162 \n",
+      "[1] \"PP abf for shared variant: 0.0162%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PDCD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.64e-02  7.03e-05  9.42e-01  1.17e-03  8.28e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0828%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-19  2.18e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-22  3.43e-25  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-21  1.66e-24  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.42e-11  6.05e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-07  6.55e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-04  2.67e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-03  3.03e-06  9.96e-01  1.35e-03  8.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LGALS3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.692000  0.000947  0.307000  0.000417  0.000287 \n",
+      "[1] \"PP abf for shared variant: 0.0287%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MIAT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.23e-02  7.17e-05  9.45e-01  1.29e-03  8.29e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0829%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.22e-18  4.40e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-07  2.61e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.34e-02  8.69e-05  9.34e-01  1.27e-03  8.26e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0826%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.21e-16  1.26e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-20  3.10e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RNF19A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-02  1.68e-05  9.85e-01  1.34e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-CO3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-06  4.88e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-22  1.73e-25  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.55e-11  8.96e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FAS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.612000  0.000838  0.386000  0.000525  0.000352 \n",
+      "[1] \"PP abf for shared variant: 0.0352%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.12e-09  7.01e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.20e-09  8.50e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GYPC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-04  2.43e-07  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.31e-21  8.64e-24  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.68e-22  1.05e-24  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPM4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.252000  0.000344  0.746000  0.001010  0.000742 \n",
+      "[1] \"PP abf for shared variant: 0.0742%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-09  5.02e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.41e-04  6.04e-07  9.97e-01  1.36e-03  9.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0959%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.30e-22  1.27e-24  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ITGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-06  2.57e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-05  4.40e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.165000  0.000226  0.833000  0.001130  0.000736 \n",
+      "[1] \"PP abf for shared variant: 0.0736%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.06e-25  8.29e-28  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.533000  0.000729  0.466000  0.000633  0.000422 \n",
+      "[1] \"PP abf for shared variant: 0.0422%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.47e-17  1.30e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-02  2.67e-05  9.78e-01  1.33e-03  8.56e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0856%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008020  0.000011  0.990000  0.001350  0.000899 \n",
+      "[1] \"PP abf for shared variant: 0.0899%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.83e-19  9.35e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-16  4.18e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.369000  0.000505  0.629000  0.000853  0.000763 \n",
+      "[1] \"PP abf for shared variant: 0.0763%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PPP1R15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.724000  0.000991  0.275000  0.000373  0.000263 \n",
+      "[1] \"PP abf for shared variant: 0.0263%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___IL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-03  3.57e-06  9.95e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.45e-24  6.09e-27  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.68e-13  6.41e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM20\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.748000  0.001020  0.250000  0.000340  0.000247 \n",
+      "[1] \"PP abf for shared variant: 0.0247%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF3F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-03  3.80e-06  9.95e-01  1.35e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.037200  0.000051  0.960000  0.001300  0.001320 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___KLF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.50e-04  6.16e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GIMAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-03  3.75e-06  9.95e-01  1.35e-03  8.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0877%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFBR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.255000  0.000349  0.743000  0.001010  0.000689 \n",
+      "[1] \"PP abf for shared variant: 0.0689%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RNF213__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.774000  0.001060  0.225000  0.000305  0.000224 \n",
+      "[1] \"PP abf for shared variant: 0.0224%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___C19orf53__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.924000  0.001260  0.074700  0.000101  0.000114 \n",
+      "[1] \"PP abf for shared variant: 0.0114%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.57e-10  7.63e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-12  1.64e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MIR4435-1HG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-09  3.24e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-14  1.51e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MZT2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.55e-03  6.23e-06  9.93e-01  1.35e-03  8.66e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0866%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___AK5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.431000  0.000590  0.566000  0.000762  0.001360 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.577000  0.000791  0.419000  0.000555  0.001960 \n",
+      "[1] \"PP abf for shared variant: 0.196%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.48e-07  7.50e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-17  2.61e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-02  1.94e-05  9.84e-01  1.34e-03  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.44e-18  6.08e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.225000  0.000308  0.773000  0.001050  0.000685 \n",
+      "[1] \"PP abf for shared variant: 0.0685%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CXCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.803000  0.001080  0.196000  0.000261  0.000192 \n",
+      "[1] \"PP abf for shared variant: 0.0192%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ARPC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.464000  0.000635  0.535000  0.000727  0.000495 \n",
+      "[1] \"PP abf for shared variant: 0.0495%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MRPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.910000  0.001250  0.088700  0.000120  0.000102 \n",
+      "[1] \"PP abf for shared variant: 0.0102%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.46e-14  1.29e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-04  1.53e-07  9.98e-01  1.36e-03  8.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0879%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EBPL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.876000  0.001200  0.122000  0.000166  0.000176 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.786000  0.001080  0.212000  0.000289  0.000200 \n",
+      "[1] \"PP abf for shared variant: 0.02%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UXT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.33e-06  5.93e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LSM5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-03  4.38e-06  9.95e-01  1.35e-03  9.06e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0906%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___KMT2E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.6569e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.39e-01  1.29e-03  5.99e-02  8.04e-05  1.64e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0164%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.92e-06  1.08e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TAGLN2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.21e-01  1.26e-03  7.78e-02  1.06e-04  9.27e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00927%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CDCA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4164e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.828000  0.001120  0.170000  0.000227  0.000391 \n",
+      "[1] \"PP abf for shared variant: 0.0391%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-04  2.18e-07  9.98e-01  1.36e-03  9.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0919%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EPB41L4A-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-05  1.95e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-07  2.52e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TATDN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.411000  0.000563  0.587000  0.000798  0.000522 \n",
+      "[1] \"PP abf for shared variant: 0.0522%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.143000  0.000196  0.855000  0.001160  0.000752 \n",
+      "[1] \"PP abf for shared variant: 0.0752%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-11  2.25e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-20  2.64e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.96e-18  8.16e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___BTG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.45e-03  1.02e-05  9.90e-01  1.35e-03  8.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0874%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___C8orf59__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.102000  0.000140  0.896000  0.001220  0.000794 \n",
+      "[1] \"PP abf for shared variant: 0.0794%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD58__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-02  3.57e-05  9.72e-01  1.32e-03  8.53e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0853%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-16  2.96e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-05  2.13e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___AKAP13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.133000  0.000182  0.865000  0.001180  0.000780 \n",
+      "[1] \"PP abf for shared variant: 0.078%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.56e-06  6.25e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___DDX5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.69e-04  1.05e-06  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.275000  0.000376  0.723000  0.000984  0.000652 \n",
+      "[1] \"PP abf for shared variant: 0.0652%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ANXA2R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.882000  0.001210  0.117000  0.000158  0.000134 \n",
+      "[1] \"PP abf for shared variant: 0.0134%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___IL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.865000  0.001180  0.133000  0.000181  0.000139 \n",
+      "[1] \"PP abf for shared variant: 0.0139%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LINC00152__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-08  3.06e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FOXP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.352000  0.000472  0.646000  0.000860  0.000691 \n",
+      "[1] \"PP abf for shared variant: 0.0691%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-08  1.79e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.26e-21  9.94e-24  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.92e-04  9.48e-07  9.97e-01  1.36e-03  9.10e-04 \n",
+      "[1] \"PP abf for shared variant: 0.091%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-14  1.40e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-11  2.52e-14  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___DAP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.88e-03  6.68e-06  9.93e-01  1.35e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-10  2.64e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SVIP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.19e-01  1.26e-03  7.94e-02  1.08e-04  8.32e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00832%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.14e-15  9.78e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-25  2.19e-28  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.80e-18  1.34e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-06  2.06e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.167000  0.000229  0.831000  0.001130  0.000760 \n",
+      "[1] \"PP abf for shared variant: 0.076%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ENTPD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.449000  0.000609  0.550000  0.000741  0.000518 \n",
+      "[1] \"PP abf for shared variant: 0.0518%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___DUSP4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-09  3.79e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-12  2.25e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.80e-03  1.21e-05  9.89e-01  1.35e-03  8.63e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0863%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.723000  0.000991  0.275000  0.000373  0.000321 \n",
+      "[1] \"PP abf for shared variant: 0.0321%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-ND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.104000  0.000143  0.894000  0.001220  0.000808 \n",
+      "[1] \"PP abf for shared variant: 0.0808%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-16  2.63e-19  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ADAM19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.856000  0.001170  0.143000  0.000193  0.000200 \n",
+      "[1] \"PP abf for shared variant: 0.02%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CLDND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.615000  0.000841  0.384000  0.000522  0.000369 \n",
+      "[1] \"PP abf for shared variant: 0.0369%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.44e-06  3.34e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.95e-08  8.15e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.612000  0.000838  0.386000  0.000524  0.000403 \n",
+      "[1] \"PP abf for shared variant: 0.0403%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___APEX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.818000  0.001120  0.180000  0.000245  0.000198 \n",
+      "[1] \"PP abf for shared variant: 0.0198%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-07  1.54e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.62e-21  7.69e-24  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LETMD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.66e-03  1.19e-05  9.89e-01  1.35e-03  8.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0896%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.91e-03  1.01e-05  9.90e-01  1.26e-03  8.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0865%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-05  2.81e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___AHNAK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.098000  0.000134  0.899000  0.001220  0.001440 \n",
+      "[1] \"PP abf for shared variant: 0.144%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SLC7A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.928000  0.001270  0.071000  0.000096  0.000123 \n",
+      "[1] \"PP abf for shared variant: 0.0123%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.45e-09  7.47e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-19  2.38e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.15e-02  4.32e-05  9.66e-01  1.31e-03  8.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.085%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-27  4.02e-30  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.24e-12  5.80e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-23  1.45e-26  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-ATP6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-03  3.10e-06  9.96e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-17  1.85e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.22e-06  8.52e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RILPL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.621000  0.000850  0.378000  0.000514  0.000350 \n",
+      "[1] \"PP abf for shared variant: 0.035%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SSR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-04  4.72e-07  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.95e-02  4.04e-05  9.68e-01  1.32e-03  8.52e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0852%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.02e-08  6.87e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-13  2.02e-16  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MAF__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.94e-05  8.13e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.52e-09  7.56e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___COMMD6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008050  0.000011  0.990000  0.001350  0.000973 \n",
+      "[1] \"PP abf for shared variant: 0.0973%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-06  3.80e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NSMCE1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.101000  0.000138  0.897000  0.001220  0.000790 \n",
+      "[1] \"PP abf for shared variant: 0.079%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.04e-04  1.10e-06  9.97e-01  1.36e-03  8.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.089%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PRDX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.432000  0.000592  0.566000  0.000769  0.000519 \n",
+      "[1] \"PP abf for shared variant: 0.0519%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-17  1.47e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FAM46C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.143000  0.000196  0.855000  0.001160  0.000751 \n",
+      "[1] \"PP abf for shared variant: 0.0751%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.39e-20  1.01e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-20  5.80e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-25  2.48e-28  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-22  2.20e-25  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RORA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.756000  0.001030  0.243000  0.000330  0.000262 \n",
+      "[1] \"PP abf for shared variant: 0.0262%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.15e-03  1.12e-05  9.90e-01  1.35e-03  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.80e-18  1.20e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD44__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.96e-04  4.05e-07  9.97e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4Y1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.839000  0.001150  0.158000  0.000209  0.000797 \n",
+      "[1] \"PP abf for shared variant: 0.0797%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-05  3.68e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___COX7A2L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.268000  0.000367  0.730000  0.000993  0.000654 \n",
+      "[1] \"PP abf for shared variant: 0.0654%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-18  2.81e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HADHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.470000  0.000644  0.528000  0.000718  0.000478 \n",
+      "[1] \"PP abf for shared variant: 0.0478%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SATB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.689000  0.000944  0.309000  0.000420  0.000289 \n",
+      "[1] \"PP abf for shared variant: 0.0289%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UGP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.566000  0.000776  0.432000  0.000587  0.000391 \n",
+      "[1] \"PP abf for shared variant: 0.0391%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SBDS\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.696000  0.000953  0.302000  0.000411  0.000288 \n",
+      "[1] \"PP abf for shared variant: 0.0288%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SYNE2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.27e-02  8.58e-05  9.35e-01  1.27e-03  8.33e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0833%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMA7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.303000  0.000415  0.695000  0.000945  0.000625 \n",
+      "[1] \"PP abf for shared variant: 0.0625%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-07  1.81e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NR3C1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.325000  0.000445  0.673000  0.000915  0.000616 \n",
+      "[1] \"PP abf for shared variant: 0.0616%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-24  2.30e-27  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CCT8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.382000  0.000523  0.615000  0.000828  0.001450 \n",
+      "[1] \"PP abf for shared variant: 0.145%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFAIP3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.525000  0.000719  0.473000  0.000644  0.000424 \n",
+      "[1] \"PP abf for shared variant: 0.0424%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH2D2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.879000  0.001110  0.119000  0.000149  0.000143 \n",
+      "[1] \"PP abf for shared variant: 0.0143%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-10  4.23e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CLNS1A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-02  3.69e-05  9.71e-01  1.32e-03  8.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0849%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.69e-08  1.33e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ATP6V0E1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.684000  0.000936  0.315000  0.000428  0.000312 \n",
+      "[1] \"PP abf for shared variant: 0.0312%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-24  5.80e-27  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.803000  0.001100  0.195000  0.000265  0.000185 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.00e-23  6.84e-26  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36L2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.254000  0.000348  0.744000  0.001010  0.000664 \n",
+      "[1] \"PP abf for shared variant: 0.0664%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF3D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-03  3.77e-06  9.95e-01  1.35e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.599000  0.000820  0.400000  0.000543  0.000414 \n",
+      "[1] \"PP abf for shared variant: 0.0414%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-17  2.46e-20  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___APRT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.154000  0.000211  0.844000  0.001150  0.000766 \n",
+      "[1] \"PP abf for shared variant: 0.0766%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FYN__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-02  2.19e-05  9.82e-01  1.34e-03  8.57e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0857%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ANP32B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.404000  0.000553  0.594000  0.000808  0.000618 \n",
+      "[1] \"PP abf for shared variant: 0.0618%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.170000  0.000233  0.828000  0.001130  0.000726 \n",
+      "[1] \"PP abf for shared variant: 0.0726%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-02  1.67e-05  9.86e-01  1.34e-03  8.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0882%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-26  3.06e-29  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CMPK1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-03  3.51e-06  9.95e-01  1.35e-03  8.68e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0868%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAZ\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.537000  0.000736  0.461000  0.000626  0.000469 \n",
+      "[1] \"PP abf for shared variant: 0.0469%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GIMAP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-06  3.51e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___COTL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-05  2.65e-08  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF2S3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.32e-10  1.28e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HSP90AA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1807e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.818000  0.001120  0.180000  0.000245  0.000177 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MT-CYB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-03  3.04e-06  9.96e-01  1.35e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HSPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.895000  0.001230  0.103000  0.000140  0.000107 \n",
+      "[1] \"PP abf for shared variant: 0.0107%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-09  1.71e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.98e-08  6.82e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-15  3.06e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXK\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-03  1.41e-06  9.97e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.45e-15  3.36e-18  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.01e-12  4.12e-15  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.59e-06  4.92e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.11e-04  1.25e-06  9.97e-01  1.36e-03  8.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0875%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.403000  0.000551  0.596000  0.000810  0.000533 \n",
+      "[1] \"PP abf for shared variant: 0.0533%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___LYRM4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.728000  0.000997  0.270000  0.000367  0.000266 \n",
+      "[1] \"PP abf for shared variant: 0.0266%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.49e-20  3.41e-23  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.25e-19  7.19e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.54e-09  1.31e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EZR__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.97e-03  8.18e-06  9.92e-01  1.35e-03  8.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0865%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.55e-03  1.03e-05  9.90e-01  1.35e-03  8.64e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0864%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ATP5O__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-07  1.69e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.470000  0.000643  0.528000  0.000717  0.000598 \n",
+      "[1] \"PP abf for shared variant: 0.0598%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-18  4.34e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUCLG2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-03  3.93e-06  9.95e-01  1.35e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CD3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.525000  0.000719  0.473000  0.000642  0.000512 \n",
+      "[1] \"PP abf for shared variant: 0.0512%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.97e-18  8.17e-21  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___NSA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.53e-07  8.94e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.353000  0.000484  0.645000  0.000877  0.000638 \n",
+      "[1] \"PP abf for shared variant: 0.0638%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___HIGD2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.609000  0.000834  0.389000  0.000528  0.000408 \n",
+      "[1] \"PP abf for shared variant: 0.0408%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.98e-08  5.45e-11  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___IGBP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.01e-02  5.49e-05  9.58e-01  1.30e-03  8.54e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0854%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.91e-19  8.09e-22  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___MYH9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.923000  0.001260  0.075500  0.000101  0.000225 \n",
+      "[1] \"PP abf for shared variant: 0.0225%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-07  2.95e-10  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___ATP2B1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.32e-03  7.28e-06  9.92e-01  1.35e-03  8.66e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0866%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-28  2.05e-31  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RBM39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.092400  0.000127  0.905000  0.001230  0.000880 \n",
+      "[1] \"PP abf for shared variant: 0.088%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___CCNG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-02  4.67e-05  9.64e-01  1.31e-03  8.52e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0852%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-14  1.40e-17  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___COX4I1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.117000  0.000160  0.881000  0.001200  0.000786 \n",
+      "[1] \"PP abf for shared variant: 0.0786%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___PMAIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.317000  0.000435  0.681000  0.000925  0.000633 \n",
+      "[1] \"PP abf for shared variant: 0.0633%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-10  4.15e-13  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-06  1.50e-09  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___FHIT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.46e-10  1.16e-12  9.98e-01  1.36e-03  8.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.087%\"\n",
+      "[1] \"Multiple Sclerosis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.69e-04  1.05e-06  9.97e-01  1.36e-03  8.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0869%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___CAPG__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02470   0.00348   0.85100   0.12000   0.00136 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___PTAFR__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06980   0.00981   0.80600   0.11300   0.00128 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___MNDA__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.5916e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.48200   0.06780   0.39400   0.05540   0.00104 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___RNASE6__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16600   0.02330   0.71000   0.09980   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__TSPO\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.22900   0.03210   0.64700   0.09100   0.00118 \n",
+      "[1] \"PP abf for shared variant: 0.118%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__VMO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5549e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.49800   0.07000   0.37700   0.05300   0.00132 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___S100A9__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26100   0.03660   0.61500   0.08650   0.00113 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___QPCT__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.8504e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.46800   0.06570   0.40800   0.05730   0.00113 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___BLVRB__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1205e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.34600   0.04860   0.53000   0.07450   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___LYZ__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0128    0.0018    0.8630    0.1210    0.0013 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_TMEM176A___CLEC4A__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.502000  0.070600  0.374000  0.052500  0.000971 \n",
+      "[1] \"PP abf for shared variant: 0.0971%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02300   0.00199   0.89600   0.07770   0.00155 \n",
+      "[1] \"PP abf for shared variant: 0.155%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL5__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3670    0.0318    0.5530    0.0479    0.0011 \n",
+      "[1] \"PP abf for shared variant: 0.11%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.22300   0.01940   0.69600   0.06040   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL32__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02860   0.00248   0.89000   0.07720   0.00157 \n",
+      "[1] \"PP abf for shared variant: 0.157%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___EEF1A1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.40400   0.03500   0.51500   0.04470   0.00105 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL38__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1320    0.0114    0.7870    0.0683    0.0014 \n",
+      "[1] \"PP abf for shared variant: 0.14%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.17800   0.01550   0.74100   0.06420   0.00136 \n",
+      "[1] \"PP abf for shared variant: 0.136%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.20900   0.01810   0.71000   0.06160   0.00125 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.32800   0.02840   0.59100   0.05130   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.37000   0.03210   0.54900   0.04760   0.00107 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS15A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.19500   0.01690   0.72400   0.06280   0.00132 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS8__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1860    0.0161    0.7330    0.0636    0.0013 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.36400   0.03160   0.55600   0.04820   0.00102 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010000  0.000867  0.909000  0.078800  0.001510 \n",
+      "[1] \"PP abf for shared variant: 0.151%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___NKG7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2740    0.0238    0.6450    0.0559    0.0012 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___B2M__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07160   0.00621   0.84700   0.07350   0.00146 \n",
+      "[1] \"PP abf for shared variant: 0.146%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14100   0.01220   0.77800   0.06750   0.00144 \n",
+      "[1] \"PP abf for shared variant: 0.144%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___PFN1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04560   0.00396   0.87300   0.07570   0.00150 \n",
+      "[1] \"PP abf for shared variant: 0.15%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS28__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13600   0.01180   0.78300   0.06790   0.00134 \n",
+      "[1] \"PP abf for shared variant: 0.134%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.18800   0.01630   0.73100   0.06340   0.00135 \n",
+      "[1] \"PP abf for shared variant: 0.135%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___GZMH__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04160   0.00361   0.87700   0.07610   0.00147 \n",
+      "[1] \"PP abf for shared variant: 0.147%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___LTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08220   0.00713   0.83700   0.07260   0.00146 \n",
+      "[1] \"PP abf for shared variant: 0.146%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL39__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.13300   0.01150   0.78600   0.06820   0.00141 \n",
+      "[1] \"PP abf for shared variant: 0.141%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16600   0.01440   0.75300   0.06530   0.00134 \n",
+      "[1] \"PP abf for shared variant: 0.134%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.12700   0.01100   0.79200   0.06870   0.00143 \n",
+      "[1] \"PP abf for shared variant: 0.143%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS23__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06470   0.00561   0.85400   0.07410   0.00149 \n",
+      "[1] \"PP abf for shared variant: 0.149%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS29__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07680   0.00666   0.84200   0.07300   0.00145 \n",
+      "[1] \"PP abf for shared variant: 0.145%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL22__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.37000   0.03210   0.54900   0.04760   0.00113 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL9__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.20000   0.01730   0.71900   0.06240   0.00133 \n",
+      "[1] \"PP abf for shared variant: 0.133%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.35000   0.03040   0.56900   0.04940   0.00111 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.31300   0.02720   0.60600   0.05250   0.00115 \n",
+      "[1] \"PP abf for shared variant: 0.115%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00027483\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.756000  0.065600  0.163000  0.014100  0.000652 \n",
+      "[1] \"PP abf for shared variant: 0.0652%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___MAL__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.20300   0.01730   0.71700   0.06100   0.00128 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___PRF1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4200    0.0365    0.4990    0.0433    0.0010 \n",
+      "[1] \"PP abf for shared variant: 0.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01220   0.00106   0.90700   0.07860   0.00159 \n",
+      "[1] \"PP abf for shared variant: 0.159%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS6__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11400   0.00989   0.80500   0.06980   0.00142 \n",
+      "[1] \"PP abf for shared variant: 0.142%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01340   0.00116   0.90500   0.07850   0.00157 \n",
+      "[1] \"PP abf for shared variant: 0.157%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL21__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08190   0.00710   0.83700   0.07260   0.00147 \n",
+      "[1] \"PP abf for shared variant: 0.147%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___SMDT1__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.003400  0.000295  0.915000  0.079400  0.001570 \n",
+      "[1] \"PP abf for shared variant: 0.157%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11300   0.00979   0.80600   0.06990   0.00139 \n",
+      "[1] \"PP abf for shared variant: 0.139%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01740   0.00151   0.90100   0.07820   0.00157 \n",
+      "[1] \"PP abf for shared variant: 0.157%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL34__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04980   0.00432   0.86900   0.07540   0.00152 \n",
+      "[1] \"PP abf for shared variant: 0.152%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL10A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07050   0.00612   0.84800   0.07360   0.00147 \n",
+      "[1] \"PP abf for shared variant: 0.147%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_SMDT1___RPL30__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.11700   0.01010   0.80200   0.06960   0.00143 \n",
+      "[1] \"PP abf for shared variant: 0.143%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01170   0.00101   0.90700   0.07870   0.00137 \n",
+      "[1] \"PP abf for shared variant: 0.137%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.31700   0.02750   0.60200   0.05220   0.00109 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05470   0.00475   0.86400   0.07500   0.00144 \n",
+      "[1] \"PP abf for shared variant: 0.144%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0461    0.0040    0.8730    0.0757    0.0015 \n",
+      "[1] \"PP abf for shared variant: 0.15%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.45200   0.03920   0.46700   0.04050   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.37400   0.03240   0.54600   0.04730   0.00098 \n",
+      "[1] \"PP abf for shared variant: 0.098%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL31__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.434000  0.037700  0.485000  0.042100  0.000986 \n",
+      "[1] \"PP abf for shared variant: 0.0986%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2270    0.0197    0.6920    0.0600    0.0012 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___ACTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01690   0.00147   0.90200   0.07830   0.00122 \n",
+      "[1] \"PP abf for shared variant: 0.122%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.25000   0.02160   0.66900   0.05810   0.00124 \n",
+      "[1] \"PP abf for shared variant: 0.124%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.004640  0.000402  0.914000  0.079300  0.001460 \n",
+      "[1] \"PP abf for shared variant: 0.146%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.503000  0.043600  0.416000  0.036100  0.000908 \n",
+      "[1] \"PP abf for shared variant: 0.0908%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16500   0.01430   0.75400   0.06540   0.00127 \n",
+      "[1] \"PP abf for shared variant: 0.127%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.21000   0.01820   0.70900   0.06150   0.00128 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL10__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05380   0.00466   0.86500   0.07510   0.00114 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.14300   0.01240   0.77600   0.06730   0.00117 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0032661\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.746000  0.064700  0.173000  0.015000  0.000947 \n",
+      "[1] \"PP abf for shared variant: 0.0947%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___CD48__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.26500   0.02300   0.65400   0.05680   0.00121 \n",
+      "[1] \"PP abf for shared variant: 0.121%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.32400   0.02810   0.59500   0.05160   0.00106 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_SMDT1___RPS27__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.401000  0.034800  0.518000  0.044900  0.000977 \n",
+      "[1] \"PP abf for shared variant: 0.0977%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.87e-243  7.40e-01 2.70e-244  1.05e-01  1.54e-01 \n",
+      "[1] \"PP abf for shared variant: 15.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.63e-243  6.44e-01 3.92e-244  1.53e-01  2.03e-01 \n",
+      "[1] \"PP abf for shared variant: 20.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.26e-243  4.99e-01 5.03e-244  1.96e-01  3.05e-01 \n",
+      "[1] \"PP abf for shared variant: 30.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___CLIC3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.38e-243  5.46e-01 4.22e-244  1.64e-01  2.90e-01 \n",
+      "[1] \"PP abf for shared variant: 29%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__PTPRCAP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.67e-243  6.61e-01 2.55e-244  9.84e-02  2.40e-01 \n",
+      "[1] \"PP abf for shared variant: 24%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___CDKN2D__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.5969e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.94e-243  7.66e-01 2.93e-244  1.15e-01  1.19e-01 \n",
+      "[1] \"PP abf for shared variant: 11.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0931e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.12e-243  8.37e-01 2.40e-244  9.43e-02  6.83e-02 \n",
+      "[1] \"PP abf for shared variant: 6.83%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.57e-243  6.22e-01 3.35e-244  1.30e-01  2.48e-01 \n",
+      "[1] \"PP abf for shared variant: 24.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.47e-243  5.81e-01 5.04e-244  1.97e-01  2.21e-01 \n",
+      "[1] \"PP abf for shared variant: 22.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MAP1A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.62e-243  6.43e-01 3.00e-244  1.16e-01  2.41e-01 \n",
+      "[1] \"PP abf for shared variant: 24.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___FAM129C__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.15e-243  4.54e-01 4.30e-244  1.66e-01  3.79e-01 \n",
+      "[1] \"PP abf for shared variant: 37.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MT-CO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1338e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.79e-243  7.08e-01 2.87e-244  1.12e-01  1.80e-01 \n",
+      "[1] \"PP abf for shared variant: 18%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "7.76e-244  3.07e-01 5.49e-244  2.13e-01  4.80e-01 \n",
+      "[1] \"PP abf for shared variant: 48%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "6.87e-245  2.72e-02 7.60e-244  2.94e-01  6.78e-01 \n",
+      "[1] \"PP abf for shared variant: 67.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "3.87e-246  1.53e-03 8.98e-244  3.49e-01  6.49e-01 \n",
+      "[1] \"PP abf for shared variant: 64.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "4.14e-246  1.64e-03 8.47e-244  3.29e-01  6.69e-01 \n",
+      "[1] \"PP abf for shared variant: 66.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "8.03e-245  3.18e-02 8.56e-244  3.33e-01  6.36e-01 \n",
+      "[1] \"PP abf for shared variant: 63.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "8.94e-246  3.54e-03 7.74e-244  3.00e-01  6.97e-01 \n",
+      "[1] \"PP abf for shared variant: 69.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.48e-246  5.86e-04 7.42e-244  2.87e-01  7.12e-01 \n",
+      "[1] \"PP abf for shared variant: 71.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMA__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "3.71e-246  1.47e-03 8.46e-244  3.29e-01  6.70e-01 \n",
+      "[1] \"PP abf for shared variant: 67%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS23\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.01e-244  7.98e-02 6.99e-244  2.71e-01  6.50e-01 \n",
+      "[1] \"PP abf for shared variant: 65%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "8.38e-244  3.32e-01 6.50e-244  2.54e-01  4.14e-01 \n",
+      "[1] \"PP abf for shared variant: 41.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "7.99e-244  3.17e-01 6.03e-244  2.34e-01  4.49e-01 \n",
+      "[1] \"PP abf for shared variant: 44.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___EEF1A1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.21e-244  8.76e-02 8.89e-244  3.47e-01  5.66e-01 \n",
+      "[1] \"PP abf for shared variant: 56.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "3.65e-244  1.45e-01 8.30e-244  3.24e-01  5.31e-01 \n",
+      "[1] \"PP abf for shared variant: 53.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "4.95e-244  1.96e-01 7.60e-244  2.96e-01  5.08e-01 \n",
+      "[1] \"PP abf for shared variant: 50.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMB__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.11e-244  4.41e-02 7.70e-244  2.98e-01  6.57e-01 \n",
+      "[1] \"PP abf for shared variant: 65.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.82e-245  7.22e-03 7.08e-244  2.74e-01  7.19e-01 \n",
+      "[1] \"PP abf for shared variant: 71.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "7.83e-246  3.10e-03 8.94e-244  3.48e-01  6.49e-01 \n",
+      "[1] \"PP abf for shared variant: 64.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.04e-243  4.14e-01 6.22e-244  2.43e-01  3.43e-01 \n",
+      "[1] \"PP abf for shared variant: 34.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RNASET2___HLA-DRB5__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.36e-243  9.33e-01 1.49e-244  5.88e-02  7.73e-03 \n",
+      "[1] \"PP abf for shared variant: 0.773%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_HLA-DQA2___CCL5__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.85e-244  1.13e-01 1.40e-243  5.53e-01  3.34e-01 \n",
+      "[1] \"PP abf for shared variant: 33.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.40e-243  5.53e-01 6.31e-244  2.48e-01  1.99e-01 \n",
+      "[1] \"PP abf for shared variant: 19.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "7.85e-244  3.11e-01 1.01e-243  3.96e-01  2.93e-01 \n",
+      "[1] \"PP abf for shared variant: 29.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__NKG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.66e-244  1.05e-01 1.93e-243  7.62e-01  1.33e-01 \n",
+      "[1] \"PP abf for shared variant: 13.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPL34\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "5.97e-244  2.37e-01 1.39e-243  5.47e-01  2.16e-01 \n",
+      "[1] \"PP abf for shared variant: 21.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "9.81e-244  3.89e-01 4.90e-244  1.90e-01  4.21e-01 \n",
+      "[1] \"PP abf for shared variant: 42.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_HLA-DQA2___CMC1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "6.81e-244  2.70e-01 1.11e-243  4.35e-01  2.95e-01 \n",
+      "[1] \"PP abf for shared variant: 29.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.47e-244  9.79e-02 1.62e-243  6.39e-01  2.63e-01 \n",
+      "[1] \"PP abf for shared variant: 26.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.97e-244  7.81e-02 5.25e-244  2.01e-01  7.21e-01 \n",
+      "[1] \"PP abf for shared variant: 72.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "5.79e-244  2.29e-01 7.01e-244  2.73e-01  4.98e-01 \n",
+      "[1] \"PP abf for shared variant: 49.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "6.45e-244  2.56e-01 1.16e-243  4.59e-01  2.86e-01 \n",
+      "[1] \"PP abf for shared variant: 28.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "6.91e-244  2.74e-01 6.36e-244  2.47e-01  4.79e-01 \n",
+      "[1] \"PP abf for shared variant: 47.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___CD52__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.48e-244  9.83e-02 1.98e-243  7.84e-01  1.17e-01 \n",
+      "[1] \"PP abf for shared variant: 11.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "5.35e-244  2.12e-01 5.28e-244  2.04e-01  5.84e-01 \n",
+      "[1] \"PP abf for shared variant: 58.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "1.05e-243  4.14e-01 6.19e-244  2.42e-01  3.44e-01 \n",
+      "[1] \"PP abf for shared variant: 34.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "3.65e-244  1.45e-01 8.20e-244  3.20e-01  5.36e-01 \n",
+      "[1] \"PP abf for shared variant: 53.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___EEF1B2__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.29e-244  9.09e-02 8.05e-244  3.13e-01  5.96e-01 \n",
+      "[1] \"PP abf for shared variant: 59.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "4.95e-244  1.96e-01 1.37e-243  5.40e-01  2.64e-01 \n",
+      "[1] \"PP abf for shared variant: 26.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___B2M__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "2.37e-244  9.39e-02 1.02e-243  4.00e-01  5.06e-01 \n",
+      "[1] \"PP abf for shared variant: 50.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___GAPDH__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "6.47e-244  2.56e-01 7.27e-244  2.84e-01  4.60e-01 \n",
+      "[1] \"PP abf for shared variant: 46%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL32\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "3.69e-244  1.46e-01 1.59e-243  6.28e-01  2.26e-01 \n",
+      "[1] \"PP abf for shared variant: 22.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "7.10e-244  2.81e-01 5.32e-244  2.06e-01  5.13e-01 \n",
+      "[1] \"PP abf for shared variant: 51.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "8.88e-245  3.52e-02 7.82e-244  3.03e-01  6.62e-01 \n",
+      "[1] \"PP abf for shared variant: 66.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RNASET2___ITGB1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.99e-26  8.48e-03  1.05e-23  9.91e-01  5.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0565%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RNASET2___CRIP1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.33e-25  8.80e-02  9.59e-24  9.05e-01  7.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.711%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RNASET2___B2M__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.19e-25  3.96e-02  1.02e-23  9.59e-01  1.65e-03 \n",
+      "[1] \"PP abf for shared variant: 0.165%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RNASET2___ALOX5AP__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-24  9.95e-02  9.47e-24  8.93e-01  7.26e-03 \n",
+      "[1] \"PP abf for shared variant: 0.726%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0253e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-05  1.93e-01  1.57e-05  7.13e-02  7.36e-01 \n",
+      "[1] \"PP abf for shared variant: 73.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.40e-05  2.20e-01  3.27e-05  1.57e-01  6.23e-01 \n",
+      "[1] \"PP abf for shared variant: 62.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"DC_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-05  2.05e-01  2.38e-05  1.12e-01  6.83e-01 \n",
+      "[1] \"PP abf for shared variant: 68.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.27e-06  3.14e-02  1.48e-05  6.50e-02  9.04e-01 \n",
+      "[1] \"PP abf for shared variant: 90.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__UBE2J1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.0878e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.77e-05  3.89e-01  4.32e-05  2.12e-01  3.99e-01 \n",
+      "[1] \"PP abf for shared variant: 39.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.37e-06  2.18e-02  3.80e-05  1.82e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.35e-05  2.17e-01  3.09e-05  1.48e-01  6.34e-01 \n",
+      "[1] \"PP abf for shared variant: 63.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1042e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-05  1.83e-01  2.78e-05  1.32e-01  6.84e-01 \n",
+      "[1] \"PP abf for shared variant: 68.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.1644e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.57e-05  3.79e-01  2.20e-05  1.05e-01  5.17e-01 \n",
+      "[1] \"PP abf for shared variant: 51.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.42e-08  1.71e-04  4.67e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.0557e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.40e-05  3.20e-01  2.53e-05  1.21e-01  5.59e-01 \n",
+      "[1] \"PP abf for shared variant: 55.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.29e-07  1.65e-03  3.28e-05  1.55e-01  8.43e-01 \n",
+      "[1] \"PP abf for shared variant: 84.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.37e-06  4.18e-02  3.81e-05  1.83e-01  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.85e-07  9.26e-04  3.70e-05  1.77e-01  8.22e-01 \n",
+      "[1] \"PP abf for shared variant: 82.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.62e-05  2.31e-01  3.28e-05  1.58e-01  6.11e-01 \n",
+      "[1] \"PP abf for shared variant: 61.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.76e-06  4.38e-02  1.19e-05  5.05e-02  9.06e-01 \n",
+      "[1] \"PP abf for shared variant: 90.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.7757e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-05  1.90e-01  2.45e-05  1.15e-01  6.95e-01 \n",
+      "[1] \"PP abf for shared variant: 69.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-06  1.84e-02  2.76e-05  1.29e-01  8.52e-01 \n",
+      "[1] \"PP abf for shared variant: 85.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.50e-05  1.75e-01  3.54e-05  1.71e-01  6.54e-01 \n",
+      "[1] \"PP abf for shared variant: 65.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2778e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-05  1.18e-01  1.60e-05  7.21e-02  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0623e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.71e-05  3.85e-01  2.17e-05  1.03e-01  5.11e-01 \n",
+      "[1] \"PP abf for shared variant: 51.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-06  1.12e-02  5.20e-05  2.53e-01  7.36e-01 \n",
+      "[1] \"PP abf for shared variant: 73.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-06  1.03e-02  2.33e-05  1.08e-01  8.82e-01 \n",
+      "[1] \"PP abf for shared variant: 88.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.12e-06  2.06e-02  1.14e-05  4.77e-02  9.32e-01 \n",
+      "[1] \"PP abf for shared variant: 93.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-09  1.47e-05  3.67e-05  1.75e-01  8.25e-01 \n",
+      "[1] \"PP abf for shared variant: 82.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1408e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-04  5.10e-01  2.53e-05  1.23e-01  3.67e-01 \n",
+      "[1] \"PP abf for shared variant: 36.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.791e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-05  2.45e-01  3.55e-05  1.71e-01  5.83e-01 \n",
+      "[1] \"PP abf for shared variant: 58.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1436e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.04e-05  3.02e-01  2.11e-05  9.94e-02  5.99e-01 \n",
+      "[1] \"PP abf for shared variant: 59.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1123e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.81e-05  4.40e-01  1.83e-05  8.70e-02  4.72e-01 \n",
+      "[1] \"PP abf for shared variant: 47.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.64e-06  2.32e-02  1.70e-05  7.60e-02  9.01e-01 \n",
+      "[1] \"PP abf for shared variant: 90.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-05  5.49e-02  4.16e-05  2.01e-01  7.44e-01 \n",
+      "[1] \"PP abf for shared variant: 74.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.57e-10  3.78e-06  4.24e-05  2.04e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.20e-06  4.60e-02  4.04e-05  1.94e-01  7.60e-01 \n",
+      "[1] \"PP abf for shared variant: 76%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-06  5.45e-03  3.63e-05  1.73e-01  8.21e-01 \n",
+      "[1] \"PP abf for shared variant: 82.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-09  5.80e-06  3.54e-05  1.69e-01  8.31e-01 \n",
+      "[1] \"PP abf for shared variant: 83.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-06  1.92e-02  1.94e-05  8.83e-02  8.92e-01 \n",
+      "[1] \"PP abf for shared variant: 89.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1639e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.44e-05  4.72e-01  2.53e-05  1.23e-01  4.05e-01 \n",
+      "[1] \"PP abf for shared variant: 40.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"B_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-06  1.08e-02  2.92e-05  1.37e-01  8.52e-01 \n",
+      "[1] \"PP abf for shared variant: 85.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.57e-13  4.29e-09  4.63e-05  2.24e-01  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.79e-17  3.89e-13  3.96e-05  1.90e-01  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-06  1.18e-02  2.12e-05  9.73e-02  8.91e-01 \n",
+      "[1] \"PP abf for shared variant: 89.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-12  7.00e-09  4.17e-05  2.00e-01  8.00e-01 \n",
+      "[1] \"PP abf for shared variant: 80%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.53e-11  2.27e-07  2.36e-05  1.09e-01  8.91e-01 \n",
+      "[1] \"PP abf for shared variant: 89.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-09  5.97e-06  1.55e-05  6.84e-02  9.32e-01 \n",
+      "[1] \"PP abf for shared variant: 93.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-21  6.94e-18  4.13e-05  1.98e-01  8.02e-01 \n",
+      "[1] \"PP abf for shared variant: 80.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-15  1.96e-11  4.68e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-16  1.19e-12  4.59e-05  2.22e-01  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-15  1.54e-11  4.31e-05  2.08e-01  7.92e-01 \n",
+      "[1] \"PP abf for shared variant: 79.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-10  2.45e-06  4.02e-05  1.93e-01  8.07e-01 \n",
+      "[1] \"PP abf for shared variant: 80.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.44e-08  2.22e-04  3.19e-05  1.51e-01  8.49e-01 \n",
+      "[1] \"PP abf for shared variant: 84.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-16  1.40e-12  1.59e-05  7.02e-02  9.30e-01 \n",
+      "[1] \"PP abf for shared variant: 93%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.73e-19  8.64e-16  5.01e-05  2.43e-01  7.57e-01 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-06  6.81e-03  3.91e-05  1.87e-01  8.06e-01 \n",
+      "[1] \"PP abf for shared variant: 80.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-06  7.58e-03  1.91e-05  8.66e-02  9.06e-01 \n",
+      "[1] \"PP abf for shared variant: 90.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-14  5.44e-11  4.72e-05  2.28e-01  7.72e-01 \n",
+      "[1] \"PP abf for shared variant: 77.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-11  1.04e-07  1.61e-05  7.13e-02  9.29e-01 \n",
+      "[1] \"PP abf for shared variant: 92.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-07  6.88e-04  1.15e-05  4.77e-02  9.52e-01 \n",
+      "[1] \"PP abf for shared variant: 95.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.17e-11  1.09e-07  4.23e-05  2.04e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.02e-22  3.01e-18  5.45e-05  2.65e-01  7.35e-01 \n",
+      "[1] \"PP abf for shared variant: 73.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-15  5.79e-12  3.19e-05  1.51e-01  8.49e-01 \n",
+      "[1] \"PP abf for shared variant: 84.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-15  9.36e-12  4.26e-05  2.05e-01  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__SPON2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-07  2.11e-03  2.91e-05  1.37e-01  8.61e-01 \n",
+      "[1] \"PP abf for shared variant: 86.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.80e-10  4.90e-06  4.48e-05  2.16e-01  7.84e-01 \n",
+      "[1] \"PP abf for shared variant: 78.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.73e-24  3.36e-20  3.99e-05  1.91e-01  8.09e-01 \n",
+      "[1] \"PP abf for shared variant: 80.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-17  6.56e-14  3.67e-05  1.75e-01  8.25e-01 \n",
+      "[1] \"PP abf for shared variant: 82.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.16e-16  1.58e-12  4.56e-05  2.20e-01  7.80e-01 \n",
+      "[1] \"PP abf for shared variant: 78%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-17  1.03e-13  4.31e-05  2.07e-01  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-11  1.78e-07  2.34e-05  1.08e-01  8.92e-01 \n",
+      "[1] \"PP abf for shared variant: 89.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.31e-06  2.15e-02  1.35e-05  5.81e-02  9.20e-01 \n",
+      "[1] \"PP abf for shared variant: 92%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-10  5.99e-07  9.61e-06  3.84e-02  9.62e-01 \n",
+      "[1] \"PP abf for shared variant: 96.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1991e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-05  6.85e-02  1.30e-05  5.61e-02  8.75e-01 \n",
+      "[1] \"PP abf for shared variant: 87.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-13  5.44e-10  3.74e-05  1.79e-01  8.21e-01 \n",
+      "[1] \"PP abf for shared variant: 82.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-16  7.23e-13  3.78e-05  1.81e-01  8.19e-01 \n",
+      "[1] \"PP abf for shared variant: 81.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.48e-14  3.74e-10  3.65e-05  1.74e-01  8.26e-01 \n",
+      "[1] \"PP abf for shared variant: 82.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.73e-17  8.63e-14  1.63e-05  7.23e-02  9.28e-01 \n",
+      "[1] \"PP abf for shared variant: 92.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.46e-20  3.23e-16  4.87e-05  2.36e-01  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-22  7.91e-19  4.88e-05  2.36e-01  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.30e-09  2.65e-05  1.24e-05  5.23e-02  9.48e-01 \n",
+      "[1] \"PP abf for shared variant: 94.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-14  1.28e-10  4.23e-05  2.03e-01  7.97e-01 \n",
+      "[1] \"PP abf for shared variant: 79.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-09  7.31e-06  1.37e-05  5.88e-02  9.41e-01 \n",
+      "[1] \"PP abf for shared variant: 94.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-14  8.89e-11  3.88e-05  1.86e-01  8.14e-01 \n",
+      "[1] \"PP abf for shared variant: 81.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.24e-18  4.62e-14  4.35e-05  2.10e-01  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-13  6.09e-10  1.16e-05  4.86e-02  9.51e-01 \n",
+      "[1] \"PP abf for shared variant: 95.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-20  1.14e-16  1.09e-05  4.48e-02  9.55e-01 \n",
+      "[1] \"PP abf for shared variant: 95.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.51e-18  2.75e-14  4.83e-05  2.34e-01  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.72e-08  2.36e-04  4.84e-05  2.34e-01  7.65e-01 \n",
+      "[1] \"PP abf for shared variant: 76.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-17  1.06e-13  5.23e-05  2.54e-01  7.46e-01 \n",
+      "[1] \"PP abf for shared variant: 74.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-05  7.11e-02  1.83e-05  8.32e-02  8.46e-01 \n",
+      "[1] \"PP abf for shared variant: 84.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-11  7.30e-08  4.18e-05  2.01e-01  7.99e-01 \n",
+      "[1] \"PP abf for shared variant: 79.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.89e-07  4.45e-03  3.63e-05  1.73e-01  8.22e-01 \n",
+      "[1] \"PP abf for shared variant: 82.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-12  5.03e-09  3.02e-05  1.42e-01  8.58e-01 \n",
+      "[1] \"PP abf for shared variant: 85.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-09  8.25e-06  6.40e-06  2.22e-02  9.78e-01 \n",
+      "[1] \"PP abf for shared variant: 97.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-22  1.94e-18  4.85e-05  2.35e-01  7.65e-01 \n",
+      "[1] \"PP abf for shared variant: 76.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.15e-12  4.58e-08  4.12e-05  1.98e-01  8.02e-01 \n",
+      "[1] \"PP abf for shared variant: 80.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.83e-14  3.41e-10  3.17e-05  1.50e-01  8.50e-01 \n",
+      "[1] \"PP abf for shared variant: 85%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.08e-11  3.04e-07  3.05e-05  1.44e-01  8.56e-01 \n",
+      "[1] \"PP abf for shared variant: 85.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-15  1.59e-11  8.92e-06  3.49e-02  9.65e-01 \n",
+      "[1] \"PP abf for shared variant: 96.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-20  1.22e-16  1.35e-05  5.83e-02  9.42e-01 \n",
+      "[1] \"PP abf for shared variant: 94.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-11  9.37e-08  8.19e-06  3.13e-02  9.69e-01 \n",
+      "[1] \"PP abf for shared variant: 96.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-10  1.70e-06  1.46e-05  6.37e-02  9.36e-01 \n",
+      "[1] \"PP abf for shared variant: 93.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-05  9.31e-02  3.45e-05  1.65e-01  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-20  9.85e-17  3.70e-05  1.77e-01  8.23e-01 \n",
+      "[1] \"PP abf for shared variant: 82.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.09e-07  2.04e-03  3.82e-05  1.83e-01  8.15e-01 \n",
+      "[1] \"PP abf for shared variant: 81.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.20e-13  2.60e-09  3.30e-05  1.56e-01  8.44e-01 \n",
+      "[1] \"PP abf for shared variant: 84.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.39e-11  3.69e-07  2.14e-05  9.77e-02  9.02e-01 \n",
+      "[1] \"PP abf for shared variant: 90.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-09  1.09e-05  3.85e-05  1.84e-01  8.16e-01 \n",
+      "[1] \"PP abf for shared variant: 81.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.64e-10  4.82e-06  4.43e-05  2.14e-01  7.86e-01 \n",
+      "[1] \"PP abf for shared variant: 78.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-11  1.37e-07  4.09e-05  1.96e-01  8.04e-01 \n",
+      "[1] \"PP abf for shared variant: 80.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4099e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-05  1.06e-01  2.38e-05  1.11e-01  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5173e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-05  4.78e-01  1.85e-05  8.80e-02  4.34e-01 \n",
+      "[1] \"PP abf for shared variant: 43.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-08  5.78e-05  9.45e-06  3.76e-02  9.62e-01 \n",
+      "[1] \"PP abf for shared variant: 96.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.59e-11  1.29e-07  2.92e-05  1.37e-01  8.63e-01 \n",
+      "[1] \"PP abf for shared variant: 86.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.53e-17  1.26e-13  4.85e-05  2.35e-01  7.65e-01 \n",
+      "[1] \"PP abf for shared variant: 76.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___KLRC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-06  5.49e-03  9.35e-06  3.72e-02  9.57e-01 \n",
+      "[1] \"PP abf for shared variant: 95.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.4275e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.72e-05  1.86e-01  1.29e-05  5.69e-02  7.57e-01 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-11  9.65e-08  1.23e-05  5.19e-02  9.48e-01 \n",
+      "[1] \"PP abf for shared variant: 94.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.17e-13  2.09e-09  3.50e-05  1.67e-01  8.33e-01 \n",
+      "[1] \"PP abf for shared variant: 83.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___MALAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.53e-07  4.26e-03  4.13e-05  1.98e-01  7.97e-01 \n",
+      "[1] \"PP abf for shared variant: 79.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.49e-08  3.24e-04  2.00e-05  9.11e-02  9.09e-01 \n",
+      "[1] \"PP abf for shared variant: 90.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-15  9.70e-12  3.11e-05  1.47e-01  8.53e-01 \n",
+      "[1] \"PP abf for shared variant: 85.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8351e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.43e-05  3.21e-01  2.80e-05  1.35e-01  5.44e-01 \n",
+      "[1] \"PP abf for shared variant: 54.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-11  1.40e-07  4.04e-05  1.94e-01  8.06e-01 \n",
+      "[1] \"PP abf for shared variant: 80.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-09  5.48e-06  1.48e-05  6.44e-02  9.36e-01 \n",
+      "[1] \"PP abf for shared variant: 93.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___FGFBP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-05  5.85e-02  2.28e-05  1.06e-01  8.36e-01 \n",
+      "[1] \"PP abf for shared variant: 83.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.97e-08  4.98e-04  1.01e-05  4.09e-02  9.59e-01 \n",
+      "[1] \"PP abf for shared variant: 95.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.58e-09  4.29e-05  1.42e-05  6.14e-02  9.39e-01 \n",
+      "[1] \"PP abf for shared variant: 93.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-08  1.54e-04  2.94e-05  1.38e-01  8.62e-01 \n",
+      "[1] \"PP abf for shared variant: 86.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.32e-07  3.16e-03  1.20e-05  5.04e-02  9.46e-01 \n",
+      "[1] \"PP abf for shared variant: 94.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.574e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-04  5.53e-01  2.91e-05  1.43e-01  3.04e-01 \n",
+      "[1] \"PP abf for shared variant: 30.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.83e-11  3.92e-07  9.24e-06  3.66e-02  9.63e-01 \n",
+      "[1] \"PP abf for shared variant: 96.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.30e-19  2.15e-15  5.21e-05  2.53e-01  7.47e-01 \n",
+      "[1] \"PP abf for shared variant: 74.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2336e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-05  2.77e-01  2.56e-05  1.22e-01  6.01e-01 \n",
+      "[1] \"PP abf for shared variant: 60.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-14  8.10e-11  2.06e-05  9.38e-02  9.06e-01 \n",
+      "[1] \"PP abf for shared variant: 90.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"NK_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-15  7.38e-12  2.45e-05  1.14e-01  8.86e-01 \n",
+      "[1] \"PP abf for shared variant: 88.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___NRGN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7437e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-05  1.22e-01  1.87e-05  8.54e-02  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-05  1.21e-01  5.55e-05  2.71e-01  6.07e-01 \n",
+      "[1] \"PP abf for shared variant: 60.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-08  1.82e-04  2.13e-05  9.75e-02  9.02e-01 \n",
+      "[1] \"PP abf for shared variant: 90.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-11  8.38e-08  8.90e-06  3.48e-02  9.65e-01 \n",
+      "[1] \"PP abf for shared variant: 96.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-20  1.21e-16  5.35e-05  2.60e-01  7.40e-01 \n",
+      "[1] \"PP abf for shared variant: 74%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.04e-11  2.52e-07  4.42e-05  2.13e-01  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-13  5.05e-10  5.66e-05  2.76e-01  7.24e-01 \n",
+      "[1] \"PP abf for shared variant: 72.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-07  1.82e-03  2.21e-05  1.01e-01  8.97e-01 \n",
+      "[1] \"PP abf for shared variant: 89.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.19e-13  2.59e-09  5.50e-05  2.68e-01  7.32e-01 \n",
+      "[1] \"PP abf for shared variant: 73.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.86e-19  2.93e-15  5.02e-05  2.43e-01  7.57e-01 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VCAN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-05  1.31e-01  2.70e-05  1.28e-01  7.41e-01 \n",
+      "[1] \"PP abf for shared variant: 74.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UQCRH\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.26e-10  4.13e-06  5.10e-05  2.47e-01  7.53e-01 \n",
+      "[1] \"PP abf for shared variant: 75.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC7A7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.35e-07  1.67e-03  2.23e-05  1.03e-01  8.96e-01 \n",
+      "[1] \"PP abf for shared variant: 89.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EPB41L3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-05  7.93e-02  4.31e-05  2.08e-01  7.12e-01 \n",
+      "[1] \"PP abf for shared variant: 71.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-07  5.33e-04  1.32e-05  5.67e-02  9.43e-01 \n",
+      "[1] \"PP abf for shared variant: 94.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-18  1.16e-14  5.13e-05  2.49e-01  7.51e-01 \n",
+      "[1] \"PP abf for shared variant: 75.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.55e-06  7.76e-03  9.54e-05  4.72e-01  5.20e-01 \n",
+      "[1] \"PP abf for shared variant: 52%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___QARS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.69e-06  2.85e-02  1.34e-05  5.77e-02  9.14e-01 \n",
+      "[1] \"PP abf for shared variant: 91.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-10  1.05e-06  4.65e-05  2.25e-01  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.80e-19  1.40e-15  5.19e-05  2.52e-01  7.48e-01 \n",
+      "[1] \"PP abf for shared variant: 74.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.60e-10  1.80e-06  4.39e-05  2.12e-01  7.88e-01 \n",
+      "[1] \"PP abf for shared variant: 78.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-19  9.30e-16  6.05e-05  2.95e-01  7.05e-01 \n",
+      "[1] \"PP abf for shared variant: 70.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.52e-13  1.26e-09  1.70e-05  7.55e-02  9.24e-01 \n",
+      "[1] \"PP abf for shared variant: 92.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-13  5.83e-10  5.98e-05  2.92e-01  7.08e-01 \n",
+      "[1] \"PP abf for shared variant: 70.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-16  1.76e-12  5.83e-05  2.84e-01  7.16e-01 \n",
+      "[1] \"PP abf for shared variant: 71.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.44e-07  4.22e-03  3.72e-05  1.78e-01  8.18e-01 \n",
+      "[1] \"PP abf for shared variant: 81.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.59e-14  2.79e-10  5.68e-05  2.77e-01  7.23e-01 \n",
+      "[1] \"PP abf for shared variant: 72.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___CSTA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-08  1.06e-04  2.32e-05  1.07e-01  8.93e-01 \n",
+      "[1] \"PP abf for shared variant: 89.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-11  5.87e-08  4.51e-05  2.18e-01  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.52e-06  2.26e-02  2.61e-05  1.22e-01  8.56e-01 \n",
+      "[1] \"PP abf for shared variant: 85.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.00e-17  4.50e-13  5.42e-05  2.63e-01  7.37e-01 \n",
+      "[1] \"PP abf for shared variant: 73.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-07  6.18e-04  4.63e-05  2.24e-01  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-19  1.93e-15  5.63e-05  2.74e-01  7.26e-01 \n",
+      "[1] \"PP abf for shared variant: 72.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-05  1.11e-01  3.80e-05  1.83e-01  7.06e-01 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TNFAIP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.75e-06  3.88e-02  1.39e-05  6.02e-02  9.01e-01 \n",
+      "[1] \"PP abf for shared variant: 90.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.88e-07  2.94e-03  3.38e-05  1.61e-01  8.36e-01 \n",
+      "[1] \"PP abf for shared variant: 83.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-16  1.59e-12  3.95e-05  1.89e-01  8.11e-01 \n",
+      "[1] \"PP abf for shared variant: 81.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.70e-24  1.85e-20  6.10e-05  2.98e-01  7.02e-01 \n",
+      "[1] \"PP abf for shared variant: 70.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-15  5.16e-12  4.98e-05  2.41e-01  7.59e-01 \n",
+      "[1] \"PP abf for shared variant: 75.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-10  1.78e-06  2.39e-05  1.11e-01  8.89e-01 \n",
+      "[1] \"PP abf for shared variant: 88.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.43e-15  2.72e-11  1.34e-05  5.75e-02  9.42e-01 \n",
+      "[1] \"PP abf for shared variant: 94.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-13  1.33e-09  5.26e-05  2.55e-01  7.45e-01 \n",
+      "[1] \"PP abf for shared variant: 74.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.92e-09  1.96e-05  3.50e-05  1.67e-01  8.33e-01 \n",
+      "[1] \"PP abf for shared variant: 83.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.71e-08  2.35e-04  2.40e-05  1.11e-01  8.88e-01 \n",
+      "[1] \"PP abf for shared variant: 88.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-08  1.31e-04  4.05e-05  1.95e-01  8.05e-01 \n",
+      "[1] \"PP abf for shared variant: 80.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-17  6.66e-14  6.05e-05  2.95e-01  7.05e-01 \n",
+      "[1] \"PP abf for shared variant: 70.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.05e-20  3.03e-16  5.53e-05  2.69e-01  7.31e-01 \n",
+      "[1] \"PP abf for shared variant: 73.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.32e-13  2.16e-09  4.61e-05  2.23e-01  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.10e-09  4.55e-05  4.25e-05  2.05e-01  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-15  1.48e-11  4.51e-05  2.18e-01  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1173e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-05  8.99e-02  2.81e-05  1.33e-01  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-11  1.33e-07  4.05e-05  1.94e-01  8.06e-01 \n",
+      "[1] \"PP abf for shared variant: 80.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-14  6.08e-11  4.60e-05  2.22e-01  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.60e-12  3.80e-08  4.83e-05  2.34e-01  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.89e-08  2.44e-04  1.44e-05  6.25e-02  9.37e-01 \n",
+      "[1] \"PP abf for shared variant: 93.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.62e-13  1.81e-09  4.48e-05  2.16e-01  7.84e-01 \n",
+      "[1] \"PP abf for shared variant: 78.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.07e-10  1.03e-06  3.97e-05  1.90e-01  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.15e-07  4.57e-03  3.44e-05  1.64e-01  8.32e-01 \n",
+      "[1] \"PP abf for shared variant: 83.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.26e-16  3.13e-12  4.61e-05  2.23e-01  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.91e-15  1.95e-11  4.83e-05  2.34e-01  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-09  1.04e-05  3.13e-05  1.48e-01  8.52e-01 \n",
+      "[1] \"PP abf for shared variant: 85.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-08  8.53e-05  3.94e-05  1.89e-01  8.11e-01 \n",
+      "[1] \"PP abf for shared variant: 81.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___CST3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7382e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.88e-05  1.94e-01  2.44e-05  1.15e-01  6.91e-01 \n",
+      "[1] \"PP abf for shared variant: 69.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-06  5.81e-03  2.93e-05  1.38e-01  8.56e-01 \n",
+      "[1] \"PP abf for shared variant: 85.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-05  5.99e-02  3.02e-05  1.43e-01  7.97e-01 \n",
+      "[1] \"PP abf for shared variant: 79.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-19  5.52e-16  3.62e-05  1.73e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-05  6.02e-02  2.39e-05  1.11e-01  8.29e-01 \n",
+      "[1] \"PP abf for shared variant: 82.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.15e-08  1.57e-04  2.61e-05  1.22e-01  8.78e-01 \n",
+      "[1] \"PP abf for shared variant: 87.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___AP1S2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-05  7.62e-02  3.93e-05  1.89e-01  7.35e-01 \n",
+      "[1] \"PP abf for shared variant: 73.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.82e-15  4.41e-11  4.75e-05  2.30e-01  7.70e-01 \n",
+      "[1] \"PP abf for shared variant: 77%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.48e-13  1.74e-09  4.78e-05  2.31e-01  7.69e-01 \n",
+      "[1] \"PP abf for shared variant: 76.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SEC11A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.13e-07  3.56e-03  4.34e-05  2.09e-01  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.43e-08  7.13e-05  3.69e-05  1.76e-01  8.24e-01 \n",
+      "[1] \"PP abf for shared variant: 82.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.36e-15  2.18e-11  4.99e-05  2.42e-01  7.58e-01 \n",
+      "[1] \"PP abf for shared variant: 75.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.28e-13  4.14e-09  5.03e-05  2.44e-01  7.56e-01 \n",
+      "[1] \"PP abf for shared variant: 75.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-15  1.84e-11  4.55e-05  2.20e-01  7.80e-01 \n",
+      "[1] \"PP abf for shared variant: 78%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-11  6.81e-08  4.72e-05  2.28e-01  7.72e-01 \n",
+      "[1] \"PP abf for shared variant: 77.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.42e-11  1.21e-07  4.97e-05  2.41e-01  7.59e-01 \n",
+      "[1] \"PP abf for shared variant: 75.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.25e-12  3.62e-08  4.21e-05  2.02e-01  7.98e-01 \n",
+      "[1] \"PP abf for shared variant: 79.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___ATP5G2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-07  1.84e-03  3.57e-05  1.70e-01  8.28e-01 \n",
+      "[1] \"PP abf for shared variant: 82.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-10  7.19e-07  1.40e-05  6.05e-02  9.40e-01 \n",
+      "[1] \"PP abf for shared variant: 94%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-13  1.57e-09  4.42e-05  2.13e-01  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.09e-07  3.04e-03  3.43e-05  1.63e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.66e-17  1.83e-13  5.51e-05  2.68e-01  7.32e-01 \n",
+      "[1] \"PP abf for shared variant: 73.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.25e-20  2.62e-16  5.94e-05  2.90e-01  7.10e-01 \n",
+      "[1] \"PP abf for shared variant: 71%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.03e-22  3.51e-18  4.65e-05  2.25e-01  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.28e-11  3.14e-07  4.26e-05  2.05e-01  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-11  6.08e-08  4.80e-05  2.32e-01  7.68e-01 \n",
+      "[1] \"PP abf for shared variant: 76.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-20  6.89e-17  5.42e-05  2.63e-01  7.37e-01 \n",
+      "[1] \"PP abf for shared variant: 73.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.32e-09  4.16e-05  3.85e-05  1.84e-01  8.16e-01 \n",
+      "[1] \"PP abf for shared variant: 81.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EVI2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-05  9.29e-02  2.92e-05  1.38e-01  7.69e-01 \n",
+      "[1] \"PP abf for shared variant: 76.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.83e-07  2.41e-03  8.62e-06  3.35e-02  9.64e-01 \n",
+      "[1] \"PP abf for shared variant: 96.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-06  9.83e-03  7.09e-05  3.48e-01  6.42e-01 \n",
+      "[1] \"PP abf for shared variant: 64.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-08  1.69e-04  3.43e-05  1.63e-01  8.37e-01 \n",
+      "[1] \"PP abf for shared variant: 83.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.08e-17  3.04e-13  5.21e-05  2.53e-01  7.47e-01 \n",
+      "[1] \"PP abf for shared variant: 74.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___LGALS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.78e-06  3.89e-02  2.60e-05  1.21e-01  8.40e-01 \n",
+      "[1] \"PP abf for shared variant: 84%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-05  6.80e-02  2.60e-05  1.22e-01  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-15  1.45e-11  4.97e-05  2.41e-01  7.59e-01 \n",
+      "[1] \"PP abf for shared variant: 75.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.02e-19  4.01e-15  5.17e-05  2.51e-01  7.49e-01 \n",
+      "[1] \"PP abf for shared variant: 74.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-15  1.88e-11  4.75e-05  2.30e-01  7.70e-01 \n",
+      "[1] \"PP abf for shared variant: 77%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-10  5.67e-07  3.99e-05  1.92e-01  8.08e-01 \n",
+      "[1] \"PP abf for shared variant: 80.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___GPX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.13e-07  4.56e-03  2.01e-05  9.13e-02  9.04e-01 \n",
+      "[1] \"PP abf for shared variant: 90.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___LTA4H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-05  7.57e-02  1.15e-05  4.89e-02  8.75e-01 \n",
+      "[1] \"PP abf for shared variant: 87.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RNASE6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-06  1.97e-02  1.04e-05  4.25e-02  9.38e-01 \n",
+      "[1] \"PP abf for shared variant: 93.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.00e-06  2.00e-02  3.32e-05  1.58e-01  8.22e-01 \n",
+      "[1] \"PP abf for shared variant: 82.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___BTF3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.09e-07  2.05e-03  3.55e-05  1.69e-01  8.29e-01 \n",
+      "[1] \"PP abf for shared variant: 82.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___DRAM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1829e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-05  1.55e-01  2.23e-05  1.04e-01  7.41e-01 \n",
+      "[1] \"PP abf for shared variant: 74.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___IL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-08  2.45e-04  4.27e-05  2.06e-01  7.94e-01 \n",
+      "[1] \"PP abf for shared variant: 79.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.14e-08  4.57e-04  9.92e-06  4.00e-02  9.60e-01 \n",
+      "[1] \"PP abf for shared variant: 96%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.10e-11  1.55e-07  4.14e-05  1.99e-01  8.01e-01 \n",
+      "[1] \"PP abf for shared variant: 80.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.51e-15  2.25e-11  4.70e-05  2.27e-01  7.73e-01 \n",
+      "[1] \"PP abf for shared variant: 77.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.30e-18  3.65e-14  4.81e-05  2.33e-01  7.67e-01 \n",
+      "[1] \"PP abf for shared variant: 76.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-05  9.22e-02  3.73e-05  1.79e-01  7.29e-01 \n",
+      "[1] \"PP abf for shared variant: 72.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.13e-19  3.06e-15  4.07e-05  1.95e-01  8.05e-01 \n",
+      "[1] \"PP abf for shared variant: 80.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.70e-16  2.85e-12  5.65e-05  2.75e-01  7.25e-01 \n",
+      "[1] \"PP abf for shared variant: 72.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___IPO7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-06  5.75e-03  1.06e-05  4.37e-02  9.51e-01 \n",
+      "[1] \"PP abf for shared variant: 95.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.32e-12  4.16e-08  5.07e-05  2.46e-01  7.54e-01 \n",
+      "[1] \"PP abf for shared variant: 75.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-16  1.71e-12  4.72e-05  2.28e-01  7.72e-01 \n",
+      "[1] \"PP abf for shared variant: 77.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-17  9.62e-14  4.60e-05  2.22e-01  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2966e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-05  6.88e-02  1.20e-05  5.14e-02  8.80e-01 \n",
+      "[1] \"PP abf for shared variant: 88%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-12  7.92e-09  4.26e-05  2.05e-01  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-10  1.79e-06  2.90e-05  1.36e-01  8.64e-01 \n",
+      "[1] \"PP abf for shared variant: 86.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___CRTAP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.79e-07  3.39e-03  4.18e-05  2.01e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-05  8.34e-02  2.33e-05  1.08e-01  8.08e-01 \n",
+      "[1] \"PP abf for shared variant: 80.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.39e-13  1.20e-09  5.99e-05  2.92e-01  7.08e-01 \n",
+      "[1] \"PP abf for shared variant: 70.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.16e-08  4.58e-04  3.72e-05  1.78e-01  8.22e-01 \n",
+      "[1] \"PP abf for shared variant: 82.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.68e-14  3.84e-10  4.68e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-05  1.19e-01  4.10e-05  1.98e-01  6.83e-01 \n",
+      "[1] \"PP abf for shared variant: 68.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-06  1.07e-02  3.87e-05  1.85e-01  8.04e-01 \n",
+      "[1] \"PP abf for shared variant: 80.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.42e-14  1.71e-10  5.54e-05  2.70e-01  7.30e-01 \n",
+      "[1] \"PP abf for shared variant: 73%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-15  5.67e-12  5.22e-05  2.54e-01  7.46e-01 \n",
+      "[1] \"PP abf for shared variant: 74.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TKT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.44e-09  1.22e-05  1.03e-05  4.21e-02  9.58e-01 \n",
+      "[1] \"PP abf for shared variant: 95.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-19  7.03e-16  3.96e-05  1.90e-01  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.66e-06  2.33e-02  3.03e-05  1.43e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ACTN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.52e-08  1.76e-04  3.87e-05  1.85e-01  8.14e-01 \n",
+      "[1] \"PP abf for shared variant: 81.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-19  1.57e-15  3.60e-05  1.71e-01  8.28e-01 \n",
+      "[1] \"PP abf for shared variant: 82.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAND1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4561e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-05  1.37e-01  3.60e-05  1.73e-01  6.89e-01 \n",
+      "[1] \"PP abf for shared variant: 68.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-19  7.61e-16  3.56e-05  1.69e-01  8.31e-01 \n",
+      "[1] \"PP abf for shared variant: 83.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PRF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-07  1.61e-03  2.23e-05  1.02e-01  8.96e-01 \n",
+      "[1] \"PP abf for shared variant: 89.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-09  5.18e-06  3.27e-05  1.55e-01  8.45e-01 \n",
+      "[1] \"PP abf for shared variant: 84.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EFHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-06  5.97e-03  2.24e-05  1.03e-01  8.91e-01 \n",
+      "[1] \"PP abf for shared variant: 89.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.63e-15  3.31e-11  4.19e-05  2.01e-01  7.98e-01 \n",
+      "[1] \"PP abf for shared variant: 79.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-20  1.92e-16  5.00e-05  2.42e-01  7.58e-01 \n",
+      "[1] \"PP abf for shared variant: 75.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-18  1.10e-14  3.36e-05  1.60e-01  8.40e-01 \n",
+      "[1] \"PP abf for shared variant: 84%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-17  7.71e-14  2.95e-05  1.39e-01  8.61e-01 \n",
+      "[1] \"PP abf for shared variant: 86.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___APBA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-06  1.22e-02  2.27e-05  1.04e-01  8.83e-01 \n",
+      "[1] \"PP abf for shared variant: 88.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-08  8.35e-05  3.70e-05  1.77e-01  8.23e-01 \n",
+      "[1] \"PP abf for shared variant: 82.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FAIM3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-06  5.11e-03  1.27e-05  5.43e-02  9.41e-01 \n",
+      "[1] \"PP abf for shared variant: 94.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-05  1.10e-01  1.61e-05  7.21e-02  8.17e-01 \n",
+      "[1] \"PP abf for shared variant: 81.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-05  5.49e-02  1.85e-05  8.40e-02  8.61e-01 \n",
+      "[1] \"PP abf for shared variant: 86.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.38e-07  2.19e-03  2.55e-05  1.19e-01  8.79e-01 \n",
+      "[1] \"PP abf for shared variant: 87.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-18  6.34e-15  4.42e-05  2.13e-01  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___C11orf1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.8471e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.37e-05  2.19e-01  2.04e-05  9.50e-02  6.86e-01 \n",
+      "[1] \"PP abf for shared variant: 68.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-08  7.55e-05  3.00e-05  1.42e-01  8.58e-01 \n",
+      "[1] \"PP abf for shared variant: 85.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-21  6.62e-18  4.80e-05  2.32e-01  7.68e-01 \n",
+      "[1] \"PP abf for shared variant: 76.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-21  1.03e-17  4.58e-05  2.21e-01  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GZMM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-05  5.51e-02  1.51e-05  6.66e-02  8.78e-01 \n",
+      "[1] \"PP abf for shared variant: 87.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-11  1.56e-07  8.96e-06  3.52e-02  9.65e-01 \n",
+      "[1] \"PP abf for shared variant: 96.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-08  1.05e-04  2.64e-05  1.23e-01  8.77e-01 \n",
+      "[1] \"PP abf for shared variant: 87.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.77e-06  2.89e-02  1.30e-05  5.58e-02  9.15e-01 \n",
+      "[1] \"PP abf for shared variant: 91.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CTSW__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.90e-06  3.45e-02  1.93e-05  8.77e-02  8.78e-01 \n",
+      "[1] \"PP abf for shared variant: 87.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.76e-10  4.88e-06  3.46e-05  1.65e-01  8.35e-01 \n",
+      "[1] \"PP abf for shared variant: 83.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.29e-23  4.15e-19  6.06e-05  2.96e-01  7.04e-01 \n",
+      "[1] \"PP abf for shared variant: 70.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FLT3LG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.81e-09  4.41e-05  2.64e-05  1.23e-01  8.77e-01 \n",
+      "[1] \"PP abf for shared variant: 87.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NKG7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-09  5.48e-06  3.01e-05  1.42e-01  8.58e-01 \n",
+      "[1] \"PP abf for shared variant: 85.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-06  5.02e-03  1.76e-05  7.90e-02  9.16e-01 \n",
+      "[1] \"PP abf for shared variant: 91.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.3964e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-05  1.33e-01  1.41e-05  6.25e-02  8.05e-01 \n",
+      "[1] \"PP abf for shared variant: 80.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-05  8.11e-02  2.95e-05  1.40e-01  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-09  2.78e-05  3.10e-05  1.46e-01  8.54e-01 \n",
+      "[1] \"PP abf for shared variant: 85.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-10  6.52e-07  3.48e-05  1.66e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.91e-14  3.95e-10  3.30e-05  1.56e-01  8.44e-01 \n",
+      "[1] \"PP abf for shared variant: 84.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-16  5.64e-13  5.00e-05  2.42e-01  7.58e-01 \n",
+      "[1] \"PP abf for shared variant: 75.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.34e-18  1.17e-14  3.30e-05  1.57e-01  8.43e-01 \n",
+      "[1] \"PP abf for shared variant: 84.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ATP1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 9.0977e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.38e-05  3.19e-01  1.90e-05  8.91e-02  5.92e-01 \n",
+      "[1] \"PP abf for shared variant: 59.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-05  5.48e-02  3.58e-05  1.71e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SYNE1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-06  1.38e-02  1.32e-05  5.66e-02  9.30e-01 \n",
+      "[1] \"PP abf for shared variant: 93%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.137e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.04e-05  2.52e-01  2.47e-05  1.17e-01  6.31e-01 \n",
+      "[1] \"PP abf for shared variant: 63.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.38e-11  4.19e-07  4.46e-05  2.15e-01  7.85e-01 \n",
+      "[1] \"PP abf for shared variant: 78.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.03e-15  3.52e-11  3.38e-05  1.61e-01  8.39e-01 \n",
+      "[1] \"PP abf for shared variant: 83.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PDE3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.82e-09  2.41e-05  3.47e-05  1.65e-01  8.35e-01 \n",
+      "[1] \"PP abf for shared variant: 83.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.10e-24  3.05e-20  5.31e-05  2.58e-01  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-09  9.58e-06  3.25e-05  1.54e-01  8.46e-01 \n",
+      "[1] \"PP abf for shared variant: 84.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZEB2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.37e-07  1.19e-03  1.86e-05  8.39e-02  9.15e-01 \n",
+      "[1] \"PP abf for shared variant: 91.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___LTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-12  5.36e-09  3.43e-05  1.63e-01  8.37e-01 \n",
+      "[1] \"PP abf for shared variant: 83.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PTPN7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.58e-06  3.79e-02  1.20e-05  5.07e-02  9.11e-01 \n",
+      "[1] \"PP abf for shared variant: 91.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.00e-17  2.00e-13  3.22e-05  1.52e-01  8.48e-01 \n",
+      "[1] \"PP abf for shared variant: 84.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.83e-15  3.42e-11  3.94e-05  1.89e-01  8.11e-01 \n",
+      "[1] \"PP abf for shared variant: 81.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___IER2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1556e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.83e-05  2.92e-01  2.22e-05  1.05e-01  6.04e-01 \n",
+      "[1] \"PP abf for shared variant: 60.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.83e-09  1.41e-05  3.59e-05  1.71e-01  8.29e-01 \n",
+      "[1] \"PP abf for shared variant: 82.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.72e-14  1.86e-10  2.85e-05  1.34e-01  8.66e-01 \n",
+      "[1] \"PP abf for shared variant: 86.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-24  6.96e-21  4.49e-05  2.17e-01  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.68e-20  2.84e-16  9.62e-06  3.85e-02  9.62e-01 \n",
+      "[1] \"PP abf for shared variant: 96.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CMC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-08  5.60e-05  1.05e-05  4.30e-02  9.57e-01 \n",
+      "[1] \"PP abf for shared variant: 95.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.16e-09  2.08e-05  3.22e-05  1.52e-01  8.47e-01 \n",
+      "[1] \"PP abf for shared variant: 84.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-17  6.14e-14  3.00e-05  1.41e-01  8.59e-01 \n",
+      "[1] \"PP abf for shared variant: 85.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GZMB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.20e-07  4.10e-03  2.53e-05  1.18e-01  8.78e-01 \n",
+      "[1] \"PP abf for shared variant: 87.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___AKNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.53e-05  2.77e-01  2.13e-05  1.00e-01  6.23e-01 \n",
+      "[1] \"PP abf for shared variant: 62.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9277e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-05  1.60e-01  2.07e-05  9.60e-02  7.44e-01 \n",
+      "[1] \"PP abf for shared variant: 74.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-24  8.98e-21  3.33e-05  1.58e-01  8.42e-01 \n",
+      "[1] \"PP abf for shared variant: 84.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.15e-13  4.08e-09  2.77e-05  1.30e-01  8.70e-01 \n",
+      "[1] \"PP abf for shared variant: 87%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.87e-09  3.94e-05  2.00e-05  9.07e-02  9.09e-01 \n",
+      "[1] \"PP abf for shared variant: 90.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-07  1.79e-03  8.57e-06  3.32e-02  9.65e-01 \n",
+      "[1] \"PP abf for shared variant: 96.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.14e-07  2.57e-03  1.15e-05  4.82e-02  9.49e-01 \n",
+      "[1] \"PP abf for shared variant: 94.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.57e-21  3.78e-17  3.63e-05  1.73e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.20e-16  4.60e-12  3.40e-05  1.62e-01  8.38e-01 \n",
+      "[1] \"PP abf for shared variant: 83.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.37e-19  2.19e-15  4.11e-05  1.98e-01  8.02e-01 \n",
+      "[1] \"PP abf for shared variant: 80.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___METRNL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4496e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.02e-05  4.01e-01  2.38e-05  1.14e-01  4.85e-01 \n",
+      "[1] \"PP abf for shared variant: 48.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-07  1.39e-03  8.67e-06  3.37e-02  9.65e-01 \n",
+      "[1] \"PP abf for shared variant: 96.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CAMK4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-11  7.32e-08  3.58e-05  1.70e-01  8.30e-01 \n",
+      "[1] \"PP abf for shared variant: 83%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___BHLHE40__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-07  1.10e-03  1.36e-05  5.87e-02  9.40e-01 \n",
+      "[1] \"PP abf for shared variant: 94%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___IFITM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2604e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-05  1.88e-01  2.38e-05  1.12e-01  7.00e-01 \n",
+      "[1] \"PP abf for shared variant: 70%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-06  6.05e-03  1.22e-05  5.14e-02  9.43e-01 \n",
+      "[1] \"PP abf for shared variant: 94.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CD8B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.03e-09  2.52e-05  2.02e-05  9.21e-02  9.08e-01 \n",
+      "[1] \"PP abf for shared variant: 90.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.01e-23  3.01e-19  3.46e-05  1.65e-01  8.35e-01 \n",
+      "[1] \"PP abf for shared variant: 83.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NGFRAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.89e-06  9.44e-03  2.40e-05  1.11e-01  8.79e-01 \n",
+      "[1] \"PP abf for shared variant: 87.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-18  1.40e-14  3.44e-05  1.64e-01  8.36e-01 \n",
+      "[1] \"PP abf for shared variant: 83.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TUBA4A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-06  5.58e-03  1.60e-05  7.08e-02  9.24e-01 \n",
+      "[1] \"PP abf for shared variant: 92.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-10  1.53e-06  8.19e-06  3.12e-02  9.69e-01 \n",
+      "[1] \"PP abf for shared variant: 96.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-23  6.29e-20  5.48e-05  2.67e-01  7.33e-01 \n",
+      "[1] \"PP abf for shared variant: 73.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RCAN3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.83e-10  1.41e-06  3.32e-05  1.58e-01  8.42e-01 \n",
+      "[1] \"PP abf for shared variant: 84.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-17  8.32e-14  3.35e-05  1.59e-01  8.41e-01 \n",
+      "[1] \"PP abf for shared variant: 84.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.66e-09  3.83e-05  1.95e-05  8.83e-02  9.12e-01 \n",
+      "[1] \"PP abf for shared variant: 91.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.89e-15  1.45e-11  3.77e-05  1.80e-01  8.20e-01 \n",
+      "[1] \"PP abf for shared variant: 82%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-12  8.20e-09  1.81e-05  8.14e-02  9.19e-01 \n",
+      "[1] \"PP abf for shared variant: 91.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___DNAJB6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.52e-07  4.26e-03  7.38e-06  2.72e-02  9.69e-01 \n",
+      "[1] \"PP abf for shared variant: 96.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RAP1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.077e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.89e-05  4.95e-01  2.49e-05  1.21e-01  3.85e-01 \n",
+      "[1] \"PP abf for shared variant: 38.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.49e-10  3.75e-06  3.52e-05  1.67e-01  8.32e-01 \n",
+      "[1] \"PP abf for shared variant: 83.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.78e-08  3.39e-04  2.33e-05  1.08e-01  8.92e-01 \n",
+      "[1] \"PP abf for shared variant: 89.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-07  1.47e-03  3.35e-05  1.59e-01  8.39e-01 \n",
+      "[1] \"PP abf for shared variant: 83.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CCDC104__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.93e-05  3.46e-01  3.19e-05  1.55e-01  4.99e-01 \n",
+      "[1] \"PP abf for shared variant: 49.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.35e-13  1.68e-09  3.67e-05  1.75e-01  8.25e-01 \n",
+      "[1] \"PP abf for shared variant: 82.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.39e-13  2.20e-09  3.35e-05  1.59e-01  8.41e-01 \n",
+      "[1] \"PP abf for shared variant: 84.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-10  5.09e-07  1.64e-05  7.29e-02  9.27e-01 \n",
+      "[1] \"PP abf for shared variant: 92.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-22  1.30e-18  5.41e-05  2.63e-01  7.37e-01 \n",
+      "[1] \"PP abf for shared variant: 73.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.43e-06  7.16e-03  2.41e-05  1.12e-01  8.81e-01 \n",
+      "[1] \"PP abf for shared variant: 88.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TESPA1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.27e-07  1.63e-03  1.40e-05  6.08e-02  9.38e-01 \n",
+      "[1] \"PP abf for shared variant: 93.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.96e-07  1.98e-03  3.40e-05  1.61e-01  8.37e-01 \n",
+      "[1] \"PP abf for shared variant: 83.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PSMA7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-05  7.93e-02  2.85e-05  1.35e-01  7.86e-01 \n",
+      "[1] \"PP abf for shared variant: 78.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PLEK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.11e-06  4.05e-02  1.69e-05  7.59e-02  8.84e-01 \n",
+      "[1] \"PP abf for shared variant: 88.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.24e-09  4.12e-05  2.25e-05  1.03e-01  8.97e-01 \n",
+      "[1] \"PP abf for shared variant: 89.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.05e-20  2.03e-16  4.57e-05  2.21e-01  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.4281e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.15e-05  1.57e-01  2.37e-05  1.11e-01  7.32e-01 \n",
+      "[1] \"PP abf for shared variant: 73.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___KLRD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-07  1.10e-03  3.01e-05  1.42e-01  8.57e-01 \n",
+      "[1] \"PP abf for shared variant: 85.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MYC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.40e-07  1.70e-03  4.56e-05  2.20e-01  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-05  6.01e-02  2.45e-05  1.14e-01  8.26e-01 \n",
+      "[1] \"PP abf for shared variant: 82.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___KLF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.391e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.11e-05  2.06e-01  3.11e-05  1.49e-01  6.45e-01 \n",
+      "[1] \"PP abf for shared variant: 64.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.84e-06  3.42e-02  3.68e-05  1.76e-01  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HNRNPA2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-05  1.12e-01  1.37e-05  6.01e-02  8.28e-01 \n",
+      "[1] \"PP abf for shared variant: 82.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ARAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3907e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-04  5.28e-01  2.29e-05  1.11e-01  3.61e-01 \n",
+      "[1] \"PP abf for shared variant: 36.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-20  5.76e-17  4.12e-05  1.98e-01  8.02e-01 \n",
+      "[1] \"PP abf for shared variant: 80.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-10  1.15e-06  1.26e-05  5.36e-02  9.46e-01 \n",
+      "[1] \"PP abf for shared variant: 94.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-09  5.80e-06  1.91e-05  8.64e-02  9.14e-01 \n",
+      "[1] \"PP abf for shared variant: 91.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.24e-17  2.12e-13  3.95e-05  1.89e-01  8.11e-01 \n",
+      "[1] \"PP abf for shared variant: 81.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-10  1.69e-06  3.99e-05  1.91e-01  8.09e-01 \n",
+      "[1] \"PP abf for shared variant: 80.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.03e-10  2.51e-06  4.27e-05  2.05e-01  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.12e-06  3.56e-02  2.70e-05  1.27e-01  8.38e-01 \n",
+      "[1] \"PP abf for shared variant: 83.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-13  5.46e-10  4.68e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-22  8.70e-19  5.48e-05  2.66e-01  7.33e-01 \n",
+      "[1] \"PP abf for shared variant: 73.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___LYPD3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.90e-06  4.45e-02  1.40e-05  6.08e-02  8.95e-01 \n",
+      "[1] \"PP abf for shared variant: 89.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-09  1.97e-05  2.57e-05  1.19e-01  8.80e-01 \n",
+      "[1] \"PP abf for shared variant: 88%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.24e-06  4.12e-02  2.79e-05  1.31e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-06  8.59e-03  1.67e-05  7.44e-02  9.17e-01 \n",
+      "[1] \"PP abf for shared variant: 91.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM243\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.64e-08  2.82e-04  2.58e-05  1.20e-01  8.80e-01 \n",
+      "[1] \"PP abf for shared variant: 88%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.89e-19  9.47e-16  4.35e-05  2.10e-01  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ID2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-06  1.71e-02  1.40e-05  6.06e-02  9.22e-01 \n",
+      "[1] \"PP abf for shared variant: 92.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-19  7.50e-16  3.34e-05  1.59e-01  8.41e-01 \n",
+      "[1] \"PP abf for shared variant: 84.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-14  1.16e-10  3.49e-05  1.66e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.39e-08  2.70e-04  1.17e-05  4.91e-02  9.51e-01 \n",
+      "[1] \"PP abf for shared variant: 95.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-29  1.15e-25  5.19e-05  2.52e-01  7.48e-01 \n",
+      "[1] \"PP abf for shared variant: 74.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.32e-06  3.16e-02  9.70e-06  3.92e-02  9.29e-01 \n",
+      "[1] \"PP abf for shared variant: 92.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NFKBIA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 7.944e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.38e-05  2.69e-01  2.26e-05  1.07e-01  6.24e-01 \n",
+      "[1] \"PP abf for shared variant: 62.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.31e-07  3.65e-03  2.10e-05  9.62e-02  9.00e-01 \n",
+      "[1] \"PP abf for shared variant: 90%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.77e-21  2.39e-17  5.26e-05  2.55e-01  7.45e-01 \n",
+      "[1] \"PP abf for shared variant: 74.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.89e-20  1.95e-16  1.95e-05  8.85e-02  9.11e-01 \n",
+      "[1] \"PP abf for shared variant: 91.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.02e-14  2.51e-10  3.95e-05  1.89e-01  8.11e-01 \n",
+      "[1] \"PP abf for shared variant: 81.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GZMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-11  6.56e-08  3.63e-05  1.73e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.78e-06  4.39e-02  1.64e-05  7.31e-02  8.83e-01 \n",
+      "[1] \"PP abf for shared variant: 88.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.23e-15  3.62e-11  3.91e-05  1.87e-01  8.13e-01 \n",
+      "[1] \"PP abf for shared variant: 81.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPI1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.53e-05  1.27e-01  2.91e-05  1.38e-01  7.35e-01 \n",
+      "[1] \"PP abf for shared variant: 73.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-23  1.66e-19  3.48e-05  1.66e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.40e-06  2.20e-02  2.47e-05  1.15e-01  8.63e-01 \n",
+      "[1] \"PP abf for shared variant: 86.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-14  7.92e-11  3.15e-05  1.49e-01  8.51e-01 \n",
+      "[1] \"PP abf for shared variant: 85.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.14e-20  4.07e-16  3.62e-05  1.73e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SC5D\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.01e-05  1.50e-01  3.60e-05  1.73e-01  6.76e-01 \n",
+      "[1] \"PP abf for shared variant: 67.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.08e-15  4.04e-11  4.24e-05  2.04e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-21  8.90e-18  5.44e-05  2.64e-01  7.36e-01 \n",
+      "[1] \"PP abf for shared variant: 73.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RIC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.89e-08  1.94e-04  2.12e-05  9.72e-02  9.03e-01 \n",
+      "[1] \"PP abf for shared variant: 90.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.37e-16  3.69e-12  1.58e-05  6.96e-02  9.30e-01 \n",
+      "[1] \"PP abf for shared variant: 93%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.50e-10  2.25e-06  1.56e-05  6.88e-02  9.31e-01 \n",
+      "[1] \"PP abf for shared variant: 93.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.9814e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.53e-05  2.26e-01  1.88e-05  8.70e-02  6.87e-01 \n",
+      "[1] \"PP abf for shared variant: 68.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRP14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.85e-09  2.43e-05  3.82e-05  1.83e-01  8.17e-01 \n",
+      "[1] \"PP abf for shared variant: 81.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0298e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.88e-05  1.94e-01  2.13e-05  9.93e-02  7.07e-01 \n",
+      "[1] \"PP abf for shared variant: 70.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.47e-08  1.23e-04  3.41e-05  1.62e-01  8.38e-01 \n",
+      "[1] \"PP abf for shared variant: 83.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-11  1.35e-07  2.55e-05  1.18e-01  8.82e-01 \n",
+      "[1] \"PP abf for shared variant: 88.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-20  6.12e-17  3.90e-05  1.87e-01  8.13e-01 \n",
+      "[1] \"PP abf for shared variant: 81.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.67e-23  3.34e-19  5.96e-05  2.91e-01  7.09e-01 \n",
+      "[1] \"PP abf for shared variant: 70.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.53e-24  4.27e-20  3.68e-05  1.76e-01  8.24e-01 \n",
+      "[1] \"PP abf for shared variant: 82.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MYL12B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-05  1.26e-01  1.79e-05  8.18e-02  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.53e-07  4.76e-03  3.06e-05  1.44e-01  8.51e-01 \n",
+      "[1] \"PP abf for shared variant: 85.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MIR142__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1648e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.05e-05  4.52e-01  2.34e-05  1.12e-01  4.35e-01 \n",
+      "[1] \"PP abf for shared variant: 43.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-07  5.96e-04  1.47e-05  6.44e-02  9.35e-01 \n",
+      "[1] \"PP abf for shared variant: 93.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.90e-22  4.95e-18  4.10e-05  1.97e-01  8.03e-01 \n",
+      "[1] \"PP abf for shared variant: 80.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-05  8.04e-02  3.48e-05  1.66e-01  7.53e-01 \n",
+      "[1] \"PP abf for shared variant: 75.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-10  1.19e-06  2.15e-05  9.84e-02  9.02e-01 \n",
+      "[1] \"PP abf for shared variant: 90.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PTP4A2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-05  6.22e-02  1.60e-05  7.14e-02  8.66e-01 \n",
+      "[1] \"PP abf for shared variant: 86.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.9666e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.38e-05  3.19e-01  2.23e-05  1.05e-01  5.76e-01 \n",
+      "[1] \"PP abf for shared variant: 57.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.97e-09  2.48e-05  4.67e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.42e-12  7.09e-09  2.94e-05  1.39e-01  8.61e-01 \n",
+      "[1] \"PP abf for shared variant: 86.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-29  8.42e-26  4.04e-05  1.94e-01  8.06e-01 \n",
+      "[1] \"PP abf for shared variant: 80.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CCDC57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.84e-11  4.42e-07  3.79e-05  1.81e-01  8.19e-01 \n",
+      "[1] \"PP abf for shared variant: 81.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ITGB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-05  1.38e-01  3.05e-05  1.45e-01  7.17e-01 \n",
+      "[1] \"PP abf for shared variant: 71.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-06  1.07e-02  2.10e-05  9.62e-02  8.93e-01 \n",
+      "[1] \"PP abf for shared variant: 89.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4185e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.54e-05  1.77e-01  1.36e-05  6.05e-02  7.62e-01 \n",
+      "[1] \"PP abf for shared variant: 76.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ARF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.80e-06  2.90e-02  3.26e-05  1.55e-01  8.16e-01 \n",
+      "[1] \"PP abf for shared variant: 81.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CD81__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.16e-06  1.58e-02  8.83e-06  3.46e-02  9.50e-01 \n",
+      "[1] \"PP abf for shared variant: 95%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-07  8.55e-04  3.86e-05  1.85e-01  8.14e-01 \n",
+      "[1] \"PP abf for shared variant: 81.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-07  8.32e-04  3.74e-05  1.79e-01  8.20e-01 \n",
+      "[1] \"PP abf for shared variant: 82%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.01e-08  1.51e-04  2.85e-05  1.34e-01  8.66e-01 \n",
+      "[1] \"PP abf for shared variant: 86.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-09  5.19e-06  3.34e-05  1.59e-01  8.41e-01 \n",
+      "[1] \"PP abf for shared variant: 84.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FOXP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-08  6.09e-05  1.45e-05  6.30e-02  9.37e-01 \n",
+      "[1] \"PP abf for shared variant: 93.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CCL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-09  8.73e-06  1.84e-05  8.30e-02  9.17e-01 \n",
+      "[1] \"PP abf for shared variant: 91.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-07  9.77e-04  1.44e-05  6.27e-02  9.36e-01 \n",
+      "[1] \"PP abf for shared variant: 93.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___KLRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9856e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-05  1.58e-01  1.74e-05  7.92e-02  7.62e-01 \n",
+      "[1] \"PP abf for shared variant: 76.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5042e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.34e-05  3.17e-01  1.89e-05  8.87e-02  5.94e-01 \n",
+      "[1] \"PP abf for shared variant: 59.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.45e-06  3.72e-02  1.91e-05  8.65e-02  8.76e-01 \n",
+      "[1] \"PP abf for shared variant: 87.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.98e-20  3.99e-16  3.84e-05  1.84e-01  8.16e-01 \n",
+      "[1] \"PP abf for shared variant: 81.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___C1orf21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1023e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.34e-05  1.67e-01  2.01e-05  9.28e-02  7.40e-01 \n",
+      "[1] \"PP abf for shared variant: 74%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.38e-15  4.69e-11  3.42e-05  1.63e-01  8.37e-01 \n",
+      "[1] \"PP abf for shared variant: 83.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CALM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-06  5.71e-03  8.31e-06  3.19e-02  9.62e-01 \n",
+      "[1] \"PP abf for shared variant: 96.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HOPX__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-05  5.38e-02  1.85e-05  8.38e-02  8.62e-01 \n",
+      "[1] \"PP abf for shared variant: 86.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.55e-07  3.28e-03  1.68e-05  7.48e-02  9.22e-01 \n",
+      "[1] \"PP abf for shared variant: 92.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-19  1.12e-15  4.91e-05  2.38e-01  7.62e-01 \n",
+      "[1] \"PP abf for shared variant: 76.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.85e-08  9.25e-05  1.93e-05  8.72e-02  9.13e-01 \n",
+      "[1] \"PP abf for shared variant: 91.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-20  6.72e-17  3.72e-05  1.78e-01  8.22e-01 \n",
+      "[1] \"PP abf for shared variant: 82.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-07  7.71e-04  1.64e-05  7.29e-02  9.26e-01 \n",
+      "[1] \"PP abf for shared variant: 92.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-15  7.63e-12  3.20e-05  1.51e-01  8.49e-01 \n",
+      "[1] \"PP abf for shared variant: 84.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.507e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-05  2.45e-01  1.55e-05  7.06e-02  6.84e-01 \n",
+      "[1] \"PP abf for shared variant: 68.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.07e-22  4.03e-18  5.29e-05  2.57e-01  7.43e-01 \n",
+      "[1] \"PP abf for shared variant: 74.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.72e-18  2.36e-14  4.18e-05  2.01e-01  7.99e-01 \n",
+      "[1] \"PP abf for shared variant: 79.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PTMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.74e-08  3.37e-04  2.18e-05  9.98e-02  9.00e-01 \n",
+      "[1] \"PP abf for shared variant: 90%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.03e-15  3.51e-11  3.78e-05  1.81e-01  8.19e-01 \n",
+      "[1] \"PP abf for shared variant: 81.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GNG2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.52e-06  2.76e-02  1.65e-05  7.34e-02  8.99e-01 \n",
+      "[1] \"PP abf for shared variant: 89.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.28e-07  2.64e-03  2.10e-05  9.62e-02  9.01e-01 \n",
+      "[1] \"PP abf for shared variant: 90.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-11  1.34e-07  3.57e-05  1.70e-01  8.30e-01 \n",
+      "[1] \"PP abf for shared variant: 83%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-14  6.49e-11  3.24e-05  1.54e-01  8.46e-01 \n",
+      "[1] \"PP abf for shared variant: 84.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.44e-08  2.22e-04  4.44e-05  2.14e-01  7.86e-01 \n",
+      "[1] \"PP abf for shared variant: 78.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CD27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.689e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-05  1.41e-01  1.69e-05  7.67e-02  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-08  1.92e-04  1.68e-05  7.48e-02  9.25e-01 \n",
+      "[1] \"PP abf for shared variant: 92.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.16e-14  3.08e-10  3.97e-05  1.91e-01  8.09e-01 \n",
+      "[1] \"PP abf for shared variant: 80.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-20  1.57e-16  4.28e-05  2.06e-01  7.94e-01 \n",
+      "[1] \"PP abf for shared variant: 79.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___REL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.691e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-05  1.49e-01  1.74e-05  7.93e-02  7.71e-01 \n",
+      "[1] \"PP abf for shared variant: 77.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.21e-16  4.10e-12  3.92e-05  1.88e-01  8.12e-01 \n",
+      "[1] \"PP abf for shared variant: 81.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.21e-06  4.60e-02  1.19e-05  5.06e-02  9.03e-01 \n",
+      "[1] \"PP abf for shared variant: 90.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.00e-13  3.00e-09  3.67e-05  1.75e-01  8.25e-01 \n",
+      "[1] \"PP abf for shared variant: 82.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-17  7.44e-14  4.49e-05  2.17e-01  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-11  9.63e-08  3.75e-05  1.79e-01  8.21e-01 \n",
+      "[1] \"PP abf for shared variant: 82.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SEPT7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-06  1.89e-02  2.33e-05  1.08e-01  8.73e-01 \n",
+      "[1] \"PP abf for shared variant: 87.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-06  1.66e-02  1.86e-05  8.40e-02  8.99e-01 \n",
+      "[1] \"PP abf for shared variant: 89.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-05  1.39e-01  2.85e-05  1.35e-01  7.26e-01 \n",
+      "[1] \"PP abf for shared variant: 72.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.92e-14  2.96e-10  1.16e-05  4.85e-02  9.51e-01 \n",
+      "[1] \"PP abf for shared variant: 95.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___FCGR3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.41e-06  1.20e-02  2.56e-05  1.19e-01  8.69e-01 \n",
+      "[1] \"PP abf for shared variant: 86.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PSMB9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.645e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.83e-05  2.42e-01  1.96e-05  9.14e-02  6.67e-01 \n",
+      "[1] \"PP abf for shared variant: 66.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.14e-14  3.57e-10  2.93e-05  1.38e-01  8.62e-01 \n",
+      "[1] \"PP abf for shared variant: 86.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-12  1.13e-08  4.65e-05  2.25e-01  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.56e-06  3.28e-02  1.01e-05  4.12e-02  9.26e-01 \n",
+      "[1] \"PP abf for shared variant: 92.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-08  7.05e-05  1.34e-05  5.73e-02  9.43e-01 \n",
+      "[1] \"PP abf for shared variant: 94.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.50e-20  1.75e-16  3.93e-05  1.88e-01  8.12e-01 \n",
+      "[1] \"PP abf for shared variant: 81.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-17  8.21e-14  4.57e-05  2.21e-01  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-06  5.24e-03  1.05e-05  4.29e-02  9.52e-01 \n",
+      "[1] \"PP abf for shared variant: 95.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-06  1.36e-02  3.19e-05  1.51e-01  8.35e-01 \n",
+      "[1] \"PP abf for shared variant: 83.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-18  7.81e-15  4.29e-05  2.07e-01  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5519e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-05  1.93e-01  3.09e-05  1.48e-01  6.58e-01 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.31e-16  2.66e-12  7.68e-06  2.87e-02  9.71e-01 \n",
+      "[1] \"PP abf for shared variant: 97.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___DOK2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-05  6.14e-02  2.10e-05  9.67e-02  8.42e-01 \n",
+      "[1] \"PP abf for shared variant: 84.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-10  7.21e-07  3.33e-05  1.58e-01  8.42e-01 \n",
+      "[1] \"PP abf for shared variant: 84.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CALR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9449e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-05  1.02e-01  1.63e-05  7.31e-02  8.25e-01 \n",
+      "[1] \"PP abf for shared variant: 82.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TTC38\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1223e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-05  7.54e-02  1.19e-05  5.06e-02  8.74e-01 \n",
+      "[1] \"PP abf for shared variant: 87.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.43e-07  2.72e-03  1.93e-05  8.73e-02  9.10e-01 \n",
+      "[1] \"PP abf for shared variant: 91%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-07  6.23e-04  2.24e-05  1.03e-01  8.96e-01 \n",
+      "[1] \"PP abf for shared variant: 89.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.22e-14  2.11e-10  1.07e-05  4.38e-02  9.56e-01 \n",
+      "[1] \"PP abf for shared variant: 95.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.59e-14  2.79e-10  4.36e-05  2.10e-01  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-10  1.02e-06  3.88e-05  1.86e-01  8.14e-01 \n",
+      "[1] \"PP abf for shared variant: 81.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.10e-20  1.55e-16  5.20e-05  2.53e-01  7.47e-01 \n",
+      "[1] \"PP abf for shared variant: 74.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PASK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-11  6.60e-08  2.45e-05  1.14e-01  8.86e-01 \n",
+      "[1] \"PP abf for shared variant: 88.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.16e-15  2.58e-11  3.48e-05  1.66e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.88e-21  1.94e-17  5.23e-05  2.54e-01  7.46e-01 \n",
+      "[1] \"PP abf for shared variant: 74.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___OXNAD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1359e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.69e-05  2.85e-01  3.39e-05  1.64e-01  5.51e-01 \n",
+      "[1] \"PP abf for shared variant: 55.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.64e-07  3.32e-03  1.39e-05  6.01e-02  9.37e-01 \n",
+      "[1] \"PP abf for shared variant: 93.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.28e-20  6.39e-17  3.78e-05  1.81e-01  8.19e-01 \n",
+      "[1] \"PP abf for shared variant: 81.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.97e-08  2.49e-04  3.46e-05  1.65e-01  8.35e-01 \n",
+      "[1] \"PP abf for shared variant: 83.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TYROBP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.40e-07  1.20e-03  3.01e-05  1.42e-01  8.57e-01 \n",
+      "[1] \"PP abf for shared variant: 85.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YBX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1331e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.71e-05  4.35e-01  2.15e-05  1.03e-01  4.62e-01 \n",
+      "[1] \"PP abf for shared variant: 46.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-11  1.14e-07  2.66e-05  1.24e-01  8.76e-01 \n",
+      "[1] \"PP abf for shared variant: 87.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-09  1.57e-05  1.01e-05  4.09e-02  9.59e-01 \n",
+      "[1] \"PP abf for shared variant: 95.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___IL7R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.76e-08  3.38e-04  3.74e-05  1.79e-01  8.21e-01 \n",
+      "[1] \"PP abf for shared variant: 82.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RHOH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.94e-07  2.47e-03  1.64e-05  7.27e-02  9.25e-01 \n",
+      "[1] \"PP abf for shared variant: 92.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-20  1.01e-16  5.04e-05  2.45e-01  7.55e-01 \n",
+      "[1] \"PP abf for shared variant: 75.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-22  1.02e-18  3.99e-05  1.91e-01  8.08e-01 \n",
+      "[1] \"PP abf for shared variant: 80.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.74e-07  1.87e-03  2.09e-05  9.56e-02  9.03e-01 \n",
+      "[1] \"PP abf for shared variant: 90.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___DBI__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-07  9.97e-04  2.35e-05  1.08e-01  8.91e-01 \n",
+      "[1] \"PP abf for shared variant: 89.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.74e-16  4.87e-12  5.41e-05  2.63e-01  7.37e-01 \n",
+      "[1] \"PP abf for shared variant: 73.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.00e-14  4.50e-10  4.30e-05  2.07e-01  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-11  8.74e-08  3.94e-05  1.89e-01  8.11e-01 \n",
+      "[1] \"PP abf for shared variant: 81.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___POMP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-05  1.09e-01  2.38e-05  1.11e-01  7.80e-01 \n",
+      "[1] \"PP abf for shared variant: 78%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.70e-19  1.85e-15  4.52e-05  2.18e-01  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RAB8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0817e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.66e-05  2.33e-01  2.08e-05  9.73e-02  6.70e-01 \n",
+      "[1] \"PP abf for shared variant: 67%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___GZMH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.00e-06  2.00e-02  2.39e-05  1.11e-01  8.69e-01 \n",
+      "[1] \"PP abf for shared variant: 86.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-10  4.78e-06  1.00e-05  4.06e-02  9.59e-01 \n",
+      "[1] \"PP abf for shared variant: 95.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-15  1.34e-11  9.09e-06  3.58e-02  9.64e-01 \n",
+      "[1] \"PP abf for shared variant: 96.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.12e-24  3.06e-20  3.86e-05  1.85e-01  8.15e-01 \n",
+      "[1] \"PP abf for shared variant: 81.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.98e-22  4.49e-18  3.32e-05  1.58e-01  8.42e-01 \n",
+      "[1] \"PP abf for shared variant: 84.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___ABLIM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.31e-08  3.65e-04  1.62e-05  7.18e-02  9.28e-01 \n",
+      "[1] \"PP abf for shared variant: 92.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EIF4A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8946e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-05  1.14e-01  1.41e-05  6.24e-02  8.23e-01 \n",
+      "[1] \"PP abf for shared variant: 82.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.53e-09  2.77e-05  1.62e-05  7.18e-02  9.28e-01 \n",
+      "[1] \"PP abf for shared variant: 92.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RP11-291B21.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.19e-10  3.60e-06  3.68e-05  1.76e-01  8.24e-01 \n",
+      "[1] \"PP abf for shared variant: 82.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-23  8.17e-20  4.88e-05  2.36e-01  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-22  1.02e-18  5.35e-05  2.60e-01  7.40e-01 \n",
+      "[1] \"PP abf for shared variant: 74%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.22e-14  4.11e-10  2.42e-05  1.12e-01  8.88e-01 \n",
+      "[1] \"PP abf for shared variant: 88.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-14  9.73e-11  3.53e-05  1.68e-01  8.32e-01 \n",
+      "[1] \"PP abf for shared variant: 83.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.41e-22  1.20e-18  4.80e-05  2.32e-01  7.68e-01 \n",
+      "[1] \"PP abf for shared variant: 76.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-07  1.09e-03  1.70e-05  7.60e-02  9.23e-01 \n",
+      "[1] \"PP abf for shared variant: 92.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.39e-18  1.19e-14  4.74e-05  2.29e-01  7.71e-01 \n",
+      "[1] \"PP abf for shared variant: 77.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.30e-16  1.15e-12  1.67e-05  7.43e-02  9.26e-01 \n",
+      "[1] \"PP abf for shared variant: 92.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S1PR5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1943e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.85e-05  4.92e-01  2.65e-05  1.29e-01  3.79e-01 \n",
+      "[1] \"PP abf for shared variant: 37.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD8T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-09  1.14e-05  3.31e-05  1.57e-01  8.43e-01 \n",
+      "[1] \"PP abf for shared variant: 84.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.95e-07  4.47e-03  1.41e-05  6.13e-02  9.34e-01 \n",
+      "[1] \"PP abf for shared variant: 93.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CHCHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-06  1.45e-02  1.60e-05  7.09e-02  9.15e-01 \n",
+      "[1] \"PP abf for shared variant: 91.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-08  1.37e-04  2.85e-05  1.34e-01  8.66e-01 \n",
+      "[1] \"PP abf for shared variant: 86.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FMNL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.61e-06  3.30e-02  2.09e-05  9.56e-02  8.71e-01 \n",
+      "[1] \"PP abf for shared variant: 87.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.75e-29  4.37e-25  4.43e-05  2.13e-01  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.44e-11  1.22e-07  3.90e-05  1.87e-01  8.13e-01 \n",
+      "[1] \"PP abf for shared variant: 81.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HERPUD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.267e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-05  1.14e-01  2.22e-05  1.03e-01  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ANXA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.76e-10  2.88e-06  3.39e-05  1.61e-01  8.39e-01 \n",
+      "[1] \"PP abf for shared variant: 83.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SOD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.05e-07  4.02e-03  4.49e-05  2.17e-01  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.73e-22  4.37e-18  3.74e-05  1.79e-01  8.21e-01 \n",
+      "[1] \"PP abf for shared variant: 82.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.29e-18  2.64e-14  3.84e-05  1.84e-01  8.16e-01 \n",
+      "[1] \"PP abf for shared variant: 81.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ATP1B3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.79e-06  4.89e-02  3.01e-05  1.42e-01  8.09e-01 \n",
+      "[1] \"PP abf for shared variant: 80.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.27e-05  1.63e-01  4.00e-05  1.94e-01  6.43e-01 \n",
+      "[1] \"PP abf for shared variant: 64.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.39e-30  2.19e-26  3.90e-05  1.87e-01  8.13e-01 \n",
+      "[1] \"PP abf for shared variant: 81.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-15  6.59e-12  4.90e-05  2.37e-01  7.63e-01 \n",
+      "[1] \"PP abf for shared variant: 76.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EML4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.04e-09  3.02e-05  1.50e-05  6.58e-02  9.34e-01 \n",
+      "[1] \"PP abf for shared variant: 93.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-07  5.09e-04  3.83e-05  1.83e-01  8.16e-01 \n",
+      "[1] \"PP abf for shared variant: 81.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MCL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.71e-11  4.36e-07  3.75e-05  1.79e-01  8.21e-01 \n",
+      "[1] \"PP abf for shared variant: 82.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NOG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.77e-06  1.88e-02  4.10e-05  1.97e-01  7.84e-01 \n",
+      "[1] \"PP abf for shared variant: 78.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PRMT2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-08  1.28e-04  1.78e-05  7.98e-02  9.20e-01 \n",
+      "[1] \"PP abf for shared variant: 92%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.30e-10  4.65e-06  3.43e-05  1.63e-01  8.37e-01 \n",
+      "[1] \"PP abf for shared variant: 83.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-05  8.94e-02  2.45e-05  1.15e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.70e-16  2.85e-12  4.12e-05  1.98e-01  8.02e-01 \n",
+      "[1] \"PP abf for shared variant: 80.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FAM129A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.01e-11  2.00e-07  3.80e-05  1.82e-01  8.18e-01 \n",
+      "[1] \"PP abf for shared variant: 81.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.85e-20  3.92e-16  3.92e-05  1.88e-01  8.12e-01 \n",
+      "[1] \"PP abf for shared variant: 81.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.2936e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-05  1.63e-01  2.34e-05  1.10e-01  7.27e-01 \n",
+      "[1] \"PP abf for shared variant: 72.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-29  6.65e-26  4.85e-05  2.35e-01  7.65e-01 \n",
+      "[1] \"PP abf for shared variant: 76.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ALOX5AP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-07  5.45e-04  2.81e-05  1.32e-01  8.68e-01 \n",
+      "[1] \"PP abf for shared variant: 86.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TSHZ2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.18e-08  1.59e-04  2.52e-05  1.17e-01  8.83e-01 \n",
+      "[1] \"PP abf for shared variant: 88.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-10  9.79e-07  2.05e-05  9.33e-02  9.07e-01 \n",
+      "[1] \"PP abf for shared variant: 90.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ARHGDIB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.64e-10  3.82e-06  3.05e-05  1.44e-01  8.56e-01 \n",
+      "[1] \"PP abf for shared variant: 85.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.26e-15  4.13e-11  3.54e-05  1.68e-01  8.31e-01 \n",
+      "[1] \"PP abf for shared variant: 83.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.76e-25  3.38e-21  4.88e-05  2.36e-01  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.49e-33  3.75e-29  4.67e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.85e-32  2.43e-28  4.68e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YBX1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-10  1.51e-06  1.29e-05  5.50e-02  9.45e-01 \n",
+      "[1] \"PP abf for shared variant: 94.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.52e-29  1.76e-25  3.97e-05  1.91e-01  8.09e-01 \n",
+      "[1] \"PP abf for shared variant: 80.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___JUND__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.279e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-05  8.77e-02  2.90e-05  1.37e-01  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-11  9.60e-08  4.38e-05  2.11e-01  7.89e-01 \n",
+      "[1] \"PP abf for shared variant: 78.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.89e-31  4.94e-27  5.01e-05  2.43e-01  7.57e-01 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-07  9.98e-04  1.76e-05  7.85e-02  9.20e-01 \n",
+      "[1] \"PP abf for shared variant: 92%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.22e-16  4.11e-12  1.39e-05  6.01e-02  9.40e-01 \n",
+      "[1] \"PP abf for shared variant: 94%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF18\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.98e-07  1.99e-03  2.35e-05  1.09e-01  8.89e-01 \n",
+      "[1] \"PP abf for shared variant: 88.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MYO1F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-07  6.35e-04  3.22e-05  1.52e-01  8.47e-01 \n",
+      "[1] \"PP abf for shared variant: 84.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.40e-28  1.70e-24  4.89e-05  2.37e-01  7.63e-01 \n",
+      "[1] \"PP abf for shared variant: 76.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-13  1.66e-09  4.46e-05  2.15e-01  7.85e-01 \n",
+      "[1] \"PP abf for shared variant: 78.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-05  1.01e-01  2.45e-05  1.14e-01  7.84e-01 \n",
+      "[1] \"PP abf for shared variant: 78.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-29  2.05e-25  4.24e-05  2.04e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.47e-09  3.73e-05  2.77e-05  1.30e-01  8.70e-01 \n",
+      "[1] \"PP abf for shared variant: 87%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VMP1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-05  5.01e-02  3.32e-05  1.58e-01  7.92e-01 \n",
+      "[1] \"PP abf for shared variant: 79.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-10  1.73e-06  4.51e-05  2.18e-01  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-29  1.07e-25  4.95e-05  2.40e-01  7.60e-01 \n",
+      "[1] \"PP abf for shared variant: 76%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-05  7.45e-02  2.82e-05  1.33e-01  7.92e-01 \n",
+      "[1] \"PP abf for shared variant: 79.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GALM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.78e-06  1.89e-02  1.07e-05  4.40e-02  9.37e-01 \n",
+      "[1] \"PP abf for shared variant: 93.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-06  1.08e-02  3.64e-05  1.74e-01  8.15e-01 \n",
+      "[1] \"PP abf for shared variant: 81.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-07  7.32e-04  7.84e-05  3.86e-01  6.13e-01 \n",
+      "[1] \"PP abf for shared variant: 61.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-18  5.76e-15  4.74e-05  2.29e-01  7.71e-01 \n",
+      "[1] \"PP abf for shared variant: 77.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-20  6.22e-17  3.41e-05  1.62e-01  8.38e-01 \n",
+      "[1] \"PP abf for shared variant: 83.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ANXA5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.36e-07  4.18e-03  4.05e-05  1.95e-01  8.01e-01 \n",
+      "[1] \"PP abf for shared variant: 80.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___IDS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.10e-08  1.55e-04  3.56e-05  1.70e-01  8.30e-01 \n",
+      "[1] \"PP abf for shared variant: 83%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ARID5B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.10e-06  2.55e-02  3.91e-05  1.88e-01  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___IMPDH2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.33e-10  2.16e-06  4.18e-05  2.01e-01  7.99e-01 \n",
+      "[1] \"PP abf for shared variant: 79.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-18  1.61e-14  4.81e-05  2.33e-01  7.67e-01 \n",
+      "[1] \"PP abf for shared variant: 76.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ST13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.37e-11  1.69e-07  3.58e-05  1.71e-01  8.29e-01 \n",
+      "[1] \"PP abf for shared variant: 82.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CXCR3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.98e-07  1.49e-03  3.16e-05  1.50e-01  8.49e-01 \n",
+      "[1] \"PP abf for shared variant: 84.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.70e-11  4.35e-07  5.02e-05  2.44e-01  7.56e-01 \n",
+      "[1] \"PP abf for shared variant: 75.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-19  5.66e-16  4.38e-05  2.11e-01  7.89e-01 \n",
+      "[1] \"PP abf for shared variant: 78.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SPOCK2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-06  8.49e-03  5.03e-05  2.44e-01  7.47e-01 \n",
+      "[1] \"PP abf for shared variant: 74.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___C15orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.82e-06  3.41e-02  3.27e-05  1.56e-01  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPF\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1448e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.12e-05  2.56e-01  2.77e-05  1.32e-01  6.11e-01 \n",
+      "[1] \"PP abf for shared variant: 61.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.53e-19  3.26e-15  4.75e-05  2.30e-01  7.70e-01 \n",
+      "[1] \"PP abf for shared variant: 77%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FAM134B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-05  5.28e-02  3.73e-05  1.79e-01  7.68e-01 \n",
+      "[1] \"PP abf for shared variant: 76.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ISG20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.25e-08  4.62e-04  1.17e-05  4.92e-02  9.50e-01 \n",
+      "[1] \"PP abf for shared variant: 95%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-14  9.99e-11  4.24e-05  2.04e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NUCB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.42e-06  1.21e-02  2.44e-05  1.13e-01  8.75e-01 \n",
+      "[1] \"PP abf for shared variant: 87.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.75e-08  2.88e-04  2.23e-05  1.03e-01  8.97e-01 \n",
+      "[1] \"PP abf for shared variant: 89.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LINC00493__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-06  9.76e-03  1.44e-05  6.26e-02  9.28e-01 \n",
+      "[1] \"PP abf for shared variant: 92.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-30  2.14e-26  3.82e-05  1.83e-01  8.17e-01 \n",
+      "[1] \"PP abf for shared variant: 81.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-07  8.13e-04  2.47e-05  1.15e-01  8.84e-01 \n",
+      "[1] \"PP abf for shared variant: 88.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.85e-16  2.42e-12  4.50e-05  2.17e-01  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CDC42__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.39e-07  1.19e-03  2.85e-05  1.34e-01  8.65e-01 \n",
+      "[1] \"PP abf for shared variant: 86.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF1B\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-07  2.39e-03  1.23e-05  5.21e-02  9.45e-01 \n",
+      "[1] \"PP abf for shared variant: 94.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.13e-06  1.07e-02  3.77e-05  1.80e-01  8.09e-01 \n",
+      "[1] \"PP abf for shared variant: 80.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.33e-23  3.66e-19  4.43e-05  2.14e-01  7.86e-01 \n",
+      "[1] \"PP abf for shared variant: 78.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ACTN4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.24e-08  4.12e-04  3.44e-05  1.64e-01  8.36e-01 \n",
+      "[1] \"PP abf for shared variant: 83.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___IKZF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.38e-06  3.69e-02  4.71e-05  2.28e-01  7.35e-01 \n",
+      "[1] \"PP abf for shared variant: 73.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.61e-06  4.31e-02  1.83e-05  8.29e-02  8.74e-01 \n",
+      "[1] \"PP abf for shared variant: 87.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-21  6.75e-18  5.05e-05  2.45e-01  7.55e-01 \n",
+      "[1] \"PP abf for shared variant: 75.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.00e-13  2.00e-09  3.93e-05  1.88e-01  8.12e-01 \n",
+      "[1] \"PP abf for shared variant: 81.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.86e-05  1.43e-01  3.69e-05  1.78e-01  6.79e-01 \n",
+      "[1] \"PP abf for shared variant: 67.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NAMPT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8087e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.13e-05  1.06e-01  2.23e-05  1.04e-01  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.00e-10  4.50e-06  4.20e-05  2.02e-01  7.98e-01 \n",
+      "[1] \"PP abf for shared variant: 79.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CALM2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.80e-09  1.40e-05  3.30e-05  1.57e-01  8.43e-01 \n",
+      "[1] \"PP abf for shared variant: 84.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-20  2.90e-16  3.69e-05  1.76e-01  8.24e-01 \n",
+      "[1] \"PP abf for shared variant: 82.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-ND2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-05  9.48e-02  7.96e-05  3.93e-01  5.12e-01 \n",
+      "[1] \"PP abf for shared variant: 51.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-19  1.73e-15  3.91e-05  1.88e-01  8.12e-01 \n",
+      "[1] \"PP abf for shared variant: 81.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD52__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-08  8.42e-05  3.17e-05  1.50e-01  8.50e-01 \n",
+      "[1] \"PP abf for shared variant: 85%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.19e-12  2.09e-08  4.11e-05  1.98e-01  8.02e-01 \n",
+      "[1] \"PP abf for shared variant: 80.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___H3F3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.92e-10  3.46e-06  1.56e-05  6.86e-02  9.31e-01 \n",
+      "[1] \"PP abf for shared variant: 93.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ADTRP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-12  6.18e-09  3.71e-05  1.77e-01  8.23e-01 \n",
+      "[1] \"PP abf for shared variant: 82.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-06  1.53e-02  1.52e-05  6.66e-02  9.18e-01 \n",
+      "[1] \"PP abf for shared variant: 91.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-06  1.32e-02  4.25e-05  2.05e-01  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-09  1.09e-05  2.21e-05  1.01e-01  8.99e-01 \n",
+      "[1] \"PP abf for shared variant: 89.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-09  6.14e-06  4.44e-05  2.14e-01  7.86e-01 \n",
+      "[1] \"PP abf for shared variant: 78.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___DYNLL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.93e-08  2.46e-04  2.77e-05  1.30e-01  8.70e-01 \n",
+      "[1] \"PP abf for shared variant: 87%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAMSN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-09  6.82e-06  3.77e-05  1.80e-01  8.20e-01 \n",
+      "[1] \"PP abf for shared variant: 82%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LMNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.91e-14  2.95e-10  3.93e-05  1.88e-01  8.12e-01 \n",
+      "[1] \"PP abf for shared variant: 81.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-ND5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.49e-12  4.24e-08  4.67e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-26  2.39e-22  4.46e-05  2.15e-01  7.85e-01 \n",
+      "[1] \"PP abf for shared variant: 78.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RUNX3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-06  1.56e-02  1.96e-05  8.91e-02  8.95e-01 \n",
+      "[1] \"PP abf for shared variant: 89.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-22  9.99e-19  4.97e-05  2.41e-01  7.59e-01 \n",
+      "[1] \"PP abf for shared variant: 75.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-10  1.11e-06  1.97e-05  8.94e-02  9.11e-01 \n",
+      "[1] \"PP abf for shared variant: 91.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ERGIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.423e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.44e-05  2.72e-01  6.06e-05  2.99e-01  4.29e-01 \n",
+      "[1] \"PP abf for shared variant: 42.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-12  5.87e-09  3.89e-05  1.86e-01  8.14e-01 \n",
+      "[1] \"PP abf for shared variant: 81.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TYMP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-06  8.07e-03  3.31e-05  1.57e-01  8.35e-01 \n",
+      "[1] \"PP abf for shared variant: 83.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-06  1.30e-02  1.63e-05  7.23e-02  9.15e-01 \n",
+      "[1] \"PP abf for shared variant: 91.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.08e-27  4.54e-23  4.98e-05  2.41e-01  7.59e-01 \n",
+      "[1] \"PP abf for shared variant: 75.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-08  5.90e-05  3.77e-05  1.80e-01  8.19e-01 \n",
+      "[1] \"PP abf for shared variant: 81.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-27  1.84e-23  4.77e-05  2.31e-01  7.69e-01 \n",
+      "[1] \"PP abf for shared variant: 76.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.70e-25  1.85e-21  4.73e-05  2.29e-01  7.71e-01 \n",
+      "[1] \"PP abf for shared variant: 77.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-ND4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.35e-05  1.18e-01  4.07e-05  1.96e-01  6.86e-01 \n",
+      "[1] \"PP abf for shared variant: 68.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-09  2.89e-05  1.02e-05  4.15e-02  9.58e-01 \n",
+      "[1] \"PP abf for shared variant: 95.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ATP5E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-07  7.52e-04  3.56e-05  1.69e-01  8.30e-01 \n",
+      "[1] \"PP abf for shared variant: 83%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-15  1.93e-11  3.49e-05  1.66e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.59e-17  1.79e-13  5.21e-05  2.53e-01  7.47e-01 \n",
+      "[1] \"PP abf for shared variant: 74.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-26  1.77e-22  4.27e-05  2.05e-01  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-13  7.49e-10  4.32e-05  2.08e-01  7.92e-01 \n",
+      "[1] \"PP abf for shared variant: 79.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ATP2B4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.83e-07  4.91e-03  1.99e-05  9.03e-02  9.05e-01 \n",
+      "[1] \"PP abf for shared variant: 90.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ARPC1B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-05  9.91e-02  1.59e-05  7.11e-02  8.30e-01 \n",
+      "[1] \"PP abf for shared variant: 83%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PDCD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-07  1.08e-03  5.18e-05  2.51e-01  7.48e-01 \n",
+      "[1] \"PP abf for shared variant: 74.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.19e-25  2.59e-21  4.97e-05  2.41e-01  7.59e-01 \n",
+      "[1] \"PP abf for shared variant: 75.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.52e-31  3.26e-27  3.50e-05  1.67e-01  8.33e-01 \n",
+      "[1] \"PP abf for shared variant: 83.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-26  7.01e-23  4.58e-05  2.21e-01  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-17  9.10e-14  4.84e-05  2.34e-01  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-12  8.22e-09  4.83e-05  2.34e-01  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-09  6.67e-06  1.86e-05  8.37e-02  9.16e-01 \n",
+      "[1] \"PP abf for shared variant: 91.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.44e-09  4.72e-05  3.70e-05  1.77e-01  8.23e-01 \n",
+      "[1] \"PP abf for shared variant: 82.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LGALS3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-05  5.66e-02  3.41e-05  1.62e-01  7.81e-01 \n",
+      "[1] \"PP abf for shared variant: 78.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MIAT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.60e-07  1.80e-03  1.89e-05  8.54e-02  9.13e-01 \n",
+      "[1] \"PP abf for shared variant: 91.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.17e-25  1.08e-21  4.83e-05  2.34e-01  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-12  8.55e-09  4.07e-05  1.95e-01  8.05e-01 \n",
+      "[1] \"PP abf for shared variant: 80.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.24e-07  2.62e-03  2.37e-05  1.10e-01  8.88e-01 \n",
+      "[1] \"PP abf for shared variant: 88.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-21  5.62e-18  4.67e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-27  1.14e-23  4.77e-05  2.31e-01  7.69e-01 \n",
+      "[1] \"PP abf for shared variant: 76.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RNF19A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-08  1.59e-04  1.50e-05  6.58e-02  9.34e-01 \n",
+      "[1] \"PP abf for shared variant: 93.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-CO3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-11  1.03e-07  4.79e-05  2.32e-01  7.68e-01 \n",
+      "[1] \"PP abf for shared variant: 76.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.26e-29  3.63e-25  4.76e-05  2.30e-01  7.70e-01 \n",
+      "[1] \"PP abf for shared variant: 77%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.06e-16  3.53e-12  3.96e-05  1.90e-01  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FAS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.05e-06  3.52e-02  2.65e-05  1.24e-01  8.40e-01 \n",
+      "[1] \"PP abf for shared variant: 84%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.75e-14  1.87e-10  4.05e-05  1.94e-01  8.06e-01 \n",
+      "[1] \"PP abf for shared variant: 80.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.86e-14  1.93e-10  4.52e-05  2.18e-01  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GYPC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-10  1.88e-06  4.15e-05  2.00e-01  8.00e-01 \n",
+      "[1] \"PP abf for shared variant: 80%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.27e-27  3.63e-23  4.27e-05  2.05e-01  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.18e-29  3.59e-25  4.01e-05  1.92e-01  8.08e-01 \n",
+      "[1] \"PP abf for shared variant: 80.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPM4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-06  5.66e-03  3.64e-05  1.74e-01  8.20e-01 \n",
+      "[1] \"PP abf for shared variant: 82%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.31e-16  2.15e-12  4.78e-05  2.31e-01  7.69e-01 \n",
+      "[1] \"PP abf for shared variant: 76.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-09  6.97e-06  1.65e-05  7.31e-02  9.27e-01 \n",
+      "[1] \"PP abf for shared variant: 92.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-27  9.95e-24  4.37e-05  2.11e-01  7.89e-01 \n",
+      "[1] \"PP abf for shared variant: 78.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ITGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-11  7.22e-08  3.86e-05  1.85e-01  8.15e-01 \n",
+      "[1] \"PP abf for shared variant: 81.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-11  8.06e-08  3.35e-05  1.59e-01  8.41e-01 \n",
+      "[1] \"PP abf for shared variant: 84.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.75e-07  2.87e-03  1.95e-05  8.82e-02  9.09e-01 \n",
+      "[1] \"PP abf for shared variant: 90.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.23e-31  4.61e-27  4.32e-05  2.08e-01  7.92e-01 \n",
+      "[1] \"PP abf for shared variant: 79.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-06  5.55e-03  8.46e-06  3.27e-02  9.62e-01 \n",
+      "[1] \"PP abf for shared variant: 96.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-22  1.45e-18  5.00e-05  2.43e-01  7.57e-01 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.76e-08  3.38e-04  3.95e-05  1.89e-01  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.70e-08  1.85e-04  2.12e-05  9.69e-02  9.03e-01 \n",
+      "[1] \"PP abf for shared variant: 90.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-27  1.03e-23  4.90e-05  2.37e-01  7.63e-01 \n",
+      "[1] \"PP abf for shared variant: 76.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.49e-21  2.24e-17  4.32e-05  2.08e-01  7.92e-01 \n",
+      "[1] \"PP abf for shared variant: 79.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.04e-06  1.52e-02  3.96e-05  1.90e-01  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PPP1R15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-05  8.94e-02  1.90e-05  8.68e-02  8.24e-01 \n",
+      "[1] \"PP abf for shared variant: 82.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___IL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-08  1.12e-04  2.64e-05  1.23e-01  8.77e-01 \n",
+      "[1] \"PP abf for shared variant: 87.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-30  1.13e-26  4.04e-05  1.94e-01  8.06e-01 \n",
+      "[1] \"PP abf for shared variant: 80.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.07e-20  1.04e-16  4.90e-05  2.37e-01  7.63e-01 \n",
+      "[1] \"PP abf for shared variant: 76.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM20\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-05  7.49e-02  1.88e-05  8.57e-02  8.39e-01 \n",
+      "[1] \"PP abf for shared variant: 83.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF3F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.56e-08  2.78e-04  5.69e-05  2.77e-01  7.22e-01 \n",
+      "[1] \"PP abf for shared variant: 72.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.69e-07  2.85e-03  1.28e-04  6.34e-01  3.63e-01 \n",
+      "[1] \"PP abf for shared variant: 36.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___KLF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.00e-10  3.00e-06  3.59e-05  1.71e-01  8.29e-01 \n",
+      "[1] \"PP abf for shared variant: 82.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GIMAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-08  1.38e-04  3.14e-05  1.48e-01  8.51e-01 \n",
+      "[1] \"PP abf for shared variant: 85.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFBR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-06  8.75e-03  2.47e-05  1.15e-01  8.76e-01 \n",
+      "[1] \"PP abf for shared variant: 87.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RNF213__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-05  7.46e-02  2.97e-05  1.40e-01  7.85e-01 \n",
+      "[1] \"PP abf for shared variant: 78.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___C19orf53__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.04e-05  2.02e-01  3.53e-05  1.70e-01  6.28e-01 \n",
+      "[1] \"PP abf for shared variant: 62.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-15  8.03e-12  1.49e-05  6.54e-02  9.35e-01 \n",
+      "[1] \"PP abf for shared variant: 93.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-19  7.99e-16  4.18e-05  2.01e-01  7.99e-01 \n",
+      "[1] \"PP abf for shared variant: 79.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MIR4435-1HG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.59e-15  3.29e-11  1.16e-05  4.84e-02  9.52e-01 \n",
+      "[1] \"PP abf for shared variant: 95.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.65e-20  4.32e-16  4.74e-05  2.29e-01  7.71e-01 \n",
+      "[1] \"PP abf for shared variant: 77.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MZT2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-08  1.14e-04  4.37e-05  2.11e-01  7.89e-01 \n",
+      "[1] \"PP abf for shared variant: 78.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___AK5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.38e-06  2.69e-02  5.31e-05  2.58e-01  7.15e-01 \n",
+      "[1] \"PP abf for shared variant: 71.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-05  5.85e-02  2.99e-05  1.41e-01  8.00e-01 \n",
+      "[1] \"PP abf for shared variant: 80%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.87e-13  3.43e-09  3.67e-05  1.75e-01  8.25e-01 \n",
+      "[1] \"PP abf for shared variant: 82.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-24  5.74e-21  4.94e-05  2.39e-01  7.61e-01 \n",
+      "[1] \"PP abf for shared variant: 76.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-08  5.20e-05  3.73e-05  1.78e-01  8.22e-01 \n",
+      "[1] \"PP abf for shared variant: 82.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.16e-23  1.58e-19  3.77e-05  1.80e-01  8.20e-01 \n",
+      "[1] \"PP abf for shared variant: 82%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.08e-07  4.54e-03  3.01e-05  1.42e-01  8.54e-01 \n",
+      "[1] \"PP abf for shared variant: 85.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CXCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-05  6.05e-02  2.07e-05  9.49e-02  8.45e-01 \n",
+      "[1] \"PP abf for shared variant: 84.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ARPC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-06  1.77e-02  2.35e-05  1.09e-01  8.73e-01 \n",
+      "[1] \"PP abf for shared variant: 87.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MRPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-05  1.92e-01  3.65e-05  1.76e-01  6.31e-01 \n",
+      "[1] \"PP abf for shared variant: 63.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.31e-20  3.15e-16  3.31e-05  1.57e-01  8.43e-01 \n",
+      "[1] \"PP abf for shared variant: 84.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.54e-11  4.27e-07  3.90e-05  1.87e-01  8.13e-01 \n",
+      "[1] \"PP abf for shared variant: 81.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EBPL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.43e-05  1.72e-01  4.52e-05  2.20e-01  6.08e-01 \n",
+      "[1] \"PP abf for shared variant: 60.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-05  8.90e-02  6.17e-05  3.02e-01  6.08e-01 \n",
+      "[1] \"PP abf for shared variant: 60.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UXT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.35e-13  3.17e-09  3.89e-05  1.86e-01  8.14e-01 \n",
+      "[1] \"PP abf for shared variant: 81.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LSM5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.71e-09  2.35e-05  1.21e-05  5.11e-02  9.49e-01 \n",
+      "[1] \"PP abf for shared variant: 94.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___KMT2E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.6569e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.56e-05  2.28e-01  3.45e-05  1.67e-01  6.05e-01 \n",
+      "[1] \"PP abf for shared variant: 60.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.91e-12  4.95e-08  5.58e-05  2.71e-01  7.28e-01 \n",
+      "[1] \"PP abf for shared variant: 72.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TAGLN2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.42e-05  1.21e-01  2.21e-05  1.03e-01  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CDCA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4164e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-05  1.72e-01  2.39e-05  1.12e-01  7.15e-01 \n",
+      "[1] \"PP abf for shared variant: 71.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-10  1.09e-06  1.33e-04  6.61e-01  3.39e-01 \n",
+      "[1] \"PP abf for shared variant: 33.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EPB41L4A-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.65e-11  4.32e-07  4.35e-05  2.10e-01  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-12  1.13e-08  3.25e-05  1.54e-01  8.46e-01 \n",
+      "[1] \"PP abf for shared variant: 84.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TATDN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-06  6.29e-03  4.12e-05  1.98e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-06  1.01e-02  2.70e-05  1.27e-01  8.63e-01 \n",
+      "[1] \"PP abf for shared variant: 86.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-17  8.06e-14  4.73e-05  2.29e-01  7.71e-01 \n",
+      "[1] \"PP abf for shared variant: 77.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.96e-28  4.48e-24  4.33e-05  2.09e-01  7.91e-01 \n",
+      "[1] \"PP abf for shared variant: 79.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.37e-25  3.18e-21  3.37e-05  1.60e-01  8.40e-01 \n",
+      "[1] \"PP abf for shared variant: 84%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___BTG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-08  8.56e-05  3.95e-05  1.89e-01  8.11e-01 \n",
+      "[1] \"PP abf for shared variant: 81.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___C8orf59__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-06  8.48e-03  4.81e-05  2.33e-01  7.59e-01 \n",
+      "[1] \"PP abf for shared variant: 75.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD58__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-07  5.08e-04  1.29e-05  5.51e-02  9.44e-01 \n",
+      "[1] \"PP abf for shared variant: 94.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.04e-22  4.02e-18  4.96e-05  2.40e-01  7.60e-01 \n",
+      "[1] \"PP abf for shared variant: 76%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-10  1.13e-06  3.87e-05  1.85e-01  8.15e-01 \n",
+      "[1] \"PP abf for shared variant: 81.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___AKAP13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-07  1.92e-03  1.00e-05  4.06e-02  9.57e-01 \n",
+      "[1] \"PP abf for shared variant: 95.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-11  7.34e-08  3.77e-05  1.80e-01  8.20e-01 \n",
+      "[1] \"PP abf for shared variant: 82%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___DDX5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-09  7.28e-06  3.91e-05  1.87e-01  8.13e-01 \n",
+      "[1] \"PP abf for shared variant: 81.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-06  6.84e-03  4.30e-05  2.07e-01  7.86e-01 \n",
+      "[1] \"PP abf for shared variant: 78.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ANXA2R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.82e-06  3.41e-02  1.59e-05  7.05e-02  8.95e-01 \n",
+      "[1] \"PP abf for shared variant: 89.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___IL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-05  9.49e-02  4.68e-05  2.27e-01  6.78e-01 \n",
+      "[1] \"PP abf for shared variant: 67.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LINC00152__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.78e-13  4.39e-09  5.16e-05  2.50e-01  7.50e-01 \n",
+      "[1] \"PP abf for shared variant: 75%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FOXP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.09e-07  2.04e-03  8.14e-06  3.10e-02  9.67e-01 \n",
+      "[1] \"PP abf for shared variant: 96.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.96e-13  1.98e-09  4.00e-05  1.92e-01  8.08e-01 \n",
+      "[1] \"PP abf for shared variant: 80.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.83e-26  3.91e-22  5.14e-05  2.50e-01  7.50e-01 \n",
+      "[1] \"PP abf for shared variant: 75%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.82e-09  1.91e-05  3.66e-05  1.74e-01  8.25e-01 \n",
+      "[1] \"PP abf for shared variant: 82.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.15e-21  2.57e-17  4.62e-05  2.23e-01  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.35e-19  3.68e-15  4.81e-05  2.33e-01  7.67e-01 \n",
+      "[1] \"PP abf for shared variant: 76.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___DAP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-08  1.40e-04  3.61e-05  1.72e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.51e-16  2.76e-12  3.58e-05  1.71e-01  8.29e-01 \n",
+      "[1] \"PP abf for shared variant: 82.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SVIP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-05  1.41e-01  3.79e-05  1.83e-01  6.77e-01 \n",
+      "[1] \"PP abf for shared variant: 67.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-19  6.47e-16  4.94e-05  2.39e-01  7.61e-01 \n",
+      "[1] \"PP abf for shared variant: 76.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-31  8.44e-28  4.50e-05  2.17e-01  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.90e-23  4.95e-19  4.93e-05  2.39e-01  7.61e-01 \n",
+      "[1] \"PP abf for shared variant: 76.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.38e-12  4.69e-08  4.04e-05  1.94e-01  8.06e-01 \n",
+      "[1] \"PP abf for shared variant: 80.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-06  6.17e-03  3.51e-05  1.67e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ENTPD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-06  1.56e-02  1.75e-05  7.82e-02  9.06e-01 \n",
+      "[1] \"PP abf for shared variant: 90.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___DUSP4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-14  6.53e-11  3.68e-05  1.76e-01  8.24e-01 \n",
+      "[1] \"PP abf for shared variant: 82.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.90e-19  4.95e-15  4.61e-05  2.23e-01  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-08  1.84e-04  1.54e-05  6.76e-02  9.32e-01 \n",
+      "[1] \"PP abf for shared variant: 93.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.83e-06  4.92e-02  4.04e-05  1.94e-01  7.57e-01 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-ND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-07  8.25e-04  8.15e-05  4.01e-01  5.98e-01 \n",
+      "[1] \"PP abf for shared variant: 59.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.07e-21  2.04e-17  4.35e-05  2.10e-01  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ADAM19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.40e-06  3.20e-02  1.23e-05  5.23e-02  9.16e-01 \n",
+      "[1] \"PP abf for shared variant: 91.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CLDND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-05  8.14e-02  3.57e-05  1.71e-01  7.47e-01 \n",
+      "[1] \"PP abf for shared variant: 74.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.50e-11  2.25e-07  4.64e-05  2.24e-01  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.98e-14  1.99e-10  3.63e-05  1.73e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-05  5.46e-02  3.55e-05  1.70e-01  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___APEX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-05  5.26e-02  2.06e-05  9.44e-02  8.53e-01 \n",
+      "[1] \"PP abf for shared variant: 85.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-12  6.19e-09  4.62e-05  2.23e-01  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.91e-26  1.95e-22  4.30e-05  2.07e-01  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LETMD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.66e-08  2.83e-04  4.45e-05  2.15e-01  7.85e-01 \n",
+      "[1] \"PP abf for shared variant: 78.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-08  1.93e-04  1.45e-05  6.32e-02  9.37e-01 \n",
+      "[1] \"PP abf for shared variant: 93.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.08e-11  1.54e-07  3.55e-05  1.69e-01  8.31e-01 \n",
+      "[1] \"PP abf for shared variant: 83.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___AHNAK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.34e-07  3.67e-03  5.39e-05  2.62e-01  7.34e-01 \n",
+      "[1] \"PP abf for shared variant: 73.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SLC7A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.58e-05  2.29e-01  4.18e-05  2.03e-01  5.68e-01 \n",
+      "[1] \"PP abf for shared variant: 56.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-14  1.23e-10  3.48e-05  1.66e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.75e-25  4.37e-21  4.59e-05  2.22e-01  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.21e-09  3.10e-05  8.89e-06  3.48e-02  9.65e-01 \n",
+      "[1] \"PP abf for shared variant: 96.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.54e-32  1.77e-28  4.50e-05  2.17e-01  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.33e-17  1.16e-13  3.78e-05  1.81e-01  8.19e-01 \n",
+      "[1] \"PP abf for shared variant: 81.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-29  7.24e-26  3.74e-05  1.79e-01  8.21e-01 \n",
+      "[1] \"PP abf for shared variant: 82.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-ATP6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-09  1.03e-05  5.28e-05  2.57e-01  7.43e-01 \n",
+      "[1] \"PP abf for shared variant: 74.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-22  6.30e-19  4.41e-05  2.13e-01  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-10  7.38e-07  4.57e-05  2.21e-01  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RILPL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-05  8.84e-02  5.86e-05  2.86e-01  6.25e-01 \n",
+      "[1] \"PP abf for shared variant: 62.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SSR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-09  7.85e-06  4.20e-05  2.02e-01  7.98e-01 \n",
+      "[1] \"PP abf for shared variant: 79.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-07  6.52e-04  3.69e-05  1.76e-01  8.23e-01 \n",
+      "[1] \"PP abf for shared variant: 82.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-13  1.05e-09  4.22e-05  2.03e-01  7.97e-01 \n",
+      "[1] \"PP abf for shared variant: 79.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.08e-18  2.04e-14  4.78e-05  2.31e-01  7.69e-01 \n",
+      "[1] \"PP abf for shared variant: 76.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MAF__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-10  2.05e-06  3.53e-05  1.68e-01  8.32e-01 \n",
+      "[1] \"PP abf for shared variant: 83.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-14  5.20e-11  3.11e-05  1.47e-01  8.53e-01 \n",
+      "[1] \"PP abf for shared variant: 85.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___COMMD6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-08  1.05e-04  4.14e-05  1.99e-01  8.01e-01 \n",
+      "[1] \"PP abf for shared variant: 80.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-11  1.29e-07  2.90e-05  1.36e-01  8.64e-01 \n",
+      "[1] \"PP abf for shared variant: 86.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NSMCE1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-07  1.08e-03  4.29e-05  2.06e-01  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-09  9.88e-06  3.76e-05  1.80e-01  8.20e-01 \n",
+      "[1] \"PP abf for shared variant: 82%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PRDX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-06  1.05e-02  2.23e-05  1.03e-01  8.87e-01 \n",
+      "[1] \"PP abf for shared variant: 88.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.40e-24  1.20e-20  3.59e-05  1.71e-01  8.29e-01 \n",
+      "[1] \"PP abf for shared variant: 82.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FAM46C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.27e-07  4.63e-03  3.08e-05  1.45e-01  8.50e-01 \n",
+      "[1] \"PP abf for shared variant: 85%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-26  2.17e-22  4.94e-05  2.39e-01  7.60e-01 \n",
+      "[1] \"PP abf for shared variant: 76%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.66e-26  3.83e-22  4.75e-05  2.30e-01  7.70e-01 \n",
+      "[1] \"PP abf for shared variant: 77%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-31  1.29e-27  3.65e-05  1.74e-01  8.26e-01 \n",
+      "[1] \"PP abf for shared variant: 82.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.29e-28  3.64e-24  5.01e-05  2.43e-01  7.57e-01 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RORA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-05  1.23e-01  3.57e-05  1.72e-01  7.06e-01 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-08  5.89e-05  3.38e-05  1.61e-01  8.39e-01 \n",
+      "[1] \"PP abf for shared variant: 83.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.28e-23  6.42e-20  3.61e-05  1.72e-01  8.28e-01 \n",
+      "[1] \"PP abf for shared variant: 82.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD44__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.52e-10  3.76e-06  4.40e-05  2.12e-01  7.88e-01 \n",
+      "[1] \"PP abf for shared variant: 78.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4Y1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-05  1.26e-01  2.82e-05  1.34e-01  7.41e-01 \n",
+      "[1] \"PP abf for shared variant: 74.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.60e-10  3.80e-06  4.30e-05  2.07e-01  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___COX7A2L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-06  1.15e-02  4.04e-05  1.94e-01  7.94e-01 \n",
+      "[1] \"PP abf for shared variant: 79.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-25  5.66e-22  4.59e-05  2.21e-01  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HADHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-06  1.97e-02  4.14e-05  1.99e-01  7.81e-01 \n",
+      "[1] \"PP abf for shared variant: 78.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SATB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.41e-06  3.20e-02  2.73e-05  1.28e-01  8.40e-01 \n",
+      "[1] \"PP abf for shared variant: 84%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UGP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-06  7.44e-03  3.82e-05  1.83e-01  8.09e-01 \n",
+      "[1] \"PP abf for shared variant: 80.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SBDS\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.80e-06  2.40e-02  1.29e-05  5.53e-02  9.21e-01 \n",
+      "[1] \"PP abf for shared variant: 92.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SYNE2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-07  1.36e-03  4.65e-05  2.25e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMA7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-06  5.12e-03  1.27e-05  5.43e-02  9.41e-01 \n",
+      "[1] \"PP abf for shared variant: 94.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.00e-14  3.50e-10  4.54e-05  2.19e-01  7.81e-01 \n",
+      "[1] \"PP abf for shared variant: 78.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NR3C1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-06  8.41e-03  3.01e-05  1.42e-01  8.50e-01 \n",
+      "[1] \"PP abf for shared variant: 85%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-31  1.35e-27  3.91e-05  1.88e-01  8.12e-01 \n",
+      "[1] \"PP abf for shared variant: 81.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CCT8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.72e-07  4.86e-03  4.40e-05  2.12e-01  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFAIP3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-06  1.84e-02  7.08e-05  3.47e-01  6.34e-01 \n",
+      "[1] \"PP abf for shared variant: 63.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH2D2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-05  1.60e-01  4.67e-05  2.27e-01  6.13e-01 \n",
+      "[1] \"PP abf for shared variant: 61.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-15  6.98e-12  3.48e-05  1.65e-01  8.35e-01 \n",
+      "[1] \"PP abf for shared variant: 83.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CLNS1A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.10e-07  1.55e-03  6.03e-05  2.94e-01  7.04e-01 \n",
+      "[1] \"PP abf for shared variant: 70.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-12  5.01e-09  4.20e-05  2.02e-01  7.98e-01 \n",
+      "[1] \"PP abf for shared variant: 79.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ATP6V0E1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.53e-06  2.26e-02  9.01e-05  4.45e-01  5.32e-01 \n",
+      "[1] \"PP abf for shared variant: 53.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.54e-31  3.27e-27  4.92e-05  2.39e-01  7.61e-01 \n",
+      "[1] \"PP abf for shared variant: 76.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.35e-05  1.17e-01  2.96e-05  1.40e-01  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.87e-32  4.44e-28  5.08e-05  2.47e-01  7.53e-01 \n",
+      "[1] \"PP abf for shared variant: 75.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36L2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-06  9.49e-03  3.29e-05  1.56e-01  8.35e-01 \n",
+      "[1] \"PP abf for shared variant: 83.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF3D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.83e-09  2.91e-05  2.46e-05  1.14e-01  8.86e-01 \n",
+      "[1] \"PP abf for shared variant: 88.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-06  1.05e-02  2.10e-05  9.59e-02  8.94e-01 \n",
+      "[1] \"PP abf for shared variant: 89.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-23  9.59e-20  4.46e-05  2.15e-01  7.85e-01 \n",
+      "[1] \"PP abf for shared variant: 78.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___APRT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.95e-07  2.47e-03  6.46e-05  3.16e-01  6.81e-01 \n",
+      "[1] \"PP abf for shared variant: 68.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FYN__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.23e-08  4.61e-04  2.26e-05  1.04e-01  8.96e-01 \n",
+      "[1] \"PP abf for shared variant: 89.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ANP32B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-06  1.33e-02  2.87e-05  1.35e-01  8.52e-01 \n",
+      "[1] \"PP abf for shared variant: 85.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-06  8.61e-03  4.24e-05  2.04e-01  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-08  8.62e-05  2.89e-05  1.36e-01  8.64e-01 \n",
+      "[1] \"PP abf for shared variant: 86.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.43e-32  4.71e-28  4.05e-05  1.94e-01  8.06e-01 \n",
+      "[1] \"PP abf for shared variant: 80.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CMPK1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-09  1.04e-05  4.30e-05  2.07e-01  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAZ\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.64e-06  2.32e-02  2.65e-05  1.24e-01  8.53e-01 \n",
+      "[1] \"PP abf for shared variant: 85.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GIMAP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-12  8.55e-09  3.87e-05  1.85e-01  8.15e-01 \n",
+      "[1] \"PP abf for shared variant: 81.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___COTL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-10  1.05e-06  3.81e-05  1.82e-01  8.18e-01 \n",
+      "[1] \"PP abf for shared variant: 81.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF2S3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.88e-15  1.94e-11  4.68e-05  2.26e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HSP90AA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1807e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-05  1.72e-01  3.40e-05  1.63e-01  6.65e-01 \n",
+      "[1] \"PP abf for shared variant: 66.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MT-CYB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-08  6.59e-05  6.98e-05  3.42e-01  6.58e-01 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HSPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-05  8.86e-02  4.28e-05  2.07e-01  7.04e-01 \n",
+      "[1] \"PP abf for shared variant: 70.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-14  8.44e-11  3.62e-05  1.73e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.34e-13  6.72e-10  4.37e-05  2.10e-01  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.57e-22  2.28e-18  4.40e-05  2.12e-01  7.88e-01 \n",
+      "[1] \"PP abf for shared variant: 78.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXK\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-09  1.03e-05  4.69e-05  2.27e-01  7.73e-01 \n",
+      "[1] \"PP abf for shared variant: 77.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.79e-21  4.39e-17  3.96e-05  1.90e-01  8.10e-01 \n",
+      "[1] \"PP abf for shared variant: 81%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-18  1.11e-14  3.80e-05  1.82e-01  8.18e-01 \n",
+      "[1] \"PP abf for shared variant: 81.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.85e-11  4.43e-07  4.63e-05  2.24e-01  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-09  6.48e-06  3.43e-05  1.63e-01  8.37e-01 \n",
+      "[1] \"PP abf for shared variant: 83.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.50e-06  1.25e-02  3.49e-05  1.66e-01  8.21e-01 \n",
+      "[1] \"PP abf for shared variant: 82.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___LYRM4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-05  7.30e-02  3.81e-05  1.83e-01  7.44e-01 \n",
+      "[1] \"PP abf for shared variant: 74.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-24  6.53e-21  5.87e-05  2.86e-01  7.13e-01 \n",
+      "[1] \"PP abf for shared variant: 71.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.42e-24  2.21e-20  4.47e-05  2.16e-01  7.84e-01 \n",
+      "[1] \"PP abf for shared variant: 78.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-14  7.31e-11  4.62e-05  2.23e-01  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EZR__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.30e-08  4.15e-04  2.96e-05  1.39e-01  8.60e-01 \n",
+      "[1] \"PP abf for shared variant: 86%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.13e-08  2.06e-04  4.24e-05  2.04e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ATP5O__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.59e-13  2.30e-09  3.62e-05  1.73e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.74e-06  1.87e-02  4.13e-05  1.99e-01  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.56e-24  2.28e-20  4.33e-05  2.09e-01  7.91e-01 \n",
+      "[1] \"PP abf for shared variant: 79.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUCLG2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-08  7.52e-05  5.00e-05  2.42e-01  7.57e-01 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CD3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.36e-06  1.18e-02  4.26e-05  2.05e-01  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-24  6.96e-21  4.40e-05  2.12e-01  7.88e-01 \n",
+      "[1] \"PP abf for shared variant: 78.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___NSA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.76e-12  2.88e-08  4.63e-05  2.24e-01  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-06  5.22e-03  1.70e-05  7.56e-02  9.19e-01 \n",
+      "[1] \"PP abf for shared variant: 91.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___HIGD2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.05e-06  2.53e-02  2.87e-05  1.35e-01  8.40e-01 \n",
+      "[1] \"PP abf for shared variant: 84%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-13  6.10e-10  3.84e-05  1.84e-01  8.16e-01 \n",
+      "[1] \"PP abf for shared variant: 81.6%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___IGBP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.68e-08  3.84e-04  2.17e-05  9.92e-02  9.00e-01 \n",
+      "[1] \"PP abf for shared variant: 90%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-23  7.18e-20  4.71e-05  2.28e-01  7.72e-01 \n",
+      "[1] \"PP abf for shared variant: 77.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___MYH9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.90e-05  3.45e-01  4.31e-05  2.11e-01  4.44e-01 \n",
+      "[1] \"PP abf for shared variant: 44.4%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.32e-12  1.66e-08  4.84e-05  2.35e-01  7.65e-01 \n",
+      "[1] \"PP abf for shared variant: 76.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___ATP2B1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.91e-08  9.56e-05  1.57e-05  6.92e-02  9.31e-01 \n",
+      "[1] \"PP abf for shared variant: 93.1%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.14e-33  4.57e-29  6.17e-05  3.02e-01  6.98e-01 \n",
+      "[1] \"PP abf for shared variant: 69.8%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RBM39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-07  1.16e-03  3.57e-05  1.70e-01  8.29e-01 \n",
+      "[1] \"PP abf for shared variant: 82.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___CCNG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.01e-08  3.00e-04  2.18e-05  1.00e-01  8.99e-01 \n",
+      "[1] \"PP abf for shared variant: 89.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-20  7.23e-17  3.67e-05  1.75e-01  8.25e-01 \n",
+      "[1] \"PP abf for shared variant: 82.5%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___COX4I1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.41e-07  1.21e-03  3.90e-05  1.87e-01  8.12e-01 \n",
+      "[1] \"PP abf for shared variant: 81.2%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___PMAIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-06  9.86e-03  3.43e-05  1.63e-01  8.27e-01 \n",
+      "[1] \"PP abf for shared variant: 82.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-15  6.79e-12  3.42e-05  1.63e-01  8.37e-01 \n",
+      "[1] \"PP abf for shared variant: 83.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-11  5.60e-08  2.24e-05  1.03e-01  8.97e-01 \n",
+      "[1] \"PP abf for shared variant: 89.7%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___FHIT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-14  8.84e-11  4.58e-05  2.21e-01  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Rheumatoid Arthritis\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.39e-10  4.19e-06  3.83e-05  1.83e-01  8.17e-01 \n",
+      "[1] \"PP abf for shared variant: 81.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___CAPG__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05570   0.00983   0.79300   0.14000   0.00200 \n",
+      "[1] \"PP abf for shared variant: 0.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___PTAFR__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.15400   0.02720   0.69400   0.12300   0.00157 \n",
+      "[1] \"PP abf for shared variant: 0.157%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___MNDA__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.5916e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.63400   0.11200   0.21500   0.03790   0.00128 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___RNASE6__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.33100   0.05830   0.51800   0.09140   0.00151 \n",
+      "[1] \"PP abf for shared variant: 0.151%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__TSPO\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.39300   0.06930   0.45600   0.08050   0.00155 \n",
+      "[1] \"PP abf for shared variant: 0.155%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__VMO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5549e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.6240    0.1100    0.2250    0.0396    0.0022 \n",
+      "[1] \"PP abf for shared variant: 0.22%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___S100A9__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.38000   0.06700   0.46900   0.08270   0.00204 \n",
+      "[1] \"PP abf for shared variant: 0.204%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___QPCT__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.8504e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5760    0.1020    0.2730    0.0481    0.0014 \n",
+      "[1] \"PP abf for shared variant: 0.14%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___BLVRB__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1205e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.55300   0.09760   0.29600   0.05220   0.00129 \n",
+      "[1] \"PP abf for shared variant: 0.129%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___LYZ__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03710   0.00654   0.81100   0.14300   0.00182 \n",
+      "[1] \"PP abf for shared variant: 0.182%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_TMEM176A___CLEC4A__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.64800   0.11400   0.20100   0.03550   0.00117 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.018200  0.000312  0.964000  0.016500  0.000702 \n",
+      "[1] \"PP abf for shared variant: 0.0702%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL5__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.373000  0.006390  0.609000  0.010400  0.000468 \n",
+      "[1] \"PP abf for shared variant: 0.0468%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.226000  0.003870  0.757000  0.012900  0.000565 \n",
+      "[1] \"PP abf for shared variant: 0.0565%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL32__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.022100  0.000377  0.960000  0.016400  0.000693 \n",
+      "[1] \"PP abf for shared variant: 0.0693%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___EEF1A1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.415000  0.007100  0.568000  0.009710  0.000455 \n",
+      "[1] \"PP abf for shared variant: 0.0455%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL38__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.112000  0.001920  0.870000  0.014900  0.000635 \n",
+      "[1] \"PP abf for shared variant: 0.0635%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.15000   0.00257   0.83200   0.01420   0.00062 \n",
+      "[1] \"PP abf for shared variant: 0.062%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.218000  0.003730  0.764000  0.013100  0.000573 \n",
+      "[1] \"PP abf for shared variant: 0.0573%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.428000  0.007330  0.554000  0.009480  0.000444 \n",
+      "[1] \"PP abf for shared variant: 0.0444%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.399000  0.006830  0.583000  0.009970  0.000463 \n",
+      "[1] \"PP abf for shared variant: 0.0463%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS15A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.16000   0.00273   0.82300   0.01410   0.00062 \n",
+      "[1] \"PP abf for shared variant: 0.062%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS8__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.176000  0.003020  0.806000  0.013800  0.000598 \n",
+      "[1] \"PP abf for shared variant: 0.0598%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.400000  0.006840  0.583000  0.009960  0.000452 \n",
+      "[1] \"PP abf for shared variant: 0.0452%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.008350  0.000143  0.974000  0.016700  0.000694 \n",
+      "[1] \"PP abf for shared variant: 0.0694%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___NKG7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.253000  0.004330  0.730000  0.012500  0.000538 \n",
+      "[1] \"PP abf for shared variant: 0.0538%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___B2M__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06130   0.00105   0.92100   0.01570   0.00066 \n",
+      "[1] \"PP abf for shared variant: 0.066%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.131000  0.002230  0.852000  0.014600  0.000629 \n",
+      "[1] \"PP abf for shared variant: 0.0629%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___PFN1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.042100  0.000720  0.940000  0.016100  0.000668 \n",
+      "[1] \"PP abf for shared variant: 0.0668%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS28__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.129000  0.002200  0.854000  0.014600  0.000616 \n",
+      "[1] \"PP abf for shared variant: 0.0616%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.159000  0.002720  0.823000  0.014100  0.000618 \n",
+      "[1] \"PP abf for shared variant: 0.0618%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___GZMH__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.039900  0.000682  0.943000  0.016100  0.000668 \n",
+      "[1] \"PP abf for shared variant: 0.0668%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___LTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.079000  0.001350  0.904000  0.015400  0.000655 \n",
+      "[1] \"PP abf for shared variant: 0.0655%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL39__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.111000  0.001910  0.871000  0.014900  0.000638 \n",
+      "[1] \"PP abf for shared variant: 0.0638%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.151000  0.002590  0.831000  0.014200  0.000597 \n",
+      "[1] \"PP abf for shared variant: 0.0597%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.10200   0.00174   0.88100   0.01510   0.00064 \n",
+      "[1] \"PP abf for shared variant: 0.064%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS23__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.051800  0.000886  0.931000  0.015900  0.000680 \n",
+      "[1] \"PP abf for shared variant: 0.068%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS29__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.067200  0.001150  0.915000  0.015600  0.000668 \n",
+      "[1] \"PP abf for shared variant: 0.0668%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL22__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.348000  0.005950  0.635000  0.010900  0.000491 \n",
+      "[1] \"PP abf for shared variant: 0.0491%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL9__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.181000  0.003100  0.802000  0.013700  0.000586 \n",
+      "[1] \"PP abf for shared variant: 0.0586%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.338000  0.005780  0.645000  0.011000  0.000503 \n",
+      "[1] \"PP abf for shared variant: 0.0503%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.296000  0.005070  0.687000  0.011700  0.000527 \n",
+      "[1] \"PP abf for shared variant: 0.0527%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00027483\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.898000  0.015400  0.084600  0.001450  0.000183 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___MAL__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.188000  0.003150  0.795000  0.013300  0.000579 \n",
+      "[1] \"PP abf for shared variant: 0.0579%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___PRF1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.415000  0.007100  0.568000  0.009710  0.000443 \n",
+      "[1] \"PP abf for shared variant: 0.0443%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.009190  0.000157  0.973000  0.016600  0.000694 \n",
+      "[1] \"PP abf for shared variant: 0.0694%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS6__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.096300  0.001650  0.886000  0.015200  0.000647 \n",
+      "[1] \"PP abf for shared variant: 0.0647%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.010200  0.000174  0.972000  0.016600  0.000716 \n",
+      "[1] \"PP abf for shared variant: 0.0716%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL21__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.066300  0.001130  0.916000  0.015700  0.000667 \n",
+      "[1] \"PP abf for shared variant: 0.0667%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___SMDT1__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-03  4.51e-05  9.80e-01  1.68e-02  7.16e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0716%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.099200  0.001700  0.883000  0.015100  0.000653 \n",
+      "[1] \"PP abf for shared variant: 0.0653%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012900  0.000221  0.970000  0.016600  0.000720 \n",
+      "[1] \"PP abf for shared variant: 0.072%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL34__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.042000  0.000719  0.941000  0.016100  0.000670 \n",
+      "[1] \"PP abf for shared variant: 0.067%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL10A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.061200  0.001050  0.921000  0.015800  0.000662 \n",
+      "[1] \"PP abf for shared variant: 0.0662%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_SMDT1___RPL30__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.100000  0.001710  0.882000  0.015100  0.000653 \n",
+      "[1] \"PP abf for shared variant: 0.0653%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.013100  0.000224  0.969000  0.016600  0.000711 \n",
+      "[1] \"PP abf for shared variant: 0.0711%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.339000  0.005810  0.643000  0.011000  0.000508 \n",
+      "[1] \"PP abf for shared variant: 0.0508%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.057800  0.000988  0.925000  0.015800  0.000682 \n",
+      "[1] \"PP abf for shared variant: 0.0682%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.037500  0.000641  0.945000  0.016200  0.000693 \n",
+      "[1] \"PP abf for shared variant: 0.0693%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.530000  0.009060  0.453000  0.007750  0.000384 \n",
+      "[1] \"PP abf for shared variant: 0.0384%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.449000  0.007670  0.534000  0.009130  0.000443 \n",
+      "[1] \"PP abf for shared variant: 0.0443%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL31__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.467000  0.007990  0.515000  0.008810  0.000427 \n",
+      "[1] \"PP abf for shared variant: 0.0427%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.299000  0.005120  0.684000  0.011700  0.000528 \n",
+      "[1] \"PP abf for shared variant: 0.0528%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___ACTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019400  0.000331  0.963000  0.016500  0.000681 \n",
+      "[1] \"PP abf for shared variant: 0.0681%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.225000  0.003850  0.758000  0.013000  0.000573 \n",
+      "[1] \"PP abf for shared variant: 0.0573%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.16e-03  7.11e-05  9.78e-01  1.67e-02  7.11e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0711%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.530000  0.009060  0.453000  0.007750  0.000389 \n",
+      "[1] \"PP abf for shared variant: 0.0389%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.166000  0.002830  0.817000  0.014000  0.000608 \n",
+      "[1] \"PP abf for shared variant: 0.0608%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.205000  0.003500  0.778000  0.013300  0.000573 \n",
+      "[1] \"PP abf for shared variant: 0.0573%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL10__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.080800  0.001380  0.902000  0.015400  0.000703 \n",
+      "[1] \"PP abf for shared variant: 0.0703%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.210000  0.003580  0.773000  0.013200  0.000633 \n",
+      "[1] \"PP abf for shared variant: 0.0633%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0032661\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.89200   0.01530   0.09100   0.00156   0.00016 \n",
+      "[1] \"PP abf for shared variant: 0.016%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___CD48__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.252000  0.004310  0.730000  0.012500  0.000544 \n",
+      "[1] \"PP abf for shared variant: 0.0544%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.349000  0.005980  0.633000  0.010800  0.000493 \n",
+      "[1] \"PP abf for shared variant: 0.0493%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_SMDT1___RPS27__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.53700   0.00919   0.44600   0.00762   0.00037 \n",
+      "[1] \"PP abf for shared variant: 0.037%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-11  6.91e-01  1.13e-12  2.58e-02  2.84e-01 \n",
+      "[1] \"PP abf for shared variant: 28.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-11  5.89e-01  1.06e-12  2.29e-02  3.88e-01 \n",
+      "[1] \"PP abf for shared variant: 38.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-11  2.91e-01  7.21e-13  1.13e-02  6.98e-01 \n",
+      "[1] \"PP abf for shared variant: 69.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___CLIC3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.92e-11  7.40e-01  1.34e-12  3.16e-02  2.28e-01 \n",
+      "[1] \"PP abf for shared variant: 22.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__PTPRCAP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-11  8.12e-01  1.32e-12  3.19e-02  1.56e-01 \n",
+      "[1] \"PP abf for shared variant: 15.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___CDKN2D__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.5969e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-11  5.27e-01  1.11e-12  2.37e-02  4.49e-01 \n",
+      "[1] \"PP abf for shared variant: 44.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0931e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-11  9.32e-01  1.39e-12  3.49e-02  3.33e-02 \n",
+      "[1] \"PP abf for shared variant: 3.33%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-11  6.85e-01  1.16e-12  2.65e-02  2.89e-01 \n",
+      "[1] \"PP abf for shared variant: 28.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.31e-12  2.11e-01  6.20e-13  7.91e-03  7.81e-01 \n",
+      "[1] \"PP abf for shared variant: 78.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MAP1A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-11  3.18e-01  7.87e-13  1.33e-02  6.68e-01 \n",
+      "[1] \"PP abf for shared variant: 66.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___FAM129C__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-11  2.80e-01  8.59e-13  1.47e-02  7.05e-01 \n",
+      "[1] \"PP abf for shared variant: 70.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MT-CO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1338e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.54e-11  8.97e-01  1.34e-12  3.32e-02  7.02e-02 \n",
+      "[1] \"PP abf for shared variant: 7.02%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-11  4.65e-01  1.01e-12  2.05e-02  5.15e-01 \n",
+      "[1] \"PP abf for shared variant: 51.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-11  8.04e-01  4.07e-12  1.02e-01  9.40e-02 \n",
+      "[1] \"PP abf for shared variant: 9.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.59e-11  6.56e-01  3.26e-12  8.01e-02  2.64e-01 \n",
+      "[1] \"PP abf for shared variant: 26.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.60e-12  2.18e-01  1.04e-12  1.88e-02  7.63e-01 \n",
+      "[1] \"PP abf for shared variant: 76.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-11  6.74e-01  1.03e-11  2.61e-01  6.46e-02 \n",
+      "[1] \"PP abf for shared variant: 6.46%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-11  7.28e-01  3.68e-12  9.16e-02  1.80e-01 \n",
+      "[1] \"PP abf for shared variant: 18%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.76e-11  6.99e-01  6.00e-12  1.51e-01  1.51e-01 \n",
+      "[1] \"PP abf for shared variant: 15.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMA__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-11  3.51e-01  2.08e-12  4.68e-02  6.02e-01 \n",
+      "[1] \"PP abf for shared variant: 60.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS23\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.04e-11  7.72e-01  3.58e-12  8.93e-02  1.39e-01 \n",
+      "[1] \"PP abf for shared variant: 13.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-11  8.28e-01  1.52e-12  3.73e-02  1.35e-01 \n",
+      "[1] \"PP abf for shared variant: 13.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-11  6.92e-01  1.69e-12  4.02e-02  2.68e-01 \n",
+      "[1] \"PP abf for shared variant: 26.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___EEF1A1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-12  7.73e-02  7.79e-13  1.06e-02  9.12e-01 \n",
+      "[1] \"PP abf for shared variant: 91.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-11  4.97e-01  1.69e-12  3.83e-02  4.65e-01 \n",
+      "[1] \"PP abf for shared variant: 46.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-11  4.99e-01  1.61e-12  3.60e-02  4.65e-01 \n",
+      "[1] \"PP abf for shared variant: 46.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMB__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.50e-11  8.89e-01  2.38e-12  5.98e-02  5.16e-02 \n",
+      "[1] \"PP abf for shared variant: 5.16%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.46e-11  8.78e-01  3.83e-12  9.68e-02  2.56e-02 \n",
+      "[1] \"PP abf for shared variant: 2.56%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-11  3.68e-01  1.52e-12  3.25e-02  6.00e-01 \n",
+      "[1] \"PP abf for shared variant: 60%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-11  7.65e-01  1.78e-12  4.33e-02  1.92e-01 \n",
+      "[1] \"PP abf for shared variant: 19.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RNASET2___HLA-DRB5__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.77e-11  9.56e-01  1.23e-12  3.12e-02  1.31e-02 \n",
+      "[1] \"PP abf for shared variant: 1.31%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_HLA-DQA2___CCL5__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-13  3.22e-03  4.03e-13  2.45e-04  9.97e-01 \n",
+      "[1] \"PP abf for shared variant: 99.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-11  3.19e-01  1.49e-12  3.13e-02  6.49e-01 \n",
+      "[1] \"PP abf for shared variant: 64.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-12  3.50e-02  4.47e-13  1.69e-03  9.63e-01 \n",
+      "[1] \"PP abf for shared variant: 96.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__NKG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-13  2.80e-03  4.20e-13  6.89e-04  9.97e-01 \n",
+      "[1] \"PP abf for shared variant: 99.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPL34\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-13  6.67e-03  4.02e-13  2.70e-04  9.93e-01 \n",
+      "[1] \"PP abf for shared variant: 99.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-11  4.54e-01  1.22e-12  2.58e-02  5.20e-01 \n",
+      "[1] \"PP abf for shared variant: 52%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_HLA-DQA2___CMC1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.27e-13  1.34e-02  4.54e-13  1.66e-03  9.85e-01 \n",
+      "[1] \"PP abf for shared variant: 98.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.16e-13  2.07e-02  4.29e-13  1.08e-03  9.78e-01 \n",
+      "[1] \"PP abf for shared variant: 97.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-12  3.90e-02  5.21e-13  3.64e-03  9.57e-01 \n",
+      "[1] \"PP abf for shared variant: 95.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.13e-11  5.40e-01  1.00e-12  2.10e-02  4.39e-01 \n",
+      "[1] \"PP abf for shared variant: 43.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.52e-13  1.65e-02  4.17e-13  7.33e-04  9.83e-01 \n",
+      "[1] \"PP abf for shared variant: 98.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.64e-12  1.43e-01  6.81e-13  8.78e-03  8.48e-01 \n",
+      "[1] \"PP abf for shared variant: 84.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___CD52__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-13  3.66e-03  4.01e-13  2.16e-04  9.96e-01 \n",
+      "[1] \"PP abf for shared variant: 99.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-11  5.20e-01  1.45e-12  3.22e-02  4.48e-01 \n",
+      "[1] \"PP abf for shared variant: 44.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.98e-12  1.52e-01  6.67e-13  8.52e-03  8.40e-01 \n",
+      "[1] \"PP abf for shared variant: 84%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.52e-12  1.91e-01  7.89e-13  1.20e-02  7.97e-01 \n",
+      "[1] \"PP abf for shared variant: 79.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___EEF1B2__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-11  4.58e-01  9.79e-13  1.96e-02  5.22e-01 \n",
+      "[1] \"PP abf for shared variant: 52.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.38e-12  1.11e-01  5.92e-13  6.18e-03  8.83e-01 \n",
+      "[1] \"PP abf for shared variant: 88.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___B2M__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-11  2.64e-01  8.43e-13  1.42e-02  7.21e-01 \n",
+      "[1] \"PP abf for shared variant: 72.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___GAPDH__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.56e-12  1.41e-01  6.91e-13  9.03e-03  8.50e-01 \n",
+      "[1] \"PP abf for shared variant: 85%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL32\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-13  8.13e-03  4.10e-13  4.69e-04  9.91e-01 \n",
+      "[1] \"PP abf for shared variant: 99.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.70e-11  6.85e-01  1.58e-12  3.74e-02  2.78e-01 \n",
+      "[1] \"PP abf for shared variant: 27.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.76e-12  1.71e-01  7.10e-13  9.82e-03  8.19e-01 \n",
+      "[1] \"PP abf for shared variant: 81.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RNASET2___ITGB1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.019900  0.000407  0.958000  0.019600  0.001890 \n",
+      "[1] \"PP abf for shared variant: 0.189%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RNASET2___CRIP1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.19500   0.00400   0.78300   0.01600   0.00163 \n",
+      "[1] \"PP abf for shared variant: 0.163%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RNASET2___B2M__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09440   0.00193   0.88400   0.01810   0.00185 \n",
+      "[1] \"PP abf for shared variant: 0.185%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RNASET2___ALOX5AP__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.22000   0.00451   0.75800   0.01550   0.00156 \n",
+      "[1] \"PP abf for shared variant: 0.156%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0253e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5760    0.2260    0.0843    0.0322    0.0813 \n",
+      "[1] \"PP abf for shared variant: 8.13%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3600    0.1410    0.1270    0.0464    0.3250 \n",
+      "[1] \"PP abf for shared variant: 32.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"DC_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.393     0.154     0.107     0.039     0.306 \n",
+      "[1] \"PP abf for shared variant: 30.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2490    0.0979    0.2220    0.0837    0.3470 \n",
+      "[1] \"PP abf for shared variant: 34.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__UBE2J1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.0878e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.505     0.198     0.106     0.040     0.151 \n",
+      "[1] \"PP abf for shared variant: 15.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0757    0.0297    0.2460    0.0911    0.5570 \n",
+      "[1] \"PP abf for shared variant: 55.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4660    0.1830    0.0987    0.0366    0.2150 \n",
+      "[1] \"PP abf for shared variant: 21.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1042e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4890    0.1920    0.1170    0.0445    0.1580 \n",
+      "[1] \"PP abf for shared variant: 15.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.1644e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5780    0.2270    0.0677    0.0256    0.1020 \n",
+      "[1] \"PP abf for shared variant: 10.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000548  0.000215  0.281000  0.104000  0.614000 \n",
+      "[1] \"PP abf for shared variant: 61.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.0557e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5800    0.2280    0.0787    0.0301    0.0835 \n",
+      "[1] \"PP abf for shared variant: 8.35%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01140   0.00448   0.28400   0.10500   0.59500 \n",
+      "[1] \"PP abf for shared variant: 59.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1210    0.0476    0.2240    0.0827    0.5250 \n",
+      "[1] \"PP abf for shared variant: 52.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00339   0.00133   0.26400   0.09740   0.63400 \n",
+      "[1] \"PP abf for shared variant: 63.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4460    0.1750    0.1160    0.0433    0.2200 \n",
+      "[1] \"PP abf for shared variant: 22%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2790    0.1100    0.1830    0.0681    0.3610 \n",
+      "[1] \"PP abf for shared variant: 36.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.7757e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4590    0.1800    0.1150    0.0432    0.2030 \n",
+      "[1] \"PP abf for shared variant: 20.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0692    0.0272    0.2140    0.0777    0.6120 \n",
+      "[1] \"PP abf for shared variant: 61.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3730    0.1470    0.1260    0.0464    0.3070 \n",
+      "[1] \"PP abf for shared variant: 30.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2778e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4300    0.1690    0.1230    0.0459    0.2320 \n",
+      "[1] \"PP abf for shared variant: 23.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0623e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5660    0.2220    0.0711    0.0268    0.1140 \n",
+      "[1] \"PP abf for shared variant: 11.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0276    0.0108    0.2640    0.0977    0.6000 \n",
+      "[1] \"PP abf for shared variant: 60%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0782    0.0307    0.2860    0.1070    0.4980 \n",
+      "[1] \"PP abf for shared variant: 49.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1560    0.0612    0.1980    0.0727    0.5120 \n",
+      "[1] \"PP abf for shared variant: 51.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.59e-05  2.59e-05  2.99e-01  1.11e-01  5.90e-01 \n",
+      "[1] \"PP abf for shared variant: 59%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1408e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.6060    0.2380    0.0630    0.0241    0.0686 \n",
+      "[1] \"PP abf for shared variant: 6.86%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.791e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4390    0.1720    0.1330    0.0502    0.2060 \n",
+      "[1] \"PP abf for shared variant: 20.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1436e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5150    0.2020    0.0801    0.0297    0.1720 \n",
+      "[1] \"PP abf for shared variant: 17.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1123e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.6180    0.2430    0.0522    0.0198    0.0668 \n",
+      "[1] \"PP abf for shared variant: 6.68%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1470    0.0577    0.2130    0.0787    0.5040 \n",
+      "[1] \"PP abf for shared variant: 50.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2270    0.0891    0.1550    0.0561    0.4730 \n",
+      "[1] \"PP abf for shared variant: 47.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.73e-05  6.78e-06  3.25e-01  1.22e-01  5.52e-01 \n",
+      "[1] \"PP abf for shared variant: 55.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1410    0.0553    0.2170    0.0802    0.5070 \n",
+      "[1] \"PP abf for shared variant: 50.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01540   0.00603   0.22600   0.08200   0.67100 \n",
+      "[1] \"PP abf for shared variant: 67.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-05  1.02e-05  2.93e-01  1.09e-01  5.98e-01 \n",
+      "[1] \"PP abf for shared variant: 59.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0950    0.0373    0.2220    0.0814    0.5650 \n",
+      "[1] \"PP abf for shared variant: 56.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1639e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5550    0.2180    0.0753    0.0283    0.1230 \n",
+      "[1] \"PP abf for shared variant: 12.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"B_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0462    0.0181    0.2580    0.0954    0.5830 \n",
+      "[1] \"PP abf for shared variant: 58.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.14e-09  3.59e-09  2.50e-01  9.16e-02  6.58e-01 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-12  5.70e-13  2.70e-01  9.96e-02  6.30e-01 \n",
+      "[1] \"PP abf for shared variant: 63%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0459    0.0180    0.2210    0.0802    0.6350 \n",
+      "[1] \"PP abf for shared variant: 63.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-08  6.34e-09  2.36e-01  8.59e-02  6.78e-01 \n",
+      "[1] \"PP abf for shared variant: 67.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-06  7.56e-07  3.52e-01  1.33e-01  5.15e-01 \n",
+      "[1] \"PP abf for shared variant: 51.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.35e-05  1.71e-05  2.87e-01  1.06e-01  6.07e-01 \n",
+      "[1] \"PP abf for shared variant: 60.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-17  8.08e-18  2.90e-01  1.08e-01  6.02e-01 \n",
+      "[1] \"PP abf for shared variant: 60.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-11  1.40e-11  2.33e-01  8.45e-02  6.83e-01 \n",
+      "[1] \"PP abf for shared variant: 68.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.70e-12  1.45e-12  3.11e-01  1.16e-01  5.72e-01 \n",
+      "[1] \"PP abf for shared variant: 57.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-11  8.41e-12  1.85e-01  6.49e-02  7.50e-01 \n",
+      "[1] \"PP abf for shared variant: 75%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-05  4.56e-06  3.12e-01  1.17e-01  5.71e-01 \n",
+      "[1] \"PP abf for shared variant: 57.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000835  0.000327  0.267000  0.098400  0.633000 \n",
+      "[1] \"PP abf for shared variant: 63.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.01e-12  3.53e-12  2.74e-01  1.01e-01  6.25e-01 \n",
+      "[1] \"PP abf for shared variant: 62.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-15  4.96e-16  2.18e-01  7.86e-02  7.03e-01 \n",
+      "[1] \"PP abf for shared variant: 70.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02350   0.00923   0.29000   0.10800   0.56900 \n",
+      "[1] \"PP abf for shared variant: 56.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0427    0.0167    0.2170    0.0788    0.6440 \n",
+      "[1] \"PP abf for shared variant: 64.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-10  5.69e-11  2.89e-01  1.07e-01  6.04e-01 \n",
+      "[1] \"PP abf for shared variant: 60.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.35e-07  2.88e-07  2.74e-01  1.01e-01  6.25e-01 \n",
+      "[1] \"PP abf for shared variant: 62.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00774   0.00303   0.22300   0.08060   0.68600 \n",
+      "[1] \"PP abf for shared variant: 68.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-07  9.74e-08  2.41e-01  8.76e-02  6.72e-01 \n",
+      "[1] \"PP abf for shared variant: 67.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-18  1.07e-18  1.81e-01  6.34e-02  7.56e-01 \n",
+      "[1] \"PP abf for shared variant: 75.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.42e-12  2.91e-12  1.70e-01  5.88e-02  7.72e-01 \n",
+      "[1] \"PP abf for shared variant: 77.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-11  1.02e-11  2.70e-01  9.95e-02  6.31e-01 \n",
+      "[1] \"PP abf for shared variant: 63.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__SPON2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00994   0.00390   0.30700   0.11500   0.56400 \n",
+      "[1] \"PP abf for shared variant: 56.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-05  5.22e-06  2.81e-01  1.04e-01  6.15e-01 \n",
+      "[1] \"PP abf for shared variant: 61.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-19  6.11e-20  2.94e-01  1.09e-01  5.97e-01 \n",
+      "[1] \"PP abf for shared variant: 59.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-13  6.02e-14  2.29e-01  8.29e-02  6.88e-01 \n",
+      "[1] \"PP abf for shared variant: 68.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-12  1.52e-12  2.68e-01  9.86e-02  6.34e-01 \n",
+      "[1] \"PP abf for shared variant: 63.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-13  5.90e-14  1.98e-01  7.04e-02  7.31e-01 \n",
+      "[1] \"PP abf for shared variant: 73.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-06  4.45e-07  3.32e-01  1.25e-01  5.43e-01 \n",
+      "[1] \"PP abf for shared variant: 54.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1340    0.0524    0.2210    0.0816    0.5110 \n",
+      "[1] \"PP abf for shared variant: 51.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.69e-06  3.41e-06  3.42e-01  1.29e-01  5.29e-01 \n",
+      "[1] \"PP abf for shared variant: 52.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1991e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.413     0.162     0.149     0.056     0.221 \n",
+      "[1] \"PP abf for shared variant: 22.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-09  4.15e-10  2.16e-01  7.76e-02  7.06e-01 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-12  8.24e-13  2.75e-01  1.01e-01  6.24e-01 \n",
+      "[1] \"PP abf for shared variant: 62.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-09  5.23e-10  2.61e-01  9.59e-02  6.43e-01 \n",
+      "[1] \"PP abf for shared variant: 64.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.50e-13  3.72e-13  4.17e-01  1.59e-01  4.24e-01 \n",
+      "[1] \"PP abf for shared variant: 42.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.73e-16  3.42e-16  3.10e-01  1.16e-01  5.74e-01 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-18  7.44e-19  2.90e-01  1.08e-01  6.03e-01 \n",
+      "[1] \"PP abf for shared variant: 60.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000442  0.000173  0.294000  0.109000  0.597000 \n",
+      "[1] \"PP abf for shared variant: 59.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.27e-10  1.67e-10  3.21e-01  1.20e-01  5.59e-01 \n",
+      "[1] \"PP abf for shared variant: 55.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.29e-05  2.07e-05  2.65e-01  9.76e-02  6.37e-01 \n",
+      "[1] \"PP abf for shared variant: 63.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.45e-10  2.14e-10  3.30e-01  1.24e-01  5.46e-01 \n",
+      "[1] \"PP abf for shared variant: 54.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-13  4.92e-14  2.66e-01  9.80e-02  6.36e-01 \n",
+      "[1] \"PP abf for shared variant: 63.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.00e-09  1.96e-09  2.85e-01  1.06e-01  6.09e-01 \n",
+      "[1] \"PP abf for shared variant: 60.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-15  7.67e-16  3.09e-01  1.15e-01  5.76e-01 \n",
+      "[1] \"PP abf for shared variant: 57.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.80e-14  3.06e-14  3.16e-01  1.18e-01  5.66e-01 \n",
+      "[1] \"PP abf for shared variant: 56.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000268  0.000105  0.185000  0.065000  0.750000 \n",
+      "[1] \"PP abf for shared variant: 75%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.74e-14  3.43e-14  1.68e-01  5.80e-02  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3230    0.1270    0.1630    0.0607    0.3270 \n",
+      "[1] \"PP abf for shared variant: 32.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.11e-07  1.22e-07  3.12e-01  1.17e-01  5.72e-01 \n",
+      "[1] \"PP abf for shared variant: 57.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02030   0.00794   0.29800   0.11100   0.56200 \n",
+      "[1] \"PP abf for shared variant: 56.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-08  1.02e-08  3.37e-01  1.27e-01  5.36e-01 \n",
+      "[1] \"PP abf for shared variant: 53.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000834  0.000327  0.645000  0.252000  0.102000 \n",
+      "[1] \"PP abf for shared variant: 10.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-18  2.18e-18  3.21e-01  1.20e-01  5.58e-01 \n",
+      "[1] \"PP abf for shared variant: 55.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-08  2.18e-08  1.81e-01  6.35e-02  7.55e-01 \n",
+      "[1] \"PP abf for shared variant: 75.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-09  5.88e-10  2.84e-01  1.05e-01  6.11e-01 \n",
+      "[1] \"PP abf for shared variant: 61.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.08e-07  3.17e-07  2.21e-01  7.95e-02  7.00e-01 \n",
+      "[1] \"PP abf for shared variant: 70%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.48e-10  1.36e-10  4.97e-01  1.92e-01  3.11e-01 \n",
+      "[1] \"PP abf for shared variant: 31.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.24e-16  3.23e-16  2.66e-01  9.80e-02  6.36e-01 \n",
+      "[1] \"PP abf for shared variant: 63.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-06  1.31e-06  2.78e-01  1.03e-01  6.19e-01 \n",
+      "[1] \"PP abf for shared variant: 61.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.31e-06  3.65e-06  2.09e-01  7.47e-02  7.17e-01 \n",
+      "[1] \"PP abf for shared variant: 71.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2410    0.0944    0.1780    0.0657    0.4210 \n",
+      "[1] \"PP abf for shared variant: 42.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-16  8.73e-17  2.38e-01  8.64e-02  6.76e-01 \n",
+      "[1] \"PP abf for shared variant: 67.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00739   0.00290   0.27900   0.10300   0.60800 \n",
+      "[1] \"PP abf for shared variant: 60.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.77e-09  2.26e-09  2.07e-01  7.40e-02  7.19e-01 \n",
+      "[1] \"PP abf for shared variant: 71.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.73e-06  6.78e-07  2.55e-01  9.36e-02  6.51e-01 \n",
+      "[1] \"PP abf for shared variant: 65.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-05  6.82e-06  1.97e-01  6.98e-02  7.33e-01 \n",
+      "[1] \"PP abf for shared variant: 73.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-05  6.98e-06  3.13e-01  1.17e-01  5.70e-01 \n",
+      "[1] \"PP abf for shared variant: 57%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.09e-07  1.60e-07  2.54e-01  9.30e-02  6.53e-01 \n",
+      "[1] \"PP abf for shared variant: 65.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4099e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3620    0.1420    0.1650    0.0622    0.2680 \n",
+      "[1] \"PP abf for shared variant: 26.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5173e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.6350    0.2490    0.0442    0.0168    0.0548 \n",
+      "[1] \"PP abf for shared variant: 5.48%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001080  0.000425  0.435000  0.167000  0.397000 \n",
+      "[1] \"PP abf for shared variant: 39.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.15e-07  1.63e-07  2.20e-01  7.93e-02  7.01e-01 \n",
+      "[1] \"PP abf for shared variant: 70.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.80e-13  1.49e-13  3.33e-01  1.25e-01  5.42e-01 \n",
+      "[1] \"PP abf for shared variant: 54.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___KLRC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0905    0.0355    0.3710    0.1420    0.3610 \n",
+      "[1] \"PP abf for shared variant: 36.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.4275e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5880    0.2310    0.0872    0.0336    0.0600 \n",
+      "[1] \"PP abf for shared variant: 6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-06  6.51e-07  4.60e-01  1.77e-01  3.64e-01 \n",
+      "[1] \"PP abf for shared variant: 36.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.25e-09  1.67e-09  2.14e-01  7.67e-02  7.10e-01 \n",
+      "[1] \"PP abf for shared variant: 71%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___MALAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01650   0.00648   0.33100   0.12500   0.52200 \n",
+      "[1] \"PP abf for shared variant: 52.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002450  0.000962  0.314000  0.117000  0.565000 \n",
+      "[1] \"PP abf for shared variant: 56.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.70e-11  1.84e-11  3.30e-01  1.24e-01  5.46e-01 \n",
+      "[1] \"PP abf for shared variant: 54.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8351e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5130    0.2010    0.0898    0.0336    0.1620 \n",
+      "[1] \"PP abf for shared variant: 16.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.02e-07  1.97e-07  3.19e-01  1.20e-01  5.61e-01 \n",
+      "[1] \"PP abf for shared variant: 56.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.96e-05  1.16e-05  2.22e-01  8.01e-02  6.98e-01 \n",
+      "[1] \"PP abf for shared variant: 69.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___FGFBP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2790    0.1090    0.2090    0.0787    0.3240 \n",
+      "[1] \"PP abf for shared variant: 32.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00731   0.00287   0.32500   0.12200   0.54200 \n",
+      "[1] \"PP abf for shared variant: 54.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000312  0.000123  0.256000  0.093900  0.650000 \n",
+      "[1] \"PP abf for shared variant: 65%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000292  0.000115  0.171000  0.059500  0.769000 \n",
+      "[1] \"PP abf for shared variant: 76.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0415    0.0163    0.3540    0.1340    0.4540 \n",
+      "[1] \"PP abf for shared variant: 45.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.574e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5990    0.2350    0.0623    0.0236    0.0799 \n",
+      "[1] \"PP abf for shared variant: 7.99%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-05  4.07e-06  4.04e-01  1.54e-01  4.42e-01 \n",
+      "[1] \"PP abf for shared variant: 44.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-15  7.21e-16  1.73e-01  6.01e-02  7.67e-01 \n",
+      "[1] \"PP abf for shared variant: 76.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2336e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5380    0.2110    0.0932    0.0353    0.1230 \n",
+      "[1] \"PP abf for shared variant: 12.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.53e-10  2.95e-10  3.10e-01  1.16e-01  5.74e-01 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"NK_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.97e-11  1.56e-11  2.88e-01  1.07e-01  6.05e-01 \n",
+      "[1] \"PP abf for shared variant: 60.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___NRGN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7437e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3850    0.1510    0.1370    0.0509    0.2770 \n",
+      "[1] \"PP abf for shared variant: 27.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1710    0.0671    0.1780    0.0646    0.5200 \n",
+      "[1] \"PP abf for shared variant: 52%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000826  0.000324  0.243000  0.088700  0.667000 \n",
+      "[1] \"PP abf for shared variant: 66.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-06  4.72e-07  2.93e-01  1.09e-01  5.98e-01 \n",
+      "[1] \"PP abf for shared variant: 59.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-16  4.21e-17  1.75e-01  6.10e-02  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.25e-07  2.06e-07  2.34e-01  8.52e-02  6.81e-01 \n",
+      "[1] \"PP abf for shared variant: 68.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-10  1.23e-10  1.58e-01  5.42e-02  7.88e-01 \n",
+      "[1] \"PP abf for shared variant: 78.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00573   0.00225   0.20400   0.07280   0.71600 \n",
+      "[1] \"PP abf for shared variant: 71.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-09  7.12e-10  1.62e-01  5.59e-02  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.45e-15  1.75e-15  2.21e-01  7.97e-02  6.99e-01 \n",
+      "[1] \"PP abf for shared variant: 69.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VCAN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2570    0.1010    0.1430    0.0516    0.4470 \n",
+      "[1] \"PP abf for shared variant: 44.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UQCRH\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.92e-06  1.54e-06  1.74e-01  6.05e-02  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC7A7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00425   0.00167   0.17700   0.06200   0.75500 \n",
+      "[1] \"PP abf for shared variant: 75.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EPB41L3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2130    0.0837    0.2200    0.0821    0.4010 \n",
+      "[1] \"PP abf for shared variant: 40.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00642   0.00252   0.28700   0.10700   0.59800 \n",
+      "[1] \"PP abf for shared variant: 59.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.98e-15  3.53e-15  1.65e-01  5.69e-02  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01230   0.00484   0.23900   0.08750   0.65600 \n",
+      "[1] \"PP abf for shared variant: 65.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___QARS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2190    0.0859    0.2000    0.0745    0.4200 \n",
+      "[1] \"PP abf for shared variant: 42%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-06  8.69e-07  2.45e-01  8.94e-02  6.66e-01 \n",
+      "[1] \"PP abf for shared variant: 66.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-15  5.00e-16  1.72e-01  5.98e-02  7.68e-01 \n",
+      "[1] \"PP abf for shared variant: 76.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.67e-06  1.83e-06  2.66e-01  9.81e-02  6.36e-01 \n",
+      "[1] \"PP abf for shared variant: 63.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.89e-16  1.92e-16  1.53e-01  5.21e-02  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.17e-09  1.64e-09  1.88e-01  6.63e-02  7.46e-01 \n",
+      "[1] \"PP abf for shared variant: 74.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-10  1.23e-10  1.54e-01  5.25e-02  7.94e-01 \n",
+      "[1] \"PP abf for shared variant: 79.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-12  3.96e-13  1.56e-01  5.32e-02  7.91e-01 \n",
+      "[1] \"PP abf for shared variant: 79.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01590   0.00625   0.23600   0.08620   0.65500 \n",
+      "[1] \"PP abf for shared variant: 65.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-10  6.89e-11  1.57e-01  5.39e-02  7.89e-01 \n",
+      "[1] \"PP abf for shared variant: 78.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___CSTA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-04  9.09e-05  1.76e-01  6.13e-02  7.63e-01 \n",
+      "[1] \"PP abf for shared variant: 76.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-07  6.29e-08  2.72e-01  1.00e-01  6.28e-01 \n",
+      "[1] \"PP abf for shared variant: 62.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0564    0.0221    0.1810    0.0644    0.6760 \n",
+      "[1] \"PP abf for shared variant: 67.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-13  1.36e-13  1.66e-01  5.73e-02  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001770  0.000697  0.258000  0.094900  0.645000 \n",
+      "[1] \"PP abf for shared variant: 64.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.28e-15  5.01e-16  1.61e-01  5.55e-02  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2160    0.0848    0.1810    0.0667    0.4510 \n",
+      "[1] \"PP abf for shared variant: 45.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TNFAIP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2160    0.0850    0.1910    0.0706    0.4370 \n",
+      "[1] \"PP abf for shared variant: 43.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00459   0.00180   0.17600   0.06160   0.75600 \n",
+      "[1] \"PP abf for shared variant: 75.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.25e-12  2.45e-12  2.54e-01  9.31e-02  6.53e-01 \n",
+      "[1] \"PP abf for shared variant: 65.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.60e-21  3.77e-21  1.53e-01  5.22e-02  7.95e-01 \n",
+      "[1] \"PP abf for shared variant: 79.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.56e-12  2.18e-12  1.86e-01  6.57e-02  7.48e-01 \n",
+      "[1] \"PP abf for shared variant: 74.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.49e-06  2.16e-06  2.16e-01  7.79e-02  7.06e-01 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-10  7.03e-11  2.48e-01  9.08e-02  6.61e-01 \n",
+      "[1] \"PP abf for shared variant: 66.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.75e-10  3.83e-10  1.62e-01  5.58e-02  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-05  9.12e-06  1.67e-01  5.78e-02  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00079   0.00031   0.21100   0.07580   0.71200 \n",
+      "[1] \"PP abf for shared variant: 71.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000310  0.000122  0.238000  0.086900  0.674000 \n",
+      "[1] \"PP abf for shared variant: 67.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.51e-14  1.38e-14  1.53e-01  5.22e-02  7.94e-01 \n",
+      "[1] \"PP abf for shared variant: 79.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-16  7.32e-17  1.56e-01  5.36e-02  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.70e-09  2.24e-09  2.79e-01  1.03e-01  6.18e-01 \n",
+      "[1] \"PP abf for shared variant: 61.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.34e-05  2.88e-05  1.99e-01  7.08e-02  7.30e-01 \n",
+      "[1] \"PP abf for shared variant: 73%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-11  9.66e-12  1.99e-01  7.09e-02  7.30e-01 \n",
+      "[1] \"PP abf for shared variant: 73%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1173e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2810    0.1100    0.2060    0.0776    0.3250 \n",
+      "[1] \"PP abf for shared variant: 32.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.81e-07  1.89e-07  2.58e-01  9.50e-02  6.47e-01 \n",
+      "[1] \"PP abf for shared variant: 64.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-10  6.22e-11  2.78e-01  1.03e-01  6.19e-01 \n",
+      "[1] \"PP abf for shared variant: 61.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-08  1.23e-08  1.64e-01  5.66e-02  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001210  0.000477  0.207000  0.074000  0.718000 \n",
+      "[1] \"PP abf for shared variant: 71.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-09  9.53e-10  1.85e-01  6.53e-02  7.49e-01 \n",
+      "[1] \"PP abf for shared variant: 74.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-06  1.04e-06  2.42e-01  8.83e-02  6.70e-01 \n",
+      "[1] \"PP abf for shared variant: 67%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00969   0.00380   0.20700   0.07400   0.70600 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.64e-12  2.22e-12  2.13e-01  7.65e-02  7.10e-01 \n",
+      "[1] \"PP abf for shared variant: 71%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.76e-11  1.48e-11  2.41e-01  8.79e-02  6.71e-01 \n",
+      "[1] \"PP abf for shared variant: 67.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-05  1.33e-05  2.25e-01  8.13e-02  6.94e-01 \n",
+      "[1] \"PP abf for shared variant: 69.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-04  4.31e-05  1.75e-01  6.11e-02  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___CST3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7382e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3940    0.1550    0.1170    0.0429    0.2920 \n",
+      "[1] \"PP abf for shared variant: 29.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01790   0.00702   0.22100   0.07990   0.67500 \n",
+      "[1] \"PP abf for shared variant: 67.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2150    0.0844    0.2110    0.0787    0.4110 \n",
+      "[1] \"PP abf for shared variant: 41.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.12e-16  2.40e-16  1.63e-01  5.63e-02  7.81e-01 \n",
+      "[1] \"PP abf for shared variant: 78.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2840    0.1120    0.1960    0.0736    0.3350 \n",
+      "[1] \"PP abf for shared variant: 33.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000741  0.000291  0.282000  0.105000  0.612000 \n",
+      "[1] \"PP abf for shared variant: 61.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___AP1S2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1390    0.0547    0.1720    0.0617    0.5730 \n",
+      "[1] \"PP abf for shared variant: 57.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.41e-11  2.52e-11  2.01e-01  7.18e-02  7.27e-01 \n",
+      "[1] \"PP abf for shared variant: 72.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.15e-09  1.24e-09  2.30e-01  8.33e-02  6.87e-01 \n",
+      "[1] \"PP abf for shared variant: 68.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SEC11A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01100   0.00433   0.23900   0.08740   0.65800 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.45e-04  9.64e-05  2.27e-01  8.21e-02  6.91e-01 \n",
+      "[1] \"PP abf for shared variant: 69.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.40e-11  1.33e-11  2.22e-01  8.01e-02  6.98e-01 \n",
+      "[1] \"PP abf for shared variant: 69.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.35e-09  1.71e-09  1.83e-01  6.43e-02  7.53e-01 \n",
+      "[1] \"PP abf for shared variant: 75.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.92e-11  1.93e-11  2.77e-01  1.03e-01  6.20e-01 \n",
+      "[1] \"PP abf for shared variant: 62%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.90e-08  2.32e-08  1.65e-01  5.69e-02  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-07  5.64e-08  1.91e-01  6.74e-02  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.66e-08  2.22e-08  1.96e-01  6.95e-02  7.35e-01 \n",
+      "[1] \"PP abf for shared variant: 73.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___ATP5G2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00527   0.00207   0.23000   0.08370   0.67900 \n",
+      "[1] \"PP abf for shared variant: 67.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.52e-06  1.38e-06  1.94e-01  6.89e-02  7.37e-01 \n",
+      "[1] \"PP abf for shared variant: 73.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-09  1.14e-09  2.04e-01  7.30e-02  7.23e-01 \n",
+      "[1] \"PP abf for shared variant: 72.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00446   0.00175   0.17500   0.06130   0.75700 \n",
+      "[1] \"PP abf for shared variant: 75.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-13  5.19e-14  1.65e-01  5.69e-02  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-16  5.83e-17  1.56e-01  5.33e-02  7.91e-01 \n",
+      "[1] \"PP abf for shared variant: 79.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.01e-18  2.75e-18  2.30e-01  8.35e-02  6.86e-01 \n",
+      "[1] \"PP abf for shared variant: 68.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.71e-07  2.64e-07  2.06e-01  7.36e-02  7.20e-01 \n",
+      "[1] \"PP abf for shared variant: 72%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.35e-08  1.71e-08  1.57e-01  5.38e-02  7.89e-01 \n",
+      "[1] \"PP abf for shared variant: 78.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.80e-17  2.28e-17  1.73e-01  6.04e-02  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.18e-05  2.04e-05  1.75e-01  6.11e-02  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EVI2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2630    0.1030    0.1770    0.0655    0.3920 \n",
+      "[1] \"PP abf for shared variant: 39.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0629    0.0247    0.3100    0.1170    0.4850 \n",
+      "[1] \"PP abf for shared variant: 48.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01400   0.00551   0.25500   0.09370   0.63200 \n",
+      "[1] \"PP abf for shared variant: 63.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-04  7.85e-05  1.65e-01  5.68e-02  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-13  1.20e-13  1.82e-01  6.40e-02  7.54e-01 \n",
+      "[1] \"PP abf for shared variant: 75.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___LGALS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0906    0.0356    0.1680    0.0594    0.6470 \n",
+      "[1] \"PP abf for shared variant: 64.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1610    0.0634    0.1660    0.0595    0.5500 \n",
+      "[1] \"PP abf for shared variant: 55%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-11  4.41e-12  1.61e-01  5.55e-02  7.83e-01 \n",
+      "[1] \"PP abf for shared variant: 78.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-15  1.03e-15  1.56e-01  5.32e-02  7.91e-01 \n",
+      "[1] \"PP abf for shared variant: 79.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-11  9.53e-12  1.94e-01  6.90e-02  7.37e-01 \n",
+      "[1] \"PP abf for shared variant: 73.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-06  5.20e-07  2.35e-01  8.54e-02  6.80e-01 \n",
+      "[1] \"PP abf for shared variant: 68%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___GPX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0183    0.0072    0.2090    0.0753    0.6900 \n",
+      "[1] \"PP abf for shared variant: 69%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___LTA4H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4560    0.1790    0.1250    0.0472    0.1930 \n",
+      "[1] \"PP abf for shared variant: 19.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RNASE6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2160    0.0850    0.1920    0.0712    0.4350 \n",
+      "[1] \"PP abf for shared variant: 43.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0802    0.0315    0.2380    0.0877    0.5630 \n",
+      "[1] \"PP abf for shared variant: 56.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___BTF3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00802   0.00315   0.24200   0.08860   0.65800 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___DRAM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1829e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4140    0.1630    0.1160    0.0429    0.2640 \n",
+      "[1] \"PP abf for shared variant: 26.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___IL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000726  0.000285  0.271000  0.100000  0.627000 \n",
+      "[1] \"PP abf for shared variant: 62.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00857   0.00337   0.32500   0.12200   0.54000 \n",
+      "[1] \"PP abf for shared variant: 54%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-07  2.18e-07  2.91e-01  1.08e-01  6.01e-01 \n",
+      "[1] \"PP abf for shared variant: 60.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.56e-11  2.18e-11  2.73e-01  1.01e-01  6.26e-01 \n",
+      "[1] \"PP abf for shared variant: 62.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.38e-14  2.51e-14  2.25e-01  8.13e-02  6.94e-01 \n",
+      "[1] \"PP abf for shared variant: 69.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1770    0.0694    0.1690    0.0610    0.5240 \n",
+      "[1] \"PP abf for shared variant: 52.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.68e-15  2.62e-15  1.88e-01  6.64e-02  7.46e-01 \n",
+      "[1] \"PP abf for shared variant: 74.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-12  7.68e-13  1.63e-01  5.62e-02  7.81e-01 \n",
+      "[1] \"PP abf for shared variant: 78.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___IPO7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0590    0.0232    0.2890    0.1080    0.5210 \n",
+      "[1] \"PP abf for shared variant: 52.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.82e-08  1.50e-08  1.72e-01  6.00e-02  7.68e-01 \n",
+      "[1] \"PP abf for shared variant: 76.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.35e-12  2.10e-12  3.27e-01  1.23e-01  5.50e-01 \n",
+      "[1] \"PP abf for shared variant: 55%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.71e-13  1.06e-13  2.86e-01  1.06e-01  6.08e-01 \n",
+      "[1] \"PP abf for shared variant: 60.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2966e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3930    0.1540    0.1440    0.0541    0.2540 \n",
+      "[1] \"PP abf for shared variant: 25.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-08  4.78e-09  1.96e-01  6.97e-02  7.34e-01 \n",
+      "[1] \"PP abf for shared variant: 73.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.56e-06  2.18e-06  2.15e-01  7.75e-02  7.07e-01 \n",
+      "[1] \"PP abf for shared variant: 70.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___CRTAP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00565   0.00222   0.20100   0.07170   0.72000 \n",
+      "[1] \"PP abf for shared variant: 72%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3190    0.1250    0.1510    0.0558    0.3480 \n",
+      "[1] \"PP abf for shared variant: 34.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.43e-10  2.53e-10  1.54e-01  5.25e-02  7.94e-01 \n",
+      "[1] \"PP abf for shared variant: 79.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000863  0.000339  0.204000  0.072700  0.723000 \n",
+      "[1] \"PP abf for shared variant: 72.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.66e-10  3.40e-10  2.63e-01  9.67e-02  6.41e-01 \n",
+      "[1] \"PP abf for shared variant: 64.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2420    0.0952    0.1780    0.0657    0.4190 \n",
+      "[1] \"PP abf for shared variant: 41.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0456    0.0179    0.2490    0.0917    0.5960 \n",
+      "[1] \"PP abf for shared variant: 59.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-10  4.83e-11  1.64e-01  5.67e-02  7.79e-01 \n",
+      "[1] \"PP abf for shared variant: 77.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.33e-12  1.70e-12  1.59e-01  5.45e-02  7.87e-01 \n",
+      "[1] \"PP abf for shared variant: 78.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TKT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-04  7.69e-05  3.53e-01  1.33e-01  5.13e-01 \n",
+      "[1] \"PP abf for shared variant: 51.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-15  6.53e-16  1.90e-01  6.73e-02  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1130    0.0441    0.2560    0.0955    0.4920 \n",
+      "[1] \"PP abf for shared variant: 49.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ACTN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000817  0.000320  0.293000  0.109000  0.597000 \n",
+      "[1] \"PP abf for shared variant: 59.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-14  4.55e-15  2.88e-01  1.07e-01  6.04e-01 \n",
+      "[1] \"PP abf for shared variant: 60.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAND1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4561e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3220    0.1260    0.1620    0.0605    0.3290 \n",
+      "[1] \"PP abf for shared variant: 32.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.25e-15  1.67e-15  2.41e-01  8.81e-02  6.70e-01 \n",
+      "[1] \"PP abf for shared variant: 67%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PRF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00566   0.00222   0.20700   0.07430   0.71000 \n",
+      "[1] \"PP abf for shared variant: 71%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-05  1.05e-05  2.51e-01  9.20e-02  6.57e-01 \n",
+      "[1] \"PP abf for shared variant: 65.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EFHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0406    0.0159    0.2690    0.1000    0.5740 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-10  5.89e-11  3.32e-01  1.25e-01  5.43e-01 \n",
+      "[1] \"PP abf for shared variant: 54.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-16  8.71e-17  1.93e-01  6.83e-02  7.39e-01 \n",
+      "[1] \"PP abf for shared variant: 73.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.77e-14  2.27e-14  2.51e-01  9.19e-02  6.57e-01 \n",
+      "[1] \"PP abf for shared variant: 65.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-13  8.93e-14  1.99e-01  7.10e-02  7.30e-01 \n",
+      "[1] \"PP abf for shared variant: 73%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___APBA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0932    0.0366    0.2830    0.1060    0.4810 \n",
+      "[1] \"PP abf for shared variant: 48.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000140  0.000055  0.192000  0.068200  0.739000 \n",
+      "[1] \"PP abf for shared variant: 73.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FAIM3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0512    0.0201    0.2500    0.0923    0.5860 \n",
+      "[1] \"PP abf for shared variant: 58.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.430     0.169     0.131     0.049     0.222 \n",
+      "[1] \"PP abf for shared variant: 22.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3040    0.1200    0.1810    0.0678    0.3270 \n",
+      "[1] \"PP abf for shared variant: 32.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01600   0.00628   0.28300   0.10500   0.58900 \n",
+      "[1] \"PP abf for shared variant: 58.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-14  7.86e-15  2.99e-01  1.11e-01  5.90e-01 \n",
+      "[1] \"PP abf for shared variant: 59%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___C11orf1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.8471e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5240    0.2060    0.0969    0.0367    0.1370 \n",
+      "[1] \"PP abf for shared variant: 13.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000414  0.000162  0.254000  0.093100  0.652000 \n",
+      "[1] \"PP abf for shared variant: 65.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-17  5.72e-18  2.60e-01  9.57e-02  6.44e-01 \n",
+      "[1] \"PP abf for shared variant: 64.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-17  6.97e-18  2.04e-01  7.31e-02  7.22e-01 \n",
+      "[1] \"PP abf for shared variant: 72.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GZMM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2620    0.1030    0.1430    0.0519    0.4410 \n",
+      "[1] \"PP abf for shared variant: 44.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.06e-06  1.20e-06  4.09e-01  1.56e-01  4.35e-01 \n",
+      "[1] \"PP abf for shared variant: 43.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000256  0.000100  0.194000  0.069000  0.736000 \n",
+      "[1] \"PP abf for shared variant: 73.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1610    0.0632    0.1910    0.0697    0.5150 \n",
+      "[1] \"PP abf for shared variant: 51.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CTSW__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1550    0.0610    0.1900    0.0693    0.5240 \n",
+      "[1] \"PP abf for shared variant: 52.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-05  1.08e-05  2.48e-01  9.10e-02  6.61e-01 \n",
+      "[1] \"PP abf for shared variant: 66.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-19  8.56e-20  1.53e-01  5.23e-02  7.94e-01 \n",
+      "[1] \"PP abf for shared variant: 79.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FLT3LG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000363  0.000142  0.305000  0.114000  0.581000 \n",
+      "[1] \"PP abf for shared variant: 58.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NKG7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-05  1.30e-05  2.77e-01  1.02e-01  6.21e-01 \n",
+      "[1] \"PP abf for shared variant: 62.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0326    0.0128    0.2830    0.1050    0.5670 \n",
+      "[1] \"PP abf for shared variant: 56.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.3964e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5030    0.1980    0.1130    0.0431    0.1420 \n",
+      "[1] \"PP abf for shared variant: 14.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2690    0.1060    0.1720    0.0635    0.3900 \n",
+      "[1] \"PP abf for shared variant: 39%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-04  4.68e-05  2.33e-01  8.48e-02  6.82e-01 \n",
+      "[1] \"PP abf for shared variant: 68.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-06  5.00e-07  2.02e-01  7.20e-02  7.26e-01 \n",
+      "[1] \"PP abf for shared variant: 72.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.48e-09  1.37e-09  2.85e-01  1.06e-01  6.09e-01 \n",
+      "[1] \"PP abf for shared variant: 60.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.00e-13  1.96e-13  1.71e-01  5.97e-02  7.69e-01 \n",
+      "[1] \"PP abf for shared variant: 76.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-13  4.82e-14  2.39e-01  8.73e-02  6.73e-01 \n",
+      "[1] \"PP abf for shared variant: 67.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ATP1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 9.0977e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5330    0.2090    0.0699    0.0258    0.1630 \n",
+      "[1] \"PP abf for shared variant: 16.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1780    0.0699    0.2430    0.0914    0.4170 \n",
+      "[1] \"PP abf for shared variant: 41.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SYNE1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0805    0.0316    0.1730    0.0613    0.6540 \n",
+      "[1] \"PP abf for shared variant: 65.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.137e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5110    0.2010    0.0989    0.0373    0.1520 \n",
+      "[1] \"PP abf for shared variant: 15.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.63e-07  2.21e-07  1.79e-01  6.28e-02  7.58e-01 \n",
+      "[1] \"PP abf for shared variant: 75.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.23e-10  1.27e-10  2.71e-01  1.00e-01  6.29e-01 \n",
+      "[1] \"PP abf for shared variant: 62.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PDE3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-04  5.07e-05  2.50e-01  9.16e-02  6.58e-01 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-20  9.07e-21  1.60e-01  5.48e-02  7.86e-01 \n",
+      "[1] \"PP abf for shared variant: 78.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.95e-05  1.16e-05  2.16e-01  7.79e-02  7.06e-01 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZEB2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00835   0.00328   0.24700   0.09070   0.65000 \n",
+      "[1] \"PP abf for shared variant: 65%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___LTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.47e-08  1.76e-08  3.02e-01  1.13e-01  5.85e-01 \n",
+      "[1] \"PP abf for shared variant: 58.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PTPN7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3220    0.1260    0.1930    0.0728    0.2860 \n",
+      "[1] \"PP abf for shared variant: 28.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.03e-13  1.58e-13  1.84e-01  6.50e-02  7.51e-01 \n",
+      "[1] \"PP abf for shared variant: 75.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.98e-11  2.35e-11  1.77e-01  6.18e-02  7.62e-01 \n",
+      "[1] \"PP abf for shared variant: 76.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___IER2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1556e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5450    0.2140    0.0777    0.0292    0.1340 \n",
+      "[1] \"PP abf for shared variant: 13.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-05  9.68e-06  1.91e-01  6.77e-02  7.41e-01 \n",
+      "[1] \"PP abf for shared variant: 74.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-09  4.68e-10  2.68e-01  9.91e-02  6.32e-01 \n",
+      "[1] \"PP abf for shared variant: 63.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.21e-21  3.62e-21  1.73e-01  6.02e-02  7.67e-01 \n",
+      "[1] \"PP abf for shared variant: 76.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.06e-15  2.38e-15  4.38e-01  1.68e-01  3.93e-01 \n",
+      "[1] \"PP abf for shared variant: 39.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CMC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000904  0.000355  0.390000  0.149000  0.460000 \n",
+      "[1] \"PP abf for shared variant: 46%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-04  4.48e-05  3.10e-01  1.16e-01  5.74e-01 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-13  8.07e-14  2.31e-01  8.37e-02  6.86e-01 \n",
+      "[1] \"PP abf for shared variant: 68.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GZMB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0274    0.0108    0.2530    0.0934    0.6150 \n",
+      "[1] \"PP abf for shared variant: 61.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___AKNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5590    0.2200    0.0832    0.0316    0.1060 \n",
+      "[1] \"PP abf for shared variant: 10.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9277e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4320    0.1700    0.1160    0.0431    0.2390 \n",
+      "[1] \"PP abf for shared variant: 23.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-19  4.73e-20  2.96e-01  1.10e-01  5.93e-01 \n",
+      "[1] \"PP abf for shared variant: 59.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-08  9.74e-09  2.20e-01  7.93e-02  7.01e-01 \n",
+      "[1] \"PP abf for shared variant: 70.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-04  7.74e-05  2.28e-01  8.25e-02  6.89e-01 \n",
+      "[1] \"PP abf for shared variant: 68.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0325    0.0128    0.2320    0.0846    0.6380 \n",
+      "[1] \"PP abf for shared variant: 63.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0308    0.0121    0.2660    0.0987    0.5920 \n",
+      "[1] \"PP abf for shared variant: 59.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-16  6.47e-17  2.10e-01  7.53e-02  7.15e-01 \n",
+      "[1] \"PP abf for shared variant: 71.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.52e-11  1.77e-11  2.95e-01  1.10e-01  5.95e-01 \n",
+      "[1] \"PP abf for shared variant: 59.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-14  4.11e-15  3.27e-01  1.23e-01  5.50e-01 \n",
+      "[1] \"PP abf for shared variant: 55%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___METRNL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4496e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5600    0.2200    0.0719    0.0270    0.1220 \n",
+      "[1] \"PP abf for shared variant: 12.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0323    0.0127    0.4080    0.1560    0.3910 \n",
+      "[1] \"PP abf for shared variant: 39.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CAMK4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-07  8.57e-08  1.88e-01  6.66e-02  7.45e-01 \n",
+      "[1] \"PP abf for shared variant: 74.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___BHLHE40__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01000   0.00393   0.24200   0.08830   0.65600 \n",
+      "[1] \"PP abf for shared variant: 65.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___IFITM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2604e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4730    0.1860    0.1180    0.0444    0.1790 \n",
+      "[1] \"PP abf for shared variant: 17.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0674    0.0265    0.3270    0.1240    0.4550 \n",
+      "[1] \"PP abf for shared variant: 45.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CD8B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-04  6.45e-05  2.61e-01  9.60e-02  6.43e-01 \n",
+      "[1] \"PP abf for shared variant: 64.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-18  6.51e-19  2.04e-01  7.28e-02  7.23e-01 \n",
+      "[1] \"PP abf for shared variant: 72.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NGFRAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0696    0.0273    0.2930    0.1100    0.5010 \n",
+      "[1] \"PP abf for shared variant: 50.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-13  4.38e-14  2.53e-01  9.27e-02  6.55e-01 \n",
+      "[1] \"PP abf for shared variant: 65.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TUBA4A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0548    0.0215    0.2640    0.0981    0.5620 \n",
+      "[1] \"PP abf for shared variant: 56.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.54e-05  1.39e-05  3.04e-01  1.14e-01  5.82e-01 \n",
+      "[1] \"PP abf for shared variant: 58.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.75e-20  1.86e-20  1.65e-01  5.72e-02  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RCAN3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-06  1.68e-06  2.08e-01  7.47e-02  7.17e-01 \n",
+      "[1] \"PP abf for shared variant: 71.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.86e-13  1.51e-13  2.19e-01  7.90e-02  7.02e-01 \n",
+      "[1] \"PP abf for shared variant: 70.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-04  7.17e-05  2.26e-01  8.17e-02  6.92e-01 \n",
+      "[1] \"PP abf for shared variant: 69.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-11  1.21e-11  1.94e-01  6.87e-02  7.38e-01 \n",
+      "[1] \"PP abf for shared variant: 73.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-08  1.19e-08  2.09e-01  7.49e-02  7.16e-01 \n",
+      "[1] \"PP abf for shared variant: 71.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___DNAJB6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1210    0.0476    0.2160    0.0795    0.5350 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RAP1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.077e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5670    0.2230    0.0639    0.0239    0.1230 \n",
+      "[1] \"PP abf for shared variant: 12.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-05  8.80e-06  2.57e-01  9.45e-02  6.48e-01 \n",
+      "[1] \"PP abf for shared variant: 64.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001510  0.000593  0.228000  0.082500  0.688000 \n",
+      "[1] \"PP abf for shared variant: 68.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00432   0.00170   0.23500   0.08570   0.67300 \n",
+      "[1] \"PP abf for shared variant: 67.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CCDC104__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5190    0.2040    0.0799    0.0297    0.1680 \n",
+      "[1] \"PP abf for shared variant: 16.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-08  4.60e-09  3.21e-01  1.21e-01  5.58e-01 \n",
+      "[1] \"PP abf for shared variant: 55.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.15e-08  1.24e-08  3.32e-01  1.25e-01  5.43e-01 \n",
+      "[1] \"PP abf for shared variant: 54.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-06  1.03e-06  2.32e-01  8.43e-02  6.84e-01 \n",
+      "[1] \"PP abf for shared variant: 68.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-18  4.12e-19  1.69e-01  5.85e-02  7.73e-01 \n",
+      "[1] \"PP abf for shared variant: 77.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0375    0.0147    0.2500    0.0919    0.6060 \n",
+      "[1] \"PP abf for shared variant: 60.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TESPA1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01150   0.00453   0.22600   0.08210   0.67600 \n",
+      "[1] \"PP abf for shared variant: 67.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01820   0.00715   0.29700   0.11100   0.56600 \n",
+      "[1] \"PP abf for shared variant: 56.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PSMA7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2640    0.1040    0.1850    0.0689    0.3780 \n",
+      "[1] \"PP abf for shared variant: 37.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PLEK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2350    0.0923    0.1940    0.0721    0.4070 \n",
+      "[1] \"PP abf for shared variant: 40.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-04  5.92e-05  2.17e-01  7.84e-02  7.04e-01 \n",
+      "[1] \"PP abf for shared variant: 70.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.82e-16  1.11e-16  1.81e-01  6.35e-02  7.56e-01 \n",
+      "[1] \"PP abf for shared variant: 75.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.4281e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3700    0.1450    0.1350    0.0499    0.3010 \n",
+      "[1] \"PP abf for shared variant: 30.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___KLRD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00394   0.00155   0.20000   0.07130   0.72300 \n",
+      "[1] \"PP abf for shared variant: 72.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MYC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00402   0.00158   0.24900   0.09140   0.65400 \n",
+      "[1] \"PP abf for shared variant: 65.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2180    0.0858    0.1820    0.0670    0.4470 \n",
+      "[1] \"PP abf for shared variant: 44.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___KLF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.391e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.449     0.176     0.130     0.049     0.196 \n",
+      "[1] \"PP abf for shared variant: 19.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3470    0.1360    0.1590    0.0595    0.2980 \n",
+      "[1] \"PP abf for shared variant: 29.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HNRNPA2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4940    0.1940    0.1090    0.0411    0.1620 \n",
+      "[1] \"PP abf for shared variant: 16.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ARAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3907e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.6120    0.2400    0.0522    0.0197    0.0759 \n",
+      "[1] \"PP abf for shared variant: 7.59%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-16  6.29e-17  2.49e-01  9.12e-02  6.60e-01 \n",
+      "[1] \"PP abf for shared variant: 66%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.15e-06  2.81e-06  1.80e-01  6.33e-02  7.56e-01 \n",
+      "[1] \"PP abf for shared variant: 75.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-05  6.54e-06  1.87e-01  6.58e-02  7.48e-01 \n",
+      "[1] \"PP abf for shared variant: 74.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.82e-13  1.50e-13  1.73e-01  6.04e-02  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.26e-06  1.67e-06  2.15e-01  7.74e-02  7.07e-01 \n",
+      "[1] \"PP abf for shared variant: 70.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.49e-06  3.33e-06  3.00e-01  1.12e-01  5.87e-01 \n",
+      "[1] \"PP abf for shared variant: 58.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1240    0.0486    0.1890    0.0684    0.5700 \n",
+      "[1] \"PP abf for shared variant: 57%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.92e-10  2.32e-10  1.67e-01  5.78e-02  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.58e-19  2.59e-19  1.65e-01  5.72e-02  7.77e-01 \n",
+      "[1] \"PP abf for shared variant: 77.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___LYPD3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3250    0.1280    0.2080    0.0789    0.2610 \n",
+      "[1] \"PP abf for shared variant: 26.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.75e-05  1.87e-05  1.91e-01  6.75e-02  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1740    0.0683    0.2090    0.0773    0.4720 \n",
+      "[1] \"PP abf for shared variant: 47.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0348    0.0136    0.1920    0.0684    0.6920 \n",
+      "[1] \"PP abf for shared variant: 69.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM243\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001360  0.000535  0.251000  0.092100  0.655000 \n",
+      "[1] \"PP abf for shared variant: 65.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-15  7.05e-16  2.23e-01  8.08e-02  6.96e-01 \n",
+      "[1] \"PP abf for shared variant: 69.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ID2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1140    0.0448    0.2200    0.0808    0.5410 \n",
+      "[1] \"PP abf for shared variant: 54.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.84e-15  1.90e-15  2.20e-01  7.95e-02  7.00e-01 \n",
+      "[1] \"PP abf for shared variant: 70%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.77e-10  3.05e-10  2.50e-01  9.16e-02  6.58e-01 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00370   0.00145   0.36900   0.14000   0.48500 \n",
+      "[1] \"PP abf for shared variant: 48.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-25  4.22e-26  1.73e-01  6.03e-02  7.67e-01 \n",
+      "[1] \"PP abf for shared variant: 76.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3440    0.1350    0.1810    0.0685    0.2710 \n",
+      "[1] \"PP abf for shared variant: 27.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NFKBIA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 7.944e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4990    0.1960    0.0977    0.0367    0.1710 \n",
+      "[1] \"PP abf for shared variant: 17.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0284    0.0111    0.2850    0.1060    0.5690 \n",
+      "[1] \"PP abf for shared variant: 56.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-17  7.61e-18  1.68e-01  5.81e-02  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-15  5.87e-16  2.65e-01  9.77e-02  6.37e-01 \n",
+      "[1] \"PP abf for shared variant: 63.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.57e-10  3.36e-10  2.52e-01  9.23e-02  6.56e-01 \n",
+      "[1] \"PP abf for shared variant: 65.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GZMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.96e-08  3.91e-08  1.70e-01  5.92e-02  7.70e-01 \n",
+      "[1] \"PP abf for shared variant: 77%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1800    0.0706    0.1850    0.0676    0.4970 \n",
+      "[1] \"PP abf for shared variant: 49.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.24e-11  1.66e-11  1.65e-01  5.69e-02  7.78e-01 \n",
+      "[1] \"PP abf for shared variant: 77.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPI1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3000    0.1180    0.1590    0.0587    0.3650 \n",
+      "[1] \"PP abf for shared variant: 36.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-18  5.09e-19  2.60e-01  9.57e-02  6.44e-01 \n",
+      "[1] \"PP abf for shared variant: 64.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1180    0.0462    0.2630    0.0986    0.4740 \n",
+      "[1] \"PP abf for shared variant: 47.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-10  4.25e-11  1.66e-01  5.76e-02  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.16e-15  8.50e-16  2.44e-01  8.92e-02  6.67e-01 \n",
+      "[1] \"PP abf for shared variant: 66.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SC5D\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3720    0.1460    0.2120    0.0812    0.1890 \n",
+      "[1] \"PP abf for shared variant: 18.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.68e-11  3.80e-11  2.23e-01  8.08e-02  6.96e-01 \n",
+      "[1] \"PP abf for shared variant: 69.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.35e-18  2.10e-18  1.55e-01  5.29e-02  7.92e-01 \n",
+      "[1] \"PP abf for shared variant: 79.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RIC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001460  0.000574  0.299000  0.111000  0.588000 \n",
+      "[1] \"PP abf for shared variant: 58.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-11  7.16e-12  2.18e-01  7.85e-02  7.04e-01 \n",
+      "[1] \"PP abf for shared variant: 70.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.82e-05  1.11e-05  3.66e-01  1.39e-01  4.95e-01 \n",
+      "[1] \"PP abf for shared variant: 49.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.9814e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5490    0.2160    0.0875    0.0332    0.1140 \n",
+      "[1] \"PP abf for shared variant: 11.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRP14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-04  5.29e-05  2.99e-01  1.11e-01  5.89e-01 \n",
+      "[1] \"PP abf for shared variant: 58.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0298e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.450     0.177     0.105     0.039     0.229 \n",
+      "[1] \"PP abf for shared variant: 22.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000965  0.000379  0.307000  0.115000  0.576000 \n",
+      "[1] \"PP abf for shared variant: 57.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.93e-07  3.51e-07  3.20e-01  1.20e-01  5.60e-01 \n",
+      "[1] \"PP abf for shared variant: 56%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-16  1.09e-16  2.69e-01  9.94e-02  6.32e-01 \n",
+      "[1] \"PP abf for shared variant: 63.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-19  7.20e-20  1.54e-01  5.26e-02  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-19  6.16e-20  2.00e-01  7.13e-02  7.29e-01 \n",
+      "[1] \"PP abf for shared variant: 72.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MYL12B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4460    0.1750    0.1300    0.0491    0.2000 \n",
+      "[1] \"PP abf for shared variant: 20%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01370   0.00539   0.19200   0.06830   0.72000 \n",
+      "[1] \"PP abf for shared variant: 72%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MIR142__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1648e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.6030    0.2370    0.0607    0.0231    0.0770 \n",
+      "[1] \"PP abf for shared variant: 7.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00570   0.00224   0.31000   0.11600   0.56500 \n",
+      "[1] \"PP abf for shared variant: 56.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.11e-18  2.79e-18  1.62e-01  5.59e-02  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1370    0.0537    0.1600    0.0570    0.5920 \n",
+      "[1] \"PP abf for shared variant: 59.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-06  1.43e-06  1.87e-01  6.59e-02  7.47e-01 \n",
+      "[1] \"PP abf for shared variant: 74.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PTP4A2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3100    0.1220    0.1650    0.0613    0.3420 \n",
+      "[1] \"PP abf for shared variant: 34.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.9666e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5470    0.2150    0.0723    0.0270    0.1390 \n",
+      "[1] \"PP abf for shared variant: 13.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.30e-05  2.08e-05  2.35e-01  8.56e-02  6.79e-01 \n",
+      "[1] \"PP abf for shared variant: 67.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-08  1.23e-08  2.58e-01  9.49e-02  6.47e-01 \n",
+      "[1] \"PP abf for shared variant: 64.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.47e-25  1.76e-25  3.35e-01  1.26e-01  5.39e-01 \n",
+      "[1] \"PP abf for shared variant: 53.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CCDC57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.50e-06  9.81e-07  3.16e-01  1.18e-01  5.65e-01 \n",
+      "[1] \"PP abf for shared variant: 56.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ITGB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4210    0.1650    0.1240    0.0462    0.2430 \n",
+      "[1] \"PP abf for shared variant: 24.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0553    0.0217    0.2480    0.0918    0.5830 \n",
+      "[1] \"PP abf for shared variant: 58.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4185e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5480    0.2150    0.0815    0.0308    0.1240 \n",
+      "[1] \"PP abf for shared variant: 12.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ARF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0799    0.0314    0.1790    0.0640    0.6450 \n",
+      "[1] \"PP abf for shared variant: 64.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CD81__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2450    0.0961    0.2630    0.1000    0.2960 \n",
+      "[1] \"PP abf for shared variant: 29.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002530  0.000995  0.224000  0.081200  0.691000 \n",
+      "[1] \"PP abf for shared variant: 69.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002510  0.000985  0.237000  0.086200  0.674000 \n",
+      "[1] \"PP abf for shared variant: 67.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000892  0.000350  0.268000  0.098800  0.632000 \n",
+      "[1] \"PP abf for shared variant: 63.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.22e-05  2.05e-05  3.06e-01  1.15e-01  5.79e-01 \n",
+      "[1] \"PP abf for shared variant: 57.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FOXP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00104   0.00041   0.37200   0.14100   0.48500 \n",
+      "[1] \"PP abf for shared variant: 48.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CCL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.27e-05  2.07e-05  2.35e-01  8.55e-02  6.80e-01 \n",
+      "[1] \"PP abf for shared variant: 68%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01110   0.00436   0.22700   0.08230   0.67500 \n",
+      "[1] \"PP abf for shared variant: 67.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___KLRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9856e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.483     0.190     0.104     0.039     0.184 \n",
+      "[1] \"PP abf for shared variant: 18.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5042e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5950    0.2340    0.0680    0.0259    0.0779 \n",
+      "[1] \"PP abf for shared variant: 7.79%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1710    0.0671    0.1910    0.0701    0.5010 \n",
+      "[1] \"PP abf for shared variant: 50.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-15  6.99e-16  2.54e-01  9.32e-02  6.53e-01 \n",
+      "[1] \"PP abf for shared variant: 65.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___C1orf21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1023e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4710    0.1850    0.1070    0.0401    0.1960 \n",
+      "[1] \"PP abf for shared variant: 19.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-10  9.53e-11  3.02e-01  1.13e-01  5.86e-01 \n",
+      "[1] \"PP abf for shared variant: 58.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CALM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1290    0.0507    0.2860    0.1080    0.4250 \n",
+      "[1] \"PP abf for shared variant: 42.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HOPX__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3220    0.1260    0.2150    0.0821    0.2540 \n",
+      "[1] \"PP abf for shared variant: 25.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0277    0.0109    0.2490    0.0916    0.6210 \n",
+      "[1] \"PP abf for shared variant: 62.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.26e-16  3.64e-16  1.66e-01  5.75e-02  7.76e-01 \n",
+      "[1] \"PP abf for shared variant: 77.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000428  0.000168  0.208000  0.074700  0.716000 \n",
+      "[1] \"PP abf for shared variant: 71.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.33e-16  1.31e-16  2.58e-01  9.48e-02  6.47e-01 \n",
+      "[1] \"PP abf for shared variant: 64.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00658   0.00258   0.27400   0.10100   0.61600 \n",
+      "[1] \"PP abf for shared variant: 61.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-10  6.52e-11  3.09e-01  1.16e-01  5.75e-01 \n",
+      "[1] \"PP abf for shared variant: 57.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.507e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5930    0.2330    0.0759    0.0291    0.0686 \n",
+      "[1] \"PP abf for shared variant: 6.86%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-18  1.40e-18  1.75e-01  6.11e-02  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.73e-14  1.86e-14  2.08e-01  7.46e-02  7.17e-01 \n",
+      "[1] \"PP abf for shared variant: 71.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PTMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00268   0.00105   0.24100   0.08810   0.66700 \n",
+      "[1] \"PP abf for shared variant: 66.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-10  6.88e-11  2.71e-01  1.00e-01  6.29e-01 \n",
+      "[1] \"PP abf for shared variant: 62.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GNG2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1760    0.0689    0.2280    0.0850    0.4430 \n",
+      "[1] \"PP abf for shared variant: 44.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02330   0.00914   0.26900   0.09950   0.59900 \n",
+      "[1] \"PP abf for shared variant: 59.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.89e-07  3.88e-07  2.95e-01  1.10e-01  5.95e-01 \n",
+      "[1] \"PP abf for shared variant: 59.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-10  1.14e-10  2.16e-01  7.77e-02  7.07e-01 \n",
+      "[1] \"PP abf for shared variant: 70.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000700  0.000275  0.287000  0.107000  0.606000 \n",
+      "[1] \"PP abf for shared variant: 60.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CD27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.689e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4430    0.1740    0.1150    0.0428    0.2260 \n",
+      "[1] \"PP abf for shared variant: 22.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00296   0.00116   0.26900   0.09920   0.62800 \n",
+      "[1] \"PP abf for shared variant: 62.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.45e-10  3.70e-10  2.37e-01  8.61e-02  6.77e-01 \n",
+      "[1] \"PP abf for shared variant: 67.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.61e-16  1.42e-16  2.17e-01  7.82e-02  7.05e-01 \n",
+      "[1] \"PP abf for shared variant: 70.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___REL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.691e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5220    0.2050    0.1160    0.0444    0.1120 \n",
+      "[1] \"PP abf for shared variant: 11.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-11  6.34e-12  2.55e-01  9.35e-02  6.52e-01 \n",
+      "[1] \"PP abf for shared variant: 65.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2950    0.1160    0.1870    0.0701    0.3310 \n",
+      "[1] \"PP abf for shared variant: 33.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.99e-09  2.75e-09  1.91e-01  6.75e-02  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-13  9.85e-14  3.12e-01  1.17e-01  5.71e-01 \n",
+      "[1] \"PP abf for shared variant: 57.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.26e-07  1.67e-07  2.75e-01  1.02e-01  6.24e-01 \n",
+      "[1] \"PP abf for shared variant: 62.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SEPT7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0879    0.0345    0.2220    0.0813    0.5740 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1100    0.0430    0.2520    0.0938    0.5020 \n",
+      "[1] \"PP abf for shared variant: 50.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3630    0.1430    0.1490    0.0557    0.2900 \n",
+      "[1] \"PP abf for shared variant: 29%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.43e-09  1.74e-09  3.76e-01  1.43e-01  4.82e-01 \n",
+      "[1] \"PP abf for shared variant: 48.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___FCGR3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0446    0.0175    0.2390    0.0877    0.6110 \n",
+      "[1] \"PP abf for shared variant: 61.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PSMB9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.645e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5180    0.2040    0.1030    0.0392    0.1360 \n",
+      "[1] \"PP abf for shared variant: 13.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.63e-09  1.42e-09  2.61e-01  9.62e-02  6.43e-01 \n",
+      "[1] \"PP abf for shared variant: 64.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.54e-08  9.96e-09  2.55e-01  9.38e-02  6.51e-01 \n",
+      "[1] \"PP abf for shared variant: 65.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3720    0.1460    0.1680    0.0633    0.2510 \n",
+      "[1] \"PP abf for shared variant: 25.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000976  0.000383  0.263000  0.096800  0.639000 \n",
+      "[1] \"PP abf for shared variant: 63.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.40e-16  2.51e-16  2.56e-01  9.42e-02  6.49e-01 \n",
+      "[1] \"PP abf for shared variant: 64.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-13  4.85e-14  2.00e-01  7.14e-02  7.28e-01 \n",
+      "[1] \"PP abf for shared variant: 72.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1000    0.0393    0.2970    0.1120    0.4510 \n",
+      "[1] \"PP abf for shared variant: 45.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0561    0.0220    0.2170    0.0791    0.6250 \n",
+      "[1] \"PP abf for shared variant: 62.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-14  6.86e-15  2.15e-01  7.72e-02  7.08e-01 \n",
+      "[1] \"PP abf for shared variant: 70.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5519e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4370    0.1720    0.1320    0.0499    0.2090 \n",
+      "[1] \"PP abf for shared variant: 20.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.32e-11  3.66e-11  5.20e-01  2.01e-01  2.79e-01 \n",
+      "[1] \"PP abf for shared variant: 27.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___DOK2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2520    0.0991    0.1840    0.0683    0.3960 \n",
+      "[1] \"PP abf for shared variant: 39.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.79e-06  2.67e-06  3.19e-01  1.20e-01  5.62e-01 \n",
+      "[1] \"PP abf for shared variant: 56.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CALR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9449e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3720    0.1460    0.1410    0.0525    0.2890 \n",
+      "[1] \"PP abf for shared variant: 28.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TTC38\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1223e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3890    0.1530    0.1390    0.0521    0.2660 \n",
+      "[1] \"PP abf for shared variant: 26.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01380   0.00543   0.25000   0.09170   0.63900 \n",
+      "[1] \"PP abf for shared variant: 63.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00515   0.00202   0.31400   0.11800   0.56200 \n",
+      "[1] \"PP abf for shared variant: 56.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.79e-09  1.10e-09  2.62e-01  9.65e-02  6.42e-01 \n",
+      "[1] \"PP abf for shared variant: 64.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.47e-10  2.93e-10  2.56e-01  9.41e-02  6.50e-01 \n",
+      "[1] \"PP abf for shared variant: 65%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-06  7.22e-07  1.94e-01  6.89e-02  7.37e-01 \n",
+      "[1] \"PP abf for shared variant: 73.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-16  4.85e-17  1.67e-01  5.80e-02  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PASK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.98e-07  2.74e-07  3.18e-01  1.19e-01  5.63e-01 \n",
+      "[1] \"PP abf for shared variant: 56.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-10  8.79e-11  2.97e-01  1.11e-01  5.93e-01 \n",
+      "[1] \"PP abf for shared variant: 59.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-17  7.01e-18  1.75e-01  6.10e-02  7.64e-01 \n",
+      "[1] \"PP abf for shared variant: 76.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___OXNAD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1359e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5110    0.2010    0.1590    0.0617    0.0675 \n",
+      "[1] \"PP abf for shared variant: 6.75%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0364    0.0143    0.3160    0.1190    0.5150 \n",
+      "[1] \"PP abf for shared variant: 51.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.41e-16  1.73e-16  3.37e-01  1.27e-01  5.36e-01 \n",
+      "[1] \"PP abf for shared variant: 53.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000471  0.000185  0.182000  0.063700  0.754000 \n",
+      "[1] \"PP abf for shared variant: 75.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TYROBP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00372   0.00146   0.20800   0.07470   0.71200 \n",
+      "[1] \"PP abf for shared variant: 71.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YBX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1331e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5960    0.2340    0.0592    0.0224    0.0888 \n",
+      "[1] \"PP abf for shared variant: 8.88%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-06  5.19e-07  2.87e-01  1.07e-01  6.06e-01 \n",
+      "[1] \"PP abf for shared variant: 60.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000347  0.000136  0.459000  0.176000  0.364000 \n",
+      "[1] \"PP abf for shared variant: 36.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___IL7R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000838  0.000329  0.219000  0.079100  0.701000 \n",
+      "[1] \"PP abf for shared variant: 70.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RHOH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0305    0.0120    0.3090    0.1160    0.5320 \n",
+      "[1] \"PP abf for shared variant: 53.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.01e-17  3.15e-17  1.62e-01  5.59e-02  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.59e-18  1.41e-18  2.44e-01  8.91e-02  6.67e-01 \n",
+      "[1] \"PP abf for shared variant: 66.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00887   0.00348   0.23200   0.08460   0.67100 \n",
+      "[1] \"PP abf for shared variant: 67.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___DBI__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00542   0.00213   0.27900   0.10300   0.61000 \n",
+      "[1] \"PP abf for shared variant: 61%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.36e-12  1.32e-12  1.57e-01  5.38e-02  7.89e-01 \n",
+      "[1] \"PP abf for shared variant: 78.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-09  6.85e-10  3.27e-01  1.23e-01  5.50e-01 \n",
+      "[1] \"PP abf for shared variant: 55%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-07  1.21e-07  3.08e-01  1.15e-01  5.76e-01 \n",
+      "[1] \"PP abf for shared variant: 57.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___POMP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.292     0.115     0.140     0.051     0.402 \n",
+      "[1] \"PP abf for shared variant: 40.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.43e-15  2.13e-15  2.86e-01  1.06e-01  6.07e-01 \n",
+      "[1] \"PP abf for shared variant: 60.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RAB8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0817e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5900    0.2320    0.0685    0.0261    0.0838 \n",
+      "[1] \"PP abf for shared variant: 8.38%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___GZMH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1030    0.0406    0.2170    0.0795    0.5600 \n",
+      "[1] \"PP abf for shared variant: 56%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.87e-05  3.48e-05  4.40e-01  1.69e-01  3.92e-01 \n",
+      "[1] \"PP abf for shared variant: 39.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-10  6.91e-11  3.12e-01  1.17e-01  5.71e-01 \n",
+      "[1] \"PP abf for shared variant: 57.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-19  7.61e-20  3.34e-01  1.26e-01  5.41e-01 \n",
+      "[1] \"PP abf for shared variant: 54.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-17  8.62e-18  2.55e-01  9.37e-02  6.51e-01 \n",
+      "[1] \"PP abf for shared variant: 65.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___ABLIM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00264   0.00104   0.25000   0.09180   0.65400 \n",
+      "[1] \"PP abf for shared variant: 65.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EIF4A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8946e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4850    0.1900    0.1080    0.0407    0.1760 \n",
+      "[1] \"PP abf for shared variant: 17.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000267  0.000105  0.282000  0.105000  0.613000 \n",
+      "[1] \"PP abf for shared variant: 61.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RP11-291B21.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.68e-06  2.23e-06  1.82e-01  6.41e-02  7.53e-01 \n",
+      "[1] \"PP abf for shared variant: 75.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-19  5.79e-20  2.41e-01  8.79e-02  6.71e-01 \n",
+      "[1] \"PP abf for shared variant: 67.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.81e-19  2.68e-19  1.59e-01  5.47e-02  7.86e-01 \n",
+      "[1] \"PP abf for shared variant: 78.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-09  8.57e-10  2.16e-01  7.77e-02  7.06e-01 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.66e-10  2.62e-10  2.75e-01  1.02e-01  6.23e-01 \n",
+      "[1] \"PP abf for shared variant: 62.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-18  1.07e-18  2.67e-01  9.86e-02  6.34e-01 \n",
+      "[1] \"PP abf for shared variant: 63.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00902   0.00354   0.29700   0.11100   0.57900 \n",
+      "[1] \"PP abf for shared variant: 57.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.96e-14  1.16e-14  2.78e-01  1.03e-01  6.20e-01 \n",
+      "[1] \"PP abf for shared variant: 62%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.00e-12  2.36e-12  2.13e-01  7.65e-02  7.11e-01 \n",
+      "[1] \"PP abf for shared variant: 71.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S1PR5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1943e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.5890    0.2310    0.0681    0.0259    0.0858 \n",
+      "[1] \"PP abf for shared variant: 8.58%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD8T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-04  6.65e-05  3.31e-01  1.25e-01  5.44e-01 \n",
+      "[1] \"PP abf for shared variant: 54.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0419    0.0164    0.2710    0.1010    0.5700 \n",
+      "[1] \"PP abf for shared variant: 57%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CHCHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1310    0.0514    0.2900    0.1100    0.4180 \n",
+      "[1] \"PP abf for shared variant: 41.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000420  0.000165  0.209000  0.074800  0.716000 \n",
+      "[1] \"PP abf for shared variant: 71.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FMNL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1820    0.0714    0.2030    0.0749    0.4690 \n",
+      "[1] \"PP abf for shared variant: 46.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-24  4.88e-25  2.65e-01  9.77e-02  6.37e-01 \n",
+      "[1] \"PP abf for shared variant: 63.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.60e-07  1.02e-07  1.86e-01  6.54e-02  7.49e-01 \n",
+      "[1] \"PP abf for shared variant: 74.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HERPUD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.267e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3830    0.1500    0.1570    0.0591    0.2500 \n",
+      "[1] \"PP abf for shared variant: 25%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ANXA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.20e-06  2.04e-06  1.81e-01  6.36e-02  7.55e-01 \n",
+      "[1] \"PP abf for shared variant: 75.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SOD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01360   0.00536   0.27300   0.10100   0.60700 \n",
+      "[1] \"PP abf for shared variant: 60.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.32e-18  1.70e-18  1.60e-01  5.49e-02  7.85e-01 \n",
+      "[1] \"PP abf for shared variant: 78.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-13  6.75e-14  3.33e-01  1.25e-01  5.42e-01 \n",
+      "[1] \"PP abf for shared variant: 54.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ATP1B3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1950    0.0764    0.2300    0.0861    0.4130 \n",
+      "[1] \"PP abf for shared variant: 41.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3600    0.1420    0.1600    0.0602    0.2770 \n",
+      "[1] \"PP abf for shared variant: 27.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.06e-26  3.56e-26  2.51e-01  9.22e-02  6.56e-01 \n",
+      "[1] \"PP abf for shared variant: 65.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-11  6.90e-12  3.10e-01  1.16e-01  5.74e-01 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EML4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000438  0.000172  0.274000  0.101000  0.625000 \n",
+      "[1] \"PP abf for shared variant: 62.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00138   0.00054   0.21800   0.07850   0.70200 \n",
+      "[1] \"PP abf for shared variant: 70.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MCL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-06  6.14e-07  2.50e-01  9.15e-02  6.59e-01 \n",
+      "[1] \"PP abf for shared variant: 65.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NOG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0408    0.0160    0.2060    0.0741    0.6630 \n",
+      "[1] \"PP abf for shared variant: 66.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PRMT2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001000  0.000393  0.274000  0.101000  0.624000 \n",
+      "[1] \"PP abf for shared variant: 62.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.55e-06  3.36e-06  1.91e-01  6.74e-02  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2950    0.1160    0.1600    0.0593    0.3700 \n",
+      "[1] \"PP abf for shared variant: 37%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.77e-12  1.87e-12  1.74e-01  6.06e-02  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FAM129A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.45e-07  2.53e-07  2.06e-01  7.35e-02  7.21e-01 \n",
+      "[1] \"PP abf for shared variant: 72.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.66e-16  3.40e-16  1.83e-01  6.42e-02  7.53e-01 \n",
+      "[1] \"PP abf for shared variant: 75.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.2936e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3990    0.1570    0.1180    0.0435    0.2830 \n",
+      "[1] \"PP abf for shared variant: 28.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-25  4.65e-26  2.30e-01  8.34e-02  6.87e-01 \n",
+      "[1] \"PP abf for shared variant: 68.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ALOX5AP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001900  0.000748  0.231000  0.083700  0.683000 \n",
+      "[1] \"PP abf for shared variant: 68.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TSHZ2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000383  0.000151  0.180000  0.063300  0.756000 \n",
+      "[1] \"PP abf for shared variant: 75.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-06  9.87e-07  1.89e-01  6.66e-02  7.45e-01 \n",
+      "[1] \"PP abf for shared variant: 74.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ARHGDIB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.62e-06  1.82e-06  1.58e-01  5.42e-02  7.88e-01 \n",
+      "[1] \"PP abf for shared variant: 78.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.98e-10  1.56e-10  3.25e-01  1.22e-01  5.53e-01 \n",
+      "[1] \"PP abf for shared variant: 55.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-20  4.00e-21  3.37e-01  1.27e-01  5.36e-01 \n",
+      "[1] \"PP abf for shared variant: 53.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-28  4.94e-29  3.35e-01  1.26e-01  5.38e-01 \n",
+      "[1] \"PP abf for shared variant: 53.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.67e-28  3.01e-28  3.22e-01  1.21e-01  5.57e-01 \n",
+      "[1] \"PP abf for shared variant: 55.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YBX1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.81e-06  3.07e-06  1.84e-01  6.48e-02  7.51e-01 \n",
+      "[1] \"PP abf for shared variant: 75.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.19e-25  2.43e-25  2.38e-01  8.69e-02  6.75e-01 \n",
+      "[1] \"PP abf for shared variant: 67.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___JUND__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.279e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.287     0.113     0.197     0.074     0.330 \n",
+      "[1] \"PP abf for shared variant: 33%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-07  1.09e-07  2.88e-01  1.07e-01  6.05e-01 \n",
+      "[1] \"PP abf for shared variant: 60.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.84e-27  3.87e-27  2.62e-01  9.65e-02  6.41e-01 \n",
+      "[1] \"PP abf for shared variant: 64.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01490   0.00585   0.43300   0.16600   0.38000 \n",
+      "[1] \"PP abf for shared variant: 38%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.23e-11  1.27e-11  2.65e-01  9.76e-02  6.38e-01 \n",
+      "[1] \"PP abf for shared variant: 63.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF18\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00696   0.00273   0.21100   0.07580   0.70300 \n",
+      "[1] \"PP abf for shared variant: 70.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MYO1F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001290  0.000505  0.187000  0.066000  0.745000 \n",
+      "[1] \"PP abf for shared variant: 74.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.10e-24  2.00e-24  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.95e-09  2.34e-09  3.23e-01  1.21e-01  5.56e-01 \n",
+      "[1] \"PP abf for shared variant: 55.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3230    0.1270    0.1460    0.0537    0.3500 \n",
+      "[1] \"PP abf for shared variant: 35%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.07e-25  3.56e-25  3.31e-01  1.24e-01  5.45e-01 \n",
+      "[1] \"PP abf for shared variant: 54.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.02e-04  7.95e-05  2.75e-01  1.02e-01  6.23e-01 \n",
+      "[1] \"PP abf for shared variant: 62.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VMP1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1810    0.0712    0.1740    0.0632    0.5100 \n",
+      "[1] \"PP abf for shared variant: 51%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.91e-06  7.51e-07  1.79e-01  6.26e-02  7.59e-01 \n",
+      "[1] \"PP abf for shared variant: 75.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-25  1.05e-25  3.01e-01  1.12e-01  5.86e-01 \n",
+      "[1] \"PP abf for shared variant: 58.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2160    0.0849    0.1640    0.0598    0.4750 \n",
+      "[1] \"PP abf for shared variant: 47.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GALM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1890    0.0743    0.1730    0.0631    0.5000 \n",
+      "[1] \"PP abf for shared variant: 50%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01760   0.00691   0.17400   0.06100   0.74000 \n",
+      "[1] \"PP abf for shared variant: 74%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001470  0.000577  0.279000  0.103000  0.615000 \n",
+      "[1] \"PP abf for shared variant: 61.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-14  7.29e-15  3.35e-01  1.26e-01  5.39e-01 \n",
+      "[1] \"PP abf for shared variant: 53.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.39e-16  2.12e-16  2.52e-01  9.23e-02  6.56e-01 \n",
+      "[1] \"PP abf for shared variant: 65.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ANXA5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01530   0.00601   0.31000   0.11600   0.55300 \n",
+      "[1] \"PP abf for shared variant: 55.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___IDS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000463  0.000182  0.215000  0.077400  0.707000 \n",
+      "[1] \"PP abf for shared variant: 70.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ARID5B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0609    0.0239    0.2070    0.0749    0.6330 \n",
+      "[1] \"PP abf for shared variant: 63.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___IMPDH2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.88e-06  2.70e-06  2.88e-01  1.07e-01  6.04e-01 \n",
+      "[1] \"PP abf for shared variant: 60.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.89e-14  1.92e-14  3.30e-01  1.24e-01  5.45e-01 \n",
+      "[1] \"PP abf for shared variant: 54.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ST13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-06  4.40e-07  2.73e-01  1.01e-01  6.26e-01 \n",
+      "[1] \"PP abf for shared variant: 62.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CXCR3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00315   0.00124   0.20000   0.07150   0.72400 \n",
+      "[1] \"PP abf for shared variant: 72.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.10e-07  1.22e-07  1.56e-01  5.35e-02  7.90e-01 \n",
+      "[1] \"PP abf for shared variant: 79%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.31e-15  9.07e-16  3.36e-01  1.27e-01  5.37e-01 \n",
+      "[1] \"PP abf for shared variant: 53.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SPOCK2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01970   0.00775   0.21700   0.07850   0.67700 \n",
+      "[1] \"PP abf for shared variant: 67.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___C15orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1160    0.0454    0.2300    0.0852    0.5230 \n",
+      "[1] \"PP abf for shared variant: 52.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPF\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1448e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4390    0.1720    0.1070    0.0398    0.2410 \n",
+      "[1] \"PP abf for shared variant: 24.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.70e-15  1.85e-15  1.96e-01  6.97e-02  7.34e-01 \n",
+      "[1] \"PP abf for shared variant: 73.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FAM134B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1240    0.0485    0.1730    0.0621    0.5930 \n",
+      "[1] \"PP abf for shared variant: 59.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ISG20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00622   0.00244   0.32000   0.12000   0.55200 \n",
+      "[1] \"PP abf for shared variant: 55.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-10  4.48e-11  1.76e-01  6.13e-02  7.63e-01 \n",
+      "[1] \"PP abf for shared variant: 76.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NUCB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0265    0.0104    0.1860    0.0660    0.7110 \n",
+      "[1] \"PP abf for shared variant: 71.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00206   0.00081   0.23500   0.08540   0.67700 \n",
+      "[1] \"PP abf for shared variant: 67.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LINC00493__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0855    0.0336    0.2720    0.1020    0.5080 \n",
+      "[1] \"PP abf for shared variant: 50.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-25  5.67e-26  3.37e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00263   0.00103   0.21700   0.07820   0.70100 \n",
+      "[1] \"PP abf for shared variant: 70.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.14e-12  1.63e-12  2.06e-01  7.38e-02  7.20e-01 \n",
+      "[1] \"PP abf for shared variant: 72%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CDC42__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00860   0.00338   0.27800   0.10300   0.60700 \n",
+      "[1] \"PP abf for shared variant: 60.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF1B\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0272    0.0107    0.2420    0.0889    0.6310 \n",
+      "[1] \"PP abf for shared variant: 63.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0381    0.0150    0.2670    0.0989    0.5810 \n",
+      "[1] \"PP abf for shared variant: 58.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.43e-18  5.62e-19  3.35e-01  1.26e-01  5.39e-01 \n",
+      "[1] \"PP abf for shared variant: 53.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ACTN4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001060  0.000415  0.219000  0.078800  0.701000 \n",
+      "[1] \"PP abf for shared variant: 70.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___IKZF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0971    0.0381    0.2070    0.0753    0.5830 \n",
+      "[1] \"PP abf for shared variant: 58.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2790    0.1100    0.2110    0.0797    0.3200 \n",
+      "[1] \"PP abf for shared variant: 32%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.32e-17  5.19e-18  2.62e-01  9.64e-02  6.42e-01 \n",
+      "[1] \"PP abf for shared variant: 64.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.08e-09  3.57e-09  2.76e-01  1.02e-01  6.21e-01 \n",
+      "[1] \"PP abf for shared variant: 62.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3140    0.1230    0.1520    0.0559    0.3560 \n",
+      "[1] \"PP abf for shared variant: 35.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NAMPT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8087e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4290    0.1690    0.1800    0.0691    0.1530 \n",
+      "[1] \"PP abf for shared variant: 15.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.38e-06  2.90e-06  1.97e-01  6.99e-02  7.33e-01 \n",
+      "[1] \"PP abf for shared variant: 73.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CALM2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.59e-05  2.20e-05  2.31e-01  8.40e-02  6.84e-01 \n",
+      "[1] \"PP abf for shared variant: 68.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.13e-15  8.35e-16  3.18e-01  1.19e-01  5.62e-01 \n",
+      "[1] \"PP abf for shared variant: 56.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-ND2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1610    0.0633    0.2400    0.0899    0.4450 \n",
+      "[1] \"PP abf for shared variant: 44.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-14  4.05e-15  3.33e-01  1.25e-01  5.42e-01 \n",
+      "[1] \"PP abf for shared variant: 54.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD52__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-04  6.02e-05  1.79e-01  6.27e-02  7.58e-01 \n",
+      "[1] \"PP abf for shared variant: 75.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.72e-08  2.25e-08  2.27e-01  8.21e-02  6.91e-01 \n",
+      "[1] \"PP abf for shared variant: 69.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___H3F3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-05  5.30e-06  2.01e-01  7.16e-02  7.28e-01 \n",
+      "[1] \"PP abf for shared variant: 72.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ADTRP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-08  1.43e-08  2.65e-01  9.75e-02  6.38e-01 \n",
+      "[1] \"PP abf for shared variant: 63.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1130    0.0445    0.2640    0.0990    0.4790 \n",
+      "[1] \"PP abf for shared variant: 47.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0405    0.0159    0.2530    0.0936    0.5960 \n",
+      "[1] \"PP abf for shared variant: 59.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.42e-05  3.31e-05  2.63e-01  9.68e-02  6.40e-01 \n",
+      "[1] \"PP abf for shared variant: 64%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.77e-05  1.09e-05  3.11e-01  1.17e-01  5.72e-01 \n",
+      "[1] \"PP abf for shared variant: 57.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___DYNLL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000489  0.000192  0.182000  0.063900  0.754000 \n",
+      "[1] \"PP abf for shared variant: 75.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAMSN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-05  6.02e-06  1.91e-01  6.75e-02  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LMNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.75e-10  3.83e-10  2.23e-01  8.07e-02  6.96e-01 \n",
+      "[1] \"PP abf for shared variant: 69.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-ND5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-07  5.35e-08  3.23e-01  1.21e-01  5.56e-01 \n",
+      "[1] \"PP abf for shared variant: 55.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.41e-22  3.30e-22  3.14e-01  1.18e-01  5.69e-01 \n",
+      "[1] \"PP abf for shared variant: 56.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RUNX3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0738    0.0290    0.2000    0.0723    0.6250 \n",
+      "[1] \"PP abf for shared variant: 62.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-18  8.60e-19  2.77e-01  1.02e-01  6.21e-01 \n",
+      "[1] \"PP abf for shared variant: 62.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.46e-06  1.36e-06  1.90e-01  6.70e-02  7.43e-01 \n",
+      "[1] \"PP abf for shared variant: 74.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ERGIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.423e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.439     0.172     0.140     0.053     0.196 \n",
+      "[1] \"PP abf for shared variant: 19.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.78e-08  1.09e-08  2.68e-01  9.89e-02  6.33e-01 \n",
+      "[1] \"PP abf for shared variant: 63.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TYMP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02530   0.00994   0.23500   0.08580   0.64400 \n",
+      "[1] \"PP abf for shared variant: 64.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0752    0.0295    0.2170    0.0793    0.5990 \n",
+      "[1] \"PP abf for shared variant: 59.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-22  5.09e-23  3.36e-01  1.27e-01  5.37e-01 \n",
+      "[1] \"PP abf for shared variant: 53.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000296  0.000116  0.279000  0.104000  0.617000 \n",
+      "[1] \"PP abf for shared variant: 61.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.97e-23  1.56e-23  2.54e-01  9.31e-02  6.53e-01 \n",
+      "[1] \"PP abf for shared variant: 65.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-21  1.52e-21  2.44e-01  8.93e-02  6.66e-01 \n",
+      "[1] \"PP abf for shared variant: 66.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-ND4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2230    0.0876    0.1780    0.0656    0.4450 \n",
+      "[1] \"PP abf for shared variant: 44.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000352  0.000138  0.324000  0.122000  0.554000 \n",
+      "[1] \"PP abf for shared variant: 55.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ATP5E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00107   0.00042   0.17200   0.05980   0.76700 \n",
+      "[1] \"PP abf for shared variant: 76.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-10  8.02e-11  3.26e-01  1.22e-01  5.52e-01 \n",
+      "[1] \"PP abf for shared variant: 55.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-13  7.71e-14  1.91e-01  6.76e-02  7.41e-01 \n",
+      "[1] \"PP abf for shared variant: 74.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.00e-22  2.75e-22  3.10e-01  1.16e-01  5.74e-01 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-09  5.37e-10  2.06e-01  7.39e-02  7.20e-01 \n",
+      "[1] \"PP abf for shared variant: 72%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ATP2B4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01910   0.00748   0.20500   0.07370   0.69400 \n",
+      "[1] \"PP abf for shared variant: 69.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ARPC1B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3690    0.1450    0.1340    0.0497    0.3020 \n",
+      "[1] \"PP abf for shared variant: 30.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PDCD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00313   0.00123   0.25900   0.09520   0.64100 \n",
+      "[1] \"PP abf for shared variant: 64.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.10e-21  2.40e-21  2.91e-01  1.08e-01  6.01e-01 \n",
+      "[1] \"PP abf for shared variant: 60.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.54e-26  1.39e-26  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-22  4.95e-23  2.10e-01  7.53e-02  7.15e-01 \n",
+      "[1] \"PP abf for shared variant: 71.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-13  1.07e-13  3.30e-01  1.24e-01  5.46e-01 \n",
+      "[1] \"PP abf for shared variant: 54.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-08  7.75e-09  2.87e-01  1.07e-01  6.06e-01 \n",
+      "[1] \"PP abf for shared variant: 60.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.52e-05  9.91e-06  1.83e-01  6.45e-02  7.52e-01 \n",
+      "[1] \"PP abf for shared variant: 75.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-04  6.92e-05  2.45e-01  8.97e-02  6.65e-01 \n",
+      "[1] \"PP abf for shared variant: 66.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LGALS3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1700    0.0669    0.1710    0.0620    0.5290 \n",
+      "[1] \"PP abf for shared variant: 52.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MIAT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00683   0.00268   0.20300   0.07270   0.71400 \n",
+      "[1] \"PP abf for shared variant: 71.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.36e-21  1.32e-21  3.36e-01  1.27e-01  5.37e-01 \n",
+      "[1] \"PP abf for shared variant: 53.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-08  8.24e-09  2.02e-01  7.21e-02  7.26e-01 \n",
+      "[1] \"PP abf for shared variant: 72.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00841   0.00330   0.19800   0.07040   0.72000 \n",
+      "[1] \"PP abf for shared variant: 72%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-17  6.83e-18  3.15e-01  1.18e-01  5.68e-01 \n",
+      "[1] \"PP abf for shared variant: 56.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.64e-23  1.43e-23  3.36e-01  1.27e-01  5.38e-01 \n",
+      "[1] \"PP abf for shared variant: 53.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RNF19A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001200  0.000472  0.286000  0.106000  0.606000 \n",
+      "[1] \"PP abf for shared variant: 60.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-CO3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-07  8.89e-08  2.60e-01  9.55e-02  6.45e-01 \n",
+      "[1] \"PP abf for shared variant: 64.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-24  4.40e-25  3.27e-01  1.23e-01  5.50e-01 \n",
+      "[1] \"PP abf for shared variant: 55%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.18e-12  3.21e-12  1.84e-01  6.47e-02  7.51e-01 \n",
+      "[1] \"PP abf for shared variant: 75.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FAS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1270    0.0498    0.1980    0.0722    0.5530 \n",
+      "[1] \"PP abf for shared variant: 55.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.09e-10  2.00e-10  2.16e-01  7.76e-02  7.07e-01 \n",
+      "[1] \"PP abf for shared variant: 70.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.60e-10  1.80e-10  2.47e-01  9.02e-02  6.63e-01 \n",
+      "[1] \"PP abf for shared variant: 66.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GYPC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.68e-06  3.02e-06  2.83e-01  1.05e-01  6.11e-01 \n",
+      "[1] \"PP abf for shared variant: 61.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-22  5.66e-23  3.10e-01  1.16e-01  5.74e-01 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-24  7.72e-25  3.35e-01  1.26e-01  5.39e-01 \n",
+      "[1] \"PP abf for shared variant: 53.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPM4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02380   0.00936   0.24600   0.09050   0.63000 \n",
+      "[1] \"PP abf for shared variant: 63%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.82e-12  2.68e-12  3.36e-01  1.26e-01  5.38e-01 \n",
+      "[1] \"PP abf for shared variant: 53.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.87e-05  2.30e-05  2.78e-01  1.03e-01  6.20e-01 \n",
+      "[1] \"PP abf for shared variant: 62%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-23  1.33e-23  2.93e-01  1.09e-01  5.98e-01 \n",
+      "[1] \"PP abf for shared variant: 59.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ITGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-07  8.18e-08  2.02e-01  7.19e-02  7.27e-01 \n",
+      "[1] \"PP abf for shared variant: 72.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.15e-07  2.02e-07  3.29e-01  1.24e-01  5.48e-01 \n",
+      "[1] \"PP abf for shared variant: 54.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01870   0.00736   0.24400   0.08960   0.64000 \n",
+      "[1] \"PP abf for shared variant: 64%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-26  6.69e-27  3.03e-01  1.13e-01  5.84e-01 \n",
+      "[1] \"PP abf for shared variant: 58.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1020    0.0400    0.2450    0.0911    0.5220 \n",
+      "[1] \"PP abf for shared variant: 52.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-18  1.34e-18  2.93e-01  1.09e-01  5.97e-01 \n",
+      "[1] \"PP abf for shared variant: 59.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001280  0.000501  0.259000  0.095300  0.644000 \n",
+      "[1] \"PP abf for shared variant: 64.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000864  0.000339  0.250000  0.091600  0.657000 \n",
+      "[1] \"PP abf for shared variant: 65.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.08e-23  1.21e-23  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.74e-17  1.47e-17  1.87e-01  6.60e-02  7.47e-01 \n",
+      "[1] \"PP abf for shared variant: 74.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0437    0.0171    0.2350    0.0859    0.6190 \n",
+      "[1] \"PP abf for shared variant: 61.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PPP1R15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2280    0.0894    0.1490    0.0538    0.4800 \n",
+      "[1] \"PP abf for shared variant: 48%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___IL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000316  0.000124  0.202000  0.072100  0.725000 \n",
+      "[1] \"PP abf for shared variant: 72.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.72e-26  2.25e-26  3.22e-01  1.21e-01  5.57e-01 \n",
+      "[1] \"PP abf for shared variant: 55.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-16  1.21e-16  3.37e-01  1.27e-01  5.36e-01 \n",
+      "[1] \"PP abf for shared variant: 53.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM20\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2450    0.0961    0.1490    0.0541    0.4560 \n",
+      "[1] \"PP abf for shared variant: 45.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF3F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000322  0.000126  0.192000  0.067900  0.740000 \n",
+      "[1] \"PP abf for shared variant: 74%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00347   0.00136   0.35900   0.13600   0.50100 \n",
+      "[1] \"PP abf for shared variant: 50.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___KLF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-05  7.64e-06  2.87e-01  1.07e-01  6.07e-01 \n",
+      "[1] \"PP abf for shared variant: 60.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GIMAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000325  0.000127  0.213000  0.076400  0.710000 \n",
+      "[1] \"PP abf for shared variant: 71%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFBR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0355    0.0139    0.2130    0.0771    0.6600 \n",
+      "[1] \"PP abf for shared variant: 66%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RNF213__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2270    0.0890    0.1780    0.0654    0.4410 \n",
+      "[1] \"PP abf for shared variant: 44.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___C19orf53__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3910    0.1530    0.1380    0.0517    0.2660 \n",
+      "[1] \"PP abf for shared variant: 26.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.88e-11  2.31e-11  2.67e-01  9.87e-02  6.34e-01 \n",
+      "[1] \"PP abf for shared variant: 63.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.77e-15  1.48e-15  3.34e-01  1.26e-01  5.40e-01 \n",
+      "[1] \"PP abf for shared variant: 54%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MIR4435-1HG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-10  1.03e-10  2.73e-01  1.01e-01  6.26e-01 \n",
+      "[1] \"PP abf for shared variant: 62.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.61e-16  3.38e-16  2.42e-01  8.82e-02  6.70e-01 \n",
+      "[1] \"PP abf for shared variant: 67%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MZT2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000296  0.000116  0.259000  0.095200  0.645000 \n",
+      "[1] \"PP abf for shared variant: 64.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___AK5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0660    0.0259    0.2620    0.0973    0.5490 \n",
+      "[1] \"PP abf for shared variant: 54.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1730    0.0680    0.2410    0.0906    0.4270 \n",
+      "[1] \"PP abf for shared variant: 42.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-08  8.97e-09  2.86e-01  1.06e-01  6.07e-01 \n",
+      "[1] \"PP abf for shared variant: 60.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-20  6.57e-21  3.37e-01  1.27e-01  5.36e-01 \n",
+      "[1] \"PP abf for shared variant: 53.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000282  0.000111  0.319000  0.120000  0.561000 \n",
+      "[1] \"PP abf for shared variant: 56.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.10e-19  2.00e-19  1.96e-01  6.98e-02  7.34e-01 \n",
+      "[1] \"PP abf for shared variant: 73.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01870   0.00733   0.26700   0.09860   0.60900 \n",
+      "[1] \"PP abf for shared variant: 60.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CXCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2640    0.1040    0.1550    0.0566    0.4210 \n",
+      "[1] \"PP abf for shared variant: 42.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ARPC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0763    0.0299    0.2050    0.0745    0.6140 \n",
+      "[1] \"PP abf for shared variant: 61.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MRPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3770    0.1480    0.1490    0.0559    0.2700 \n",
+      "[1] \"PP abf for shared variant: 27%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.12e-15  1.62e-15  2.84e-01  1.06e-01  6.10e-01 \n",
+      "[1] \"PP abf for shared variant: 61%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-06  8.81e-07  3.15e-01  1.18e-01  5.66e-01 \n",
+      "[1] \"PP abf for shared variant: 56.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EBPL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3210    0.1260    0.1690    0.0631    0.3210 \n",
+      "[1] \"PP abf for shared variant: 32.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2000    0.0784    0.1820    0.0667    0.4730 \n",
+      "[1] \"PP abf for shared variant: 47.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UXT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-08  7.82e-09  3.32e-01  1.25e-01  5.43e-01 \n",
+      "[1] \"PP abf for shared variant: 54.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LSM5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000295  0.000116  0.326000  0.123000  0.551000 \n",
+      "[1] \"PP abf for shared variant: 55.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___KMT2E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.6569e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4680    0.1840    0.1420    0.0542    0.1530 \n",
+      "[1] \"PP abf for shared variant: 15.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.43e-07  5.60e-08  3.09e-01  1.16e-01  5.75e-01 \n",
+      "[1] \"PP abf for shared variant: 57.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TAGLN2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4130    0.1620    0.1360    0.0512    0.2380 \n",
+      "[1] \"PP abf for shared variant: 23.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CDCA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4164e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3550    0.1400    0.1300    0.0479    0.3270 \n",
+      "[1] \"PP abf for shared variant: 32.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-06  7.86e-07  2.89e-01  1.07e-01  6.04e-01 \n",
+      "[1] \"PP abf for shared variant: 60.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EPB41L4A-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-06  4.34e-07  2.42e-01  8.85e-02  6.69e-01 \n",
+      "[1] \"PP abf for shared variant: 66.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-08  8.88e-09  1.88e-01  6.64e-02  7.46e-01 \n",
+      "[1] \"PP abf for shared variant: 74.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TATDN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02140   0.00841   0.29400   0.11000   0.56600 \n",
+      "[1] \"PP abf for shared variant: 56.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02120   0.00834   0.18400   0.06490   0.72200 \n",
+      "[1] \"PP abf for shared variant: 72.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.45e-13  9.64e-14  3.19e-01  1.20e-01  5.61e-01 \n",
+      "[1] \"PP abf for shared variant: 56.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.89e-23  7.43e-24  3.37e-01  1.27e-01  5.37e-01 \n",
+      "[1] \"PP abf for shared variant: 53.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.43e-20  1.74e-20  3.29e-01  1.24e-01  5.48e-01 \n",
+      "[1] \"PP abf for shared variant: 54.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___BTG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000303  0.000119  0.294000  0.109000  0.596000 \n",
+      "[1] \"PP abf for shared variant: 59.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___C8orf59__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01230   0.00485   0.20200   0.07220   0.70900 \n",
+      "[1] \"PP abf for shared variant: 70.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD58__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00337   0.00132   0.22500   0.08140   0.68900 \n",
+      "[1] \"PP abf for shared variant: 68.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.27e-18  3.64e-18  2.85e-01  1.06e-01  6.09e-01 \n",
+      "[1] \"PP abf for shared variant: 60.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-06  7.20e-07  1.96e-01  6.95e-02  7.35e-01 \n",
+      "[1] \"PP abf for shared variant: 73.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___AKAP13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01850   0.00728   0.23200   0.08460   0.65800 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-07  1.28e-07  2.51e-01  9.19e-02  6.57e-01 \n",
+      "[1] \"PP abf for shared variant: 65.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___DDX5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.46e-05  1.36e-05  2.83e-01  1.05e-01  6.12e-01 \n",
+      "[1] \"PP abf for shared variant: 61.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01950   0.00768   0.27300   0.10100   0.59900 \n",
+      "[1] \"PP abf for shared variant: 59.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ANXA2R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2760    0.1080    0.2200    0.0834    0.3120 \n",
+      "[1] \"PP abf for shared variant: 31.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___IL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2310    0.0906    0.2210    0.0829    0.3750 \n",
+      "[1] \"PP abf for shared variant: 37.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LINC00152__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.28e-09  1.29e-09  1.58e-01  5.41e-02  7.88e-01 \n",
+      "[1] \"PP abf for shared variant: 78.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FOXP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0459    0.0180    0.3280    0.1240    0.4840 \n",
+      "[1] \"PP abf for shared variant: 48.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-09  7.53e-10  1.60e-01  5.50e-02  7.85e-01 \n",
+      "[1] \"PP abf for shared variant: 78.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.17e-22  2.42e-22  2.33e-01  8.48e-02  6.82e-01 \n",
+      "[1] \"PP abf for shared variant: 68.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.25e-05  2.46e-05  2.29e-01  8.31e-02  6.88e-01 \n",
+      "[1] \"PP abf for shared variant: 68.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.63e-17  3.39e-17  3.27e-01  1.23e-01  5.50e-01 \n",
+      "[1] \"PP abf for shared variant: 55%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-14  4.52e-15  3.37e-01  1.27e-01  5.36e-01 \n",
+      "[1] \"PP abf for shared variant: 53.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___DAP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000508  0.000199  0.214000  0.077000  0.708000 \n",
+      "[1] \"PP abf for shared variant: 70.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-11  6.15e-12  2.38e-01  8.68e-02  6.75e-01 \n",
+      "[1] \"PP abf for shared variant: 67.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SVIP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3190    0.1250    0.1630    0.0608    0.3320 \n",
+      "[1] \"PP abf for shared variant: 33.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.08e-16  3.17e-16  1.99e-01  7.08e-02  7.30e-01 \n",
+      "[1] \"PP abf for shared variant: 73%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-27  1.14e-27  3.15e-01  1.18e-01  5.66e-01 \n",
+      "[1] \"PP abf for shared variant: 56.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.52e-19  3.35e-19  2.31e-01  8.38e-02  6.85e-01 \n",
+      "[1] \"PP abf for shared variant: 68.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-07  5.13e-08  2.31e-01  8.39e-02  6.85e-01 \n",
+      "[1] \"PP abf for shared variant: 68.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01970   0.00773   0.21400   0.07720   0.68100 \n",
+      "[1] \"PP abf for shared variant: 68.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ENTPD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0791    0.0310    0.2130    0.0775    0.5990 \n",
+      "[1] \"PP abf for shared variant: 59.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___DUSP4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.61e-10  1.02e-10  2.22e-01  8.04e-02  6.97e-01 \n",
+      "[1] \"PP abf for shared variant: 69.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-14  6.50e-15  3.25e-01  1.22e-01  5.52e-01 \n",
+      "[1] \"PP abf for shared variant: 55.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001010  0.000397  0.224000  0.081200  0.693000 \n",
+      "[1] \"PP abf for shared variant: 69.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1610    0.0631    0.2180    0.0809    0.4770 \n",
+      "[1] \"PP abf for shared variant: 47.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-ND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00196   0.00077   0.29500   0.11000   0.59200 \n",
+      "[1] \"PP abf for shared variant: 59.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-17  1.07e-17  1.63e-01  5.63e-02  7.80e-01 \n",
+      "[1] \"PP abf for shared variant: 78%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ADAM19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3280    0.1290    0.1850    0.0696    0.2890 \n",
+      "[1] \"PP abf for shared variant: 28.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CLDND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1460    0.0572    0.1680    0.0605    0.5680 \n",
+      "[1] \"PP abf for shared variant: 56.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-07  1.13e-07  1.94e-01  6.90e-02  7.37e-01 \n",
+      "[1] \"PP abf for shared variant: 73.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-09  6.06e-10  3.06e-01  1.14e-01  5.80e-01 \n",
+      "[1] \"PP abf for shared variant: 58%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1310    0.0514    0.1870    0.0679    0.5630 \n",
+      "[1] \"PP abf for shared variant: 56.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___APEX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2420    0.0952    0.1910    0.0709    0.4010 \n",
+      "[1] \"PP abf for shared variant: 40.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-08  4.24e-09  2.20e-01  7.95e-02  7.00e-01 \n",
+      "[1] \"PP abf for shared variant: 70%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.92e-22  1.93e-22  2.30e-01  8.34e-02  6.87e-01 \n",
+      "[1] \"PP abf for shared variant: 68.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LETMD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000719  0.000282  0.255000  0.093600  0.651000 \n",
+      "[1] \"PP abf for shared variant: 65.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001030  0.000406  0.197000  0.070200  0.731000 \n",
+      "[1] \"PP abf for shared variant: 73.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-06  4.23e-07  2.74e-01  1.01e-01  6.24e-01 \n",
+      "[1] \"PP abf for shared variant: 62.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___AHNAK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01040   0.00408   0.33500   0.12600   0.52400 \n",
+      "[1] \"PP abf for shared variant: 52.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SLC7A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4700    0.1850    0.1100    0.0411    0.1940 \n",
+      "[1] \"PP abf for shared variant: 19.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.63e-10  2.21e-10  2.12e-01  7.61e-02  7.12e-01 \n",
+      "[1] \"PP abf for shared variant: 71.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-20  4.28e-21  2.61e-01  9.59e-02  6.44e-01 \n",
+      "[1] \"PP abf for shared variant: 64.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000943  0.000371  0.550000  0.214000  0.234000 \n",
+      "[1] \"PP abf for shared variant: 23.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-28  1.25e-28  2.04e-01  7.29e-02  7.23e-01 \n",
+      "[1] \"PP abf for shared variant: 72.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.27e-13  1.68e-13  2.14e-01  7.69e-02  7.09e-01 \n",
+      "[1] \"PP abf for shared variant: 70.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-25  1.71e-25  2.87e-01  1.07e-01  6.06e-01 \n",
+      "[1] \"PP abf for shared variant: 60.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-ATP6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-05  1.23e-05  3.15e-01  1.18e-01  5.66e-01 \n",
+      "[1] \"PP abf for shared variant: 56.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-18  5.23e-19  2.16e-01  7.79e-02  7.06e-01 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.54e-07  3.35e-07  1.69e-01  5.87e-02  7.72e-01 \n",
+      "[1] \"PP abf for shared variant: 77.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RILPL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1250    0.0489    0.2090    0.0766    0.5410 \n",
+      "[1] \"PP abf for shared variant: 54.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SSR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.33e-05  9.17e-06  2.55e-01  9.38e-02  6.51e-01 \n",
+      "[1] \"PP abf for shared variant: 65.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00275   0.00108   0.22600   0.08180   0.68900 \n",
+      "[1] \"PP abf for shared variant: 68.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.28e-09  1.29e-09  2.58e-01  9.47e-02  6.48e-01 \n",
+      "[1] \"PP abf for shared variant: 64.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-14  8.04e-15  1.67e-01  5.79e-02  7.75e-01 \n",
+      "[1] \"PP abf for shared variant: 77.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MAF__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.00e-06  2.36e-06  2.16e-01  7.77e-02  7.06e-01 \n",
+      "[1] \"PP abf for shared variant: 70.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.35e-10  9.23e-11  2.92e-01  1.09e-01  6.00e-01 \n",
+      "[1] \"PP abf for shared variant: 60%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___COMMD6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000345  0.000135  0.288000  0.107000  0.605000 \n",
+      "[1] \"PP abf for shared variant: 60.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-07  1.35e-07  1.91e-01  6.74e-02  7.42e-01 \n",
+      "[1] \"PP abf for shared variant: 74.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NSMCE1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00366   0.00144   0.29800   0.11100   0.58600 \n",
+      "[1] \"PP abf for shared variant: 58.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.99e-05  1.96e-05  2.64e-01  9.73e-02  6.39e-01 \n",
+      "[1] \"PP abf for shared variant: 63.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PRDX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0598    0.0235    0.2640    0.0981    0.5550 \n",
+      "[1] \"PP abf for shared variant: 55.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-19  4.18e-20  3.26e-01  1.22e-01  5.52e-01 \n",
+      "[1] \"PP abf for shared variant: 55.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FAM46C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01670   0.00655   0.21700   0.07850   0.68100 \n",
+      "[1] \"PP abf for shared variant: 68.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.10e-22  2.40e-22  3.28e-01  1.23e-01  5.49e-01 \n",
+      "[1] \"PP abf for shared variant: 54.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-21  4.28e-22  3.06e-01  1.14e-01  5.80e-01 \n",
+      "[1] \"PP abf for shared variant: 58%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.49e-27  3.33e-27  2.80e-01  1.04e-01  6.16e-01 \n",
+      "[1] \"PP abf for shared variant: 61.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.89e-24  3.10e-24  2.77e-01  1.03e-01  6.20e-01 \n",
+      "[1] \"PP abf for shared variant: 62%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RORA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2270    0.0892    0.1550    0.0562    0.4720 \n",
+      "[1] \"PP abf for shared variant: 47.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000397  0.000156  0.304000  0.114000  0.581000 \n",
+      "[1] \"PP abf for shared variant: 58.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.39e-19  1.72e-19  2.76e-01  1.02e-01  6.21e-01 \n",
+      "[1] \"PP abf for shared variant: 62.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD44__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-05  4.47e-06  2.92e-01  1.09e-01  5.99e-01 \n",
+      "[1] \"PP abf for shared variant: 59.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4Y1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3730    0.1470    0.1730    0.0655    0.2410 \n",
+      "[1] \"PP abf for shared variant: 24.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-06  1.51e-06  1.62e-01  5.57e-02  7.82e-01 \n",
+      "[1] \"PP abf for shared variant: 78.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___COX7A2L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0287    0.0113    0.2370    0.0866    0.6370 \n",
+      "[1] \"PP abf for shared variant: 63.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-21  7.91e-22  3.37e-01  1.27e-01  5.37e-01 \n",
+      "[1] \"PP abf for shared variant: 53.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HADHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0598    0.0235    0.2310    0.0848    0.6010 \n",
+      "[1] \"PP abf for shared variant: 60.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SATB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1520    0.0597    0.1990    0.0729    0.5170 \n",
+      "[1] \"PP abf for shared variant: 51.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UGP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0469    0.0184    0.2720    0.1010    0.5620 \n",
+      "[1] \"PP abf for shared variant: 56.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SBDS\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1950    0.0764    0.1590    0.0575    0.5120 \n",
+      "[1] \"PP abf for shared variant: 51.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SYNE2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00541   0.00213   0.23800   0.08670   0.66800 \n",
+      "[1] \"PP abf for shared variant: 66.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMA7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0448    0.0176    0.2260    0.0824    0.6290 \n",
+      "[1] \"PP abf for shared variant: 62.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-09  4.81e-10  3.26e-01  1.23e-01  5.51e-01 \n",
+      "[1] \"PP abf for shared variant: 55.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NR3C1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0396    0.0156    0.2280    0.0832    0.6340 \n",
+      "[1] \"PP abf for shared variant: 63.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.05e-27  3.16e-27  3.32e-01  1.25e-01  5.43e-01 \n",
+      "[1] \"PP abf for shared variant: 54.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CCT8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01870   0.00734   0.30900   0.11600   0.54900 \n",
+      "[1] \"PP abf for shared variant: 54.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFAIP3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0400    0.0157    0.2600    0.0964    0.5870 \n",
+      "[1] \"PP abf for shared variant: 58.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH2D2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.3190    0.1250    0.1510    0.0558    0.3480 \n",
+      "[1] \"PP abf for shared variant: 34.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.38e-11  1.33e-11  2.04e-01  7.31e-02  7.23e-01 \n",
+      "[1] \"PP abf for shared variant: 72.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CLNS1A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002450  0.000963  0.223000  0.080500  0.693000 \n",
+      "[1] \"PP abf for shared variant: 69.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.50e-09  3.73e-09  2.20e-01  7.93e-02  7.01e-01 \n",
+      "[1] \"PP abf for shared variant: 70.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ATP6V0E1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0534    0.0210    0.2770    0.1030    0.5460 \n",
+      "[1] \"PP abf for shared variant: 54.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-27  3.75e-27  3.35e-01  1.26e-01  5.39e-01 \n",
+      "[1] \"PP abf for shared variant: 53.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2590    0.1020    0.1550    0.0564    0.4280 \n",
+      "[1] \"PP abf for shared variant: 42.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-27  4.76e-28  3.38e-01  1.27e-01  5.35e-01 \n",
+      "[1] \"PP abf for shared variant: 53.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36L2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0309    0.0121    0.2240    0.0815    0.6510 \n",
+      "[1] \"PP abf for shared variant: 65.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF3D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-04  7.61e-05  2.76e-01  1.02e-01  6.21e-01 \n",
+      "[1] \"PP abf for shared variant: 62.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0743    0.0292    0.2850    0.1070    0.5050 \n",
+      "[1] \"PP abf for shared variant: 50.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.39e-19  1.33e-19  3.15e-01  1.18e-01  5.68e-01 \n",
+      "[1] \"PP abf for shared variant: 56.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___APRT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00755   0.00296   0.28000   0.10400   0.60600 \n",
+      "[1] \"PP abf for shared variant: 60.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FYN__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001790  0.000704  0.216000  0.077700  0.704000 \n",
+      "[1] \"PP abf for shared variant: 70.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ANP32B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0646    0.0254    0.2340    0.0861    0.5900 \n",
+      "[1] \"PP abf for shared variant: 59%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01950   0.00768   0.21200   0.07640   0.68400 \n",
+      "[1] \"PP abf for shared variant: 68.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000564  0.000222  0.294000  0.109000  0.596000 \n",
+      "[1] \"PP abf for shared variant: 59.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-27  6.42e-28  2.48e-01  9.07e-02  6.62e-01 \n",
+      "[1] \"PP abf for shared variant: 66.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CMPK1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.00e-05  1.57e-05  3.19e-01  1.20e-01  5.61e-01 \n",
+      "[1] \"PP abf for shared variant: 56.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAZ\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0880    0.0346    0.2330    0.0857    0.5590 \n",
+      "[1] \"PP abf for shared variant: 55.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GIMAP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.97e-08  1.95e-08  3.14e-01  1.18e-01  5.68e-01 \n",
+      "[1] \"PP abf for shared variant: 56.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___COTL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-06  8.74e-07  2.00e-01  7.11e-02  7.29e-01 \n",
+      "[1] \"PP abf for shared variant: 72.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF2S3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.91e-11  1.93e-11  2.73e-01  1.01e-01  6.26e-01 \n",
+      "[1] \"PP abf for shared variant: 62.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HSP90AA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1807e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2990    0.1170    0.1460    0.0536    0.3840 \n",
+      "[1] \"PP abf for shared variant: 38.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MT-CYB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-04  4.54e-05  2.60e-01  9.55e-02  6.45e-01 \n",
+      "[1] \"PP abf for shared variant: 64.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HSPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.2240    0.0878    0.2070    0.0772    0.4040 \n",
+      "[1] \"PP abf for shared variant: 40.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-10  6.39e-11  1.80e-01  6.33e-02  7.56e-01 \n",
+      "[1] \"PP abf for shared variant: 75.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-09  8.66e-10  2.83e-01  1.05e-01  6.12e-01 \n",
+      "[1] \"PP abf for shared variant: 61.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.02e-18  3.54e-18  3.33e-01  1.25e-01  5.42e-01 \n",
+      "[1] \"PP abf for shared variant: 54.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXK\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.33e-05  1.70e-05  2.70e-01  9.98e-02  6.30e-01 \n",
+      "[1] \"PP abf for shared variant: 63%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-16  4.81e-17  2.80e-01  1.04e-01  6.16e-01 \n",
+      "[1] \"PP abf for shared variant: 61.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.84e-14  2.69e-14  3.10e-01  1.16e-01  5.74e-01 \n",
+      "[1] \"PP abf for shared variant: 57.4%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.95e-07  1.95e-07  1.68e-01  5.84e-02  7.73e-01 \n",
+      "[1] \"PP abf for shared variant: 77.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.67e-05  1.83e-05  2.79e-01  1.03e-01  6.17e-01 \n",
+      "[1] \"PP abf for shared variant: 61.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0529    0.0208    0.2250    0.0823    0.6190 \n",
+      "[1] \"PP abf for shared variant: 61.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___LYRM4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1650    0.0650    0.1950    0.0716    0.5030 \n",
+      "[1] \"PP abf for shared variant: 50.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.71e-21  1.46e-21  1.54e-01  5.27e-02  7.93e-01 \n",
+      "[1] \"PP abf for shared variant: 79.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-20  1.88e-20  2.26e-01  8.17e-02  6.93e-01 \n",
+      "[1] \"PP abf for shared variant: 69.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-10  8.84e-11  3.09e-01  1.16e-01  5.76e-01 \n",
+      "[1] \"PP abf for shared variant: 57.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EZR__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000813  0.000319  0.175000  0.061200  0.762000 \n",
+      "[1] \"PP abf for shared variant: 76.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000588  0.000231  0.242000  0.088400  0.669000 \n",
+      "[1] \"PP abf for shared variant: 66.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ATP5O__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-08  4.42e-09  2.26e-01  8.17e-02  6.93e-01 \n",
+      "[1] \"PP abf for shared variant: 69.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0860    0.0338    0.2960    0.1120    0.4720 \n",
+      "[1] \"PP abf for shared variant: 47.2%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.40e-20  3.30e-20  3.05e-01  1.14e-01  5.81e-01 \n",
+      "[1] \"PP abf for shared variant: 58.1%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUCLG2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000183  0.000072  0.258000  0.094800  0.647000 \n",
+      "[1] \"PP abf for shared variant: 64.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CD3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0538    0.0211    0.2640    0.0980    0.5630 \n",
+      "[1] \"PP abf for shared variant: 56.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-20  1.08e-20  3.32e-01  1.25e-01  5.43e-01 \n",
+      "[1] \"PP abf for shared variant: 54.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___NSA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.68e-08  2.23e-08  2.31e-01  8.38e-02  6.85e-01 \n",
+      "[1] \"PP abf for shared variant: 68.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0424    0.0166    0.2580    0.0954    0.5880 \n",
+      "[1] \"PP abf for shared variant: 58.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___HIGD2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.1020    0.0402    0.2400    0.0889    0.5290 \n",
+      "[1] \"PP abf for shared variant: 52.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-09  1.03e-09  2.57e-01  9.44e-02  6.49e-01 \n",
+      "[1] \"PP abf for shared variant: 64.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___IGBP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.002430  0.000953  0.304000  0.114000  0.579000 \n",
+      "[1] \"PP abf for shared variant: 57.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.12e-20  3.19e-20  1.69e-01  5.85e-02  7.73e-01 \n",
+      "[1] \"PP abf for shared variant: 77.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___MYH9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.4750    0.1870    0.1220    0.0464    0.1690 \n",
+      "[1] \"PP abf for shared variant: 16.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-08  9.35e-09  2.02e-01  7.20e-02  7.26e-01 \n",
+      "[1] \"PP abf for shared variant: 72.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___ATP2B1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000584  0.000229  0.246000  0.090000  0.663000 \n",
+      "[1] \"PP abf for shared variant: 66.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-29  8.89e-30  1.52e-01  5.15e-02  7.97e-01 \n",
+      "[1] \"PP abf for shared variant: 79.7%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RBM39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00504   0.00198   0.33100   0.12400   0.53800 \n",
+      "[1] \"PP abf for shared variant: 53.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___CCNG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001950  0.000765  0.307000  0.115000  0.575000 \n",
+      "[1] \"PP abf for shared variant: 57.5%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.61e-16  1.81e-16  2.82e-01  1.05e-01  6.13e-01 \n",
+      "[1] \"PP abf for shared variant: 61.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___COX4I1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00582   0.00229   0.28300   0.10500   0.60300 \n",
+      "[1] \"PP abf for shared variant: 60.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___PMAIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0377    0.0148    0.2400    0.0881    0.6190 \n",
+      "[1] \"PP abf for shared variant: 61.9%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.64e-11  1.43e-11  1.90e-01  6.72e-02  7.43e-01 \n",
+      "[1] \"PP abf for shared variant: 74.3%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.43e-07  5.63e-08  1.93e-01  6.85e-02  7.38e-01 \n",
+      "[1] \"PP abf for shared variant: 73.8%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___FHIT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-10  4.43e-11  1.74e-01  6.05e-02  7.66e-01 \n",
+      "[1] \"PP abf for shared variant: 76.6%\"\n",
+      "[1] \"Asthma\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-05  8.22e-06  3.07e-01  1.15e-01  5.78e-01 \n",
+      "[1] \"PP abf for shared variant: 57.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___CAPG__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.022600  0.002380  0.881000  0.092900  0.000823 \n",
+      "[1] \"PP abf for shared variant: 0.0823%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___PTAFR__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.068700  0.007250  0.835000  0.088100  0.000912 \n",
+      "[1] \"PP abf for shared variant: 0.0912%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___MNDA__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.5916e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.45100   0.04760   0.45300   0.04770   0.00077 \n",
+      "[1] \"PP abf for shared variant: 0.077%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___RNASE6__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.162000  0.017100  0.742000  0.078200  0.000821 \n",
+      "[1] \"PP abf for shared variant: 0.0821%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__TSPO\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.202000  0.021300  0.702000  0.074100  0.000813 \n",
+      "[1] \"PP abf for shared variant: 0.0813%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___TMEM176A__VMO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5549e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.448000  0.046700  0.457000  0.047600  0.000977 \n",
+      "[1] \"PP abf for shared variant: 0.0977%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___S100A9__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.204000  0.021500  0.700000  0.073800  0.000788 \n",
+      "[1] \"PP abf for shared variant: 0.0788%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___QPCT__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.8504e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.384000  0.040500  0.520000  0.054700  0.000793 \n",
+      "[1] \"PP abf for shared variant: 0.0793%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___BLVRB__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1205e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.324000  0.034200  0.580000  0.061100  0.000891 \n",
+      "[1] \"PP abf for shared variant: 0.0891%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___LYZ__TMEM176A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.012900  0.001360  0.891000  0.094000  0.000888 \n",
+      "[1] \"PP abf for shared variant: 0.0888%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_TMEM176A___CLEC4A__TMEM176A\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.5652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.471000  0.049700  0.432000  0.045600  0.000761 \n",
+      "[1] \"PP abf for shared variant: 0.0761%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00157   0.00152   0.50400   0.49000   0.00211 \n",
+      "[1] \"PP abf for shared variant: 0.211%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL5__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05090   0.04950   0.45500   0.44200   0.00228 \n",
+      "[1] \"PP abf for shared variant: 0.228%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02470   0.02400   0.48100   0.46800   0.00247 \n",
+      "[1] \"PP abf for shared variant: 0.247%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL32__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00186   0.00181   0.50400   0.49000   0.00210 \n",
+      "[1] \"PP abf for shared variant: 0.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___EEF1A1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05750   0.05590   0.44800   0.43600   0.00257 \n",
+      "[1] \"PP abf for shared variant: 0.257%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL38__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01070   0.01040   0.49500   0.48100   0.00231 \n",
+      "[1] \"PP abf for shared variant: 0.231%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01410   0.01370   0.49200   0.47800   0.00227 \n",
+      "[1] \"PP abf for shared variant: 0.227%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02490   0.02420   0.48100   0.46700   0.00247 \n",
+      "[1] \"PP abf for shared variant: 0.247%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05990   0.05820   0.44600   0.43300   0.00282 \n",
+      "[1] \"PP abf for shared variant: 0.282%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05460   0.05310   0.45100   0.43800   0.00355 \n",
+      "[1] \"PP abf for shared variant: 0.355%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS15A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01590   0.01550   0.49000   0.47600   0.00225 \n",
+      "[1] \"PP abf for shared variant: 0.225%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS8__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01900   0.01840   0.48700   0.47300   0.00236 \n",
+      "[1] \"PP abf for shared variant: 0.236%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05460   0.05300   0.45100   0.43800   0.00314 \n",
+      "[1] \"PP abf for shared variant: 0.314%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000834  0.000810  0.505000  0.491000  0.002090 \n",
+      "[1] \"PP abf for shared variant: 0.209%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___NKG7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0283    0.0275    0.4780    0.4640    0.0024 \n",
+      "[1] \"PP abf for shared variant: 0.24%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___B2M__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00581   0.00565   0.50000   0.48600   0.00215 \n",
+      "[1] \"PP abf for shared variant: 0.215%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01200   0.01170   0.49400   0.48000   0.00236 \n",
+      "[1] \"PP abf for shared variant: 0.236%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___PFN1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00402   0.00391   0.50200   0.48800   0.00211 \n",
+      "[1] \"PP abf for shared variant: 0.211%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS28__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01380   0.01340   0.49200   0.47800   0.00251 \n",
+      "[1] \"PP abf for shared variant: 0.251%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01530   0.01490   0.49100   0.47700   0.00215 \n",
+      "[1] \"PP abf for shared variant: 0.215%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___GZMH__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00410   0.00398   0.50200   0.48800   0.00209 \n",
+      "[1] \"PP abf for shared variant: 0.209%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___LTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00791   0.00769   0.49800   0.48400   0.00231 \n",
+      "[1] \"PP abf for shared variant: 0.231%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL39__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01090   0.01060   0.49500   0.48100   0.00217 \n",
+      "[1] \"PP abf for shared variant: 0.217%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.01570   0.01530   0.49000   0.47700   0.00217 \n",
+      "[1] \"PP abf for shared variant: 0.217%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00952   0.00925   0.49700   0.48300   0.00214 \n",
+      "[1] \"PP abf for shared variant: 0.214%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS23__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00447   0.00434   0.50200   0.48700   0.00225 \n",
+      "[1] \"PP abf for shared variant: 0.225%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS29__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00666   0.00648   0.49900   0.48500   0.00212 \n",
+      "[1] \"PP abf for shared variant: 0.212%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL22__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04420   0.04290   0.46200   0.44900   0.00233 \n",
+      "[1] \"PP abf for shared variant: 0.233%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL9__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0186    0.0181    0.4870    0.4740    0.0022 \n",
+      "[1] \"PP abf for shared variant: 0.22%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04290   0.04170   0.46300   0.45000   0.00225 \n",
+      "[1] \"PP abf for shared variant: 0.225%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03520   0.03420   0.47100   0.45700   0.00248 \n",
+      "[1] \"PP abf for shared variant: 0.248%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.00027483\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.37700   0.36700   0.12700   0.12400   0.00468 \n",
+      "[1] \"PP abf for shared variant: 0.468%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___MAL__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02040   0.01980   0.48600   0.47100   0.00263 \n",
+      "[1] \"PP abf for shared variant: 0.263%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___PRF1__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.05840   0.05670   0.44700   0.43500   0.00258 \n",
+      "[1] \"PP abf for shared variant: 0.258%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000782  0.000760  0.505000  0.491000  0.002090 \n",
+      "[1] \"PP abf for shared variant: 0.209%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS6__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00909   0.00884   0.49700   0.48300   0.00224 \n",
+      "[1] \"PP abf for shared variant: 0.224%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000888  0.000863  0.505000  0.491000  0.002110 \n",
+      "[1] \"PP abf for shared variant: 0.211%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL21__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00581   0.00565   0.50000   0.48600   0.00226 \n",
+      "[1] \"PP abf for shared variant: 0.226%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___SMDT1__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000215  0.000209  0.506000  0.492000  0.002110 \n",
+      "[1] \"PP abf for shared variant: 0.211%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00985   0.00957   0.49600   0.48200   0.00221 \n",
+      "[1] \"PP abf for shared variant: 0.221%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.001010  0.000985  0.505000  0.491000  0.002120 \n",
+      "[1] \"PP abf for shared variant: 0.212%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL34__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00375   0.00364   0.50200   0.48800   0.00212 \n",
+      "[1] \"PP abf for shared variant: 0.212%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL10A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00581   0.00564   0.50000   0.48600   0.00210 \n",
+      "[1] \"PP abf for shared variant: 0.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_SMDT1___RPL30__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00861   0.00837   0.49700   0.48300   0.00225 \n",
+      "[1] \"PP abf for shared variant: 0.225%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL3__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00139   0.00135   0.50400   0.49000   0.00307 \n",
+      "[1] \"PP abf for shared variant: 0.307%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS25__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.04450   0.04320   0.46100   0.44800   0.00314 \n",
+      "[1] \"PP abf for shared variant: 0.314%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL13A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00529   0.00514   0.50100   0.48700   0.00215 \n",
+      "[1] \"PP abf for shared variant: 0.215%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS13__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00327   0.00318   0.50300   0.48900   0.00213 \n",
+      "[1] \"PP abf for shared variant: 0.213%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS4X__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09340   0.09080   0.41200   0.40000   0.00363 \n",
+      "[1] \"PP abf for shared variant: 0.363%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS18__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0763    0.0741    0.4280    0.4160    0.0048 \n",
+      "[1] \"PP abf for shared variant: 0.48%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL31__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07290   0.07090   0.43300   0.42100   0.00296 \n",
+      "[1] \"PP abf for shared variant: 0.296%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS15__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.03610   0.03510   0.47000   0.45700   0.00261 \n",
+      "[1] \"PP abf for shared variant: 0.261%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___ACTB__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00548   0.00533   0.50000   0.48600   0.00235 \n",
+      "[1] \"PP abf for shared variant: 0.235%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL36__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02550   0.02480   0.48000   0.46700   0.00217 \n",
+      "[1] \"PP abf for shared variant: 0.217%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL35A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000409  0.000397  0.506000  0.491000  0.002130 \n",
+      "[1] \"PP abf for shared variant: 0.213%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS12__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.08800   0.08550   0.41800   0.40600   0.00276 \n",
+      "[1] \"PP abf for shared variant: 0.276%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL11__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02030   0.01970   0.48600   0.47200   0.00233 \n",
+      "[1] \"PP abf for shared variant: 0.233%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL14__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0221    0.0215    0.4840    0.4700    0.0022 \n",
+      "[1] \"PP abf for shared variant: 0.22%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL10__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00811   0.00789   0.49500   0.48100   0.00746 \n",
+      "[1] \"PP abf for shared variant: 0.746%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS3A__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02730   0.02650   0.47500   0.46200   0.00936 \n",
+      "[1] \"PP abf for shared variant: 0.936%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS26__SMDT1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 0.0032661\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.37100   0.36100   0.13300   0.12900   0.00622 \n",
+      "[1] \"PP abf for shared variant: 0.622%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___CD48__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02940   0.02850   0.47700   0.46300   0.00218 \n",
+      "[1] \"PP abf for shared variant: 0.218%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPL7__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0517    0.0502    0.4540    0.4410    0.0026 \n",
+      "[1] \"PP abf for shared variant: 0.26%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_SMDT1___RPS27__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.09520   0.09260   0.41100   0.39900   0.00274 \n",
+      "[1] \"PP abf for shared variant: 0.274%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.7380    0.0000    0.2340    0.0277 \n",
+      "[1] \"PP abf for shared variant: 2.77%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.6220    0.0000    0.3640    0.0147 \n",
+      "[1] \"PP abf for shared variant: 1.47%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.3930    0.0000    0.5810    0.0254 \n",
+      "[1] \"PP abf for shared variant: 2.54%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___CLIC3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.4860    0.0000    0.4820    0.0317 \n",
+      "[1] \"PP abf for shared variant: 3.17%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__PTPRCAP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.7190    0.0000    0.2580    0.0238 \n",
+      "[1] \"PP abf for shared variant: 2.38%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___CDKN2D__HLA-DQA2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.5969e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.76700   0.00000   0.22300   0.00959 \n",
+      "[1] \"PP abf for shared variant: 0.959%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__YBX1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0931e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.7410    0.0000    0.1850    0.0738 \n",
+      "[1] \"PP abf for shared variant: 7.38%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.6640    0.0000    0.2810    0.0556 \n",
+      "[1] \"PP abf for shared variant: 5.56%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.43700   0.00000   0.55300   0.00954 \n",
+      "[1] \"PP abf for shared variant: 0.954%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MAP1A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.6980    0.0000    0.2860    0.0157 \n",
+      "[1] \"PP abf for shared variant: 1.57%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___FAM129C__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.5240    0.0000    0.4660    0.0104 \n",
+      "[1] \"PP abf for shared variant: 1.04%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DQA2__MT-CO1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1338e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.7210    0.0000    0.2560    0.0226 \n",
+      "[1] \"PP abf for shared variant: 2.26%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.3630    0.0000    0.6250    0.0117 \n",
+      "[1] \"PP abf for shared variant: 1.17%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___CST3__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.039200  0.000000  0.960000  0.000602 \n",
+      "[1] \"PP abf for shared variant: 0.0602%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.001270  0.000000  0.999000  0.000101 \n",
+      "[1] \"PP abf for shared variant: 0.0101%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.00e+00  1.59e-03  0.00e+00  9.98e-01  8.81e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00881%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.026100  0.000000  0.973000  0.000485 \n",
+      "[1] \"PP abf for shared variant: 0.0485%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.004520  0.000000  0.995000  0.000315 \n",
+      "[1] \"PP abf for shared variant: 0.0315%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DPA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.001110  0.000000  0.999000  0.000119 \n",
+      "[1] \"PP abf for shared variant: 0.0119%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMA__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.002080  0.000000  0.998000  0.000199 \n",
+      "[1] \"PP abf for shared variant: 0.0199%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS23\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.102000  0.000000  0.897000  0.000839 \n",
+      "[1] \"PP abf for shared variant: 0.0839%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DQB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.01480   0.00000   0.98500   0.00045 \n",
+      "[1] \"PP abf for shared variant: 0.045%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.34100   0.00000   0.65600   0.00277 \n",
+      "[1] \"PP abf for shared variant: 0.277%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___EEF1A1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.03810   0.00000   0.96000   0.00173 \n",
+      "[1] \"PP abf for shared variant: 0.173%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPS2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.08120   0.00000   0.91700   0.00185 \n",
+      "[1] \"PP abf for shared variant: 0.185%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.12100   0.00000   0.87700   0.00224 \n",
+      "[1] \"PP abf for shared variant: 0.224%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DMB__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.06740   0.00000   0.93200   0.00103 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.012600  0.000000  0.986000  0.000968 \n",
+      "[1] \"PP abf for shared variant: 0.0968%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__HLA-DRA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.002130  0.000000  0.997000  0.000497 \n",
+      "[1] \"PP abf for shared variant: 0.0497%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_HLA-DQA2___HLA-DQA2__RPL5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.000     0.188     0.000     0.802     0.010 \n",
+      "[1] \"PP abf for shared variant: 1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RNASET2___HLA-DRB5__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.89100   0.00000   0.09940   0.00931 \n",
+      "[1] \"PP abf for shared variant: 0.931%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_HLA-DQA2___CCL5__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.000000  0.028500  0.000000  0.971000  0.000747 \n",
+      "[1] \"PP abf for shared variant: 0.0747%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_HLA-DQA2___CD74__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.4610    0.0000    0.4980    0.0402 \n",
+      "[1] \"PP abf for shared variant: 4.02%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.2920    0.0000    0.6980    0.0102 \n",
+      "[1] \"PP abf for shared variant: 1.02%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__NKG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.03350   0.00000   0.96300   0.00346 \n",
+      "[1] \"PP abf for shared variant: 0.346%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA2__RPL34\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.1370    0.0000    0.8530    0.0102 \n",
+      "[1] \"PP abf for shared variant: 1.02%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_HLA-DQA2___HLA-DQA1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.45200   0.00000   0.53800   0.00993 \n",
+      "[1] \"PP abf for shared variant: 0.993%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_HLA-DQA2___CMC1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.00000   0.10400   0.00000   0.88800   0.00881 \n",
+      "[1] \"PP abf for shared variant: 0.881%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.0691    0.0000    0.8680    0.0633 \n",
+      "[1] \"PP abf for shared variant: 6.33%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.000     0.149     0.000     0.832     0.019 \n",
+      "[1] \"PP abf for shared variant: 1.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.2420    0.0000    0.7180    0.0401 \n",
+      "[1] \"PP abf for shared variant: 4.01%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DPB1__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.2030    0.0000    0.7300    0.0674 \n",
+      "[1] \"PP abf for shared variant: 6.74%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.3470    0.0000    0.6160    0.0368 \n",
+      "[1] \"PP abf for shared variant: 3.68%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___CD52__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.0396    0.0000    0.9480    0.0127 \n",
+      "[1] \"PP abf for shared variant: 1.27%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPS13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.3150    0.0000    0.6590    0.0263 \n",
+      "[1] \"PP abf for shared variant: 2.63%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.4190    0.0000    0.5290    0.0523 \n",
+      "[1] \"PP abf for shared variant: 5.23%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.000     0.138     0.000     0.756     0.106 \n",
+      "[1] \"PP abf for shared variant: 10.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___EEF1B2__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.1170    0.0000    0.8630    0.0198 \n",
+      "[1] \"PP abf for shared variant: 1.98%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "    0.000     0.138     0.000     0.755     0.107 \n",
+      "[1] \"PP abf for shared variant: 10.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___B2M__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.0941    0.0000    0.8490    0.0568 \n",
+      "[1] \"PP abf for shared variant: 5.68%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___GAPDH__HLA-DQA2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.2560    0.0000    0.6590    0.0844 \n",
+      "[1] \"PP abf for shared variant: 8.44%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL32\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.0998    0.0000    0.7940    0.1060 \n",
+      "[1] \"PP abf for shared variant: 10.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__RPL7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.3540    0.0000    0.6360    0.0102 \n",
+      "[1] \"PP abf for shared variant: 1.02%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_HLA-DQA2___HLA-DQA2__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "   0.0000    0.0534    0.0000    0.9270    0.0198 \n",
+      "[1] \"PP abf for shared variant: 1.98%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RNASET2___ITGB1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 0.006240  0.000817  0.873000  0.114000  0.005550 \n",
+      "[1] \"PP abf for shared variant: 0.555%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RNASET2___CRIP1__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.06520   0.00853   0.81400   0.10600   0.00560 \n",
+      "[1] \"PP abf for shared variant: 0.56%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RNASET2___B2M__RNASET2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.02880   0.00377   0.85000   0.11100   0.00605 \n",
+      "[1] \"PP abf for shared variant: 0.605%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RNASET2___ALOX5AP__RNASET2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      "  0.07280   0.00952   0.80600   0.10500   0.00586 \n",
+      "[1] \"PP abf for shared variant: 0.586%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_RPS26___RPS26__RPS8\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.0253e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.63e-57  5.42e-01  3.27e-57  3.82e-01  7.53e-02 \n",
+      "[1] \"PP abf for shared variant: 7.53%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-57  4.19e-01  4.20e-57  4.91e-01  9.04e-02 \n",
+      "[1] \"PP abf for shared variant: 9.04%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"DC_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-57  4.13e-01  3.32e-57  3.87e-01  2.00e-01 \n",
+      "[1] \"PP abf for shared variant: 20%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.34e-57  1.57e-01  5.77e-57  6.74e-01  1.69e-01 \n",
+      "[1] \"PP abf for shared variant: 16.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__UBE2J1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.0878e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-57  3.08e-01  1.99e-57  2.28e-01  4.64e-01 \n",
+      "[1] \"PP abf for shared variant: 46.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.84e-58  4.49e-02  6.38e-57  7.45e-01  2.10e-01 \n",
+      "[1] \"PP abf for shared variant: 21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-57  4.14e-01  3.90e-57  4.55e-01  1.31e-01 \n",
+      "[1] \"PP abf for shared variant: 13.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS13__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1042e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.62e-57  4.24e-01  4.35e-57  5.09e-01  6.74e-02 \n",
+      "[1] \"PP abf for shared variant: 6.74%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__RPS28\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.1644e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.42e-57  6.34e-01  2.68e-57  3.13e-01  5.27e-02 \n",
+      "[1] \"PP abf for shared variant: 5.27%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-60  2.41e-04  4.77e-57  5.55e-01  4.45e-01 \n",
+      "[1] \"PP abf for shared variant: 44.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL39__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.0557e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.17e-57  6.05e-01  3.20e-57  3.75e-01  1.97e-02 \n",
+      "[1] \"PP abf for shared variant: 1.97%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.10e-59  5.97e-03  8.41e-57  9.84e-01  9.70e-03 \n",
+      "[1] \"PP abf for shared variant: 0.97%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.83e-58  5.65e-02  3.31e-57  3.82e-01  5.61e-01 \n",
+      "[1] \"PP abf for shared variant: 56.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.35e-59  2.75e-03  8.34e-57  9.76e-01  2.14e-02 \n",
+      "[1] \"PP abf for shared variant: 2.14%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.29e-57  3.85e-01  4.06e-57  4.74e-01  1.42e-01 \n",
+      "[1] \"PP abf for shared variant: 14.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.45e-57  2.87e-01  5.77e-57  6.75e-01  3.85e-02 \n",
+      "[1] \"PP abf for shared variant: 3.85%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL26__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.7757e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-57  3.76e-01  3.31e-57  3.86e-01  2.38e-01 \n",
+      "[1] \"PP abf for shared variant: 23.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.07e-58  4.76e-02  5.77e-57  6.73e-01  2.80e-01 \n",
+      "[1] \"PP abf for shared variant: 28%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.08e-57  3.61e-01  5.00e-57  5.84e-01  5.49e-02 \n",
+      "[1] \"PP abf for shared variant: 5.49%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS25__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2778e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.44e-57  4.03e-01  4.14e-57  4.84e-01  1.13e-01 \n",
+      "[1] \"PP abf for shared variant: 11.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__RPS29\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0623e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.55e-57  6.50e-01  2.49e-57  2.91e-01  5.93e-02 \n",
+      "[1] \"PP abf for shared variant: 5.93%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.87e-59  9.21e-03  2.69e-57  3.08e-01  6.83e-01 \n",
+      "[1] \"PP abf for shared variant: 68.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.64e-58  4.27e-02  7.79e-57  9.12e-01  4.53e-02 \n",
+      "[1] \"PP abf for shared variant: 4.53%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-57  1.58e-01  7.01e-57  8.21e-01  2.15e-02 \n",
+      "[1] \"PP abf for shared variant: 2.15%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.71e-61  4.34e-05  8.50e-57  9.95e-01  4.97e-03 \n",
+      "[1] \"PP abf for shared variant: 0.497%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS19__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1408e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.93e-57  5.77e-01  1.84e-57  2.13e-01  2.10e-01 \n",
+      "[1] \"PP abf for shared variant: 21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL23__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.791e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-57  3.08e-01  2.53e-57  2.93e-01  3.99e-01 \n",
+      "[1] \"PP abf for shared variant: 39.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL18__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1436e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.84e-57  5.67e-01  2.98e-57  3.49e-01  8.44e-02 \n",
+      "[1] \"PP abf for shared variant: 8.44%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1123e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.72e-57  6.70e-01  1.92e-57  2.23e-01  1.07e-01 \n",
+      "[1] \"PP abf for shared variant: 10.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-57  1.22e-01  6.78e-57  7.93e-01  8.49e-02 \n",
+      "[1] \"PP abf for shared variant: 8.49%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-57  1.49e-01  6.50e-57  7.61e-01  9.04e-02 \n",
+      "[1] \"PP abf for shared variant: 9.04%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.67e-62  8.98e-06  7.67e-57  8.98e-01  1.02e-01 \n",
+      "[1] \"PP abf for shared variant: 10.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.36e-58  7.45e-02  4.58e-57  5.32e-01  3.94e-01 \n",
+      "[1] \"PP abf for shared variant: 39.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-58  1.61e-02  8.27e-57  9.68e-01  1.57e-02 \n",
+      "[1] \"PP abf for shared variant: 1.57%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-61  1.83e-05  8.53e-57  9.99e-01  9.11e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0911%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.89e-58  9.23e-02  7.54e-57  8.83e-01  2.50e-02 \n",
+      "[1] \"PP abf for shared variant: 2.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPL23A__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1639e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.80e-57  5.62e-01  1.85e-57  2.14e-01  2.24e-01 \n",
+      "[1] \"PP abf for shared variant: 22.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"B_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-58  3.10e-02  7.83e-57  9.17e-01  5.24e-02 \n",
+      "[1] \"PP abf for shared variant: 5.24%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.00e-65  1.05e-08  8.54e-57  1.00e+00  3.63e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0363%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.87e-69  9.22e-13  8.54e-57  1.00e+00  1.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0143%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.59e-58  5.38e-02  7.46e-57  8.73e-01  7.36e-02 \n",
+      "[1] \"PP abf for shared variant: 7.36%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-64  2.01e-08  8.54e-57  1.00e+00  2.97e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0297%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.76e-63  1.03e-06  8.54e-57  1.00e+00  2.31e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0231%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-61  4.31e-05  8.50e-57  9.95e-01  4.70e-03 \n",
+      "[1] \"PP abf for shared variant: 0.47%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.39e-73  1.63e-17  8.54e-57  1.00e+00  3.37e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0337%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.37e-67  3.95e-11  8.54e-57  9.99e-01  5.07e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0507%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-68  2.04e-12  8.54e-57  1.00e+00  4.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0499%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-67  4.06e-11  8.54e-57  1.00e+00  2.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0218%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.47e-62  7.58e-06  8.54e-57  1.00e+00  2.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0218%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.71e-60  6.69e-04  7.41e-57  8.66e-01  1.33e-01 \n",
+      "[1] \"PP abf for shared variant: 13.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-67  1.19e-11  8.54e-57  1.00e+00  1.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0178%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.17e-71  2.55e-15  8.54e-57  1.00e+00  2.10e-04 \n",
+      "[1] \"PP abf for shared variant: 0.021%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-58  1.75e-02  7.51e-57  8.78e-01  1.04e-01 \n",
+      "[1] \"PP abf for shared variant: 10.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.99e-58  4.67e-02  7.77e-57  9.09e-01  4.41e-02 \n",
+      "[1] \"PP abf for shared variant: 4.41%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-66  1.23e-10  8.54e-57  1.00e+00  3.57e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0357%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.19e-63  6.08e-07  8.54e-57  1.00e+00  3.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0322%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.65e-59  7.79e-03  8.44e-57  9.88e-01  3.93e-03 \n",
+      "[1] \"PP abf for shared variant: 0.393%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.56e-63  3.00e-07  8.51e-57  9.97e-01  3.48e-03 \n",
+      "[1] \"PP abf for shared variant: 0.348%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.55e-74  7.66e-18  8.54e-57  1.00e+00  3.16e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0316%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-67  3.02e-11  8.54e-57  1.00e+00  3.17e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0317%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-67  2.35e-11  8.51e-57  9.96e-01  4.08e-03 \n",
+      "[1] \"PP abf for shared variant: 0.408%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__SPON2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.79e-59  7.95e-03  8.20e-57  9.59e-01  3.26e-02 \n",
+      "[1] \"PP abf for shared variant: 3.26%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-61  1.36e-05  8.47e-57  9.91e-01  8.54e-03 \n",
+      "[1] \"PP abf for shared variant: 0.854%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.33e-76  9.75e-20  8.54e-57  1.00e+00  6.01e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00601%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.56e-69  1.83e-13  8.54e-57  1.00e+00  3.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0367%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-68  3.50e-12  8.54e-57  1.00e+00  4.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0483%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-69  2.68e-13  8.54e-57  1.00e+00  3.46e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0346%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.75e-63  9.08e-07  8.52e-57  9.98e-01  2.09e-03 \n",
+      "[1] \"PP abf for shared variant: 0.209%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-57  1.52e-01  7.01e-57  8.21e-01  2.76e-02 \n",
+      "[1] \"PP abf for shared variant: 2.76%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.12e-62  7.16e-06  8.54e-57  1.00e+00  3.05e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0305%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___PRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1991e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-57  3.59e-01  5.02e-57  5.87e-01  5.37e-02 \n",
+      "[1] \"PP abf for shared variant: 5.37%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.44e-65  1.69e-09  8.54e-57  1.00e+00  3.58e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0358%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-68  2.41e-12  8.54e-57  1.00e+00  2.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0293%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.19e-66  8.42e-10  8.54e-57  1.00e+00  3.84e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0384%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.50e-69  6.44e-13  8.54e-57  1.00e+00  1.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0183%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-72  3.20e-16  8.53e-57  9.99e-01  7.64e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0764%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.73e-74  2.03e-18  8.54e-57  1.00e+00  3.63e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0363%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.23e-60  2.62e-04  8.43e-57  9.86e-01  1.33e-02 \n",
+      "[1] \"PP abf for shared variant: 1.33%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-66  3.67e-10  8.54e-57  1.00e+00  1.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0165%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.50e-61  6.44e-05  8.53e-57  9.99e-01  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-66  1.64e-10  8.54e-57  1.00e+00  3.89e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00389%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.40e-70  9.84e-14  8.54e-57  1.00e+00  4.36e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0436%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.44e-65  7.54e-09  8.54e-57  1.00e+00  1.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0175%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-71  1.21e-15  8.54e-57  1.00e+00  2.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0219%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-70  4.07e-14  8.53e-57  9.99e-01  7.38e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0738%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.53e-60  5.31e-04  7.98e-57  9.34e-01  6.57e-02 \n",
+      "[1] \"PP abf for shared variant: 6.57%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-69  2.52e-13  8.54e-57  1.00e+00  4.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0459%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.37e-57  2.78e-01  5.42e-57  6.34e-01  8.82e-02 \n",
+      "[1] \"PP abf for shared variant: 8.82%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.67e-63  1.95e-07  8.54e-57  1.00e+00  2.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0289%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.70e-59  1.14e-02  8.24e-57  9.65e-01  2.37e-02 \n",
+      "[1] \"PP abf for shared variant: 2.37%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-64  2.20e-08  8.54e-57  1.00e+00  2.17e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0217%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-60  1.79e-04  8.54e-57  1.00e+00  2.38e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0238%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.77e-74  5.58e-18  8.54e-57  1.00e+00  2.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0283%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-63  1.58e-07  8.28e-57  9.69e-01  3.11e-02 \n",
+      "[1] \"PP abf for shared variant: 3.11%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-65  1.28e-09  8.54e-57  1.00e+00  1.48e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0148%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.92e-63  9.27e-07  8.53e-57  9.98e-01  1.73e-03 \n",
+      "[1] \"PP abf for shared variant: 0.173%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-66  2.48e-10  8.54e-57  1.00e+00  3.39e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00339%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.24e-72  9.65e-16  8.54e-57  1.00e+00  2.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0277%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-62  1.74e-06  8.54e-57  1.00e+00  2.26e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0226%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-61  1.35e-05  8.54e-57  9.99e-01  5.25e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0525%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-57  1.72e-01  4.57e-57  5.32e-01  2.96e-01 \n",
+      "[1] \"PP abf for shared variant: 29.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.55e-72  2.99e-16  8.54e-57  1.00e+00  4.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0483%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.41e-59  5.17e-03  8.15e-57  9.54e-01  4.07e-02 \n",
+      "[1] \"PP abf for shared variant: 4.07%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.45e-65  7.55e-09  8.54e-57  1.00e+00  3.31e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0331%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-62  1.80e-06  8.54e-57  1.00e+00  3.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0327%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.30e-61  3.86e-05  8.53e-57  9.99e-01  7.41e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0741%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.70e-62  1.02e-05  7.34e-57  8.58e-01  1.42e-01 \n",
+      "[1] \"PP abf for shared variant: 14.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-63  2.68e-07  8.53e-57  9.99e-01  7.58e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0758%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___GZMB__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4099e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-57  2.41e-01  3.52e-57  4.09e-01  3.50e-01 \n",
+      "[1] \"PP abf for shared variant: 35%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___EEF1D__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5173e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.37e-57  7.46e-01  2.05e-57  2.39e-01  1.44e-02 \n",
+      "[1] \"PP abf for shared variant: 1.44%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.79e-60  5.61e-04  8.52e-57  9.98e-01  1.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.131%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.23e-63  3.79e-07  8.54e-57  1.00e+00  4.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0477%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.59e-69  3.03e-13  8.54e-57  1.00e+00  2.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0265%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___KLRC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.45e-58  6.38e-02  7.91e-57  9.26e-01  1.04e-02 \n",
+      "[1] \"PP abf for shared variant: 1.04%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL17__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.4275e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.45e-57  6.38e-01  2.89e-57  3.38e-01  2.45e-02 \n",
+      "[1] \"PP abf for shared variant: 2.45%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.17e-63  9.57e-07  8.54e-57  1.00e+00  3.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0318%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.49e-65  7.60e-09  8.51e-57  9.96e-01  3.79e-03 \n",
+      "[1] \"PP abf for shared variant: 0.379%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___MALAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.97e-59  1.17e-02  7.77e-57  9.09e-01  7.92e-02 \n",
+      "[1] \"PP abf for shared variant: 7.92%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-59  1.45e-03  8.21e-57  9.61e-01  3.74e-02 \n",
+      "[1] \"PP abf for shared variant: 3.74%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.98e-67  3.50e-11  8.54e-57  1.00e+00  2.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0274%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___HLA-B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8351e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.16e-57  4.87e-01  3.21e-57  3.75e-01  1.39e-01 \n",
+      "[1] \"PP abf for shared variant: 13.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-63  4.27e-07  8.50e-57  9.95e-01  4.82e-03 \n",
+      "[1] \"PP abf for shared variant: 0.482%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.98e-61  4.66e-05  8.54e-57  1.00e+00  4.21e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0421%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___FGFBP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-57  2.04e-01  6.47e-57  7.57e-01  3.92e-02 \n",
+      "[1] \"PP abf for shared variant: 3.92%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.43e-59  6.35e-03  8.44e-57  9.88e-01  5.39e-03 \n",
+      "[1] \"PP abf for shared variant: 0.539%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-60  3.82e-04  8.41e-57  9.84e-01  1.55e-02 \n",
+      "[1] \"PP abf for shared variant: 1.55%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.52e-60  5.29e-04  8.49e-57  9.94e-01  5.34e-03 \n",
+      "[1] \"PP abf for shared variant: 0.534%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-58  2.40e-02  8.28e-57  9.69e-01  6.88e-03 \n",
+      "[1] \"PP abf for shared variant: 0.688%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__ZEB2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.574e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.56e-57  5.34e-01  1.78e-57  2.06e-01  2.60e-01 \n",
+      "[1] \"PP abf for shared variant: 26%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.44e-62  5.20e-06  8.52e-57  9.98e-01  2.33e-03 \n",
+      "[1] \"PP abf for shared variant: 0.233%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.56e-71  4.16e-15  8.53e-57  9.99e-01  6.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0698%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___NACA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2336e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-57  4.13e-01  2.53e-57  2.93e-01  2.94e-01 \n",
+      "[1] \"PP abf for shared variant: 29.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-66  3.13e-10  8.53e-57  9.99e-01  6.71e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0671%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"NK_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-67  3.21e-11  8.54e-57  1.00e+00  4.21e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0421%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___NRGN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7437e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.64e-57  4.26e-01  4.37e-57  5.11e-01  6.35e-02 \n",
+      "[1] \"PP abf for shared variant: 6.35%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.99e-58  7.02e-02  1.89e-57  2.14e-01  7.16e-01 \n",
+      "[1] \"PP abf for shared variant: 71.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.31e-60  9.73e-04  7.54e-57  8.81e-01  1.18e-01 \n",
+      "[1] \"PP abf for shared variant: 11.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-62  1.23e-06  8.54e-57  9.99e-01  5.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0518%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-72  1.71e-16  8.53e-57  9.99e-01  8.11e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0811%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.51e-63  5.28e-07  8.53e-57  9.99e-01  6.35e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0635%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.89e-66  1.04e-09  8.53e-57  9.99e-01  6.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0675%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.79e-59  9.12e-03  8.25e-57  9.66e-01  2.53e-02 \n",
+      "[1] \"PP abf for shared variant: 2.53%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.17e-65  6.05e-09  8.53e-57  9.99e-01  6.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0649%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-71  2.57e-15  8.53e-57  9.99e-01  9.95e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0995%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VCAN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-57  2.42e-01  4.32e-57  5.03e-01  2.55e-01 \n",
+      "[1] \"PP abf for shared variant: 25.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UQCRH\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.45e-62  7.55e-06  7.83e-57  9.16e-01  8.43e-02 \n",
+      "[1] \"PP abf for shared variant: 8.43%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC7A7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.08e-59  1.06e-02  8.41e-57  9.84e-01  5.04e-03 \n",
+      "[1] \"PP abf for shared variant: 0.504%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EPB41L3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.40e-58  7.49e-02  2.53e-57  2.90e-01  6.35e-01 \n",
+      "[1] \"PP abf for shared variant: 63.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.08e-59  3.60e-03  8.04e-57  9.41e-01  5.53e-02 \n",
+      "[1] \"PP abf for shared variant: 5.53%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-70  2.65e-14  8.53e-57  9.99e-01  6.20e-04 \n",
+      "[1] \"PP abf for shared variant: 0.062%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.30e-59  2.69e-03  1.93e-57  2.18e-01  7.80e-01 \n",
+      "[1] \"PP abf for shared variant: 78%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___QARS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-57  1.76e-01  6.82e-57  7.99e-01  2.50e-02 \n",
+      "[1] \"PP abf for shared variant: 2.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-62  2.63e-06  8.53e-57  9.99e-01  1.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.13%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.50e-71  2.93e-15  8.54e-57  1.00e+00  4.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0465%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.68e-62  3.14e-06  8.53e-57  9.99e-01  8.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0896%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-71  2.46e-15  8.54e-57  1.00e+00  4.72e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0472%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.42e-65  8.68e-09  8.54e-57  1.00e+00  3.64e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0364%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-65  1.52e-09  8.53e-57  9.99e-01  5.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0565%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-68  4.30e-12  8.53e-57  9.99e-01  6.10e-04 \n",
+      "[1] \"PP abf for shared variant: 0.061%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.23e-59  8.47e-03  6.66e-57  7.78e-01  2.14e-01 \n",
+      "[1] \"PP abf for shared variant: 21.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.67e-66  5.47e-10  8.53e-57  9.99e-01  6.23e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0623%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___CSTA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.74e-60  5.55e-04  7.64e-57  8.93e-01  1.06e-01 \n",
+      "[1] \"PP abf for shared variant: 10.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.83e-64  1.15e-07  8.53e-57  9.99e-01  8.15e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0815%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.84e-58  8.00e-02  7.08e-57  8.29e-01  9.13e-02 \n",
+      "[1] \"PP abf for shared variant: 9.13%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.87e-69  1.04e-12  8.54e-57  1.00e+00  4.66e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0466%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.22e-60  7.29e-04  3.79e-57  4.38e-01  5.61e-01 \n",
+      "[1] \"PP abf for shared variant: 56.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.50e-71  2.92e-15  8.53e-57  9.99e-01  8.52e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0852%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-57  1.40e-01  2.93e-57  3.38e-01  5.23e-01 \n",
+      "[1] \"PP abf for shared variant: 52.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TNFAIP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-57  2.13e-01  6.62e-57  7.75e-01  1.21e-02 \n",
+      "[1] \"PP abf for shared variant: 1.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.61e-59  8.91e-03  7.72e-57  9.04e-01  8.76e-02 \n",
+      "[1] \"PP abf for shared variant: 8.76%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-68  1.89e-12  8.54e-57  1.00e+00  3.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0322%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.80e-76  4.45e-20  8.53e-57  9.99e-01  6.16e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0616%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.48e-68  8.76e-12  8.53e-57  9.99e-01  7.58e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0758%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.59e-62  6.55e-06  8.36e-57  9.79e-01  2.11e-02 \n",
+      "[1] \"PP abf for shared variant: 2.11%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-66  1.97e-10  8.54e-57  1.00e+00  4.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0499%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.67e-65  3.13e-09  8.53e-57  9.99e-01  6.09e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0609%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.59e-61  6.55e-05  8.33e-57  9.75e-01  2.52e-02 \n",
+      "[1] \"PP abf for shared variant: 2.52%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.99e-60  8.18e-04  7.45e-57  8.72e-01  1.28e-01 \n",
+      "[1] \"PP abf for shared variant: 12.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.54e-60  2.97e-04  8.52e-57  9.98e-01  1.96e-03 \n",
+      "[1] \"PP abf for shared variant: 0.196%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-69  1.49e-13  8.53e-57  9.99e-01  6.56e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0656%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.60e-72  6.55e-16  8.53e-57  9.99e-01  6.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0693%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.08e-65  5.95e-09  8.54e-57  1.00e+00  3.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0347%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.96e-61  9.32e-05  8.51e-57  9.97e-01  3.20e-03 \n",
+      "[1] \"PP abf for shared variant: 0.32%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.24e-67  2.63e-11  8.54e-57  1.00e+00  2.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0292%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPSA\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1173e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-57  1.35e-01  2.70e-57  3.10e-01  5.55e-01 \n",
+      "[1] \"PP abf for shared variant: 55.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-63  1.94e-07  8.53e-57  9.99e-01  7.28e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0728%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-66  1.37e-10  8.54e-57  1.00e+00  4.45e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0445%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.15e-64  1.07e-07  8.53e-57  9.99e-01  7.13e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0713%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-59  2.07e-03  8.48e-57  9.93e-01  5.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.523%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-65  3.48e-09  8.53e-57  9.99e-01  6.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0677%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-62  2.52e-06  7.91e-57  9.25e-01  7.47e-02 \n",
+      "[1] \"PP abf for shared variant: 7.47%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-58  1.61e-02  7.08e-57  8.28e-01  1.56e-01 \n",
+      "[1] \"PP abf for shared variant: 15.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-68  1.72e-12  8.53e-57  9.99e-01  1.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.111%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.30e-67  2.69e-11  8.53e-57  9.99e-01  8.33e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0833%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-61  3.50e-05  8.53e-57  9.99e-01  5.63e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0563%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-60  1.87e-04  6.51e-57  7.59e-01  2.40e-01 \n",
+      "[1] \"PP abf for shared variant: 24%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___CST3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7382e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.98e-57  4.66e-01  3.94e-57  4.60e-01  7.37e-02 \n",
+      "[1] \"PP abf for shared variant: 7.37%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-58  2.14e-02  8.00e-57  9.36e-01  4.21e-02 \n",
+      "[1] \"PP abf for shared variant: 4.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-57  1.77e-01  6.72e-57  7.86e-01  3.70e-02 \n",
+      "[1] \"PP abf for shared variant: 3.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-71  1.58e-15  8.53e-57  9.99e-01  6.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0627%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.55e-58  1.12e-01  2.61e-57  3.00e-01  5.88e-01 \n",
+      "[1] \"PP abf for shared variant: 58.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.95e-60  6.96e-04  8.25e-57  9.66e-01  3.36e-02 \n",
+      "[1] \"PP abf for shared variant: 3.36%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___AP1S2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-57  1.24e-01  4.64e-57  5.41e-01  3.36e-01 \n",
+      "[1] \"PP abf for shared variant: 33.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.33e-67  6.24e-11  8.53e-57  9.99e-01  7.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0718%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.02e-65  2.36e-09  8.53e-57  9.99e-01  8.21e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0821%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SEC11A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-59  4.43e-03  3.87e-57  4.48e-01  5.48e-01 \n",
+      "[1] \"PP abf for shared variant: 54.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-60  1.72e-04  7.67e-57  8.97e-01  1.03e-01 \n",
+      "[1] \"PP abf for shared variant: 10.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-67  1.64e-11  8.53e-57  9.99e-01  1.07e-03 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.67e-65  7.81e-09  8.53e-57  9.98e-01  1.51e-03 \n",
+      "[1] \"PP abf for shared variant: 0.151%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.41e-67  2.83e-11  8.53e-57  9.99e-01  5.94e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0594%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-63  1.71e-07  8.53e-57  9.99e-01  7.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0747%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-63  2.26e-07  8.53e-57  9.99e-01  1.17e-03 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.76e-64  9.08e-08  8.54e-57  9.99e-01  5.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0547%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___ATP5G2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.63e-59  6.59e-03  8.40e-57  9.84e-01  9.45e-03 \n",
+      "[1] \"PP abf for shared variant: 0.945%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.04e-62  5.90e-06  8.50e-57  9.95e-01  4.91e-03 \n",
+      "[1] \"PP abf for shared variant: 0.491%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-65  2.27e-09  8.54e-57  9.99e-01  5.17e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0517%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__SLC25A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.86e-59  1.04e-02  7.36e-57  8.60e-01  1.29e-01 \n",
+      "[1] \"PP abf for shared variant: 12.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.78e-69  2.08e-13  8.53e-57  9.99e-01  9.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.098%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.00e-72  5.85e-16  8.53e-57  9.99e-01  6.44e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0644%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-74  2.20e-18  8.53e-57  9.99e-01  1.03e-03 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.91e-63  4.58e-07  8.42e-57  9.85e-01  1.46e-02 \n",
+      "[1] \"PP abf for shared variant: 1.46%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-63  1.98e-07  8.53e-57  9.99e-01  1.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.48e-73  9.92e-17  8.53e-57  9.99e-01  8.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0867%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-60  1.33e-04  8.40e-57  9.83e-01  1.68e-02 \n",
+      "[1] \"PP abf for shared variant: 1.68%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EVI2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.83e-57  2.14e-01  4.38e-57  5.10e-01  2.76e-01 \n",
+      "[1] \"PP abf for shared variant: 27.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-58  3.54e-02  8.02e-57  9.39e-01  2.56e-02 \n",
+      "[1] \"PP abf for shared variant: 2.56%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.44e-59  5.20e-03  2.06e-57  2.34e-01  7.61e-01 \n",
+      "[1] \"PP abf for shared variant: 76.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.95e-60  5.79e-04  8.48e-57  9.93e-01  6.44e-03 \n",
+      "[1] \"PP abf for shared variant: 0.644%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.36e-69  3.93e-13  8.53e-57  9.99e-01  9.08e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0908%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___LGALS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-57  1.39e-01  6.81e-57  7.97e-01  6.45e-02 \n",
+      "[1] \"PP abf for shared variant: 6.45%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.00e-57  2.35e-01  5.76e-57  6.74e-01  9.18e-02 \n",
+      "[1] \"PP abf for shared variant: 9.18%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-67  2.39e-11  8.53e-57  9.99e-01  8.58e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0858%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.03e-71  9.40e-15  8.53e-57  9.99e-01  6.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.069%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-67  2.41e-11  8.53e-57  9.99e-01  9.12e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0912%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-63  1.12e-06  8.53e-57  9.99e-01  6.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.069%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___GPX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-58  1.84e-02  7.68e-57  8.98e-01  8.32e-02 \n",
+      "[1] \"PP abf for shared variant: 8.32%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___LTA4H__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-57  4.04e-01  3.94e-57  4.60e-01  1.36e-01 \n",
+      "[1] \"PP abf for shared variant: 13.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RNASE6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-57  1.73e-01  6.71e-57  7.86e-01  4.19e-02 \n",
+      "[1] \"PP abf for shared variant: 4.19%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.39e-58  5.14e-02  7.52e-57  8.80e-01  6.86e-02 \n",
+      "[1] \"PP abf for shared variant: 6.86%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___BTF3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-59  5.59e-03  8.34e-57  9.76e-01  1.82e-02 \n",
+      "[1] \"PP abf for shared variant: 1.82%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___DRAM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1829e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.34e-57  3.91e-01  3.81e-57  4.44e-01  1.65e-01 \n",
+      "[1] \"PP abf for shared variant: 16.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___IL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.95e-60  4.63e-04  5.15e-57  5.99e-01  4.00e-01 \n",
+      "[1] \"PP abf for shared variant: 40%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-59  5.74e-03  7.71e-57  9.02e-01  9.22e-02 \n",
+      "[1] \"PP abf for shared variant: 9.22%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-63  3.54e-07  8.51e-57  9.96e-01  3.58e-03 \n",
+      "[1] \"PP abf for shared variant: 0.358%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-67  1.81e-11  8.53e-57  9.99e-01  9.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0974%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-70  3.09e-14  8.53e-57  9.99e-01  1.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-57  1.41e-01  4.15e-57  4.82e-01  3.77e-01 \n",
+      "[1] \"PP abf for shared variant: 37.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-71  3.08e-15  8.54e-57  1.00e+00  4.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0493%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.83e-68  4.48e-12  8.53e-57  9.99e-01  8.03e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0803%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___IPO7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-58  6.78e-02  7.70e-57  9.01e-01  3.08e-02 \n",
+      "[1] \"PP abf for shared variant: 3.08%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.40e-64  7.49e-08  8.51e-57  9.96e-01  4.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.406%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.71e-68  4.35e-12  8.54e-57  1.00e+00  3.13e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0313%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.05e-70  5.92e-14  8.53e-57  9.99e-01  1.07e-03 \n",
+      "[1] \"PP abf for shared variant: 0.107%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___CXCR4__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.2966e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-57  4.27e-01  4.70e-57  5.50e-01  2.31e-02 \n",
+      "[1] \"PP abf for shared variant: 2.31%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-64  1.61e-08  8.53e-57  9.99e-01  6.29e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0629%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.42e-62  4.01e-06  4.94e-57  5.75e-01  4.25e-01 \n",
+      "[1] \"PP abf for shared variant: 42.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___CRTAP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.65e-59  1.01e-02  8.25e-57  9.65e-01  2.46e-02 \n",
+      "[1] \"PP abf for shared variant: 2.46%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-57  2.94e-01  5.65e-57  6.62e-01  4.40e-02 \n",
+      "[1] \"PP abf for shared variant: 4.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.88e-65  3.37e-09  8.54e-57  1.00e+00  4.53e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0453%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-59  1.53e-03  8.42e-57  9.86e-01  1.24e-02 \n",
+      "[1] \"PP abf for shared variant: 1.24%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-66  3.15e-10  8.53e-57  9.99e-01  1.03e-03 \n",
+      "[1] \"PP abf for shared variant: 0.103%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-57  2.03e-01  5.31e-57  6.20e-01  1.77e-01 \n",
+      "[1] \"PP abf for shared variant: 17.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___GPR183__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.84e-58  2.15e-02  7.64e-57  8.94e-01  8.42e-02 \n",
+      "[1] \"PP abf for shared variant: 8.42%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.86e-66  3.35e-10  8.53e-57  9.99e-01  6.24e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0624%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-67  1.19e-11  8.54e-57  1.00e+00  3.02e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0302%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"monocyte_RPS26___RPS26__TKT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.59e-61  8.89e-05  8.50e-57  9.96e-01  4.10e-03 \n",
+      "[1] \"PP abf for shared variant: 0.41%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.13e-72  1.07e-15  8.53e-57  9.99e-01  5.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0592%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.84e-58  6.84e-02  7.31e-57  8.55e-01  7.68e-02 \n",
+      "[1] \"PP abf for shared variant: 7.68%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ACTN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.94e-60  4.62e-04  8.50e-57  9.96e-01  3.79e-03 \n",
+      "[1] \"PP abf for shared variant: 0.379%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-71  2.18e-15  8.54e-57  1.00e+00  7.63e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00763%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAND1\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4561e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-57  1.61e-01  2.62e-57  3.01e-01  5.37e-01 \n",
+      "[1] \"PP abf for shared variant: 53.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-71  1.43e-15  8.54e-57  1.00e+00  2.34e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0234%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PRF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.49e-59  7.59e-03  8.43e-57  9.87e-01  5.85e-03 \n",
+      "[1] \"PP abf for shared variant: 0.585%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.41e-61  1.65e-05  8.51e-57  9.97e-01  2.95e-03 \n",
+      "[1] \"PP abf for shared variant: 0.295%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EFHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-58  2.20e-02  8.29e-57  9.71e-01  7.29e-03 \n",
+      "[1] \"PP abf for shared variant: 0.729%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.20e-67  8.43e-11  8.54e-57  1.00e+00  1.17e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0117%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.62e-72  4.24e-16  8.53e-57  9.99e-01  5.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0581%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-70  1.87e-14  8.54e-57  1.00e+00  2.46e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0246%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-69  2.29e-13  8.54e-57  9.99e-01  5.29e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0529%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___APBA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-58  4.95e-02  7.10e-57  8.31e-01  1.20e-01 \n",
+      "[1] \"PP abf for shared variant: 12%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-60  3.07e-04  8.50e-57  9.95e-01  4.69e-03 \n",
+      "[1] \"PP abf for shared variant: 0.469%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FAIM3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.55e-58  4.15e-02  7.98e-57  9.35e-01  2.40e-02 \n",
+      "[1] \"PP abf for shared variant: 2.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF3G__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.7746e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.07e-57  3.60e-01  3.79e-57  4.42e-01  1.98e-01 \n",
+      "[1] \"PP abf for shared variant: 19.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.08e-57  2.44e-01  6.24e-57  7.31e-01  2.51e-02 \n",
+      "[1] \"PP abf for shared variant: 2.51%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.19e-59  6.08e-03  8.46e-57  9.91e-01  3.20e-03 \n",
+      "[1] \"PP abf for shared variant: 0.32%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.35e-71  6.27e-15  8.53e-57  9.99e-01  9.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0943%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___C11orf1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.8471e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.27e-57  5.00e-01  3.33e-57  3.89e-01  1.11e-01 \n",
+      "[1] \"PP abf for shared variant: 11.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-60  2.38e-04  8.53e-57  9.99e-01  9.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0999%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-74  4.27e-18  8.53e-57  9.99e-01  1.14e-03 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-73  2.67e-17  8.54e-57  1.00e+00  3.52e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0352%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GZMM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.22e-57  2.60e-01  5.70e-57  6.66e-01  7.39e-02 \n",
+      "[1] \"PP abf for shared variant: 7.39%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.85e-62  2.17e-06  8.54e-57  1.00e+00  1.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0177%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.21e-60  4.92e-04  8.43e-57  9.87e-01  1.21e-02 \n",
+      "[1] \"PP abf for shared variant: 1.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-57  2.02e-01  6.43e-57  7.52e-01  4.57e-02 \n",
+      "[1] \"PP abf for shared variant: 4.57%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CTSW__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-57  1.53e-01  7.09e-57  8.30e-01  1.69e-02 \n",
+      "[1] \"PP abf for shared variant: 1.69%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-61  1.27e-05  8.53e-57  9.99e-01  5.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.059%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.95e-75  9.31e-19  8.53e-57  9.99e-01  6.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0659%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FLT3LG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.77e-61  1.14e-04  8.51e-57  9.97e-01  2.87e-03 \n",
+      "[1] \"PP abf for shared variant: 0.287%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NKG7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-61  1.46e-05  8.53e-57  9.99e-01  1.04e-03 \n",
+      "[1] \"PP abf for shared variant: 0.104%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-58  2.00e-02  4.76e-57  5.54e-01  4.26e-01 \n",
+      "[1] \"PP abf for shared variant: 42.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YWHAZ\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.3964e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.31e-57  5.05e-01  4.12e-57  4.82e-01  1.28e-02 \n",
+      "[1] \"PP abf for shared variant: 1.28%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-57  1.93e-01  6.09e-57  7.13e-01  9.42e-02 \n",
+      "[1] \"PP abf for shared variant: 9.42%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.83e-61  9.17e-05  8.52e-57  9.98e-01  2.17e-03 \n",
+      "[1] \"PP abf for shared variant: 0.217%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-62  2.17e-06  8.32e-57  9.74e-01  2.57e-02 \n",
+      "[1] \"PP abf for shared variant: 2.57%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.38e-66  1.10e-09  8.54e-57  1.00e+00  1.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0147%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-68  1.46e-12  8.54e-57  9.99e-01  5.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0547%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-70  2.18e-14  8.54e-57  1.00e+00  1.39e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0139%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ATP1A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 9.0977e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.20e-57  6.09e-01  2.43e-57  2.83e-01  1.08e-01 \n",
+      "[1] \"PP abf for shared variant: 10.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.51e-58  7.62e-02  3.27e-57  3.77e-01  5.47e-01 \n",
+      "[1] \"PP abf for shared variant: 54.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SYNE1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.43e-58  9.88e-02  6.87e-57  8.04e-01  9.74e-02 \n",
+      "[1] \"PP abf for shared variant: 9.74%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FYN__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.137e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.03e-57  4.71e-01  3.15e-57  3.67e-01  1.62e-01 \n",
+      "[1] \"PP abf for shared variant: 16.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.85e-63  9.20e-07  8.53e-57  9.99e-01  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.81e-67  6.81e-11  8.54e-57  1.00e+00  3.38e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0338%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PDE3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.84e-61  6.84e-05  8.50e-57  9.95e-01  5.21e-03 \n",
+      "[1] \"PP abf for shared variant: 0.521%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.58e-76  3.02e-20  8.53e-57  9.99e-01  1.01e-03 \n",
+      "[1] \"PP abf for shared variant: 0.101%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.92e-61  3.42e-05  8.49e-57  9.94e-01  5.84e-03 \n",
+      "[1] \"PP abf for shared variant: 0.584%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZEB2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.79e-59  6.78e-03  8.37e-57  9.80e-01  1.33e-02 \n",
+      "[1] \"PP abf for shared variant: 1.33%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___LTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.26e-64  1.48e-08  8.54e-57  1.00e+00  2.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0286%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PTPN7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-57  2.46e-01  5.94e-57  6.95e-01  5.94e-02 \n",
+      "[1] \"PP abf for shared variant: 5.94%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-69  5.08e-13  8.53e-57  9.99e-01  6.29e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0629%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.92e-67  8.10e-11  8.54e-57  1.00e+00  3.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0386%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___IER2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.1556e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.73e-57  5.53e-01  3.44e-57  4.03e-01  4.39e-02 \n",
+      "[1] \"PP abf for shared variant: 4.39%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.17e-61  4.89e-05  8.54e-57  1.00e+00  3.35e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0335%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.47e-66  6.40e-10  8.54e-57  1.00e+00  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.50e-77  5.27e-21  8.53e-57  9.99e-01  1.09e-03 \n",
+      "[1] \"PP abf for shared variant: 0.109%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.12e-71  3.65e-15  8.54e-57  1.00e+00  1.44e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0144%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CMC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.84e-60  6.83e-04  8.41e-57  9.85e-01  1.45e-02 \n",
+      "[1] \"PP abf for shared variant: 1.45%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.03e-61  5.89e-05  8.53e-57  9.99e-01  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.92e-69  2.25e-13  8.54e-57  1.00e+00  3.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0377%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GZMB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-58  1.26e-02  8.40e-57  9.83e-01  4.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.411%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___AKNA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.47e-57  5.23e-01  2.88e-57  3.36e-01  1.41e-01 \n",
+      "[1] \"PP abf for shared variant: 14.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HLA-DPB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9277e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.36e-57  3.94e-01  3.70e-57  4.31e-01  1.75e-01 \n",
+      "[1] \"PP abf for shared variant: 17.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.13e-77  1.07e-20  8.53e-57  9.99e-01  6.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0659%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.16e-64  1.36e-08  8.54e-57  1.00e+00  1.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0167%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-60  1.59e-04  8.53e-57  9.99e-01  1.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.128%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-58  2.55e-02  8.28e-57  9.70e-01  4.62e-03 \n",
+      "[1] \"PP abf for shared variant: 0.462%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-58  2.22e-02  8.24e-57  9.65e-01  1.28e-02 \n",
+      "[1] \"PP abf for shared variant: 1.28%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.89e-73  5.73e-17  8.53e-57  9.99e-01  7.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0747%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.99e-68  1.05e-11  8.54e-57  1.00e+00  2.33e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0233%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.72e-71  4.36e-15  8.54e-57  9.99e-01  5.42e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0542%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___METRNL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4496e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.62e-57  6.58e-01  2.65e-57  3.10e-01  3.14e-02 \n",
+      "[1] \"PP abf for shared variant: 3.14%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.34e-59  9.76e-03  8.44e-57  9.88e-01  1.98e-03 \n",
+      "[1] \"PP abf for shared variant: 0.198%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CAMK4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-63  1.86e-07  8.53e-57  9.99e-01  1.46e-03 \n",
+      "[1] \"PP abf for shared variant: 0.146%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___BHLHE40__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.69e-59  1.02e-02  8.42e-57  9.86e-01  4.31e-03 \n",
+      "[1] \"PP abf for shared variant: 0.431%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___IFITM2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 5.2604e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.24e-57  4.96e-01  4.02e-57  4.70e-01  3.34e-02 \n",
+      "[1] \"PP abf for shared variant: 3.34%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-58  4.60e-02  7.98e-57  9.34e-01  2.03e-02 \n",
+      "[1] \"PP abf for shared variant: 2.03%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CD8B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-60  1.31e-04  8.51e-57  9.96e-01  3.52e-03 \n",
+      "[1] \"PP abf for shared variant: 0.352%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-75  2.31e-19  8.54e-57  1.00e+00  9.34e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00934%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NGFRAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.06e-58  3.58e-02  7.44e-57  8.70e-01  9.40e-02 \n",
+      "[1] \"PP abf for shared variant: 9.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-70  4.13e-14  8.54e-57  1.00e+00  1.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0127%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TUBA4A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-58  2.90e-02  6.13e-57  7.15e-01  2.56e-01 \n",
+      "[1] \"PP abf for shared variant: 25.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-61  1.87e-05  8.54e-57  1.00e+00  2.66e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0266%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-75  1.23e-19  8.53e-57  9.99e-01  7.14e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0714%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RCAN3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.51e-62  4.10e-06  8.54e-57  1.00e+00  3.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0392%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-69  1.75e-13  8.54e-57  1.00e+00  2.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0227%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.94e-60  2.27e-04  8.49e-57  9.94e-01  6.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.611%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.64e-67  3.09e-11  8.53e-57  9.99e-01  5.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0582%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.91e-64  5.75e-08  8.54e-57  1.00e+00  3.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.035%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___DNAJB6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.85e-58  6.85e-02  7.74e-57  9.07e-01  2.49e-02 \n",
+      "[1] \"PP abf for shared variant: 2.49%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RAP1B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.077e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.64e-57  6.61e-01  2.28e-57  2.66e-01  7.32e-02 \n",
+      "[1] \"PP abf for shared variant: 7.32%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.81e-62  7.98e-06  8.54e-57  1.00e+00  2.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.027%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PABPC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-59  1.46e-03  8.37e-57  9.80e-01  1.82e-02 \n",
+      "[1] \"PP abf for shared variant: 1.82%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-59  4.80e-03  8.12e-57  9.51e-01  4.44e-02 \n",
+      "[1] \"PP abf for shared variant: 4.44%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CCDC104__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.9652e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.64e-57  4.26e-01  2.30e-57  2.66e-01  3.07e-01 \n",
+      "[1] \"PP abf for shared variant: 30.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.46e-65  2.88e-09  8.54e-57  1.00e+00  1.62e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0162%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.72e-65  4.36e-09  8.54e-57  1.00e+00  7.42e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00742%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-62  3.44e-06  8.53e-57  9.99e-01  6.25e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0625%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.11e-74  1.30e-18  8.53e-57  9.99e-01  9.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0919%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-58  2.38e-02  5.06e-57  5.89e-01  3.88e-01 \n",
+      "[1] \"PP abf for shared variant: 38.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TESPA1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-58  1.29e-02  7.76e-57  9.08e-01  7.93e-02 \n",
+      "[1] \"PP abf for shared variant: 7.93%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-59  4.33e-03  8.45e-57  9.90e-01  5.89e-03 \n",
+      "[1] \"PP abf for shared variant: 0.589%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PSMA7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-57  1.96e-01  6.09e-57  7.12e-01  9.13e-02 \n",
+      "[1] \"PP abf for shared variant: 9.13%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PLEK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-57  2.10e-01  5.95e-57  6.95e-01  9.51e-02 \n",
+      "[1] \"PP abf for shared variant: 9.51%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-60  1.52e-04  8.52e-57  9.97e-01  2.58e-03 \n",
+      "[1] \"PP abf for shared variant: 0.258%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-72  3.74e-16  8.53e-57  9.99e-01  6.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0659%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MT-ND5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.4281e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-57  3.18e-01  3.05e-57  3.53e-01  3.29e-01 \n",
+      "[1] \"PP abf for shared variant: 32.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___KLRD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.18e-59  3.72e-03  8.40e-57  9.84e-01  1.26e-02 \n",
+      "[1] \"PP abf for shared variant: 1.26%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MYC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.01e-59  3.53e-03  7.11e-57  8.30e-01  1.66e-01 \n",
+      "[1] \"PP abf for shared variant: 16.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-57  1.49e-01  4.50e-57  5.24e-01  3.28e-01 \n",
+      "[1] \"PP abf for shared variant: 32.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___KLF2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.391e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-57  4.32e-01  4.72e-57  5.53e-01  1.51e-02 \n",
+      "[1] \"PP abf for shared variant: 1.51%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SLC25A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.04e-58  1.06e-01  6.27e-57  7.32e-01  1.62e-01 \n",
+      "[1] \"PP abf for shared variant: 16.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HNRNPA2B1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-57  4.30e-01  3.96e-57  4.63e-01  1.08e-01 \n",
+      "[1] \"PP abf for shared variant: 10.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ARAP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3907e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.90e-57  6.91e-01  2.12e-57  2.47e-01  6.13e-02 \n",
+      "[1] \"PP abf for shared variant: 6.13%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-73  5.60e-17  8.53e-57  9.99e-01  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-61  1.18e-05  8.53e-57  9.99e-01  8.53e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0853%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.35e-61  3.92e-05  8.53e-57  9.99e-01  9.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0965%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.18e-69  3.73e-13  8.54e-57  1.00e+00  3.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0322%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.07e-62  4.77e-06  8.33e-57  9.75e-01  2.47e-02 \n",
+      "[1] \"PP abf for shared variant: 2.47%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.46e-62  5.22e-06  8.52e-57  9.98e-01  1.78e-03 \n",
+      "[1] \"PP abf for shared variant: 0.178%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.13e-58  1.07e-01  7.19e-57  8.42e-01  5.12e-02 \n",
+      "[1] \"PP abf for shared variant: 5.12%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-65  1.22e-09  8.54e-57  1.00e+00  4.71e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0471%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.91e-75  4.58e-19  8.53e-57  9.99e-01  1.20e-03 \n",
+      "[1] \"PP abf for shared variant: 0.12%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___LYPD3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-57  2.51e-01  5.41e-57  6.32e-01  1.17e-01 \n",
+      "[1] \"PP abf for shared variant: 11.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.11e-61  8.33e-05  7.92e-57  9.27e-01  7.32e-02 \n",
+      "[1] \"PP abf for shared variant: 7.32%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.15e-58  1.07e-01  7.40e-57  8.66e-01  2.63e-02 \n",
+      "[1] \"PP abf for shared variant: 2.63%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.19e-58  4.90e-02  6.61e-57  7.73e-01  1.78e-01 \n",
+      "[1] \"PP abf for shared variant: 17.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM243\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-59  1.20e-03  8.52e-57  9.98e-01  8.37e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0837%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-71  1.64e-15  8.53e-57  9.99e-01  6.57e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0657%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ID2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.68e-58  1.02e-01  7.53e-57  8.82e-01  1.67e-02 \n",
+      "[1] \"PP abf for shared variant: 1.67%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-71  1.62e-15  8.54e-57  1.00e+00  4.04e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0404%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.10e-66  2.46e-10  8.54e-57  1.00e+00  1.48e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0148%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.47e-59  2.89e-03  8.37e-57  9.80e-01  1.67e-02 \n",
+      "[1] \"PP abf for shared variant: 1.67%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-81  1.41e-25  8.53e-57  9.99e-01  8.94e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0894%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.49e-57  2.92e-01  5.85e-57  6.85e-01  2.29e-02 \n",
+      "[1] \"PP abf for shared variant: 2.29%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NFKBIA__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 7.944e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.62e-57  5.41e-01  3.29e-57  3.84e-01  7.51e-02 \n",
+      "[1] \"PP abf for shared variant: 7.51%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-58  1.87e-02  7.47e-57  8.74e-01  1.08e-01 \n",
+      "[1] \"PP abf for shared variant: 10.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-73  2.45e-17  8.53e-57  9.99e-01  9.54e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0954%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.06e-72  8.27e-16  8.54e-57  1.00e+00  3.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0374%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.25e-66  4.97e-10  8.54e-57  1.00e+00  2.16e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0216%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GZMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-63  1.92e-07  8.54e-57  1.00e+00  2.91e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0291%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-57  2.20e-01  5.88e-57  6.87e-01  9.33e-02 \n",
+      "[1] \"PP abf for shared variant: 9.33%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.28e-67  7.35e-11  8.53e-57  9.99e-01  5.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0573%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TPI1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.34e-57  2.74e-01  5.69e-57  6.66e-01  5.97e-02 \n",
+      "[1] \"PP abf for shared variant: 5.97%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.18e-75  2.55e-19  8.53e-57  9.99e-01  7.17e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0717%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.98e-58  5.83e-02  4.15e-57  4.82e-01  4.60e-01 \n",
+      "[1] \"PP abf for shared variant: 46%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.55e-66  2.99e-10  8.54e-57  1.00e+00  3.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0367%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.02e-72  9.40e-16  8.54e-57  1.00e+00  4.41e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0441%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SC5D\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.89e-57  3.39e-01  5.41e-57  6.33e-01  2.82e-02 \n",
+      "[1] \"PP abf for shared variant: 2.82%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.10e-67  8.31e-11  8.53e-57  9.99e-01  5.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0583%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-73  1.86e-17  8.53e-57  9.99e-01  7.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0747%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RIC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.38e-60  8.64e-04  8.52e-57  9.97e-01  1.88e-03 \n",
+      "[1] \"PP abf for shared variant: 0.188%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-67  1.77e-11  8.54e-57  9.99e-01  5.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0543%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-61  1.54e-05  8.53e-57  9.99e-01  7.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0718%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CCNG1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 4.9814e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.57e-57  5.35e-01  3.29e-57  3.85e-01  8.01e-02 \n",
+      "[1] \"PP abf for shared variant: 8.01%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRP14\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.91e-61  4.57e-05  8.35e-57  9.77e-01  2.28e-02 \n",
+      "[1] \"PP abf for shared variant: 2.28%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SPON2\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.0298e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.91e-57  4.58e-01  4.28e-57  5.01e-01  4.06e-02 \n",
+      "[1] \"PP abf for shared variant: 4.06%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.83e-60  3.31e-04  8.46e-57  9.91e-01  8.77e-03 \n",
+      "[1] \"PP abf for shared variant: 0.877%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.02e-63  5.88e-07  8.54e-57  1.00e+00  1.31e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0131%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.06e-73  1.06e-16  8.54e-57  1.00e+00  2.51e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0251%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.20e-75  4.92e-19  8.53e-57  9.99e-01  8.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0875%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.87e-76  1.04e-19  8.54e-57  1.00e+00  2.34e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0234%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MYL12B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0233e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.72e-57  4.36e-01  4.70e-57  5.50e-01  1.36e-02 \n",
+      "[1] \"PP abf for shared variant: 1.36%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GNLY__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-58  1.73e-02  8.03e-57  9.40e-01  4.25e-02 \n",
+      "[1] \"PP abf for shared variant: 4.25%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MIR142__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1648e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.12e-57  5.99e-01  2.22e-57  2.59e-01  1.42e-01 \n",
+      "[1] \"PP abf for shared variant: 14.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.35e-59  3.92e-03  7.82e-57  9.15e-01  8.14e-02 \n",
+      "[1] \"PP abf for shared variant: 8.14%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.98e-74  9.34e-18  8.54e-57  1.00e+00  3.29e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0329%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.72e-57  2.01e-01  6.37e-57  7.45e-01  5.36e-02 \n",
+      "[1] \"PP abf for shared variant: 5.36%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.48e-62  6.42e-06  8.22e-57  9.62e-01  3.82e-02 \n",
+      "[1] \"PP abf for shared variant: 3.82%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PTP4A2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-57  3.19e-01  5.51e-57  6.45e-01  3.55e-02 \n",
+      "[1] \"PP abf for shared variant: 3.55%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FGFBP2__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.9666e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.79e-57  5.60e-01  3.04e-57  3.55e-01  8.46e-02 \n",
+      "[1] \"PP abf for shared variant: 8.46%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-61  5.01e-05  7.63e-57  8.92e-01  1.07e-01 \n",
+      "[1] \"PP abf for shared variant: 10.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.38e-64  1.62e-08  8.53e-57  9.99e-01  6.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.067%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.90e-81  2.23e-25  8.54e-57  1.00e+00  2.54e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0254%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CCDC57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.02e-63  1.06e-06  8.48e-57  9.93e-01  6.98e-03 \n",
+      "[1] \"PP abf for shared variant: 0.698%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ITGB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.02e-57  3.54e-01  5.03e-57  5.88e-01  5.75e-02 \n",
+      "[1] \"PP abf for shared variant: 5.75%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.90e-58  4.56e-02  6.23e-57  7.27e-01  2.27e-01 \n",
+      "[1] \"PP abf for shared variant: 22.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MYO1F__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.4185e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.09e-57  5.96e-01  3.03e-57  3.54e-01  5.06e-02 \n",
+      "[1] \"PP abf for shared variant: 5.06%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ARF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.19e-58  7.25e-02  6.31e-57  7.37e-01  1.90e-01 \n",
+      "[1] \"PP abf for shared variant: 19%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CD81__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-57  1.79e-01  6.81e-57  7.98e-01  2.33e-02 \n",
+      "[1] \"PP abf for shared variant: 2.33%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.76e-59  2.06e-03  8.47e-57  9.92e-01  5.71e-03 \n",
+      "[1] \"PP abf for shared variant: 0.571%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-59  2.13e-03  8.43e-57  9.87e-01  1.06e-02 \n",
+      "[1] \"PP abf for shared variant: 1.06%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.31e-60  5.04e-04  8.51e-57  9.97e-01  2.60e-03 \n",
+      "[1] \"PP abf for shared variant: 0.26%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-61  1.52e-05  8.54e-57  1.00e+00  4.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0474%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FOXP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.04e-60  4.73e-04  8.51e-57  9.97e-01  2.49e-03 \n",
+      "[1] \"PP abf for shared variant: 0.249%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CCL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.53e-61  4.14e-05  8.53e-57  9.99e-01  5.83e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0583%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.69e-59  9.00e-03  8.45e-57  9.89e-01  1.90e-03 \n",
+      "[1] \"PP abf for shared variant: 0.19%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___KLRF1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9856e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.99e-57  4.67e-01  4.25e-57  4.98e-01  3.51e-02 \n",
+      "[1] \"PP abf for shared variant: 3.51%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___BTF3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5042e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.70e-57  5.51e-01  2.30e-57  2.67e-01  1.82e-01 \n",
+      "[1] \"PP abf for shared variant: 18.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-57  1.80e-01  5.67e-57  6.62e-01  1.57e-01 \n",
+      "[1] \"PP abf for shared variant: 15.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.67e-72  8.98e-16  8.54e-57  1.00e+00  3.24e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0324%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___C1orf21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1023e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-57  4.30e-01  4.26e-57  4.98e-01  7.25e-02 \n",
+      "[1] \"PP abf for shared variant: 7.25%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.19e-66  1.40e-10  8.54e-57  1.00e+00  1.72e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0172%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CALM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.84e-58  8.01e-02  7.52e-57  8.80e-01  3.96e-02 \n",
+      "[1] \"PP abf for shared variant: 3.96%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HOPX__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.25e-57  2.63e-01  6.21e-57  7.27e-01  9.86e-03 \n",
+      "[1] \"PP abf for shared variant: 0.986%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-58  1.85e-02  7.98e-57  9.34e-01  4.71e-02 \n",
+      "[1] \"PP abf for shared variant: 4.71%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.53e-71  1.79e-15  8.53e-57  9.99e-01  8.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0896%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.02e-60  4.71e-04  8.52e-57  9.97e-01  2.18e-03 \n",
+      "[1] \"PP abf for shared variant: 0.218%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.90e-73  9.25e-17  8.54e-57  1.00e+00  2.70e-04 \n",
+      "[1] \"PP abf for shared variant: 0.027%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.89e-59  3.39e-03  7.08e-57  8.28e-01  1.69e-01 \n",
+      "[1] \"PP abf for shared variant: 16.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.40e-68  1.10e-11  8.54e-57  1.00e+00  7.83e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00783%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HLA-DRB1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 6.507e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.31e-57  6.21e-01  3.08e-57  3.61e-01  1.82e-02 \n",
+      "[1] \"PP abf for shared variant: 1.82%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-74  3.57e-18  8.53e-57  9.99e-01  1.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.47e-70  2.90e-14  8.53e-57  9.99e-01  6.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0618%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PTMA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-59  1.38e-03  8.50e-57  9.95e-01  3.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.328%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.54e-67  7.66e-11  8.54e-57  1.00e+00  2.82e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0282%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GNG2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-57  1.31e-01  5.84e-57  6.82e-01  1.87e-01 \n",
+      "[1] \"PP abf for shared variant: 18.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.73e-59  1.02e-02  7.80e-57  9.12e-01  7.77e-02 \n",
+      "[1] \"PP abf for shared variant: 7.77%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.62e-63  3.07e-07  8.50e-57  9.95e-01  5.21e-03 \n",
+      "[1] \"PP abf for shared variant: 0.521%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.74e-67  1.02e-10  8.53e-57  9.99e-01  5.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0579%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.70e-60  5.51e-04  7.01e-57  8.19e-01  1.80e-01 \n",
+      "[1] \"PP abf for shared variant: 18%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CD27__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.689e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.99e-57  4.67e-01  3.81e-57  4.45e-01  8.78e-02 \n",
+      "[1] \"PP abf for shared variant: 8.78%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.70e-60  1.14e-03  8.40e-57  9.84e-01  1.50e-02 \n",
+      "[1] \"PP abf for shared variant: 1.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.24e-66  7.30e-10  8.54e-57  1.00e+00  2.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0292%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.88e-72  2.20e-16  8.53e-57  9.99e-01  8.06e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0806%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___REL__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.691e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-57  4.19e-01  3.40e-57  3.97e-01  1.84e-01 \n",
+      "[1] \"PP abf for shared variant: 18.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.77e-68  5.59e-12  8.53e-57  9.99e-01  7.15e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0715%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.56e-57  3.00e-01  5.09e-57  5.94e-01  1.06e-01 \n",
+      "[1] \"PP abf for shared variant: 10.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.78e-65  7.94e-09  8.54e-57  1.00e+00  2.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0259%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.56e-70  1.00e-13  8.53e-57  9.99e-01  8.61e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0861%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.85e-63  2.16e-07  8.54e-57  1.00e+00  4.45e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0445%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SEPT7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.29e-58  7.37e-02  7.81e-57  9.14e-01  1.20e-02 \n",
+      "[1] \"PP abf for shared variant: 1.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.76e-58  9.09e-02  7.53e-57  8.81e-01  2.77e-02 \n",
+      "[1] \"PP abf for shared variant: 2.77%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-57  3.68e-01  5.20e-57  6.09e-01  2.31e-02 \n",
+      "[1] \"PP abf for shared variant: 2.31%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.54e-65  2.97e-09  8.54e-57  1.00e+00  2.01e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0201%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___FCGR3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-58  4.53e-02  8.06e-57  9.44e-01  1.11e-02 \n",
+      "[1] \"PP abf for shared variant: 1.11%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PSMB9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 8.645e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.07e-57  5.93e-01  3.19e-57  3.73e-01  3.34e-02 \n",
+      "[1] \"PP abf for shared variant: 3.34%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.04e-65  1.21e-09  8.54e-57  1.00e+00  8.86e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00886%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.77e-64  2.07e-08  8.53e-57  9.99e-01  6.07e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0607%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.19e-57  2.56e-01  6.13e-57  7.17e-01  2.69e-02 \n",
+      "[1] \"PP abf for shared variant: 2.69%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.27e-60  5.00e-04  8.48e-57  9.93e-01  6.59e-03 \n",
+      "[1] \"PP abf for shared variant: 0.659%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.55e-72  4.15e-16  8.54e-57  1.00e+00  4.21e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0421%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.08e-69  1.27e-13  8.53e-57  9.99e-01  8.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0827%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.00e-58  5.86e-02  7.88e-57  9.23e-01  1.83e-02 \n",
+      "[1] \"PP abf for shared variant: 1.83%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-58  3.36e-02  7.92e-57  9.27e-01  3.99e-02 \n",
+      "[1] \"PP abf for shared variant: 3.99%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.61e-71  1.01e-14  8.53e-57  9.99e-01  8.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0818%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___DDX5__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.5519e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.99e-57  3.50e-01  3.76e-57  4.38e-01  2.12e-01 \n",
+      "[1] \"PP abf for shared variant: 21.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.97e-67  5.82e-11  8.54e-57  1.00e+00  3.80e-05 \n",
+      "[1] \"PP abf for shared variant: 0.0038%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___DOK2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-57  2.44e-01  5.82e-57  6.80e-01  7.54e-02 \n",
+      "[1] \"PP abf for shared variant: 7.54%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-62  1.80e-06  8.47e-57  9.92e-01  8.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.806%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CALR__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.9449e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.48e-57  4.08e-01  4.74e-57  5.55e-01  3.78e-02 \n",
+      "[1] \"PP abf for shared variant: 3.78%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TTC38\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1223e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.58e-57  4.19e-01  4.58e-57  5.36e-01  4.55e-02 \n",
+      "[1] \"PP abf for shared variant: 4.55%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-58  1.47e-02  8.13e-57  9.51e-01  3.40e-02 \n",
+      "[1] \"PP abf for shared variant: 3.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.20e-59  2.57e-03  8.45e-57  9.90e-01  7.85e-03 \n",
+      "[1] \"PP abf for shared variant: 0.785%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-65  2.67e-09  8.54e-57  1.00e+00  1.71e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0171%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.38e-66  6.30e-10  8.54e-57  1.00e+00  4.79e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0479%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-62  2.39e-06  8.54e-57  1.00e+00  4.72e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0472%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-72  1.69e-16  8.53e-57  9.99e-01  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PASK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.87e-63  2.19e-07  8.54e-57  1.00e+00  2.13e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0213%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-67  5.08e-11  8.54e-57  1.00e+00  2.62e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0262%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.24e-73  3.79e-17  8.53e-57  9.99e-01  6.62e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0662%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___OXNAD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1359e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.42e-57  5.17e-01  4.01e-57  4.70e-01  1.30e-02 \n",
+      "[1] \"PP abf for shared variant: 1.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-58  2.72e-02  8.05e-57  9.42e-01  3.07e-02 \n",
+      "[1] \"PP abf for shared variant: 3.07%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-72  1.28e-16  8.54e-57  1.00e+00  3.46e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00346%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.21e-60  6.10e-04  8.25e-57  9.66e-01  3.37e-02 \n",
+      "[1] \"PP abf for shared variant: 3.37%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TYROBP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.08e-59  4.78e-03  8.46e-57  9.91e-01  4.27e-03 \n",
+      "[1] \"PP abf for shared variant: 0.427%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__YBX3\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1331e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.76e-57  6.74e-01  2.35e-57  2.75e-01  5.05e-02 \n",
+      "[1] \"PP abf for shared variant: 5.05%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.14e-63  3.67e-07  8.54e-57  1.00e+00  2.17e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0217%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-60  1.79e-04  8.38e-57  9.81e-01  1.84e-02 \n",
+      "[1] \"PP abf for shared variant: 1.84%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___IL7R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.07e-60  8.27e-04  8.47e-57  9.91e-01  7.79e-03 \n",
+      "[1] \"PP abf for shared variant: 0.779%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RHOH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-58  1.85e-02  8.33e-57  9.75e-01  6.64e-03 \n",
+      "[1] \"PP abf for shared variant: 0.664%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.75e-72  2.05e-16  8.53e-57  9.99e-01  6.12e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0612%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-74  1.58e-18  8.53e-57  9.99e-01  7.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0769%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.58e-59  1.00e-02  8.42e-57  9.86e-01  3.49e-03 \n",
+      "[1] \"PP abf for shared variant: 0.349%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___DBI__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.55e-59  2.98e-03  8.09e-57  9.47e-01  5.04e-02 \n",
+      "[1] \"PP abf for shared variant: 5.04%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.41e-68  9.85e-12  8.54e-57  9.99e-01  5.44e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0544%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-65  1.19e-09  8.53e-57  9.99e-01  9.40e-04 \n",
+      "[1] \"PP abf for shared variant: 0.094%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.47e-63  2.89e-07  8.54e-57  1.00e+00  3.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0373%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___POMP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.59e-57  3.04e-01  5.07e-57  5.92e-01  1.04e-01 \n",
+      "[1] \"PP abf for shared variant: 10.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-71  1.60e-15  8.53e-57  9.99e-01  9.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0998%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RAB8B__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.0817e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.61e-57  5.39e-01  3.34e-57  3.91e-01  6.99e-02 \n",
+      "[1] \"PP abf for shared variant: 6.99%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___GZMH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.38e-58  7.47e-02  7.72e-57  9.04e-01  2.15e-02 \n",
+      "[1] \"PP abf for shared variant: 2.15%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.39e-61  6.31e-05  8.54e-57  1.00e+00  3.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0387%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.54e-66  1.81e-10  8.54e-57  1.00e+00  6.84e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00684%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.32e-76  3.89e-20  8.54e-57  1.00e+00  1.38e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0138%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.52e-74  7.63e-18  8.53e-57  9.99e-01  6.37e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0637%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___ABLIM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-59  2.65e-03  8.43e-57  9.87e-01  1.05e-02 \n",
+      "[1] \"PP abf for shared variant: 1.05%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EIF4A1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8946e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.77e-57  4.41e-01  4.55e-57  5.32e-01  2.69e-02 \n",
+      "[1] \"PP abf for shared variant: 2.69%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___APOBEC3G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.46e-60  1.70e-04  8.53e-57  9.99e-01  8.37e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0837%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RP11-291B21.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.01e-62  1.06e-05  8.51e-57  9.96e-01  3.50e-03 \n",
+      "[1] \"PP abf for shared variant: 0.35%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-76  1.20e-20  8.53e-57  9.99e-01  1.32e-03 \n",
+      "[1] \"PP abf for shared variant: 0.132%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-74  1.84e-18  8.53e-57  9.99e-01  8.14e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0814%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.34e-65  1.57e-09  8.54e-57  1.00e+00  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-66  2.32e-10  8.54e-57  1.00e+00  2.34e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0234%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-74  1.64e-18  8.53e-57  9.99e-01  8.60e-04 \n",
+      "[1] \"PP abf for shared variant: 0.086%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.25e-59  6.14e-03  7.28e-57  8.51e-01  1.42e-01 \n",
+      "[1] \"PP abf for shared variant: 14.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.72e-70  3.18e-14  8.54e-57  1.00e+00  3.73e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0373%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.39e-68  7.48e-12  8.54e-57  1.00e+00  2.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0219%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___RPS26__S1PR5\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1943e-05\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.94e-57  6.95e-01  2.40e-57  2.81e-01  2.35e-02 \n",
+      "[1] \"PP abf for shared variant: 2.35%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD8T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-61  2.91e-05  8.50e-57  9.95e-01  4.91e-03 \n",
+      "[1] \"PP abf for shared variant: 0.491%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.26e-58  2.65e-02  6.85e-57  8.00e-01  1.74e-01 \n",
+      "[1] \"PP abf for shared variant: 17.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CHCHD2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.75e-58  6.73e-02  5.86e-57  6.84e-01  2.49e-01 \n",
+      "[1] \"PP abf for shared variant: 24.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EMP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-60  4.80e-04  8.51e-57  9.97e-01  2.77e-03 \n",
+      "[1] \"PP abf for shared variant: 0.277%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FMNL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-57  1.46e-01  7.23e-57  8.46e-01  7.40e-03 \n",
+      "[1] \"PP abf for shared variant: 0.74%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.41e-81  9.84e-25  8.54e-57  1.00e+00  4.37e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0437%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LEF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-63  3.08e-07  8.54e-57  1.00e+00  3.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0392%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HERPUD1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.267e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.44e-57  2.86e-01  3.90e-57  4.54e-01  2.61e-01 \n",
+      "[1] \"PP abf for shared variant: 26.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ANXA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.45e-62  9.90e-06  7.71e-57  9.01e-01  9.86e-02 \n",
+      "[1] \"PP abf for shared variant: 9.86%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SOD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-59  4.44e-03  3.96e-57  4.59e-01  5.37e-01 \n",
+      "[1] \"PP abf for shared variant: 53.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MYL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-73  1.73e-17  8.54e-57  1.00e+00  4.97e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0497%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GNB2L1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.67e-70  7.81e-14  8.49e-57  9.95e-01  5.39e-03 \n",
+      "[1] \"PP abf for shared variant: 0.539%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ATP1B3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.10e-57  1.29e-01  5.88e-57  6.87e-01  1.84e-01 \n",
+      "[1] \"PP abf for shared variant: 18.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.02e-57  2.37e-01  4.56e-57  5.31e-01  2.32e-01 \n",
+      "[1] \"PP abf for shared variant: 23.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.96e-82  3.46e-26  8.54e-57  9.99e-01  5.29e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0529%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL28__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-67  1.24e-11  8.53e-57  9.99e-01  7.15e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0715%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EML4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.12e-60  1.31e-04  6.63e-57  7.74e-01  2.26e-01 \n",
+      "[1] \"PP abf for shared variant: 22.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SCML1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.04e-60  8.24e-04  4.75e-57  5.52e-01  4.47e-01 \n",
+      "[1] \"PP abf for shared variant: 44.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MCL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.70e-63  1.02e-06  6.45e-57  7.53e-01  2.47e-01 \n",
+      "[1] \"PP abf for shared variant: 24.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NOG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-58  2.66e-02  4.42e-57  5.13e-01  4.61e-01 \n",
+      "[1] \"PP abf for shared variant: 46.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PRMT2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.85e-60  6.86e-04  8.42e-57  9.86e-01  1.36e-02 \n",
+      "[1] \"PP abf for shared variant: 1.36%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-61  1.87e-05  8.48e-57  9.92e-01  7.56e-03 \n",
+      "[1] \"PP abf for shared variant: 0.756%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-57  2.26e-01  4.16e-57  4.84e-01  2.89e-01 \n",
+      "[1] \"PP abf for shared variant: 28.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMSB4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.78e-68  6.77e-12  8.54e-57  1.00e+00  2.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0249%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FAM129A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.42e-63  5.18e-07  8.54e-57  1.00e+00  3.05e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0305%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.40e-72  9.84e-16  8.54e-57  1.00e+00  2.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0259%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ABLIM1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.2936e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.19e-57  3.73e-01  3.54e-57  4.12e-01  2.15e-01 \n",
+      "[1] \"PP abf for shared variant: 21.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPLP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-82  2.52e-26  8.53e-57  9.99e-01  1.23e-03 \n",
+      "[1] \"PP abf for shared variant: 0.123%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ALOX5AP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.48e-59  1.73e-03  6.72e-57  7.85e-01  2.13e-01 \n",
+      "[1] \"PP abf for shared variant: 21.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TSHZ2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.94e-60  6.96e-04  7.81e-57  9.14e-01  8.52e-02 \n",
+      "[1] \"PP abf for shared variant: 8.52%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TIGIT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.06e-62  7.10e-06  8.39e-57  9.83e-01  1.73e-02 \n",
+      "[1] \"PP abf for shared variant: 1.73%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ARHGDIB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-61  2.04e-05  8.52e-57  9.98e-01  1.75e-03 \n",
+      "[1] \"PP abf for shared variant: 0.175%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FAU__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.73e-67  1.02e-10  8.54e-57  1.00e+00  1.28e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0128%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS29\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.47e-77  9.92e-21  8.54e-57  1.00e+00  2.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0296%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-84  1.23e-28  8.54e-57  1.00e+00  1.47e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0147%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.58e-84  6.53e-28  8.54e-57  1.00e+00  3.75e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0375%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YBX1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.86e-62  1.15e-05  8.53e-57  9.99e-01  1.13e-03 \n",
+      "[1] \"PP abf for shared variant: 0.113%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.39e-81  5.14e-25  8.54e-57  1.00e+00  1.39e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0139%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___JUND__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.279e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-57  1.43e-01  3.01e-57  3.47e-01  5.10e-01 \n",
+      "[1] \"PP abf for shared variant: 51%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3YL1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-63  3.01e-07  8.53e-57  9.99e-01  6.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.065%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS27\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.36e-83  8.62e-27  8.53e-57  9.99e-01  6.98e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0698%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___C12orf75__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.93e-59  6.95e-03  8.44e-57  9.88e-01  5.13e-03 \n",
+      "[1] \"PP abf for shared variant: 0.513%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CYBA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.13e-67  3.66e-11  8.54e-57  1.00e+00  1.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0159%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF18\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.86e-59  8.03e-03  6.78e-57  7.92e-01  2.00e-01 \n",
+      "[1] \"PP abf for shared variant: 20%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MYO1F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.69e-59  1.98e-03  8.07e-57  9.44e-01  5.36e-02 \n",
+      "[1] \"PP abf for shared variant: 5.36%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL12__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.95e-80  4.62e-24  8.54e-57  1.00e+00  3.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0378%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PTPRC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.35e-65  3.92e-09  8.54e-57  1.00e+00  3.00e-04 \n",
+      "[1] \"PP abf for shared variant: 0.03%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD55__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.27e-57  2.66e-01  4.83e-57  5.64e-01  1.70e-01 \n",
+      "[1] \"PP abf for shared variant: 17%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.14e-81  6.02e-25  8.54e-57  1.00e+00  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CREM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-60  1.45e-04  8.48e-57  9.93e-01  6.88e-03 \n",
+      "[1] \"PP abf for shared variant: 0.688%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VMP1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.79e-58  1.03e-01  7.23e-57  8.46e-01  5.15e-02 \n",
+      "[1] \"PP abf for shared variant: 5.15%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HMGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.32e-62  3.89e-06  8.48e-57  9.93e-01  6.72e-03 \n",
+      "[1] \"PP abf for shared variant: 0.672%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL31__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.73e-82  9.05e-26  8.53e-57  9.99e-01  1.05e-03 \n",
+      "[1] \"PP abf for shared variant: 0.105%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___C1orf228__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.80e-57  2.10e-01  6.22e-57  7.28e-01  6.18e-02 \n",
+      "[1] \"PP abf for shared variant: 6.18%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GALM__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-57  1.56e-01  6.48e-57  7.58e-01  8.65e-02 \n",
+      "[1] \"PP abf for shared variant: 8.65%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TRABD2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.59e-58  3.04e-02  7.66e-57  8.97e-01  7.29e-02 \n",
+      "[1] \"PP abf for shared variant: 7.29%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.85e-60  3.34e-04  1.94e-57  2.19e-01  7.80e-01 \n",
+      "[1] \"PP abf for shared variant: 78%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL17__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.49e-70  1.74e-14  8.54e-57  1.00e+00  2.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0243%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.23e-73  6.12e-17  8.54e-57  1.00e+00  2.07e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00207%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ANXA5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.40e-59  1.10e-02  8.15e-57  9.55e-01  3.45e-02 \n",
+      "[1] \"PP abf for shared variant: 3.45%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___IDS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.05e-60  3.57e-04  7.95e-57  9.30e-01  6.95e-02 \n",
+      "[1] \"PP abf for shared variant: 6.95%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ARID5B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.55e-58  5.33e-02  7.68e-57  8.99e-01  4.76e-02 \n",
+      "[1] \"PP abf for shared variant: 4.76%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___IMPDH2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.90e-62  5.73e-06  8.38e-57  9.82e-01  1.84e-02 \n",
+      "[1] \"PP abf for shared variant: 1.84%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.65e-70  3.10e-14  8.54e-57  1.00e+00  4.41e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0441%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ST13\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.28e-63  3.84e-07  8.54e-57  1.00e+00  1.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CXCR3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.34e-59  5.08e-03  8.39e-57  9.82e-01  1.31e-02 \n",
+      "[1] \"PP abf for shared variant: 1.31%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.15e-62  1.34e-06  8.53e-57  9.99e-01  5.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0585%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL37A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-71  1.23e-15  8.54e-57  1.00e+00  2.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0222%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SPOCK2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.77e-59  7.93e-03  3.43e-57  3.95e-01  5.97e-01 \n",
+      "[1] \"PP abf for shared variant: 59.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___C15orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.56e-58  8.85e-02  6.72e-57  7.86e-01  1.26e-01 \n",
+      "[1] \"PP abf for shared variant: 12.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPF\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 3.1448e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.20e-57  4.91e-01  3.79e-57  4.43e-01  6.57e-02 \n",
+      "[1] \"PP abf for shared variant: 6.57%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___H3F3B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.68e-71  9.00e-15  8.54e-57  1.00e+00  3.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0318%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FAM134B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-57  1.23e-01  6.72e-57  7.86e-01  9.02e-02 \n",
+      "[1] \"PP abf for shared variant: 9.02%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ISG20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.89e-59  4.56e-03  8.49e-57  9.95e-01  7.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0743%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CFL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.57e-66  3.01e-10  8.54e-57  1.00e+00  4.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.049%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NUCB2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.10e-58  4.80e-02  6.15e-57  7.17e-01  2.35e-01 \n",
+      "[1] \"PP abf for shared variant: 23.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ALKBH7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.14e-59  1.34e-03  8.51e-57  9.96e-01  2.34e-03 \n",
+      "[1] \"PP abf for shared variant: 0.234%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LINC00493__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.72e-58  7.87e-02  7.63e-57  8.93e-01  2.79e-02 \n",
+      "[1] \"PP abf for shared variant: 2.79%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.31e-82  5.04e-26  8.54e-57  1.00e+00  8.95e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00895%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__VIM\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-59  2.33e-03  4.25e-57  4.93e-01  5.05e-01 \n",
+      "[1] \"PP abf for shared variant: 50.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG8\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.68e-68  6.65e-12  8.54e-57  1.00e+00  3.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0378%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CDC42__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-59  2.58e-03  6.89e-57  8.05e-01  1.92e-01 \n",
+      "[1] \"PP abf for shared variant: 19.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF1B\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.93e-58  2.26e-02  8.10e-57  9.48e-01  2.93e-02 \n",
+      "[1] \"PP abf for shared variant: 2.93%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NELL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.75e-58  3.21e-02  7.90e-57  9.24e-01  4.36e-02 \n",
+      "[1] \"PP abf for shared variant: 4.36%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.35e-75  1.09e-18  8.54e-57  1.00e+00  1.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0189%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ACTN4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.18e-59  1.38e-03  8.19e-57  9.59e-01  3.96e-02 \n",
+      "[1] \"PP abf for shared variant: 3.96%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___IKZF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.04e-58  3.56e-02  3.54e-57  4.09e-01  5.56e-01 \n",
+      "[1] \"PP abf for shared variant: 55.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LDHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.74e-57  2.04e-01  6.44e-57  7.53e-01  4.30e-02 \n",
+      "[1] \"PP abf for shared variant: 4.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL41__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.17e-74  3.71e-18  8.53e-57  9.99e-01  1.17e-03 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS16__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.12e-65  4.82e-09  8.54e-57  1.00e+00  3.46e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0346%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.68e-57  1.96e-01  4.35e-57  5.06e-01  2.97e-01 \n",
+      "[1] \"PP abf for shared variant: 29.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NAMPT__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.8087e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-57  3.39e-01  4.73e-57  5.52e-01  1.08e-01 \n",
+      "[1] \"PP abf for shared variant: 10.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFAS1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.79e-62  1.15e-05  8.47e-57  9.91e-01  8.78e-03 \n",
+      "[1] \"PP abf for shared variant: 0.878%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CALM2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.62e-61  4.24e-05  8.27e-57  9.68e-01  3.21e-02 \n",
+      "[1] \"PP abf for shared variant: 3.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.19e-72  8.42e-16  8.54e-57  1.00e+00  1.05e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0105%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-ND2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.24e-58  3.80e-02  1.62e-57  1.82e-01  7.80e-01 \n",
+      "[1] \"PP abf for shared variant: 78%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.68e-71  4.31e-15  8.54e-57  1.00e+00  1.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0176%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD52__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.85e-60  3.33e-04  8.42e-57  9.86e-01  1.41e-02 \n",
+      "[1] \"PP abf for shared variant: 1.41%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF3L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.28e-64  5.02e-08  8.53e-57  9.99e-01  9.89e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0989%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___H3F3A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-61  2.12e-05  8.24e-57  9.64e-01  3.59e-02 \n",
+      "[1] \"PP abf for shared variant: 3.59%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ADTRP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.72e-65  1.14e-08  8.14e-57  9.52e-01  4.75e-02 \n",
+      "[1] \"PP abf for shared variant: 4.75%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.54e-58  1.00e-01  6.95e-57  8.13e-01  8.68e-02 \n",
+      "[1] \"PP abf for shared variant: 8.68%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNRPD2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-58  1.87e-02  4.13e-57  4.78e-01  5.03e-01 \n",
+      "[1] \"PP abf for shared variant: 50.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.42e-61  5.18e-05  8.22e-57  9.62e-01  3.77e-02 \n",
+      "[1] \"PP abf for shared variant: 3.77%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CXCR4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.71e-62  9.03e-06  5.64e-57  6.58e-01  3.42e-01 \n",
+      "[1] \"PP abf for shared variant: 34.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___DYNLL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.69e-60  1.02e-03  7.27e-57  8.50e-01  1.49e-01 \n",
+      "[1] \"PP abf for shared variant: 14.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAMSN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-61  1.93e-05  8.47e-57  9.92e-01  8.22e-03 \n",
+      "[1] \"PP abf for shared variant: 0.822%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LMNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.65e-66  5.45e-10  6.82e-57  7.96e-01  2.04e-01 \n",
+      "[1] \"PP abf for shared variant: 20.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-ND5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.87e-64  1.16e-07  8.28e-57  9.69e-01  3.09e-02 \n",
+      "[1] \"PP abf for shared variant: 3.09%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4X\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.61e-78  6.57e-22  8.54e-57  1.00e+00  3.08e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0308%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RUNX3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.80e-58  7.97e-02  7.10e-57  8.30e-01  9.03e-02 \n",
+      "[1] \"PP abf for shared variant: 9.03%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HLA-B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.50e-74  1.76e-18  8.53e-57  9.99e-01  7.31e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0731%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RGS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.22e-62  7.28e-06  8.53e-57  9.98e-01  1.55e-03 \n",
+      "[1] \"PP abf for shared variant: 0.155%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ERGIC3__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.423e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.79e-57  2.10e-01  2.64e-57  3.05e-01  4.85e-01 \n",
+      "[1] \"PP abf for shared variant: 48.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SELL\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-64  1.64e-08  8.13e-57  9.52e-01  4.83e-02 \n",
+      "[1] \"PP abf for shared variant: 4.83%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TYMP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-58  2.67e-02  7.86e-57  9.20e-01  5.33e-02 \n",
+      "[1] \"PP abf for shared variant: 5.33%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.82e-58  7.99e-02  6.58e-57  7.69e-01  1.51e-01 \n",
+      "[1] \"PP abf for shared variant: 15.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.24e-78  1.45e-22  8.54e-57  1.00e+00  1.96e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0196%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UQCRB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-60  1.47e-04  8.02e-57  9.39e-01  6.12e-02 \n",
+      "[1] \"PP abf for shared variant: 6.12%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-79  4.51e-23  8.54e-57  1.00e+00  4.30e-04 \n",
+      "[1] \"PP abf for shared variant: 0.043%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRGN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.72e-77  5.53e-21  8.54e-57  1.00e+00  2.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0219%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-ND4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-57  1.41e-01  3.16e-57  3.65e-01  4.95e-01 \n",
+      "[1] \"PP abf for shared variant: 49.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ABHD14B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.61e-60  1.89e-04  8.48e-57  9.93e-01  6.44e-03 \n",
+      "[1] \"PP abf for shared variant: 0.644%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ATP5E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-59  2.47e-03  7.23e-57  8.45e-01  1.53e-01 \n",
+      "[1] \"PP abf for shared variant: 15.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSAP58\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.48e-67  5.24e-11  8.53e-57  9.99e-01  9.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0967%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-69  3.45e-13  8.54e-57  9.99e-01  5.33e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0533%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.67e-78  5.47e-22  8.54e-57  1.00e+00  1.40e-04 \n",
+      "[1] \"PP abf for shared variant: 0.014%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MAL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-65  2.18e-09  8.53e-57  9.99e-01  1.17e-03 \n",
+      "[1] \"PP abf for shared variant: 0.117%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ATP2B4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-58  2.51e-02  7.55e-57  8.83e-01  9.19e-02 \n",
+      "[1] \"PP abf for shared variant: 9.19%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ARPC1B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.95e-57  3.46e-01  5.03e-57  5.88e-01  6.56e-02 \n",
+      "[1] \"PP abf for shared variant: 6.56%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PDCD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.94e-60  8.12e-04  3.24e-57  3.73e-01  6.26e-01 \n",
+      "[1] \"PP abf for shared variant: 62.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.59e-77  5.38e-21  8.54e-57  9.99e-01  5.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.055%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL9__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.89e-83  9.24e-27  8.54e-57  1.00e+00  7.65e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00765%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS25__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-78  1.77e-22  8.54e-57  1.00e+00  3.54e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0354%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SAT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-69  1.70e-13  8.53e-57  9.99e-01  6.92e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0692%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HLA-E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-64  2.12e-08  8.53e-57  9.99e-01  6.28e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0628%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TCF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.22e-61  4.94e-05  8.06e-57  9.43e-01  5.69e-02 \n",
+      "[1] \"PP abf for shared variant: 5.69%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PIK3IP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-60  1.17e-04  7.95e-57  9.30e-01  6.97e-02 \n",
+      "[1] \"PP abf for shared variant: 6.97%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LGALS3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.56e-58  1.12e-01  6.89e-57  8.06e-01  8.16e-02 \n",
+      "[1] \"PP abf for shared variant: 8.16%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MIAT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.09e-59  8.30e-03  5.54e-57  6.45e-01  3.47e-01 \n",
+      "[1] \"PP abf for shared variant: 34.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL26__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.49e-77  2.91e-21  8.54e-57  1.00e+00  2.55e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0255%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-64  1.24e-08  8.53e-57  9.99e-01  6.17e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0617%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CCR7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.89e-59  1.16e-02  7.27e-57  8.50e-01  1.39e-01 \n",
+      "[1] \"PP abf for shared variant: 13.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL14__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-73  1.44e-17  8.54e-57  1.00e+00  3.63e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0363%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL18A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.91e-79  3.41e-23  8.54e-57  1.00e+00  2.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0276%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RNF19A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.39e-60  9.82e-04  6.93e-57  8.10e-01  1.89e-01 \n",
+      "[1] \"PP abf for shared variant: 18.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-CO3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.54e-63  2.97e-07  8.40e-57  9.83e-01  1.68e-02 \n",
+      "[1] \"PP abf for shared variant: 1.68%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EEF1A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.40e-81  5.15e-25  8.53e-57  9.99e-01  8.48e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0848%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF3H__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.64e-68  8.94e-12  8.52e-57  9.98e-01  1.84e-03 \n",
+      "[1] \"PP abf for shared variant: 0.184%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FAS__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.97e-58  1.05e-01  7.17e-57  8.39e-01  5.64e-02 \n",
+      "[1] \"PP abf for shared variant: 5.64%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EEF1D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.83e-66  3.31e-10  8.53e-57  9.99e-01  7.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0799%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPLP0__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-66  4.32e-10  8.53e-57  9.99e-01  5.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0567%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GYPC__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.45e-62  4.04e-06  7.58e-57  8.87e-01  1.13e-01 \n",
+      "[1] \"PP abf for shared variant: 11.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL30__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.00e-79  7.02e-23  8.53e-57  9.99e-01  5.97e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0597%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL34__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.75e-81  1.02e-24  8.54e-57  1.00e+00  1.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0193%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TPM4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.00e-58  1.18e-02  8.09e-57  9.47e-01  4.09e-02 \n",
+      "[1] \"PP abf for shared variant: 4.09%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LDHB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.67e-68  6.64e-12  8.54e-57  1.00e+00  2.69e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0269%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___AIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.85e-61  4.51e-05  8.17e-57  9.56e-01  4.38e-02 \n",
+      "[1] \"PP abf for shared variant: 4.38%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL35A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-79  2.52e-23  8.54e-57  1.00e+00  1.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0185%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ITGB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.51e-63  1.77e-07  8.53e-57  9.99e-01  8.43e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0843%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXN\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.99e-63  2.33e-07  8.52e-57  9.98e-01  1.86e-03 \n",
+      "[1] \"PP abf for shared variant: 0.186%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FTH1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-58  1.56e-02  7.94e-57  9.29e-01  5.50e-02 \n",
+      "[1] \"PP abf for shared variant: 5.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.92e-83  1.16e-26  8.54e-57  1.00e+00  2.41e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0241%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___COX7C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.56e-58  7.68e-02  7.60e-57  8.89e-01  3.40e-02 \n",
+      "[1] \"PP abf for shared variant: 3.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HLA-A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.28e-74  2.67e-18  8.53e-57  9.99e-01  7.06e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0706%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LCP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.73e-60  7.88e-04  8.33e-57  9.75e-01  2.39e-02 \n",
+      "[1] \"PP abf for shared variant: 2.39%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.68e-60  9.00e-04  6.95e-57  8.12e-01  1.87e-01 \n",
+      "[1] \"PP abf for shared variant: 18.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-79  2.78e-23  8.54e-57  1.00e+00  2.77e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0277%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ARPC2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.42e-73  5.18e-17  8.54e-57  9.99e-01  5.12e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0512%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMEM123\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.20e-58  3.75e-02  7.94e-57  9.29e-01  3.35e-02 \n",
+      "[1] \"PP abf for shared variant: 3.35%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PPP1R15A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-57  3.36e-01  4.51e-57  5.27e-01  1.37e-01 \n",
+      "[1] \"PP abf for shared variant: 13.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___IL32__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.09e-60  4.79e-04  8.37e-57  9.80e-01  1.93e-02 \n",
+      "[1] \"PP abf for shared variant: 1.93%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-82  2.31e-26  8.54e-57  9.99e-01  5.51e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0551%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-72  1.85e-16  8.53e-57  9.99e-01  6.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0686%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM20\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-57  3.12e-01  5.26e-57  6.15e-01  7.33e-02 \n",
+      "[1] \"PP abf for shared variant: 7.33%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF3F__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.20e-60  2.58e-04  3.44e-57  3.97e-01  6.03e-01 \n",
+      "[1] \"PP abf for shared variant: 60.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ERP29__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.55e-60  7.67e-04  1.80e-57  2.03e-01  7.96e-01 \n",
+      "[1] \"PP abf for shared variant: 79.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___KLF6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.66e-62  7.79e-06  8.53e-57  9.98e-01  1.73e-03 \n",
+      "[1] \"PP abf for shared variant: 0.173%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GIMAP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.47e-60  4.07e-04  5.72e-57  6.66e-01  3.34e-01 \n",
+      "[1] \"PP abf for shared variant: 33.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFBR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.90e-58  3.39e-02  7.79e-57  9.12e-01  5.40e-02 \n",
+      "[1] \"PP abf for shared variant: 5.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RNF213__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-57  1.37e-01  4.48e-57  5.21e-01  3.42e-01 \n",
+      "[1] \"PP abf for shared variant: 34.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___C19orf53__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.95e-57  3.45e-01  4.45e-57  5.19e-01  1.36e-01 \n",
+      "[1] \"PP abf for shared variant: 13.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SERF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.84e-67  3.33e-11  8.53e-57  9.99e-01  5.87e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0587%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.36e-71  1.59e-15  8.54e-57  1.00e+00  4.67e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0467%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MIR4435-1HG__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.03e-66  3.54e-10  8.54e-57  1.00e+00  9.32e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00932%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.95e-72  2.29e-16  8.53e-57  9.99e-01  1.18e-03 \n",
+      "[1] \"PP abf for shared variant: 0.118%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MZT2B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.41e-60  2.82e-04  8.39e-57  9.82e-01  1.78e-02 \n",
+      "[1] \"PP abf for shared variant: 1.78%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___AK5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-58  2.35e-02  3.17e-57  3.65e-01  6.11e-01 \n",
+      "[1] \"PP abf for shared variant: 61.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NDFIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-57  1.94e-01  6.19e-57  7.24e-01  8.14e-02 \n",
+      "[1] \"PP abf for shared variant: 8.14%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HNRNPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.75e-65  6.73e-09  8.44e-57  9.89e-01  1.13e-02 \n",
+      "[1] \"PP abf for shared variant: 1.13%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL7A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.44e-77  1.11e-20  8.53e-57  9.99e-01  6.59e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0659%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-60  1.24e-04  8.13e-57  9.51e-01  4.86e-02 \n",
+      "[1] \"PP abf for shared variant: 4.86%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL22__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.42e-75  4.01e-19  8.54e-57  1.00e+00  2.01e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0201%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___C1QBP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.13e-58  1.32e-02  6.56e-57  7.66e-01  2.21e-01 \n",
+      "[1] \"PP abf for shared variant: 22.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CXCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-57  1.84e-01  4.65e-57  5.42e-01  2.74e-01 \n",
+      "[1] \"PP abf for shared variant: 27.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ARPC3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.81e-58  6.80e-02  7.56e-57  8.84e-01  4.78e-02 \n",
+      "[1] \"PP abf for shared variant: 4.78%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MRPS21__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.3464e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-57  2.29e-01  2.97e-57  3.43e-01  4.28e-01 \n",
+      "[1] \"PP abf for shared variant: 42.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.27e-72  9.69e-16  8.54e-57  1.00e+00  4.36e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00436%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-62  1.24e-06  8.30e-57  9.71e-01  2.88e-02 \n",
+      "[1] \"PP abf for shared variant: 2.88%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EBPL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-57  1.49e-01  2.51e-57  2.88e-01  5.63e-01 \n",
+      "[1] \"PP abf for shared variant: 56.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FTL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.94e-58  8.13e-02  3.16e-57  3.64e-01  5.55e-01 \n",
+      "[1] \"PP abf for shared variant: 55.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UXT\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.41e-65  7.50e-09  8.36e-57  9.79e-01  2.12e-02 \n",
+      "[1] \"PP abf for shared variant: 2.12%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LSM5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.96e-60  2.30e-04  8.36e-57  9.79e-01  2.07e-02 \n",
+      "[1] \"PP abf for shared variant: 2.07%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___KMT2E__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.6569e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.42e-57  4.00e-01  4.33e-57  5.07e-01  9.34e-02 \n",
+      "[1] \"PP abf for shared variant: 9.34%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-CO2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.92e-64  5.76e-08  4.60e-57  5.34e-01  4.66e-01 \n",
+      "[1] \"PP abf for shared variant: 46.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TAGLN2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.50e-57  2.93e-01  4.34e-57  5.06e-01  2.01e-01 \n",
+      "[1] \"PP abf for shared variant: 20.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CDCA7__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.4164e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.79e-57  4.43e-01  3.87e-57  4.52e-01  1.05e-01 \n",
+      "[1] \"PP abf for shared variant: 10.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EEF2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.06e-63  2.41e-07  1.49e-57  1.66e-01  8.34e-01 \n",
+      "[1] \"PP abf for shared variant: 83.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EPB41L4A-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.70e-63  7.85e-07  8.53e-57  9.99e-01  7.25e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0725%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FLNA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.78e-64  4.42e-08  8.46e-57  9.91e-01  9.11e-03 \n",
+      "[1] \"PP abf for shared variant: 0.911%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TATDN1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-58  1.70e-02  7.74e-57  9.06e-01  7.72e-02 \n",
+      "[1] \"PP abf for shared variant: 7.72%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HLA-DPA1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.82e-58  4.48e-02  7.47e-57  8.74e-01  8.16e-02 \n",
+      "[1] \"PP abf for shared variant: 8.16%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___C12orf57__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.97e-69  2.31e-13  8.54e-57  1.00e+00  1.80e-04 \n",
+      "[1] \"PP abf for shared variant: 0.018%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-79  1.22e-23  8.54e-57  1.00e+00  1.39e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0139%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS21__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.12e-77  9.51e-21  8.54e-57  1.00e+00  8.01e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00801%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___BTG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-60  2.40e-04  7.25e-57  8.47e-01  1.53e-01 \n",
+      "[1] \"PP abf for shared variant: 15.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___C8orf59__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-58  1.49e-02  5.55e-57  6.47e-01  3.38e-01 \n",
+      "[1] \"PP abf for shared variant: 33.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD58__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.94e-59  3.44e-03  8.08e-57  9.46e-01  5.03e-02 \n",
+      "[1] \"PP abf for shared variant: 5.03%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-CO1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.62e-74  8.93e-18  8.54e-57  9.99e-01  5.57e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0557%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPLP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.01e-62  2.36e-06  7.27e-57  8.50e-01  1.50e-01 \n",
+      "[1] \"PP abf for shared variant: 15%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___AKAP13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.04e-58  2.39e-02  8.28e-57  9.70e-01  6.34e-03 \n",
+      "[1] \"PP abf for shared variant: 0.634%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF4B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.73e-63  2.03e-07  8.53e-57  9.99e-01  8.11e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0811%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___DDX5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-61  1.85e-05  8.02e-57  9.38e-01  6.15e-02 \n",
+      "[1] \"PP abf for shared variant: 6.15%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS24__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.64e-59  1.13e-02  4.86e-57  5.65e-01  4.24e-01 \n",
+      "[1] \"PP abf for shared variant: 42.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ANXA2R__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-57  1.52e-01  6.40e-57  7.48e-01  1.00e-01 \n",
+      "[1] \"PP abf for shared variant: 10%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___IL8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.42e-58  8.69e-02  2.65e-57  3.04e-01  6.09e-01 \n",
+      "[1] \"PP abf for shared variant: 60.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LINC00152__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.07e-64  1.25e-08  8.51e-57  9.97e-01  3.50e-03 \n",
+      "[1] \"PP abf for shared variant: 0.35%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FOXP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.81e-58  3.29e-02  8.24e-57  9.65e-01  2.21e-03 \n",
+      "[1] \"PP abf for shared variant: 0.221%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RGS10__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.83e-65  6.83e-09  8.51e-57  9.96e-01  3.94e-03 \n",
+      "[1] \"PP abf for shared variant: 0.394%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___B2M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.54e-78  8.83e-22  8.54e-57  1.00e+00  4.65e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0465%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___KLRB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.78e-61  5.60e-05  8.53e-57  9.99e-01  1.16e-03 \n",
+      "[1] \"PP abf for shared variant: 0.116%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL36A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.15e-73  4.85e-17  8.53e-57  9.99e-01  6.14e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0614%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL35__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.06e-71  9.44e-15  8.54e-57  1.00e+00  2.58e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0258%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___DAP3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.87e-60  3.37e-04  7.03e-57  8.22e-01  1.78e-01 \n",
+      "[1] \"PP abf for shared variant: 17.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___C6orf48__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.25e-68  4.97e-12  8.54e-57  1.00e+00  1.51e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0151%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SVIP\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.15e-57  2.52e-01  4.95e-57  5.78e-01  1.70e-01 \n",
+      "[1] \"PP abf for shared variant: 17%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HLA-C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.48e-72  7.58e-16  8.53e-57  9.99e-01  9.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0985%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL10A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-83  1.90e-27  8.54e-57  1.00e+00  4.86e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0486%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL23A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.45e-75  1.69e-19  8.53e-57  9.99e-01  1.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.125%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PRKCQ-AS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-63  1.20e-07  8.51e-57  9.96e-01  3.51e-03 \n",
+      "[1] \"PP abf for shared variant: 0.351%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GIMAP7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-58  1.92e-02  8.17e-57  9.56e-01  2.44e-02 \n",
+      "[1] \"PP abf for shared variant: 2.44%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ENTPD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.92e-58  8.11e-02  7.43e-57  8.69e-01  4.97e-02 \n",
+      "[1] \"PP abf for shared variant: 4.97%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___DUSP4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-66  1.47e-10  8.54e-57  1.00e+00  2.19e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0219%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.62e-71  6.58e-15  8.53e-57  9.99e-01  8.50e-04 \n",
+      "[1] \"PP abf for shared variant: 0.085%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAB\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.23e-59  1.44e-03  8.45e-57  9.89e-01  9.67e-03 \n",
+      "[1] \"PP abf for shared variant: 0.967%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CCR6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.17e-58  7.23e-02  4.20e-57  4.88e-01  4.40e-01 \n",
+      "[1] \"PP abf for shared variant: 44%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-ND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.31e-60  3.88e-04  2.03e-57  2.30e-01  7.70e-01 \n",
+      "[1] \"PP abf for shared variant: 77%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PFN1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.35e-73  5.09e-17  8.54e-57  1.00e+00  2.85e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0285%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ADAM19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-57  1.88e-01  5.13e-57  5.98e-01  2.14e-01 \n",
+      "[1] \"PP abf for shared variant: 21.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CLDND1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-57  1.23e-01  3.62e-57  4.19e-01  4.58e-01 \n",
+      "[1] \"PP abf for shared variant: 45.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PFDN5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.28e-63  6.18e-07  8.53e-57  9.99e-01  6.00e-04 \n",
+      "[1] \"PP abf for shared variant: 0.06%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FBL__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.21e-66  4.94e-10  8.41e-57  9.85e-01  1.51e-02 \n",
+      "[1] \"PP abf for shared variant: 1.51%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD37__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.72e-58  1.14e-01  5.35e-57  6.24e-01  2.62e-01 \n",
+      "[1] \"PP abf for shared variant: 26.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___APEX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-57  1.94e-01  5.04e-57  5.88e-01  2.18e-01 \n",
+      "[1] \"PP abf for shared variant: 21.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD74__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.17e-64  1.37e-08  8.53e-57  9.99e-01  6.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0622%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS20__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-78  1.78e-22  8.53e-57  9.99e-01  1.02e-03 \n",
+      "[1] \"PP abf for shared variant: 0.102%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LETMD1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.46e-60  4.05e-04  4.13e-57  4.78e-01  5.22e-01 \n",
+      "[1] \"PP abf for shared variant: 52.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.47e-59  1.73e-03  8.39e-57  9.83e-01  1.57e-02 \n",
+      "[1] \"PP abf for shared variant: 1.57%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NOSIP__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.84e-63  4.50e-07  8.54e-57  1.00e+00  4.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0476%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___AHNAK__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.91e-59  8.09e-03  6.31e-57  7.36e-01  2.56e-01 \n",
+      "[1] \"PP abf for shared variant: 25.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SLC7A5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.98e-57  3.48e-01  4.58e-57  5.35e-01  1.17e-01 \n",
+      "[1] \"PP abf for shared variant: 11.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GLTSCR2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.57e-66  1.83e-10  8.54e-57  1.00e+00  9.00e-05 \n",
+      "[1] \"PP abf for shared variant: 0.009%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.44e-77  1.11e-20  8.54e-57  1.00e+00  3.78e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0378%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HLA-DRA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.60e-60  5.39e-04  8.51e-57  9.97e-01  2.70e-03 \n",
+      "[1] \"PP abf for shared variant: 0.27%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS3A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.21e-84  3.76e-28  8.54e-57  9.99e-01  5.01e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0501%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.63e-69  3.08e-13  8.54e-57  1.00e+00  4.36e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0436%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS5\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.66e-81  1.94e-25  8.54e-57  1.00e+00  1.40e-04 \n",
+      "[1] \"PP abf for shared variant: 0.014%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-ATP6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.25e-61  1.47e-05  4.71e-57  5.47e-01  4.53e-01 \n",
+      "[1] \"PP abf for shared variant: 45.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A11\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-74  1.42e-18  8.54e-57  9.99e-01  5.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0518%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CCL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.58e-62  1.84e-06  8.53e-57  9.99e-01  9.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0922%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RILPL2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.23e-58  6.12e-02  2.32e-57  2.65e-01  6.74e-01 \n",
+      "[1] \"PP abf for shared variant: 67.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SSR2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.63e-61  1.91e-05  8.51e-57  9.97e-01  3.45e-03 \n",
+      "[1] \"PP abf for shared variant: 0.345%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFRSF4\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.40e-59  1.64e-03  8.24e-57  9.65e-01  3.33e-02 \n",
+      "[1] \"PP abf for shared variant: 3.33%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBC\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.81e-65  2.13e-09  8.53e-57  9.99e-01  7.42e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0742%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__S100A10\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.50e-70  6.44e-14  8.54e-57  1.00e+00  3.15e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0315%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MAF__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.75e-62  6.73e-06  8.51e-57  9.96e-01  3.95e-03 \n",
+      "[1] \"PP abf for shared variant: 0.395%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NACA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-66  1.99e-10  8.54e-57  1.00e+00  3.15e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0315%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___COMMD6__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.38e-60  2.79e-04  8.43e-57  9.86e-01  1.32e-02 \n",
+      "[1] \"PP abf for shared variant: 1.32%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS11__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.36e-63  3.94e-07  8.54e-57  1.00e+00  2.13e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0213%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NSMCE1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.73e-59  3.20e-03  7.95e-57  9.30e-01  6.67e-02 \n",
+      "[1] \"PP abf for shared variant: 6.67%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TGFB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.21e-61  2.59e-05  8.49e-57  9.94e-01  6.04e-03 \n",
+      "[1] \"PP abf for shared variant: 0.604%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PRDX1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.28e-58  3.84e-02  5.58e-57  6.51e-01  3.11e-01 \n",
+      "[1] \"PP abf for shared variant: 31.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS9\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.09e-76  3.61e-20  8.54e-57  1.00e+00  7.79e-05 \n",
+      "[1] \"PP abf for shared variant: 0.00779%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FAM46C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.09e-58  1.27e-02  7.22e-57  8.45e-01  1.43e-01 \n",
+      "[1] \"PP abf for shared variant: 14.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.41e-78  3.99e-22  8.53e-57  9.99e-01  7.08e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0708%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS23__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.22e-78  9.63e-22  8.54e-57  1.00e+00  3.99e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0399%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS13__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.66e-83  3.11e-27  8.54e-57  1.00e+00  1.22e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0122%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.98e-80  2.32e-24  8.53e-57  9.99e-01  1.14e-03 \n",
+      "[1] \"PP abf for shared variant: 0.114%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RORA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.43e-57  2.85e-01  5.79e-57  6.78e-01  3.73e-02 \n",
+      "[1] \"PP abf for shared variant: 3.73%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-60  1.24e-04  6.34e-57  7.39e-01  2.61e-01 \n",
+      "[1] \"PP abf for shared variant: 26.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-75  1.51e-19  8.54e-57  1.00e+00  1.07e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0107%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD44__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.43e-62  8.70e-06  8.42e-57  9.86e-01  1.36e-02 \n",
+      "[1] \"PP abf for shared variant: 1.36%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS4Y1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.71e-57  3.18e-01  5.37e-57  6.28e-01  5.42e-02 \n",
+      "[1] \"PP abf for shared variant: 5.42%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LGALS1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.69e-62  1.13e-05  8.06e-57  9.44e-01  5.65e-02 \n",
+      "[1] \"PP abf for shared variant: 5.65%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___COX7A2L__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-58  1.40e-02  2.97e-57  3.41e-01  6.45e-01 \n",
+      "[1] \"PP abf for shared variant: 64.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL15__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.30e-77  1.53e-21  8.54e-57  1.00e+00  3.24e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0324%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HADHA__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.40e-58  5.15e-02  7.14e-57  8.35e-01  1.13e-01 \n",
+      "[1] \"PP abf for shared variant: 11.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SATB1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.01e-58  1.05e-01  7.10e-57  8.30e-01  6.41e-02 \n",
+      "[1] \"PP abf for shared variant: 6.41%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UGP2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.22e-58  1.43e-02  5.95e-57  6.94e-01  2.92e-01 \n",
+      "[1] \"PP abf for shared variant: 29.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SBDS\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-57  1.78e-01  6.61e-57  7.74e-01  4.82e-02 \n",
+      "[1] \"PP abf for shared variant: 4.82%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SYNE2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.64e-59  1.92e-03  5.02e-57  5.84e-01  4.14e-01 \n",
+      "[1] \"PP abf for shared variant: 41.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TMA7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.67e-58  4.29e-02  7.78e-57  9.11e-01  4.63e-02 \n",
+      "[1] \"PP abf for shared variant: 4.63%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NEAT1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.98e-66  9.35e-10  8.54e-57  1.00e+00  2.35e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0235%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NR3C1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.11e-58  2.47e-02  7.17e-57  8.38e-01  1.38e-01 \n",
+      "[1] \"PP abf for shared variant: 13.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS28\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.74e-83  3.21e-27  8.54e-57  1.00e+00  1.10e-04 \n",
+      "[1] \"PP abf for shared variant: 0.011%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CCT8__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.20e-59  1.08e-02  6.84e-57  7.99e-01  1.90e-01 \n",
+      "[1] \"PP abf for shared variant: 19%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TNFAIP3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.09e-59  9.47e-03  2.14e-57  2.43e-01  7.48e-01 \n",
+      "[1] \"PP abf for shared variant: 74.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH2D2A\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.29e-57  1.51e-01  3.22e-57  3.73e-01  4.77e-01 \n",
+      "[1] \"PP abf for shared variant: 47.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NPM1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.43e-67  1.68e-11  8.51e-57  9.97e-01  3.18e-03 \n",
+      "[1] \"PP abf for shared variant: 0.318%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CLNS1A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.03e-59  1.20e-03  2.97e-57  3.41e-01  6.58e-01 \n",
+      "[1] \"PP abf for shared variant: 65.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RSL1D1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.33e-64  1.56e-08  8.49e-57  9.95e-01  5.30e-03 \n",
+      "[1] \"PP abf for shared variant: 0.53%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ATP6V0E1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.48e-59  8.76e-03  1.92e-57  2.17e-01  7.74e-01 \n",
+      "[1] \"PP abf for shared variant: 77.4%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL27A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 6.14e-83  7.20e-27  8.54e-57  9.99e-01  5.44e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0544%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___DUSP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.05e-57  2.40e-01  4.17e-57  4.86e-01  2.75e-01 \n",
+      "[1] \"PP abf for shared variant: 27.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL13A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-84  4.61e-28  8.53e-57  9.99e-01  1.00e-03 \n",
+      "[1] \"PP abf for shared variant: 0.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__ZFP36L2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.34e-58  1.57e-02  4.04e-57  4.68e-01  5.17e-01 \n",
+      "[1] \"PP abf for shared variant: 51.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF3D__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.60e-61  1.12e-04  8.22e-57  9.63e-01  3.73e-02 \n",
+      "[1] \"PP abf for shared variant: 3.73%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RP11-138A9.2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.65e-58  4.27e-02  6.14e-57  7.17e-01  2.40e-01 \n",
+      "[1] \"PP abf for shared variant: 24%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL27__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.71e-75  2.00e-19  8.54e-57  1.00e+00  4.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0493%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___APRT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.29e-59  2.68e-03  3.72e-57  4.29e-01  5.68e-01 \n",
+      "[1] \"PP abf for shared variant: 56.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FYN__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.86e-59  2.18e-03  8.48e-57  9.93e-01  5.25e-03 \n",
+      "[1] \"PP abf for shared variant: 0.525%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ANP32B__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.93e-58  4.60e-02  7.68e-57  8.99e-01  5.55e-02 \n",
+      "[1] \"PP abf for shared variant: 5.55%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PPP2R5C__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.35e-58  1.58e-02  7.75e-57  9.07e-01  7.75e-02 \n",
+      "[1] \"PP abf for shared variant: 7.75%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF3M__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.71e-60  3.17e-04  8.48e-57  9.92e-01  7.28e-03 \n",
+      "[1] \"PP abf for shared variant: 0.728%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL5__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.27e-84  8.52e-28  8.54e-57  1.00e+00  1.28e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0128%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CMPK1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.48e-61  2.91e-05  8.51e-57  9.97e-01  3.00e-03 \n",
+      "[1] \"PP abf for shared variant: 0.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__YWHAZ\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.18e-58  8.40e-02  7.59e-57  8.89e-01  2.72e-02 \n",
+      "[1] \"PP abf for shared variant: 2.72%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GIMAP2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.03e-64  2.38e-08  8.47e-57  9.91e-01  8.62e-03 \n",
+      "[1] \"PP abf for shared variant: 0.862%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___COTL1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.82e-62  3.31e-06  8.53e-57  9.99e-01  9.62e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0962%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF2S3__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.38e-67  6.30e-11  8.54e-57  1.00e+00  2.81e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0281%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HSP90AA1__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 1.1807e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.32e-57  2.71e-01  3.68e-57  4.28e-01  3.00e-01 \n",
+      "[1] \"PP abf for shared variant: 30%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MT-CYB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.26e-61  3.82e-05  2.30e-57  2.61e-01  7.39e-01 \n",
+      "[1] \"PP abf for shared variant: 73.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HSPB1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.05e-57  1.23e-01  3.74e-57  4.34e-01  4.43e-01 \n",
+      "[1] \"PP abf for shared variant: 44.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CRIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.39e-66  2.80e-10  8.53e-57  9.99e-01  6.93e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0693%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SMDT1\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.37e-65  1.60e-09  8.33e-57  9.75e-01  2.47e-02 \n",
+      "[1] \"PP abf for shared variant: 2.47%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL18__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.69e-74  4.32e-18  8.54e-57  9.99e-01  5.40e-04 \n",
+      "[1] \"PP abf for shared variant: 0.054%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TXK\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.02e-61  1.19e-05  3.78e-57  4.38e-01  5.62e-01 \n",
+      "[1] \"PP abf for shared variant: 56.2%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL36__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.87e-73  4.53e-17  8.53e-57  9.99e-01  1.06e-03 \n",
+      "[1] \"PP abf for shared variant: 0.106%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___GAPDH__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.69e-70  3.15e-14  8.54e-57  1.00e+00  2.51e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0251%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ANXA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.83e-63  1.03e-06  7.80e-57  9.12e-01  8.76e-02 \n",
+      "[1] \"PP abf for shared variant: 8.76%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CLIC1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.01e-61  1.18e-05  8.42e-57  9.86e-01  1.39e-02 \n",
+      "[1] \"PP abf for shared variant: 1.39%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD99__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.12e-58  2.48e-02  6.59e-57  7.69e-01  2.06e-01 \n",
+      "[1] \"PP abf for shared variant: 20.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___LYRM4__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.62e-57  1.90e-01  6.56e-57  7.68e-01  4.21e-02 \n",
+      "[1] \"PP abf for shared variant: 4.21%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EEF1B2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-76  1.93e-20  8.54e-57  1.00e+00  3.51e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0351%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ACTB__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.00e-76  3.51e-20  8.53e-57  9.99e-01  6.74e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0674%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS19__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 7.06e-67  8.26e-11  8.53e-57  9.99e-01  9.37e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0937%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EZR__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.27e-59  1.49e-03  7.52e-57  8.79e-01  1.19e-01 \n",
+      "[1] \"PP abf for shared variant: 11.9%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ATP5A1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 3.57e-60  4.18e-04  6.30e-57  7.35e-01  2.65e-01 \n",
+      "[1] \"PP abf for shared variant: 26.5%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ATP5O__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.45e-65  6.39e-09  8.54e-57  1.00e+00  2.49e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0249%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EIF3K__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.51e-58  2.94e-02  5.52e-57  6.43e-01  3.27e-01 \n",
+      "[1] \"PP abf for shared variant: 32.7%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPL38__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.97e-76  3.48e-20  8.54e-57  1.00e+00  2.76e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0276%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SUCLG2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.60e-61  1.01e-04  5.34e-57  6.22e-01  3.78e-01 \n",
+      "[1] \"PP abf for shared variant: 37.8%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CD3E__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.70e-58  1.99e-02  5.79e-57  6.74e-01  3.06e-01 \n",
+      "[1] \"PP abf for shared variant: 30.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPSA\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.31e-76  1.53e-20  8.54e-57  1.00e+00  2.27e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0227%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___NSA2__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.32e-64  6.23e-08  8.53e-57  9.99e-01  8.18e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0818%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CST7__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.53e-58  2.97e-02  8.07e-57  9.45e-01  2.58e-02 \n",
+      "[1] \"PP abf for shared variant: 2.58%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___HIGD2A__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.67e-58  1.02e-01  7.54e-57  8.83e-01  1.55e-02 \n",
+      "[1] \"PP abf for shared variant: 1.55%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___EEF1G__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.60e-65  1.87e-09  8.45e-57  9.89e-01  1.05e-02 \n",
+      "[1] \"PP abf for shared variant: 1.05%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___IGBP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.20e-59  1.41e-03  5.14e-57  5.98e-01  4.01e-01 \n",
+      "[1] \"PP abf for shared variant: 40.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___OAZ1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.82e-75  2.13e-19  8.54e-57  1.00e+00  2.72e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0272%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___MYH9__RPS26\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in check_dataset(d = dataset2, 2):\n",
+      "“minimum p value is: 2.8842e-06\n",
+      "If this is what you expected, this is not a problem.\n",
+      "If this is not as small as you expected, please check the 02_data vignette.”\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 4.23e-57  4.96e-01  3.84e-57  4.49e-01  5.49e-02 \n",
+      "[1] \"PP abf for shared variant: 5.49%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__UBA52\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.65e-64  1.94e-08  8.53e-57  9.99e-01  8.57e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0857%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___ATP2B1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 5.09e-60  5.95e-04  8.29e-57  9.70e-01  2.93e-02 \n",
+      "[1] \"PP abf for shared variant: 2.93%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__RPS6\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.21e-84  1.41e-28  8.54e-57  1.00e+00  4.11e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0411%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RBM39__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.06e-59  1.24e-03  2.22e-57  2.52e-01  7.46e-01 \n",
+      "[1] \"PP abf for shared variant: 74.6%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___CCNG1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.52e-59  1.78e-03  8.39e-57  9.83e-01  1.56e-02 \n",
+      "[1] \"PP abf for shared variant: 1.56%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SH3BGRL3\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.59e-72  1.86e-16  8.54e-57  1.00e+00  2.90e-04 \n",
+      "[1] \"PP abf for shared variant: 0.029%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___COX4I1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.14e-59  2.51e-03  6.29e-57  7.34e-01  2.63e-01 \n",
+      "[1] \"PP abf for shared variant: 26.3%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___PMAIP1__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 1.89e-58  2.21e-02  5.97e-57  6.97e-01  2.81e-01 \n",
+      "[1] \"PP abf for shared variant: 28.1%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__TOMM7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 8.90e-68  1.04e-11  8.54e-57  1.00e+00  1.36e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0136%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SNHG7\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.86e-63  3.35e-07  8.54e-57  1.00e+00  4.39e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0439%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___FHIT__RPS26\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 2.09e-66  2.45e-10  8.53e-57  9.99e-01  7.36e-04 \n",
+      "[1] \"PP abf for shared variant: 0.0736%\"\n",
+      "[1] \"Type_1_Diabetes\"\n",
+      "[1] \"CD4T_RPS26___RPS26__SRSF2\"\n",
+      "PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf \n",
+      " 9.49e-62  1.11e-05  8.50e-57  9.95e-01  4.54e-03 \n",
+      "[1] \"PP abf for shared variant: 0.454%\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "for(i in names(gwas_input_list)){\n",
+    "   for(ident in names(data_input_coeqtl)){      \n",
+    "        print(i)\n",
+    "        print(ident)\n",
+    "        \n",
+    "        colocalization_result = coloc.abf(\n",
+    "            dataset1=gwas_input_list[[i]],  # GWAS\n",
+    "            dataset2=data_input_coeqtl[[ident]],   # co-EQTL\n",
+    "            p1 = 1e-04, p2 = 1e-04, p12 = 1e-06)\n",
+    "\n",
+    "        result_summary = data.frame(parameter = names(colocalization_result$summary), value = colocalization_result$summary, trait = i, identifier = ident)\n",
+    "        coloc_result_summary = rbind(coloc_result_summary, result_summary)\n",
+    "\n",
+    "        result_detail = colocalization_result$results\n",
+    "        result_detail$trait = i\n",
+    "        result_detail$identifier = ident\n",
+    "        if(save_detail == TRUE){\n",
+    "           coloc_result_detail = rbind(coloc_result_detail, result_detail)\n",
+    "           }\n",
+    "    }\n",
+    "    }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "34f503fc-e9fb-4b9d-ae38-347c072d1e39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##\n",
+    "# HO: locus is not associated with any of the traits\n",
+    "# H1: locus is only significant in the GWAS\n",
+    "# H2: locus is only a significant eQTL\n",
+    "# H3: locus is associated with both traits due to two independent signals \n",
+    "# H4: locus is associated with both traits due to a single colocalizing SNP\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "id": "6dc83030-8b12-4070-a46d-43b2b6f9e8fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Check out example results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "783f870c-8553-4970-adee-42c3e3b43ce3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_result_summary_wide = coloc_result_summary %>% dcast(trait + identifier~ parameter, value.var = 'value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "12931a1a-9795-4142-b5b3-c238100719b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 8</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>nsnps</th><th scope=col>PP.H0.abf</th><th scope=col>PP.H1.abf</th><th scope=col>PP.H2.abf</th><th scope=col>PP.H3.abf</th><th scope=col>PP.H4.abf</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>Asthma</td><td>B_RPS26___EEF1A1__RPS26</td><td>381</td><td>0.373471300</td><td>0.14667123</td><td>0.1260356</td><td>0.04642325</td><td>0.3073986</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>Asthma</td><td>B_RPS26___RPL10__RPS26 </td><td>381</td><td>0.003393068</td><td>0.00133254</td><td>0.2640670</td><td>0.09736711</td><td>0.6338403</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 8\n",
+       "\\begin{tabular}{r|llllllll}\n",
+       "  & trait & identifier & nsnps & PP.H0.abf & PP.H1.abf & PP.H2.abf & PP.H3.abf & PP.H4.abf\\\\\n",
+       "  & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & Asthma & B\\_RPS26\\_\\_\\_EEF1A1\\_\\_RPS26 & 381 & 0.373471300 & 0.14667123 & 0.1260356 & 0.04642325 & 0.3073986\\\\\n",
+       "\t2 & Asthma & B\\_RPS26\\_\\_\\_RPL10\\_\\_RPS26  & 381 & 0.003393068 & 0.00133254 & 0.2640670 & 0.09736711 & 0.6338403\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 8\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | nsnps &lt;dbl&gt; | PP.H0.abf &lt;dbl&gt; | PP.H1.abf &lt;dbl&gt; | PP.H2.abf &lt;dbl&gt; | PP.H3.abf &lt;dbl&gt; | PP.H4.abf &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | Asthma | B_RPS26___EEF1A1__RPS26 | 381 | 0.373471300 | 0.14667123 | 0.1260356 | 0.04642325 | 0.3073986 |\n",
+       "| 2 | Asthma | B_RPS26___RPL10__RPS26  | 381 | 0.003393068 | 0.00133254 | 0.2640670 | 0.09736711 | 0.6338403 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  trait  identifier              nsnps PP.H0.abf   PP.H1.abf  PP.H2.abf\n",
+       "1 Asthma B_RPS26___EEF1A1__RPS26 381   0.373471300 0.14667123 0.1260356\n",
+       "2 Asthma B_RPS26___RPL10__RPS26  381   0.003393068 0.00133254 0.2640670\n",
+       "  PP.H3.abf  PP.H4.abf\n",
+       "1 0.04642325 0.3073986\n",
+       "2 0.09736711 0.6338403"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coloc_result_summary_wide,2) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "6b9417a8-2111-46f3-a6fc-4041c16c2ab6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Save the results - Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "f86ae713-e427-498f-815a-cccea9297b02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.table(coloc_result_summary, file = paste0(path, \"/colocalization_results/\", \"COEQTL_summary_update.csv\"), append =FALSE, sep = \",\", row.names = FALSE, col.names = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "5359ac4d-3fc8-4df2-8dc3-4915ebe18b0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Save the results - Detail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "id": "dd75cdb3-703b-415d-910c-69bd8a58abbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if(save_detail == TRUE){\n",
+    "     write.table(coloc_result_detail, file = paste0(path, \"/colocalization_results/\", \"COEQTL_detail_update.csv\"), append =FALSE, sep = \",\", row.names = FALSE, col.names =TRUE)\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31ddb938-63d2-43b7-9f00-2f3966c8613f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55e34797-c096-428a-8ff2-ec99e4b0b59e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "4.1.1"
+  },
+  "toc-autonumbering": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/05_coeqtl_interpretation/R3_Coloc_Evaluation.ipynb b/05_coeqtl_interpretation/R3_Coloc_Evaluation.ipynb
new file mode 100644
index 0000000..1272ac4
--- /dev/null
+++ b/05_coeqtl_interpretation/R3_Coloc_Evaluation.ipynb
@@ -0,0 +1,5000 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "253ab18f-d2dc-4dee-a9db-5255571056d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Evaluate colocalization results\n",
+    "### Load the results saved in R2 script and check out some summaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "802f0d1c-925e-480b-a547-865cc6d997b2",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d187711e-139c-4eaf-bcaf-5795174c795d",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Attaching package: ‘dplyr’\n",
+      "\n",
+      "\n",
+      "The following objects are masked from ‘package:stats’:\n",
+      "\n",
+      "    filter, lag\n",
+      "\n",
+      "\n",
+      "The following objects are masked from ‘package:base’:\n",
+      "\n",
+      "    intersect, setdiff, setequal, union\n",
+      "\n",
+      "\n",
+      "\n",
+      "Attaching package: ‘data.table’\n",
+      "\n",
+      "\n",
+      "The following objects are masked from ‘package:dplyr’:\n",
+      "\n",
+      "    between, first, last\n",
+      "\n",
+      "\n",
+      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.1 ──\n",
+      "\n",
+      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.6     \u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.2\n",
+      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.7     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
+      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.0     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n",
+      "\n",
+      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdata.table\u001b[39m::\u001b[32mbetween()\u001b[39m masks \u001b[34mdplyr\u001b[39m::between()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m       masks \u001b[34mstats\u001b[39m::filter()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdata.table\u001b[39m::\u001b[32mfirst()\u001b[39m   masks \u001b[34mdplyr\u001b[39m::first()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m          masks \u001b[34mstats\u001b[39m::lag()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mdata.table\u001b[39m::\u001b[32mlast()\u001b[39m    masks \u001b[34mdplyr\u001b[39m::last()\n",
+      "\u001b[31m✖\u001b[39m \u001b[34mpurrr\u001b[39m::\u001b[32mtranspose()\u001b[39m    masks \u001b[34mdata.table\u001b[39m::transpose()\n",
+      "\n",
+      "\n",
+      "Attaching package: ‘reshape2’\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:tidyr’:\n",
+      "\n",
+      "    smiths\n",
+      "\n",
+      "\n",
+      "The following objects are masked from ‘package:data.table’:\n",
+      "\n",
+      "    dcast, melt\n",
+      "\n",
+      "\n",
+      "Loading required package: lattice\n",
+      "\n",
+      "\n",
+      "Attaching package: ‘caret’\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:purrr’:\n",
+      "\n",
+      "    lift\n",
+      "\n",
+      "\n",
+      "This is a new update to coloc.\n",
+      "\n",
+      "\n",
+      "Attaching package: ‘coloc’\n",
+      "\n",
+      "\n",
+      "The following object is masked from ‘package:caret’:\n",
+      "\n",
+      "    sensitivity\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "source('MS1_Libraries.r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e59e15e0-23cd-457b-b704-05dbfe404876",
+   "metadata": {},
+   "source": [
+    "# Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "535e2290-2e3b-4cca-880f-8ce2d00f55b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path<-\"\"\n",
+    "outdir<-\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bfac0756-8f6a-4388-93a1-3e76a178b56e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_type_var = c(\"CD4T\",\"CD8T\",\"monocyte\",\"NK\",\"B\",\"DC\")\n",
+    "# c(\"CD4T\",\"CD8T\",\"monocyte\",\"NK\",\"B\",\"DC\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96ec5300-7663-481d-b632-a0d23a9f5093",
+   "metadata": {},
+   "source": [
+    "# Data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9333eff6-72ae-4d8f-85a0-6030d4a6320c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Summaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9cbb9461-52d2-43e6-8027-9f2afb401615",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### EQTL coloclaization summaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8bb77e4e-8642-4bdc-a5d0-0c194374034b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_summary = read.table(paste0(path, '/colocalization_results/EQTL_summary_update.csv'), row.names = NULL, sep = \",\", header = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "40f8a037-6668-41f4-b681-605a7a89e211",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_summary$X = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "060ad489-167f-46a5-b56d-15d87c406773",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1260"
+      ],
+      "text/latex": [
+       "1260"
+      ],
+      "text/markdown": [
+       "1260"
+      ],
+      "text/plain": [
+       "[1] 1260"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(eqtl_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "204331e6-b4ee-4b70-adc6-7c3d617bf01f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>parameter</th><th scope=col>value</th><th scope=col>trait</th><th scope=col>identifier</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>nsnps    </td><td>2.46200e+03</td><td>White blood cell count</td><td>DC1MB_TMEM176A</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>PP.H0.abf</td><td>8.18261e-04</td><td>White blood cell count</td><td>DC1MB_TMEM176A</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 4\n",
+       "\\begin{tabular}{r|llll}\n",
+       "  & parameter & value & trait & identifier\\\\\n",
+       "  & <chr> & <dbl> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & nsnps     & 2.46200e+03 & White blood cell count & DC1MB\\_TMEM176A\\\\\n",
+       "\t2 & PP.H0.abf & 8.18261e-04 & White blood cell count & DC1MB\\_TMEM176A\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 4\n",
+       "\n",
+       "| <!--/--> | parameter &lt;chr&gt; | value &lt;dbl&gt; | trait &lt;chr&gt; | identifier &lt;chr&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| 1 | nsnps     | 2.46200e+03 | White blood cell count | DC1MB_TMEM176A |\n",
+       "| 2 | PP.H0.abf | 8.18261e-04 | White blood cell count | DC1MB_TMEM176A |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  parameter value       trait                  identifier    \n",
+       "1 nsnps     2.46200e+03 White blood cell count DC1MB_TMEM176A\n",
+       "2 PP.H0.abf 8.18261e-04 White blood cell count DC1MB_TMEM176A"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(eqtl_summary,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c2a206a2-6df9-4748-a5e8-9faf15dce2fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'White blood cell count'</li><li>'Crohn\\'s Disease'</li><li>'Inflammatory Bowel Disease'</li><li>'Multiple Sclerosis'</li><li>'Rheumatoid Arthritis'</li><li>'Asthma'</li><li>'Type_1_Diabetes'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'White blood cell count'\n",
+       "\\item 'Crohn\\textbackslash{}'s Disease'\n",
+       "\\item 'Inflammatory Bowel Disease'\n",
+       "\\item 'Multiple Sclerosis'\n",
+       "\\item 'Rheumatoid Arthritis'\n",
+       "\\item 'Asthma'\n",
+       "\\item 'Type\\_1\\_Diabetes'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'White blood cell count'\n",
+       "2. 'Crohn\\'s Disease'\n",
+       "3. 'Inflammatory Bowel Disease'\n",
+       "4. 'Multiple Sclerosis'\n",
+       "5. 'Rheumatoid Arthritis'\n",
+       "6. 'Asthma'\n",
+       "7. 'Type_1_Diabetes'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"White blood cell count\"     \"Crohn's Disease\"           \n",
+       "[3] \"Inflammatory Bowel Disease\" \"Multiple Sclerosis\"        \n",
+       "[5] \"Rheumatoid Arthritis\"       \"Asthma\"                    \n",
+       "[7] \"Type_1_Diabetes\"           "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(eqtl_summary$trait)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ac50d287-9b5d-4556-ada8-6048d1d16386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_summary = unique(eqtl_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0c57ec7e-f676-4fce-b0d4-65c2dffb2960",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "duplicates = eqtl_summary %>% group_by(parameter, trait, identifier) %>% count() %>% filter(n>= 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "83f668f5-507d-4ea9-9423-ceca78858a10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/latex": [],
+      "text/markdown": [],
+      "text/plain": [
+       "character(0)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(duplicates$trait)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "909e941b-5c5c-4d46-88ed-66327f54c6e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Co-EQTL colocalization summaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "74c85e40-2a30-4306-ad8e-778bd62b3a52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary = read.table(paste0(path, '/colocalization_results/COEQTL_summary_update.csv'), row.names = NULL, sep = \",\", header = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2e60f497-0d9c-4ccf-b00e-be12c19c998e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary$X = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "62aede66-8929-4efd-b76e-5145490f3fde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "44688"
+      ],
+      "text/latex": [
+       "44688"
+      ],
+      "text/markdown": [
+       "44688"
+      ],
+      "text/plain": [
+       "[1] 44688"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(coeqtl_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "414849c2-215a-414f-af15-a5dbc9b32016",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>parameter</th><th scope=col>value</th><th scope=col>trait</th><th scope=col>identifier</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>nsnps    </td><td>2.461000e+03</td><td>White blood cell count</td><td>monocyte_TMEM176A___CAPG__TMEM176A</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>PP.H0.abf</td><td>2.606173e-02</td><td>White blood cell count</td><td>monocyte_TMEM176A___CAPG__TMEM176A</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 4\n",
+       "\\begin{tabular}{r|llll}\n",
+       "  & parameter & value & trait & identifier\\\\\n",
+       "  & <chr> & <dbl> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & nsnps     & 2.461000e+03 & White blood cell count & monocyte\\_TMEM176A\\_\\_\\_CAPG\\_\\_TMEM176A\\\\\n",
+       "\t2 & PP.H0.abf & 2.606173e-02 & White blood cell count & monocyte\\_TMEM176A\\_\\_\\_CAPG\\_\\_TMEM176A\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 4\n",
+       "\n",
+       "| <!--/--> | parameter &lt;chr&gt; | value &lt;dbl&gt; | trait &lt;chr&gt; | identifier &lt;chr&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| 1 | nsnps     | 2.461000e+03 | White blood cell count | monocyte_TMEM176A___CAPG__TMEM176A |\n",
+       "| 2 | PP.H0.abf | 2.606173e-02 | White blood cell count | monocyte_TMEM176A___CAPG__TMEM176A |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  parameter value        trait                 \n",
+       "1 nsnps     2.461000e+03 White blood cell count\n",
+       "2 PP.H0.abf 2.606173e-02 White blood cell count\n",
+       "  identifier                        \n",
+       "1 monocyte_TMEM176A___CAPG__TMEM176A\n",
+       "2 monocyte_TMEM176A___CAPG__TMEM176A"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coeqtl_summary,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "bbe38511-c264-4bc6-a756-9752bcadf19d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary = unique(coeqtl_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a9c7f658-f3d3-4f73-81d8-0bc498bf0b4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "duplicates = coeqtl_summary %>% group_by(parameter, trait, identifier) %>% count() %>% filter(n>= 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "289a0d48-77c4-4db7-ac5c-32d61bd7f4d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/latex": [],
+      "text/markdown": [],
+      "text/plain": [
+       "character(0)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(duplicates$trait)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8925b1ae-e0fe-4c96-8c61-d4003d06a2fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#unique(duplicates$parameter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f2ade7f4-16e7-4018-9b14-f1f4c85c6b0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 0 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>parameter</th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 0 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " parameter & trait & identifier & n\\\\\n",
+       " <chr> & <chr> & <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 0 × 4\n",
+       "\n",
+       "| parameter &lt;chr&gt; | trait &lt;chr&gt; | identifier &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     parameter trait identifier n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(duplicates,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e035efd4-9848-4a64-919f-7a48b79bcafd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Extract egene"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "dc53ab43-4ab9-4f52-8c35-458e10b23406",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary$egene = str_extract(coeqtl_summary$identifier, '.*___')\n",
+    "coeqtl_summary$egene = str_replace(coeqtl_summary$egene, '___', '')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5e259ac-c2b6-4888-ae21-a4e6b4dabec0",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Evaluate summaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3c11cc1-129a-4313-a4b9-5e57278343d9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## For EQTLs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f357d86b-10c4-403b-a79d-907ea09f4947",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>parameter</th><th scope=col>value</th><th scope=col>trait</th><th scope=col>identifier</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>nsnps    </td><td>2.46200e+03</td><td>White blood cell count</td><td>DC1MB_TMEM176A</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>PP.H0.abf</td><td>8.18261e-04</td><td>White blood cell count</td><td>DC1MB_TMEM176A</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 4\n",
+       "\\begin{tabular}{r|llll}\n",
+       "  & parameter & value & trait & identifier\\\\\n",
+       "  & <chr> & <dbl> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & nsnps     & 2.46200e+03 & White blood cell count & DC1MB\\_TMEM176A\\\\\n",
+       "\t2 & PP.H0.abf & 8.18261e-04 & White blood cell count & DC1MB\\_TMEM176A\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 4\n",
+       "\n",
+       "| <!--/--> | parameter &lt;chr&gt; | value &lt;dbl&gt; | trait &lt;chr&gt; | identifier &lt;chr&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| 1 | nsnps     | 2.46200e+03 | White blood cell count | DC1MB_TMEM176A |\n",
+       "| 2 | PP.H0.abf | 8.18261e-04 | White blood cell count | DC1MB_TMEM176A |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  parameter value       trait                  identifier    \n",
+       "1 nsnps     2.46200e+03 White blood cell count DC1MB_TMEM176A\n",
+       "2 PP.H0.abf 8.18261e-04 White blood cell count DC1MB_TMEM176A"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(eqtl_summary,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "28ebc623-2ba4-4382-95cb-d8b652ba881e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Extract amount of SNPS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "79bafba6-9302-49ff-b9a3-caa818c18655",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_snps = eqtl_summary[eqtl_summary$parameter == 'nsnps',]\n",
+    "n_snps$overlapping_snps = n_snps$value\n",
+    "n_snps$parameter = NULL\n",
+    "n_snps$value = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "e5e7f024-b1fb-4e22-8828-cc3cf531de98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#eqtl_summary = eqtl_summary[eqtl_summary$parameter != 'nsnps',]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "77624013-cc23-4fdb-bce3-b867be56b8ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Extract cell-type, gene etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d64fe822-3b10-476b-a132-a49ce34e5449",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_summary$cell_type = str_replace(eqtl_summary$identifier, '_.*', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "ad06f965-dc2e-42c2-aa1c-19ae04cfdadf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_summary$gene = str_replace(eqtl_summary$identifier, '.*_', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d5a3d03e-50d1-4429-a6ec-dd636f510609",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'DC1MB'</li><li>'B1MB'</li><li>'NK1MB'</li><li>'monocyte1MB'</li><li>'CD8T1MB'</li><li>'CD4T1MB'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'DC1MB'\n",
+       "\\item 'B1MB'\n",
+       "\\item 'NK1MB'\n",
+       "\\item 'monocyte1MB'\n",
+       "\\item 'CD8T1MB'\n",
+       "\\item 'CD4T1MB'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'DC1MB'\n",
+       "2. 'B1MB'\n",
+       "3. 'NK1MB'\n",
+       "4. 'monocyte1MB'\n",
+       "5. 'CD8T1MB'\n",
+       "6. 'CD4T1MB'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"DC1MB\"       \"B1MB\"        \"NK1MB\"       \"monocyte1MB\" \"CD8T1MB\"    \n",
+       "[6] \"CD4T1MB\"    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(eqtl_summary$cell_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "c7e6fc78-ac1a-451f-bc14-e6dce54f61cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Add number of SNPS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "7d80cf0c-4730-44b8-b678-50f3e5958a29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_summary = merge(eqtl_summary, n_snps, by.x = c('trait', 'identifier'), by.y = c('trait', 'identifier'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "a5e3f9ca-c3d2-445e-ba82-ed242f6a01a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>parameter</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>Asthma</td><td>B1MB_HLA-DQA2</td><td>nsnps    </td><td>3.880000e+02</td><td>B1MB</td><td>HLA-DQA2</td><td>388</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>Asthma</td><td>B1MB_HLA-DQA2</td><td>PP.H0.abf</td><td>1.212062e-25</td><td>B1MB</td><td>HLA-DQA2</td><td>388</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 7\n",
+       "\\begin{tabular}{r|lllllll}\n",
+       "  & trait & identifier & parameter & value & cell\\_type & gene & overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & Asthma & B1MB\\_HLA-DQA2 & nsnps     & 3.880000e+02 & B1MB & HLA-DQA2 & 388\\\\\n",
+       "\t2 & Asthma & B1MB\\_HLA-DQA2 & PP.H0.abf & 1.212062e-25 & B1MB & HLA-DQA2 & 388\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 7\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | parameter &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|\n",
+       "| 1 | Asthma | B1MB_HLA-DQA2 | nsnps     | 3.880000e+02 | B1MB | HLA-DQA2 | 388 |\n",
+       "| 2 | Asthma | B1MB_HLA-DQA2 | PP.H0.abf | 1.212062e-25 | B1MB | HLA-DQA2 | 388 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  trait  identifier    parameter value        cell_type gene    \n",
+       "1 Asthma B1MB_HLA-DQA2 nsnps     3.880000e+02 B1MB      HLA-DQA2\n",
+       "2 Asthma B1MB_HLA-DQA2 PP.H0.abf 1.212062e-25 B1MB      HLA-DQA2\n",
+       "  overlapping_snps\n",
+       "1 388             \n",
+       "2 388             "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(eqtl_summary,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b4dcc4f2-deb6-49d5-b737-6dab933a6f21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Maximum probability per identifier and trait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "6fbce627-914d-4a99-a610-34fa11114dbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'trait', 'identifier', 'cell_type'. You can\n",
+      "override using the `.groups` argument.\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_prob = eqtl_summary[eqtl_summary$parameter != 'nsnps',] %>% group_by(trait, identifier, cell_type, gene) %>% summarise(value = max(value))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e44b29b1-259b-4774-a6ba-3b32e7ffe47a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_summary_filtered = merge(eqtl_summary, max_prob)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "79ab3696-b199-4999-a43b-40f08288fc43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'parameter'. You can override using the\n",
+      "`.groups` argument.\n"
+     ]
+    }
+   ],
+   "source": [
+    "overview_h_amounts = eqtl_summary_filtered %>% group_by(parameter, trait) %>% summarise(n = n(), mean_value = mean(value), amount_greater_0.9 = sum(value > 0.9), amount_greater_0.75 = sum(value > 0.75),amount_greater_0.5 = sum(value > 0.5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "42d9bc14-d39c-4514-8db8-c6a25d0ba778",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Add snp information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "418546a6-526f-4478-a6d5-75bd891fad4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_snps = n_snps %>% group_by(trait) %>% summarise(average_overlapping_snps = mean(overlapping_snps), min_overlapping_snps = min(overlapping_snps), max_overlapping_snps = max(overlapping_snps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "a6fd814e-abfd-4b2a-9857-23600340fe97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#n_snps = unique(n_snps[,c('trait', 'overlapping_snps')])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "410c5391-6f89-4699-a0da-20eda9e82d37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overview_h_amounts = merge(overview_h_amounts, n_snps, by.x = c('trait'), by.y = c('trait'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "bc03666e-598c-4ea1-a03f-042cd0136e4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#head(n_snps,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "2f7826aa-c5cf-4ddc-9745-1c1b72236235",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 5 × 10</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>parameter</th><th scope=col>n</th><th scope=col>mean_value</th><th scope=col>amount_greater_0.9</th><th scope=col>amount_greater_0.75</th><th scope=col>amount_greater_0.5</th><th scope=col>average_overlapping_snps</th><th scope=col>min_overlapping_snps</th><th scope=col>max_overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>26</th><td>White blood cell count</td><td>PP.H0.abf</td><td> 7</td><td>0.7627689</td><td> 0</td><td> 6</td><td> 7</td><td>1736.267</td><td> 517</td><td>2639</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td>White blood cell count</td><td>PP.H1.abf</td><td> 1</td><td>0.8050249</td><td> 0</td><td> 1</td><td> 1</td><td>1736.267</td><td> 517</td><td>2639</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>White blood cell count</td><td>PP.H2.abf</td><td>17</td><td>0.9584354</td><td>16</td><td>17</td><td>17</td><td>1736.267</td><td> 517</td><td>2639</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>White blood cell count</td><td>PP.H3.abf</td><td> 5</td><td>0.9526789</td><td> 4</td><td> 5</td><td> 5</td><td>1736.267</td><td> 517</td><td>2639</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td>Type_1_Diabetes       </td><td>PP.H3.abf</td><td>12</td><td>0.9998213</td><td>12</td><td>12</td><td>12</td><td>2284.000</td><td>1341</td><td>3161</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 5 × 10\n",
+       "\\begin{tabular}{r|llllllllll}\n",
+       "  & trait & parameter & n & mean\\_value & amount\\_greater\\_0.9 & amount\\_greater\\_0.75 & amount\\_greater\\_0.5 & average\\_overlapping\\_snps & min\\_overlapping\\_snps & max\\_overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <int> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t26 & White blood cell count & PP.H0.abf &  7 & 0.7627689 &  0 &  6 &  7 & 1736.267 &  517 & 2639\\\\\n",
+       "\t27 & White blood cell count & PP.H1.abf &  1 & 0.8050249 &  0 &  1 &  1 & 1736.267 &  517 & 2639\\\\\n",
+       "\t28 & White blood cell count & PP.H2.abf & 17 & 0.9584354 & 16 & 17 & 17 & 1736.267 &  517 & 2639\\\\\n",
+       "\t29 & White blood cell count & PP.H3.abf &  5 & 0.9526789 &  4 &  5 &  5 & 1736.267 &  517 & 2639\\\\\n",
+       "\t23 & Type\\_1\\_Diabetes        & PP.H3.abf & 12 & 0.9998213 & 12 & 12 & 12 & 2284.000 & 1341 & 3161\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 5 × 10\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | parameter &lt;chr&gt; | n &lt;int&gt; | mean_value &lt;dbl&gt; | amount_greater_0.9 &lt;int&gt; | amount_greater_0.75 &lt;int&gt; | amount_greater_0.5 &lt;int&gt; | average_overlapping_snps &lt;dbl&gt; | min_overlapping_snps &lt;dbl&gt; | max_overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 26 | White blood cell count | PP.H0.abf |  7 | 0.7627689 |  0 |  6 |  7 | 1736.267 |  517 | 2639 |\n",
+       "| 27 | White blood cell count | PP.H1.abf |  1 | 0.8050249 |  0 |  1 |  1 | 1736.267 |  517 | 2639 |\n",
+       "| 28 | White blood cell count | PP.H2.abf | 17 | 0.9584354 | 16 | 17 | 17 | 1736.267 |  517 | 2639 |\n",
+       "| 29 | White blood cell count | PP.H3.abf |  5 | 0.9526789 |  4 |  5 |  5 | 1736.267 |  517 | 2639 |\n",
+       "| 23 | Type_1_Diabetes        | PP.H3.abf | 12 | 0.9998213 | 12 | 12 | 12 | 2284.000 | 1341 | 3161 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   trait                  parameter n  mean_value amount_greater_0.9\n",
+       "26 White blood cell count PP.H0.abf  7 0.7627689   0                \n",
+       "27 White blood cell count PP.H1.abf  1 0.8050249   0                \n",
+       "28 White blood cell count PP.H2.abf 17 0.9584354  16                \n",
+       "29 White blood cell count PP.H3.abf  5 0.9526789   4                \n",
+       "23 Type_1_Diabetes        PP.H3.abf 12 0.9998213  12                \n",
+       "   amount_greater_0.75 amount_greater_0.5 average_overlapping_snps\n",
+       "26  6                   7                 1736.267                \n",
+       "27  1                   1                 1736.267                \n",
+       "28 17                  17                 1736.267                \n",
+       "29  5                   5                 1736.267                \n",
+       "23 12                  12                 2284.000                \n",
+       "   min_overlapping_snps max_overlapping_snps\n",
+       "26  517                 2639                \n",
+       "27  517                 2639                \n",
+       "28  517                 2639                \n",
+       "29  517                 2639                \n",
+       "23 1341                 3161                "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(overview_h_amounts[order(overview_h_amounts$trait, decreasing = TRUE),],5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12afd501-1018-4ffd-a512-7ebca28dd146",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "61d3bee9-9b7d-4477-a8fe-8ad131aa3b8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Inspect interesting hypothesis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "e88a2cfb-7f69-4f17-805e-87f8f4896b76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_var = 'PP.H4.abf'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "5dd1d320-92d1-48f6-a318-c8e90f782c6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 29 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>parameter</th><th scope=col>overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>Asthma                    </td><td>B1MB_HLA-DQA2       </td><td>0.9997353</td><td>B1MB       </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 388</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>Asthma                    </td><td>B1MB_RPS26          </td><td>0.5349101</td><td>B1MB       </td><td>RPS26   </td><td>PP.H4.abf</td><td> 381</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>Asthma                    </td><td>CD4T1MB_HLA-DQA2    </td><td>0.9999956</td><td>CD4T1MB    </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 388</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>Asthma                    </td><td>CD4T1MB_RPS26       </td><td>0.5349363</td><td>CD4T1MB    </td><td>RPS26   </td><td>PP.H4.abf</td><td> 381</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td>Asthma                    </td><td>CD8T1MB_HLA-DQA2    </td><td>1.0000000</td><td>CD8T1MB    </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 388</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>Asthma                    </td><td>CD8T1MB_RPS26       </td><td>0.5350001</td><td>CD8T1MB    </td><td>RPS26   </td><td>PP.H4.abf</td><td> 381</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>Asthma                    </td><td>DC1MB_HLA-DQA2      </td><td>0.6589821</td><td>DC1MB      </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 388</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td>Asthma                    </td><td>DC1MB_RPS26         </td><td>0.5349096</td><td>DC1MB      </td><td>RPS26   </td><td>PP.H4.abf</td><td> 381</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>Asthma                    </td><td>monocyte1MB_HLA-DQA2</td><td>0.9746826</td><td>monocyte1MB</td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 388</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td>Asthma                    </td><td>monocyte1MB_RPS26   </td><td>0.5349095</td><td>monocyte1MB</td><td>RPS26   </td><td>PP.H4.abf</td><td> 381</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td>Asthma                    </td><td>NK1MB_HLA-DQA2      </td><td>0.9999953</td><td>NK1MB      </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 388</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>Asthma                    </td><td>NK1MB_RPS26         </td><td>0.5429194</td><td>NK1MB      </td><td>RPS26   </td><td>PP.H4.abf</td><td> 381</td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>Crohn's Disease           </td><td>CD4T1MB_RNASET2     </td><td>0.9046377</td><td>CD4T1MB    </td><td>RNASET2 </td><td>PP.H4.abf</td><td>2707</td></tr>\n",
+       "\t<tr><th scope=row>42</th><td>Crohn's Disease           </td><td>CD8T1MB_RNASET2     </td><td>0.5453104</td><td>CD8T1MB    </td><td>RNASET2 </td><td>PP.H4.abf</td><td>2707</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td>Inflammatory Bowel Disease</td><td>CD4T1MB_RNASET2     </td><td>0.9309073</td><td>CD4T1MB    </td><td>RNASET2 </td><td>PP.H4.abf</td><td>2704</td></tr>\n",
+       "\t<tr><th scope=row>91</th><td>Multiple Sclerosis        </td><td>B1MB_HLA-DQA2       </td><td>0.9999979</td><td>B1MB       </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td>  50</td></tr>\n",
+       "\t<tr><th scope=row>96</th><td>Multiple Sclerosis        </td><td>CD4T1MB_HLA-DQA2    </td><td>0.7868654</td><td>CD4T1MB    </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td>  50</td></tr>\n",
+       "\t<tr><th scope=row>101</th><td>Multiple Sclerosis        </td><td>CD8T1MB_HLA-DQA2    </td><td>0.9978392</td><td>CD8T1MB    </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td>  50</td></tr>\n",
+       "\t<tr><th scope=row>106</th><td>Multiple Sclerosis        </td><td>DC1MB_HLA-DQA2      </td><td>0.9999978</td><td>DC1MB      </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td>  50</td></tr>\n",
+       "\t<tr><th scope=row>111</th><td>Multiple Sclerosis        </td><td>monocyte1MB_HLA-DQA2</td><td>0.9999995</td><td>monocyte1MB</td><td>HLA-DQA2</td><td>PP.H4.abf</td><td>  50</td></tr>\n",
+       "\t<tr><th scope=row>121</th><td>Rheumatoid Arthritis      </td><td>B1MB_HLA-DQA2       </td><td>0.6648626</td><td>B1MB       </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 834</td></tr>\n",
+       "\t<tr><th scope=row>123</th><td>Rheumatoid Arthritis      </td><td>B1MB_RPS26          </td><td>0.7520723</td><td>B1MB       </td><td>RPS26   </td><td>PP.H4.abf</td><td> 882</td></tr>\n",
+       "\t<tr><th scope=row>128</th><td>Rheumatoid Arthritis      </td><td>CD4T1MB_RPS26       </td><td>0.7536740</td><td>CD4T1MB    </td><td>RPS26   </td><td>PP.H4.abf</td><td> 882</td></tr>\n",
+       "\t<tr><th scope=row>133</th><td>Rheumatoid Arthritis      </td><td>CD8T1MB_RPS26       </td><td>0.7532601</td><td>CD8T1MB    </td><td>RPS26   </td><td>PP.H4.abf</td><td> 882</td></tr>\n",
+       "\t<tr><th scope=row>136</th><td>Rheumatoid Arthritis      </td><td>DC1MB_HLA-DQA2      </td><td>0.6717926</td><td>DC1MB      </td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 834</td></tr>\n",
+       "\t<tr><th scope=row>138</th><td>Rheumatoid Arthritis      </td><td>DC1MB_RPS26         </td><td>0.7534094</td><td>DC1MB      </td><td>RPS26   </td><td>PP.H4.abf</td><td> 882</td></tr>\n",
+       "\t<tr><th scope=row>141</th><td>Rheumatoid Arthritis      </td><td>monocyte1MB_HLA-DQA2</td><td>0.6669792</td><td>monocyte1MB</td><td>HLA-DQA2</td><td>PP.H4.abf</td><td> 834</td></tr>\n",
+       "\t<tr><th scope=row>143</th><td>Rheumatoid Arthritis      </td><td>monocyte1MB_RPS26   </td><td>0.7551725</td><td>monocyte1MB</td><td>RPS26   </td><td>PP.H4.abf</td><td> 882</td></tr>\n",
+       "\t<tr><th scope=row>148</th><td>Rheumatoid Arthritis      </td><td>NK1MB_RPS26         </td><td>0.7545729</td><td>NK1MB      </td><td>RPS26   </td><td>PP.H4.abf</td><td> 882</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 29 × 7\n",
+       "\\begin{tabular}{r|lllllll}\n",
+       "  & trait & identifier & value & cell\\_type & gene & parameter & overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <dbl> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & Asthma                     & B1MB\\_HLA-DQA2        & 0.9997353 & B1MB        & HLA-DQA2 & PP.H4.abf &  388\\\\\n",
+       "\t3 & Asthma                     & B1MB\\_RPS26           & 0.5349101 & B1MB        & RPS26    & PP.H4.abf &  381\\\\\n",
+       "\t6 & Asthma                     & CD4T1MB\\_HLA-DQA2     & 0.9999956 & CD4T1MB     & HLA-DQA2 & PP.H4.abf &  388\\\\\n",
+       "\t8 & Asthma                     & CD4T1MB\\_RPS26        & 0.5349363 & CD4T1MB     & RPS26    & PP.H4.abf &  381\\\\\n",
+       "\t11 & Asthma                     & CD8T1MB\\_HLA-DQA2     & 1.0000000 & CD8T1MB     & HLA-DQA2 & PP.H4.abf &  388\\\\\n",
+       "\t13 & Asthma                     & CD8T1MB\\_RPS26        & 0.5350001 & CD8T1MB     & RPS26    & PP.H4.abf &  381\\\\\n",
+       "\t16 & Asthma                     & DC1MB\\_HLA-DQA2       & 0.6589821 & DC1MB       & HLA-DQA2 & PP.H4.abf &  388\\\\\n",
+       "\t18 & Asthma                     & DC1MB\\_RPS26          & 0.5349096 & DC1MB       & RPS26    & PP.H4.abf &  381\\\\\n",
+       "\t21 & Asthma                     & monocyte1MB\\_HLA-DQA2 & 0.9746826 & monocyte1MB & HLA-DQA2 & PP.H4.abf &  388\\\\\n",
+       "\t23 & Asthma                     & monocyte1MB\\_RPS26    & 0.5349095 & monocyte1MB & RPS26    & PP.H4.abf &  381\\\\\n",
+       "\t26 & Asthma                     & NK1MB\\_HLA-DQA2       & 0.9999953 & NK1MB       & HLA-DQA2 & PP.H4.abf &  388\\\\\n",
+       "\t28 & Asthma                     & NK1MB\\_RPS26          & 0.5429194 & NK1MB       & RPS26    & PP.H4.abf &  381\\\\\n",
+       "\t37 & Crohn's Disease            & CD4T1MB\\_RNASET2      & 0.9046377 & CD4T1MB     & RNASET2  & PP.H4.abf & 2707\\\\\n",
+       "\t42 & Crohn's Disease            & CD8T1MB\\_RNASET2      & 0.5453104 & CD8T1MB     & RNASET2  & PP.H4.abf & 2707\\\\\n",
+       "\t67 & Inflammatory Bowel Disease & CD4T1MB\\_RNASET2      & 0.9309073 & CD4T1MB     & RNASET2  & PP.H4.abf & 2704\\\\\n",
+       "\t91 & Multiple Sclerosis         & B1MB\\_HLA-DQA2        & 0.9999979 & B1MB        & HLA-DQA2 & PP.H4.abf &   50\\\\\n",
+       "\t96 & Multiple Sclerosis         & CD4T1MB\\_HLA-DQA2     & 0.7868654 & CD4T1MB     & HLA-DQA2 & PP.H4.abf &   50\\\\\n",
+       "\t101 & Multiple Sclerosis         & CD8T1MB\\_HLA-DQA2     & 0.9978392 & CD8T1MB     & HLA-DQA2 & PP.H4.abf &   50\\\\\n",
+       "\t106 & Multiple Sclerosis         & DC1MB\\_HLA-DQA2       & 0.9999978 & DC1MB       & HLA-DQA2 & PP.H4.abf &   50\\\\\n",
+       "\t111 & Multiple Sclerosis         & monocyte1MB\\_HLA-DQA2 & 0.9999995 & monocyte1MB & HLA-DQA2 & PP.H4.abf &   50\\\\\n",
+       "\t121 & Rheumatoid Arthritis       & B1MB\\_HLA-DQA2        & 0.6648626 & B1MB        & HLA-DQA2 & PP.H4.abf &  834\\\\\n",
+       "\t123 & Rheumatoid Arthritis       & B1MB\\_RPS26           & 0.7520723 & B1MB        & RPS26    & PP.H4.abf &  882\\\\\n",
+       "\t128 & Rheumatoid Arthritis       & CD4T1MB\\_RPS26        & 0.7536740 & CD4T1MB     & RPS26    & PP.H4.abf &  882\\\\\n",
+       "\t133 & Rheumatoid Arthritis       & CD8T1MB\\_RPS26        & 0.7532601 & CD8T1MB     & RPS26    & PP.H4.abf &  882\\\\\n",
+       "\t136 & Rheumatoid Arthritis       & DC1MB\\_HLA-DQA2       & 0.6717926 & DC1MB       & HLA-DQA2 & PP.H4.abf &  834\\\\\n",
+       "\t138 & Rheumatoid Arthritis       & DC1MB\\_RPS26          & 0.7534094 & DC1MB       & RPS26    & PP.H4.abf &  882\\\\\n",
+       "\t141 & Rheumatoid Arthritis       & monocyte1MB\\_HLA-DQA2 & 0.6669792 & monocyte1MB & HLA-DQA2 & PP.H4.abf &  834\\\\\n",
+       "\t143 & Rheumatoid Arthritis       & monocyte1MB\\_RPS26    & 0.7551725 & monocyte1MB & RPS26    & PP.H4.abf &  882\\\\\n",
+       "\t148 & Rheumatoid Arthritis       & NK1MB\\_RPS26          & 0.7545729 & NK1MB       & RPS26    & PP.H4.abf &  882\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 29 × 7\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | parameter &lt;chr&gt; | overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|\n",
+       "| 1 | Asthma                     | B1MB_HLA-DQA2        | 0.9997353 | B1MB        | HLA-DQA2 | PP.H4.abf |  388 |\n",
+       "| 3 | Asthma                     | B1MB_RPS26           | 0.5349101 | B1MB        | RPS26    | PP.H4.abf |  381 |\n",
+       "| 6 | Asthma                     | CD4T1MB_HLA-DQA2     | 0.9999956 | CD4T1MB     | HLA-DQA2 | PP.H4.abf |  388 |\n",
+       "| 8 | Asthma                     | CD4T1MB_RPS26        | 0.5349363 | CD4T1MB     | RPS26    | PP.H4.abf |  381 |\n",
+       "| 11 | Asthma                     | CD8T1MB_HLA-DQA2     | 1.0000000 | CD8T1MB     | HLA-DQA2 | PP.H4.abf |  388 |\n",
+       "| 13 | Asthma                     | CD8T1MB_RPS26        | 0.5350001 | CD8T1MB     | RPS26    | PP.H4.abf |  381 |\n",
+       "| 16 | Asthma                     | DC1MB_HLA-DQA2       | 0.6589821 | DC1MB       | HLA-DQA2 | PP.H4.abf |  388 |\n",
+       "| 18 | Asthma                     | DC1MB_RPS26          | 0.5349096 | DC1MB       | RPS26    | PP.H4.abf |  381 |\n",
+       "| 21 | Asthma                     | monocyte1MB_HLA-DQA2 | 0.9746826 | monocyte1MB | HLA-DQA2 | PP.H4.abf |  388 |\n",
+       "| 23 | Asthma                     | monocyte1MB_RPS26    | 0.5349095 | monocyte1MB | RPS26    | PP.H4.abf |  381 |\n",
+       "| 26 | Asthma                     | NK1MB_HLA-DQA2       | 0.9999953 | NK1MB       | HLA-DQA2 | PP.H4.abf |  388 |\n",
+       "| 28 | Asthma                     | NK1MB_RPS26          | 0.5429194 | NK1MB       | RPS26    | PP.H4.abf |  381 |\n",
+       "| 37 | Crohn's Disease            | CD4T1MB_RNASET2      | 0.9046377 | CD4T1MB     | RNASET2  | PP.H4.abf | 2707 |\n",
+       "| 42 | Crohn's Disease            | CD8T1MB_RNASET2      | 0.5453104 | CD8T1MB     | RNASET2  | PP.H4.abf | 2707 |\n",
+       "| 67 | Inflammatory Bowel Disease | CD4T1MB_RNASET2      | 0.9309073 | CD4T1MB     | RNASET2  | PP.H4.abf | 2704 |\n",
+       "| 91 | Multiple Sclerosis         | B1MB_HLA-DQA2        | 0.9999979 | B1MB        | HLA-DQA2 | PP.H4.abf |   50 |\n",
+       "| 96 | Multiple Sclerosis         | CD4T1MB_HLA-DQA2     | 0.7868654 | CD4T1MB     | HLA-DQA2 | PP.H4.abf |   50 |\n",
+       "| 101 | Multiple Sclerosis         | CD8T1MB_HLA-DQA2     | 0.9978392 | CD8T1MB     | HLA-DQA2 | PP.H4.abf |   50 |\n",
+       "| 106 | Multiple Sclerosis         | DC1MB_HLA-DQA2       | 0.9999978 | DC1MB       | HLA-DQA2 | PP.H4.abf |   50 |\n",
+       "| 111 | Multiple Sclerosis         | monocyte1MB_HLA-DQA2 | 0.9999995 | monocyte1MB | HLA-DQA2 | PP.H4.abf |   50 |\n",
+       "| 121 | Rheumatoid Arthritis       | B1MB_HLA-DQA2        | 0.6648626 | B1MB        | HLA-DQA2 | PP.H4.abf |  834 |\n",
+       "| 123 | Rheumatoid Arthritis       | B1MB_RPS26           | 0.7520723 | B1MB        | RPS26    | PP.H4.abf |  882 |\n",
+       "| 128 | Rheumatoid Arthritis       | CD4T1MB_RPS26        | 0.7536740 | CD4T1MB     | RPS26    | PP.H4.abf |  882 |\n",
+       "| 133 | Rheumatoid Arthritis       | CD8T1MB_RPS26        | 0.7532601 | CD8T1MB     | RPS26    | PP.H4.abf |  882 |\n",
+       "| 136 | Rheumatoid Arthritis       | DC1MB_HLA-DQA2       | 0.6717926 | DC1MB       | HLA-DQA2 | PP.H4.abf |  834 |\n",
+       "| 138 | Rheumatoid Arthritis       | DC1MB_RPS26          | 0.7534094 | DC1MB       | RPS26    | PP.H4.abf |  882 |\n",
+       "| 141 | Rheumatoid Arthritis       | monocyte1MB_HLA-DQA2 | 0.6669792 | monocyte1MB | HLA-DQA2 | PP.H4.abf |  834 |\n",
+       "| 143 | Rheumatoid Arthritis       | monocyte1MB_RPS26    | 0.7551725 | monocyte1MB | RPS26    | PP.H4.abf |  882 |\n",
+       "| 148 | Rheumatoid Arthritis       | NK1MB_RPS26          | 0.7545729 | NK1MB       | RPS26    | PP.H4.abf |  882 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    trait                      identifier           value     cell_type  \n",
+       "1   Asthma                     B1MB_HLA-DQA2        0.9997353 B1MB       \n",
+       "3   Asthma                     B1MB_RPS26           0.5349101 B1MB       \n",
+       "6   Asthma                     CD4T1MB_HLA-DQA2     0.9999956 CD4T1MB    \n",
+       "8   Asthma                     CD4T1MB_RPS26        0.5349363 CD4T1MB    \n",
+       "11  Asthma                     CD8T1MB_HLA-DQA2     1.0000000 CD8T1MB    \n",
+       "13  Asthma                     CD8T1MB_RPS26        0.5350001 CD8T1MB    \n",
+       "16  Asthma                     DC1MB_HLA-DQA2       0.6589821 DC1MB      \n",
+       "18  Asthma                     DC1MB_RPS26          0.5349096 DC1MB      \n",
+       "21  Asthma                     monocyte1MB_HLA-DQA2 0.9746826 monocyte1MB\n",
+       "23  Asthma                     monocyte1MB_RPS26    0.5349095 monocyte1MB\n",
+       "26  Asthma                     NK1MB_HLA-DQA2       0.9999953 NK1MB      \n",
+       "28  Asthma                     NK1MB_RPS26          0.5429194 NK1MB      \n",
+       "37  Crohn's Disease            CD4T1MB_RNASET2      0.9046377 CD4T1MB    \n",
+       "42  Crohn's Disease            CD8T1MB_RNASET2      0.5453104 CD8T1MB    \n",
+       "67  Inflammatory Bowel Disease CD4T1MB_RNASET2      0.9309073 CD4T1MB    \n",
+       "91  Multiple Sclerosis         B1MB_HLA-DQA2        0.9999979 B1MB       \n",
+       "96  Multiple Sclerosis         CD4T1MB_HLA-DQA2     0.7868654 CD4T1MB    \n",
+       "101 Multiple Sclerosis         CD8T1MB_HLA-DQA2     0.9978392 CD8T1MB    \n",
+       "106 Multiple Sclerosis         DC1MB_HLA-DQA2       0.9999978 DC1MB      \n",
+       "111 Multiple Sclerosis         monocyte1MB_HLA-DQA2 0.9999995 monocyte1MB\n",
+       "121 Rheumatoid Arthritis       B1MB_HLA-DQA2        0.6648626 B1MB       \n",
+       "123 Rheumatoid Arthritis       B1MB_RPS26           0.7520723 B1MB       \n",
+       "128 Rheumatoid Arthritis       CD4T1MB_RPS26        0.7536740 CD4T1MB    \n",
+       "133 Rheumatoid Arthritis       CD8T1MB_RPS26        0.7532601 CD8T1MB    \n",
+       "136 Rheumatoid Arthritis       DC1MB_HLA-DQA2       0.6717926 DC1MB      \n",
+       "138 Rheumatoid Arthritis       DC1MB_RPS26          0.7534094 DC1MB      \n",
+       "141 Rheumatoid Arthritis       monocyte1MB_HLA-DQA2 0.6669792 monocyte1MB\n",
+       "143 Rheumatoid Arthritis       monocyte1MB_RPS26    0.7551725 monocyte1MB\n",
+       "148 Rheumatoid Arthritis       NK1MB_RPS26          0.7545729 NK1MB      \n",
+       "    gene     parameter overlapping_snps\n",
+       "1   HLA-DQA2 PP.H4.abf  388            \n",
+       "3   RPS26    PP.H4.abf  381            \n",
+       "6   HLA-DQA2 PP.H4.abf  388            \n",
+       "8   RPS26    PP.H4.abf  381            \n",
+       "11  HLA-DQA2 PP.H4.abf  388            \n",
+       "13  RPS26    PP.H4.abf  381            \n",
+       "16  HLA-DQA2 PP.H4.abf  388            \n",
+       "18  RPS26    PP.H4.abf  381            \n",
+       "21  HLA-DQA2 PP.H4.abf  388            \n",
+       "23  RPS26    PP.H4.abf  381            \n",
+       "26  HLA-DQA2 PP.H4.abf  388            \n",
+       "28  RPS26    PP.H4.abf  381            \n",
+       "37  RNASET2  PP.H4.abf 2707            \n",
+       "42  RNASET2  PP.H4.abf 2707            \n",
+       "67  RNASET2  PP.H4.abf 2704            \n",
+       "91  HLA-DQA2 PP.H4.abf   50            \n",
+       "96  HLA-DQA2 PP.H4.abf   50            \n",
+       "101 HLA-DQA2 PP.H4.abf   50            \n",
+       "106 HLA-DQA2 PP.H4.abf   50            \n",
+       "111 HLA-DQA2 PP.H4.abf   50            \n",
+       "121 HLA-DQA2 PP.H4.abf  834            \n",
+       "123 RPS26    PP.H4.abf  882            \n",
+       "128 RPS26    PP.H4.abf  882            \n",
+       "133 RPS26    PP.H4.abf  882            \n",
+       "136 HLA-DQA2 PP.H4.abf  834            \n",
+       "138 RPS26    PP.H4.abf  882            \n",
+       "141 HLA-DQA2 PP.H4.abf  834            \n",
+       "143 RPS26    PP.H4.abf  882            \n",
+       "148 RPS26    PP.H4.abf  882            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eqtl_summary_filtered[(eqtl_summary_filtered$parameter == parameter_var) ,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "511df027-bdf8-45d0-9e06-c789a47a810a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_var = 'PP.H3.abf'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "663e5826-d961-4933-ba17-d2096e8b1b37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#trait = c('Rheumatoid Arthritis', 'Type_1_Diabetes', 'Crohn\\'s Disease')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "3656aff9-7838-4c4e-86c8-a2fcfe9b558c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 38 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>parameter</th><th scope=col>overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>31</th><td>Crohn's Disease           </td><td>B1MB_HLA-DQA2       </td><td>0.9999567</td><td>B1MB       </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4698</td></tr>\n",
+       "\t<tr><th scope=row>36</th><td>Crohn's Disease           </td><td>CD4T1MB_HLA-DQA2    </td><td>0.9998617</td><td>CD4T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4698</td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>Crohn's Disease           </td><td>CD8T1MB_HLA-DQA2    </td><td>0.9995537</td><td>CD8T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4698</td></tr>\n",
+       "\t<tr><th scope=row>46</th><td>Crohn's Disease           </td><td>DC1MB_HLA-DQA2      </td><td>0.9999567</td><td>DC1MB      </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4698</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td>Crohn's Disease           </td><td>monocyte1MB_HLA-DQA2</td><td>0.9999567</td><td>monocyte1MB</td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4698</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>Crohn's Disease           </td><td>NK1MB_HLA-DQA2      </td><td>0.9998702</td><td>NK1MB      </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4698</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>Crohn's Disease           </td><td>NK1MB_RNASET2       </td><td>0.5016812</td><td>NK1MB      </td><td>RNASET2 </td><td>PP.H3.abf</td><td>2707</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>Inflammatory Bowel Disease</td><td>B1MB_HLA-DQA2       </td><td>0.9263285</td><td>B1MB       </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4700</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td>Inflammatory Bowel Disease</td><td>CD4T1MB_HLA-DQA2    </td><td>0.8770284</td><td>CD4T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4700</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td>Inflammatory Bowel Disease</td><td>CD8T1MB_HLA-DQA2    </td><td>0.9360076</td><td>CD8T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4700</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>Inflammatory Bowel Disease</td><td>CD8T1MB_RNASET2     </td><td>0.6274067</td><td>CD8T1MB    </td><td>RNASET2 </td><td>PP.H3.abf</td><td>2704</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>Inflammatory Bowel Disease</td><td>DC1MB_HLA-DQA2      </td><td>0.9242179</td><td>DC1MB      </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4700</td></tr>\n",
+       "\t<tr><th scope=row>81</th><td>Inflammatory Bowel Disease</td><td>monocyte1MB_HLA-DQA2</td><td>0.9066889</td><td>monocyte1MB</td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4700</td></tr>\n",
+       "\t<tr><th scope=row>86</th><td>Inflammatory Bowel Disease</td><td>NK1MB_HLA-DQA2      </td><td>0.9820401</td><td>NK1MB      </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>4700</td></tr>\n",
+       "\t<tr><th scope=row>87</th><td>Inflammatory Bowel Disease</td><td>NK1MB_RNASET2       </td><td>0.4851461</td><td>NK1MB      </td><td>RNASET2 </td><td>PP.H3.abf</td><td>2704</td></tr>\n",
+       "\t<tr><th scope=row>126</th><td>Rheumatoid Arthritis      </td><td>CD4T1MB_HLA-DQA2    </td><td>0.6547936</td><td>CD4T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td> 834</td></tr>\n",
+       "\t<tr><th scope=row>127</th><td>Rheumatoid Arthritis      </td><td>CD4T1MB_RNASET2     </td><td>1.0000000</td><td>CD4T1MB    </td><td>RNASET2 </td><td>PP.H3.abf</td><td>2031</td></tr>\n",
+       "\t<tr><th scope=row>131</th><td>Rheumatoid Arthritis      </td><td>CD8T1MB_HLA-DQA2    </td><td>0.7767070</td><td>CD8T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td> 834</td></tr>\n",
+       "\t<tr><th scope=row>132</th><td>Rheumatoid Arthritis      </td><td>CD8T1MB_RNASET2     </td><td>0.9994734</td><td>CD8T1MB    </td><td>RNASET2 </td><td>PP.H3.abf</td><td>2031</td></tr>\n",
+       "\t<tr><th scope=row>146</th><td>Rheumatoid Arthritis      </td><td>NK1MB_HLA-DQA2      </td><td>0.6625783</td><td>NK1MB      </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td> 834</td></tr>\n",
+       "\t<tr><th scope=row>147</th><td>Rheumatoid Arthritis      </td><td>NK1MB_RNASET2       </td><td>0.7544265</td><td>NK1MB      </td><td>RNASET2 </td><td>PP.H3.abf</td><td>2031</td></tr>\n",
+       "\t<tr><th scope=row>151</th><td>Type_1_Diabetes           </td><td>B1MB_HLA-DQA2       </td><td>1.0000000</td><td>B1MB       </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>1344</td></tr>\n",
+       "\t<tr><th scope=row>153</th><td>Type_1_Diabetes           </td><td>B1MB_RPS26          </td><td>0.9998778</td><td>B1MB       </td><td>RPS26   </td><td>PP.H3.abf</td><td>1341</td></tr>\n",
+       "\t<tr><th scope=row>156</th><td>Type_1_Diabetes           </td><td>CD4T1MB_HLA-DQA2    </td><td>0.9995561</td><td>CD4T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>1344</td></tr>\n",
+       "\t<tr><th scope=row>158</th><td>Type_1_Diabetes           </td><td>CD4T1MB_RPS26       </td><td>0.9997676</td><td>CD4T1MB    </td><td>RPS26   </td><td>PP.H3.abf</td><td>1341</td></tr>\n",
+       "\t<tr><th scope=row>161</th><td>Type_1_Diabetes           </td><td>CD8T1MB_HLA-DQA2    </td><td>1.0000000</td><td>CD8T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>1344</td></tr>\n",
+       "\t<tr><th scope=row>163</th><td>Type_1_Diabetes           </td><td>CD8T1MB_RPS26       </td><td>0.9995874</td><td>CD8T1MB    </td><td>RPS26   </td><td>PP.H3.abf</td><td>1341</td></tr>\n",
+       "\t<tr><th scope=row>166</th><td>Type_1_Diabetes           </td><td>DC1MB_HLA-DQA2      </td><td>1.0000000</td><td>DC1MB      </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>1344</td></tr>\n",
+       "\t<tr><th scope=row>168</th><td>Type_1_Diabetes           </td><td>DC1MB_RPS26         </td><td>0.9995863</td><td>DC1MB      </td><td>RPS26   </td><td>PP.H3.abf</td><td>1341</td></tr>\n",
+       "\t<tr><th scope=row>171</th><td>Type_1_Diabetes           </td><td>monocyte1MB_HLA-DQA2</td><td>1.0000000</td><td>monocyte1MB</td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>1344</td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>Type_1_Diabetes           </td><td>monocyte1MB_RPS26   </td><td>0.9998459</td><td>monocyte1MB</td><td>RPS26   </td><td>PP.H3.abf</td><td>1341</td></tr>\n",
+       "\t<tr><th scope=row>176</th><td>Type_1_Diabetes           </td><td>NK1MB_HLA-DQA2      </td><td>0.9999993</td><td>NK1MB      </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>1344</td></tr>\n",
+       "\t<tr><th scope=row>178</th><td>Type_1_Diabetes           </td><td>NK1MB_RPS26         </td><td>0.9996350</td><td>NK1MB      </td><td>RPS26   </td><td>PP.H3.abf</td><td>1341</td></tr>\n",
+       "\t<tr><th scope=row>181</th><td>White blood cell count    </td><td>B1MB_HLA-DQA2       </td><td>1.0000000</td><td>B1MB       </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td> 517</td></tr>\n",
+       "\t<tr><th scope=row>186</th><td>White blood cell count    </td><td>CD4T1MB_HLA-DQA2    </td><td>0.7634488</td><td>CD4T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td> 517</td></tr>\n",
+       "\t<tr><th scope=row>191</th><td>White blood cell count    </td><td>CD8T1MB_HLA-DQA2    </td><td>0.9999464</td><td>CD8T1MB    </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td> 517</td></tr>\n",
+       "\t<tr><th scope=row>196</th><td>White blood cell count    </td><td>DC1MB_HLA-DQA2      </td><td>1.0000000</td><td>DC1MB      </td><td>HLA-DQA2</td><td>PP.H3.abf</td><td> 517</td></tr>\n",
+       "\t<tr><th scope=row>201</th><td>White blood cell count    </td><td>monocyte1MB_HLA-DQA2</td><td>0.9999996</td><td>monocyte1MB</td><td>HLA-DQA2</td><td>PP.H3.abf</td><td> 517</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 38 × 7\n",
+       "\\begin{tabular}{r|lllllll}\n",
+       "  & trait & identifier & value & cell\\_type & gene & parameter & overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <dbl> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t31 & Crohn's Disease            & B1MB\\_HLA-DQA2        & 0.9999567 & B1MB        & HLA-DQA2 & PP.H3.abf & 4698\\\\\n",
+       "\t36 & Crohn's Disease            & CD4T1MB\\_HLA-DQA2     & 0.9998617 & CD4T1MB     & HLA-DQA2 & PP.H3.abf & 4698\\\\\n",
+       "\t41 & Crohn's Disease            & CD8T1MB\\_HLA-DQA2     & 0.9995537 & CD8T1MB     & HLA-DQA2 & PP.H3.abf & 4698\\\\\n",
+       "\t46 & Crohn's Disease            & DC1MB\\_HLA-DQA2       & 0.9999567 & DC1MB       & HLA-DQA2 & PP.H3.abf & 4698\\\\\n",
+       "\t51 & Crohn's Disease            & monocyte1MB\\_HLA-DQA2 & 0.9999567 & monocyte1MB & HLA-DQA2 & PP.H3.abf & 4698\\\\\n",
+       "\t56 & Crohn's Disease            & NK1MB\\_HLA-DQA2       & 0.9998702 & NK1MB       & HLA-DQA2 & PP.H3.abf & 4698\\\\\n",
+       "\t57 & Crohn's Disease            & NK1MB\\_RNASET2        & 0.5016812 & NK1MB       & RNASET2  & PP.H3.abf & 2707\\\\\n",
+       "\t61 & Inflammatory Bowel Disease & B1MB\\_HLA-DQA2        & 0.9263285 & B1MB        & HLA-DQA2 & PP.H3.abf & 4700\\\\\n",
+       "\t66 & Inflammatory Bowel Disease & CD4T1MB\\_HLA-DQA2     & 0.8770284 & CD4T1MB     & HLA-DQA2 & PP.H3.abf & 4700\\\\\n",
+       "\t71 & Inflammatory Bowel Disease & CD8T1MB\\_HLA-DQA2     & 0.9360076 & CD8T1MB     & HLA-DQA2 & PP.H3.abf & 4700\\\\\n",
+       "\t72 & Inflammatory Bowel Disease & CD8T1MB\\_RNASET2      & 0.6274067 & CD8T1MB     & RNASET2  & PP.H3.abf & 2704\\\\\n",
+       "\t76 & Inflammatory Bowel Disease & DC1MB\\_HLA-DQA2       & 0.9242179 & DC1MB       & HLA-DQA2 & PP.H3.abf & 4700\\\\\n",
+       "\t81 & Inflammatory Bowel Disease & monocyte1MB\\_HLA-DQA2 & 0.9066889 & monocyte1MB & HLA-DQA2 & PP.H3.abf & 4700\\\\\n",
+       "\t86 & Inflammatory Bowel Disease & NK1MB\\_HLA-DQA2       & 0.9820401 & NK1MB       & HLA-DQA2 & PP.H3.abf & 4700\\\\\n",
+       "\t87 & Inflammatory Bowel Disease & NK1MB\\_RNASET2        & 0.4851461 & NK1MB       & RNASET2  & PP.H3.abf & 2704\\\\\n",
+       "\t126 & Rheumatoid Arthritis       & CD4T1MB\\_HLA-DQA2     & 0.6547936 & CD4T1MB     & HLA-DQA2 & PP.H3.abf &  834\\\\\n",
+       "\t127 & Rheumatoid Arthritis       & CD4T1MB\\_RNASET2      & 1.0000000 & CD4T1MB     & RNASET2  & PP.H3.abf & 2031\\\\\n",
+       "\t131 & Rheumatoid Arthritis       & CD8T1MB\\_HLA-DQA2     & 0.7767070 & CD8T1MB     & HLA-DQA2 & PP.H3.abf &  834\\\\\n",
+       "\t132 & Rheumatoid Arthritis       & CD8T1MB\\_RNASET2      & 0.9994734 & CD8T1MB     & RNASET2  & PP.H3.abf & 2031\\\\\n",
+       "\t146 & Rheumatoid Arthritis       & NK1MB\\_HLA-DQA2       & 0.6625783 & NK1MB       & HLA-DQA2 & PP.H3.abf &  834\\\\\n",
+       "\t147 & Rheumatoid Arthritis       & NK1MB\\_RNASET2        & 0.7544265 & NK1MB       & RNASET2  & PP.H3.abf & 2031\\\\\n",
+       "\t151 & Type\\_1\\_Diabetes            & B1MB\\_HLA-DQA2        & 1.0000000 & B1MB        & HLA-DQA2 & PP.H3.abf & 1344\\\\\n",
+       "\t153 & Type\\_1\\_Diabetes            & B1MB\\_RPS26           & 0.9998778 & B1MB        & RPS26    & PP.H3.abf & 1341\\\\\n",
+       "\t156 & Type\\_1\\_Diabetes            & CD4T1MB\\_HLA-DQA2     & 0.9995561 & CD4T1MB     & HLA-DQA2 & PP.H3.abf & 1344\\\\\n",
+       "\t158 & Type\\_1\\_Diabetes            & CD4T1MB\\_RPS26        & 0.9997676 & CD4T1MB     & RPS26    & PP.H3.abf & 1341\\\\\n",
+       "\t161 & Type\\_1\\_Diabetes            & CD8T1MB\\_HLA-DQA2     & 1.0000000 & CD8T1MB     & HLA-DQA2 & PP.H3.abf & 1344\\\\\n",
+       "\t163 & Type\\_1\\_Diabetes            & CD8T1MB\\_RPS26        & 0.9995874 & CD8T1MB     & RPS26    & PP.H3.abf & 1341\\\\\n",
+       "\t166 & Type\\_1\\_Diabetes            & DC1MB\\_HLA-DQA2       & 1.0000000 & DC1MB       & HLA-DQA2 & PP.H3.abf & 1344\\\\\n",
+       "\t168 & Type\\_1\\_Diabetes            & DC1MB\\_RPS26          & 0.9995863 & DC1MB       & RPS26    & PP.H3.abf & 1341\\\\\n",
+       "\t171 & Type\\_1\\_Diabetes            & monocyte1MB\\_HLA-DQA2 & 1.0000000 & monocyte1MB & HLA-DQA2 & PP.H3.abf & 1344\\\\\n",
+       "\t173 & Type\\_1\\_Diabetes            & monocyte1MB\\_RPS26    & 0.9998459 & monocyte1MB & RPS26    & PP.H3.abf & 1341\\\\\n",
+       "\t176 & Type\\_1\\_Diabetes            & NK1MB\\_HLA-DQA2       & 0.9999993 & NK1MB       & HLA-DQA2 & PP.H3.abf & 1344\\\\\n",
+       "\t178 & Type\\_1\\_Diabetes            & NK1MB\\_RPS26          & 0.9996350 & NK1MB       & RPS26    & PP.H3.abf & 1341\\\\\n",
+       "\t181 & White blood cell count     & B1MB\\_HLA-DQA2        & 1.0000000 & B1MB        & HLA-DQA2 & PP.H3.abf &  517\\\\\n",
+       "\t186 & White blood cell count     & CD4T1MB\\_HLA-DQA2     & 0.7634488 & CD4T1MB     & HLA-DQA2 & PP.H3.abf &  517\\\\\n",
+       "\t191 & White blood cell count     & CD8T1MB\\_HLA-DQA2     & 0.9999464 & CD8T1MB     & HLA-DQA2 & PP.H3.abf &  517\\\\\n",
+       "\t196 & White blood cell count     & DC1MB\\_HLA-DQA2       & 1.0000000 & DC1MB       & HLA-DQA2 & PP.H3.abf &  517\\\\\n",
+       "\t201 & White blood cell count     & monocyte1MB\\_HLA-DQA2 & 0.9999996 & monocyte1MB & HLA-DQA2 & PP.H3.abf &  517\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 38 × 7\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | parameter &lt;chr&gt; | overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|\n",
+       "| 31 | Crohn's Disease            | B1MB_HLA-DQA2        | 0.9999567 | B1MB        | HLA-DQA2 | PP.H3.abf | 4698 |\n",
+       "| 36 | Crohn's Disease            | CD4T1MB_HLA-DQA2     | 0.9998617 | CD4T1MB     | HLA-DQA2 | PP.H3.abf | 4698 |\n",
+       "| 41 | Crohn's Disease            | CD8T1MB_HLA-DQA2     | 0.9995537 | CD8T1MB     | HLA-DQA2 | PP.H3.abf | 4698 |\n",
+       "| 46 | Crohn's Disease            | DC1MB_HLA-DQA2       | 0.9999567 | DC1MB       | HLA-DQA2 | PP.H3.abf | 4698 |\n",
+       "| 51 | Crohn's Disease            | monocyte1MB_HLA-DQA2 | 0.9999567 | monocyte1MB | HLA-DQA2 | PP.H3.abf | 4698 |\n",
+       "| 56 | Crohn's Disease            | NK1MB_HLA-DQA2       | 0.9998702 | NK1MB       | HLA-DQA2 | PP.H3.abf | 4698 |\n",
+       "| 57 | Crohn's Disease            | NK1MB_RNASET2        | 0.5016812 | NK1MB       | RNASET2  | PP.H3.abf | 2707 |\n",
+       "| 61 | Inflammatory Bowel Disease | B1MB_HLA-DQA2        | 0.9263285 | B1MB        | HLA-DQA2 | PP.H3.abf | 4700 |\n",
+       "| 66 | Inflammatory Bowel Disease | CD4T1MB_HLA-DQA2     | 0.8770284 | CD4T1MB     | HLA-DQA2 | PP.H3.abf | 4700 |\n",
+       "| 71 | Inflammatory Bowel Disease | CD8T1MB_HLA-DQA2     | 0.9360076 | CD8T1MB     | HLA-DQA2 | PP.H3.abf | 4700 |\n",
+       "| 72 | Inflammatory Bowel Disease | CD8T1MB_RNASET2      | 0.6274067 | CD8T1MB     | RNASET2  | PP.H3.abf | 2704 |\n",
+       "| 76 | Inflammatory Bowel Disease | DC1MB_HLA-DQA2       | 0.9242179 | DC1MB       | HLA-DQA2 | PP.H3.abf | 4700 |\n",
+       "| 81 | Inflammatory Bowel Disease | monocyte1MB_HLA-DQA2 | 0.9066889 | monocyte1MB | HLA-DQA2 | PP.H3.abf | 4700 |\n",
+       "| 86 | Inflammatory Bowel Disease | NK1MB_HLA-DQA2       | 0.9820401 | NK1MB       | HLA-DQA2 | PP.H3.abf | 4700 |\n",
+       "| 87 | Inflammatory Bowel Disease | NK1MB_RNASET2        | 0.4851461 | NK1MB       | RNASET2  | PP.H3.abf | 2704 |\n",
+       "| 126 | Rheumatoid Arthritis       | CD4T1MB_HLA-DQA2     | 0.6547936 | CD4T1MB     | HLA-DQA2 | PP.H3.abf |  834 |\n",
+       "| 127 | Rheumatoid Arthritis       | CD4T1MB_RNASET2      | 1.0000000 | CD4T1MB     | RNASET2  | PP.H3.abf | 2031 |\n",
+       "| 131 | Rheumatoid Arthritis       | CD8T1MB_HLA-DQA2     | 0.7767070 | CD8T1MB     | HLA-DQA2 | PP.H3.abf |  834 |\n",
+       "| 132 | Rheumatoid Arthritis       | CD8T1MB_RNASET2      | 0.9994734 | CD8T1MB     | RNASET2  | PP.H3.abf | 2031 |\n",
+       "| 146 | Rheumatoid Arthritis       | NK1MB_HLA-DQA2       | 0.6625783 | NK1MB       | HLA-DQA2 | PP.H3.abf |  834 |\n",
+       "| 147 | Rheumatoid Arthritis       | NK1MB_RNASET2        | 0.7544265 | NK1MB       | RNASET2  | PP.H3.abf | 2031 |\n",
+       "| 151 | Type_1_Diabetes            | B1MB_HLA-DQA2        | 1.0000000 | B1MB        | HLA-DQA2 | PP.H3.abf | 1344 |\n",
+       "| 153 | Type_1_Diabetes            | B1MB_RPS26           | 0.9998778 | B1MB        | RPS26    | PP.H3.abf | 1341 |\n",
+       "| 156 | Type_1_Diabetes            | CD4T1MB_HLA-DQA2     | 0.9995561 | CD4T1MB     | HLA-DQA2 | PP.H3.abf | 1344 |\n",
+       "| 158 | Type_1_Diabetes            | CD4T1MB_RPS26        | 0.9997676 | CD4T1MB     | RPS26    | PP.H3.abf | 1341 |\n",
+       "| 161 | Type_1_Diabetes            | CD8T1MB_HLA-DQA2     | 1.0000000 | CD8T1MB     | HLA-DQA2 | PP.H3.abf | 1344 |\n",
+       "| 163 | Type_1_Diabetes            | CD8T1MB_RPS26        | 0.9995874 | CD8T1MB     | RPS26    | PP.H3.abf | 1341 |\n",
+       "| 166 | Type_1_Diabetes            | DC1MB_HLA-DQA2       | 1.0000000 | DC1MB       | HLA-DQA2 | PP.H3.abf | 1344 |\n",
+       "| 168 | Type_1_Diabetes            | DC1MB_RPS26          | 0.9995863 | DC1MB       | RPS26    | PP.H3.abf | 1341 |\n",
+       "| 171 | Type_1_Diabetes            | monocyte1MB_HLA-DQA2 | 1.0000000 | monocyte1MB | HLA-DQA2 | PP.H3.abf | 1344 |\n",
+       "| 173 | Type_1_Diabetes            | monocyte1MB_RPS26    | 0.9998459 | monocyte1MB | RPS26    | PP.H3.abf | 1341 |\n",
+       "| 176 | Type_1_Diabetes            | NK1MB_HLA-DQA2       | 0.9999993 | NK1MB       | HLA-DQA2 | PP.H3.abf | 1344 |\n",
+       "| 178 | Type_1_Diabetes            | NK1MB_RPS26          | 0.9996350 | NK1MB       | RPS26    | PP.H3.abf | 1341 |\n",
+       "| 181 | White blood cell count     | B1MB_HLA-DQA2        | 1.0000000 | B1MB        | HLA-DQA2 | PP.H3.abf |  517 |\n",
+       "| 186 | White blood cell count     | CD4T1MB_HLA-DQA2     | 0.7634488 | CD4T1MB     | HLA-DQA2 | PP.H3.abf |  517 |\n",
+       "| 191 | White blood cell count     | CD8T1MB_HLA-DQA2     | 0.9999464 | CD8T1MB     | HLA-DQA2 | PP.H3.abf |  517 |\n",
+       "| 196 | White blood cell count     | DC1MB_HLA-DQA2       | 1.0000000 | DC1MB       | HLA-DQA2 | PP.H3.abf |  517 |\n",
+       "| 201 | White blood cell count     | monocyte1MB_HLA-DQA2 | 0.9999996 | monocyte1MB | HLA-DQA2 | PP.H3.abf |  517 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    trait                      identifier           value     cell_type  \n",
+       "31  Crohn's Disease            B1MB_HLA-DQA2        0.9999567 B1MB       \n",
+       "36  Crohn's Disease            CD4T1MB_HLA-DQA2     0.9998617 CD4T1MB    \n",
+       "41  Crohn's Disease            CD8T1MB_HLA-DQA2     0.9995537 CD8T1MB    \n",
+       "46  Crohn's Disease            DC1MB_HLA-DQA2       0.9999567 DC1MB      \n",
+       "51  Crohn's Disease            monocyte1MB_HLA-DQA2 0.9999567 monocyte1MB\n",
+       "56  Crohn's Disease            NK1MB_HLA-DQA2       0.9998702 NK1MB      \n",
+       "57  Crohn's Disease            NK1MB_RNASET2        0.5016812 NK1MB      \n",
+       "61  Inflammatory Bowel Disease B1MB_HLA-DQA2        0.9263285 B1MB       \n",
+       "66  Inflammatory Bowel Disease CD4T1MB_HLA-DQA2     0.8770284 CD4T1MB    \n",
+       "71  Inflammatory Bowel Disease CD8T1MB_HLA-DQA2     0.9360076 CD8T1MB    \n",
+       "72  Inflammatory Bowel Disease CD8T1MB_RNASET2      0.6274067 CD8T1MB    \n",
+       "76  Inflammatory Bowel Disease DC1MB_HLA-DQA2       0.9242179 DC1MB      \n",
+       "81  Inflammatory Bowel Disease monocyte1MB_HLA-DQA2 0.9066889 monocyte1MB\n",
+       "86  Inflammatory Bowel Disease NK1MB_HLA-DQA2       0.9820401 NK1MB      \n",
+       "87  Inflammatory Bowel Disease NK1MB_RNASET2        0.4851461 NK1MB      \n",
+       "126 Rheumatoid Arthritis       CD4T1MB_HLA-DQA2     0.6547936 CD4T1MB    \n",
+       "127 Rheumatoid Arthritis       CD4T1MB_RNASET2      1.0000000 CD4T1MB    \n",
+       "131 Rheumatoid Arthritis       CD8T1MB_HLA-DQA2     0.7767070 CD8T1MB    \n",
+       "132 Rheumatoid Arthritis       CD8T1MB_RNASET2      0.9994734 CD8T1MB    \n",
+       "146 Rheumatoid Arthritis       NK1MB_HLA-DQA2       0.6625783 NK1MB      \n",
+       "147 Rheumatoid Arthritis       NK1MB_RNASET2        0.7544265 NK1MB      \n",
+       "151 Type_1_Diabetes            B1MB_HLA-DQA2        1.0000000 B1MB       \n",
+       "153 Type_1_Diabetes            B1MB_RPS26           0.9998778 B1MB       \n",
+       "156 Type_1_Diabetes            CD4T1MB_HLA-DQA2     0.9995561 CD4T1MB    \n",
+       "158 Type_1_Diabetes            CD4T1MB_RPS26        0.9997676 CD4T1MB    \n",
+       "161 Type_1_Diabetes            CD8T1MB_HLA-DQA2     1.0000000 CD8T1MB    \n",
+       "163 Type_1_Diabetes            CD8T1MB_RPS26        0.9995874 CD8T1MB    \n",
+       "166 Type_1_Diabetes            DC1MB_HLA-DQA2       1.0000000 DC1MB      \n",
+       "168 Type_1_Diabetes            DC1MB_RPS26          0.9995863 DC1MB      \n",
+       "171 Type_1_Diabetes            monocyte1MB_HLA-DQA2 1.0000000 monocyte1MB\n",
+       "173 Type_1_Diabetes            monocyte1MB_RPS26    0.9998459 monocyte1MB\n",
+       "176 Type_1_Diabetes            NK1MB_HLA-DQA2       0.9999993 NK1MB      \n",
+       "178 Type_1_Diabetes            NK1MB_RPS26          0.9996350 NK1MB      \n",
+       "181 White blood cell count     B1MB_HLA-DQA2        1.0000000 B1MB       \n",
+       "186 White blood cell count     CD4T1MB_HLA-DQA2     0.7634488 CD4T1MB    \n",
+       "191 White blood cell count     CD8T1MB_HLA-DQA2     0.9999464 CD8T1MB    \n",
+       "196 White blood cell count     DC1MB_HLA-DQA2       1.0000000 DC1MB      \n",
+       "201 White blood cell count     monocyte1MB_HLA-DQA2 0.9999996 monocyte1MB\n",
+       "    gene     parameter overlapping_snps\n",
+       "31  HLA-DQA2 PP.H3.abf 4698            \n",
+       "36  HLA-DQA2 PP.H3.abf 4698            \n",
+       "41  HLA-DQA2 PP.H3.abf 4698            \n",
+       "46  HLA-DQA2 PP.H3.abf 4698            \n",
+       "51  HLA-DQA2 PP.H3.abf 4698            \n",
+       "56  HLA-DQA2 PP.H3.abf 4698            \n",
+       "57  RNASET2  PP.H3.abf 2707            \n",
+       "61  HLA-DQA2 PP.H3.abf 4700            \n",
+       "66  HLA-DQA2 PP.H3.abf 4700            \n",
+       "71  HLA-DQA2 PP.H3.abf 4700            \n",
+       "72  RNASET2  PP.H3.abf 2704            \n",
+       "76  HLA-DQA2 PP.H3.abf 4700            \n",
+       "81  HLA-DQA2 PP.H3.abf 4700            \n",
+       "86  HLA-DQA2 PP.H3.abf 4700            \n",
+       "87  RNASET2  PP.H3.abf 2704            \n",
+       "126 HLA-DQA2 PP.H3.abf  834            \n",
+       "127 RNASET2  PP.H3.abf 2031            \n",
+       "131 HLA-DQA2 PP.H3.abf  834            \n",
+       "132 RNASET2  PP.H3.abf 2031            \n",
+       "146 HLA-DQA2 PP.H3.abf  834            \n",
+       "147 RNASET2  PP.H3.abf 2031            \n",
+       "151 HLA-DQA2 PP.H3.abf 1344            \n",
+       "153 RPS26    PP.H3.abf 1341            \n",
+       "156 HLA-DQA2 PP.H3.abf 1344            \n",
+       "158 RPS26    PP.H3.abf 1341            \n",
+       "161 HLA-DQA2 PP.H3.abf 1344            \n",
+       "163 RPS26    PP.H3.abf 1341            \n",
+       "166 HLA-DQA2 PP.H3.abf 1344            \n",
+       "168 RPS26    PP.H3.abf 1341            \n",
+       "171 HLA-DQA2 PP.H3.abf 1344            \n",
+       "173 RPS26    PP.H3.abf 1341            \n",
+       "176 HLA-DQA2 PP.H3.abf 1344            \n",
+       "178 RPS26    PP.H3.abf 1341            \n",
+       "181 HLA-DQA2 PP.H3.abf  517            \n",
+       "186 HLA-DQA2 PP.H3.abf  517            \n",
+       "191 HLA-DQA2 PP.H3.abf  517            \n",
+       "196 HLA-DQA2 PP.H3.abf  517            \n",
+       "201 HLA-DQA2 PP.H3.abf  517            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eqtl_summary_filtered[(eqtl_summary_filtered$parameter == parameter_var) ,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "2583253d-247b-439f-978c-6bdfd8067b16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Save eqtl summary for supplementary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "50a4382c-24d8-419e-985c-146799b862eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_supp = eqtl_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "0b4ff2b7-8343-48cf-a977-871cedea631c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>parameter</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>Asthma</td><td>B1MB_HLA-DQA2</td><td>nsnps    </td><td>3.880000e+02</td><td>B1MB</td><td>HLA-DQA2</td><td>388</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>Asthma</td><td>B1MB_HLA-DQA2</td><td>PP.H0.abf</td><td>1.212062e-25</td><td>B1MB</td><td>HLA-DQA2</td><td>388</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 7\n",
+       "\\begin{tabular}{r|lllllll}\n",
+       "  & trait & identifier & parameter & value & cell\\_type & gene & overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & Asthma & B1MB\\_HLA-DQA2 & nsnps     & 3.880000e+02 & B1MB & HLA-DQA2 & 388\\\\\n",
+       "\t2 & Asthma & B1MB\\_HLA-DQA2 & PP.H0.abf & 1.212062e-25 & B1MB & HLA-DQA2 & 388\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 7\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | parameter &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|\n",
+       "| 1 | Asthma | B1MB_HLA-DQA2 | nsnps     | 3.880000e+02 | B1MB | HLA-DQA2 | 388 |\n",
+       "| 2 | Asthma | B1MB_HLA-DQA2 | PP.H0.abf | 1.212062e-25 | B1MB | HLA-DQA2 | 388 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  trait  identifier    parameter value        cell_type gene    \n",
+       "1 Asthma B1MB_HLA-DQA2 nsnps     3.880000e+02 B1MB      HLA-DQA2\n",
+       "2 Asthma B1MB_HLA-DQA2 PP.H0.abf 1.212062e-25 B1MB      HLA-DQA2\n",
+       "  overlapping_snps\n",
+       "1 388             \n",
+       "2 388             "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(eqtl_supp,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "533f9da1-2a30-4b9a-a067-aafa9bd4e634",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_supp = eqtl_supp[(eqtl_supp$parameter %in% c('PP.H3.abf','PP.H4.abf' )) ,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "124508a4-1019-407b-9dc8-f66cb4abaf92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_supp$identifier = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "bdd4eeaf-fbf4-4d16-8828-025858231946",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_supp$egene = eqtl_supp$gene\n",
+    "eqtl_supp$gene = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "eb687509-1146-44a3-be0b-795ada02ef07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_supp$coloc_hypothesis = eqtl_supp$parameter\n",
+    "eqtl_supp$parameter = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "a68b4cb1-ef86-45e0-9f62-9718162a2cfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_supp$cell_type = str_replace(eqtl_supp$cell_type, '1MB', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "76b55b08-401d-4871-b45d-8bb75859e7da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_supp$pp_value = eqtl_supp$value\n",
+    "eqtl_supp$value = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "08ee4113-b4e3-41b8-8ec4-93e353f1c5bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eqtl_supp$amount_overlapping_snps = eqtl_supp$overlapping_snps \n",
+    "eqtl_supp$overlapping_snps = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "fb8814c1-663a-4b8f-bd46-9d81eab02035",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 3 × 6</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>cell_type</th><th scope=col>egene</th><th scope=col>coloc_hypothesis</th><th scope=col>pp_value</th><th scope=col>amount_overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>5</th><td>Asthma</td><td>B</td><td>HLA-DQA2</td><td>PP.H3.abf</td><td>0.0002647404</td><td> 388</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>Asthma</td><td>B</td><td>HLA-DQA2</td><td>PP.H4.abf</td><td>0.9997352596</td><td> 388</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td>Asthma</td><td>B</td><td>RNASET2 </td><td>PP.H3.abf</td><td>0.0018053181</td><td>1133</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 3 × 6\n",
+       "\\begin{tabular}{r|llllll}\n",
+       "  & trait & cell\\_type & egene & coloc\\_hypothesis & pp\\_value & amount\\_overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t5 & Asthma & B & HLA-DQA2 & PP.H3.abf & 0.0002647404 &  388\\\\\n",
+       "\t6 & Asthma & B & HLA-DQA2 & PP.H4.abf & 0.9997352596 &  388\\\\\n",
+       "\t11 & Asthma & B & RNASET2  & PP.H3.abf & 0.0018053181 & 1133\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 3 × 6\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | cell_type &lt;chr&gt; | egene &lt;chr&gt; | coloc_hypothesis &lt;chr&gt; | pp_value &lt;dbl&gt; | amount_overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| 5 | Asthma | B | HLA-DQA2 | PP.H3.abf | 0.0002647404 |  388 |\n",
+       "| 6 | Asthma | B | HLA-DQA2 | PP.H4.abf | 0.9997352596 |  388 |\n",
+       "| 11 | Asthma | B | RNASET2  | PP.H3.abf | 0.0018053181 | 1133 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   trait  cell_type egene    coloc_hypothesis pp_value    \n",
+       "5  Asthma B         HLA-DQA2 PP.H3.abf        0.0002647404\n",
+       "6  Asthma B         HLA-DQA2 PP.H4.abf        0.9997352596\n",
+       "11 Asthma B         RNASET2  PP.H3.abf        0.0018053181\n",
+       "   amount_overlapping_snps\n",
+       "5   388                   \n",
+       "6   388                   \n",
+       "11 1133                   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(eqtl_supp,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fdbde2f-a449-4270-be11-8a4f9857a123",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "e67d6835-f0e1-4696-b12f-b0eb7de6b573",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.table(eqtl_supp, file = paste0(path, \"/colocalization_results/\", \"Coloc_EQTL_supp_table.csv\"), append =FALSE, sep = \",\", row.names = FALSE, col.names =TRUE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8b5a092-17bc-4e53-b279-4fdf271ee30c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## For Co-EQTLs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bff68b2-5251-4f43-88ef-8dad37dcd0f5",
+   "metadata": {},
+   "source": [
+    "### General summaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "14c7f569-6d8d-4286-a74a-4e9b8a2dd6c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 5</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>parameter</th><th scope=col>value</th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>nsnps    </td><td>2.461000e+03</td><td>White blood cell count</td><td>monocyte_TMEM176A___CAPG__TMEM176A</td><td>monocyte_TMEM176A</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>PP.H0.abf</td><td>2.606173e-02</td><td>White blood cell count</td><td>monocyte_TMEM176A___CAPG__TMEM176A</td><td>monocyte_TMEM176A</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 5\n",
+       "\\begin{tabular}{r|lllll}\n",
+       "  & parameter & value & trait & identifier & egene\\\\\n",
+       "  & <chr> & <dbl> & <chr> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & nsnps     & 2.461000e+03 & White blood cell count & monocyte\\_TMEM176A\\_\\_\\_CAPG\\_\\_TMEM176A & monocyte\\_TMEM176A\\\\\n",
+       "\t2 & PP.H0.abf & 2.606173e-02 & White blood cell count & monocyte\\_TMEM176A\\_\\_\\_CAPG\\_\\_TMEM176A & monocyte\\_TMEM176A\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 5\n",
+       "\n",
+       "| <!--/--> | parameter &lt;chr&gt; | value &lt;dbl&gt; | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 1 | nsnps     | 2.461000e+03 | White blood cell count | monocyte_TMEM176A___CAPG__TMEM176A | monocyte_TMEM176A |\n",
+       "| 2 | PP.H0.abf | 2.606173e-02 | White blood cell count | monocyte_TMEM176A___CAPG__TMEM176A | monocyte_TMEM176A |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  parameter value        trait                 \n",
+       "1 nsnps     2.461000e+03 White blood cell count\n",
+       "2 PP.H0.abf 2.606173e-02 White blood cell count\n",
+       "  identifier                         egene            \n",
+       "1 monocyte_TMEM176A___CAPG__TMEM176A monocyte_TMEM176A\n",
+       "2 monocyte_TMEM176A___CAPG__TMEM176A monocyte_TMEM176A"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coeqtl_summary,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "e3201d61-d405-45c2-ae35-97452c21af24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## extract amount of SNPs info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "06bb4100-9486-4566-9d1e-b622f04b53f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_snps = coeqtl_summary[coeqtl_summary$parameter == 'nsnps',]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "4a5940e1-a100-4e9f-a90e-a95bddf3d371",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_snps$overlapping_snps = n_snps$value\n",
+    "n_snps$parameter = NULL\n",
+    "n_snps$value = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "2e06514d-3f8c-45ab-8ebe-cba6301ba0e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Extract relevant columsn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "c7f7ddde-d16b-42b5-a9b2-b8dd5fd9a615",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary = coeqtl_summary[coeqtl_summary$parameter != 'nsnps',]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "13dbdd66-9c1b-483e-9307-b5fc88370e57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary$cell_type = str_replace(coeqtl_summary$identifier, '_.*', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "9baece63-8981-49e2-b3f0-e94e15165618",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#tail(coeqtl_summary,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "cee1a263-8956-421f-ad12-6be2337287d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'monocyte'</li><li>'CD8T'</li><li>'CD4T'</li><li>'DC'</li><li>'B'</li><li>'NK'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'monocyte'\n",
+       "\\item 'CD8T'\n",
+       "\\item 'CD4T'\n",
+       "\\item 'DC'\n",
+       "\\item 'B'\n",
+       "\\item 'NK'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'monocyte'\n",
+       "2. 'CD8T'\n",
+       "3. 'CD4T'\n",
+       "4. 'DC'\n",
+       "5. 'B'\n",
+       "6. 'NK'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] \"monocyte\" \"CD8T\"     \"CD4T\"     \"DC\"       \"B\"        \"NK\"      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(coeqtl_summary$cell_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "e368cc1e-8bbd-49d2-9bb9-c67c52744dc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary$gene = str_extract(coeqtl_summary$identifier, '_.*')\n",
+    "coeqtl_summary$gene = str_replace(coeqtl_summary$gene, '.*___', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "0953053b-c925-494d-b0e6-de02fa3335f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "604"
+      ],
+      "text/latex": [
+       "604"
+      ],
+      "text/markdown": [
+       "604"
+      ],
+      "text/plain": [
+       "[1] 604"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(coeqtl_summary$gene))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "c0cfee92-8655-4048-92ec-b416594996a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Add number of snps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "34f0729b-889f-4901-9d7f-e94d4788877f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th><th scope=col>overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>White blood cell count</td><td>monocyte_TMEM176A___CAPG__TMEM176A </td><td>monocyte_TMEM176A</td><td>2461</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td>White blood cell count</td><td>monocyte_TMEM176A___PTAFR__TMEM176A</td><td>monocyte_TMEM176A</td><td>2461</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 4\n",
+       "\\begin{tabular}{r|llll}\n",
+       "  & trait & identifier & egene & overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & White blood cell count & monocyte\\_TMEM176A\\_\\_\\_CAPG\\_\\_TMEM176A  & monocyte\\_TMEM176A & 2461\\\\\n",
+       "\t7 & White blood cell count & monocyte\\_TMEM176A\\_\\_\\_PTAFR\\_\\_TMEM176A & monocyte\\_TMEM176A & 2461\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 4\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| 1 | White blood cell count | monocyte_TMEM176A___CAPG__TMEM176A  | monocyte_TMEM176A | 2461 |\n",
+       "| 7 | White blood cell count | monocyte_TMEM176A___PTAFR__TMEM176A | monocyte_TMEM176A | 2461 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  trait                  identifier                          egene            \n",
+       "1 White blood cell count monocyte_TMEM176A___CAPG__TMEM176A  monocyte_TMEM176A\n",
+       "7 White blood cell count monocyte_TMEM176A___PTAFR__TMEM176A monocyte_TMEM176A\n",
+       "  overlapping_snps\n",
+       "1 2461            \n",
+       "7 2461            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(n_snps,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "e692030d-cd63-4a82-9165-b3e3ffc90c44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary = merge(coeqtl_summary, n_snps, by.x = c('trait', 'identifier', 'egene'), by.y = c('trait', 'identifier', 'egene'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "9eb0ffa6-70a6-45f9-96a3-ee526c66a888",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Check out maximum probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "52fe9faa-5c7b-4ebc-8917-c7f256ca1d6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'trait', 'identifier', 'cell_type', 'gene',\n",
+      "'overlapping_snps'. You can override using the `.groups` argument.\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_value = coeqtl_summary %>% group_by(trait, identifier, cell_type, gene, overlapping_snps, egene) %>% summarise(value = max(value))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "98b9eb7f-7dce-4c8d-86bb-5939ba75a195",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary_filtered = merge(coeqtl_summary, max_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "d465a856-bf99-46eb-b6d1-105e08447328",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 8</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th><th scope=col>parameter</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>Asthma</td><td>B_RPS26___EEF1A1__RPS26</td><td>B_RPS26</td><td>0.3734713</td><td>B</td><td>EEF1A1__RPS26</td><td>381</td><td>PP.H0.abf</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>Asthma</td><td>B_RPS26___RPL10__RPS26 </td><td>B_RPS26</td><td>0.6338403</td><td>B</td><td>RPL10__RPS26 </td><td>381</td><td>PP.H4.abf</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 8\n",
+       "\\begin{tabular}{r|llllllll}\n",
+       "  & trait & identifier & egene & value & cell\\_type & gene & overlapping\\_snps & parameter\\\\\n",
+       "  & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & Asthma & B\\_RPS26\\_\\_\\_EEF1A1\\_\\_RPS26 & B\\_RPS26 & 0.3734713 & B & EEF1A1\\_\\_RPS26 & 381 & PP.H0.abf\\\\\n",
+       "\t2 & Asthma & B\\_RPS26\\_\\_\\_RPL10\\_\\_RPS26  & B\\_RPS26 & 0.6338403 & B & RPL10\\_\\_RPS26  & 381 & PP.H4.abf\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 8\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; | parameter &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | Asthma | B_RPS26___EEF1A1__RPS26 | B_RPS26 | 0.3734713 | B | EEF1A1__RPS26 | 381 | PP.H0.abf |\n",
+       "| 2 | Asthma | B_RPS26___RPL10__RPS26  | B_RPS26 | 0.6338403 | B | RPL10__RPS26  | 381 | PP.H4.abf |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  trait  identifier              egene   value     cell_type gene         \n",
+       "1 Asthma B_RPS26___EEF1A1__RPS26 B_RPS26 0.3734713 B         EEF1A1__RPS26\n",
+       "2 Asthma B_RPS26___RPL10__RPS26  B_RPS26 0.6338403 B         RPL10__RPS26 \n",
+       "  overlapping_snps parameter\n",
+       "1 381              PP.H0.abf\n",
+       "2 381              PP.H4.abf"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coeqtl_summary_filtered,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "c2c75a27-e6cd-453d-a301-94440f25406d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'parameter', 'trait'. You can override\n",
+      "using the `.groups` argument.\n"
+     ]
+    }
+   ],
+   "source": [
+    "overview_h_amounts = coeqtl_summary_filtered %>% group_by(parameter, trait, egene ) %>% summarise(n = n(), mean_value = mean(value), amount_greater_0.9 = sum(value > 0.9),  amount_greater_0.75 = sum(value > 0.75),amount_greater_0.5 = sum(value > 0.5),  max_overlap_snps = max(overlapping_snps), min_overlap_snps = min(overlapping_snps), mean_overlap_snps = mean(overlapping_snps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "fd026fa0-f255-41a3-aa9a-7d028d97c351",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overview_h_amounts = overview_h_amounts[order(overview_h_amounts$trait),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "54f14012-d8b9-4463-b114-96e3231963cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[22m`summarise()` has grouped output by 'egene'. You can override using the\n",
+      "`.groups` argument.\n"
+     ]
+    }
+   ],
+   "source": [
+    "amount_coegenes_per_e_gene = overview_h_amounts %>% group_by(egene, trait) %>% summarise(total_n = sum(n))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "8d0b44db-5ef6-4821-be87-ca2740bf2803",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overview_h_amounts = merge(overview_h_amounts, amount_coegenes_per_e_gene)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "1cfdbc1e-9270-4158-a9f3-fac044fdfbe9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### Inspect the results for eGenes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "53698756-d176-4716-a5bb-5c46cda6e69e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### All RPS26 - co-egene examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "d6bca892-26b6-40fe-8da0-164cf7437ce7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 15 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>egene</th><th scope=col>parameter</th><th scope=col>n</th><th scope=col>mean_value</th><th scope=col>amount_greater_0.9</th><th scope=col>amount_greater_0.75</th><th scope=col>amount_greater_0.5</th><th scope=col>max_overlap_snps</th><th scope=col>min_overlap_snps</th><th scope=col>mean_overlap_snps</th><th scope=col>total_n</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>2</th><td>Asthma              </td><td>B_RPS26       </td><td>PP.H4.abf</td><td> 20</td><td>0.5448488</td><td> 0</td><td>  0</td><td> 16</td><td> 381</td><td> 381</td><td> 381.0000</td><td> 35</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>Asthma              </td><td>CD4T_RPS26    </td><td>PP.H4.abf</td><td>353</td><td>0.6166249</td><td> 0</td><td> 28</td><td>323</td><td> 381</td><td> 368</td><td> 380.7734</td><td>372</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>Asthma              </td><td>CD8T_RPS26    </td><td>PP.H4.abf</td><td>241</td><td>0.6293361</td><td> 0</td><td> 31</td><td>212</td><td> 381</td><td> 367</td><td> 380.6307</td><td>293</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>Asthma              </td><td>monocyte_RPS26</td><td>PP.H4.abf</td><td>127</td><td>0.6724475</td><td> 0</td><td> 40</td><td>114</td><td> 381</td><td> 379</td><td> 380.9685</td><td>132</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>Asthma              </td><td>NK_RPS26      </td><td>PP.H4.abf</td><td> 84</td><td>0.6149269</td><td> 0</td><td>  7</td><td> 78</td><td> 379</td><td> 379</td><td> 379.0000</td><td> 96</td></tr>\n",
+       "\t<tr><th scope=row>105</th><td>Rheumatoid Arthritis</td><td>B_RPS26       </td><td>PP.H4.abf</td><td> 33</td><td>0.7385906</td><td> 4</td><td> 19</td><td> 31</td><td> 878</td><td> 876</td><td> 877.9394</td><td> 35</td></tr>\n",
+       "\t<tr><th scope=row>111</th><td>Rheumatoid Arthritis</td><td>CD4T_RPS26    </td><td>PP.H4.abf</td><td>370</td><td>0.8044940</td><td>41</td><td>325</td><td>368</td><td> 882</td><td> 835</td><td> 881.1541</td><td>372</td></tr>\n",
+       "\t<tr><th scope=row>118</th><td>Rheumatoid Arthritis</td><td>CD8T_RPS26    </td><td>PP.H4.abf</td><td>289</td><td>0.8303538</td><td>64</td><td>247</td><td>286</td><td> 882</td><td> 831</td><td> 880.4567</td><td>293</td></tr>\n",
+       "\t<tr><th scope=row>123</th><td>Rheumatoid Arthritis</td><td>DC_RPS26      </td><td>PP.H4.abf</td><td>  3</td><td>0.6804751</td><td> 0</td><td>  0</td><td>  3</td><td> 855</td><td> 855</td><td> 855.0000</td><td>  3</td></tr>\n",
+       "\t<tr><th scope=row>127</th><td>Rheumatoid Arthritis</td><td>monocyte_RPS26</td><td>PP.H4.abf</td><td>132</td><td>0.7954287</td><td>15</td><td> 98</td><td>132</td><td> 882</td><td> 879</td><td> 881.9545</td><td>132</td></tr>\n",
+       "\t<tr><th scope=row>131</th><td>Rheumatoid Arthritis</td><td>NK_RPS26      </td><td>PP.H4.abf</td><td> 94</td><td>0.8435984</td><td>28</td><td> 88</td><td> 94</td><td> 879</td><td> 879</td><td> 879.0000</td><td> 96</td></tr>\n",
+       "\t<tr><th scope=row>134</th><td>Type_1_Diabetes     </td><td>B_RPS26       </td><td>PP.H4.abf</td><td>  4</td><td>0.5270015</td><td> 0</td><td>  0</td><td>  2</td><td>1335</td><td>1332</td><td>1334.2500</td><td> 35</td></tr>\n",
+       "\t<tr><th scope=row>138</th><td>Type_1_Diabetes     </td><td>CD4T_RPS26    </td><td>PP.H4.abf</td><td> 34</td><td>0.6080222</td><td> 0</td><td>  6</td><td> 28</td><td>1341</td><td>1273</td><td>1337.0882</td><td>372</td></tr>\n",
+       "\t<tr><th scope=row>143</th><td>Type_1_Diabetes     </td><td>CD8T_RPS26    </td><td>PP.H4.abf</td><td>  2</td><td>0.5419573</td><td> 0</td><td>  0</td><td>  2</td><td>1341</td><td>1341</td><td>1341.0000</td><td>293</td></tr>\n",
+       "\t<tr><th scope=row>155</th><td>Type_1_Diabetes     </td><td>monocyte_RPS26</td><td>PP.H4.abf</td><td>  9</td><td>0.6295441</td><td> 0</td><td>  2</td><td>  9</td><td>1341</td><td>1334</td><td>1340.2222</td><td>132</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 15 × 12\n",
+       "\\begin{tabular}{r|llllllllllll}\n",
+       "  & trait & egene & parameter & n & mean\\_value & amount\\_greater\\_0.9 & amount\\_greater\\_0.75 & amount\\_greater\\_0.5 & max\\_overlap\\_snps & min\\_overlap\\_snps & mean\\_overlap\\_snps & total\\_n\\\\\n",
+       "  & <chr> & <chr> & <chr> & <int> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
+       "\\hline\n",
+       "\t2 & Asthma               & B\\_RPS26        & PP.H4.abf &  20 & 0.5448488 &  0 &   0 &  16 &  381 &  381 &  381.0000 &  35\\\\\n",
+       "\t8 & Asthma               & CD4T\\_RPS26     & PP.H4.abf & 353 & 0.6166249 &  0 &  28 & 323 &  381 &  368 &  380.7734 & 372\\\\\n",
+       "\t14 & Asthma               & CD8T\\_RPS26     & PP.H4.abf & 241 & 0.6293361 &  0 &  31 & 212 &  381 &  367 &  380.6307 & 293\\\\\n",
+       "\t24 & Asthma               & monocyte\\_RPS26 & PP.H4.abf & 127 & 0.6724475 &  0 &  40 & 114 &  381 &  379 &  380.9685 & 132\\\\\n",
+       "\t28 & Asthma               & NK\\_RPS26       & PP.H4.abf &  84 & 0.6149269 &  0 &   7 &  78 &  379 &  379 &  379.0000 &  96\\\\\n",
+       "\t105 & Rheumatoid Arthritis & B\\_RPS26        & PP.H4.abf &  33 & 0.7385906 &  4 &  19 &  31 &  878 &  876 &  877.9394 &  35\\\\\n",
+       "\t111 & Rheumatoid Arthritis & CD4T\\_RPS26     & PP.H4.abf & 370 & 0.8044940 & 41 & 325 & 368 &  882 &  835 &  881.1541 & 372\\\\\n",
+       "\t118 & Rheumatoid Arthritis & CD8T\\_RPS26     & PP.H4.abf & 289 & 0.8303538 & 64 & 247 & 286 &  882 &  831 &  880.4567 & 293\\\\\n",
+       "\t123 & Rheumatoid Arthritis & DC\\_RPS26       & PP.H4.abf &   3 & 0.6804751 &  0 &   0 &   3 &  855 &  855 &  855.0000 &   3\\\\\n",
+       "\t127 & Rheumatoid Arthritis & monocyte\\_RPS26 & PP.H4.abf & 132 & 0.7954287 & 15 &  98 & 132 &  882 &  879 &  881.9545 & 132\\\\\n",
+       "\t131 & Rheumatoid Arthritis & NK\\_RPS26       & PP.H4.abf &  94 & 0.8435984 & 28 &  88 &  94 &  879 &  879 &  879.0000 &  96\\\\\n",
+       "\t134 & Type\\_1\\_Diabetes      & B\\_RPS26        & PP.H4.abf &   4 & 0.5270015 &  0 &   0 &   2 & 1335 & 1332 & 1334.2500 &  35\\\\\n",
+       "\t138 & Type\\_1\\_Diabetes      & CD4T\\_RPS26     & PP.H4.abf &  34 & 0.6080222 &  0 &   6 &  28 & 1341 & 1273 & 1337.0882 & 372\\\\\n",
+       "\t143 & Type\\_1\\_Diabetes      & CD8T\\_RPS26     & PP.H4.abf &   2 & 0.5419573 &  0 &   0 &   2 & 1341 & 1341 & 1341.0000 & 293\\\\\n",
+       "\t155 & Type\\_1\\_Diabetes      & monocyte\\_RPS26 & PP.H4.abf &   9 & 0.6295441 &  0 &   2 &   9 & 1341 & 1334 & 1340.2222 & 132\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 15 × 12\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | egene &lt;chr&gt; | parameter &lt;chr&gt; | n &lt;int&gt; | mean_value &lt;dbl&gt; | amount_greater_0.9 &lt;int&gt; | amount_greater_0.75 &lt;int&gt; | amount_greater_0.5 &lt;int&gt; | max_overlap_snps &lt;dbl&gt; | min_overlap_snps &lt;dbl&gt; | mean_overlap_snps &lt;dbl&gt; | total_n &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 2 | Asthma               | B_RPS26        | PP.H4.abf |  20 | 0.5448488 |  0 |   0 |  16 |  381 |  381 |  381.0000 |  35 |\n",
+       "| 8 | Asthma               | CD4T_RPS26     | PP.H4.abf | 353 | 0.6166249 |  0 |  28 | 323 |  381 |  368 |  380.7734 | 372 |\n",
+       "| 14 | Asthma               | CD8T_RPS26     | PP.H4.abf | 241 | 0.6293361 |  0 |  31 | 212 |  381 |  367 |  380.6307 | 293 |\n",
+       "| 24 | Asthma               | monocyte_RPS26 | PP.H4.abf | 127 | 0.6724475 |  0 |  40 | 114 |  381 |  379 |  380.9685 | 132 |\n",
+       "| 28 | Asthma               | NK_RPS26       | PP.H4.abf |  84 | 0.6149269 |  0 |   7 |  78 |  379 |  379 |  379.0000 |  96 |\n",
+       "| 105 | Rheumatoid Arthritis | B_RPS26        | PP.H4.abf |  33 | 0.7385906 |  4 |  19 |  31 |  878 |  876 |  877.9394 |  35 |\n",
+       "| 111 | Rheumatoid Arthritis | CD4T_RPS26     | PP.H4.abf | 370 | 0.8044940 | 41 | 325 | 368 |  882 |  835 |  881.1541 | 372 |\n",
+       "| 118 | Rheumatoid Arthritis | CD8T_RPS26     | PP.H4.abf | 289 | 0.8303538 | 64 | 247 | 286 |  882 |  831 |  880.4567 | 293 |\n",
+       "| 123 | Rheumatoid Arthritis | DC_RPS26       | PP.H4.abf |   3 | 0.6804751 |  0 |   0 |   3 |  855 |  855 |  855.0000 |   3 |\n",
+       "| 127 | Rheumatoid Arthritis | monocyte_RPS26 | PP.H4.abf | 132 | 0.7954287 | 15 |  98 | 132 |  882 |  879 |  881.9545 | 132 |\n",
+       "| 131 | Rheumatoid Arthritis | NK_RPS26       | PP.H4.abf |  94 | 0.8435984 | 28 |  88 |  94 |  879 |  879 |  879.0000 |  96 |\n",
+       "| 134 | Type_1_Diabetes      | B_RPS26        | PP.H4.abf |   4 | 0.5270015 |  0 |   0 |   2 | 1335 | 1332 | 1334.2500 |  35 |\n",
+       "| 138 | Type_1_Diabetes      | CD4T_RPS26     | PP.H4.abf |  34 | 0.6080222 |  0 |   6 |  28 | 1341 | 1273 | 1337.0882 | 372 |\n",
+       "| 143 | Type_1_Diabetes      | CD8T_RPS26     | PP.H4.abf |   2 | 0.5419573 |  0 |   0 |   2 | 1341 | 1341 | 1341.0000 | 293 |\n",
+       "| 155 | Type_1_Diabetes      | monocyte_RPS26 | PP.H4.abf |   9 | 0.6295441 |  0 |   2 |   9 | 1341 | 1334 | 1340.2222 | 132 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    trait                egene          parameter n   mean_value\n",
+       "2   Asthma               B_RPS26        PP.H4.abf  20 0.5448488 \n",
+       "8   Asthma               CD4T_RPS26     PP.H4.abf 353 0.6166249 \n",
+       "14  Asthma               CD8T_RPS26     PP.H4.abf 241 0.6293361 \n",
+       "24  Asthma               monocyte_RPS26 PP.H4.abf 127 0.6724475 \n",
+       "28  Asthma               NK_RPS26       PP.H4.abf  84 0.6149269 \n",
+       "105 Rheumatoid Arthritis B_RPS26        PP.H4.abf  33 0.7385906 \n",
+       "111 Rheumatoid Arthritis CD4T_RPS26     PP.H4.abf 370 0.8044940 \n",
+       "118 Rheumatoid Arthritis CD8T_RPS26     PP.H4.abf 289 0.8303538 \n",
+       "123 Rheumatoid Arthritis DC_RPS26       PP.H4.abf   3 0.6804751 \n",
+       "127 Rheumatoid Arthritis monocyte_RPS26 PP.H4.abf 132 0.7954287 \n",
+       "131 Rheumatoid Arthritis NK_RPS26       PP.H4.abf  94 0.8435984 \n",
+       "134 Type_1_Diabetes      B_RPS26        PP.H4.abf   4 0.5270015 \n",
+       "138 Type_1_Diabetes      CD4T_RPS26     PP.H4.abf  34 0.6080222 \n",
+       "143 Type_1_Diabetes      CD8T_RPS26     PP.H4.abf   2 0.5419573 \n",
+       "155 Type_1_Diabetes      monocyte_RPS26 PP.H4.abf   9 0.6295441 \n",
+       "    amount_greater_0.9 amount_greater_0.75 amount_greater_0.5 max_overlap_snps\n",
+       "2    0                   0                  16                 381            \n",
+       "8    0                  28                 323                 381            \n",
+       "14   0                  31                 212                 381            \n",
+       "24   0                  40                 114                 381            \n",
+       "28   0                   7                  78                 379            \n",
+       "105  4                  19                  31                 878            \n",
+       "111 41                 325                 368                 882            \n",
+       "118 64                 247                 286                 882            \n",
+       "123  0                   0                   3                 855            \n",
+       "127 15                  98                 132                 882            \n",
+       "131 28                  88                  94                 879            \n",
+       "134  0                   0                   2                1335            \n",
+       "138  0                   6                  28                1341            \n",
+       "143  0                   0                   2                1341            \n",
+       "155  0                   2                   9                1341            \n",
+       "    min_overlap_snps mean_overlap_snps total_n\n",
+       "2    381              381.0000          35    \n",
+       "8    368              380.7734         372    \n",
+       "14   367              380.6307         293    \n",
+       "24   379              380.9685         132    \n",
+       "28   379              379.0000          96    \n",
+       "105  876              877.9394          35    \n",
+       "111  835              881.1541         372    \n",
+       "118  831              880.4567         293    \n",
+       "123  855              855.0000           3    \n",
+       "127  879              881.9545         132    \n",
+       "131  879              879.0000          96    \n",
+       "134 1332             1334.2500          35    \n",
+       "138 1273             1337.0882         372    \n",
+       "143 1341             1341.0000         293    \n",
+       "155 1334             1340.2222         132    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "overview_h_amounts[(overview_h_amounts$egene %in%  c('CD4T_RPS26', 'CD8T_RPS26', 'monocyte_RPS26', 'DC_RPS26', 'NK_RPS26','B_RPS26')) & (overview_h_amounts$parameter %in% c('PP.H4.abf')),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "8892fdf4-cb58-4887-847f-cc1aa744ae37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 7 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>egene</th><th scope=col>parameter</th><th scope=col>n</th><th scope=col>mean_value</th><th scope=col>amount_greater_0.9</th><th scope=col>amount_greater_0.75</th><th scope=col>amount_greater_0.5</th><th scope=col>max_overlap_snps</th><th scope=col>min_overlap_snps</th><th scope=col>mean_overlap_snps</th><th scope=col>total_n</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>110</th><td>Rheumatoid Arthritis</td><td>CD4T_RPS26    </td><td>PP.H3.abf</td><td>  2</td><td>0.6476383</td><td>  0</td><td>  0</td><td>  2</td><td> 882</td><td> 882</td><td> 882.000</td><td>372</td></tr>\n",
+       "\t<tr><th scope=row>133</th><td>Type_1_Diabetes     </td><td>B_RPS26       </td><td>PP.H3.abf</td><td> 24</td><td>0.7354662</td><td>  7</td><td> 12</td><td> 20</td><td>1335</td><td>1335</td><td>1335.000</td><td> 35</td></tr>\n",
+       "\t<tr><th scope=row>139</th><td>Type_1_Diabetes     </td><td>CD4T_RPS26    </td><td>PP.H3.abf</td><td>336</td><td>0.9025018</td><td>234</td><td>282</td><td>329</td><td>1341</td><td>1274</td><td>1339.973</td><td>372</td></tr>\n",
+       "\t<tr><th scope=row>144</th><td>Type_1_Diabetes     </td><td>CD8T_RPS26    </td><td>PP.H3.abf</td><td>266</td><td>0.9176685</td><td>204</td><td>223</td><td>258</td><td>1341</td><td>1268</td><td>1339.011</td><td>293</td></tr>\n",
+       "\t<tr><th scope=row>150</th><td>Type_1_Diabetes     </td><td>DC_RPS26      </td><td>PP.H3.abf</td><td>  1</td><td>0.4908023</td><td>  0</td><td>  0</td><td>  0</td><td>1297</td><td>1297</td><td>1297.000</td><td>  3</td></tr>\n",
+       "\t<tr><th scope=row>154</th><td>Type_1_Diabetes     </td><td>monocyte_RPS26</td><td>PP.H3.abf</td><td>122</td><td>0.9210668</td><td> 92</td><td>109</td><td>119</td><td>1341</td><td>1334</td><td>1340.934</td><td>132</td></tr>\n",
+       "\t<tr><th scope=row>159</th><td>Type_1_Diabetes     </td><td>NK_RPS26      </td><td>PP.H3.abf</td><td> 91</td><td>0.9621223</td><td> 81</td><td> 87</td><td> 90</td><td>1334</td><td>1334</td><td>1334.000</td><td> 96</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 7 × 12\n",
+       "\\begin{tabular}{r|llllllllllll}\n",
+       "  & trait & egene & parameter & n & mean\\_value & amount\\_greater\\_0.9 & amount\\_greater\\_0.75 & amount\\_greater\\_0.5 & max\\_overlap\\_snps & min\\_overlap\\_snps & mean\\_overlap\\_snps & total\\_n\\\\\n",
+       "  & <chr> & <chr> & <chr> & <int> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
+       "\\hline\n",
+       "\t110 & Rheumatoid Arthritis & CD4T\\_RPS26     & PP.H3.abf &   2 & 0.6476383 &   0 &   0 &   2 &  882 &  882 &  882.000 & 372\\\\\n",
+       "\t133 & Type\\_1\\_Diabetes      & B\\_RPS26        & PP.H3.abf &  24 & 0.7354662 &   7 &  12 &  20 & 1335 & 1335 & 1335.000 &  35\\\\\n",
+       "\t139 & Type\\_1\\_Diabetes      & CD4T\\_RPS26     & PP.H3.abf & 336 & 0.9025018 & 234 & 282 & 329 & 1341 & 1274 & 1339.973 & 372\\\\\n",
+       "\t144 & Type\\_1\\_Diabetes      & CD8T\\_RPS26     & PP.H3.abf & 266 & 0.9176685 & 204 & 223 & 258 & 1341 & 1268 & 1339.011 & 293\\\\\n",
+       "\t150 & Type\\_1\\_Diabetes      & DC\\_RPS26       & PP.H3.abf &   1 & 0.4908023 &   0 &   0 &   0 & 1297 & 1297 & 1297.000 &   3\\\\\n",
+       "\t154 & Type\\_1\\_Diabetes      & monocyte\\_RPS26 & PP.H3.abf & 122 & 0.9210668 &  92 & 109 & 119 & 1341 & 1334 & 1340.934 & 132\\\\\n",
+       "\t159 & Type\\_1\\_Diabetes      & NK\\_RPS26       & PP.H3.abf &  91 & 0.9621223 &  81 &  87 &  90 & 1334 & 1334 & 1334.000 &  96\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 7 × 12\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | egene &lt;chr&gt; | parameter &lt;chr&gt; | n &lt;int&gt; | mean_value &lt;dbl&gt; | amount_greater_0.9 &lt;int&gt; | amount_greater_0.75 &lt;int&gt; | amount_greater_0.5 &lt;int&gt; | max_overlap_snps &lt;dbl&gt; | min_overlap_snps &lt;dbl&gt; | mean_overlap_snps &lt;dbl&gt; | total_n &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 110 | Rheumatoid Arthritis | CD4T_RPS26     | PP.H3.abf |   2 | 0.6476383 |   0 |   0 |   2 |  882 |  882 |  882.000 | 372 |\n",
+       "| 133 | Type_1_Diabetes      | B_RPS26        | PP.H3.abf |  24 | 0.7354662 |   7 |  12 |  20 | 1335 | 1335 | 1335.000 |  35 |\n",
+       "| 139 | Type_1_Diabetes      | CD4T_RPS26     | PP.H3.abf | 336 | 0.9025018 | 234 | 282 | 329 | 1341 | 1274 | 1339.973 | 372 |\n",
+       "| 144 | Type_1_Diabetes      | CD8T_RPS26     | PP.H3.abf | 266 | 0.9176685 | 204 | 223 | 258 | 1341 | 1268 | 1339.011 | 293 |\n",
+       "| 150 | Type_1_Diabetes      | DC_RPS26       | PP.H3.abf |   1 | 0.4908023 |   0 |   0 |   0 | 1297 | 1297 | 1297.000 |   3 |\n",
+       "| 154 | Type_1_Diabetes      | monocyte_RPS26 | PP.H3.abf | 122 | 0.9210668 |  92 | 109 | 119 | 1341 | 1334 | 1340.934 | 132 |\n",
+       "| 159 | Type_1_Diabetes      | NK_RPS26       | PP.H3.abf |  91 | 0.9621223 |  81 |  87 |  90 | 1334 | 1334 | 1334.000 |  96 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    trait                egene          parameter n   mean_value\n",
+       "110 Rheumatoid Arthritis CD4T_RPS26     PP.H3.abf   2 0.6476383 \n",
+       "133 Type_1_Diabetes      B_RPS26        PP.H3.abf  24 0.7354662 \n",
+       "139 Type_1_Diabetes      CD4T_RPS26     PP.H3.abf 336 0.9025018 \n",
+       "144 Type_1_Diabetes      CD8T_RPS26     PP.H3.abf 266 0.9176685 \n",
+       "150 Type_1_Diabetes      DC_RPS26       PP.H3.abf   1 0.4908023 \n",
+       "154 Type_1_Diabetes      monocyte_RPS26 PP.H3.abf 122 0.9210668 \n",
+       "159 Type_1_Diabetes      NK_RPS26       PP.H3.abf  91 0.9621223 \n",
+       "    amount_greater_0.9 amount_greater_0.75 amount_greater_0.5 max_overlap_snps\n",
+       "110   0                  0                   2                 882            \n",
+       "133   7                 12                  20                1335            \n",
+       "139 234                282                 329                1341            \n",
+       "144 204                223                 258                1341            \n",
+       "150   0                  0                   0                1297            \n",
+       "154  92                109                 119                1341            \n",
+       "159  81                 87                  90                1334            \n",
+       "    min_overlap_snps mean_overlap_snps total_n\n",
+       "110  882              882.000          372    \n",
+       "133 1335             1335.000           35    \n",
+       "139 1274             1339.973          372    \n",
+       "144 1268             1339.011          293    \n",
+       "150 1297             1297.000            3    \n",
+       "154 1334             1340.934          132    \n",
+       "159 1334             1334.000           96    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "overview_h_amounts[(overview_h_amounts$egene %in%  c('CD4T_RPS26', 'CD8T_RPS26', 'monocyte_RPS26', 'DC_RPS26', 'NK_RPS26','B_RPS26')) & (overview_h_amounts$parameter %in% c( 'PP.H3.abf')),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "c484189b-0860-452d-a2c8-0b3fd0e92493",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### All HLA-DQA2  - co-egene examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "1b9cd94a-04fd-4e2d-abb8-9bb3fffcc2f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#unique(overview_h_amounts$egene )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "efef7428-1374-453c-a65d-9c68313bde68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 24 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>egene</th><th scope=col>parameter</th><th scope=col>n</th><th scope=col>mean_value</th><th scope=col>amount_greater_0.9</th><th scope=col>amount_greater_0.75</th><th scope=col>amount_greater_0.5</th><th scope=col>max_overlap_snps</th><th scope=col>min_overlap_snps</th><th scope=col>mean_overlap_snps</th><th scope=col>total_n</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3</th><td>Asthma                    </td><td>CD4T_HLA-DQA2    </td><td>PP.H4.abf</td><td>13</td><td>0.8605121</td><td> 5</td><td>11</td><td>13</td><td> 387</td><td> 387</td><td> 387.0000</td><td>16</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td>Asthma                    </td><td>CD8T_HLA-DQA2    </td><td>PP.H4.abf</td><td> 7</td><td>0.8719882</td><td> 5</td><td> 5</td><td> 7</td><td> 386</td><td> 386</td><td> 386.0000</td><td> 7</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td>Asthma                    </td><td>DC_HLA-DQA2      </td><td>PP.H4.abf</td><td> 5</td><td>0.6734167</td><td> 0</td><td> 1</td><td> 5</td><td> 358</td><td> 348</td><td> 351.8000</td><td>13</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>Asthma                    </td><td>monocyte_HLA-DQA2</td><td>PP.H4.abf</td><td> 4</td><td>0.7193509</td><td> 1</td><td> 2</td><td> 4</td><td> 388</td><td> 388</td><td> 388.0000</td><td>17</td></tr>\n",
+       "\t<tr><th scope=row>32</th><td>Crohn's Disease           </td><td>CD4T_HLA-DQA2    </td><td>PP.H3.abf</td><td>16</td><td>0.8674851</td><td> 8</td><td>14</td><td>16</td><td>4525</td><td>4525</td><td>4525.0000</td><td>16</td></tr>\n",
+       "\t<tr><th scope=row>39</th><td>Crohn's Disease           </td><td>CD8T_HLA-DQA2    </td><td>PP.H3.abf</td><td> 7</td><td>0.8685034</td><td> 2</td><td> 6</td><td> 7</td><td>4491</td><td>4383</td><td>4475.5714</td><td> 7</td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>Crohn's Disease           </td><td>DC_HLA-DQA2      </td><td>PP.H3.abf</td><td> 9</td><td>0.6909695</td><td> 0</td><td> 3</td><td> 9</td><td>3930</td><td>3758</td><td>3810.6667</td><td>13</td></tr>\n",
+       "\t<tr><th scope=row>47</th><td>Crohn's Disease           </td><td>monocyte_HLA-DQA2</td><td>PP.H3.abf</td><td>17</td><td>0.9558094</td><td>14</td><td>17</td><td>17</td><td>4436</td><td>4331</td><td>4363.8235</td><td>17</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>Inflammatory Bowel Disease</td><td>CD4T_HLA-DQA2    </td><td>PP.H3.abf</td><td>16</td><td>0.7763020</td><td> 1</td><td> 9</td><td>16</td><td>4527</td><td>4527</td><td>4527.0000</td><td>16</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td>Inflammatory Bowel Disease</td><td>CD8T_HLA-DQA2    </td><td>PP.H3.abf</td><td> 7</td><td>0.8173129</td><td> 1</td><td> 5</td><td> 7</td><td>4493</td><td>4385</td><td>4477.5714</td><td> 7</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>Inflammatory Bowel Disease</td><td>DC_HLA-DQA2      </td><td>PP.H3.abf</td><td> 9</td><td>0.6542293</td><td> 0</td><td> 3</td><td> 9</td><td>3932</td><td>3760</td><td>3812.4444</td><td>13</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td>Inflammatory Bowel Disease</td><td>monocyte_HLA-DQA2</td><td>PP.H3.abf</td><td>17</td><td>0.8218316</td><td> 4</td><td>12</td><td>17</td><td>4438</td><td>4333</td><td>4365.8235</td><td>17</td></tr>\n",
+       "\t<tr><th scope=row>82</th><td>Multiple Sclerosis        </td><td>CD4T_HLA-DQA2    </td><td>PP.H4.abf</td><td> 1</td><td>0.9211400</td><td> 1</td><td> 1</td><td> 1</td><td>  50</td><td>  50</td><td>  50.0000</td><td>16</td></tr>\n",
+       "\t<tr><th scope=row>96</th><td>Multiple Sclerosis        </td><td>monocyte_HLA-DQA2</td><td>PP.H4.abf</td><td> 1</td><td>0.7952340</td><td> 0</td><td> 1</td><td> 1</td><td>  50</td><td>  50</td><td>  50.0000</td><td>17</td></tr>\n",
+       "\t<tr><th scope=row>106</th><td>Rheumatoid Arthritis      </td><td>CD4T_HLA-DQA2    </td><td>PP.H4.abf</td><td>10</td><td>0.5554058</td><td> 0</td><td> 0</td><td> 7</td><td> 819</td><td> 819</td><td> 819.0000</td><td>16</td></tr>\n",
+       "\t<tr><th scope=row>108</th><td>Rheumatoid Arthritis      </td><td>CD4T_HLA-DQA2    </td><td>PP.H3.abf</td><td> 5</td><td>0.6100632</td><td> 0</td><td> 1</td><td> 4</td><td> 819</td><td> 819</td><td> 819.0000</td><td>16</td></tr>\n",
+       "\t<tr><th scope=row>114</th><td>Rheumatoid Arthritis      </td><td>CD8T_HLA-DQA2    </td><td>PP.H4.abf</td><td> 1</td><td>0.4211959</td><td> 0</td><td> 0</td><td> 0</td><td> 801</td><td> 801</td><td> 801.0000</td><td> 7</td></tr>\n",
+       "\t<tr><th scope=row>115</th><td>Rheumatoid Arthritis      </td><td>CD8T_HLA-DQA2    </td><td>PP.H3.abf</td><td> 5</td><td>0.5385873</td><td> 0</td><td> 1</td><td> 3</td><td> 817</td><td> 817</td><td> 817.0000</td><td> 7</td></tr>\n",
+       "\t<tr><th scope=row>122</th><td>Rheumatoid Arthritis      </td><td>DC_HLA-DQA2      </td><td>PP.H4.abf</td><td> 1</td><td>0.4800992</td><td> 0</td><td> 0</td><td> 0</td><td> 706</td><td> 706</td><td> 706.0000</td><td>13</td></tr>\n",
+       "\t<tr><th scope=row>125</th><td>Rheumatoid Arthritis      </td><td>monocyte_HLA-DQA2</td><td>PP.H4.abf</td><td>16</td><td>0.6159706</td><td> 0</td><td> 0</td><td>14</td><td> 817</td><td> 815</td><td> 815.8125</td><td>17</td></tr>\n",
+       "\t<tr><th scope=row>135</th><td>Type_1_Diabetes           </td><td>CD4T_HLA-DQA2    </td><td>PP.H3.abf</td><td>16</td><td>0.7585537</td><td> 2</td><td> 9</td><td>16</td><td>1319</td><td>1319</td><td>1319.0000</td><td>16</td></tr>\n",
+       "\t<tr><th scope=row>142</th><td>Type_1_Diabetes           </td><td>CD8T_HLA-DQA2    </td><td>PP.H3.abf</td><td> 7</td><td>0.7726785</td><td> 2</td><td> 4</td><td> 6</td><td>1317</td><td>1282</td><td>1312.0000</td><td> 7</td></tr>\n",
+       "\t<tr><th scope=row>148</th><td>Type_1_Diabetes           </td><td>DC_HLA-DQA2      </td><td>PP.H3.abf</td><td> 3</td><td>0.5865191</td><td> 0</td><td> 0</td><td> 3</td><td>1115</td><td>1115</td><td>1115.0000</td><td>13</td></tr>\n",
+       "\t<tr><th scope=row>152</th><td>Type_1_Diabetes           </td><td>monocyte_HLA-DQA2</td><td>PP.H3.abf</td><td>17</td><td>0.9371937</td><td>13</td><td>16</td><td>17</td><td>1300</td><td>1289</td><td>1292.4706</td><td>17</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 24 × 12\n",
+       "\\begin{tabular}{r|llllllllllll}\n",
+       "  & trait & egene & parameter & n & mean\\_value & amount\\_greater\\_0.9 & amount\\_greater\\_0.75 & amount\\_greater\\_0.5 & max\\_overlap\\_snps & min\\_overlap\\_snps & mean\\_overlap\\_snps & total\\_n\\\\\n",
+       "  & <chr> & <chr> & <chr> & <int> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
+       "\\hline\n",
+       "\t3 & Asthma                     & CD4T\\_HLA-DQA2     & PP.H4.abf & 13 & 0.8605121 &  5 & 11 & 13 &  387 &  387 &  387.0000 & 16\\\\\n",
+       "\t11 & Asthma                     & CD8T\\_HLA-DQA2     & PP.H4.abf &  7 & 0.8719882 &  5 &  5 &  7 &  386 &  386 &  386.0000 &  7\\\\\n",
+       "\t18 & Asthma                     & DC\\_HLA-DQA2       & PP.H4.abf &  5 & 0.6734167 &  0 &  1 &  5 &  358 &  348 &  351.8000 & 13\\\\\n",
+       "\t20 & Asthma                     & monocyte\\_HLA-DQA2 & PP.H4.abf &  4 & 0.7193509 &  1 &  2 &  4 &  388 &  388 &  388.0000 & 17\\\\\n",
+       "\t32 & Crohn's Disease            & CD4T\\_HLA-DQA2     & PP.H3.abf & 16 & 0.8674851 &  8 & 14 & 16 & 4525 & 4525 & 4525.0000 & 16\\\\\n",
+       "\t39 & Crohn's Disease            & CD8T\\_HLA-DQA2     & PP.H3.abf &  7 & 0.8685034 &  2 &  6 &  7 & 4491 & 4383 & 4475.5714 &  7\\\\\n",
+       "\t45 & Crohn's Disease            & DC\\_HLA-DQA2       & PP.H3.abf &  9 & 0.6909695 &  0 &  3 &  9 & 3930 & 3758 & 3810.6667 & 13\\\\\n",
+       "\t47 & Crohn's Disease            & monocyte\\_HLA-DQA2 & PP.H3.abf & 17 & 0.9558094 & 14 & 17 & 17 & 4436 & 4331 & 4363.8235 & 17\\\\\n",
+       "\t57 & Inflammatory Bowel Disease & CD4T\\_HLA-DQA2     & PP.H3.abf & 16 & 0.7763020 &  1 &  9 & 16 & 4527 & 4527 & 4527.0000 & 16\\\\\n",
+       "\t63 & Inflammatory Bowel Disease & CD8T\\_HLA-DQA2     & PP.H3.abf &  7 & 0.8173129 &  1 &  5 &  7 & 4493 & 4385 & 4477.5714 &  7\\\\\n",
+       "\t69 & Inflammatory Bowel Disease & DC\\_HLA-DQA2       & PP.H3.abf &  9 & 0.6542293 &  0 &  3 &  9 & 3932 & 3760 & 3812.4444 & 13\\\\\n",
+       "\t71 & Inflammatory Bowel Disease & monocyte\\_HLA-DQA2 & PP.H3.abf & 17 & 0.8218316 &  4 & 12 & 17 & 4438 & 4333 & 4365.8235 & 17\\\\\n",
+       "\t82 & Multiple Sclerosis         & CD4T\\_HLA-DQA2     & PP.H4.abf &  1 & 0.9211400 &  1 &  1 &  1 &   50 &   50 &   50.0000 & 16\\\\\n",
+       "\t96 & Multiple Sclerosis         & monocyte\\_HLA-DQA2 & PP.H4.abf &  1 & 0.7952340 &  0 &  1 &  1 &   50 &   50 &   50.0000 & 17\\\\\n",
+       "\t106 & Rheumatoid Arthritis       & CD4T\\_HLA-DQA2     & PP.H4.abf & 10 & 0.5554058 &  0 &  0 &  7 &  819 &  819 &  819.0000 & 16\\\\\n",
+       "\t108 & Rheumatoid Arthritis       & CD4T\\_HLA-DQA2     & PP.H3.abf &  5 & 0.6100632 &  0 &  1 &  4 &  819 &  819 &  819.0000 & 16\\\\\n",
+       "\t114 & Rheumatoid Arthritis       & CD8T\\_HLA-DQA2     & PP.H4.abf &  1 & 0.4211959 &  0 &  0 &  0 &  801 &  801 &  801.0000 &  7\\\\\n",
+       "\t115 & Rheumatoid Arthritis       & CD8T\\_HLA-DQA2     & PP.H3.abf &  5 & 0.5385873 &  0 &  1 &  3 &  817 &  817 &  817.0000 &  7\\\\\n",
+       "\t122 & Rheumatoid Arthritis       & DC\\_HLA-DQA2       & PP.H4.abf &  1 & 0.4800992 &  0 &  0 &  0 &  706 &  706 &  706.0000 & 13\\\\\n",
+       "\t125 & Rheumatoid Arthritis       & monocyte\\_HLA-DQA2 & PP.H4.abf & 16 & 0.6159706 &  0 &  0 & 14 &  817 &  815 &  815.8125 & 17\\\\\n",
+       "\t135 & Type\\_1\\_Diabetes            & CD4T\\_HLA-DQA2     & PP.H3.abf & 16 & 0.7585537 &  2 &  9 & 16 & 1319 & 1319 & 1319.0000 & 16\\\\\n",
+       "\t142 & Type\\_1\\_Diabetes            & CD8T\\_HLA-DQA2     & PP.H3.abf &  7 & 0.7726785 &  2 &  4 &  6 & 1317 & 1282 & 1312.0000 &  7\\\\\n",
+       "\t148 & Type\\_1\\_Diabetes            & DC\\_HLA-DQA2       & PP.H3.abf &  3 & 0.5865191 &  0 &  0 &  3 & 1115 & 1115 & 1115.0000 & 13\\\\\n",
+       "\t152 & Type\\_1\\_Diabetes            & monocyte\\_HLA-DQA2 & PP.H3.abf & 17 & 0.9371937 & 13 & 16 & 17 & 1300 & 1289 & 1292.4706 & 17\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 24 × 12\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | egene &lt;chr&gt; | parameter &lt;chr&gt; | n &lt;int&gt; | mean_value &lt;dbl&gt; | amount_greater_0.9 &lt;int&gt; | amount_greater_0.75 &lt;int&gt; | amount_greater_0.5 &lt;int&gt; | max_overlap_snps &lt;dbl&gt; | min_overlap_snps &lt;dbl&gt; | mean_overlap_snps &lt;dbl&gt; | total_n &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3 | Asthma                     | CD4T_HLA-DQA2     | PP.H4.abf | 13 | 0.8605121 |  5 | 11 | 13 |  387 |  387 |  387.0000 | 16 |\n",
+       "| 11 | Asthma                     | CD8T_HLA-DQA2     | PP.H4.abf |  7 | 0.8719882 |  5 |  5 |  7 |  386 |  386 |  386.0000 |  7 |\n",
+       "| 18 | Asthma                     | DC_HLA-DQA2       | PP.H4.abf |  5 | 0.6734167 |  0 |  1 |  5 |  358 |  348 |  351.8000 | 13 |\n",
+       "| 20 | Asthma                     | monocyte_HLA-DQA2 | PP.H4.abf |  4 | 0.7193509 |  1 |  2 |  4 |  388 |  388 |  388.0000 | 17 |\n",
+       "| 32 | Crohn's Disease            | CD4T_HLA-DQA2     | PP.H3.abf | 16 | 0.8674851 |  8 | 14 | 16 | 4525 | 4525 | 4525.0000 | 16 |\n",
+       "| 39 | Crohn's Disease            | CD8T_HLA-DQA2     | PP.H3.abf |  7 | 0.8685034 |  2 |  6 |  7 | 4491 | 4383 | 4475.5714 |  7 |\n",
+       "| 45 | Crohn's Disease            | DC_HLA-DQA2       | PP.H3.abf |  9 | 0.6909695 |  0 |  3 |  9 | 3930 | 3758 | 3810.6667 | 13 |\n",
+       "| 47 | Crohn's Disease            | monocyte_HLA-DQA2 | PP.H3.abf | 17 | 0.9558094 | 14 | 17 | 17 | 4436 | 4331 | 4363.8235 | 17 |\n",
+       "| 57 | Inflammatory Bowel Disease | CD4T_HLA-DQA2     | PP.H3.abf | 16 | 0.7763020 |  1 |  9 | 16 | 4527 | 4527 | 4527.0000 | 16 |\n",
+       "| 63 | Inflammatory Bowel Disease | CD8T_HLA-DQA2     | PP.H3.abf |  7 | 0.8173129 |  1 |  5 |  7 | 4493 | 4385 | 4477.5714 |  7 |\n",
+       "| 69 | Inflammatory Bowel Disease | DC_HLA-DQA2       | PP.H3.abf |  9 | 0.6542293 |  0 |  3 |  9 | 3932 | 3760 | 3812.4444 | 13 |\n",
+       "| 71 | Inflammatory Bowel Disease | monocyte_HLA-DQA2 | PP.H3.abf | 17 | 0.8218316 |  4 | 12 | 17 | 4438 | 4333 | 4365.8235 | 17 |\n",
+       "| 82 | Multiple Sclerosis         | CD4T_HLA-DQA2     | PP.H4.abf |  1 | 0.9211400 |  1 |  1 |  1 |   50 |   50 |   50.0000 | 16 |\n",
+       "| 96 | Multiple Sclerosis         | monocyte_HLA-DQA2 | PP.H4.abf |  1 | 0.7952340 |  0 |  1 |  1 |   50 |   50 |   50.0000 | 17 |\n",
+       "| 106 | Rheumatoid Arthritis       | CD4T_HLA-DQA2     | PP.H4.abf | 10 | 0.5554058 |  0 |  0 |  7 |  819 |  819 |  819.0000 | 16 |\n",
+       "| 108 | Rheumatoid Arthritis       | CD4T_HLA-DQA2     | PP.H3.abf |  5 | 0.6100632 |  0 |  1 |  4 |  819 |  819 |  819.0000 | 16 |\n",
+       "| 114 | Rheumatoid Arthritis       | CD8T_HLA-DQA2     | PP.H4.abf |  1 | 0.4211959 |  0 |  0 |  0 |  801 |  801 |  801.0000 |  7 |\n",
+       "| 115 | Rheumatoid Arthritis       | CD8T_HLA-DQA2     | PP.H3.abf |  5 | 0.5385873 |  0 |  1 |  3 |  817 |  817 |  817.0000 |  7 |\n",
+       "| 122 | Rheumatoid Arthritis       | DC_HLA-DQA2       | PP.H4.abf |  1 | 0.4800992 |  0 |  0 |  0 |  706 |  706 |  706.0000 | 13 |\n",
+       "| 125 | Rheumatoid Arthritis       | monocyte_HLA-DQA2 | PP.H4.abf | 16 | 0.6159706 |  0 |  0 | 14 |  817 |  815 |  815.8125 | 17 |\n",
+       "| 135 | Type_1_Diabetes            | CD4T_HLA-DQA2     | PP.H3.abf | 16 | 0.7585537 |  2 |  9 | 16 | 1319 | 1319 | 1319.0000 | 16 |\n",
+       "| 142 | Type_1_Diabetes            | CD8T_HLA-DQA2     | PP.H3.abf |  7 | 0.7726785 |  2 |  4 |  6 | 1317 | 1282 | 1312.0000 |  7 |\n",
+       "| 148 | Type_1_Diabetes            | DC_HLA-DQA2       | PP.H3.abf |  3 | 0.5865191 |  0 |  0 |  3 | 1115 | 1115 | 1115.0000 | 13 |\n",
+       "| 152 | Type_1_Diabetes            | monocyte_HLA-DQA2 | PP.H3.abf | 17 | 0.9371937 | 13 | 16 | 17 | 1300 | 1289 | 1292.4706 | 17 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    trait                      egene             parameter n  mean_value\n",
+       "3   Asthma                     CD4T_HLA-DQA2     PP.H4.abf 13 0.8605121 \n",
+       "11  Asthma                     CD8T_HLA-DQA2     PP.H4.abf  7 0.8719882 \n",
+       "18  Asthma                     DC_HLA-DQA2       PP.H4.abf  5 0.6734167 \n",
+       "20  Asthma                     monocyte_HLA-DQA2 PP.H4.abf  4 0.7193509 \n",
+       "32  Crohn's Disease            CD4T_HLA-DQA2     PP.H3.abf 16 0.8674851 \n",
+       "39  Crohn's Disease            CD8T_HLA-DQA2     PP.H3.abf  7 0.8685034 \n",
+       "45  Crohn's Disease            DC_HLA-DQA2       PP.H3.abf  9 0.6909695 \n",
+       "47  Crohn's Disease            monocyte_HLA-DQA2 PP.H3.abf 17 0.9558094 \n",
+       "57  Inflammatory Bowel Disease CD4T_HLA-DQA2     PP.H3.abf 16 0.7763020 \n",
+       "63  Inflammatory Bowel Disease CD8T_HLA-DQA2     PP.H3.abf  7 0.8173129 \n",
+       "69  Inflammatory Bowel Disease DC_HLA-DQA2       PP.H3.abf  9 0.6542293 \n",
+       "71  Inflammatory Bowel Disease monocyte_HLA-DQA2 PP.H3.abf 17 0.8218316 \n",
+       "82  Multiple Sclerosis         CD4T_HLA-DQA2     PP.H4.abf  1 0.9211400 \n",
+       "96  Multiple Sclerosis         monocyte_HLA-DQA2 PP.H4.abf  1 0.7952340 \n",
+       "106 Rheumatoid Arthritis       CD4T_HLA-DQA2     PP.H4.abf 10 0.5554058 \n",
+       "108 Rheumatoid Arthritis       CD4T_HLA-DQA2     PP.H3.abf  5 0.6100632 \n",
+       "114 Rheumatoid Arthritis       CD8T_HLA-DQA2     PP.H4.abf  1 0.4211959 \n",
+       "115 Rheumatoid Arthritis       CD8T_HLA-DQA2     PP.H3.abf  5 0.5385873 \n",
+       "122 Rheumatoid Arthritis       DC_HLA-DQA2       PP.H4.abf  1 0.4800992 \n",
+       "125 Rheumatoid Arthritis       monocyte_HLA-DQA2 PP.H4.abf 16 0.6159706 \n",
+       "135 Type_1_Diabetes            CD4T_HLA-DQA2     PP.H3.abf 16 0.7585537 \n",
+       "142 Type_1_Diabetes            CD8T_HLA-DQA2     PP.H3.abf  7 0.7726785 \n",
+       "148 Type_1_Diabetes            DC_HLA-DQA2       PP.H3.abf  3 0.5865191 \n",
+       "152 Type_1_Diabetes            monocyte_HLA-DQA2 PP.H3.abf 17 0.9371937 \n",
+       "    amount_greater_0.9 amount_greater_0.75 amount_greater_0.5 max_overlap_snps\n",
+       "3    5                 11                  13                  387            \n",
+       "11   5                  5                   7                  386            \n",
+       "18   0                  1                   5                  358            \n",
+       "20   1                  2                   4                  388            \n",
+       "32   8                 14                  16                 4525            \n",
+       "39   2                  6                   7                 4491            \n",
+       "45   0                  3                   9                 3930            \n",
+       "47  14                 17                  17                 4436            \n",
+       "57   1                  9                  16                 4527            \n",
+       "63   1                  5                   7                 4493            \n",
+       "69   0                  3                   9                 3932            \n",
+       "71   4                 12                  17                 4438            \n",
+       "82   1                  1                   1                   50            \n",
+       "96   0                  1                   1                   50            \n",
+       "106  0                  0                   7                  819            \n",
+       "108  0                  1                   4                  819            \n",
+       "114  0                  0                   0                  801            \n",
+       "115  0                  1                   3                  817            \n",
+       "122  0                  0                   0                  706            \n",
+       "125  0                  0                  14                  817            \n",
+       "135  2                  9                  16                 1319            \n",
+       "142  2                  4                   6                 1317            \n",
+       "148  0                  0                   3                 1115            \n",
+       "152 13                 16                  17                 1300            \n",
+       "    min_overlap_snps mean_overlap_snps total_n\n",
+       "3    387              387.0000         16     \n",
+       "11   386              386.0000          7     \n",
+       "18   348              351.8000         13     \n",
+       "20   388              388.0000         17     \n",
+       "32  4525             4525.0000         16     \n",
+       "39  4383             4475.5714          7     \n",
+       "45  3758             3810.6667         13     \n",
+       "47  4331             4363.8235         17     \n",
+       "57  4527             4527.0000         16     \n",
+       "63  4385             4477.5714          7     \n",
+       "69  3760             3812.4444         13     \n",
+       "71  4333             4365.8235         17     \n",
+       "82    50               50.0000         16     \n",
+       "96    50               50.0000         17     \n",
+       "106  819              819.0000         16     \n",
+       "108  819              819.0000         16     \n",
+       "114  801              801.0000          7     \n",
+       "115  817              817.0000          7     \n",
+       "122  706              706.0000         13     \n",
+       "125  815              815.8125         17     \n",
+       "135 1319             1319.0000         16     \n",
+       "142 1282             1312.0000          7     \n",
+       "148 1115             1115.0000         13     \n",
+       "152 1289             1292.4706         17     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "overview_h_amounts[(overview_h_amounts$egene %in% c('CD4T_HLA-DQA2' , 'CD8T_HLA-DQA2', 'monocyte_HLA-DQA2', 'DC_HLA-DQA2'))  & (overview_h_amounts$parameter %in% c('PP.H4.abf', 'PP.H3.abf')),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "553b946a-6572-4f74-873b-fc2638551e85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### All SMDT1  - co-egene examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "e575afe8-2da1-4b46-ac20-6a8fd81b0c49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 0 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>trait</th><th scope=col>egene</th><th scope=col>parameter</th><th scope=col>n</th><th scope=col>mean_value</th><th scope=col>amount_greater_0.9</th><th scope=col>amount_greater_0.75</th><th scope=col>amount_greater_0.5</th><th scope=col>max_overlap_snps</th><th scope=col>min_overlap_snps</th><th scope=col>mean_overlap_snps</th><th scope=col>total_n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 12\n",
+       "\\begin{tabular}{llllllllllll}\n",
+       " trait & egene & parameter & n & mean\\_value & amount\\_greater\\_0.9 & amount\\_greater\\_0.75 & amount\\_greater\\_0.5 & max\\_overlap\\_snps & min\\_overlap\\_snps & mean\\_overlap\\_snps & total\\_n\\\\\n",
+       " <chr> & <chr> & <chr> & <int> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 12\n",
+       "\n",
+       "| trait &lt;chr&gt; | egene &lt;chr&gt; | parameter &lt;chr&gt; | n &lt;int&gt; | mean_value &lt;dbl&gt; | amount_greater_0.9 &lt;int&gt; | amount_greater_0.75 &lt;int&gt; | amount_greater_0.5 &lt;int&gt; | max_overlap_snps &lt;dbl&gt; | min_overlap_snps &lt;dbl&gt; | mean_overlap_snps &lt;dbl&gt; | total_n &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     trait egene parameter n mean_value amount_greater_0.9 amount_greater_0.75\n",
+       "     amount_greater_0.5 max_overlap_snps min_overlap_snps mean_overlap_snps\n",
+       "     total_n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "overview_h_amounts[(overview_h_amounts$egene %in%  c('CD4T_SMDT1', 'CD8T_SMDT1')) & (overview_h_amounts$parameter %in% c('PP.H4.abf', 'PP.H3.abf')),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "3ce6e5c0-f029-4051-b125-af8d6047ccf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### All TMEM176A  - co-egene examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "4f8fac25-b1a9-4daa-80d4-a7a36464bcbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 0 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>trait</th><th scope=col>egene</th><th scope=col>parameter</th><th scope=col>n</th><th scope=col>mean_value</th><th scope=col>amount_greater_0.9</th><th scope=col>amount_greater_0.75</th><th scope=col>amount_greater_0.5</th><th scope=col>max_overlap_snps</th><th scope=col>min_overlap_snps</th><th scope=col>mean_overlap_snps</th><th scope=col>total_n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 12\n",
+       "\\begin{tabular}{llllllllllll}\n",
+       " trait & egene & parameter & n & mean\\_value & amount\\_greater\\_0.9 & amount\\_greater\\_0.75 & amount\\_greater\\_0.5 & max\\_overlap\\_snps & min\\_overlap\\_snps & mean\\_overlap\\_snps & total\\_n\\\\\n",
+       " <chr> & <chr> & <chr> & <int> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 12\n",
+       "\n",
+       "| trait &lt;chr&gt; | egene &lt;chr&gt; | parameter &lt;chr&gt; | n &lt;int&gt; | mean_value &lt;dbl&gt; | amount_greater_0.9 &lt;int&gt; | amount_greater_0.75 &lt;int&gt; | amount_greater_0.5 &lt;int&gt; | max_overlap_snps &lt;dbl&gt; | min_overlap_snps &lt;dbl&gt; | mean_overlap_snps &lt;dbl&gt; | total_n &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     trait egene parameter n mean_value amount_greater_0.9 amount_greater_0.75\n",
+       "     amount_greater_0.5 max_overlap_snps min_overlap_snps mean_overlap_snps\n",
+       "     total_n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "overview_h_amounts[(overview_h_amounts$egene %in%  c('monocyte_TMEM176A'))  & (overview_h_amounts$parameter %in% c('PP.H4.abf', 'PP.H3.abf')),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "0fc7ba1c-6fb4-4def-b2e5-206deaa3a101",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### All RNASET2 - co-egene examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "78c7efed-46e2-4e17-a2f6-970497c695c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 6 × 12</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>egene</th><th scope=col>parameter</th><th scope=col>n</th><th scope=col>mean_value</th><th scope=col>amount_greater_0.9</th><th scope=col>amount_greater_0.75</th><th scope=col>amount_greater_0.5</th><th scope=col>max_overlap_snps</th><th scope=col>min_overlap_snps</th><th scope=col>mean_overlap_snps</th><th scope=col>total_n</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>33</th><td>Crohn's Disease           </td><td>CD4T_RNASET2    </td><td>PP.H3.abf</td><td>2</td><td>0.5193770</td><td>0</td><td>0</td><td>1</td><td>2707</td><td>2707</td><td>2707</td><td>4</td></tr>\n",
+       "\t<tr><th scope=row>34</th><td>Crohn's Disease           </td><td>CD4T_RNASET2    </td><td>PP.H4.abf</td><td>2</td><td>0.5193536</td><td>0</td><td>0</td><td>2</td><td>2707</td><td>2707</td><td>2707</td><td>4</td></tr>\n",
+       "\t<tr><th scope=row>48</th><td>Crohn's Disease           </td><td>monocyte_RNASET2</td><td>PP.H3.abf</td><td>1</td><td>0.8485668</td><td>0</td><td>1</td><td>1</td><td>4390</td><td>4390</td><td>4390</td><td>1</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td>Inflammatory Bowel Disease</td><td>CD4T_RNASET2    </td><td>PP.H3.abf</td><td>4</td><td>0.5968840</td><td>0</td><td>0</td><td>4</td><td>2704</td><td>2704</td><td>2704</td><td>4</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>Inflammatory Bowel Disease</td><td>monocyte_RNASET2</td><td>PP.H3.abf</td><td>1</td><td>0.8485287</td><td>0</td><td>1</td><td>1</td><td>4391</td><td>4391</td><td>4391</td><td>1</td></tr>\n",
+       "\t<tr><th scope=row>109</th><td>Rheumatoid Arthritis      </td><td>CD4T_RNASET2    </td><td>PP.H3.abf</td><td>4</td><td>0.9369664</td><td>3</td><td>4</td><td>4</td><td>2031</td><td>2031</td><td>2031</td><td>4</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 12\n",
+       "\\begin{tabular}{r|llllllllllll}\n",
+       "  & trait & egene & parameter & n & mean\\_value & amount\\_greater\\_0.9 & amount\\_greater\\_0.75 & amount\\_greater\\_0.5 & max\\_overlap\\_snps & min\\_overlap\\_snps & mean\\_overlap\\_snps & total\\_n\\\\\n",
+       "  & <chr> & <chr> & <chr> & <int> & <dbl> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
+       "\\hline\n",
+       "\t33 & Crohn's Disease            & CD4T\\_RNASET2     & PP.H3.abf & 2 & 0.5193770 & 0 & 0 & 1 & 2707 & 2707 & 2707 & 4\\\\\n",
+       "\t34 & Crohn's Disease            & CD4T\\_RNASET2     & PP.H4.abf & 2 & 0.5193536 & 0 & 0 & 2 & 2707 & 2707 & 2707 & 4\\\\\n",
+       "\t48 & Crohn's Disease            & monocyte\\_RNASET2 & PP.H3.abf & 1 & 0.8485668 & 0 & 1 & 1 & 4390 & 4390 & 4390 & 1\\\\\n",
+       "\t58 & Inflammatory Bowel Disease & CD4T\\_RNASET2     & PP.H3.abf & 4 & 0.5968840 & 0 & 0 & 4 & 2704 & 2704 & 2704 & 4\\\\\n",
+       "\t72 & Inflammatory Bowel Disease & monocyte\\_RNASET2 & PP.H3.abf & 1 & 0.8485287 & 0 & 1 & 1 & 4391 & 4391 & 4391 & 1\\\\\n",
+       "\t109 & Rheumatoid Arthritis       & CD4T\\_RNASET2     & PP.H3.abf & 4 & 0.9369664 & 3 & 4 & 4 & 2031 & 2031 & 2031 & 4\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 12\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | egene &lt;chr&gt; | parameter &lt;chr&gt; | n &lt;int&gt; | mean_value &lt;dbl&gt; | amount_greater_0.9 &lt;int&gt; | amount_greater_0.75 &lt;int&gt; | amount_greater_0.5 &lt;int&gt; | max_overlap_snps &lt;dbl&gt; | min_overlap_snps &lt;dbl&gt; | mean_overlap_snps &lt;dbl&gt; | total_n &lt;int&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 33 | Crohn's Disease            | CD4T_RNASET2     | PP.H3.abf | 2 | 0.5193770 | 0 | 0 | 1 | 2707 | 2707 | 2707 | 4 |\n",
+       "| 34 | Crohn's Disease            | CD4T_RNASET2     | PP.H4.abf | 2 | 0.5193536 | 0 | 0 | 2 | 2707 | 2707 | 2707 | 4 |\n",
+       "| 48 | Crohn's Disease            | monocyte_RNASET2 | PP.H3.abf | 1 | 0.8485668 | 0 | 1 | 1 | 4390 | 4390 | 4390 | 1 |\n",
+       "| 58 | Inflammatory Bowel Disease | CD4T_RNASET2     | PP.H3.abf | 4 | 0.5968840 | 0 | 0 | 4 | 2704 | 2704 | 2704 | 4 |\n",
+       "| 72 | Inflammatory Bowel Disease | monocyte_RNASET2 | PP.H3.abf | 1 | 0.8485287 | 0 | 1 | 1 | 4391 | 4391 | 4391 | 1 |\n",
+       "| 109 | Rheumatoid Arthritis       | CD4T_RNASET2     | PP.H3.abf | 4 | 0.9369664 | 3 | 4 | 4 | 2031 | 2031 | 2031 | 4 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    trait                      egene            parameter n mean_value\n",
+       "33  Crohn's Disease            CD4T_RNASET2     PP.H3.abf 2 0.5193770 \n",
+       "34  Crohn's Disease            CD4T_RNASET2     PP.H4.abf 2 0.5193536 \n",
+       "48  Crohn's Disease            monocyte_RNASET2 PP.H3.abf 1 0.8485668 \n",
+       "58  Inflammatory Bowel Disease CD4T_RNASET2     PP.H3.abf 4 0.5968840 \n",
+       "72  Inflammatory Bowel Disease monocyte_RNASET2 PP.H3.abf 1 0.8485287 \n",
+       "109 Rheumatoid Arthritis       CD4T_RNASET2     PP.H3.abf 4 0.9369664 \n",
+       "    amount_greater_0.9 amount_greater_0.75 amount_greater_0.5 max_overlap_snps\n",
+       "33  0                  0                   1                  2707            \n",
+       "34  0                  0                   2                  2707            \n",
+       "48  0                  1                   1                  4390            \n",
+       "58  0                  0                   4                  2704            \n",
+       "72  0                  1                   1                  4391            \n",
+       "109 3                  4                   4                  2031            \n",
+       "    min_overlap_snps mean_overlap_snps total_n\n",
+       "33  2707             2707              4      \n",
+       "34  2707             2707              4      \n",
+       "48  4390             4390              1      \n",
+       "58  2704             2704              4      \n",
+       "72  4391             4391              1      \n",
+       "109 2031             2031              4      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "overview_h_amounts[(overview_h_amounts$egene %in%  c('CD4T_RNASET2', 'monocyte_RNASET2')) & (overview_h_amounts$parameter %in% c('PP.H4.abf', 'PP.H3.abf')),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "fab6e933-d042-4133-af29-44c80e4ce21b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#overview_h_amounts[order(overview_h_amounts$trait),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "e768d157-0704-486a-983f-7f8e56920126",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Save supplementary table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "b7704adf-ab97-4153-9ef8-7fd8a28014c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp = coeqtl_summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "6d779b12-1651-4484-af1d-84d2bc4f9b37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#coeqtl_supp[is.na(coeqtl_supp$co_egene),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "749e9031-ce6b-44db-9d6d-371a0977c721",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 8</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th><th scope=col>parameter</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>Asthma</td><td>B_RPS26___EEF1A1__RPS26</td><td>B_RPS26</td><td>PP.H0.abf</td><td>0.3734713</td><td>B</td><td>EEF1A1__RPS26</td><td>381</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>Asthma</td><td>B_RPS26___EEF1A1__RPS26</td><td>B_RPS26</td><td>PP.H1.abf</td><td>0.1466712</td><td>B</td><td>EEF1A1__RPS26</td><td>381</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 8\n",
+       "\\begin{tabular}{r|llllllll}\n",
+       "  & trait & identifier & egene & parameter & value & cell\\_type & gene & overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & Asthma & B\\_RPS26\\_\\_\\_EEF1A1\\_\\_RPS26 & B\\_RPS26 & PP.H0.abf & 0.3734713 & B & EEF1A1\\_\\_RPS26 & 381\\\\\n",
+       "\t2 & Asthma & B\\_RPS26\\_\\_\\_EEF1A1\\_\\_RPS26 & B\\_RPS26 & PP.H1.abf & 0.1466712 & B & EEF1A1\\_\\_RPS26 & 381\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 8\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; | parameter &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | Asthma | B_RPS26___EEF1A1__RPS26 | B_RPS26 | PP.H0.abf | 0.3734713 | B | EEF1A1__RPS26 | 381 |\n",
+       "| 2 | Asthma | B_RPS26___EEF1A1__RPS26 | B_RPS26 | PP.H1.abf | 0.1466712 | B | EEF1A1__RPS26 | 381 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  trait  identifier              egene   parameter value     cell_type\n",
+       "1 Asthma B_RPS26___EEF1A1__RPS26 B_RPS26 PP.H0.abf 0.3734713 B        \n",
+       "2 Asthma B_RPS26___EEF1A1__RPS26 B_RPS26 PP.H1.abf 0.1466712 B        \n",
+       "  gene          overlapping_snps\n",
+       "1 EEF1A1__RPS26 381             \n",
+       "2 EEF1A1__RPS26 381             "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coeqtl_supp,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "c67011d0-0a18-45d5-8486-39952c05c04b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp = coeqtl_supp[(coeqtl_supp$parameter %in% c('PP.H3.abf','PP.H4.abf' )) ,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "0fea5320-e597-4b9e-a4e0-acff27541522",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp$identifier = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "49fd8e2d-6c83-4406-88fe-52f5cf93e3eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp$egene = str_replace(coeqtl_supp$egene, '.*_', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ca781c3-4e3b-4b58-b8f8-c0b0ebbdc16e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "a8ee389d-0557-425e-81e3-602b7372c41a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp$co_egene = coeqtl_supp$gene\n",
+    "coeqtl_supp$gene = NULL\n",
+    "coeqtl_supp$co_egene = str_replace(coeqtl_supp$co_egene, coeqtl_supp$egene, '')\n",
+    "coeqtl_supp$co_egene = str_replace(coeqtl_supp$co_egene, '__', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "2e94abd9-f085-482c-9cac-0dba5e2778bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp$coloc_hypothesis = coeqtl_supp$parameter\n",
+    "coeqtl_supp$parameter = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "ede70c26-c4a2-431a-a0a2-111d9a9e27dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp$cell_type = str_replace(coeqtl_supp$cell_type, '1MB', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "d25696dd-ab8d-4e66-9f30-e2547e01203e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp$pp_value = coeqtl_supp$value\n",
+    "coeqtl_supp$value = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "49195825-a93d-42f0-8192-2662fb1210c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_supp$amount_overlapping_snps = coeqtl_supp$overlapping_snps \n",
+    "coeqtl_supp$overlapping_snps = NULL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "616b1427-8a33-47e2-9b02-5c47dc6ee1a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 3 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>egene</th><th scope=col>cell_type</th><th scope=col>co_egene</th><th scope=col>coloc_hypothesis</th><th scope=col>pp_value</th><th scope=col>amount_overlapping_snps</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>4</th><td>Asthma</td><td>RPS26</td><td>B</td><td>EEF1A1</td><td>PP.H3.abf</td><td>0.04642325</td><td>381</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>Asthma</td><td>RPS26</td><td>B</td><td>EEF1A1</td><td>PP.H4.abf</td><td>0.30739863</td><td>381</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>Asthma</td><td>RPS26</td><td>B</td><td>RPL10 </td><td>PP.H3.abf</td><td>0.09736711</td><td>381</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 3 × 7\n",
+       "\\begin{tabular}{r|lllllll}\n",
+       "  & trait & egene & cell\\_type & co\\_egene & coloc\\_hypothesis & pp\\_value & amount\\_overlapping\\_snps\\\\\n",
+       "  & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t4 & Asthma & RPS26 & B & EEF1A1 & PP.H3.abf & 0.04642325 & 381\\\\\n",
+       "\t5 & Asthma & RPS26 & B & EEF1A1 & PP.H4.abf & 0.30739863 & 381\\\\\n",
+       "\t9 & Asthma & RPS26 & B & RPL10  & PP.H3.abf & 0.09736711 & 381\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 3 × 7\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | egene &lt;chr&gt; | cell_type &lt;chr&gt; | co_egene &lt;chr&gt; | coloc_hypothesis &lt;chr&gt; | pp_value &lt;dbl&gt; | amount_overlapping_snps &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|\n",
+       "| 4 | Asthma | RPS26 | B | EEF1A1 | PP.H3.abf | 0.04642325 | 381 |\n",
+       "| 5 | Asthma | RPS26 | B | EEF1A1 | PP.H4.abf | 0.30739863 | 381 |\n",
+       "| 9 | Asthma | RPS26 | B | RPL10  | PP.H3.abf | 0.09736711 | 381 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  trait  egene cell_type co_egene coloc_hypothesis pp_value  \n",
+       "4 Asthma RPS26 B         EEF1A1   PP.H3.abf        0.04642325\n",
+       "5 Asthma RPS26 B         EEF1A1   PP.H4.abf        0.30739863\n",
+       "9 Asthma RPS26 B         RPL10    PP.H3.abf        0.09736711\n",
+       "  amount_overlapping_snps\n",
+       "4 381                    \n",
+       "5 381                    \n",
+       "9 381                    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coeqtl_supp,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "773b7f6e-3d4c-474b-8894-b0c903d45cf1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "9dc5dd51-e271-40f0-aa52-ddca64539cd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.table(coeqtl_supp, file = paste0(path, \"/colocalization_results/\", \"Coloc_COEQTL_supp_table.csv\"), append =FALSE, sep = \",\", row.names = FALSE, col.names =TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bb01b2b-7374-40f2-9373-a70e15748a32",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53c973ec-70fa-4c5a-af44-59dcc8f24931",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d9c3785-33d1-41c5-94f7-6780f6004305",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e8f8e2d-26e4-488a-b766-6524f22e52da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc7c75df-5623-4f72-825b-c13e25887317",
+   "metadata": {},
+   "source": [
+    "### Get certain co-eqtl examples (RA - RPS26 H4 coegenes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "730e0957-f42d-4007-aa35-08d3b568cda4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## 41 co-eGenes for Rheumatoid Arthritis with strong colocalization signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "0c725133-2476-4726-8b6a-1628bbe02929",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coeqtl_summary_filtered$gene = str_replace(coeqtl_summary_filtered$gene, '_', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "da16bf8c-95d2-4e10-8c62-21dc3580c04d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "152"
+      ],
+      "text/latex": [
+       "152"
+      ],
+      "text/markdown": [
+       "152"
+      ],
+      "text/plain": [
+       "[1] 152"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(coeqtl_summary_filtered[(coeqtl_summary_filtered$trait == 'Rheumatoid Arthritis' ) & (coeqtl_summary_filtered$value > 0.9 )& (coeqtl_summary_filtered$parameter == 'PP.H4.abf' ),])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "53fb50d0-478c-428b-b011-922383bd7353",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 6 × 8</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th><th scope=col>parameter</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>Asthma</td><td>B_RPS26___EEF1A1__RPS26</td><td>B_RPS26</td><td>0.3734713</td><td>B</td><td>EEF1A1_RPS26</td><td>381</td><td>PP.H0.abf</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>Asthma</td><td>B_RPS26___RPL10__RPS26 </td><td>B_RPS26</td><td>0.6338403</td><td>B</td><td>RPL10_RPS26 </td><td>381</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>Asthma</td><td>B_RPS26___RPL11__RPS26 </td><td>B_RPS26</td><td>0.3473292</td><td>B</td><td>RPL11_RPS26 </td><td>381</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>Asthma</td><td>B_RPS26___RPL13__RPS26 </td><td>B_RPS26</td><td>0.5521870</td><td>B</td><td>RPL13_RPS26 </td><td>381</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>Asthma</td><td>B_RPS26___RPL18__RPS26 </td><td>B_RPS26</td><td>0.5154468</td><td>B</td><td>RPL18_RPS26 </td><td>381</td><td>PP.H0.abf</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>Asthma</td><td>B_RPS26___RPL21__RPS26 </td><td>B_RPS26</td><td>0.5951100</td><td>B</td><td>RPL21_RPS26 </td><td>381</td><td>PP.H4.abf</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 8\n",
+       "\\begin{tabular}{r|llllllll}\n",
+       "  & trait & identifier & egene & value & cell\\_type & gene & overlapping\\_snps & parameter\\\\\n",
+       "  & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 & Asthma & B\\_RPS26\\_\\_\\_EEF1A1\\_\\_RPS26 & B\\_RPS26 & 0.3734713 & B & EEF1A1\\_RPS26 & 381 & PP.H0.abf\\\\\n",
+       "\t2 & Asthma & B\\_RPS26\\_\\_\\_RPL10\\_\\_RPS26  & B\\_RPS26 & 0.6338403 & B & RPL10\\_RPS26  & 381 & PP.H4.abf\\\\\n",
+       "\t3 & Asthma & B\\_RPS26\\_\\_\\_RPL11\\_\\_RPS26  & B\\_RPS26 & 0.3473292 & B & RPL11\\_RPS26  & 381 & PP.H4.abf\\\\\n",
+       "\t4 & Asthma & B\\_RPS26\\_\\_\\_RPL13\\_\\_RPS26  & B\\_RPS26 & 0.5521870 & B & RPL13\\_RPS26  & 381 & PP.H4.abf\\\\\n",
+       "\t5 & Asthma & B\\_RPS26\\_\\_\\_RPL18\\_\\_RPS26  & B\\_RPS26 & 0.5154468 & B & RPL18\\_RPS26  & 381 & PP.H0.abf\\\\\n",
+       "\t6 & Asthma & B\\_RPS26\\_\\_\\_RPL21\\_\\_RPS26  & B\\_RPS26 & 0.5951100 & B & RPL21\\_RPS26  & 381 & PP.H4.abf\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 8\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; | parameter &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | Asthma | B_RPS26___EEF1A1__RPS26 | B_RPS26 | 0.3734713 | B | EEF1A1_RPS26 | 381 | PP.H0.abf |\n",
+       "| 2 | Asthma | B_RPS26___RPL10__RPS26  | B_RPS26 | 0.6338403 | B | RPL10_RPS26  | 381 | PP.H4.abf |\n",
+       "| 3 | Asthma | B_RPS26___RPL11__RPS26  | B_RPS26 | 0.3473292 | B | RPL11_RPS26  | 381 | PP.H4.abf |\n",
+       "| 4 | Asthma | B_RPS26___RPL13__RPS26  | B_RPS26 | 0.5521870 | B | RPL13_RPS26  | 381 | PP.H4.abf |\n",
+       "| 5 | Asthma | B_RPS26___RPL18__RPS26  | B_RPS26 | 0.5154468 | B | RPL18_RPS26  | 381 | PP.H0.abf |\n",
+       "| 6 | Asthma | B_RPS26___RPL21__RPS26  | B_RPS26 | 0.5951100 | B | RPL21_RPS26  | 381 | PP.H4.abf |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  trait  identifier              egene   value     cell_type gene        \n",
+       "1 Asthma B_RPS26___EEF1A1__RPS26 B_RPS26 0.3734713 B         EEF1A1_RPS26\n",
+       "2 Asthma B_RPS26___RPL10__RPS26  B_RPS26 0.6338403 B         RPL10_RPS26 \n",
+       "3 Asthma B_RPS26___RPL11__RPS26  B_RPS26 0.3473292 B         RPL11_RPS26 \n",
+       "4 Asthma B_RPS26___RPL13__RPS26  B_RPS26 0.5521870 B         RPL13_RPS26 \n",
+       "5 Asthma B_RPS26___RPL18__RPS26  B_RPS26 0.5154468 B         RPL18_RPS26 \n",
+       "6 Asthma B_RPS26___RPL21__RPS26  B_RPS26 0.5951100 B         RPL21_RPS26 \n",
+       "  overlapping_snps parameter\n",
+       "1 381              PP.H0.abf\n",
+       "2 381              PP.H4.abf\n",
+       "3 381              PP.H4.abf\n",
+       "4 381              PP.H4.abf\n",
+       "5 381              PP.H0.abf\n",
+       "6 381              PP.H4.abf"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coeqtl_summary_filtered)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "62fe9449-5c40-4497-9544-5b9c08615d4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_examples_rheomatoid_arthritis = coeqtl_summary_filtered[(coeqtl_summary_filtered$trait == 'Rheumatoid Arthritis' ) & (coeqtl_summary_filtered$value > 0.9 )& (coeqtl_summary_filtered$parameter == 'PP.H4.abf' ),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "7cd4a65c-a3a2-4d98-8d09-c0a89072db99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#coloc_examples_rheomatoid_arthritis = coeqtl_summary_filtered[(coeqtl_summary_filtered$trait == 'Rheumatoid Arthritis' ) & (coeqtl_summary_filtered$value > 0.9 )& (coeqtl_summary_filtered$parameter == 'PP.H4.abf' ) & (coeqtl_summary_filtered$egene == 'CD4T_RPS26' ),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "64ca6b9a-3593-4431-9d98-be89635b311f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "analyze = coloc_examples_rheomatoid_arthritis %>% group_by( gene) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "e9a5b7c1-f312-4d0f-9acc-8e237aa16f30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 21 × 8</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th><th scope=col>parameter</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>194</th><td>Asthma                    </td><td>CD4T_RPS26___HLA-DPB1__RPS26    </td><td>CD4T_RPS26    </td><td>0.5988243</td><td>CD4T    </td><td>HLA-DPB1_RPS26</td><td> 381</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>550</th><td>Asthma                    </td><td>CD8T_RPS26___HLA-DPB1__RPS26    </td><td>CD8T_RPS26    </td><td>0.4321920</td><td>CD8T    </td><td>HLA-DPB1_RPS26</td><td> 381</td><td>PP.H0.abf</td></tr>\n",
+       "\t<tr><th scope=row>860</th><td>Asthma                    </td><td>monocyte_RPS26___HLA-DPB1__RPS26</td><td>monocyte_RPS26</td><td>0.6660012</td><td>monocyte</td><td>HLA-DPB1_RPS26</td><td> 381</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>1258</th><td>Crohn's Disease           </td><td>CD4T_RPS26___HLA-DPB1__RPS26    </td><td>CD4T_RPS26    </td><td>0.8220487</td><td>CD4T    </td><td>HLA-DPB1_RPS26</td><td>1112</td><td>PP.H2.abf</td></tr>\n",
+       "\t<tr><th scope=row>1614</th><td>Crohn's Disease           </td><td>CD8T_RPS26___HLA-DPB1__RPS26    </td><td>CD8T_RPS26    </td><td>0.5654940</td><td>CD8T    </td><td>HLA-DPB1_RPS26</td><td>1112</td><td>PP.H0.abf</td></tr>\n",
+       "\t<tr><th scope=row>1924</th><td>Crohn's Disease           </td><td>monocyte_RPS26___HLA-DPB1__RPS26</td><td>monocyte_RPS26</td><td>0.9525225</td><td>monocyte</td><td>HLA-DPB1_RPS26</td><td>1112</td><td>PP.H2.abf</td></tr>\n",
+       "\t<tr><th scope=row>2322</th><td>Inflammatory Bowel Disease</td><td>CD4T_RPS26___HLA-DPB1__RPS26    </td><td>CD4T_RPS26    </td><td>0.8474188</td><td>CD4T    </td><td>HLA-DPB1_RPS26</td><td>1110</td><td>PP.H2.abf</td></tr>\n",
+       "\t<tr><th scope=row>2678</th><td>Inflammatory Bowel Disease</td><td>CD8T_RPS26___HLA-DPB1__RPS26    </td><td>CD8T_RPS26    </td><td>0.5830098</td><td>CD8T    </td><td>HLA-DPB1_RPS26</td><td>1110</td><td>PP.H0.abf</td></tr>\n",
+       "\t<tr><th scope=row>2988</th><td>Inflammatory Bowel Disease</td><td>monocyte_RPS26___HLA-DPB1__RPS26</td><td>monocyte_RPS26</td><td>0.9819645</td><td>monocyte</td><td>HLA-DPB1_RPS26</td><td>1110</td><td>PP.H2.abf</td></tr>\n",
+       "\t<tr><th scope=row>3386</th><td>Multiple Sclerosis        </td><td>CD4T_RPS26___HLA-DPB1__RPS26    </td><td>CD4T_RPS26    </td><td>0.5847966</td><td>CD4T    </td><td>HLA-DPB1_RPS26</td><td>  58</td><td>PP.H2.abf</td></tr>\n",
+       "\t<tr><th scope=row>3742</th><td>Multiple Sclerosis        </td><td>CD8T_RPS26___HLA-DPB1__RPS26    </td><td>CD8T_RPS26    </td><td>0.9170029</td><td>CD8T    </td><td>HLA-DPB1_RPS26</td><td>  58</td><td>PP.H0.abf</td></tr>\n",
+       "\t<tr><th scope=row>4052</th><td>Multiple Sclerosis        </td><td>monocyte_RPS26___HLA-DPB1__RPS26</td><td>monocyte_RPS26</td><td>0.9977439</td><td>monocyte</td><td>HLA-DPB1_RPS26</td><td>  58</td><td>PP.H2.abf</td></tr>\n",
+       "\t<tr><th scope=row>4450</th><td>Rheumatoid Arthritis      </td><td>CD4T_RPS26___HLA-DPB1__RPS26    </td><td>CD4T_RPS26    </td><td>0.9147419</td><td>CD4T    </td><td>HLA-DPB1_RPS26</td><td> 882</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>4806</th><td>Rheumatoid Arthritis      </td><td>CD8T_RPS26___HLA-DPB1__RPS26    </td><td>CD8T_RPS26    </td><td>0.7436868</td><td>CD8T    </td><td>HLA-DPB1_RPS26</td><td> 882</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>5116</th><td>Rheumatoid Arthritis      </td><td>monocyte_RPS26___HLA-DPB1__RPS26</td><td>monocyte_RPS26</td><td>0.7753756</td><td>monocyte</td><td>HLA-DPB1_RPS26</td><td> 882</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>5514</th><td>Type_1_Diabetes           </td><td>CD4T_RPS26___HLA-DPB1__RPS26    </td><td>CD4T_RPS26    </td><td>0.7693859</td><td>CD4T    </td><td>HLA-DPB1_RPS26</td><td>1341</td><td>PP.H3.abf</td></tr>\n",
+       "\t<tr><th scope=row>5870</th><td>Type_1_Diabetes           </td><td>CD8T_RPS26___HLA-DPB1__RPS26    </td><td>CD8T_RPS26    </td><td>0.4314387</td><td>CD8T    </td><td>HLA-DPB1_RPS26</td><td>1341</td><td>PP.H3.abf</td></tr>\n",
+       "\t<tr><th scope=row>6180</th><td>Type_1_Diabetes           </td><td>monocyte_RPS26___HLA-DPB1__RPS26</td><td>monocyte_RPS26</td><td>0.9986931</td><td>monocyte</td><td>HLA-DPB1_RPS26</td><td>1341</td><td>PP.H3.abf</td></tr>\n",
+       "\t<tr><th scope=row>6578</th><td>White blood cell count    </td><td>CD4T_RPS26___HLA-DPB1__RPS26    </td><td>CD4T_RPS26    </td><td>0.8243175</td><td>CD4T    </td><td>HLA-DPB1_RPS26</td><td>1078</td><td>PP.H2.abf</td></tr>\n",
+       "\t<tr><th scope=row>6934</th><td>White blood cell count    </td><td>CD8T_RPS26___HLA-DPB1__RPS26    </td><td>CD8T_RPS26    </td><td>0.5682385</td><td>CD8T    </td><td>HLA-DPB1_RPS26</td><td>1078</td><td>PP.H0.abf</td></tr>\n",
+       "\t<tr><th scope=row>7244</th><td>White blood cell count    </td><td>monocyte_RPS26___HLA-DPB1__RPS26</td><td>monocyte_RPS26</td><td>0.9553423</td><td>monocyte</td><td>HLA-DPB1_RPS26</td><td>1078</td><td>PP.H2.abf</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 21 × 8\n",
+       "\\begin{tabular}{r|llllllll}\n",
+       "  & trait & identifier & egene & value & cell\\_type & gene & overlapping\\_snps & parameter\\\\\n",
+       "  & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t194 & Asthma                     & CD4T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD4T\\_RPS26     & 0.5988243 & CD4T     & HLA-DPB1\\_RPS26 &  381 & PP.H4.abf\\\\\n",
+       "\t550 & Asthma                     & CD8T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD8T\\_RPS26     & 0.4321920 & CD8T     & HLA-DPB1\\_RPS26 &  381 & PP.H0.abf\\\\\n",
+       "\t860 & Asthma                     & monocyte\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26 & monocyte\\_RPS26 & 0.6660012 & monocyte & HLA-DPB1\\_RPS26 &  381 & PP.H4.abf\\\\\n",
+       "\t1258 & Crohn's Disease            & CD4T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD4T\\_RPS26     & 0.8220487 & CD4T     & HLA-DPB1\\_RPS26 & 1112 & PP.H2.abf\\\\\n",
+       "\t1614 & Crohn's Disease            & CD8T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD8T\\_RPS26     & 0.5654940 & CD8T     & HLA-DPB1\\_RPS26 & 1112 & PP.H0.abf\\\\\n",
+       "\t1924 & Crohn's Disease            & monocyte\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26 & monocyte\\_RPS26 & 0.9525225 & monocyte & HLA-DPB1\\_RPS26 & 1112 & PP.H2.abf\\\\\n",
+       "\t2322 & Inflammatory Bowel Disease & CD4T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD4T\\_RPS26     & 0.8474188 & CD4T     & HLA-DPB1\\_RPS26 & 1110 & PP.H2.abf\\\\\n",
+       "\t2678 & Inflammatory Bowel Disease & CD8T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD8T\\_RPS26     & 0.5830098 & CD8T     & HLA-DPB1\\_RPS26 & 1110 & PP.H0.abf\\\\\n",
+       "\t2988 & Inflammatory Bowel Disease & monocyte\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26 & monocyte\\_RPS26 & 0.9819645 & monocyte & HLA-DPB1\\_RPS26 & 1110 & PP.H2.abf\\\\\n",
+       "\t3386 & Multiple Sclerosis         & CD4T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD4T\\_RPS26     & 0.5847966 & CD4T     & HLA-DPB1\\_RPS26 &   58 & PP.H2.abf\\\\\n",
+       "\t3742 & Multiple Sclerosis         & CD8T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD8T\\_RPS26     & 0.9170029 & CD8T     & HLA-DPB1\\_RPS26 &   58 & PP.H0.abf\\\\\n",
+       "\t4052 & Multiple Sclerosis         & monocyte\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26 & monocyte\\_RPS26 & 0.9977439 & monocyte & HLA-DPB1\\_RPS26 &   58 & PP.H2.abf\\\\\n",
+       "\t4450 & Rheumatoid Arthritis       & CD4T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD4T\\_RPS26     & 0.9147419 & CD4T     & HLA-DPB1\\_RPS26 &  882 & PP.H4.abf\\\\\n",
+       "\t4806 & Rheumatoid Arthritis       & CD8T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD8T\\_RPS26     & 0.7436868 & CD8T     & HLA-DPB1\\_RPS26 &  882 & PP.H4.abf\\\\\n",
+       "\t5116 & Rheumatoid Arthritis       & monocyte\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26 & monocyte\\_RPS26 & 0.7753756 & monocyte & HLA-DPB1\\_RPS26 &  882 & PP.H4.abf\\\\\n",
+       "\t5514 & Type\\_1\\_Diabetes            & CD4T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD4T\\_RPS26     & 0.7693859 & CD4T     & HLA-DPB1\\_RPS26 & 1341 & PP.H3.abf\\\\\n",
+       "\t5870 & Type\\_1\\_Diabetes            & CD8T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD8T\\_RPS26     & 0.4314387 & CD8T     & HLA-DPB1\\_RPS26 & 1341 & PP.H3.abf\\\\\n",
+       "\t6180 & Type\\_1\\_Diabetes            & monocyte\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26 & monocyte\\_RPS26 & 0.9986931 & monocyte & HLA-DPB1\\_RPS26 & 1341 & PP.H3.abf\\\\\n",
+       "\t6578 & White blood cell count     & CD4T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD4T\\_RPS26     & 0.8243175 & CD4T     & HLA-DPB1\\_RPS26 & 1078 & PP.H2.abf\\\\\n",
+       "\t6934 & White blood cell count     & CD8T\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26     & CD8T\\_RPS26     & 0.5682385 & CD8T     & HLA-DPB1\\_RPS26 & 1078 & PP.H0.abf\\\\\n",
+       "\t7244 & White blood cell count     & monocyte\\_RPS26\\_\\_\\_HLA-DPB1\\_\\_RPS26 & monocyte\\_RPS26 & 0.9553423 & monocyte & HLA-DPB1\\_RPS26 & 1078 & PP.H2.abf\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 21 × 8\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; | parameter &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 194 | Asthma                     | CD4T_RPS26___HLA-DPB1__RPS26     | CD4T_RPS26     | 0.5988243 | CD4T     | HLA-DPB1_RPS26 |  381 | PP.H4.abf |\n",
+       "| 550 | Asthma                     | CD8T_RPS26___HLA-DPB1__RPS26     | CD8T_RPS26     | 0.4321920 | CD8T     | HLA-DPB1_RPS26 |  381 | PP.H0.abf |\n",
+       "| 860 | Asthma                     | monocyte_RPS26___HLA-DPB1__RPS26 | monocyte_RPS26 | 0.6660012 | monocyte | HLA-DPB1_RPS26 |  381 | PP.H4.abf |\n",
+       "| 1258 | Crohn's Disease            | CD4T_RPS26___HLA-DPB1__RPS26     | CD4T_RPS26     | 0.8220487 | CD4T     | HLA-DPB1_RPS26 | 1112 | PP.H2.abf |\n",
+       "| 1614 | Crohn's Disease            | CD8T_RPS26___HLA-DPB1__RPS26     | CD8T_RPS26     | 0.5654940 | CD8T     | HLA-DPB1_RPS26 | 1112 | PP.H0.abf |\n",
+       "| 1924 | Crohn's Disease            | monocyte_RPS26___HLA-DPB1__RPS26 | monocyte_RPS26 | 0.9525225 | monocyte | HLA-DPB1_RPS26 | 1112 | PP.H2.abf |\n",
+       "| 2322 | Inflammatory Bowel Disease | CD4T_RPS26___HLA-DPB1__RPS26     | CD4T_RPS26     | 0.8474188 | CD4T     | HLA-DPB1_RPS26 | 1110 | PP.H2.abf |\n",
+       "| 2678 | Inflammatory Bowel Disease | CD8T_RPS26___HLA-DPB1__RPS26     | CD8T_RPS26     | 0.5830098 | CD8T     | HLA-DPB1_RPS26 | 1110 | PP.H0.abf |\n",
+       "| 2988 | Inflammatory Bowel Disease | monocyte_RPS26___HLA-DPB1__RPS26 | monocyte_RPS26 | 0.9819645 | monocyte | HLA-DPB1_RPS26 | 1110 | PP.H2.abf |\n",
+       "| 3386 | Multiple Sclerosis         | CD4T_RPS26___HLA-DPB1__RPS26     | CD4T_RPS26     | 0.5847966 | CD4T     | HLA-DPB1_RPS26 |   58 | PP.H2.abf |\n",
+       "| 3742 | Multiple Sclerosis         | CD8T_RPS26___HLA-DPB1__RPS26     | CD8T_RPS26     | 0.9170029 | CD8T     | HLA-DPB1_RPS26 |   58 | PP.H0.abf |\n",
+       "| 4052 | Multiple Sclerosis         | monocyte_RPS26___HLA-DPB1__RPS26 | monocyte_RPS26 | 0.9977439 | monocyte | HLA-DPB1_RPS26 |   58 | PP.H2.abf |\n",
+       "| 4450 | Rheumatoid Arthritis       | CD4T_RPS26___HLA-DPB1__RPS26     | CD4T_RPS26     | 0.9147419 | CD4T     | HLA-DPB1_RPS26 |  882 | PP.H4.abf |\n",
+       "| 4806 | Rheumatoid Arthritis       | CD8T_RPS26___HLA-DPB1__RPS26     | CD8T_RPS26     | 0.7436868 | CD8T     | HLA-DPB1_RPS26 |  882 | PP.H4.abf |\n",
+       "| 5116 | Rheumatoid Arthritis       | monocyte_RPS26___HLA-DPB1__RPS26 | monocyte_RPS26 | 0.7753756 | monocyte | HLA-DPB1_RPS26 |  882 | PP.H4.abf |\n",
+       "| 5514 | Type_1_Diabetes            | CD4T_RPS26___HLA-DPB1__RPS26     | CD4T_RPS26     | 0.7693859 | CD4T     | HLA-DPB1_RPS26 | 1341 | PP.H3.abf |\n",
+       "| 5870 | Type_1_Diabetes            | CD8T_RPS26___HLA-DPB1__RPS26     | CD8T_RPS26     | 0.4314387 | CD8T     | HLA-DPB1_RPS26 | 1341 | PP.H3.abf |\n",
+       "| 6180 | Type_1_Diabetes            | monocyte_RPS26___HLA-DPB1__RPS26 | monocyte_RPS26 | 0.9986931 | monocyte | HLA-DPB1_RPS26 | 1341 | PP.H3.abf |\n",
+       "| 6578 | White blood cell count     | CD4T_RPS26___HLA-DPB1__RPS26     | CD4T_RPS26     | 0.8243175 | CD4T     | HLA-DPB1_RPS26 | 1078 | PP.H2.abf |\n",
+       "| 6934 | White blood cell count     | CD8T_RPS26___HLA-DPB1__RPS26     | CD8T_RPS26     | 0.5682385 | CD8T     | HLA-DPB1_RPS26 | 1078 | PP.H0.abf |\n",
+       "| 7244 | White blood cell count     | monocyte_RPS26___HLA-DPB1__RPS26 | monocyte_RPS26 | 0.9553423 | monocyte | HLA-DPB1_RPS26 | 1078 | PP.H2.abf |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     trait                      identifier                       egene         \n",
+       "194  Asthma                     CD4T_RPS26___HLA-DPB1__RPS26     CD4T_RPS26    \n",
+       "550  Asthma                     CD8T_RPS26___HLA-DPB1__RPS26     CD8T_RPS26    \n",
+       "860  Asthma                     monocyte_RPS26___HLA-DPB1__RPS26 monocyte_RPS26\n",
+       "1258 Crohn's Disease            CD4T_RPS26___HLA-DPB1__RPS26     CD4T_RPS26    \n",
+       "1614 Crohn's Disease            CD8T_RPS26___HLA-DPB1__RPS26     CD8T_RPS26    \n",
+       "1924 Crohn's Disease            monocyte_RPS26___HLA-DPB1__RPS26 monocyte_RPS26\n",
+       "2322 Inflammatory Bowel Disease CD4T_RPS26___HLA-DPB1__RPS26     CD4T_RPS26    \n",
+       "2678 Inflammatory Bowel Disease CD8T_RPS26___HLA-DPB1__RPS26     CD8T_RPS26    \n",
+       "2988 Inflammatory Bowel Disease monocyte_RPS26___HLA-DPB1__RPS26 monocyte_RPS26\n",
+       "3386 Multiple Sclerosis         CD4T_RPS26___HLA-DPB1__RPS26     CD4T_RPS26    \n",
+       "3742 Multiple Sclerosis         CD8T_RPS26___HLA-DPB1__RPS26     CD8T_RPS26    \n",
+       "4052 Multiple Sclerosis         monocyte_RPS26___HLA-DPB1__RPS26 monocyte_RPS26\n",
+       "4450 Rheumatoid Arthritis       CD4T_RPS26___HLA-DPB1__RPS26     CD4T_RPS26    \n",
+       "4806 Rheumatoid Arthritis       CD8T_RPS26___HLA-DPB1__RPS26     CD8T_RPS26    \n",
+       "5116 Rheumatoid Arthritis       monocyte_RPS26___HLA-DPB1__RPS26 monocyte_RPS26\n",
+       "5514 Type_1_Diabetes            CD4T_RPS26___HLA-DPB1__RPS26     CD4T_RPS26    \n",
+       "5870 Type_1_Diabetes            CD8T_RPS26___HLA-DPB1__RPS26     CD8T_RPS26    \n",
+       "6180 Type_1_Diabetes            monocyte_RPS26___HLA-DPB1__RPS26 monocyte_RPS26\n",
+       "6578 White blood cell count     CD4T_RPS26___HLA-DPB1__RPS26     CD4T_RPS26    \n",
+       "6934 White blood cell count     CD8T_RPS26___HLA-DPB1__RPS26     CD8T_RPS26    \n",
+       "7244 White blood cell count     monocyte_RPS26___HLA-DPB1__RPS26 monocyte_RPS26\n",
+       "     value     cell_type gene           overlapping_snps parameter\n",
+       "194  0.5988243 CD4T      HLA-DPB1_RPS26  381             PP.H4.abf\n",
+       "550  0.4321920 CD8T      HLA-DPB1_RPS26  381             PP.H0.abf\n",
+       "860  0.6660012 monocyte  HLA-DPB1_RPS26  381             PP.H4.abf\n",
+       "1258 0.8220487 CD4T      HLA-DPB1_RPS26 1112             PP.H2.abf\n",
+       "1614 0.5654940 CD8T      HLA-DPB1_RPS26 1112             PP.H0.abf\n",
+       "1924 0.9525225 monocyte  HLA-DPB1_RPS26 1112             PP.H2.abf\n",
+       "2322 0.8474188 CD4T      HLA-DPB1_RPS26 1110             PP.H2.abf\n",
+       "2678 0.5830098 CD8T      HLA-DPB1_RPS26 1110             PP.H0.abf\n",
+       "2988 0.9819645 monocyte  HLA-DPB1_RPS26 1110             PP.H2.abf\n",
+       "3386 0.5847966 CD4T      HLA-DPB1_RPS26   58             PP.H2.abf\n",
+       "3742 0.9170029 CD8T      HLA-DPB1_RPS26   58             PP.H0.abf\n",
+       "4052 0.9977439 monocyte  HLA-DPB1_RPS26   58             PP.H2.abf\n",
+       "4450 0.9147419 CD4T      HLA-DPB1_RPS26  882             PP.H4.abf\n",
+       "4806 0.7436868 CD8T      HLA-DPB1_RPS26  882             PP.H4.abf\n",
+       "5116 0.7753756 monocyte  HLA-DPB1_RPS26  882             PP.H4.abf\n",
+       "5514 0.7693859 CD4T      HLA-DPB1_RPS26 1341             PP.H3.abf\n",
+       "5870 0.4314387 CD8T      HLA-DPB1_RPS26 1341             PP.H3.abf\n",
+       "6180 0.9986931 monocyte  HLA-DPB1_RPS26 1341             PP.H3.abf\n",
+       "6578 0.8243175 CD4T      HLA-DPB1_RPS26 1078             PP.H2.abf\n",
+       "6934 0.5682385 CD8T      HLA-DPB1_RPS26 1078             PP.H0.abf\n",
+       "7244 0.9553423 monocyte  HLA-DPB1_RPS26 1078             PP.H2.abf"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coeqtl_summary_filtered[coeqtl_summary_filtered$gene == 'HLA-DPB1_RPS26',]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "83e33803-4c43-41b2-9afe-11c33feb3eaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 141 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>gene</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>AIF1_RPS26    </td><td>2</td></tr>\n",
+       "\t<tr><td>C12orf75_RPS26</td><td>2</td></tr>\n",
+       "\t<tr><td>COX7C_RPS26   </td><td>2</td></tr>\n",
+       "\t<tr><td>NACA_RPS26    </td><td>2</td></tr>\n",
+       "\t<tr><td>RPL11_RPS26   </td><td>2</td></tr>\n",
+       "\t<tr><td>RPL4_RPS26    </td><td>2</td></tr>\n",
+       "\t<tr><td>RPS10_RPS26   </td><td>2</td></tr>\n",
+       "\t<tr><td>RPS24_RPS26   </td><td>2</td></tr>\n",
+       "\t<tr><td>RPS26_TIGIT   </td><td>2</td></tr>\n",
+       "\t<tr><td>RPS26_TOMM7   </td><td>2</td></tr>\n",
+       "\t<tr><td>RPS26_UBB     </td><td>2</td></tr>\n",
+       "\t<tr><td>ABHD14B_RPS26 </td><td>1</td></tr>\n",
+       "\t<tr><td>ABLIM1_RPS26  </td><td>1</td></tr>\n",
+       "\t<tr><td>ACTB_RPS26    </td><td>1</td></tr>\n",
+       "\t<tr><td>ADAM19_RPS26  </td><td>1</td></tr>\n",
+       "\t<tr><td>AKAP13_RPS26  </td><td>1</td></tr>\n",
+       "\t<tr><td>ANXA2_RPS26   </td><td>1</td></tr>\n",
+       "\t<tr><td>APOBEC3G_RPS26</td><td>1</td></tr>\n",
+       "\t<tr><td>ARPC2_RPS26   </td><td>1</td></tr>\n",
+       "\t<tr><td>ATP2B1_RPS26  </td><td>1</td></tr>\n",
+       "\t<tr><td>ATP2B4_RPS26  </td><td>1</td></tr>\n",
+       "\t<tr><td>ATP5A1_RPS26  </td><td>1</td></tr>\n",
+       "\t<tr><td>B2M_RPS26     </td><td>1</td></tr>\n",
+       "\t<tr><td>BHLHE40_RPS26 </td><td>1</td></tr>\n",
+       "\t<tr><td>C12orf57_RPS26</td><td>1</td></tr>\n",
+       "\t<tr><td>C1orf228_RPS26</td><td>1</td></tr>\n",
+       "\t<tr><td>CALM1_RPS26   </td><td>1</td></tr>\n",
+       "\t<tr><td>CCL4_RPS26    </td><td>1</td></tr>\n",
+       "\t<tr><td>CD48_RPS26    </td><td>1</td></tr>\n",
+       "\t<tr><td>CD55_RPS26    </td><td>1</td></tr>\n",
+       "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><td>RPS26_RPS27A  </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_RPS3    </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_RPS4X   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_RPS5    </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_RPS9    </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_RPSA    </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_S100A11 </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_S100A6  </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_SBDS    </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_SERF2   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_SH3YL1  </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_SLA     </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_SMDT1   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_SRSF2   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_SRSF7   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_SYNE1   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TCF7    </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TESPA1  </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TKT     </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TMA7    </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TMSB10  </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TNFAIP2 </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TNFRSF1B</td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TUBA4A  </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_TXN     </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_UBA52   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_UQCRB   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_YBX1    </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_YWHAB   </td><td>1</td></tr>\n",
+       "\t<tr><td>RPS26_ZEB2    </td><td>1</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 141 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " gene & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t AIF1\\_RPS26     & 2\\\\\n",
+       "\t C12orf75\\_RPS26 & 2\\\\\n",
+       "\t COX7C\\_RPS26    & 2\\\\\n",
+       "\t NACA\\_RPS26     & 2\\\\\n",
+       "\t RPL11\\_RPS26    & 2\\\\\n",
+       "\t RPL4\\_RPS26     & 2\\\\\n",
+       "\t RPS10\\_RPS26    & 2\\\\\n",
+       "\t RPS24\\_RPS26    & 2\\\\\n",
+       "\t RPS26\\_TIGIT    & 2\\\\\n",
+       "\t RPS26\\_TOMM7    & 2\\\\\n",
+       "\t RPS26\\_UBB      & 2\\\\\n",
+       "\t ABHD14B\\_RPS26  & 1\\\\\n",
+       "\t ABLIM1\\_RPS26   & 1\\\\\n",
+       "\t ACTB\\_RPS26     & 1\\\\\n",
+       "\t ADAM19\\_RPS26   & 1\\\\\n",
+       "\t AKAP13\\_RPS26   & 1\\\\\n",
+       "\t ANXA2\\_RPS26    & 1\\\\\n",
+       "\t APOBEC3G\\_RPS26 & 1\\\\\n",
+       "\t ARPC2\\_RPS26    & 1\\\\\n",
+       "\t ATP2B1\\_RPS26   & 1\\\\\n",
+       "\t ATP2B4\\_RPS26   & 1\\\\\n",
+       "\t ATP5A1\\_RPS26   & 1\\\\\n",
+       "\t B2M\\_RPS26      & 1\\\\\n",
+       "\t BHLHE40\\_RPS26  & 1\\\\\n",
+       "\t C12orf57\\_RPS26 & 1\\\\\n",
+       "\t C1orf228\\_RPS26 & 1\\\\\n",
+       "\t CALM1\\_RPS26    & 1\\\\\n",
+       "\t CCL4\\_RPS26     & 1\\\\\n",
+       "\t CD48\\_RPS26     & 1\\\\\n",
+       "\t CD55\\_RPS26     & 1\\\\\n",
+       "\t ⋮ & ⋮\\\\\n",
+       "\t RPS26\\_RPS27A   & 1\\\\\n",
+       "\t RPS26\\_RPS3     & 1\\\\\n",
+       "\t RPS26\\_RPS4X    & 1\\\\\n",
+       "\t RPS26\\_RPS5     & 1\\\\\n",
+       "\t RPS26\\_RPS9     & 1\\\\\n",
+       "\t RPS26\\_RPSA     & 1\\\\\n",
+       "\t RPS26\\_S100A11  & 1\\\\\n",
+       "\t RPS26\\_S100A6   & 1\\\\\n",
+       "\t RPS26\\_SBDS     & 1\\\\\n",
+       "\t RPS26\\_SERF2    & 1\\\\\n",
+       "\t RPS26\\_SH3YL1   & 1\\\\\n",
+       "\t RPS26\\_SLA      & 1\\\\\n",
+       "\t RPS26\\_SMDT1    & 1\\\\\n",
+       "\t RPS26\\_SRSF2    & 1\\\\\n",
+       "\t RPS26\\_SRSF7    & 1\\\\\n",
+       "\t RPS26\\_SYNE1    & 1\\\\\n",
+       "\t RPS26\\_TCF7     & 1\\\\\n",
+       "\t RPS26\\_TESPA1   & 1\\\\\n",
+       "\t RPS26\\_TKT      & 1\\\\\n",
+       "\t RPS26\\_TMA7     & 1\\\\\n",
+       "\t RPS26\\_TMSB10   & 1\\\\\n",
+       "\t RPS26\\_TNFAIP2  & 1\\\\\n",
+       "\t RPS26\\_TNFRSF1B & 1\\\\\n",
+       "\t RPS26\\_TUBA4A   & 1\\\\\n",
+       "\t RPS26\\_TXN      & 1\\\\\n",
+       "\t RPS26\\_UBA52    & 1\\\\\n",
+       "\t RPS26\\_UQCRB    & 1\\\\\n",
+       "\t RPS26\\_YBX1     & 1\\\\\n",
+       "\t RPS26\\_YWHAB    & 1\\\\\n",
+       "\t RPS26\\_ZEB2     & 1\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 141 × 2\n",
+       "\n",
+       "| gene &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| AIF1_RPS26     | 2 |\n",
+       "| C12orf75_RPS26 | 2 |\n",
+       "| COX7C_RPS26    | 2 |\n",
+       "| NACA_RPS26     | 2 |\n",
+       "| RPL11_RPS26    | 2 |\n",
+       "| RPL4_RPS26     | 2 |\n",
+       "| RPS10_RPS26    | 2 |\n",
+       "| RPS24_RPS26    | 2 |\n",
+       "| RPS26_TIGIT    | 2 |\n",
+       "| RPS26_TOMM7    | 2 |\n",
+       "| RPS26_UBB      | 2 |\n",
+       "| ABHD14B_RPS26  | 1 |\n",
+       "| ABLIM1_RPS26   | 1 |\n",
+       "| ACTB_RPS26     | 1 |\n",
+       "| ADAM19_RPS26   | 1 |\n",
+       "| AKAP13_RPS26   | 1 |\n",
+       "| ANXA2_RPS26    | 1 |\n",
+       "| APOBEC3G_RPS26 | 1 |\n",
+       "| ARPC2_RPS26    | 1 |\n",
+       "| ATP2B1_RPS26   | 1 |\n",
+       "| ATP2B4_RPS26   | 1 |\n",
+       "| ATP5A1_RPS26   | 1 |\n",
+       "| B2M_RPS26      | 1 |\n",
+       "| BHLHE40_RPS26  | 1 |\n",
+       "| C12orf57_RPS26 | 1 |\n",
+       "| C1orf228_RPS26 | 1 |\n",
+       "| CALM1_RPS26    | 1 |\n",
+       "| CCL4_RPS26     | 1 |\n",
+       "| CD48_RPS26     | 1 |\n",
+       "| CD55_RPS26     | 1 |\n",
+       "| ⋮ | ⋮ |\n",
+       "| RPS26_RPS27A   | 1 |\n",
+       "| RPS26_RPS3     | 1 |\n",
+       "| RPS26_RPS4X    | 1 |\n",
+       "| RPS26_RPS5     | 1 |\n",
+       "| RPS26_RPS9     | 1 |\n",
+       "| RPS26_RPSA     | 1 |\n",
+       "| RPS26_S100A11  | 1 |\n",
+       "| RPS26_S100A6   | 1 |\n",
+       "| RPS26_SBDS     | 1 |\n",
+       "| RPS26_SERF2    | 1 |\n",
+       "| RPS26_SH3YL1   | 1 |\n",
+       "| RPS26_SLA      | 1 |\n",
+       "| RPS26_SMDT1    | 1 |\n",
+       "| RPS26_SRSF2    | 1 |\n",
+       "| RPS26_SRSF7    | 1 |\n",
+       "| RPS26_SYNE1    | 1 |\n",
+       "| RPS26_TCF7     | 1 |\n",
+       "| RPS26_TESPA1   | 1 |\n",
+       "| RPS26_TKT      | 1 |\n",
+       "| RPS26_TMA7     | 1 |\n",
+       "| RPS26_TMSB10   | 1 |\n",
+       "| RPS26_TNFAIP2  | 1 |\n",
+       "| RPS26_TNFRSF1B | 1 |\n",
+       "| RPS26_TUBA4A   | 1 |\n",
+       "| RPS26_TXN      | 1 |\n",
+       "| RPS26_UBA52    | 1 |\n",
+       "| RPS26_UQCRB    | 1 |\n",
+       "| RPS26_YBX1     | 1 |\n",
+       "| RPS26_YWHAB    | 1 |\n",
+       "| RPS26_ZEB2     | 1 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    gene           n\n",
+       "1   AIF1_RPS26     2\n",
+       "2   C12orf75_RPS26 2\n",
+       "3   COX7C_RPS26    2\n",
+       "4   NACA_RPS26     2\n",
+       "5   RPL11_RPS26    2\n",
+       "6   RPL4_RPS26     2\n",
+       "7   RPS10_RPS26    2\n",
+       "8   RPS24_RPS26    2\n",
+       "9   RPS26_TIGIT    2\n",
+       "10  RPS26_TOMM7    2\n",
+       "11  RPS26_UBB      2\n",
+       "12  ABHD14B_RPS26  1\n",
+       "13  ABLIM1_RPS26   1\n",
+       "14  ACTB_RPS26     1\n",
+       "15  ADAM19_RPS26   1\n",
+       "16  AKAP13_RPS26   1\n",
+       "17  ANXA2_RPS26    1\n",
+       "18  APOBEC3G_RPS26 1\n",
+       "19  ARPC2_RPS26    1\n",
+       "20  ATP2B1_RPS26   1\n",
+       "21  ATP2B4_RPS26   1\n",
+       "22  ATP5A1_RPS26   1\n",
+       "23  B2M_RPS26      1\n",
+       "24  BHLHE40_RPS26  1\n",
+       "25  C12orf57_RPS26 1\n",
+       "26  C1orf228_RPS26 1\n",
+       "27  CALM1_RPS26    1\n",
+       "28  CCL4_RPS26     1\n",
+       "29  CD48_RPS26     1\n",
+       "30  CD55_RPS26     1\n",
+       "⋮   ⋮              ⋮\n",
+       "112 RPS26_RPS27A   1\n",
+       "113 RPS26_RPS3     1\n",
+       "114 RPS26_RPS4X    1\n",
+       "115 RPS26_RPS5     1\n",
+       "116 RPS26_RPS9     1\n",
+       "117 RPS26_RPSA     1\n",
+       "118 RPS26_S100A11  1\n",
+       "119 RPS26_S100A6   1\n",
+       "120 RPS26_SBDS     1\n",
+       "121 RPS26_SERF2    1\n",
+       "122 RPS26_SH3YL1   1\n",
+       "123 RPS26_SLA      1\n",
+       "124 RPS26_SMDT1    1\n",
+       "125 RPS26_SRSF2    1\n",
+       "126 RPS26_SRSF7    1\n",
+       "127 RPS26_SYNE1    1\n",
+       "128 RPS26_TCF7     1\n",
+       "129 RPS26_TESPA1   1\n",
+       "130 RPS26_TKT      1\n",
+       "131 RPS26_TMA7     1\n",
+       "132 RPS26_TMSB10   1\n",
+       "133 RPS26_TNFAIP2  1\n",
+       "134 RPS26_TNFRSF1B 1\n",
+       "135 RPS26_TUBA4A   1\n",
+       "136 RPS26_TXN      1\n",
+       "137 RPS26_UBA52    1\n",
+       "138 RPS26_UQCRB    1\n",
+       "139 RPS26_YBX1     1\n",
+       "140 RPS26_YWHAB    1\n",
+       "141 RPS26_ZEB2     1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "analyze[order(analyze$n, decreasing = TRUE),]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "c63c24c3-091a-4fde-ace2-60db188bf889",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "141"
+      ],
+      "text/latex": [
+       "141"
+      ],
+      "text/markdown": [
+       "141"
+      ],
+      "text/plain": [
+       "[1] 141"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(coloc_examples_rheomatoid_arthritis$gene))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e881916b-4910-47dd-85db-e5827ca216fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "c0d63942-e313-4d77-b2ec-36a1e9ae7857",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "152"
+      ],
+      "text/latex": [
+       "152"
+      ],
+      "text/markdown": [
+       "152"
+      ],
+      "text/plain": [
+       "[1] 152"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(coloc_examples_rheomatoid_arthritis %>% group_by(cell_type, gene) %>% count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a2253b6-6e05-4d98-98da-336a9eb2fc4d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "a0327199-e95c-4ddd-9cbb-32935629a723",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 6 × 8</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th><th scope=col>parameter</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>5255</th><td>Rheumatoid Arthritis</td><td>NK_RPS26___RPL23__RPS26   </td><td>NK_RPS26  </td><td>0.9777943</td><td>NK  </td><td>RPL23_RPS26 </td><td>879</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>4839</th><td>Rheumatoid Arthritis</td><td>CD8T_RPS26___NACA__RPS26  </td><td>CD8T_RPS26</td><td>0.9713088</td><td>CD8T</td><td>NACA_RPS26  </td><td>882</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>4995</th><td>Rheumatoid Arthritis</td><td>CD8T_RPS26___RPS26__UBA52 </td><td>CD8T_RPS26</td><td>0.9687585</td><td>CD8T</td><td>RPS26_UBA52 </td><td>882</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>5249</th><td>Rheumatoid Arthritis</td><td>NK_RPS26___RPL15__RPS26   </td><td>NK_RPS26  </td><td>0.9687209</td><td>NK  </td><td>RPL15_RPS26 </td><td>879</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>4764</th><td>Rheumatoid Arthritis</td><td>CD8T_RPS26___DNAJB6__RPS26</td><td>CD8T_RPS26</td><td>0.9685069</td><td>CD8T</td><td>DNAJB6_RPS26</td><td>882</td><td>PP.H4.abf</td></tr>\n",
+       "\t<tr><th scope=row>4428</th><td>Rheumatoid Arthritis</td><td>CD4T_RPS26___FOXP3__RPS26 </td><td>CD4T_RPS26</td><td>0.9669022</td><td>CD4T</td><td>FOXP3_RPS26 </td><td>870</td><td>PP.H4.abf</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 8\n",
+       "\\begin{tabular}{r|llllllll}\n",
+       "  & trait & identifier & egene & value & cell\\_type & gene & overlapping\\_snps & parameter\\\\\n",
+       "  & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t5255 & Rheumatoid Arthritis & NK\\_RPS26\\_\\_\\_RPL23\\_\\_RPS26    & NK\\_RPS26   & 0.9777943 & NK   & RPL23\\_RPS26  & 879 & PP.H4.abf\\\\\n",
+       "\t4839 & Rheumatoid Arthritis & CD8T\\_RPS26\\_\\_\\_NACA\\_\\_RPS26   & CD8T\\_RPS26 & 0.9713088 & CD8T & NACA\\_RPS26   & 882 & PP.H4.abf\\\\\n",
+       "\t4995 & Rheumatoid Arthritis & CD8T\\_RPS26\\_\\_\\_RPS26\\_\\_UBA52  & CD8T\\_RPS26 & 0.9687585 & CD8T & RPS26\\_UBA52  & 882 & PP.H4.abf\\\\\n",
+       "\t5249 & Rheumatoid Arthritis & NK\\_RPS26\\_\\_\\_RPL15\\_\\_RPS26    & NK\\_RPS26   & 0.9687209 & NK   & RPL15\\_RPS26  & 879 & PP.H4.abf\\\\\n",
+       "\t4764 & Rheumatoid Arthritis & CD8T\\_RPS26\\_\\_\\_DNAJB6\\_\\_RPS26 & CD8T\\_RPS26 & 0.9685069 & CD8T & DNAJB6\\_RPS26 & 882 & PP.H4.abf\\\\\n",
+       "\t4428 & Rheumatoid Arthritis & CD4T\\_RPS26\\_\\_\\_FOXP3\\_\\_RPS26  & CD4T\\_RPS26 & 0.9669022 & CD4T & FOXP3\\_RPS26  & 870 & PP.H4.abf\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 8\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; | parameter &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 5255 | Rheumatoid Arthritis | NK_RPS26___RPL23__RPS26    | NK_RPS26   | 0.9777943 | NK   | RPL23_RPS26  | 879 | PP.H4.abf |\n",
+       "| 4839 | Rheumatoid Arthritis | CD8T_RPS26___NACA__RPS26   | CD8T_RPS26 | 0.9713088 | CD8T | NACA_RPS26   | 882 | PP.H4.abf |\n",
+       "| 4995 | Rheumatoid Arthritis | CD8T_RPS26___RPS26__UBA52  | CD8T_RPS26 | 0.9687585 | CD8T | RPS26_UBA52  | 882 | PP.H4.abf |\n",
+       "| 5249 | Rheumatoid Arthritis | NK_RPS26___RPL15__RPS26    | NK_RPS26   | 0.9687209 | NK   | RPL15_RPS26  | 879 | PP.H4.abf |\n",
+       "| 4764 | Rheumatoid Arthritis | CD8T_RPS26___DNAJB6__RPS26 | CD8T_RPS26 | 0.9685069 | CD8T | DNAJB6_RPS26 | 882 | PP.H4.abf |\n",
+       "| 4428 | Rheumatoid Arthritis | CD4T_RPS26___FOXP3__RPS26  | CD4T_RPS26 | 0.9669022 | CD4T | FOXP3_RPS26  | 870 | PP.H4.abf |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     trait                identifier                 egene      value    \n",
+       "5255 Rheumatoid Arthritis NK_RPS26___RPL23__RPS26    NK_RPS26   0.9777943\n",
+       "4839 Rheumatoid Arthritis CD8T_RPS26___NACA__RPS26   CD8T_RPS26 0.9713088\n",
+       "4995 Rheumatoid Arthritis CD8T_RPS26___RPS26__UBA52  CD8T_RPS26 0.9687585\n",
+       "5249 Rheumatoid Arthritis NK_RPS26___RPL15__RPS26    NK_RPS26   0.9687209\n",
+       "4764 Rheumatoid Arthritis CD8T_RPS26___DNAJB6__RPS26 CD8T_RPS26 0.9685069\n",
+       "4428 Rheumatoid Arthritis CD4T_RPS26___FOXP3__RPS26  CD4T_RPS26 0.9669022\n",
+       "     cell_type gene         overlapping_snps parameter\n",
+       "5255 NK        RPL23_RPS26  879              PP.H4.abf\n",
+       "4839 CD8T      NACA_RPS26   882              PP.H4.abf\n",
+       "4995 CD8T      RPS26_UBA52  882              PP.H4.abf\n",
+       "5249 NK        RPL15_RPS26  879              PP.H4.abf\n",
+       "4764 CD8T      DNAJB6_RPS26 882              PP.H4.abf\n",
+       "4428 CD4T      FOXP3_RPS26  870              PP.H4.abf"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coloc_examples_rheomatoid_arthritis[order(coloc_examples_rheomatoid_arthritis$value, decreasing = TRUE),],6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "65a31f3a-08d3-49b7-b722-cf610b6c8639",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A grouped_df: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>egene</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>B_RPS26       </td><td> 4</td></tr>\n",
+       "\t<tr><td>CD4T_RPS26    </td><td>41</td></tr>\n",
+       "\t<tr><td>CD8T_RPS26    </td><td>64</td></tr>\n",
+       "\t<tr><td>monocyte_RPS26</td><td>15</td></tr>\n",
+       "\t<tr><td>NK_RPS26      </td><td>28</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A grouped\\_df: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " egene & n\\\\\n",
+       " <chr> & <int>\\\\\n",
+       "\\hline\n",
+       "\t B\\_RPS26        &  4\\\\\n",
+       "\t CD4T\\_RPS26     & 41\\\\\n",
+       "\t CD8T\\_RPS26     & 64\\\\\n",
+       "\t monocyte\\_RPS26 & 15\\\\\n",
+       "\t NK\\_RPS26       & 28\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A grouped_df: 5 × 2\n",
+       "\n",
+       "| egene &lt;chr&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| B_RPS26        |  4 |\n",
+       "| CD4T_RPS26     | 41 |\n",
+       "| CD8T_RPS26     | 64 |\n",
+       "| monocyte_RPS26 | 15 |\n",
+       "| NK_RPS26       | 28 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  egene          n \n",
+       "1 B_RPS26         4\n",
+       "2 CD4T_RPS26     41\n",
+       "3 CD8T_RPS26     64\n",
+       "4 monocyte_RPS26 15\n",
+       "5 NK_RPS26       28"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coloc_examples_rheomatoid_arthritis %>% group_by(egene) %>% count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "ec24cb29-579b-4e4f-b17b-4a2035091438",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "152"
+      ],
+      "text/latex": [
+       "152"
+      ],
+      "text/markdown": [
+       "152"
+      ],
+      "text/plain": [
+       "[1] 152"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(coloc_examples_rheomatoid_arthritis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "30b932e7-cbb0-423d-b183-ce4e04d4e7bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Mapp positions for coegenes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "5ce1bbeb-d244-44f4-870d-ab98915db9b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_examples_rheomatoid_arthritis$cogene = str_replace(coloc_examples_rheomatoid_arthritis$gene, 'RPS26', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "c2f1cb78-40a2-47da-8987-920eef2b7774",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_examples_rheomatoid_arthritis$cogene = str_replace(coloc_examples_rheomatoid_arthritis$cogene, '__', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "746db8c7-a868-474c-900c-c93c75701808",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_examples_rheomatoid_arthritis$cogene = str_replace(coloc_examples_rheomatoid_arthritis$cogene, '_', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "0e0962a6-9bf5-4bfa-a04e-c723e96e39be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'RPL11'</li><li>'RPL35A'</li><li>'RPL41'</li><li>'RPLP2'</li><li>'ABHD14B'</li><li>'ADAM19'</li><li>'AIF1'</li><li>'AKAP13'</li><li>'ATP2B1'</li><li>'ATP2B4'</li><li>'C12orf75'</li><li>'CD58'</li><li>'CHCHD2'</li><li>'COX7C'</li><li>'CST7'</li><li>'CYBA'</li><li>'EML4'</li><li>'ENTPD1'</li><li>'FOXP3'</li><li>'FTH1'</li><li>'GALM'</li><li>'GK'</li><li>'H3F3A'</li><li>'HLA-DPB1'</li><li>'HLA-DRA'</li><li>'IGBP1'</li><li>'ISG20'</li><li>'LINC00493'</li><li>'LSM5'</li><li>'MIAT'</li><li>'MIR4435-1HG'</li><li>'MT2A'</li><li>'PRMT2'</li><li>'RGS1'</li><li>'RNF19A'</li><li>'SBDS'</li><li>'SERF2'</li><li>'TCF7'</li><li>'TIGIT'</li><li>'TMA7'</li><li>'TMSB10'</li><li>'TNFRSF1B'</li><li>'UBB'</li><li>'YBX1'</li><li>'YWHAB'</li><li>'ABLIM1'</li><li>'ANXA2'</li><li>'APOBEC3G'</li><li>'BHLHE40'</li><li>'C12orf57'</li><li>'C1orf228'</li><li>'CALM1'</li><li>'CCL4'</li><li>'CD55'</li><li>'CD81'</li><li>'CD8B'</li><li>'CFL1'</li><li>'CMC1'</li><li>'DNAJB6'</li><li>'EEF1D'</li><li>'EEF2'</li><li>'EIF1'</li><li>'EIF3E'</li><li>'EIF3K'</li><li>'EIF4B'</li><li>'FAIM3'</li><li>'FLNA'</li><li>'FOXP1'</li><li>'FTL'</li><li>'GLTSCR2'</li><li>'ID2'</li><li>'KLRB1'</li><li>'NACA'</li><li>'NDFIP1'</li><li>'NEAT1'</li><li>'NPM1'</li><li>'PFDN5'</li><li>'PPA1'</li><li>'PPP2R5C'</li><li>'PTPN7'</li><li>'RHOH'</li><li>'RIC3'</li><li>'RPL17'</li><li>'RPL24'</li><li>'RPL39'</li><li>'RPL4'</li><li>'RPS10'</li><li>'RPS11'</li><li>'RPS24'</li><li>'RPS4X'</li><li>'S100A6'</li><li>'SH3YL1'</li><li>'SLA'</li><li>'SMDT1'</li><li>'SRSF2'</li><li>'SRSF7'</li><li>'SYNE1'</li><li>'TESPA1'</li><li>'TOMM7'</li><li>'TUBA4A'</li><li>'TXN'</li><li>'UBA52'</li><li>'UQCRB'</li><li>'ZEB2'</li><li>'ATP5A1'</li><li>'CD48'</li><li>'EIF3L'</li><li>'FAU'</li><li>'GPX1'</li><li>'IPO7'</li><li>'QARS'</li><li>'RNASE6'</li><li>'RPL7A'</li><li>'RPS9'</li><li>'S100A11'</li><li>'TKT'</li><li>'TNFAIP2'</li><li>'ACTB'</li><li>'ARPC2'</li><li>'B2M'</li><li>'EEF1B2'</li><li>'GNB2L1'</li><li>'KLRC1'</li><li>'RPL15'</li><li>'RPL18'</li><li>'RPL23'</li><li>'RPL29'</li><li>'RPL30'</li><li>'RPL34'</li><li>'RPL35'</li><li>'RPL37'</li><li>'RPL37A'</li><li>'RPL38'</li><li>'RPL6'</li><li>'RPLP0'</li><li>'RPS16'</li><li>'RPS20'</li><li>'RPS27A'</li><li>'RPS3'</li><li>'RPS5'</li><li>'RPSA'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'RPL11'\n",
+       "\\item 'RPL35A'\n",
+       "\\item 'RPL41'\n",
+       "\\item 'RPLP2'\n",
+       "\\item 'ABHD14B'\n",
+       "\\item 'ADAM19'\n",
+       "\\item 'AIF1'\n",
+       "\\item 'AKAP13'\n",
+       "\\item 'ATP2B1'\n",
+       "\\item 'ATP2B4'\n",
+       "\\item 'C12orf75'\n",
+       "\\item 'CD58'\n",
+       "\\item 'CHCHD2'\n",
+       "\\item 'COX7C'\n",
+       "\\item 'CST7'\n",
+       "\\item 'CYBA'\n",
+       "\\item 'EML4'\n",
+       "\\item 'ENTPD1'\n",
+       "\\item 'FOXP3'\n",
+       "\\item 'FTH1'\n",
+       "\\item 'GALM'\n",
+       "\\item 'GK'\n",
+       "\\item 'H3F3A'\n",
+       "\\item 'HLA-DPB1'\n",
+       "\\item 'HLA-DRA'\n",
+       "\\item 'IGBP1'\n",
+       "\\item 'ISG20'\n",
+       "\\item 'LINC00493'\n",
+       "\\item 'LSM5'\n",
+       "\\item 'MIAT'\n",
+       "\\item 'MIR4435-1HG'\n",
+       "\\item 'MT2A'\n",
+       "\\item 'PRMT2'\n",
+       "\\item 'RGS1'\n",
+       "\\item 'RNF19A'\n",
+       "\\item 'SBDS'\n",
+       "\\item 'SERF2'\n",
+       "\\item 'TCF7'\n",
+       "\\item 'TIGIT'\n",
+       "\\item 'TMA7'\n",
+       "\\item 'TMSB10'\n",
+       "\\item 'TNFRSF1B'\n",
+       "\\item 'UBB'\n",
+       "\\item 'YBX1'\n",
+       "\\item 'YWHAB'\n",
+       "\\item 'ABLIM1'\n",
+       "\\item 'ANXA2'\n",
+       "\\item 'APOBEC3G'\n",
+       "\\item 'BHLHE40'\n",
+       "\\item 'C12orf57'\n",
+       "\\item 'C1orf228'\n",
+       "\\item 'CALM1'\n",
+       "\\item 'CCL4'\n",
+       "\\item 'CD55'\n",
+       "\\item 'CD81'\n",
+       "\\item 'CD8B'\n",
+       "\\item 'CFL1'\n",
+       "\\item 'CMC1'\n",
+       "\\item 'DNAJB6'\n",
+       "\\item 'EEF1D'\n",
+       "\\item 'EEF2'\n",
+       "\\item 'EIF1'\n",
+       "\\item 'EIF3E'\n",
+       "\\item 'EIF3K'\n",
+       "\\item 'EIF4B'\n",
+       "\\item 'FAIM3'\n",
+       "\\item 'FLNA'\n",
+       "\\item 'FOXP1'\n",
+       "\\item 'FTL'\n",
+       "\\item 'GLTSCR2'\n",
+       "\\item 'ID2'\n",
+       "\\item 'KLRB1'\n",
+       "\\item 'NACA'\n",
+       "\\item 'NDFIP1'\n",
+       "\\item 'NEAT1'\n",
+       "\\item 'NPM1'\n",
+       "\\item 'PFDN5'\n",
+       "\\item 'PPA1'\n",
+       "\\item 'PPP2R5C'\n",
+       "\\item 'PTPN7'\n",
+       "\\item 'RHOH'\n",
+       "\\item 'RIC3'\n",
+       "\\item 'RPL17'\n",
+       "\\item 'RPL24'\n",
+       "\\item 'RPL39'\n",
+       "\\item 'RPL4'\n",
+       "\\item 'RPS10'\n",
+       "\\item 'RPS11'\n",
+       "\\item 'RPS24'\n",
+       "\\item 'RPS4X'\n",
+       "\\item 'S100A6'\n",
+       "\\item 'SH3YL1'\n",
+       "\\item 'SLA'\n",
+       "\\item 'SMDT1'\n",
+       "\\item 'SRSF2'\n",
+       "\\item 'SRSF7'\n",
+       "\\item 'SYNE1'\n",
+       "\\item 'TESPA1'\n",
+       "\\item 'TOMM7'\n",
+       "\\item 'TUBA4A'\n",
+       "\\item 'TXN'\n",
+       "\\item 'UBA52'\n",
+       "\\item 'UQCRB'\n",
+       "\\item 'ZEB2'\n",
+       "\\item 'ATP5A1'\n",
+       "\\item 'CD48'\n",
+       "\\item 'EIF3L'\n",
+       "\\item 'FAU'\n",
+       "\\item 'GPX1'\n",
+       "\\item 'IPO7'\n",
+       "\\item 'QARS'\n",
+       "\\item 'RNASE6'\n",
+       "\\item 'RPL7A'\n",
+       "\\item 'RPS9'\n",
+       "\\item 'S100A11'\n",
+       "\\item 'TKT'\n",
+       "\\item 'TNFAIP2'\n",
+       "\\item 'ACTB'\n",
+       "\\item 'ARPC2'\n",
+       "\\item 'B2M'\n",
+       "\\item 'EEF1B2'\n",
+       "\\item 'GNB2L1'\n",
+       "\\item 'KLRC1'\n",
+       "\\item 'RPL15'\n",
+       "\\item 'RPL18'\n",
+       "\\item 'RPL23'\n",
+       "\\item 'RPL29'\n",
+       "\\item 'RPL30'\n",
+       "\\item 'RPL34'\n",
+       "\\item 'RPL35'\n",
+       "\\item 'RPL37'\n",
+       "\\item 'RPL37A'\n",
+       "\\item 'RPL38'\n",
+       "\\item 'RPL6'\n",
+       "\\item 'RPLP0'\n",
+       "\\item 'RPS16'\n",
+       "\\item 'RPS20'\n",
+       "\\item 'RPS27A'\n",
+       "\\item 'RPS3'\n",
+       "\\item 'RPS5'\n",
+       "\\item 'RPSA'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'RPL11'\n",
+       "2. 'RPL35A'\n",
+       "3. 'RPL41'\n",
+       "4. 'RPLP2'\n",
+       "5. 'ABHD14B'\n",
+       "6. 'ADAM19'\n",
+       "7. 'AIF1'\n",
+       "8. 'AKAP13'\n",
+       "9. 'ATP2B1'\n",
+       "10. 'ATP2B4'\n",
+       "11. 'C12orf75'\n",
+       "12. 'CD58'\n",
+       "13. 'CHCHD2'\n",
+       "14. 'COX7C'\n",
+       "15. 'CST7'\n",
+       "16. 'CYBA'\n",
+       "17. 'EML4'\n",
+       "18. 'ENTPD1'\n",
+       "19. 'FOXP3'\n",
+       "20. 'FTH1'\n",
+       "21. 'GALM'\n",
+       "22. 'GK'\n",
+       "23. 'H3F3A'\n",
+       "24. 'HLA-DPB1'\n",
+       "25. 'HLA-DRA'\n",
+       "26. 'IGBP1'\n",
+       "27. 'ISG20'\n",
+       "28. 'LINC00493'\n",
+       "29. 'LSM5'\n",
+       "30. 'MIAT'\n",
+       "31. 'MIR4435-1HG'\n",
+       "32. 'MT2A'\n",
+       "33. 'PRMT2'\n",
+       "34. 'RGS1'\n",
+       "35. 'RNF19A'\n",
+       "36. 'SBDS'\n",
+       "37. 'SERF2'\n",
+       "38. 'TCF7'\n",
+       "39. 'TIGIT'\n",
+       "40. 'TMA7'\n",
+       "41. 'TMSB10'\n",
+       "42. 'TNFRSF1B'\n",
+       "43. 'UBB'\n",
+       "44. 'YBX1'\n",
+       "45. 'YWHAB'\n",
+       "46. 'ABLIM1'\n",
+       "47. 'ANXA2'\n",
+       "48. 'APOBEC3G'\n",
+       "49. 'BHLHE40'\n",
+       "50. 'C12orf57'\n",
+       "51. 'C1orf228'\n",
+       "52. 'CALM1'\n",
+       "53. 'CCL4'\n",
+       "54. 'CD55'\n",
+       "55. 'CD81'\n",
+       "56. 'CD8B'\n",
+       "57. 'CFL1'\n",
+       "58. 'CMC1'\n",
+       "59. 'DNAJB6'\n",
+       "60. 'EEF1D'\n",
+       "61. 'EEF2'\n",
+       "62. 'EIF1'\n",
+       "63. 'EIF3E'\n",
+       "64. 'EIF3K'\n",
+       "65. 'EIF4B'\n",
+       "66. 'FAIM3'\n",
+       "67. 'FLNA'\n",
+       "68. 'FOXP1'\n",
+       "69. 'FTL'\n",
+       "70. 'GLTSCR2'\n",
+       "71. 'ID2'\n",
+       "72. 'KLRB1'\n",
+       "73. 'NACA'\n",
+       "74. 'NDFIP1'\n",
+       "75. 'NEAT1'\n",
+       "76. 'NPM1'\n",
+       "77. 'PFDN5'\n",
+       "78. 'PPA1'\n",
+       "79. 'PPP2R5C'\n",
+       "80. 'PTPN7'\n",
+       "81. 'RHOH'\n",
+       "82. 'RIC3'\n",
+       "83. 'RPL17'\n",
+       "84. 'RPL24'\n",
+       "85. 'RPL39'\n",
+       "86. 'RPL4'\n",
+       "87. 'RPS10'\n",
+       "88. 'RPS11'\n",
+       "89. 'RPS24'\n",
+       "90. 'RPS4X'\n",
+       "91. 'S100A6'\n",
+       "92. 'SH3YL1'\n",
+       "93. 'SLA'\n",
+       "94. 'SMDT1'\n",
+       "95. 'SRSF2'\n",
+       "96. 'SRSF7'\n",
+       "97. 'SYNE1'\n",
+       "98. 'TESPA1'\n",
+       "99. 'TOMM7'\n",
+       "100. 'TUBA4A'\n",
+       "101. 'TXN'\n",
+       "102. 'UBA52'\n",
+       "103. 'UQCRB'\n",
+       "104. 'ZEB2'\n",
+       "105. 'ATP5A1'\n",
+       "106. 'CD48'\n",
+       "107. 'EIF3L'\n",
+       "108. 'FAU'\n",
+       "109. 'GPX1'\n",
+       "110. 'IPO7'\n",
+       "111. 'QARS'\n",
+       "112. 'RNASE6'\n",
+       "113. 'RPL7A'\n",
+       "114. 'RPS9'\n",
+       "115. 'S100A11'\n",
+       "116. 'TKT'\n",
+       "117. 'TNFAIP2'\n",
+       "118. 'ACTB'\n",
+       "119. 'ARPC2'\n",
+       "120. 'B2M'\n",
+       "121. 'EEF1B2'\n",
+       "122. 'GNB2L1'\n",
+       "123. 'KLRC1'\n",
+       "124. 'RPL15'\n",
+       "125. 'RPL18'\n",
+       "126. 'RPL23'\n",
+       "127. 'RPL29'\n",
+       "128. 'RPL30'\n",
+       "129. 'RPL34'\n",
+       "130. 'RPL35'\n",
+       "131. 'RPL37'\n",
+       "132. 'RPL37A'\n",
+       "133. 'RPL38'\n",
+       "134. 'RPL6'\n",
+       "135. 'RPLP0'\n",
+       "136. 'RPS16'\n",
+       "137. 'RPS20'\n",
+       "138. 'RPS27A'\n",
+       "139. 'RPS3'\n",
+       "140. 'RPS5'\n",
+       "141. 'RPSA'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  [1] \"RPL11\"       \"RPL35A\"      \"RPL41\"       \"RPLP2\"       \"ABHD14B\"    \n",
+       "  [6] \"ADAM19\"      \"AIF1\"        \"AKAP13\"      \"ATP2B1\"      \"ATP2B4\"     \n",
+       " [11] \"C12orf75\"    \"CD58\"        \"CHCHD2\"      \"COX7C\"       \"CST7\"       \n",
+       " [16] \"CYBA\"        \"EML4\"        \"ENTPD1\"      \"FOXP3\"       \"FTH1\"       \n",
+       " [21] \"GALM\"        \"GK\"          \"H3F3A\"       \"HLA-DPB1\"    \"HLA-DRA\"    \n",
+       " [26] \"IGBP1\"       \"ISG20\"       \"LINC00493\"   \"LSM5\"        \"MIAT\"       \n",
+       " [31] \"MIR4435-1HG\" \"MT2A\"        \"PRMT2\"       \"RGS1\"        \"RNF19A\"     \n",
+       " [36] \"SBDS\"        \"SERF2\"       \"TCF7\"        \"TIGIT\"       \"TMA7\"       \n",
+       " [41] \"TMSB10\"      \"TNFRSF1B\"    \"UBB\"         \"YBX1\"        \"YWHAB\"      \n",
+       " [46] \"ABLIM1\"      \"ANXA2\"       \"APOBEC3G\"    \"BHLHE40\"     \"C12orf57\"   \n",
+       " [51] \"C1orf228\"    \"CALM1\"       \"CCL4\"        \"CD55\"        \"CD81\"       \n",
+       " [56] \"CD8B\"        \"CFL1\"        \"CMC1\"        \"DNAJB6\"      \"EEF1D\"      \n",
+       " [61] \"EEF2\"        \"EIF1\"        \"EIF3E\"       \"EIF3K\"       \"EIF4B\"      \n",
+       " [66] \"FAIM3\"       \"FLNA\"        \"FOXP1\"       \"FTL\"         \"GLTSCR2\"    \n",
+       " [71] \"ID2\"         \"KLRB1\"       \"NACA\"        \"NDFIP1\"      \"NEAT1\"      \n",
+       " [76] \"NPM1\"        \"PFDN5\"       \"PPA1\"        \"PPP2R5C\"     \"PTPN7\"      \n",
+       " [81] \"RHOH\"        \"RIC3\"        \"RPL17\"       \"RPL24\"       \"RPL39\"      \n",
+       " [86] \"RPL4\"        \"RPS10\"       \"RPS11\"       \"RPS24\"       \"RPS4X\"      \n",
+       " [91] \"S100A6\"      \"SH3YL1\"      \"SLA\"         \"SMDT1\"       \"SRSF2\"      \n",
+       " [96] \"SRSF7\"       \"SYNE1\"       \"TESPA1\"      \"TOMM7\"       \"TUBA4A\"     \n",
+       "[101] \"TXN\"         \"UBA52\"       \"UQCRB\"       \"ZEB2\"        \"ATP5A1\"     \n",
+       "[106] \"CD48\"        \"EIF3L\"       \"FAU\"         \"GPX1\"        \"IPO7\"       \n",
+       "[111] \"QARS\"        \"RNASE6\"      \"RPL7A\"       \"RPS9\"        \"S100A11\"    \n",
+       "[116] \"TKT\"         \"TNFAIP2\"     \"ACTB\"        \"ARPC2\"       \"B2M\"        \n",
+       "[121] \"EEF1B2\"      \"GNB2L1\"      \"KLRC1\"       \"RPL15\"       \"RPL18\"      \n",
+       "[126] \"RPL23\"       \"RPL29\"       \"RPL30\"       \"RPL34\"       \"RPL35\"      \n",
+       "[131] \"RPL37\"       \"RPL37A\"      \"RPL38\"       \"RPL6\"        \"RPLP0\"      \n",
+       "[136] \"RPS16\"       \"RPS20\"       \"RPS27A\"      \"RPS3\"        \"RPS5\"       \n",
+       "[141] \"RPSA\"       "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(coloc_examples_rheomatoid_arthritis$cogene)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "2d8a881d-640d-4d98-8b42-2aa34b9c4d2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Retrieve gene positions an map to the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "3fbadfc4-a9cf-40b7-956a-ebc825d536f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mart = useEnsembl(biomart = \"genes\", dataset = \"hsapiens_gene_ensembl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "3b843643-ee9d-43e1-97f6-6c8aab485c7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geneSet =  unique(coloc_examples_rheomatoid_arthritis$cogene)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "0f5db24f-2b50-4f0a-a46a-0d3ef60fc4c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resultTable = biomaRt::getBM(attributes = c(\"start_position\",\"end_position\",\"description\", 'hgnc_symbol', 'chromosome_name'),       \n",
+    "                  filters    = \"hgnc_symbol\",       \n",
+    "                  values     = geneSet,         \n",
+    "                  mart       = mart)     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "87c93488-8c93-4e9f-ba07-0ef60310fe2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "170"
+      ],
+      "text/latex": [
+       "170"
+      ],
+      "text/markdown": [
+       "170"
+      ],
+      "text/plain": [
+       "[1] 170"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(resultTable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "a3029f51-434b-4993-bc58-fa412e6ae2fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "132"
+      ],
+      "text/latex": [
+       "132"
+      ],
+      "text/markdown": [
+       "132"
+      ],
+      "text/plain": [
+       "[1] 132"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(unique(resultTable$hgnc_symbol))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "1e7dae2b-02e8-4209-8c0c-b38baec966c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Filter out duplicate mappings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "18effb38-a5c8-4001-a1eb-6c9d0805b6bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter = resultTable %>% group_by(hgnc_symbol) %>% count() %>% filter(n >= 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "f49e9ca8-6519-4952-9263-4369e56540d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter = filter$hgnc_symbol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "90076d21-ce15-4c7e-baa6-7a1b8165331f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resultTable = resultTable[!resultTable$hgnc_symbol %in% filter,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "db8d7392-bf41-4d9c-b44f-d102e0f571b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>'3'</li><li>'10'</li><li>'7'</li><li>'5'</li><li>'15'</li><li>'22'</li><li>'2'</li><li>'12'</li><li>'1'</li><li>'14'</li><li>'11'</li><li>'20'</li><li>'16'</li><li>'19'</li><li>'17'</li><li>'8'</li><li>'X'</li><li>'21'</li><li>'4'</li><li>'18'</li><li>'9'</li><li>'6'</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item '3'\n",
+       "\\item '10'\n",
+       "\\item '7'\n",
+       "\\item '5'\n",
+       "\\item '15'\n",
+       "\\item '22'\n",
+       "\\item '2'\n",
+       "\\item '12'\n",
+       "\\item '1'\n",
+       "\\item '14'\n",
+       "\\item '11'\n",
+       "\\item '20'\n",
+       "\\item '16'\n",
+       "\\item '19'\n",
+       "\\item '17'\n",
+       "\\item '8'\n",
+       "\\item 'X'\n",
+       "\\item '21'\n",
+       "\\item '4'\n",
+       "\\item '18'\n",
+       "\\item '9'\n",
+       "\\item '6'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. '3'\n",
+       "2. '10'\n",
+       "3. '7'\n",
+       "4. '5'\n",
+       "5. '15'\n",
+       "6. '22'\n",
+       "7. '2'\n",
+       "8. '12'\n",
+       "9. '1'\n",
+       "10. '14'\n",
+       "11. '11'\n",
+       "12. '20'\n",
+       "13. '16'\n",
+       "14. '19'\n",
+       "15. '17'\n",
+       "16. '8'\n",
+       "17. 'X'\n",
+       "18. '21'\n",
+       "19. '4'\n",
+       "20. '18'\n",
+       "21. '9'\n",
+       "22. '6'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       " [1] \"3\"  \"10\" \"7\"  \"5\"  \"15\" \"22\" \"2\"  \"12\" \"1\"  \"14\" \"11\" \"20\" \"16\" \"19\" \"17\"\n",
+       "[16] \"8\"  \"X\"  \"21\" \"4\"  \"18\" \"9\"  \"6\" "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique(resultTable$chromosome_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "5cd66cd4-be5b-4aee-b318-94482723a064",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 5</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>start_position</th><th scope=col>end_position</th><th scope=col>description</th><th scope=col>hgnc_symbol</th><th scope=col>chromosome_name</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td> 51968510</td><td> 51983409</td><td>abhydrolase domain containing 14B [Source:HGNC Symbol;Acc:HGNC:28235]</td><td>ABHD14B</td><td>3 </td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>114431112</td><td>114768061</td><td>actin binding LIM protein 1 [Source:HGNC Symbol;Acc:HGNC:78]         </td><td>ABLIM1 </td><td>10</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 5\n",
+       "\\begin{tabular}{r|lllll}\n",
+       "  & start\\_position & end\\_position & description & hgnc\\_symbol & chromosome\\_name\\\\\n",
+       "  & <int> & <int> & <chr> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t1 &  51968510 &  51983409 & abhydrolase domain containing 14B {[}Source:HGNC Symbol;Acc:HGNC:28235{]} & ABHD14B & 3 \\\\\n",
+       "\t2 & 114431112 & 114768061 & actin binding LIM protein 1 {[}Source:HGNC Symbol;Acc:HGNC:78{]}          & ABLIM1  & 10\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 5\n",
+       "\n",
+       "| <!--/--> | start_position &lt;int&gt; | end_position &lt;int&gt; | description &lt;chr&gt; | hgnc_symbol &lt;chr&gt; | chromosome_name &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 1 |  51968510 |  51983409 | abhydrolase domain containing 14B [Source:HGNC Symbol;Acc:HGNC:28235] | ABHD14B | 3  |\n",
+       "| 2 | 114431112 | 114768061 | actin binding LIM protein 1 [Source:HGNC Symbol;Acc:HGNC:78]          | ABLIM1  | 10 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  start_position end_position\n",
+       "1  51968510       51983409   \n",
+       "2 114431112      114768061   \n",
+       "  description                                                          \n",
+       "1 abhydrolase domain containing 14B [Source:HGNC Symbol;Acc:HGNC:28235]\n",
+       "2 actin binding LIM protein 1 [Source:HGNC Symbol;Acc:HGNC:78]         \n",
+       "  hgnc_symbol chromosome_name\n",
+       "1 ABHD14B     3              \n",
+       "2 ABLIM1      10             "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(resultTable,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "c507579c-c4a1-4b4a-9e3c-edeabfca3da3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 2 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th><th scope=col>parameter</th><th scope=col>cogene</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>4259</th><td>Rheumatoid Arthritis</td><td>B_RPS26___RPL11__RPS26 </td><td>B_RPS26</td><td>0.9035670</td><td>B</td><td>RPL11_RPS26 </td><td>878</td><td>PP.H4.abf</td><td>RPL11 </td></tr>\n",
+       "\t<tr><th scope=row>4269</th><td>Rheumatoid Arthritis</td><td>B_RPS26___RPL35A__RPS26</td><td>B_RPS26</td><td>0.9008136</td><td>B</td><td>RPL35A_RPS26</td><td>878</td><td>PP.H4.abf</td><td>RPL35A</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 2 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & trait & identifier & egene & value & cell\\_type & gene & overlapping\\_snps & parameter & cogene\\\\\n",
+       "  & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t4259 & Rheumatoid Arthritis & B\\_RPS26\\_\\_\\_RPL11\\_\\_RPS26  & B\\_RPS26 & 0.9035670 & B & RPL11\\_RPS26  & 878 & PP.H4.abf & RPL11 \\\\\n",
+       "\t4269 & Rheumatoid Arthritis & B\\_RPS26\\_\\_\\_RPL35A\\_\\_RPS26 & B\\_RPS26 & 0.9008136 & B & RPL35A\\_RPS26 & 878 & PP.H4.abf & RPL35A\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 2 × 9\n",
+       "\n",
+       "| <!--/--> | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; | parameter &lt;chr&gt; | cogene &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 4259 | Rheumatoid Arthritis | B_RPS26___RPL11__RPS26  | B_RPS26 | 0.9035670 | B | RPL11_RPS26  | 878 | PP.H4.abf | RPL11  |\n",
+       "| 4269 | Rheumatoid Arthritis | B_RPS26___RPL35A__RPS26 | B_RPS26 | 0.9008136 | B | RPL35A_RPS26 | 878 | PP.H4.abf | RPL35A |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     trait                identifier              egene   value     cell_type\n",
+       "4259 Rheumatoid Arthritis B_RPS26___RPL11__RPS26  B_RPS26 0.9035670 B        \n",
+       "4269 Rheumatoid Arthritis B_RPS26___RPL35A__RPS26 B_RPS26 0.9008136 B        \n",
+       "     gene         overlapping_snps parameter cogene\n",
+       "4259 RPL11_RPS26  878              PP.H4.abf RPL11 \n",
+       "4269 RPL35A_RPS26 878              PP.H4.abf RPL35A"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coloc_examples_rheomatoid_arthritis,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "5fbdf162-b458-4ec4-91a0-630ddebe2724",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coloc_examples_rheomatoid_arthritis = merge(coloc_examples_rheomatoid_arthritis, resultTable, by.x = 'cogene', by.y = 'hgnc_symbol', all.x = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "56c5705e-36df-4c78-8f2b-cb3b74de65f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>cogene</th><th scope=col>trait</th><th scope=col>identifier</th><th scope=col>egene</th><th scope=col>value</th><th scope=col>cell_type</th><th scope=col>gene</th><th scope=col>overlapping_snps</th><th scope=col>parameter</th><th scope=col>start_position</th><th scope=col>end_position</th><th scope=col>description</th><th scope=col>chromosome_name</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>5</th><td>AIF1    </td><td>Rheumatoid Arthritis</td><td>CD4T_RPS26___AIF1__RPS26      </td><td>CD4T_RPS26    </td><td>0.9269214</td><td>CD4T    </td><td>AIF1_RPS26    </td><td>882</td><td>PP.H4.abf</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>AIF1    </td><td>Rheumatoid Arthritis</td><td>CD8T_RPS26___AIF1__RPS26      </td><td>CD8T_RPS26    </td><td>0.9590870</td><td>CD8T    </td><td>AIF1_RPS26    </td><td>882</td><td>PP.H4.abf</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>ATP5A1  </td><td>Rheumatoid Arthritis</td><td>monocyte_RPS26___ATP5A1__RPS26</td><td>monocyte_RPS26</td><td>0.9595247</td><td>monocyte</td><td>ATP5A1_RPS26  </td><td>882</td><td>PP.H4.abf</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>B2M     </td><td>Rheumatoid Arthritis</td><td>NK_RPS26___B2M__RPS26         </td><td>NK_RPS26      </td><td>0.9481042</td><td>NK      </td><td>B2M_RPS26     </td><td>879</td><td>PP.H4.abf</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td>C1orf228</td><td>Rheumatoid Arthritis</td><td>CD8T_RPS26___C1orf228__RPS26  </td><td>CD8T_RPS26    </td><td>0.9100051</td><td>CD8T    </td><td>C1orf228_RPS26</td><td>881</td><td>PP.H4.abf</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>CCL4    </td><td>Rheumatoid Arthritis</td><td>CD8T_RPS26___CCL4__RPS26      </td><td>CD8T_RPS26    </td><td>0.9169790</td><td>CD8T    </td><td>CCL4_RPS26    </td><td>882</td><td>PP.H4.abf</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & cogene & trait & identifier & egene & value & cell\\_type & gene & overlapping\\_snps & parameter & start\\_position & end\\_position & description & chromosome\\_name\\\\\n",
+       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <dbl> & <chr> & <int> & <int> & <chr> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t5 & AIF1     & Rheumatoid Arthritis & CD4T\\_RPS26\\_\\_\\_AIF1\\_\\_RPS26       & CD4T\\_RPS26     & 0.9269214 & CD4T     & AIF1\\_RPS26     & 882 & PP.H4.abf & NA & NA & NA & NA\\\\\n",
+       "\t6 & AIF1     & Rheumatoid Arthritis & CD8T\\_RPS26\\_\\_\\_AIF1\\_\\_RPS26       & CD8T\\_RPS26     & 0.9590870 & CD8T     & AIF1\\_RPS26     & 882 & PP.H4.abf & NA & NA & NA & NA\\\\\n",
+       "\t13 & ATP5A1   & Rheumatoid Arthritis & monocyte\\_RPS26\\_\\_\\_ATP5A1\\_\\_RPS26 & monocyte\\_RPS26 & 0.9595247 & monocyte & ATP5A1\\_RPS26   & 882 & PP.H4.abf & NA & NA & NA & NA\\\\\n",
+       "\t14 & B2M      & Rheumatoid Arthritis & NK\\_RPS26\\_\\_\\_B2M\\_\\_RPS26          & NK\\_RPS26       & 0.9481042 & NK       & B2M\\_RPS26      & 879 & PP.H4.abf & NA & NA & NA & NA\\\\\n",
+       "\t19 & C1orf228 & Rheumatoid Arthritis & CD8T\\_RPS26\\_\\_\\_C1orf228\\_\\_RPS26   & CD8T\\_RPS26     & 0.9100051 & CD8T     & C1orf228\\_RPS26 & 881 & PP.H4.abf & NA & NA & NA & NA\\\\\n",
+       "\t21 & CCL4     & Rheumatoid Arthritis & CD8T\\_RPS26\\_\\_\\_CCL4\\_\\_RPS26       & CD8T\\_RPS26     & 0.9169790 & CD8T     & CCL4\\_RPS26     & 882 & PP.H4.abf & NA & NA & NA & NA\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | cogene &lt;chr&gt; | trait &lt;chr&gt; | identifier &lt;chr&gt; | egene &lt;chr&gt; | value &lt;dbl&gt; | cell_type &lt;chr&gt; | gene &lt;chr&gt; | overlapping_snps &lt;dbl&gt; | parameter &lt;chr&gt; | start_position &lt;int&gt; | end_position &lt;int&gt; | description &lt;chr&gt; | chromosome_name &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 5 | AIF1     | Rheumatoid Arthritis | CD4T_RPS26___AIF1__RPS26       | CD4T_RPS26     | 0.9269214 | CD4T     | AIF1_RPS26     | 882 | PP.H4.abf | NA | NA | NA | NA |\n",
+       "| 6 | AIF1     | Rheumatoid Arthritis | CD8T_RPS26___AIF1__RPS26       | CD8T_RPS26     | 0.9590870 | CD8T     | AIF1_RPS26     | 882 | PP.H4.abf | NA | NA | NA | NA |\n",
+       "| 13 | ATP5A1   | Rheumatoid Arthritis | monocyte_RPS26___ATP5A1__RPS26 | monocyte_RPS26 | 0.9595247 | monocyte | ATP5A1_RPS26   | 882 | PP.H4.abf | NA | NA | NA | NA |\n",
+       "| 14 | B2M      | Rheumatoid Arthritis | NK_RPS26___B2M__RPS26          | NK_RPS26       | 0.9481042 | NK       | B2M_RPS26      | 879 | PP.H4.abf | NA | NA | NA | NA |\n",
+       "| 19 | C1orf228 | Rheumatoid Arthritis | CD8T_RPS26___C1orf228__RPS26   | CD8T_RPS26     | 0.9100051 | CD8T     | C1orf228_RPS26 | 881 | PP.H4.abf | NA | NA | NA | NA |\n",
+       "| 21 | CCL4     | Rheumatoid Arthritis | CD8T_RPS26___CCL4__RPS26       | CD8T_RPS26     | 0.9169790 | CD8T     | CCL4_RPS26     | 882 | PP.H4.abf | NA | NA | NA | NA |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   cogene   trait                identifier                     egene         \n",
+       "5  AIF1     Rheumatoid Arthritis CD4T_RPS26___AIF1__RPS26       CD4T_RPS26    \n",
+       "6  AIF1     Rheumatoid Arthritis CD8T_RPS26___AIF1__RPS26       CD8T_RPS26    \n",
+       "13 ATP5A1   Rheumatoid Arthritis monocyte_RPS26___ATP5A1__RPS26 monocyte_RPS26\n",
+       "14 B2M      Rheumatoid Arthritis NK_RPS26___B2M__RPS26          NK_RPS26      \n",
+       "19 C1orf228 Rheumatoid Arthritis CD8T_RPS26___C1orf228__RPS26   CD8T_RPS26    \n",
+       "21 CCL4     Rheumatoid Arthritis CD8T_RPS26___CCL4__RPS26       CD8T_RPS26    \n",
+       "   value     cell_type gene           overlapping_snps parameter start_position\n",
+       "5  0.9269214 CD4T      AIF1_RPS26     882              PP.H4.abf NA            \n",
+       "6  0.9590870 CD8T      AIF1_RPS26     882              PP.H4.abf NA            \n",
+       "13 0.9595247 monocyte  ATP5A1_RPS26   882              PP.H4.abf NA            \n",
+       "14 0.9481042 NK        B2M_RPS26      879              PP.H4.abf NA            \n",
+       "19 0.9100051 CD8T      C1orf228_RPS26 881              PP.H4.abf NA            \n",
+       "21 0.9169790 CD8T      CCL4_RPS26     882              PP.H4.abf NA            \n",
+       "   end_position description chromosome_name\n",
+       "5  NA           NA          NA             \n",
+       "6  NA           NA          NA             \n",
+       "13 NA           NA          NA             \n",
+       "14 NA           NA          NA             \n",
+       "19 NA           NA          NA             \n",
+       "21 NA           NA          NA             "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(coloc_examples_rheomatoid_arthritis[is.na(coloc_examples_rheomatoid_arthritis$chromosome_name),])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "5ff8f920-e5f3-4a88-8658-380368b1ee69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#head(coloc_examples_rheomatoid_arthritis,4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71e571e3-a131-4ff4-b10b-ca361e795c60",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "7f584316-cf94-4cf6-8480-1fd1dc05cc53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "152"
+      ],
+      "text/latex": [
+       "152"
+      ],
+      "text/markdown": [
+       "152"
+      ],
+      "text/plain": [
+       "[1] 152"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(coloc_examples_rheomatoid_arthritis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "095b56c8-4aee-4d32-9c52-75c0d363877e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.table(coloc_examples_rheomatoid_arthritis, file = paste0(path, \"/colocalization_results/\", \"CD4T_RPS26_Rheomatoid_Arthritis_Colocalization_CoeGenes.csv\"), append =FALSE, sep = \",\", row.names = FALSE, col.names =TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77eeb1ed-b252-4e30-a6ed-94bb73f2fdd1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e18e4ba-3e2f-4809-9933-ca9fe8d0cab3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "4.1.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/05_coeqtl_interpretation/README.md b/05_coeqtl_interpretation/README.md
new file mode 100644
index 0000000..de5e6c7
--- /dev/null
+++ b/05_coeqtl_interpretation/README.md
@@ -0,0 +1,33 @@
+# 05_coeqtl_interpretation
+
+*enrichment_GO_terms.R* :  checks GO enrichment among co-eGenes
+
+*enrichment_GO_terms_reduced_background.R* : alternative version of *enrichment_GO_terms.R*  with a more specific background, including only genes tested for the respective SNP-eGene combination
+
+*enrichment_TFs_Remap_preprocessing.R*: filters TFBS annotations from Remap2022 for blood-related cell lines
+
+*enrichment_TFs_Remap.R* : checks enrichment of TFBS among co-eGenes using Remap 2022 annotations in three steps: 1) checks for each cell type and coeGene cluster the enrichment (FDR corrected), 2) checks if the enriched TF is itself part of the coeGenes, 3) checks if the SNP or a SNP in LD is part of the TF
+
+*general_stats.R*: shows general distribution of co-eQTLs (coeGenes per eQTL, distribution of direction of effect)
+
+*magma_coeqtl.Rmd*: R-markdown to perform MAGMA analysis for  GWAS enrichment separately for each set of coeGenes that share the same eQTL (for all eQTLs with at least 5 coeGenes)
+
+*plot_CD4T_mono_network.R*: Create network of all co-eQTLs from CD4+ T cells and/or Monocytes (nodes: eQTLs and coeGenes), color edges by direction of effect
+
+*plot_CD4T_mono_RPS26_subnetwork.R*: Part of the co-eQTL network from *plot_CD4T_mono_network.R* connected with rs1131017-RPS26
+
+*snipe.R*: help functions to query SNiPA website for SNPs in high LD with query SNPs, used in *enrichment_TFs_Remap.R*
+
+*LDTRAIT.ipynb*: Add GWAS annotation to SNP / SNP in LD
+
+*TEM_NAIVE.ipynb*: Examine the impact of rs1131017 on the ratio between TEM and naive T cells
+
+*MS1_Libraries.r*: libraries used for R1_TRANSFAC_enrichment.ipynb, R2_Coloc.ipynb, R3_Coloc_Evaluation.ipynb scripts
+
+*R1_TRANFAC_enrichment.ipynb*: checks TRANSFAC enrichment among co-eGenes and compares them to the Remap enrichment results
+
+*R2_Coloc.ipynb*: runs colocalization analysis on the output of the eqtl and co-eqtl pipeline
+
+*R3_Coloc_Evaluation.ipynb*: investigates and summarizes the results from the colocalization analysis of script R2_Coloc.ipynb
+
+
diff --git a/05_coeqtl_interpretation/TEM_NAIVE.ipynb b/05_coeqtl_interpretation/TEM_NAIVE.ipynb
new file mode 100644
index 0000000..314c2f6
--- /dev/null
+++ b/05_coeqtl_interpretation/TEM_NAIVE.ipynb
@@ -0,0 +1,1437 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import re\n",
+    "from itertools import combinations\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
+    "from scipy.stats import spearmanr\n",
+    "from scipy.stats import t, norm\n",
+    "from tqdm import tqdm\n",
+    "import argparse\n",
+    "from scipy.stats import rankdata\n",
+    "from collections import namedtuple\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "def get_time(x):\n",
+    "    if x == 'UT':\n",
+    "        return x\n",
+    "    else:\n",
+    "        pattern = re.compile(r'\\d+h')\n",
+    "        return re.findall(pattern, x)[0]\n",
+    "\n",
+    "\n",
+    "class DATASET:\n",
+    "    def __init__(self, datasetname):\n",
+    "        self.name = datasetname\n",
+    "        self.path_prefix = Path(\"./seurat_objects\")\n",
+    "        self.information = self.get_information()\n",
+    "    def get_information(self):\n",
+    "        if self.name == 'onemillionv2':\n",
+    "            self.path = '1M_v2_mediumQC_ctd_rnanormed_demuxids_20201029.sct.h5ad'\n",
+    "            self.individual_id_col = 'assignment'\n",
+    "            self.timepoint_id_col = 'time'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 'UT',\n",
+    "                                     'stimulated': '3h'}\n",
+    "        elif self.name == 'onemillionv3':\n",
+    "            self.path = '1M_v3_mediumQC_ctd_rnanormed_demuxids_20201106.SCT.h5ad'\n",
+    "            self.individual_id_col = 'assignment'\n",
+    "            self.timepoint_id_col = 'time'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 'UT',\n",
+    "                                     'stimulated': '3h'}\n",
+    "        elif self.name == 'stemiv2':\n",
+    "            self.path = 'cardio.integrated.20210301.stemiv2.h5ad'\n",
+    "            self.individual_id_col = 'assignment.final'\n",
+    "            self.timepoint_id_col = 'timepoint.final'\n",
+    "            self.celltype_id = 'cell_type_lowerres'\n",
+    "            self.chosen_condition = {'UT': 't8w',\n",
+    "                                     'stimulated': 'Baseline'}\n",
+    "        elif self.name == 'ng':\n",
+    "            self.path = 'pilot3_seurat3_200420_sct_azimuth.h5ad'\n",
+    "            self.individual_id_col = 'snumber'\n",
+    "            self.celltype_id = 'cell_type_mapped_to_onemillion'\n",
+    "        else:\n",
+    "            raise IOError(\"Dataset name not understood.\")\n",
+    "    def load_dataset(self):\n",
+    "        self.get_information()\n",
+    "        print(f'Loading dataset {self.name} from {self.path_prefix} {self.path}')\n",
+    "        self.data_sc = sc.read_h5ad(self.path_prefix / self.path)\n",
+    "        if self.name.startswith('onemillion'):\n",
+    "            self.data_sc.obs['time'] = [get_time(item) for item in self.data_sc.obs['timepoint']]\n",
+    "        elif self.name == 'ng':\n",
+    "            celltype_maping = {'CD4 T': 'CD4T', 'CD8 T': 'CD8T', 'Mono': 'monocyte', 'DC': 'DC', 'NK': 'NK',\n",
+    "                               'other T': 'otherT', 'other': 'other', 'B': 'B'}\n",
+    "            self.data_sc.obs['cell_type_mapped_to_onemillion'] = [celltype_maping.get(name) for name in\n",
+    "                                                          self.data_sc.obs['predicted.celltype.l1']]\n",
+    "\n",
+    "def corr_to_z(coef, num):\n",
+    "    t_statistic = coef * np.sqrt((num - 2) / (1 - coef ** 2))\n",
+    "    prob = t.cdf(t_statistic, num - 2)\n",
+    "    z_score = norm.ppf(prob)\n",
+    "    positive_coef_probs = 1 - prob\n",
+    "    positive_coef_probs[coef < 0] = 0\n",
+    "    negative_coef_probs = prob\n",
+    "    negative_coef_probs[coef > 0] = 0\n",
+    "    probs = negative_coef_probs + positive_coef_probs\n",
+    "    return z_score, probs\n",
+    "\n",
+    "\n",
+    "def get_individual_networks_selected_genepairs(data_df, data_sc, individual_colname, genepair):\n",
+    "#     data_df = pd.DataFrame(data=data_sc.X.toarray(),\n",
+    "#                            index=data_sc.obs.index,\n",
+    "#                            columns=data_sc.var.index)\n",
+    "    gene1, gene2 = genepair.split(';')\n",
+    "    sorted_genepair = [';'.join(sorted([gene1, gene2]))]\n",
+    "    coef_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    coef_p_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    zscore_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    zscore_p_df = pd.DataFrame(index=sorted_genepair)\n",
+    "    data_selected_df = data_df[[gene1, gene2]]\n",
+    "    print(\n",
+    "        f\"Begin calculating networks for {len(data_sc.obs[individual_colname].unique())} individuals and;\\n{genepair}\"\n",
+    "    )\n",
+    "    for ind_id in tqdm(data_sc.obs[individual_colname].unique()):\n",
+    "        cell_num = data_sc.obs[data_sc.obs[individual_colname] == ind_id].shape[0]\n",
+    "        if cell_num > 10:\n",
+    "            individual_df = data_selected_df.loc[data_sc.obs[individual_colname] == ind_id]\n",
+    "            individual_coefs, individual_coef_ps = spearmanr(individual_df.values, axis=0)\n",
+    "            if data_selected_df.shape[1] == 2:\n",
+    "                individual_coefs_flatten = pd.DataFrame(data = [individual_coefs],\n",
+    "                                                        index = sorted_genepair)\n",
+    "                individual_coef_ps_flatten = \\\n",
+    "                pd.DataFrame(data=[individual_coef_ps],\n",
+    "                             index=sorted_genepair)\n",
+    "            else:\n",
+    "                individual_coefs_flatten = pd.DataFrame(\n",
+    "                    data=individual_coefs[np.triu_indices_from(individual_coefs, 1)],\n",
+    "                    index=selected_genes_sorted_genepairs).loc[sorted_genepair]\n",
+    "                individual_coef_ps_flatten = \\\n",
+    "                pd.DataFrame(data=individual_coef_ps[np.triu_indices_from(individual_coefs, 1)],\n",
+    "                             index=selected_genes_sorted_genepairs).loc[sorted_genepair]\n",
+    "            coef_df[ind_id] = individual_coefs_flatten\n",
+    "            coef_p_df[ind_id] = individual_coef_ps_flatten\n",
+    "            try:\n",
+    "#                 print(individual_coefs_flatten.values, cell_num)\n",
+    "                individual_zscores_flatten, individual_zscore_ps_flatten = corr_to_z(\n",
+    "                    individual_coefs_flatten.values, \n",
+    "                    cell_num\n",
+    "                )\n",
+    "                zscore_df[ind_id] = individual_zscores_flatten\n",
+    "                zscore_p_df[ind_id] = individual_zscore_ps_flatten\n",
+    "            except:\n",
+    "                continue\n",
+    "        else:\n",
+    "            print(\"Deleted this individual because of low cell number\", cell_num)\n",
+    "    return data_selected_df, zscore_df, zscore_p_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### One million data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the GT data\n",
+    "gt = pd.read_csv('./coeqtl_interpretation/rs1131017_TEM_ratio/rs1131017.vcf',\n",
+    "                 skiprows=6, sep='\\t')\n",
+    "change_colnames = lambda col:'_'.join(col.split('_')[1:]) if 'LLDeep' in col else col\n",
+    "gt = gt.rename({col:change_colnames(col) for col in gt.columns}, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>#CHROM</th>\n",
+       "      <th>POS</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>REF</th>\n",
+       "      <th>ALT</th>\n",
+       "      <th>QUAL</th>\n",
+       "      <th>FILTER</th>\n",
+       "      <th>INFO</th>\n",
+       "      <th>FORMAT</th>\n",
+       "      <th>LLDeep_1191</th>\n",
+       "      <th>...</th>\n",
+       "      <th>s21</th>\n",
+       "      <th>s43</th>\n",
+       "      <th>s24</th>\n",
+       "      <th>s23</th>\n",
+       "      <th>s45</th>\n",
+       "      <th>s26</th>\n",
+       "      <th>s25</th>\n",
+       "      <th>s28</th>\n",
+       "      <th>s27</th>\n",
+       "      <th>s29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12</td>\n",
+       "      <td>56435929</td>\n",
+       "      <td>rs1131017</td>\n",
+       "      <td>C</td>\n",
+       "      <td>G</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>.</td>\n",
+       "      <td>GT:DS</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>0/1:1.0</td>\n",
+       "      <td>0/0:0.0</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "      <td>0/0:0.06000000000000005</td>\n",
+       "      <td>1/1:2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 182 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   #CHROM       POS         ID REF ALT QUAL FILTER INFO FORMAT LLDeep_1191  \\\n",
+       "0      12  56435929  rs1131017   C   G    .      .    .  GT:DS     0/0:0.0   \n",
+       "\n",
+       "   ...      s21      s43      s24      s23      s45      s26      s25  \\\n",
+       "0  ...  1/1:2.0  1/1:2.0  1/1:2.0  0/1:1.0  1/1:2.0  0/1:1.0  0/0:0.0   \n",
+       "\n",
+       "       s28                      s27      s29  \n",
+       "0  1/1:2.0  0/0:0.06000000000000005  1/1:2.0  \n",
+       "\n",
+       "[1 rows x 182 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gt.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CD4T+CD8T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # load onemillion v2 data\n",
+    "# dataset = DATASET('onemillionv2')\n",
+    "# dataset.load_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>assignment</th>\n",
+       "      <th>predicted.celltype.l1</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">LLDeep_0022</th>\n",
+       "      <th>B</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD4 T</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD8 T</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DC</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mono</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   0\n",
+       "assignment  predicted.celltype.l1   \n",
+       "LLDeep_0022 B                      0\n",
+       "            CD4 T                  0\n",
+       "            CD8 T                  0\n",
+       "            DC                     0\n",
+       "            Mono                   0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>assignment</th>\n",
+       "      <th>predicted.celltype.l2</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">LLDeep_0022</th>\n",
+       "      <th>ASDC</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B intermediate</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B memory</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B naive</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CD14 Mono</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   0\n",
+       "assignment  predicted.celltype.l2   \n",
+       "LLDeep_0022 ASDC                   0\n",
+       "            B intermediate         0\n",
+       "            B memory               0\n",
+       "            B naive                0\n",
+       "            CD14 Mono              0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T TEM</th>\n",
+       "      <th>CD4T Naive</th>\n",
+       "      <th>CD8T TEM</th>\n",
+       "      <th>CD8T Naive</th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>CD4T TCM</th>\n",
+       "      <th>CD8T TCM</th>\n",
+       "      <th>all_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1370</th>\n",
+       "      <td>20</td>\n",
+       "      <td>85</td>\n",
+       "      <td>74</td>\n",
+       "      <td>13</td>\n",
+       "      <td>224</td>\n",
+       "      <td>89</td>\n",
+       "      <td>85</td>\n",
+       "      <td>8</td>\n",
+       "      <td>827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0434</th>\n",
+       "      <td>42</td>\n",
+       "      <td>417</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "      <td>989</td>\n",
+       "      <td>209</td>\n",
+       "      <td>406</td>\n",
+       "      <td>29</td>\n",
+       "      <td>1496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1319</th>\n",
+       "      <td>110</td>\n",
+       "      <td>132</td>\n",
+       "      <td>223</td>\n",
+       "      <td>4</td>\n",
+       "      <td>922</td>\n",
+       "      <td>236</td>\n",
+       "      <td>546</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0269</th>\n",
+       "      <td>21</td>\n",
+       "      <td>58</td>\n",
+       "      <td>80</td>\n",
+       "      <td>7</td>\n",
+       "      <td>211</td>\n",
+       "      <td>88</td>\n",
+       "      <td>101</td>\n",
+       "      <td>8</td>\n",
+       "      <td>529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0471</th>\n",
+       "      <td>27</td>\n",
+       "      <td>242</td>\n",
+       "      <td>81</td>\n",
+       "      <td>14</td>\n",
+       "      <td>570</td>\n",
+       "      <td>103</td>\n",
+       "      <td>254</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1241</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
+       "LLDeep_1370        20          85        74          13   224    89        85   \n",
+       "LLDeep_0434        42         417        95          93   989   209       406   \n",
+       "LLDeep_1319       110         132       223           4   922   236       546   \n",
+       "LLDeep_0269        21          58        80           7   211    88       101   \n",
+       "LLDeep_0471        27         242        81          14   570   103       254   \n",
+       "\n",
+       "             CD8T TCM  all_num  \n",
+       "LLDeep_1370         8      827  \n",
+       "LLDeep_0434        29     1496  \n",
+       "LLDeep_1319        19     1504  \n",
+       "LLDeep_0269         8      529  \n",
+       "LLDeep_0471        10     1241  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onemillionv2 = dataset.data_sc.obs.copy()\n",
+    "onemillionv2_celltype_df = pd.read_csv(\n",
+    "    './1M_v2_20201029_azimuth.tsv',\n",
+    "    sep='\\t', index_col=0\n",
+    ")\n",
+    "onemillionv2 = pd.concat([onemillionv2, onemillionv2_celltype_df], axis=1)\n",
+    "onemillionv2 = onemillionv2[onemillionv2['timepoint']=='UT']\n",
+    "onemillionv2_l1_cellratio_df = onemillionv2.groupby(['assignment', 'predicted.celltype.l1']).size().to_frame()\n",
+    "display(onemillionv2_l1_cellratio_df.head())\n",
+    "onemillionv2_celltyperatio = onemillionv2.groupby(['assignment', 'predicted.celltype.l2']).size().to_frame()\n",
+    "display(onemillionv2_celltyperatio.head())\n",
+    "onemillionv2_allcells = onemillionv2['assignment'].value_counts()\n",
+    "\n",
+    "# caluclate the individual CD4T TEM and NAIVE ratio\n",
+    "individual_ratio = pd.DataFrame()\n",
+    "for individual in onemillionv2['assignment'].unique():\n",
+    "    tem_num = onemillionv2_celltyperatio.loc[individual, \"CD4 TEM\"].values[0]\n",
+    "    naive_num = onemillionv2_celltyperatio.loc[individual, \"CD4 Naive\"].values[0]\n",
+    "    cd8t_tem_num = onemillionv2_celltyperatio.loc[individual, \"CD8 TEM\"].values[0]\n",
+    "    tcm_num = onemillionv2_celltyperatio.loc[individual, \"CD4 TCM\"].values[0]\n",
+    "    cd8t_tcm_num = onemillionv2_celltyperatio.loc[individual, \"CD8 TCM\"].values[0]\n",
+    "    cd8t_naive_num = onemillionv2_celltyperatio.loc[individual, \"CD8 Naive\"].values[0]\n",
+    "    cd4t_num = onemillionv2_l1_cellratio_df.loc[individual, 'CD4 T'].values[0]\n",
+    "    cd8t_num = onemillionv2_l1_cellratio_df.loc[individual, 'CD8 T'].values[0]\n",
+    "    all_num = onemillionv2_allcells.loc[individual]\n",
+    "    individual_ratio[individual] = [tem_num,  naive_num, \n",
+    "                                    cd8t_tem_num, cd8t_naive_num,\n",
+    "                                    cd4t_num, cd8t_num,\n",
+    "                                    tcm_num, cd8t_tcm_num, all_num]\n",
+    "\n",
+    "individual_ratio_df = individual_ratio.T\n",
+    "individual_ratio_df = individual_ratio_df.rename({0: 'CD4T TEM', 1:'CD4T Naive', \n",
+    "                                                  2: 'CD8T TEM', 3: 'CD8T Naive',\n",
+    "                                                  4: 'CD4T', 5: 'CD8T',\n",
+    "                                                  6: 'CD4T TCM', 7: 'CD8T TCM',\n",
+    "                                                  8: 'all_num'}, \n",
+    "                                                 axis=1)\n",
+    "display(individual_ratio_df.head())\n",
+    "\n",
+    "\n",
+    "common_individuals = list(set(individual_ratio_df.index) & set(gt.columns))\n",
+    "common_individuals_individual_ratio_df = individual_ratio_df.loc[common_individuals]\n",
+    "common_individuals_individual_ratio_df['gt'] = [float(gt[col].values[0].split(':')[1]) for col in \n",
+    "                                                common_individuals_individual_ratio_df.index]\n",
+    "common_individuals_individual_ratio_df['chemistry'] = 'v2'\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax1, ax2, ax3 = axes\n",
+    "cd4ydata = (common_individuals_individual_ratio_df['CD4T TEM']) / common_individuals_individual_ratio_df['CD4T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD4 TEM / CD4T')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_df['CD4T Naive']) / common_individuals_individual_ratio_df['CD4T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax2)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax2.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax2.set_ylabel('CD4 Naive / CD4T')\n",
+    "ax2.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_df['CD4T Naive']) / common_individuals_individual_ratio_df['CD4T TEM']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax3)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax3.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax3.set_ylabel('CD4 Naive / CD4T TEM')\n",
+    "ax3.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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zBdzDn/mic9PVffVuWrkFa3C6+4i7j0XX7wHSZrZp9ZoojcgsOjdgZZfy7SIidRCTz15ZRQrwpKEcG8qSMEgkDDMjkTASFm6Xxrd3dy+3vv5yertaOTeRp7erlVtff3mjTbF9GNhlZjvNLANcT1iXc5qZbbHoW7uZXUXYVw6tekuloYxOFdi6vpVUwigGTiphbF3fythUod5NE5E1LCafvbKKtAZPRFbU3t29Df2h4u4FM3sncB9hmYRPuPtjZvaOaP+dwHXAfzGzAjABXB+VdpE1bFtPO4Ojk/Rt7pzels0V6O1qrWOrREQa/7NXVpcCPGkofZs6ODo4hrljBu4QOOza3FHvpskixSFJTjTt8p6KbXeWXf9j4I9Xu13S2G66uo9bDjxGNlegLZ1kIl/UNCgREWk4TTlF8+CRQW6460Feefv93HDXg0rQESM3X7ObnvY0BhSKAQb0tKe5+Zrd9W6aLIJq8Ugz0zQoERGJg6YbwSt9wUwnbdYXzFtBH8IxsHd3Lx+87gr2Hern+HCWSxp0BEjmVl6LB6A9kyKbK7DvUL9eQ2kKmgYlIiKNrulG8FTssXlowVP8DAxnaUvPThmvWjwiIiIiq6fpAjx9wYw31cGLN9XiEREREamvpgvw9AUz3lQHL95Ui0dERESkvpouwNMXzHg7NpSlGDhTxYDJQsBUMaAYuOrgxYSSUIiIiIjUV9MlWdm7u5dbQUk6YipfCM5be+fRdokHJaEQERERqZ+mC/BAXzBjzZg7u4qtdkNERERWl5l9AvgJYNDdX1zluCuBB4Gfdvf9q9U+EYmHppuiKfEWzJM6c77tIiIiTeSTwDXVDjCzJHA7cN9qNEhE4kcBnjSUZGLuobr5touIiDQLdz8EnFngsP8KfAZQemkRmVNTTtGU+OrIJBmZLMyakenRdhERkeU4eGSQfYf6GRjOsi2Ga/TNbCvwRuBVwJV1bo6INCiN4ElDufx56+hsSeIwfelsSXL589bVuWUiIhJnB48McsuBxxgcnWR9W5rB0UluOfBY3Oqsfhi42d2LCx1oZjea2SNm9sipU6dq3zIRaRgawZOG8oq+DTz01BnSSSNh4dq7iXzAK/o21LtpskhxP0MuIs1p36F+0kmjPRN+9WnPpMjmCuw71B+nPmoPcLeZAWwCXmtmBXf/fOWB7n4XcBfAnj17tJJdZA3RCJ40lAf6z7C5M0MmmSBwyCQTbO7M8ED/QksSpBEcPDLIe/cf5msDw5wcmeRrA8O8d//huJ0hF5EmNDCcpS09e7p/WzrJ8eH41Fl1953uvsPddwD7gV+cK7gTkbVNI3jSUAaGs2zqbGFzV+v0NneP1QfwWnbbvY8zPJ4j8HB6baHo5As5brv38TidIReRJrStp53B0cnpETyAiXyRS3ra69iq2czs08BeYJOZHQd+E0gDuPuddWyaiMSIAjxpKHH4AJb5PXlqnKKHZQtLiXKKDt85NV7PZomIcNPVfdxy4DGyuQJt6SQT+SL5onPT1X31bto0d7/hAo59Ww2bIiIxpima0lBuurqPfNHJ5gq4hz8b7QNY5lcsFSw0ZkV5BRUyFJE627u7l1tffzm9Xa2cm8jT29XKra+/XLMLRKTpaARPGsre3b3cSrgY/vhwlkuUpCNWUkkjX4zmZxrhz2i7iEi97d3dq88TEWl6GsGThqUxn/h5/qYOSrGcRy9g0sLtIiIiIlJ7CvCkoSgLY7y9/9rLaM8ksSjIM4P2TJL3X3tZfRsmskIOHhnkhrse5JW3388Ndz2ovklERBpOU07RVB2u+Lrt3sc5m82TNCNphgdwNptXFsYYaU0nyRedQhCQSiRorUhLLhJXpULZ6aTNKpR9K6h/EhGRhtF0I3ilD+DB0clZH8A6yxoPx4ayJAwSCcPMSCTCgufHhlQmIQ72Heqnuy3Nrou6uOzidey6qIvutjT7DvXXu2kiy1ZeKNss/JlOmt7fIiLSUGoa4JnZNWb2bTN70szeP8f+HjP7nJl9w8weMrMXL/cx9QEsUj/NUEhYZD56f4uISBzULMAzsyTwUeBa4EXADWb2oorDfh34uru/BPhZ4I7lPq4+gOOtb1MHgUPgjuME7gQebpfGt62nndNjU/SfGuPIcyP0nxrj9NiU6hhKU9jW085Evjhrm+p0iohIo6nlCN5VwJPu3u/uOeBu4A0Vx7wI+GcAdz8C7DCzi5bzoPoAjrebr9lNT3saAwrFAAN62tPcfM3uejdNFuEVfRs4NZYjVwxIGOSKAafGcryib0O9myaybKrTKSIicVDLAG8rMFD2+/FoW7nDwE8BmNlVwKXAJZV3ZGY3mtkjZvbIqVOnqj6oPoDjbe/uXj543RW8dHsPF69r46Xbe/jgdVcogUFMPNB/ht6uDJlkgsAhk0zQ25Xhgf4z9W6ayLKpULaIiMRBLbNozlXZuLK02W3AHWb2deDfga8BhfNu5H4XcBfAnj17qpZHU6Hs+FMh2vgaGM6SSc4+b5RJJjRFWpqG+icREWl0tQzwjgPbyn6/BHim/AB3HwF+HsDMDDgWXZZFH8Ai9dHVkuLo4BjJhJFMGIXAOXF2kl29nfVumoiIiMiaUMspmg8Du8xsp5llgOuBA+UHmNn6aB/A24FDUdAnIjHkHg2we9mlfLuIiIiI1FTNRvDcvWBm7wTuA5LAJ9z9MTN7R7T/TuAy4FNmVgS+BfzCSjy2Cp2L1MdYrkhPe4qh8TyBQ8JgY0ea8Vxx4RuLiIiIyLLVcoom7n4PcE/FtjvLrj8A7FrJxywVOk8nbVah81tBQV5MKECPr85MkufOTZJOJDADdxjOFnjB5pZ6N01ERKRp6buTlKtpofN6UKHzeCsF6IOjk7MC9INHBuvdNFmEcCktYYql0qV8u4iIiKwofXeSSk0X4KnQebztO9RPvljkuXOTfPvkKM+dmyRfLCpAj4nRqQJb17eSShjFwEkljK3rWxmbOi85roiIiKwADW5IpZpO0ayHbT3tDI5O0p6ZeWoqdB4fRwdHOZfNkyjLwnh6NEe+OFrvpskilP7++jbPZM3M5gr0drXWsVUiIiLNa2A4y/q29KxtGtxY25puBE+FzuMtVwjAIGGGYSTMwKLt0vD097ewg0cGueGuB3nl7fdzw10PagqNiIgsy7aedibys5OZaXBjbWu6AG/v7l5uff3l9Ha1cm4iT29XK7e+/nItNI2JdDKc2jeZLzKRLzKZL1IMnExSa7jiQH9/1WmdhIiIrDSdXJVKTTdFE1ToPM42d7ZwNpunvGqaAZs6lYUxLvT3N7/ydRIA7ZkU2VyBfYf69X8mIiJLsnd3L7cSfsYcH85yibJornlNGeBJfJkZZkYmadNp9ouBKwujNAWtkxARkVrQyVUp13RTNCXelIVRmpnWSYiIiEitNeUInoo9xte2nnaOnR6btW2qELBzU+c8txCJj5uu7uOWA4+RzRVoSyeZyBe1TiJm9PkiIiKNrulG8JTEIN5e0beBU2M5csWAhEGuGHBqLMcr+jbUu2nSRMzsGjP7tpk9aWbvr3LclWZWNLPrVuJxlYQm3vT5IrVmZp8ws0Ez++Y8+99iZt+ILv9mZlesdhtFpPE13QiekhjE2wP9Z+jtyjAyUSBXDMgkE3S3pXig/wzvqnfjZFGKgU+PUKWSjXcOycySwEeB1wDHgYfN7IC7f2uO424H7lvJx9c6ifjad6ifXKHI0NhM/9TVmtLni6ykTwJ/DHxqnv3HgB9y92Ezuxa4C/j+VWqbiMRE0wV4SmIQbwPDWTZ2tLCpc6Ywtrvr9WtwuULARK7IeK7AZLTGbGtPW6N2MFcBT7p7P4CZ3Q28AfhWxXH/FfgMcOXqNk8a1RMnRxiZLJDASJpRKDpD4zkKxZF6N02ahLsfMrMdVfb/W9mvDwKX1LxRIhI7Dfr9a+m29bQzODo5PYIHSmIQJ3r94iEInImoVuFErki+GKtC9FuBgbLfj1NxBtzMtgJvBF6FAjyJ5IthAZdEIszqaxb+LeSKXu1mIrXyC8C99W6EiDSexps/tUw3Xd3HyESeoydHefzZcxw9OcrIRF5JDGJCxTob11ShyNlsjmfOTvD0mSwnRyYZmcjHLbiDsLRipcpv6B8Gbnb34hzHzr4zsxvN7BEze+TUqVMr0T5pUJlUgiBwJgtFJvNFJgtFgsDJpJruo1QanJn9MGGAd3OVY9Q3iaxRTTeCB9E3NQtrqmHnf3OTxqVinY3D3ZnMB2RzBbLxG6Wr5jiwrez3S4BnKo7ZA9wd1V/cBLzWzAru/vnKO3P3uwjXwbBnzx51N01sc2cLw+M58PBzxRywcLvIajGzlwAfB65196H5jlPfJLJ2NV2At+9QP+va0ly8rm16m5KsxIuSUNRPKUHKRC6cflkMLvw7wUP9Z7j74QFOjU2yfUNHIwboDwO7zGwncAK4Hnhz+QHuvrN03cw+CfyfuYI7WVvcHTMjkzDMwB2K7rjru7OsDjPbDnwWeKu7P1Hv9ohIY2q6AE9JVkQWr3yUbiJfJFdY3ijdQ/1nuOP+o6QSxrqyNPK3QsMEee5eMLN3EmbHTAKfcPfHzOwd0f4769pAaVhjuSJb17dyOirlkkkm2NLZwnhuwZm8IotiZp8G9gKbzOw48JtAGqb7pluAjcDHohkGBXffU5/WikijaroAT0k6RKoLAiebL5KdCqdeBis4+nD3wwOkEkZbOomZNWyZEne/B7inYtucgZ27v2012iSNb1tPO08Njc3alisG7NjYWacWSbNx9xsW2P924O2r1BwRiammC/BuurqPWw48Nl2HayJfVJKOmDl4ZJB9h/oZGM6yTWvwVkSpjEE2X2AyH9RsStmzIxMkDQaGp8gXA1pSSTZ1ZjSCLk3hFX0beOipMyQMEhYGd4OjOW64ckO9myYiIjKt6VJ/7d3dy62vv5zerlbOTeTp7Wrl1tdfrgAhJg4eGeSWA48xODrJ+rIpfgePDNa7abHi7kzkigyNTTFwJsvx4SxD41NM5Io1XS/UkU5ycmSKQtGn64SdODtJRyZZs8cUWS0P9J9hc2eGTDJB4JBJJtjcmeGB/jP1bpqIiMi0phvBAyXpiLN9h/pJJ216im2jTvFrRPliQHaq9qN0VVlUgcCYKUbgUUZbATRCHWcDw1k2dbawuat1epu7a4RaREQaSlMGePoCFV9KkrN4pbV0E7mwJlcjlDEYzxW4qLuF4Ww+mqKZYEt3C2NThXo3rSGURqjTSZs1Qt1ISWhkflrjLSIicdB0UzQ1xS/etvW0M5GfnZFOX6BCxcAZmypwZjzHs+fCYuODI5OMTjZOsfGLu9tIJhJs62nne7Z007e5k1QyodcvUj5CXUpCk04a+w7117tpsgg3Xd1HvhiWEnEPf2qNt4iINJqmC/D0BSre9AVqtsl8kTPjOY4PZ3l6aJzBkUnOZnM1X0u3VNdfuY1C4Ezki3r95jAwnKUtPXs9okao40NrvEVEJA6aboqmpvjF297dvVx3/Cwf/8oxxnNFOjJJ3v7KnWvmC1SuEDBZKDK5jELj9XRV3wbezS7ufniA02OTbGvMQud1oyl+8ac13iIi0uiaLsDTF6h4O3hkkP2PnmBzVwvbozIX+x89wUsuWd90X6qCwJkqBEzmi0wWikzlgxWtSVcvV/Vt4Kq+DWztaaMlpeyZ5VTGRURERGqt6QI8fYGKt2bOopkrBEwVikzmw5+5QmOsm5PVs3d3L7cSvs+PD2e5REmgRERE1ryVThDZdAGevkDFW7NMsS2NzpUHdHGbbim1oSl+IiIiUlKLDNtNF+CBvkDFWVyn2BYDD6da5sO1cxqdExEREZGF1GL2Wk2zaJrZNWb2bTN70szeP8f+dWb2v83ssJk9ZmY/X8v2SOOLSxbNUsmC02NTDJwJM1yeHJnk3ERewZ2IiIiILEotMmzXbATPzJLAR4HXAMeBh83sgLt/q+ywXwK+5e6vM7PNwLfN7K/cPVerdklja9QptuXr5xqlqLiIiIiIxFstZq/VcormVcCT7t4PYGZ3A28AygM8B7rMzIBO4AxQqGGbJAbqPcU2CJxcMSBXDJjKB0zkihQCBXSLlc0VeOp0ln998jQ/8ZLn0dORqXeTRERERBpSLRJE1jLA2woMlP1+HPj+imP+GDgAPAN0AT/t7vomLasqCDysPZcPmMgXmcoX692kWMgXAwbOZDl2epxjp8fpPz3OU6ezPDcyOX3MpRs7uPqFm+vYSpGVtdKZzqS5mNlPVdvv7p9drbaISDzUYvZaLQM8m2NbZRrBHwO+DrwKeD7wj2b2ZXcfmXVHZjcCNwJs3759wQfWB7DMx72U3TKccjmVDzTdcgGBO8+dm5wO5EqXgeGJqplBe7taGJvSgLw0j1pkOpOms5/we83Xo9/Lvws5oABPRM6z0rPXahngHQe2lf1+CeFIXbmfB25zdweeNLNjwG7gofKD3P0u4C6APXv2VM01rw9gKXEPp1pOFcKpllOFcMjbm6CYeK0MZ3McOzXOsaFxjp2KRuWGxpnMzx8Ed7Qk6dvUwY5NHdM/d27s4LLndavQuTSVZq7TKSvmTcBPAy8BvgB82t2frG+TRGStqWWA9zCwy8x2AieA64E3VxzzXeDVwJfN7CLge4D+5TyoPoDjb6kjsIViwGQhTIIyVQjIFQIFc/OYyBVnj8hFAd3Zify8t0knjUs3dLBzcwc7N7ZHPzvY3NVCuIw29FD/GX7rwLc4NTbJ9g0dGkGXptEsdTqldtz9c8DnzKyDMO/AH5jZRuA33P1L9W2diKwVNQvw3L1gZu8E7gOSwCfc/TEze0e0/07gd4BPmtm/E05juNndTy/ncfUBHG+LHYEtBj49xbKUDEWJUM5XKAYMDE/QfyociSv9fPbc5Ly3MWBrTxs7NoYjcqVAbmtPG8nEXDOvZzzUf4Y77j9KKmGs0wi6NJm41umUupgEzgEjwHagtb7NEZG1pKaFzt39HuCeim13ll1/BvjRlXxMfQDH21wjsONTeT528Du8ZNt6ctHInIK52QJ3To5MTgdwx06HyU8GzmQpVFknt7Ezw86NHezcFF76Nndw6YZ2WtJLm1p598MDpBJGWzqJmWkEXZpKLTKdSXMxsx8GbiDMJP5PwB3u/kh9WyUia01NA7x60AdwvH33zDjr2tIUAydwxx2SCeO7Z8Y5m1V5RICz2dyszJXHouyVE1Wyf3ZkktNBXOmyY1MH6ypGu5fr2ZEJultndysaQZdm0ah1OqWh/DPwDeArQAvws2b2s6Wd7v6uejVMRNaOpgvw9u7u5brjZ/n4V44xnivSkUny9lfu1AdwgwmCMJtlPggoFp18EJAvOpu7Whkam6KtbARpMh+wpbutjq2tj4lcMRqNK5UgCK8PZ6uvk9u+of28YK63Yp1crVzc3cbQ+OzXTyPosynLb7zVu06nNLz/xPkZw0VEVlXTBXgHjwyy/9ETbO5qYXs0grf/0RO85JL1+lCuE3cnXwzXzE3mwyQo85UmuH7PNu64/ygT+SKt6QST+YBC4Fx/5bY5j28GhWLA8bMT01krS6NzC62Te976NnZsag/XyW3qZOemdi7paV9wnVwtXX/lzOuXShpZjaDPoiy/Is3N3T853z4za7rvXCLSmJqus1EWzfoolSTIF51i0SlEI3L5YhigLTab5VV9G3g3u7j74QGeG5lgS3cb11+5jav6NtT4GdSeu3NydCosQ1CWvXLgTJZ8sco6uY7MTAmCKHvlpRs7Zo2SNYry1+/02CTblEVzFvVPIs3NzL7i7q+Mrv+lu7+1bPdDwMvq0zIRWUuaLsBTFs2VUxp5KwZhwBYEUAgCitHauMDDfe5cUBC3kKv6NsQ+oDuXzdN/eoxjp7Ozsldmc9XXye2omFq5c2MH69pXdp1crZVev609baqDV0H9k0jT6yi7fnnFvgWnV5jZJ4CfAAbd/cVz7DfgDuC1QBZ4m7s/uvTmikgzaroAT1k0F68QjbiVRtsKxYB84ARRgpNileyLEprIF3k6qiFXXhy82jq5VMLYvrF9Ontl3+bVXSfXDMzs5e7+YL3bcaHUP4k0vWofnIv5UP0k8MfAp+bZfy2wK7p8P/An0U8RkWlNF+Api2Y48lYIwgCtGHg44hZA0cMpk2GZgZUbcVsLioFzfDg7K3PlsdPjPHt2ct5PbAMuXt86U4JgUzi1cltPG6lkYjWb34w+RgynOql/ij8lyZEFrDezNwKJ6PpPRdsNWLfQjd39kJntqHLIG4BPefgB/qCZrTezi9392eU2XESaR9MFeM2axtp9JlgLomCtWAx/L5UUKAQz699kadydwdGpmTVyUUC30Dq5DR0Zdkbr43ZuDEsQ7NjUmOvkpH6atX9aK5QkRxbhS8Dry66/rmzfoRW4/63AQNnvx6NtCvBEZFrTBXiw+mmsSyNmpbptAGaQMMMAMyPwcH9pHVt5qOABOOFtHWYdmy8Gmi5ZI+cm8jxVMSL31Olxxqusk2vPJNkxXRh8phzB+vbMKrZcgD4zOzDfTnd//Xz76k1p9uNLSXLibxVGYP+3u392Je+wwlzz+Of8gmBmNwI3Amzfvr2GTZJGoNkFUm7eAM/Mfs/df301G9MIpteflY+URevSij4TxBXLpkAGmurY0CbzRZ4eys4alTt2epyh8fkLp6cSYT256eyVm9rp29TJRd1aJ9cgTgF/UO9GyNqiJDnxtkojsP8dqGWAdxworxt0CfDMXAe6+13AXQB79uzRF5UmptkFUqnaCN41QCwDvNJZjKfPjPO8dW38zPdfyg+8YOPMAaXv5x4GcIWiArVmUAycE8MT00XBSyNzz5ydqLqy/eJ1recVBtc6uYY36u5fqncjlkJnWeNLSXLirUlGYA8A7zSzuwmTq5zT+jtpkve2rKBqAV7SzHqYJ62vu5+pTZOWp/wsRndLipMjk9x+3xHend8V+9T7a8VD/We4++EBnh2Z4OI56uC5O6dGp84L5L67wDq5nvb0dBmCvujnjo0dtGW0Ti6Gnqp3A5ZCZ1njTUly4m2VRmB3m9k35thugLv7S6rd2Mw+DewFNpnZceA3gTThje8E7iEskfAkYZmEn1+5pktcaXaBVKoW4O0Gvsr8870b8hOt/CxGvhhMfwjf/fCAArwYeKj/DLffd4TxqQLFwDkzNsXv3jPKq3dfRIBPlyMYn5p/nVxrOnHeiNzOTR30aJ1c03D3nzKzq4GT7v5tM3sl8HLgcXf/+zo3b146yxpvSpITb6s0AnuM2YlVLoi737DAfgd+aan3L81JswukUrUA71vu/tJVa8kKGRjOkjToPzXGVKFIOpmgpz3NcyMT9W6aVDGVL/L0mSx/8I/f5mxUQ86BokN+qsgXDp+/xCBZWie3sX26ltzOTR1c1N1KQuvkmpqZfRi4CkiZ2X3Aq4F7gV82s73u/r56tm8+Ossaf0qSE1+rNAKbc/enV/IORRai2QVSqemyaHa1pDg6OEYyYSQTRiFwTo5McenGjno3TYjWyZ2dOC975TNnJ1goUegr+jbSt7mDHRvD7JXbNrST1jq5teo1wIuBNuAEsNXds2Z2G/A1oCEDPJ1lFamfVRqB/deVvDORxdDsAqlULcC7Y9VasYKmi3eXgoXpn0qgsprcndNjOfpPj3HsdHY6oHt6aLzqOrn5pAz+vze+uAYtlZhyd3czKxV9LL2pAsICww1JZ1lF6qvWI7Du/s6a3blIFZpdIOWqBXhfNrNPEKbfvQ34Q+AVwOPA+9z9qdo378KN5YpsXd/K6bEcU4UgmqKZIZuff82WLM/oZL6s/EBYjuCpoXFGJwvz3qY1nWDHxlIJgijpyeYO3vynDzJVOD8ATCY15VJm+Xsz+zLQCnwc+FszexD4IVammHBN6CyriIiI1Fq1AO/PgU8D64AHo99vBX4U+ATwqpq3bglKU6D6NneGRcIDZyJfpLejpd5Ni72pfJHvngkDuPIMlqfH5q8nl0wY23rawoyVZdkrt6ybe51ceyZFrpCfVdbAou0SD6UsqKfGJtm+oaMmAYy732xmrwiv+oNm9nzgjYTB3v4VfbAVprOsIs3LzF7u7g/Wux0isrZV+9bc5e5/AmBmv+jupaLCf2ZmDTsFoXwKVDphTOSLFALn+iu3LXxjAcJ1cs+cnTivMPiJBdbJXdTdMp3opBTIXeg6uUs3dJBKjDM2VSRfDEdgO1uSbF2vNZRx8FD/Ge64/yiphLGuxmUA3P0BM1tvZldGm/7U3c+t6IOIVFAdQ1nAx4CX1bsRIrK2VQvwAjN7IeEIXruZ7XH3R8zsBUDDFg4rnwL19NA4vV2t59VRk1BpndxTQ+P0n5oJ5J4+kyVXCOa93bq2dFRDbiZ75Y6NHXS0LH+U7fort3HH/UfZ1JmiNZ1gMh8oQI+Rux8eIJUw2tJJzKxmZQDMLAPcBfwkYVpyAy41s88B73D3+YeVRZZIdQxFRCQOqn0j/2/A/yZMWvCTwK+Z2RVAN/Cfa9+0pStNgTo1OsXoZL7ezWkIY5OFMIAbGufYqWiK5ULr5FIJLt3Uwc6N4fq4nRvb6dvcSU97GqtRGYKr+jbwbnZx98MDPDcywZY5Cp1L43p2ZILu1tndSo3KAPx3wuK/29x9FMDMuoCPAh+ILvMys2sIE0klgY+7+20V+98A/A5h/1cA3uPuX1npJyHxojqGsgh9ZnZgvp3u/vrVbIyIrE3zBnju/s/A95Rt+oqZbQKG3V0ZSxpUrhDw3TPZWWvknjo9zuDo1Ly3SVi4dnFWYfDNHVw8zzq51aK8p8uXSiRIJcOSIbNeSg//f93BLBz+wqBy8WPSwtvObDIsER5qZlS+Oy7d0MHTZ8YYnSiQKwa0pJJ0t6XYsbFzpZ/aTwFXuft05Ojuo2b2i4RrhucN8MwsSRgIvgY4DjxsZgfc/Vtlh/0zcCDK1PkS4G+B3Sv9JCReVMdQFuEU8AcLHiUiUkNV59SZ2Ubgzcx8sXkc+GvgTI3bJQsoBs6z5yYqArksx4ezVdfJ9Xa1zCoKvnNjuE4uk2qMzPLla7i6W1MMjU9xx/1HeTe7NIpXoVTrMZVIkDCmryeTRjpppBMJEonVDdBf+YKNfPX+4en25IoBg6M5brhyxV+7oDy4K3H3MTNb6NzAVcCT7t4PYGZ3A28ApgM8dx8rO74DnW8QVMdQFmXU3b9U70aIyNo2b4BnZpcB9wP3ERYONuBK4NfN7FXufmR1mri2uTtnxnOzioIfOz3O00NZpqqsk+tuTc0ekYuyWHauwDq5WipfwwVM1wq7++GBpgjwzCwcFUsa6YSRSiZI2syoWMJs1qhp+ahbONJmJCw6bpWDt8V4oP8MmzszjE6GI3iZZIKu1hQP9J/hXSv7UG5mPXDeICKE0yqr2QoMlP1+HPj+yoPM7I3A7wO9wI8vsZ3SRG66uo/37T/MieEJCkFAKhG+vz/w4y+qd9OkcTxV7waIiFT7tv87wLvd/W/LN5rZm4D/D3hTLRu2Fo1NFXiqIpA7dnqckSrr5FpSYT25HZvaZ9WU29CRqdk6uVqaaw1XazrBcyMTdWpRdaWALJEIR6xKv5dG15IJm57mWLo0s4HhLJs6W9jc1Tq9zd1rMYVtHfBV5g7wFhptW9Rt3P1zwOfM7GrC/vBH5rwzsxuBGwG2b9++wENL3DmAhSdrMA3tymzu/lNRn3HS3b9tZq8EXg487u5/X+fmicgaUS3A+3/c/brKje7+GTP7vRq2qenlCgED0Tq5UlHw/lMLr5O7ZHqdXDs7N3Wyc1M7F69ra6qg4eLuNobGp6ZH8AAm8wFbuttW5fGTCSOdTIRr1gCi0bKkGalkuK8UwCVKX/Jk2mpNYXP3Hcu4+XGgPC3rJcAzVR7rkJk938w2ufvpOfbfRZjRkz179iz4fV9p9uNr36F+1rWluXjdTH+kJCtSzsw+TDgNPGVm9wGvBu4FftnM9rr7++rZPhFZG6oFeONL3CeRwJ1nz05Oj8SV1ssNLGKd3I5NYdbKnZs72bmxnUs3djTMOrlaKpVJmMgXV7xMQvnIWioRTnEsTZfMJBPTgZ0s3WpNYTOzHyOs1bm/YvubgVPu/o9Vbv4wsMvMdgIngOsJ1xqX388LgO9ESVZeBmSAoeW2W2n2401JVmQRXgO8GGgj7F+2unvWzG4jXO6iAE9Eaq5agNdrZr8yx3YDNteoPbHk7gxn8/SfGuPYUJZjp8JyBE+fHmeyyjq5rop1cn1RPbnO1sZeJ1dLSymTUArMZqZIRglHkglSZQGdRttWxypNYftt4HVzbL8f+Bwwb4Dn7gUzeyfh+uIk8Al3f8zM3hHtv5NwCvrPmlkemAB+2t2X/VSUZj/elGRFFsGjE0OlD/9SvxEAzX+WVkQaQrVI4k+Brnn2fbwGbYmF8akCTw1FI3KnZqZXVlsnl0kluHRD+6zslTs2drCpM57r5FZL+bfpdDJBSyoaZUvadOCWSSb0f9hAVnEKW7u7n6rc6O7PmVnHQjd293uAeyq23Vl2/Xbg9pVoaDmNAMXbTVf3ccuBx8jmCtMJoPJF56ar++rdNGkcf29mXwZaCb8r/a2ZPQj8EHCori0TkTWjWh28317unS+imPD7gLeUteUyYLO7170MQ74Y1pOrTHhycqT6Ormt69vOy175vPXNtU6ulsrLJKxrTXN2IsdHDz7JlnWtGuGIgVUMYFrNLOXus86smFmacGpUQ9IIULzt3d3LrYQnMo4PZ7lEayilgrvfbGavCK/6g2b2fOCNhMHe/uq3FhFZGdXKJPwPoL/8rHa0/ZeBLe5+c7U7XkwxYXf/IPDB6PjXAb+82sFd4M6z5yZnZa/sPz3O8eEJilUWym3qzNA3XRS8kx0b27l0QzstZclB5MIkE8bfffU4LakEnS0pzIyWdFJT2GJkFQOYzwJ/ambvdPdxgGjk7iPRvoakEaD427u7V32RVOXuD5jZejO7Mtr0p+5+rq6NEpE1pdoUzZ8gXChc6Q7gG0DVAI9FFBOucAPw6QXuc1nOjOdmjcb1nx7n6aFxJvPzr5PrbEnNrI+b/tlOV2t63tvIwtLJBJlUgkzpZzT98uToJOvb0rOmXWoKW3ysYgDz34HfBZ42s6ejbduBPwM+sNIPtlI0AiTS3MwsQ5hV9yeBY4R5Cy41s88B73D3XB2bJyJrRLUAz939vMjH3QNb3KKnRRUTBjCzduAa4J2LuN9F+cbxs/zf/jM8/uwIx4bGOXZqnLMT+XmPTyeNSzfODuR2btI6ueUqlR0oBXQtUVA3X5FuTWGLt9UKYKKpme83s98GXhBtftLdG7NgYhmNAIk0tf8OpIFt7j4KYGZdhDOaPkADn4ASkeZRLcDLmtkudz9avtHMdhFmlVvIhRQgfh3wr/NNz1xKIeFf/9y/880TI+dtTxg8b451clu1Tm7JkgmjJZUkk0qQSs4U9l5K2QFNYYu/1QxgooDu31flwUREFvZTwFXuPj3txN1HzewXgQdRgCciq6BagHcLcK+Z/S7w1WjbHuDXgPcs4r4vpJjw9VSZnnmhhYQBXnhRF4MjU1y6sZ0dG6MyBJs7tE5uBSQTRms6SUsqQWs6SesK/n9qCpuIiMRYUB7clbj7mJnVqGqMiMhs1bJo3mtmP0lYlPO/Rpu/CbzJ3RdzxnzBYsIAZraOMH3wz1xY06v7H296CcPZPKOT80/LlOoSZqSS4UhcKmFkooAunaxtKR9NYZOFzJVBMy4OHhlk36F+BoazbNMJDJFm42bWw9yzmOZf8C8isoKqVtR2928CP7eUO15kMWEI0wf/QykT3kpJ1TgIaTZmRms6QVs6OT3dUlNWpYE9aGbHgS8CX3T3p+rcnkU5eGSQ9+0/zOhkgUIQcHp0ivftP8wHr7tCQZ5Ic1hHOOvpQpapiIisqKoB3nItVEw4+v2TwCdr2Q45X8KMlnSC1lQymmapguESH+6+x8wuBa4FPmxmW4GvAPcCX3L3+QtW1tHtXzzCcDZPMmGkkgncYTib5/YvHlGAJ9IE3H3Hcu9jETWE1wH/izBzcAr4kLv/+XIfV0SaR00DPGksLekk7ekkbZlw/ZwCOokzd38auBO4Mypw/oOE2Xh/18xOufuP17WBc+g/PU7CwhMsAGbg5vSfXtEJDCJSJ2b2Y0CXu++v2P5m4JS7/+MCt1+whjDwS8C33P11ZrYZ+LaZ/ZVKMIhISbVC5zcQTp0cWsX2yApKWJgMpS2TpCOT1LRVaVrungfujy5EI3oiIqvttwkzg1e6H/gcUDXAY3E1hB3oikpWdQJngFiuSRaR2qg2gncp8HfRmfF/Jpz69JC7aw55g0olErRmEtMZLltSyhYqa5O7n6h3G+ayc2M7T54axwIPR+8cAocXbFKdR5Em0e7upyo3uvtzZtaxiNsvpobwHwMHCDOTdwE/PVfdYhFZu6pl0bwNuC0q0PkjwH8inAr1OGFig/vc/eTqNFPmMj1Cl07SmmmegE5ZBqVZvf/ay3jv/sOMTRUoBk4yYaxvSfP+ay+rd9NEZGW0zpXlNzpZ3raI2y8mOcuPAV8HXgU8H/hHM/uyu88q/nuhNYT12SvSPBacs+fuo+7+OXe/yd1fCvwusBn4VM1bt0QHjwxyw10P8vo//gq/8jeHeah/zvrpsdWSTrKpq4XtG9rZsq6Vde3ppgrubjnwGIOjk6xvSzM4OsktBx7j4JHBejdNGtgiz4zX3d7dvXzouit46bYetnS38tJtPXxIGTRFmslngT8t75Oi63dG+xaymBrCPw981kNPAseA3ZV35O53ufsed9+zefPmqg+qz16R5nLBSVaihb7fAv5g5ZuzfKVOKp001rWmGBqf4o77j/JudnFV34Z6N++CJaP6c+lkgkwqQXu6udfS7TvUTzpptGfCt2Z7JkU2V2DfoX59CZbzmNkPAB8nXIey3cyuAG5y91+sb8vmpzqPIk3tvxOeCH/azJ6Otm0H/gz4wCJuv5gawt8FXg182cwuAr4H6F9Oo/XZK9Jcmi6LZnknlS8GtKWTTOSL3P3wQGwCvJZ0mBQlzHbZHCNzizUwnGV9W3rWtrZ0kuPD2Tq1SBrcHxJOVzoA4O6Hzezq+jZJmpmmsUk10dTM95vZbwMviDY/6e4Ti739ImoI/w7wSTP7d8IpnTe7++nltFufvfGnvknKNV2AN1cn1ZpO8NzIovrWumnLJGnPpNZ8tsttPe0Mjk5On0UEmMgXuaRHSShkbu4+UFHyo1ivtkhzK58hUj6N7VbQFymZJQro/n2Jt61aQ9jdnwF+dFkNrKDP3nhT3ySV5o0kzGxDtctqNvJCbOtpZyI/+/vdZD5gS/di1javrkwqwYaODNs3tHPxujbWtaXXdHAHcNPVfeSLTjZXwD38mS86N13dV++mSWMaiKZpupllzOy9wOP1btRCpgpFjg9nGRqbYjKveDQuymeImIU/00lj36FlzY4Tqbubru5jZCLP0ZOjPP7sOY6eHGVkIq/P3phQ3ySVqo3gnSZc7FvKBFV+ityBhvyrv+nqPm458BjZXIF0wpjIFykEzvVXblv4xjWWTIRZL1tT4fTLTGptB3Nz2bu7l1sJO6vjw1ku0TQDqe4dwB2EqcWPA/9AWAS4oblDrhCQKwScm8iTSiToaEnS0ZKiNb22pmXHiaaxyULmyqAZFw5gYGZg56fulMalvkkqVQvw/gjYC/wr8GngK3GogVceIDw9NE5vVyvXX7mtbuvvMqkEnS2pNbmebqmUhEIugLn7W+rdiOUqBAHnJmaCvZZ0gkwy/NmSSpJMzJU5XVabprHJIjxoZscJy0l90d2fqnN7FmXfoX7WtaW5eN3MbCclWYkP9U1SqVodvHdbuLBlL/BW4I/M7B+AP3H3Y6vUviUpBQinRqcYncyv6mMnzGjLaJROZJX8m5kdA/4G+Iy7n61ze5atEAQUpgLGy7alk4lonW7YtyQU8NVF+QyRUgIvTSGXcu6+x8wuBa4FPmxmW4GvAPcCX3L3qbo2cB4aAYq3m67u4337D3NieIJCEJBKJOhqTfGBH39RvZsmdVI1yUo0YvcvZvY1wlS9vwMcBf50FdoWG8lEGNR1tqRoSyepSPggIjXi7rvM7CrC/uk3zOxbwN3u/r/q3LQVlS8G5CcCRibymBktqUQY7KWTtKQS6nNWiaaQy2K4+9OEde/ujAqc/yBwDfC7ZnbK3X+8rg2cg0aA4m8yXyRXDAgcAg+0vnuNmzfAiwpzvgH4acLC5p8FXubuA6vUtobWkk7Sng5H6bRmRqR+3P0h4CEz+z3gfwJ/ATRVgFfO3ZnMF6c/vM2MdNJoSSVpTSdoa/JamfWmKeRyIdw9D9wfXYhG9BqORqfj7bZ7H2ciH5BOJDAL13lP5ANuu/dx9VdrVLURvEHC0bpPA08Srre90syuBHD3z9a+eY0jmTDaooCuPZPSmpgaUi0XWSwz6wbeSDiC93zgc8BVdW3UKnN3cgUnVwgYnQy3ae2vSGNy9xP1bsNcNDodb8eGsiSM6en7ZuCBc2xIU2zXqmoB3t8RBnW7o0s5JxzRa2oapVt9quUiF+gw8HngVnd/oM5taRi5QsCZQg7Gw/V7pWQtLakweYvW8C2dTkBJs9LotEjzqJZk5W2r2I6GoFG6+tt3qJ98scjQWIFcMSCTTNDdllImL5lPXxyy+9ZTvhiQLwaMMZO5PZ1MkE4myKTCSzppZJJay7cQnYCKv9UO0M2sw93HFz5SZOn6NnVwdHAMc5+eohk47NrcUe+mSZ1UK3T+4bLr767Y98naNWl1ZVIJ1rdneN76Ni7d2EFvdytdrWkFd3VydHCU06M5CoGTTBiFwDk9muPo4Gi9myYNpKx/OmBm513q2bY4yBcDsrkCZ7M5BkcmOTE8wbHT4wycyTI4MsnZbI6JXBHFzrOpmHC8lQL0wdHJWQH6wSODK/5YZvYDUdKnx6PfrzCzj634A62gg0cGueGuB3nl7fdzw10P1uT/RWrj5mt209OexoBCMcCAnvY0N19TOQFP1opqUzSvLrv+c4TFhEteUpvmrI7SCF1HRskIGk2uEICF5SYgnEceWLi+SKTMX0Y/P1TXVjSZ0mgfUSJ3MyOTChO3tKXDJC5reZRPqeTjrTxAB2jPpGpZ6+0PgR8DDgC4+2Ezu7r6TepHo9Pxtnd3L299+aV8/CvHyOecTCrBW19+qV67NaxagGfzXI8dM6M1naCjJUWHpl42tHTSmMhDEMxMMwDIJPWayQx3/2r080v1bkszc3em8kWm8kXOwnSJhtY1GvAplXy8rXaA7u4DFX8fDZu3fpWDX1lhB48Msv/RE2zuamF7lAV1/6MneMkl6/X6rVHVhq8SZtZjZhvLrm8wsw1ALDKOtKQTbOpqYfuGdi5e10a3pl42vBde1M3GjgyppFF0J5U0NnZk2HVRd72bJg3IzHaZ2X4z+5aZ9Zcu9W5XsyqVaDibzfHsuQmeGsryzNkJzmZzTOabf0rnTVf3kS862VwB9/CnUsnHx7aediYqaoPVMEAfMLMfANzMMmb2XqLpmo1oYDhLW0UyOY1Ox4emj0ulaiN464CvMjN692jZvlh8ine3phc+SBpKqRbPlnUp1eKRxfhz4DcJp0P9MPDzNPiMg4NHBvmTg9/hqTPjXNzdxvVXbuOqvg31btaSVNbkS5jNjO5lEk1XokGp5ONtlWu9vYNwactW4DjwD8Av1eKBVoJGp+NN08elUrUA74fc/elVa4kI+gIlF6zN3f/ZzCzqr37LzL5MGPQ1nNI6l2QCultTDI1Pccf9R3k3u2Ib5JULolGtbK4A4zOZiVszYcmZZljzrFTy8bXKny/m7m+pxR3Xggqdx5sCdKlULcD7HPCy1WqISIm+QMkFmDSzBHDUzN4JnAAa9s1TmkbTmk6SLwTTX6TufnigKQK8SsXAGZsqMDYVlmhIJ6P1e5kkralEUwR8Ei+r+Pnyb2Z2DPgb4DPufnY1HnSpdHI13hSgS6XFJlkREWlE7wHagXcBvwO8ijDrb0MqTaMpn+Pemk7w3MhE3dq0mkpZOkcn80BUhD0VFWFPqwi7NA9332VmVwHXA78RlUy4293/V52bNi+dXI0vBehSqVqAt9XMPjLfTnd/Vw3aIyKyaO7+cHR1jHD9XUMrTaNpLUtmMJkP2NLdVsdW1c90Efap2UXYW9IJWpJJ0imbLsouEjfu/hDwkJn9HvA/gb8AGjbAk3hTgC7lqgV4E4RJVkREGoqZ/TnzJ3tyd/+F1WzPYpWm0QReIJUwJvMBhcC5/spt9W5aw5gO+pgJ+hJRPb5U0kgnEmSiUg3KiiyNysy6gTcSjuA9n3DZy1V1bZSIrBnVArwhd/+LVWuJiMji/Z85tm0nnLLZsKkbS9No/uTgd3j6zDhbYp5Fc7UEUbZO8rO3p5NhsBeO8tn0lM+1VJtPGtZh4PPAre7+QJ3bIiIN7uCRQfYd6mdgOMu2FZhiWy3Ayy35XkVEasjdP1O6bmZ9wK8DVwO3AX9Wr3Ytxt7dvbz8+Rt55uzaWHdXS6XRvnIWjfalk0YqEf7MpML1fSsR+K30h7A0rT5v9sKQ0lDUN8VXKcN2Ommsb0szODrJLQce41ZY8ms4b4Dn7i9fakNLzOwawjowSeDj7n7bHMfsBT4MpIHT7v5Dy31cEWl+ZnYZ8BvAS4EPAu9w90L1W0mzc3em8kWm8ufvK63nSyZs+pJKGKmkLapmXy0+hKW5mNmH3f09wAEzOy/Ac/fXr36rpNmpb4q38kL1AO2ZFNlcgX2H+lc+wFsuM0sCHwVeQ1jk82EzO+Du3yo7Zj3wMeAad/+umeldKDoLJQsys78D9gAfAn4ZKALdpREadz9Tv9ZJo5prxK9keuQvYSQSM9M9UwkjYeG2fYf6yReLDI0VyBUDMskE3W2pZX0IS9P5y+jnh+raCllTahEgyOqpRaH6mgV4hIuJn3T3fgAzuxt4A/CtsmPeDHzW3b8L4O6DK/HAChDiS2ehZJGuJEyy8l7gV6Ntpfl3Dqj4j1yQ6ZG/efabGUeeG2F0Ik8iYSQsDBhPjUyRKwQUA1fSF8Hdvxr9/FK92yJrRy0CBFk9tShUXzXAM7NUacqTmXUCu4H+RZ4d3woMlP1+HPj+imNeCKTN7CDQBdzh7p9aZNvnpAAh3nQWShbD3Xcs5/YLTR83s7cAN0e/jgH/xd0PL+cxS/700Hf40D88gbvT2ZLmZZeu5/su7eGi7la2dLeyuatFgUIDcndyhQCM6bV8ZoA5k/mAp4fGp49NmGHG9MhfaVQwVf7TSpeZ4+Z6zPBx9H5YKfd/6yR3fbmf42cnanoC2Mx2Ab8PvAhoLW139wVPPml5i1yobT3tHDs9xujkzOyCrtYUOzd11rtpsgi1KFQ/b4BnZm8D/sDMhoB3E063PAa80Mz+m7t/eoH7nusTqXI+egr4PuDVQBvwgJk96O5PVLTlRuBGgO3bt1d90H2H+skVZk+h6WrVFJq40FkoqbXFTB8n7Ot+yN2Hzexa4C7OP0F1wQ4eGeTOL/UzVQinCZ7J5vinxwf5p8dnJi8kDDZ1toQB37pWLupu4aKumeu9Xa1kUqoLVw/ppDFVgCBwzKCUQiOTnP1xF7iDQzH8Z95RwUplU4zP25dKhPUB08nErMAvEQWcyYSRtJm1hQmbOzB0d8wMdyfwsK3FwAmb7NEx0bHRc/Eg/BmUPa4RBriGRUFv9Byi27kz6xO/dHufdf9lD1R2+Hn7y/+Poq8WTtT+YOaYytt5xf3+3+8Mccf9R8mkEqtxAvjPgd8E/hD4YcI6nQtG6vVc3qLZT/H1ir4NPPTUmeiEEeSKAafGcrz5KmVojoNaFKqvNoL3q8D3EI6sHQZe6u7fMbOLgH8EFgrwjgPlxZ0uAZ6Z45jT7j4OjJvZIeAKYFaA5+53EX7BYs+ePVWzUj1xcoSRyQIJwg+7QtEZGs9RKI4s0FxpBDoLJatgwenj7v5vZcc/SNh/Ldu+Q/0kDNIJoxD49JdwA/LRF9XAYXB0isHRKf79xLk572djRyYM/LpbZweC0e9t6YatFBFrOzZ2cnx4nPFckXwxIJ1M0JFJcUlPx4rcf7Wki4UgoDA19/rB+ZRGCUvBaNG96mM0u7sfHiCVMNrSScys1jNE2tz9n83M3P1p4LfM7MuEQV81dVneotlP8fZA/xl6uzKMTMxeH/xA/xneVe/GyaKsdKH6agFe0d1PA6fNbMzdvwPg7icXOV3kYWCXme0EThAW+3xzxTFfAP7YzFJAhvAM+R9e4HOYJV8MP7xK013MwjN8ueLa/VCLE52FklWwmOnj5X4BuHclHvjo4CjnsnnMIJ2ycNQkcDrb0nz6P7+cU6NTPDcyycmRyejnFCej30+NTlEarBgazzE0nuNbz47O+Tjr2tJs6Z4d9F3U3RIFgq10ttRy+XXzuv7Kbdxx/1E2ZVK0phMNX6i+fNRN4NmRCbpbZ7/3azhDZNLMEsBRM3sn4fegxXx7q8vyFi2PiLeB4SwbO1rY1Dk9Gxh31+ynNazap/x3zez3CTuPI2b2B8BngR8Bnl3ojt29EHVq9xHOI/+Euz9mZu+I9t/p7o+b2ReBbwAB4Vzzby7nCWVSCSZyRQIvm0LjaEpTTOgslCzWMtYIL2b6eOkxfpgwwHtllXYsegp5aQ1XwgwnPAFVtHBtVyaVYGtPG1t72ua8bTFwTo2GAd+z5yajwG+Kk6OTPHduksHRKYpRBHhuIs+5iTzfPjl3ANjRkowCwNaZQHBddL2rle62lNZ9zeGqvg28m13c/fAAz41MqFB9zFzc3cbQ+BQdLTPfB5abyKCK9wDtwLuA3wFeBfzcIm5Xl+UtWh4Rb7VI0iHxVi3A+xngl4BzwPuBa4BfA74LvG0xd+7u9wD3VGy7s+L3DxLWsFoRu3q7eGpobHaA0JFmx0ZN8YsDnYWSxVjmGuHFTB/HzF4CfBy41t2H5ruzC5lCnk4aE/lo3VCVNVxzSSaMLevC6ZhXzDFgVAycM+O56RG/0gjgc6VgcDTM9ggwPlXkO6fG+c6p8fPvCGhNJ8qCv2j0b3oqaCs97ek1GwBe1bdBAV1MXX/lNm6/7wiDI5MU3UklwiUAH/jxF634Y7n7w9HVMcL1d4tVl+UtChDi7aar+3jf/sOcGJ6gEAQ1fW9LPFQrdD5CmAGqZH90aWilTDRb1qVWLBONrB59yMgiLWeN8ILTx81sO+GMhbdWnhVfjhde1M2x02OMTOTJrfAarmTC2NzVwuauFl68dd15+92d4Wy+LACc4uS5yekRwJMjU0zkiwBRVsgsTw/NfWIlk0rQ29UyMwq4LpoKGiWD2dCRUSZQaVxlyWFWegKrmf0589+tu/svLHAXdVneUossfrK6HGay/NbgvS3xslCZhJ8jPDv+PdGmx4GPLHeudy3VIhONrB59yMgiLXmN8GKmjwO3ABuBj0X3V3D3PcttdOn9fdG6JKmEreoaLjNjQ0eGDR0ZLru4+7z97s7oZGFm5C8KBE+em5kKOjpZAMKppseHJzg+PDHnY6WiYLN89K88GczmzhZSSU2bl9V198MDJA2SZgTuJC0sWbHC68z+zxzbthNO2Vww+1G9lrfou1O87TvUz7q2NBevm5nirzWUa1u1Mgk/S9gh/QrwKOG88JcBH4xSLDd0kKc3dDzpQ0YWablrhKtOH3f3twNvX9EWM/P+/pOD3+HpM+MNtYbLzOhuS9PdluaFF3XNecz4VGGOBDBhMDg4MslwNg9AIXCePReuFZzLTCmIskygFUlhtG5aVtpTQ2OMTUVZthO1ybLt7p8pXTezPuDXgauB24A/W+R9rPryFtB3pzjTGkqpVG0E7xeBN7r7U2Xb7jezNwF3Aw0b4Elz0PQCqWLZa4TrZe/uXl7+/I08c3bu0a9G1tGSom9zJ32b517TPJkvMliW+GV6KuhI+PvQeA6oLAUx95frDR0ZtszKAjo7E6hKQciFKs+ybVH5iFpk2Tazy4DfAF5KGIS9o5QQSqQWtLxFKlUL8LorgjsA3P0pMzt/fo/ICjh4ZJD37j/M2FSBYuCcHpvivfsP86HrrtCZRZkW1zXCza41nWT7xna2b5z7S0WuEDA4OjP6V5kI5vTYTCmIM+M5zlQpBdHdmpoO9spH/0rTQTtbVQpCZsukEkzlwyzbiRpl2TazvwP2AB8CfhkoAt1lRewXyvIrcsG0vEUqVfsErHZ6OX6nniUWbrv3cc5m8yQtLFTvAZzN5rnt3scV4MkscVwjvNZlUgku6Wmf96xyoRhweixXFvxN8ty5cETw5MgkgyNTFKIIcGSywMjkGE+cHJvzvjpakrMCvtJoYCko7G5dWimIh/rPcPfDAzw7MsHFDTTFVhZ26YYOTpwdZ3yqSD7wWmXZvpJwAsp7CZNBwUzpAwf0jVtWnJa3SKVqAd5lZvaNObYb6qCkRo4NZcMi52WF6j1wjs2TzU/WpjivEZb5pZKJmVIQc+wP3BkamykFcd5I4MjkrFIQ/afG6V9kKYjy6aBb1s1dCuKh/jPccf9RUgmjuzXF0PgUd9x/lHezS0FeDJQK1W/uTtPVkqrJKIe771ixOxO5AFpDKeWqBnir1goRkQujNcJrUMIWLgVxdiI/XfahfCSw9Hs2t7hSEOmkzV77193KP31rkGIQ0JpKgTM9FeruhwcU4MVAqVD93331OIOjkxrlEJGmVS3ASwMXufu/lm80sx9kjqLAIiuhb1MHRwfHMPdw9M7DhAy7Ni+/Tpg0Fa0RlvOYGT3tGXraM1x28fn7S6UgypO/VGYFLZWCyBe9SimIMFlMOmGkksbpsSk++W9PzVoL2NulUhCN6Kq+Dbz6RRexuaul3k0REamZagHehwnT+1aaiPa9rgbtkTXu5mt28779hxmdLFAoBqQSCXra09x8ze56N00ai9YIywUrLwWxa55SENlcYWb0rywT6HPnJvnOqbHpNYAA+cDJR79/6oGnZ91PwmBjR8tMEfiK0UCVghARkVqpFuDtcPfz1uC5+yNmtqN2TVq+j/zTE3z8K8cYzxXpyCR5+yt38q4feWG9myWLsHd3L299+aV8/CvHyOecTCrBW19+qabQSCWtEZaaaM+k2Lkpxc5N588aeKj/DB/+5yeAMICbyAfkigEv2NxJIXCeG5nkzFgOJ5x5cGpsilNj85eC6GlPh0lfumaKwJfXBWzLqBREHJlZqlQWwcw6gd1AvzJoishqqRbgtVbZ17bSDVkpH/mnJ7jj/idJGKQSYR2QO+5/EkBBXgwcPDLI/kdPsLmrhe3R+pb9j57gJZesV5An5bRGWFbdVX0beA8v5O6HB3huZIJLN3Scl0UzVwg4NTp7/V/5dNBTozOlIIazeYazeR5foBTElrLRv+nEMOta6WxRKYhGY2ZvA/7AzIYIs/x+FDgGvNDM/pu7f7qe7RORtaHap8PDZvaf3f1Pyzea2S8AX61ts5bu4185FgV34dSXhEEhCPj4V44pwIuBfYf6yRWKDI0VyBUDMskEXa0p9h3qV4An5bRGWOpqvtLYmVSCrT1tbO2Z+zxoMXBOjU1x8tz5mUCfW6FSEKVt3W1LKwUhy/KrhKVbuoDDwEvd/TtmdhHwj4ACPKmJg0cG2Xeon4HhLNuUQGjNqxbgvQf4nJm9hZmAbg+QAd5Y43Yt2XiuSOWyhoSF26XxPXFyhJHJAgnCOniFojM0nqNQnHuKk6xZH0ZrhGWVrUSZhGTC2BIFYHMJ3DkznpupATidBXRmJHDqAktBVK79KyWD6enIkFAAuNKK7n4aOG1mY+7+HQB3P9nowbYChPg6eGSQWw48RjpprG9LMzg6yS0HHuNW0Gu4Rs0b4Ln7SeAHzOyHgRdHm//e3e9flZYtUUcmyXiugHsR97COmhl0ZDSVJQ7yxfDMdXkdvCBwcsX5zpfLGhXbNcISX3c/PEAqYbSlw7VxtSiTkDBjU2cLmzpbuPx55+93d85N5Gdq/52byQQ6GNUCvOBSEF0tXLRudvB3UXcrmzpbSCYaOyhpQN81s98nHME7YmZ/AHwW+BHg2bq2rAoFCPG271A/6aTRHn3Xbc+kyOYKmv20hi0Y9bj7vwD/sgptWRGv3r2Zz319pg91BzzcLo0vk0owkSsSlJVJwFG2OakUyzXCEm/PjkzQ3Tr7Y7M1neC5kdVL3GpmrG/PsL49w+4t5+93d8amwkygz56bPK8W4ODIJCOLKgURjjZu7izLBNrVGgWC4Wjg5q4W0ioFUelngF8CzgHvB64Bfg34LvC2+jWrOgUI8TYwnGV9W3rWtrZ0kuPDc5/ckebXdMNaz43kWN+WYmSyQODh9Mzu1hTPjeTq3TRZhF29XTw1NMbIxMwavO6ONDs2dta7adJYYrlGWOLt4u42hsanpkfwIBwl29LdOOcUzIyu1jRdrWle0Dt3v5nNFaaLwVcWgj85MslwNg+E6wVLawPDeKXisYCNnZmZqZ/rKhLBrMFSEO4+Avx+2ab90aWhKUCIt2097QyOTk4H6BAmGbykp72OrZJ6aroAb2A4yyU97bMWlru7OqmYuOnqPt63/zDFwHF3ioFTKDo3Xa3M9zLLe4jhGmGJt+uv3MYd9x9lIl+kNZ1gMh9QCJzrr9xW76ZdkPZMir7NnfRtnjsAnMwXGRyZ4uToZFk9wJlgcCgqBeHA6bEcp8dyfPOZuddJb+jIzFr/txZKQZjZzxFm0PyeaNPjwEfc/VP1a1V1ChDi7aar+7jlwGNkc4XpqeN5fXda05ouwFMnFX9T+SK5YkDgEHjAVF4JcmS2uK4Rlni7qm8D72bXdJmELd1t55VJaAat6STbN7azfePcn5v5YsBgVAriZEXw91xFKYgz4znOjOeqloIoH/2bCQTDALCzNV5fU8zsZwlPQP0K8CjhQOfLgA+aGY0a5N10dR/v3X+YE2cnKBQDkgmjoyXF+6/ZjbsrG2uD27u7l1sJp9oejwY6lCRnbYtXz7kIOosRb7d/8QjjuTBBDoRr8MZzRW7/4hF1VHKeuK0Rlvi7qm9D0wV0FyqdTLB1fRtb189fCuL02NR0IpiTs9YCTjE4OjmdUKtUCuLo4DylIDLJcN1fVxQAViSDWdeWbrTg4xeBN7r7U2Xb7jezNwF3Aw0Z4EEYieLh527pMjSW49jpcdLJBJlUouynkUkmGu3/fk3bu7tX35NkWtMFeDqLEW9PDo5R9PCDxgxwKHq4XaRZJMxIJxME7riHv5d/T0okjISBYTjhMaVjy4VZgm3678Ww6czB09cJ64KmkjadEdGd6fst3U9JaVp0IXCCaJp06bFL7QgqGyJSJpmw6VG4uQTuDI/nwvV9C5WCyC1QCiKVmJn6GQWC5WsBN6x+KYjuiuAOAHd/ysy6V7MhF2LfoX6629JsWddGrhDg7rMyxOaLAflicN7t0smwb0kno+AvGQZ/KSXfEamrpgvwQGcx4qww/Y2TmZ9etl2kCWRSCbZtiO+08en1sbOCQMKz/1EJcHem9xcCJ4iuB2V/4qVAFMLbVe5LmJFI2HR34NFjl7qD0mNLvCTM2NjZwsYqpSDOTuSnk79ULQVRCHj6TJanz8xfCqK3a6YIfL4YcHRwjGyuyPM3d9biBHC1lKqrl271Ag0MZ0ka9J8aY6pQJJ1M0NOeXjBDbBj4wQSzl1KYGamETY/6paMgMJNMTJdBEpHaacoAT+IraVCIRgoqt4tIYzAzUkkj1SD5MQrFgKI7QQCFIJj+WQycoocjkgoG48PM6GnP0LNAKYhS8peTo7MTwVSWgjhxdoITZ2cHKu2ZZK1qvV1mZufV6CQ8b9Gwa0U6M0mePDVO0oykGYWic3JkikuXeCLK3ckXfc5Rv1QiQTplpBLRiF90PZ00TfkUWSEK8KShvGBzJ0+cHJvO0GbR5QXzZHsTEUklE2UfZtWjzmIwM+20EDiFYpgJszQiWSgGCgQbXHkpiF0Xdc15TDZXmFX64blzk9z32EmyuQJFh0wyUatab5et1B2tpunAqjy+cmbP314hhSCgkAM4P4FaKfhrSSW11k9kGRTgSUN5/7WX8d79hxmbKlAMnGTC6GxJ8f5rY/mZuSYdPDLIvkP9DAxn2aY1sNJgkomZtYjz8Sj4C6eflQV90c2CAIru5AuB1iM2qPZMip2bUuzc1DG97eATp9jc1U4ykSAVvQdqUOstDVzk7v9avtHMfhB4ZiUfaCWNThXYur6V02M5pgpBOEWzM0M2V1jVdpSCv4ncTPCn6Z4iF04BnjSUvbt7+dB1VyhJTkwdPDLILQceI5001relazUFSqSmzGz6S+RCSsknSiODpWmhpSAwKBsxlPoqFarvaJkZEapBGaUPA78+x/aJaN/rVvLBVkqpxFTf5s5ZSVZ6O1rq3bRFTfdUkheR2RTgScNRkpz42neon3TSputQ1mgKlEjdLHWE2qMEM+VBYDHw6aQxzkzSGE0VrY3yQvWphNWqjNIOdz9vDZ67P2JmO1bygVZSeYmppIX/N4XAuf7KbfVuWlXTI34V0z0TZqSjKZ4tySTpVDjVU4GfrBUK8ERkxQwMZ1nflp61rQZToETqYjkj1GZG0lhwemi50lTRUtBXLDq5YhBeolEWWbxSofq/++pxBkcnazVDZO7aEKG5CwdWMLNrgDsIF5R+3N1vm+e4K4EHgZ929/0X2tBy5SWmnhoa46KuNq6/cltsaz4G7kzli0zlYYyZaaapRFjHL5UMp30mEzOjfxfytynS6BTgiciKKU3zKY3gQU2mQInUxb5D/eQKRYbGCuSKAZlkgq7WVM1GqGemis69vxitE8wVA6by4U+tC6zuqr4NvPpFF7G5q2ZTDx82s//s7n9avtHMfgH46kI3NrMk8FHgNcDx6P4OuPu35jjuduC+lWp4afbMibMTTOXPT4DSDMIRv/OnekLpJIyRjIK/8vV+iWgdYCOv+dP6dylX0wBvobNQZrYX+AJwLNr0WXe/tZZtEpHaKZ/m05ZO1moKlEhdPHFyhJHJAglmUskPjecoFEfq0p4wYUyS1nRy1rhRoVgK9pzJQpGpfEAhmPtLray49wCfM7O3MBPQ7QEywBsXcfurgCfdvR/AzO4G3gB8q+K4/wp8BrhyBdosRCPm7hQCmJrnmFQiHP1LJiys01lWrzOZiIJAs0Ulc1pJWv8ulWoW4C32LBTwZXf/iVq1Q0RWT/k0HyXJkWaTL4YjY6Wz+GYQBE6u2FgjZqnSWqMMrCOcMj1VKDI+VWQiX9T0zhpy95PAD5jZDwMvjjb/vbvfv8i72AoMlP1+HPj+8gPMbCthsPgqFOCtqkIQUFjkuZJEqV5oFBSmo5+l6ys5Gqj171KpliN4iz0LJSJNRElypFllUgkmckUCd8zAo4KdmVTjJ25oSSVpKatMny8GFIpOPgh/FoJwXV++6Ar+VoC7/wvwL0u46Vzf+itfkA8DN7t7sVp9ODO7EbgRYPv27Qs+cGmK37GhMbbEfA1eIwjcyRWcHHNHhOWJYDLROsBSQHiho38Dw1mSBv2nxqanj2/qzGj9e4Py6TqsYd87vc46cC7qrraMd/FqGeAteBYq8gozO0xYH+a97v5YDdskIiKyJLt6u3hqaIyRiZk1eN0daXZs7Kx30y5YuL4I2ioKw7uHiVwm8wFThXCKtdb1rarjQHnqyks4v37eHuDuKLjbBLzWzAru/vnyg9z9LuAugD179lR9Acun+HW3pBkan+KO+4/ybnYpyKuR8kQwlUq1/9LJBIkEJG1mJHCuILCrJcXRwbHpqaGFwDlxdpJdvfHrm+KsslxOsRTAuc/etwoZkmsZ4C3mLNSjwKXuPmZmrwU+D+w6744u8CyUiEij0kL4+CqtMd2yLtW0a0zNrGy0byYj7lShyGQ+HOVTMpeaehjYZWY7gRPA9cCbyw9w952l62b2SeD/VAZ3F6p8il+uEEy/v+9+eEABXh1Uq/1XrrQWMFcohiVXAiCaXeDuFIOAQjGYXico5yv9XxeCcN1yKSALAvDzwpbzBQ7FYhjQNdLsh1oGeAuehXL3kbLr95jZx8xsk7ufrjhu0WehQF+gRKQxaSF8vO3d3cv3ff04B77xHMXASSaM179ky5p47SqneMJMFs+pQjjaN5lTMpflcveCmb2TMDtmEviEuz9mZu+I9t9Zi8edq8RNazrBcyMTtXg4WSHFwCnijOWKXNTdwnA2T74YkE4m2NSeYWSywHfPhNM0S8lfSqOC6URieopoMtpXbcpvrZSXgynVAnXCUSIzi36CYZjNPI9SvOpO2VTz8Pal25T2l25PtG46KAugC0FjBWYrpZYB3oJnocxsC3DS3d3MrgISwNByHvTgkUHeu/8wY1MFioFzemyK9+4/zIeuu2JNfAiLSOPSQvh4+8g/PcEXDj9LaXZNMXC+cPhZdm56gnf9yAvr27g6mJXFMxrtyxcDJvJFJvMK+JbK3e8B7qnYNmdg5+5vW4nHnKvEzWQ+YEv3okr3SZ1d3N3G0PgU28pKEk3ki/R2zJQDCdwJFhgVTE5nAw2DwMR0gGWz7yfwcOQquu7R9fJAyWwmCCsFZ+UCD++jGYOrRlCzleHuXgBKZ6EeB/62dBaqdCYKuA74ZrQG7yPA9b7MV/q2ex/nbDaPB+GcZQ/gbDbPbfc+vpy7FRFZtoHhLG0VRc1UCD4+7vxSP5VLJwIPt0sonUzQ3Zqmt6uV7Rvb2bahnc1dLXS1pkknGz8ZzVp109V95IvOyZEJzmVznMnmyOaKvOZFFzE+VdCX8AZ3/ZXbKATORL6IE/4sBM71V25b+MZlioGTKwRkcwXGJguMTOQ5N5HnbDY3fRmZyDM2VSCbKzCVL0ajYOdn5vWydWeFICBfnH0pNunIWaOoaR28hc5CufsfA3+8ko95bCgb1iUpS2PtgXNsSF+gRKS+VAg+3rLzFH+eb7uUkrkk6IoSw5WSuEwVAiZz4bo+jfLVX6nEzX/+y0emy4EAfPAfvs0H/+HbpBJGd1uadW1p1rWl6G4Nr3dHl3VtabpbU9Pb1rWl6cgk6zLlby26qm8D72YXdz88wHMjE2zpVhbUta6mAZ6IiMxQIXhZ68qTuHS3ztTom8yFUzsn8kWd1a+Tvbt7uezibp45O8G5ifysQK8QOGfGc5wZzy36/pIJiwLC84O/7rY061pTZUFjuE1B4dJd1bdBAZ1Ma7oAr29TB0cHx7CyOkWBw67NHfVumoiscSoEH29Jg7lqmif1fXRZSgHfOtK4O5P5MNibKhSZyitb52o68M5XcuLsBJO5AhP5IiMTBc5N5BmZDKfqTU/Zm8jP7JvIc24y/FkeFBaXGBSWgsHywG+uIFFBocj8mi7Au/ma3bxv/2FGJwsUigGpRIKe9jQ3X7O73k0TEVEh+Bh74UVdHHludFbibIu2y8owM9oySdoyM2tVc4WAyUKYuGUqHyyYOl6WzyxMBtWeSbFl3eIKL7v7rKCwdBmZLAWG4ZqusxUBY2VQOJzNM5ydozjcPMqDwvLgb66RwmaePvpQ/xnufniAZ0cmuFhTNGOn9PqdjpLlLPfkb9MFeHt39/LB667QGXIREVlRs04gBuEJxK7WlE4g1lgmlSCTSkxP6QwCny7NkM2FgZ/U31KDwsl8MB0Ins3OBISlUcLyEcJS0LiSQeF5o4JzTSdt8KDwof4z3HH/0XCtZGtKhepjpvz1W6kSSk0X4IHOkIuIyMrTCcTGkEjMjPKtbw+/4GdzBSbzAZNRVj+Jh/IR26UEhbNHCPOzRg9Lo4RnazhSGCabOT9QDEcO03S0rE5QePfDA6QSNp2lWYXq46X89SudKFluCaWmDPBERKoxs2uAOwgLCX/c3W+r2L8b+HPgZcBvuPuHVr+V0oh0ArHxJBNGV2t6OlNnoTiTsEW1+JrPcoPCUuBXvqbwXMVI4UoHhbNHCGeCwsr1hEsNCp8dmaC7dfZXehWqj4+5Xr/lllBSgCcia4qZJYGPAq8BjgMPm9kBd/9W2WFngHcBP7nSj3/wyCD7DvUzMJxdkXn2IjJbKpmgK5mgK5rSmSvMFF+fyGk651q0rKBw1gjhzEhhKQis1fTR89YTzhkohpctXa2cyeZm1VlVofr4KBWqL3/9lltCSQGeiKw1VwFPuns/gJndDbwBmA7w3H0QGDSzH1/JBz54ZJD37j/M2FSBYuCcHpvivfsP86HrrlCQJ1IjpTV869rS08WXRRYyKyjsXm5QWODcRG6OrKQFzk7klh0URqWfSZiRnk7razx/cyd/98hAxZTSaE3hKk0flYVdf+U27rj/KBP5IplUYkVKKCnAE5G1ZiswUPb7ceD7V+OBb7v3cc5m8yTNSJrhAZzN5rnt3scV4ImsAjMjpboWUiNLDgoLQcUIYfWg8NxknlxhZupx6ZxF4E6h7ATGwSdOcfCJU3M+bvlIYXngt64tNWdA2N2WoqMlRUJB4YorL1Q/ND61Iuu7FeBJw9EUNqmxuT6dlnxK38xuBG4E2L59e9Vjjw1lSViYJCK8LXjgHBta+jx7ERGJL7MwuUZbevFBIYRT+OYKCqd/rxglHJkszAoKlzpSWFovWAr+utsq6hYqKFySUqH6vs2dK3J/CvCkoRw8MsgtBx4jnVy5VLEiFY4D28p+vwR4Zql35u53AXcB7NmzR3O/RESk5iqDwof6z/D333iuah28sE5heZKZstHBqERFaW3hfCOFSw4KW8uzjKbOCwbXelCoOnjS1PYd6iedDFPEAiuSKlakwsPALjPbCZwArgfevBoP3LepgydOjpIvFnHCocSEwQsvWpkzdiIiS1WaPXNsaIwtXSqUHSeLrYNXCgovuoCRwsmykcKz5UFhWYKZWftWKCicGSGsPlJY2h7noPCh/jPcft8RxqcKBO6cHp3iffsP88FlrM9XgCcNZWA4y/q29Kxty00VK1LO3Qtm9k7gPsIyCZ9w98fM7B3R/jvNbAvwCNANBGb2HuBF7j6ynMe+9sVb+PbJ0en5oB5drn3xluXcrYgskpYAzO3gkUHet/8wo5MF8sWAM2M5br9vnJt/bLeCvBioZR281nSS1iUGhdVKUUxPKY1GDSuDwrNR0LhYCwWFc00pbZSg8K4v9zMykSeRMFLJBB4Fxbd/8YgCPGkO23raGRydnB7Bg+WnihWp5O73APdUbLuz7PpzhFM3V9QD/Wfo7WphdLJArhiQSSboak3xQP8Z3rXSDyYisyiL7fxu/+IRhrN5kokwCU3gMDKR564v9yvAi4FnRyZIGgwMT5EvBqSTCXra03Wrg7eiQeHkzLYwQCxMTyedWtGgMDVdi7C7Yspo+chhLYLCgeFofb4ZhoXr883pPz2+5PtUgCcN5aar+7jlwGNkc4XpM1DLTRUr0igGhrNs6mxhc9fMh567a4RaZBXcdu/jDI/nCDwcOS8UnXwhpyy2QP/p8ekvmIFHaffNGVDfFAsd6SRPn8mSMCNhRqHonByZ4tIN8Tk5vtSgcNaawslCxXTSmbWGI9HI4eQKjxR2t6XqEhQuRAGeNJS9u3u5lXAt3vHh7IqkihVpFBqhFqmfJ0+NU1ZuDAeKDt85tfSz5CINoRQ8GDN5or1se5MqBYW9S1xTWB4UzpmRdIWnj3aXl6EoCwrXt6YZGs8ReEAyYeAQAC/YpELn0kT27u5VQCdNSSPUIvVTmKfAeV6Fz9m5sZ0nT41jQbgyOPDwi+ylPW31bhrAeQW53ed+zRJm09NME2a4hzGORfcxM0oZPkd3pxhdLwkCxx2c8Gcwz2M1kvFcgYu6WxjO5memaHZmyOYK9W5aw1mpkcKz0TTRWUHhZGF6KumSRgodikXHgM6WJO+/9rIlPksFeCIiq2bv7l6uO36Wj3/lGOO5Ih2ZJG9/5U6d0IgRJemQZvT+ay+bXp9YKDrJhNHVkuZXXvM99LRnoiBpZpSoPGiKNoU/y44rj8kSZtO38VnH2+zbXsCIUzFwAvfpIC6VsAu6/WK5O8UgDARLAV8Q/XSfCTYteo7TtwtKx85x2yiIDLcvP4C8uLuNoSi9fslEvkhvR8uy71uWPlI4MkdAeN5I4WSBU6NTnCsL/jJJozVKmLNUCvBERFbJwSOD7H/0BJu7WtgejeDtf/QEL7lkvYKEGFCdTmlWe3f38qHrrojV8ohkwkhS+ymIZuGIYK2+MJcHkGHQGgWCQfi7w3QgWYi2FYLZgeH1V27jjvuPMpEv0ppOMJkPKATO9Vdum/+BpaYuJCj8lb85zND4FC2pBMlEgnTSmMgXl1UiTAGeiMgqUZ3HeNPrF2+l0aO5touWR9TLUgPIYuDTo5g/+bKtnDib5ZP/9tT07JC3/cAOXve9z5s1aliMAsdC2W0Dn3/Kq6yOZ0cm6G5NYRiZVAJYfokwBXgiIqtEdR7jTa9fvCUTULYsZtZ2kbhJJsL1hhDOLvjC4Wfp7W6dXt/9hcPP8n2XblhU0B6OCgYEAdMjifliQL4YUCieP2IoK6s0xbatbFrmchOwKcATEVklyqIZb3r94m1XbxdHnhudNYpn0XaROFvu7IIwWKy+5qs08ldKOjM9AhhAPigFguHPOCSmaSTlU2wzqcSKJGDTeSsRkVVy09V95ItONlfAPfypLJrxcdPVfYxM5Dl6cpTHnz3H0ZOjjEzk9frFxLUv3nJe1nizcHsjMbNrzOzbZvakmb1/jv1vMbNvRJd/M7Mr6tFOaRwDw9lZoz+w8rMLEolw+mBpbVlHS4qu1jTr2tNs6mxhy7pWLulpZ8emDi7d2MHz1rexuauFnvYMna0p2jJJ0slETRLhxN1VfRt496t2sbGjhXMTeXq7Wrn19Zcva8q0RvBEZEUpy+D8lEUz/hyiLIJhSkCdp46PB/rP0NvVwuhkgVwxIJNM0NWa4oH+M7yr3o2LmFkS+CjwGuA48LCZHXD3b5Uddgz4IXcfNrNrgbuA71/91kqjaLTZBaURwbkyQbo7udLUz6KTD8KpoPlCOAK4Vl3Vt4Gr+jbQt7lzRe5PAZ6IrBhlGaxOWTTjbd+hfta1pbl43UxtMCVZiY+B4SybOlvY3DWT1c7dG20N5VXAk+7eD2BmdwNvAKYDPHf/t7LjHwQuWdUWSsOJU41VM6MllaRljgikPPjLFwNyxYB80SkUA4qqV3lBFOCJyIpRlsHq9h3qJ1coMjQ2ewRB/z/xoCQr8batp52nhsYYmZj5++tuS7Fj48qcMV8hW4GBst+PU3107heAe2vaIml4e3f3civEqszFXKoFf+WJX0pBXykAVAKY8ynAE5EVoy/A1T1xcoSRyQIJjKQZhaIzNJ6jUBypd9NkERptGpRcmFf0beChp86QMEgY5IoBg6M5brhyQ72bVm6uBUpzfns1sx8mDPBeOc/+G4EbAbZv375S7ZMG1exlLqpN+yxl+wxH/8KgrxQMrlUK8ERkxegLcHX5Yvg9LRGltjYLM5Plijr7GAc3Xd3H+/Yf5sTwBIUgIJUIR2A/8OMvqnfTZBEe6D/D5s5MQ6/BIxyxK69OfQnwTOVBZvYS4OPAte4+NNcduftdhOvz2LNnjzqZJreW17+nkwnSSWhjdvBXKg5fyvBZLKsBmG+w0b+H+s9w98MDnB6fWpHXTwGeiKyYOK0DqIdMKsFErkjgjhm4A850YVNpfEqyEl8xWYP3MLDLzHYCJ4DrgTeXH2Bm24HPAm919ydWv4nSaA4eGeS9+w8zNlWgGDinx6Z47/7DfOi6K9ZMkDcXMyOdNMJBv7nLQExP+yx4NOUzYKoQrGrg91D/Ge64/yipxMrlL6jpt4qFUv2WHXelmRXN7LpatkdEamvv7l5uff3l9Ha1rliq32ayq7eLTV0ZUgmjGDiphLGpK6M6XDFRSrKyq7eL3Vu62dXbxbq2NPsO9de7abII23ramcgXZ21rtBkG7l4A3gncBzwO/K27P2Zm7zCzd0SH3QJsBD5mZl83s0fq1FxpELfd+zhns3k8gKQZHsDZbJ7b7n283k1reOlkgvZMinXtaTZ3tfC89W3s2NjO1p42ertbp8s8tKST04XlV9rdDw+QShht6SRmYR6DdNKW9dlSsxG8Rab6LR13O2FnJiIx1+zrAJajNMK5ZV1KI5wxNDCcJWnQf2pseorfps5Mo40AyTziMsPA3e8B7qnYdmfZ9bcDb1/tdknjOjaUxd2ZKss0mbRwu1y4WcleWmbvyxcDcoVgOstnKePncrJ8PjsyQXfr7JBsufkLajlFc8FUv5H/CnwGuLKGbRERqbtmyXS2VnW1pDg6OBYt9jcKgXPi7CS7ehsqC6PMQ39/0qwKxYDKpdxFB9ZwkpFaCdf7nT8BcjlZPi/ubmNofGpWsfrlzi6oZYC3YKpfM9sKvBF4FQrwRGQN0AhnfE1/SJc+q71iuzQ8/f3Nby0n6Yi7+cI4hXerp1qWz1Jmz9Iav9KoXyEIg7/rr9zGHfcfZSJfDNfqr8DsgloGeItJ9fth4GZ3L5rNP69VqX5FRKTexnJFtq5v5fRYbnqK5pbOFsZzxYVvLNLADh4Z5JYDj5FOrlySB1k9851j0rmnxpBKJkjNk+UzX3Qu+t5WulpT/Pm/PcXJkckVmV1QywBvMal+9wB3R8HdJuC1ZlZw98+XH6RUv2uLziKKSCMqlQHp2zwzJTObK9BblpVRJI72HeonXywyNDa7CPy+Q/36/BWpETMjkzIyqQQ/fsXz+PErnrdi913LLJrTqX7NLEOY6vdA+QHuvtPdd7j7DmA/8IuVwZ2sLaWziIOjk7POIh48MljvponIGnfT1X3ki042V8A9/NmISTpELtTRwVFOj+YoBD69vvT0aI6jg6P1bposQnKeSXDzbZfmV7MAb5GpfkVm2Xeon3QyTBG7UqliRURWwt7dvXzf9nU8NZTlm8+M8NRQlu/bvk4jHBJ7uUJA0Z2pQsBkPqwDVnQnV9Aqrjh44UVd562Lsmi7rE01LXS+UKrfiu1vq2VbJB4GhrOsb0vP2rbcVLEiIivhI//0BJ//+rPTi8mLgfP5rz/Lzk1P8K4feWFd2yayHLlCkcos74GH26Xx3XzNbt63/zCjkwUKQUAqkaCrNcXN1+yud9OkTmpa6FzkQsWhEK2IrE0fPfid8zKFebRdJM7y8wzUzbddGsve3b289eWXkkklcMI1XW99+aWaXRAjB48McsNdD/LK2+/nhrseXPbSJAV40lC0xkVEGtXUPNPV5tsuEhelUh9WdinfLo3t4JFB9j96gs1dLVy2pYvNXS3sf/SE8hfERC3yTyjAk4ayd3cvt77+cnq7Wjk3kae3q5VbX3+5zkKJiIjUSGdLiqRBqWKVWZigo7Olpit5ZIUof0G87TvUz+nRSb5zapxvPjPCd06Nc3p0clmvn/5ypeGoEK2IiMjqefsrd/KH/3QUiAoWe/jz7a/cWc9mySINDGdJGvSfGpsuc7GpM6P8BTHx6HeHmCrM3pbNB3ztu0NLvk+N4ImIiCxCd+vc50Tn2y4SFy+5ZD3dbbPfx91tKV5yyfr6NEguSGcmyYmzkxSKTtKMQtE5cXaSjkxy4RtL3VUGdyWT82xfDAV4IiIii3D589axoT1NIprGljDY0J7m8uetq2/DRJZp36F+2jNJ2jPJaKpfeF1T/OLBpufWMmsR5fR2WXN02lFEZBUdPDLIvkP9DAxn2dbTzk1X92lKckzcdHUftxx4jJ6ODG3pJBP5opJAxYz+/uZ2dHCUM2M53MAdCkGRiVz4/pbGNzpVYOv6Vk6P5aanaG7pbmFsvqEhaXoawRMRWSW1yJQlq2fv7l6ue9lWTo1O8fhzo5waneK6l21VgBAT+vub3/hUgYAwuCP6GUTbpfFt62knV5ydzTdXDFRiag1TgCciskqU6SzeDh4Z5C8ffJpcIcBwcoWAv3zwaQUIMaG/v/mVRuoqyyQUNIIXC6/o28DJkSnGo1HX8VyRkyNTvKJvQ72bJoswXzC2nCBNAZ6IyCoZGM7Slp696L0tnVSms5i4/YtHGBrLMVUMKAQwVQwYGstx+xeP1Ltpsgj6+5tfMmFzlklIJLSGKw7u/eZz4LNfPzzaLg0vnUpQ+Zdm0falUoAnIrJKtvW0M5Evzto2kS9qGk1MHB0cnXMa29HB0Xo2SxZJf3/z69vUgZmRTiZoSSdIJxOYGX2bOurdNFmE/tPjOLP7Jo+2S+PbubGdZMJoSSZoTSVoSSZIJoydG5feNynAExFZJTdd3Ue+6GRzBdzDn0rSER+l2WpmM5fy7dLY9Pc3v5uv2U1PexoDCsUAA3ra09x8ze56N00WIV8IqOyGPNouje/9117G+vY0loCiO5aA9e1p3n/tZUu+TwV4IiKrZO/uXm59/eX0drVybiJPb1crt77+ciXpiIlUKaLzskv5dmlo+vub397dvXzwuit46fYeLl7Xxku39/DB667Q/01MzHeOSeee4mHv7l4+dN0VvHRbD1u6W3npth4+tMy/P5VJEBFZRXt39+pLU0y9oLeTI8+NzvrSZNF2iQf9/c1P/zfxpQAv/lb6708BnoiIyCJc++ItfPvk6PQ6FwinaV774i31a5TIClGNQJH6Wem/P03RFBERWYQH+s/Q29VCRyZJOml0ZJL0drXwQP+ZejdNZFlUI1Ckfmrx96cAT0REZBEGhrNs6myhb3Mnu7d007e5k02dLUqzL7GnGoEi9VOLvz9N0ZSGo2kiUmtmdg1wB5AEPu7ut1Xst2j/a4Es8DZ3f3TVGyoNZVtPO8dOjzE6WSBXDMgkE3S1pti5SWvw4iIOny/16J8GhrOsb0vP2qYagSKrY2A4S9Kg/9TY9GfLps7Msv7+NIInDUXTRKTWzCwJfBS4FngRcIOZvajisGuBXdHlRuBPVrWR0pBe0beBU2M5csWAhEGuGHBqLMcr+jbUu2myCHH4fKlX/6QagSL109WS4sTZSQqBk0wYhcA5cXaSzpalj8MpwJOGomkisgquAp509353zwF3A2+oOOYNwKc89CCw3swuXu2GSmMJ1+BlyCQTBA6ZZILerozW4MVETD5f6tI/qUZgvCXmqdQy33ZpLD5doZ5ZJXjcl54HVQGeNJSB4Sxt6eSsbZomIitsKzBQ9vvxaNuFHgOAmd1oZo+Y2SOnTp1a0YZKYxkYzrKxY/YavI0dWoMXFzH5fFmx/ulC+ibVCIy3dDJBZSxn0XZpfGO5IlvXt5JKGkV3Uklj6/pWxnPFhW88D63Bk4ayraedwdFJ2jMzb01NE5EVNtc5zcrTZIs5JtzofhdwF8CePXtUdqiJqX+Kt5i8fivWP11o36Q6ePHVt6mDo4NjJBOGGbhDMXD6NnXUu2myCKW+qW/zzHrubK5Ab1frku9Tob00FE0TkVVwHNhW9vslwDNLOEbWGPVP8RaT10/9k1ywm6/ZTU97GgMKxQADetrT3HzN7no3TRahFn2TAjxpKJomIqvgYWCXme00swxwPXCg4pgDwM9a6OXAOXd/drUbKo1F/VO8xeT1U/8kF2zv7l4+eN0VvHR7Dxeva+Ol23v44HVXNNp7W+ZRi75JUzSl4WiaiNSSuxfM7J3AfYRpyD/h7o+Z2Tui/XcC9xCmIH+SMA35z9ervdJY1D/FW6O/fuqfZKka/b0t1a3066cAT0TWHHe/h/BLUvm2O8uuO/BLq90uERH1TyKyXJqiKSIiIiIi0iQU4ImIiIiIiDQJBXgiIiIiIiJNQgGeiIiIiIhIk1CAJyIiIiIi0iQU4ImIiIiIiDQJBXgiIiIiIiJNwsJyKvFhZqeApxd5+CbgdA2bU296fvGm5zfjUnffXMvGrAb1T7Po+cWbnt+M2PdP6ptm0fOLNz2/GfP2TbEL8C6EmT3i7nvq3Y5a0fOLNz2/ta3Z/3/0/OJNz2/tavb/Gz2/eNPzWxxN0RQREREREWkSCvBERERERESaRLMHeHfVuwE1pucXb3p+a1uz///o+cWbnt/a1ez/N3p+8abntwhNvQZPRERERERkLWn2ETwREREREZE1oykCPDO7xsy+bWZPmtn759hvZvaRaP83zOxl9WjnUi3i+e01s3Nm9vXocks92rkUZvYJMxs0s2/Osz/ur91Czy+2rx2AmW0zs38xs8fN7DEze/ccx8T6NVwO9U2xf3+rf4rp66e+aWHqn2L9/lbfFNPXDlapf3L3WF+AJPAdoA/IAIeBF1Uc81rgXsCAlwP/t97tXuHntxf4P/Vu6xKf39XAy4BvzrM/tq/dIp9fbF+7qP0XAy+LrncBTzTT398y/2/UN8X//a3+Kaavn/qmBf9/1D/F+/2tvimmr13U/pr3T80wgncV8KS797t7DrgbeEPFMW8APuWhB4H1Znbxajd0iRbz/GLL3Q8BZ6ocEufXbjHPL9bc/Vl3fzS6Pgo8DmytOCzWr+EyqG+KOfVP8aW+aUHqn2JMfVO8rUb/1AwB3lZgoOz345z/n7SYYxrVYtv+CjM7bGb3mtnlq9O0VRHn126xmuK1M7MdwEuB/1uxay28hnNR3xRqivf3POL8+i1W7F8/9U1zUv8Uiv37ex5xfu0Wqyleu1r1T6llt6z+bI5tlalBF3NMo1pM2x8FLnX3MTN7LfB5YFetG7ZK4vzaLUZTvHZm1gl8BniPu49U7p7jJs30Gs5HfVOTvL+riPPrtxixf/3UN81L/VMTvL+riPNrtxhN8drVsn9qhhG848C2st8vAZ5ZwjGNasG2u/uIu49F1+8B0ma2afWaWFNxfu0W1AyvnZmlCTuov3L3z85xSFO/hlWob2qC9/cC4vz6LSjur5/6pqrUP8X8/b2AOL92C2qG167W/VMzBHgPA7vMbKeZZYDrgQMVxxwAfjbKSPNy4Jy7P7vaDV2iBZ+fmW0xM4uuX0X4ug6tektrI86v3YLi/tpFbf8z4HF3/5/zHNbUr2EV6pti/v5ehDi/fguK8+unvmlB6p9i/P5ehDi/dguK+2u3Gv1T7KdounvBzN4J3EeYNekT7v6Ymb0j2n8ncA9hNpongSzw8/Vq74Va5PO7DvgvZlYAJoDr3T0WQ/Fm9mnCbEibzOw48JtAGuL/2sGinl9sX7vI/wu8Ffh3M/t6tO3Xge3QHK/hUqlviv/7W/1TrF8/9U1VqH+K9/tbfVN8X7tIzfsni9f/h4iIiIiIiMynGaZoioiIiIiICArwREREREREmoYCPBERERERkSahAE9ERERERKRJKMATERERERFpEgrwpCbM7D+Y2WNmFpjZnrLtG83sX8xszMz+uOI2XzSzw9Ht7jSzZLT9ajN71MwKZnZdxW1+zsyORpefK9v+TjN70szcyopfmtn7zOzr0eWbZlY0sw21+58QkUaj/klEGpH6JlkpCvBkWSw01/vom8BPAYcqtk8CHwDeO8dt/qO7XwG8GNgM/Ido+3eBtwF/XfHYGwhro3w/cBXwm2bWE+3+V+BHgKfLb+PuH3T373X37wV+DfiSu59Z+JmKSNyofxKRRqS+SWot9oXOZfWZ2Q7gXuBfgFcAZ81sM+CExUT/0N0fj46ddVt3Hwe+YmYvqLxfdx+JrqaATHR/uPtT0X0FFTf5MeAfS52Mmf0jcA3waXf/2lyPX+EG4NOLec4iEg/qn0SkEalvktWkETxZqu8BPgW8HSi4+4vd/f8B/nw5d2pm9wGDwCiwf4HDtwIDZb8fj7Yt5nHaCTu0zyyhmSLS2NQ/iUgjUt8kq0IBnizV0+7+INAP9JnZH5nZNcDIAreryt1/DLgYaAFetcDhc51i8kU+1OuAf9UUA5GmpP5JRBqR+iZZFQrwZKnGAdx9GLgCOAj8EvDx5d6xu08CB4A3LHDocWBb2e+XAM8s8mGuR1MMRJqV+icRaUTqm2RVKMCTZYmyLCXc/TOEC4BftsT76TSzi6PrKeC1wJEFbnYf8KNm1hMtEP7RaNtCj7UO+CHgC0tpq4jEg/onEWlE6puk1hTgyXJtBQ6a2deBTxJmV8LM3mhmxwkXEv99ND+caN9TwP8E3mZmx83sRUAHcMDMvgEcJpxLfmd0/JXRff0HYJ+ZPQYQTRH4HeDh6HJr2aLhd0W3uQT4hpmVnx17I/AP0aJlEWle6p9EpBGpb5KaMvfFTrsVERERERGRRqYRPBERERERkSahAE9ERERERKRJKMATERERERFpEgrwREREREREmoQCPBERERERkSahAE9ERERERKRJKMATERERERFpEgrwREREREREmsT/D3T2BGUMqlJ+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax1, ax2, ax3 = axes\n",
+    "cd4ydata = (common_individuals_individual_ratio_df['CD8T TEM']) / common_individuals_individual_ratio_df['CD8T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD8 TEM / CD8T')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_df['CD8T Naive']) / common_individuals_individual_ratio_df['CD8T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax2)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax2.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax2.set_ylabel('CD8 Naive / CD8T')\n",
+    "ax2.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_df['CD8T Naive']) / common_individuals_individual_ratio_df['CD8T TEM']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax3)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax3.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax3.set_ylabel('CD8 Naive / CD8T TEM')\n",
+    "ax3.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cd8tydata = (common_individuals_individual_ratio_df['CD8T TEM'] + \\\n",
+    "            common_individuals_individual_ratio_df['CD4T TEM']) / (\n",
+    "    common_individuals_individual_ratio_df['CD8T Naive'] + \\\n",
+    "    common_individuals_individual_ratio_df['CD4T Naive']\n",
+    ")\n",
+    "fig, ax = plt.subplots()\n",
+    "sns.regplot(x=common_individuals_individual_ratio_df['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax)\n",
+    "r, p = stats.spearmanr(common_individuals_individual_ratio_df['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax.set_title('Oelen v2 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax.set_ylabel('CD8+CD4 TEM / CD8+CD4 Naive')\n",
+    "ax.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Begin calculating networks for 72 individuals and;\n",
+      "RPS26;RUNX3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 72/72 [00:00<00:00, 207.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SpearmanrResult(correlation=0.29651955126400936, pvalue=0.011433091246178868)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='gt', ylabel='RPS26;RUNX3'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onemillionv2_datasc = dataset.data_sc\n",
+    "onemillionv2_monocytes_ut = onemillionv2_datasc[(onemillionv2_datasc.obs['cell_type_lowerres']=='CD4T') & \n",
+    "                                                (onemillionv2_datasc.obs['time']=='UT')]\n",
+    "onemillionv2_monocytes_ut_df = pd.DataFrame(\n",
+    "    data=onemillionv2_monocytes_ut.X.toarray(),\n",
+    "    columns=onemillionv2_monocytes_ut.var.index,\n",
+    "    index=onemillionv2_monocytes_ut.obs.index\n",
+    ")\n",
+    "data_selected_df, zscore_df, zscore_p_df = \\\n",
+    "get_individual_networks_selected_genepairs(onemillionv2_monocytes_ut_df, \n",
+    "                                           onemillionv2_monocytes_ut, \n",
+    "                                           'assignment', \n",
+    "                                           ';'.join(['RPS26', 'RUNX3']))\n",
+    "concated_df = pd.concat([zscore_df.T,\n",
+    "           gt.T],\n",
+    "          axis=1).dropna()\n",
+    "concated_df['gt'] = [item.split(':')[0].count('1') for item in concated_df[0]]\n",
+    "print(spearmanr(concated_df['RPS26;RUNX3'], concated_df['gt']))\n",
+    "sns.regplot(x='gt', y='RPS26;RUNX3', data=concated_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Begin calculating networks for 72 individuals and;\n",
+      "RPS26;RUNX3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 72/72 [00:00<00:00, 214.76it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SpearmanrResult(correlation=0.2201691525430256, pvalue=0.06311568667519006)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='gt', ylabel='RPS26;RUNX3'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onemillionv2_datasc = dataset.data_sc\n",
+    "onemillionv2_monocytes_ut = onemillionv2_datasc[(onemillionv2_datasc.obs['cell_type_lowerres']=='CD8T') & \n",
+    "                                                (onemillionv2_datasc.obs['time']=='UT')]\n",
+    "onemillionv2_monocytes_ut_df = pd.DataFrame(\n",
+    "    data=onemillionv2_monocytes_ut.X.toarray(),\n",
+    "    columns=onemillionv2_monocytes_ut.var.index,\n",
+    "    index=onemillionv2_monocytes_ut.obs.index\n",
+    ")\n",
+    "data_selected_df, zscore_df, zscore_p_df = \\\n",
+    "get_individual_networks_selected_genepairs(onemillionv2_monocytes_ut_df, \n",
+    "                                           onemillionv2_monocytes_ut, \n",
+    "                                           'assignment', \n",
+    "                                           ';'.join(['RPS26', 'RUNX3']))\n",
+    "concated_df = pd.concat([zscore_df.T,\n",
+    "           gt.T],\n",
+    "          axis=1).dropna()\n",
+    "concated_df['gt'] = [item.split(':')[0].count('1') for item in concated_df[0]]\n",
+    "print(spearmanr(concated_df['RPS26;RUNX3'], concated_df['gt']))\n",
+    "sns.regplot(x='gt', y='RPS26;RUNX3', data=concated_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### onemillion v3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # load onemillion v2 data\n",
+    "# datasetv3 = DATASET('onemillionv3')\n",
+    "# datasetv3.load_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T TEM</th>\n",
+       "      <th>CD4T Naive</th>\n",
+       "      <th>CD8T TEM</th>\n",
+       "      <th>CD8T Naive</th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>CD4T TCM</th>\n",
+       "      <th>CD8T TCM</th>\n",
+       "      <th>all_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0117</th>\n",
+       "      <td>39</td>\n",
+       "      <td>248</td>\n",
+       "      <td>70</td>\n",
+       "      <td>75</td>\n",
+       "      <td>625</td>\n",
+       "      <td>170</td>\n",
+       "      <td>289</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1300</th>\n",
+       "      <td>34</td>\n",
+       "      <td>149</td>\n",
+       "      <td>73</td>\n",
+       "      <td>39</td>\n",
+       "      <td>658</td>\n",
+       "      <td>119</td>\n",
+       "      <td>450</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0615</th>\n",
+       "      <td>68</td>\n",
+       "      <td>84</td>\n",
+       "      <td>175</td>\n",
+       "      <td>23</td>\n",
+       "      <td>550</td>\n",
+       "      <td>212</td>\n",
+       "      <td>347</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0923</th>\n",
+       "      <td>39</td>\n",
+       "      <td>80</td>\n",
+       "      <td>205</td>\n",
+       "      <td>40</td>\n",
+       "      <td>316</td>\n",
+       "      <td>249</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6</td>\n",
+       "      <td>907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0705</th>\n",
+       "      <td>13</td>\n",
+       "      <td>114</td>\n",
+       "      <td>123</td>\n",
+       "      <td>61</td>\n",
+       "      <td>322</td>\n",
+       "      <td>188</td>\n",
+       "      <td>166</td>\n",
+       "      <td>3</td>\n",
+       "      <td>947</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
+       "LLDeep_0117        39         248        70          75   625   170       289   \n",
+       "LLDeep_1300        34         149        73          39   658   119       450   \n",
+       "LLDeep_0615        68          84       175          23   550   212       347   \n",
+       "LLDeep_0923        39          80       205          40   316   249       140   \n",
+       "LLDeep_0705        13         114       123          61   322   188       166   \n",
+       "\n",
+       "             CD8T TCM  all_num  \n",
+       "LLDeep_0117        26     1599  \n",
+       "LLDeep_1300        10     1382  \n",
+       "LLDeep_0615        19     1277  \n",
+       "LLDeep_0923         6      907  \n",
+       "LLDeep_0705         3      947  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onemillionv3 = datasetv3.data_sc.obs.copy()\n",
+    "onemillionv3_celltype_df = pd.read_csv(\n",
+    "    './1M_v3_20201106_azimuth.tsv',\n",
+    "    sep='\\t', index_col=0\n",
+    ")\n",
+    "onemillionv3 = pd.concat([onemillionv3, onemillionv3_celltype_df], axis=1)\n",
+    "onemillionv3 = onemillionv3[onemillionv3['timepoint']=='UT']\n",
+    "onemillionv3_l1_cellratio_df = onemillionv3.groupby(['assignment', 'predicted.celltype.l1']).size().to_frame()\n",
+    "# display(onemillionv3_l1_cellratio_df.head())\n",
+    "onemillionv3_celltyperatio = onemillionv3.groupby(['assignment', 'predicted.celltype.l2']).size().to_frame()\n",
+    "# display(onemillionv3_celltyperatio.head())\n",
+    "onemillionv3_allcells = onemillionv3['assignment'].value_counts()\n",
+    "\n",
+    "# caluclate the individual CD4T TEM and NAIVE ratio\n",
+    "individual_ratio = pd.DataFrame()\n",
+    "for individual in onemillionv3['assignment'].unique():\n",
+    "    tem_num = onemillionv3_celltyperatio.loc[individual, \"CD4 TEM\"].values[0]\n",
+    "    naive_num = onemillionv3_celltyperatio.loc[individual, \"CD4 Naive\"].values[0]\n",
+    "    cd8t_tem_num = onemillionv3_celltyperatio.loc[individual, \"CD8 TEM\"].values[0]\n",
+    "    tcm_num = onemillionv3_celltyperatio.loc[individual, \"CD4 TCM\"].values[0]\n",
+    "    cd8t_tcm_num = onemillionv3_celltyperatio.loc[individual, \"CD8 TCM\"].values[0]\n",
+    "    cd8t_naive_num = onemillionv3_celltyperatio.loc[individual, \"CD8 Naive\"].values[0]\n",
+    "    cd4t_num = onemillionv3_l1_cellratio_df.loc[individual, 'CD4 T'].values[0]\n",
+    "    cd8t_num = onemillionv3_l1_cellratio_df.loc[individual, 'CD8 T'].values[0]\n",
+    "    all_num = onemillionv3_allcells.loc[individual]\n",
+    "    individual_ratio[individual] = [tem_num,  naive_num, \n",
+    "                                    cd8t_tem_num, cd8t_naive_num,\n",
+    "                                    cd4t_num, cd8t_num,\n",
+    "                                    tcm_num, cd8t_tcm_num, all_num]\n",
+    "\n",
+    "individual_ratio_dfv3 = individual_ratio.T\n",
+    "individual_ratio_dfv3 = individual_ratio_dfv3.rename({0: 'CD4T TEM', 1:'CD4T Naive', \n",
+    "                                                  2: 'CD8T TEM', 3: 'CD8T Naive',\n",
+    "                                                  4: 'CD4T', 5: 'CD8T',\n",
+    "                                                  6: 'CD4T TCM', 7: 'CD8T TCM',\n",
+    "                                                  8: 'all_num'}, \n",
+    "                                                 axis=1)\n",
+    "display(individual_ratio_dfv3.head())\n",
+    "\n",
+    "\n",
+    "common_individuals = list(set(individual_ratio_dfv3.index) & set(gt.columns))\n",
+    "common_individuals_individual_ratio_dfv3 = individual_ratio_dfv3.loc[common_individuals]\n",
+    "common_individuals_individual_ratio_dfv3['gt'] = [float(gt[col].values[0].split(':')[1]) for col in \n",
+    "                                                  common_individuals_individual_ratio_dfv3.index]\n",
+    "common_individuals_individual_ratio_dfv3['chemistry'] = 'v2'\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax1, ax2, ax3 = axes\n",
+    "cd4ydata = (common_individuals_individual_ratio_dfv3['CD4T TEM']) / common_individuals_individual_ratio_dfv3['CD4T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD4 TEM / CD4T')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD4T Naive']) / common_individuals_individual_ratio_dfv3['CD4T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax2)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax2.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax2.set_ylabel('CD4 Naive / CD4T')\n",
+    "ax2.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD4T Naive']) / common_individuals_individual_ratio_dfv3['CD4T TEM']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax3)\n",
+    "r, p = stats.pearsonr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax3.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax3.set_ylabel('CD4 Naive / CD4T TEM')\n",
+    "ax3.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'rs1131017')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax1, ax2, ax3 = axes\n",
+    "cd4ydata = (common_individuals_individual_ratio_dfv3['CD8T TEM']) / common_individuals_individual_ratio_dfv3['CD8T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD8 TEM / CD4T')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD8T Naive']) / common_individuals_individual_ratio_dfv3['CD8T']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax2)\n",
+    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax2.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax2.set_ylabel('CD8 Naive / CD8T')\n",
+    "ax2.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "cd8tydata = (common_individuals_individual_ratio_dfv3['CD8T Naive']) / common_individuals_individual_ratio_dfv3['CD8T TEM']\n",
+    "sns.regplot(x=common_individuals_individual_ratio_dfv3['gt'],\n",
+    "            y= cd8tydata, \n",
+    "            ax=ax3)\n",
+    "r, p = stats.spearmanr(common_individuals_individual_ratio_dfv3['gt'],\n",
+    "                       cd8tydata)\n",
+    "ax3.set_title('Oelen v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax3.set_ylabel('CD8 Naive / CD8T TEM')\n",
+    "ax3.set_xlabel(\"rs1131017\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(104, 11) (72, 11) (32, 11)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CD4T TEM</th>\n",
+       "      <th>CD4T Naive</th>\n",
+       "      <th>CD8T TEM</th>\n",
+       "      <th>CD8T Naive</th>\n",
+       "      <th>CD4T</th>\n",
+       "      <th>CD8T</th>\n",
+       "      <th>CD4T TCM</th>\n",
+       "      <th>CD8T TCM</th>\n",
+       "      <th>all_num</th>\n",
+       "      <th>gt</th>\n",
+       "      <th>chemistry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1035</th>\n",
+       "      <td>17</td>\n",
+       "      <td>152</td>\n",
+       "      <td>36</td>\n",
+       "      <td>54</td>\n",
+       "      <td>625</td>\n",
+       "      <td>108</td>\n",
+       "      <td>424</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1006</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0960</th>\n",
+       "      <td>36</td>\n",
+       "      <td>89</td>\n",
+       "      <td>113</td>\n",
+       "      <td>13</td>\n",
+       "      <td>586</td>\n",
+       "      <td>143</td>\n",
+       "      <td>374</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_1004</th>\n",
+       "      <td>65</td>\n",
+       "      <td>54</td>\n",
+       "      <td>440</td>\n",
+       "      <td>9</td>\n",
+       "      <td>583</td>\n",
+       "      <td>462</td>\n",
+       "      <td>437</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1493</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0918</th>\n",
+       "      <td>19</td>\n",
+       "      <td>34</td>\n",
+       "      <td>119</td>\n",
+       "      <td>7</td>\n",
+       "      <td>249</td>\n",
+       "      <td>136</td>\n",
+       "      <td>168</td>\n",
+       "      <td>14</td>\n",
+       "      <td>598</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LLDeep_0067</th>\n",
+       "      <td>51</td>\n",
+       "      <td>101</td>\n",
+       "      <td>122</td>\n",
+       "      <td>89</td>\n",
+       "      <td>753</td>\n",
+       "      <td>231</td>\n",
+       "      <td>546</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1429</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>v2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             CD4T TEM  CD4T Naive  CD8T TEM  CD8T Naive  CD4T  CD8T  CD4T TCM  \\\n",
+       "LLDeep_1035        17         152        36          54   625   108       424   \n",
+       "LLDeep_0960        36          89       113          13   586   143       374   \n",
+       "LLDeep_1004        65          54       440           9   583   462       437   \n",
+       "LLDeep_0918        19          34       119           7   249   136       168   \n",
+       "LLDeep_0067        51         101       122          89   753   231       546   \n",
+       "\n",
+       "             CD8T TCM  all_num   gt chemistry  \n",
+       "LLDeep_1035        28     1006  1.0        v2  \n",
+       "LLDeep_0960        26     1546  0.0        v2  \n",
+       "LLDeep_1004        16     1493  1.0        v2  \n",
+       "LLDeep_0918        14      598  1.0        v2  \n",
+       "LLDeep_0067        30     1429  1.0        v2  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "concate_v2_v3 = pd.concat([common_individuals_individual_ratio_df,\n",
+    "                           common_individuals_individual_ratio_dfv3],\n",
+    "                         axis=0)\n",
+    "print(concate_v2_v3.shape,common_individuals_individual_ratio_df.shape, common_individuals_individual_ratio_dfv3.shape)\n",
+    "concate_v2_v3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots()\n",
+    "cd4ydata = (concate_v2_v3['CD8T Naive'] + \\\n",
+    "            concate_v2_v3['CD4T Naive']\n",
+    "            ) / (\n",
+    "    concate_v2_v3['CD8T TEM'] + \\\n",
+    "            concate_v2_v3['CD4T TEM']\n",
+    ")\n",
+    "sns.regplot(x=concate_v2_v3['gt'],\n",
+    "            y=cd4ydata, \n",
+    "            ax=ax1)\n",
+    "r, p = stats.spearmanr(concate_v2_v3['gt'],\n",
+    "                       cd4ydata)\n",
+    "ax1.set_title('Oelen v2 & v3 r={:.2f}, p={:.2g}'.format(r, p))\n",
+    "ax1.set_ylabel('CD8+CD4 TEM / CD8+CD4 Naive')\n",
+    "ax1.set_xlabel(\"rs1131017\")\n",
+    "\n",
+    "plt.savefig('TEM_naive_CD4_CD8_v2_v3_rs1131017.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05_coeqtl_interpretation/enrichment_GO_terms.R b/05_coeqtl_interpretation/enrichment_GO_terms.R
new file mode 100644
index 0000000..8c497b8
--- /dev/null
+++ b/05_coeqtl_interpretation/enrichment_GO_terms.R
@@ -0,0 +1,163 @@
+# ------------------------------------------------------------------------------
+# This scripts runs GO enrichment analysis of significant co-eQTLs results 
+# (one analysis per co-eGene group associated with the same eQTL).
+# It consists of three functions and requires all cell-type specific eQTL results 
+# 'coeqtls_fullresults_fixed.all.tsv.gz' (with all genes tested) and 
+# significant results 'coeqtls_fullresults_fixed.sig.tsv.gz' to run it. 
+# ------------------------------------------------------------------------------
+
+#################################################################
+##                          Libraries                          ##
+#################################################################
+
+library(enrichplot)
+library(stringr)
+library(ggplot2)
+library(clusterProfiler)
+
+##################################################################
+##          Function for enrichment of subset of genes          ##
+##################################################################
+
+# enrich_small function run enrichment analysis of test_genes with background set of genes, 
+# store it to the table and also as a dotplot to output pdf file.
+
+enrich_small <- function(test_genes, background_genes, mark_gene, cell_type){
+
+  enrich_out <-enrichGO(gene=c(test_genes),
+                        OrgDb='org.Hs.eg.db',
+                        keyType="SYMBOL",
+                        pvalueCutoff = 0.05,
+                        #pAdjustMethod = "none",
+                        universe = background_genes,
+                        ont="all",
+                        minGSSize=5)
+  
+  enrich_out_df <- data.frame(enrich_out)
+  
+  # save the data if GO terms found
+  if(nrow(enrich_out_df)>0){
+    enrich_out_df$id<-mark_gene
+    dotplot(enrich_out, showCategory=15) + ggtitle(paste(cell_type, mark_gene))
+    ggsave(paste(outdir,  cell_type, "_", mark_gene,  ".pdf", sep=''), width = 10, height = 8)
+  }
+  return(enrich_out_df)
+}
+
+# enrichment_for_zscore_subsets function split the table of co-eQTL results 
+# based on the Zscore and run enrichment analysis for these subsets. 
+# Also, it creates  hubs of genes and run GO enrichmentfor particular gene hub. 
+# All the results are stored to the one datatable, visualized into pdf and saved 
+# and separated files. 
+
+enrichment_for_zscore_subsets <- function(tab_all, tab_sign, zscore_id, cell_type){
+  
+  background_genes  <- unique(c(str_split_fixed(tab_all$Gene, ";", 2)))
+  
+  #Identify eQTL gene and "second gene"
+  test_genes <- str_split_fixed(tab_sign$Gene, ";", 2)
+  tab_sign$gene1 <- test_genes[,1]
+  tab_sign$gene2 <- test_genes[,2]
+  tab_sign$eqtlgene<- str_split_fixed(tab_sign$snp_eqtlgene, "_", 2)[,2]
+  tab_sign$secondgene<- with(tab_sign,ifelse(gene1==eqtlgene,gene2,gene1))
+  tab_sign$gene1 <- NULL
+  tab_sign$gene2 <- NULL
+  print(paste(cell_type, dim(tab_sign)[1]))
+  
+  #Perform enrichment analysis over all coeQTL genes together (and all eQTL / second genes)
+  
+  all_genes <- enrich_small(test_genes, background_genes, mark_gene=paste(zscore_id, 'all', sep="_"),cell_type)
+  gene1_genes <- enrich_small(tab_sign$eqtlgene, background_genes, mark_gene=paste(zscore_id, 'eqtlgene'),cell_type)
+  gene2_genes <- enrich_small(tab_sign$secondgene, background_genes, mark_gene=paste(zscore_id, 'secondgene'),cell_type)
+  enrichment<-rbind(all_genes,gene1_genes,gene2_genes)
+  
+  count_hubs <- as.data.frame(table(tab_sign$eqtlgene))
+  count_hubs$Var1<-as.character(count_hubs$Var1)
+  count_hubs <- count_hubs[order(count_hubs$Freq, decreasing = T),]
+  
+  #Filter all hub genes to contain more than 5 second genes
+  count_hubs<-count_hubs[count_hubs$Freq>5,]
+  
+  for(hub in seq_len(nrow(count_hubs))){
+    hub_gene <- count_hubs$Var1[hub]
+    second_genes<-tab_sign$secondgene[tab_sign$eqtlgene == hub_gene] 
+    hub_enrich <- enrich_small(second_genes, background_genes, 
+                               mark_gene=paste( hub_gene, count_hubs$Freq[hub],zscore_id, sep="_"),cell_type)
+    enrichment<-rbind(enrichment,hub_enrich)
+  }
+  
+  #Save cell type in the table
+  enrichment$cell_type <- cell_type
+  
+  #Save results
+  name_GO<- paste(outdir, "GO_", zscore_id, cell_type, ".tsv", sep='')
+  write.table(enrichment, name_GO, sep='\t', quote=F, row.names = F, col.names = T)
+  
+}
+
+# enrichment_function load the input files and apply previously described functions 
+# to selected genes.
+
+enrichment_function <- function(path, cell_type, outdir){
+  
+  coeqtls<-fread(paste0(path,"UT_",cell_type,"/coeqtls_fullresults_fixed.all.tsv.gz"))
+  
+  #Load eqtls on which this is based on
+  eqtls<-fread(paste0("coeqtl_mapping/input/snp_selection/eqtl/UT_",
+                      cell_type,"_eQTLProbesFDR0.05-ProbeLevel_withAF.tsv"))
+  eqtls$SNPpair<-paste0(eqtls$SNPName,"_",eqtls$genename)
+  coeqtls<-merge(coeqtls,eqtls[,c("SNPpair","SNPType","AlleleAssessed","OverallZScore",
+                                  "AF","alt_allele")],
+                 by.x="snp_eqtlgene",by.y="SNPpair")
+  
+  #Swap the Z score
+  coeqtls$MetaPZ<-ifelse(coeqtls$AF>=0.5,coeqtls$MetaPZ*(-1),
+                         coeqtls$MetaPZ)
+  
+  tab_all <- coeqtls
+  
+  coeqtls_sign<-fread(paste0(path,"UT_",cell_type,"/coeqtls_fullresults_fixed.sig.tsv.gz"))
+  
+  #Load eqtls on which this is based on
+  eqtls_sign<-fread(paste0("coeqtl_mapping/input/snp_selection/eqtl/UT_",
+                           cell_type,"_eQTLProbesFDR0.05-ProbeLevel_withAF.tsv"))
+  eqtls_sign$SNPpair<-paste0(eqtls_sign$SNPName,"_",eqtls_sign$genename)
+  coeqtls_sign<-merge(coeqtls_sign,eqtls_sign[,c("SNPpair","SNPType","AlleleAssessed","OverallZScore",
+                                                 "AF","alt_allele")],
+                      by.x="snp_eqtlgene",by.y="SNPpair")
+  
+  #Swap the Z score
+  coeqtls_sign$MetaPZ<-ifelse(coeqtls_sign$AF>=0.5,coeqtls_sign$MetaPZ*(-1),
+                              coeqtls_sign$MetaPZ)
+  tab_sign <- coeqtls_sign
+  
+  enrichment_for_zscore_subsets(tab_all, tab_sign, zscore_id="All_Zscores",cell_type)
+  enrichment_for_zscore_subsets(tab_all[tab_all$MetaPZ >0, ], tab_sign[tab_sign$MetaPZ >0, ], zscore_id="Positive_Zscores",cell_type)
+  enrichment_for_zscore_subsets(tab_all[tab_all$MetaPZ < 0, ], tab_sign[tab_sign$MetaPZ <0, ], zscore_id="Negative_Zscores",cell_type)
+  
+}
+
+##################################################################
+##                           Analysis                           ##
+##################################################################
+
+
+for(ct in c("CD8T","CD4T",
+            "monocyte","NK","B","DC")){
+  enrichment_function(path, cell_type=ct, outdir)
+}
+
+# Path where coeQTL results are stored 
+path <- '/path/to/coeQTLt/results/'
+# Path where results of GO enrich will be stored 
+outdir <- '/path/to/outputs/'
+# for tests
+cell_type ="CD8T"
+
+# 
+# run_mono <- enrichment_function(path, cell_type="monocyte", outdir)
+# run_cd8t <- enrichment_function(path, cell_type="CD8T", outdir)
+# run_cd4t <- enrichment_function(path, cell_type="CD4T", outdir)
+# run_b <- enrichment_function(path, cell_type="B", outdir)
+# run_dc <- enrichment_function(path, cell_type="DC", outdir)
+# run_nk <- enrichment_function(path, cell_type="NK", outdir)
diff --git a/05_coeqtl_interpretation/enrichment_GO_terms_reduced_background.R b/05_coeqtl_interpretation/enrichment_GO_terms_reduced_background.R
new file mode 100644
index 0000000..06120e9
--- /dev/null
+++ b/05_coeqtl_interpretation/enrichment_GO_terms_reduced_background.R
@@ -0,0 +1,167 @@
+# ------------------------------------------------------------------------------
+# Check for each co-eQTL with at least 5 co-eGenes if there is any enrichment
+# using all genes correlated with the respective eGene as background
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(dplyr)
+library(clusterProfiler)
+
+path<-"coeqtl_mapping/output/filtered_results/"
+outdir<-"coeqtl_interpretation/go_enrichment/"
+
+#Run the GO enrichment for each cell type
+enrichment<-NULL
+enrichment_summary<-NULL
+coegenes_counts_total<-NULL
+for(cell_type in c("CD4T","CD8T","monocyte","NK","B","DC")){
+
+  coeqtls <- fread(paste0(path, "UT_",cell_type, 
+                         "/coeqtls_fullresults_fixed.all.tsv.gz"))
+  coeqtls$gene1<-gsub(";.*","",coeqtls$Gene)
+  coeqtls$gene2<-gsub(".*;","",coeqtls$Gene)
+  coeqtls$second_gene<-ifelse(coeqtls$gene1 == coeqtls$eqtlgen, coeqtls$gene2,
+                        coeqtls$gene1)
+  coeqtls$gene1<-NULL
+  coeqtls$gene2<-NULL
+  
+  # Take all tested genes as background
+  background_genes  <- union(coeqtls$eqtlgen,coeqtls$second_gene)
+  coeqtls_sign<-coeqtls[coeqtls$gene2_isSig,]
+  
+  print(paste(cell_type,"with",nrow(coeqtls_sign),"co-eQTLs"))
+  print(paste("Size of the combined background set:",
+              length(background_genes)))
+  
+  # Identify all eQTLs with at least 5 coeGenes
+  coegene_count<-coeqtls_sign%>%
+    group_by(snp_eqtlgene)%>%
+    summarize(count_coeGenes=n())%>%
+    filter(count_coeGenes>4)
+  
+  coegene_count$cell_type<-cell_type
+  coegenes_counts_total<-rbind(coegenes_counts_total,
+                               coegene_count)
+  
+  #Size of the reduced background set
+  coeqtls_reduced_background<-coeqtls%>%
+    filter(snp_eqtlgene %in% coegene_count$snp_eqtlgene)%>%
+    group_by(snp_eqtlgene)%>%
+    summarize(count_secondgene=n())
+  
+  print("Size of the reduced gene sets")
+  print(summary(coeqtls_reduced_background$count_secondgene))
+
+  enrichment_found<-0
+  #Perform GO enrichemt separately for each eQTL
+  for(eqtl in coegene_count$snp_eqtlgene){
+    
+    # Run enrichment analysis with background set
+    enrich_out <-enrichGO(gene=coeqtls_sign$second_gene[coeqtls_sign$snp_eqtlgene == eqtl],
+                          OrgDb='org.Hs.eg.db',
+                          keyType="SYMBOL",
+                          pvalueCutoff = 0.05,
+                          pAdjustMethod = "BH",
+                          universe = coeqtls$second_gene[coeqtls$snp_eqtlgene == eqtl],
+                          ont="all",
+                          minGSSize=5)
+    
+    if(nrow(enrich_out@result)>0){
+      
+      # Save if a enrichment was found
+      enrichment_found<-enrichment_found+1
+      
+      # Save result dataframe
+      res<-enrich_out@result
+      res$cell_type<-cell_type
+      res$snp_eGene<-eqtl
+      enrichment<-rbind(enrichment,
+                        res[,c("cell_type","snp_eGene","ONTOLOGY","ID",
+                               "Description","pvalue","p.adjust","GeneRatio","BgRatio")])
+    }
+
+  }
+  
+  enrichment_summary<-rbind(enrichment_summary,
+                            data.frame(cell_type,
+                                       n_eqtls_freq=nrow(coegene_count),
+                                       n_enrich=enrichment_found,
+                                       freq_enrich=enrichment_found/nrow(coegene_count)))
+  
+  
+  #Check for CD4T specificallly for RPS26 the positive & negative coeGenes separately
+  if(cell_type=="CD4T"){
+    eqtl<-"rs1131017_RPS26"
+    
+    #Test positive coeGenes (MAF not correctly flipped here)
+    enrich_out <-enrichGO(gene=coeqtls_sign$second_gene[coeqtls_sign$snp_eqtlgene == eqtl &
+                                                          coeqtls_sign$MetaPZ < 0],
+                          OrgDb='org.Hs.eg.db',
+                          keyType="SYMBOL",
+                          pvalueCutoff = 0.05,
+                          pAdjustMethod = "BH",
+                          universe = coeqtls$second_gene[coeqtls$snp_eqtlgene == eqtl],
+                          ont="all",
+                          minGSSize=5)
+    
+    if(nrow(enrich_out@result)>0){
+      
+      # Save if a enrichment was found
+      enrichment_found<-enrichment_found+1
+      
+      # Save result dataframe
+      res<-enrich_out@result
+      res$cell_type<-cell_type
+      res$snp_eGene<-paste0(eqtl,"_positive")
+      enrichment<-rbind(enrichment,
+                        res[,c("cell_type","snp_eGene","ONTOLOGY","ID",
+                               "Description","pvalue","p.adjust","GeneRatio","BgRatio")])
+    }
+    
+    #Test negative coeGenes (MAF not correctly flipped here)
+    enrich_out <-enrichGO(gene=coeqtls_sign$second_gene[coeqtls_sign$snp_eqtlgene == eqtl &
+                                                          coeqtls_sign$MetaPZ > 0],
+                          OrgDb='org.Hs.eg.db',
+                          keyType="SYMBOL",
+                          pvalueCutoff = 0.05,
+                          pAdjustMethod = "BH",
+                          universe = coeqtls$second_gene[coeqtls$snp_eqtlgene == eqtl],
+                          ont="all",
+                          minGSSize=5)
+    
+    if(nrow(enrich_out@result)>0){
+      
+      # Save if a enrichment was found
+      enrichment_found<-enrichment_found+1
+      
+      # Save result dataframe
+      res<-enrich_out@result
+      res$cell_type<-cell_type
+      res$snp_eGene<-paste0(eqtl,"_negative")
+      enrichment<-rbind(enrichment,
+                        res[,c("cell_type","snp_eGene","ONTOLOGY","ID",
+                               "Description","pvalue","p.adjust","GeneRatio","BgRatio")])
+    }
+  }
+}
+
+
+#Format p-values
+enrichment$pvalue<-format(enrichment$pvalue,digits=3)
+enrichment$p.adjust<-format(enrichment$p.adjust,digits=3)
+write.table(enrichment,
+            file=paste0(outdir,"GOenrichment_coeGenes_allcelltypes_otherbackground.tsv"),
+            sep="\t",quote=FALSE,row.names=FALSE)
+
+#Check general statistics (per eQTL - cell type)
+sum(enrichment_summary$n_eqtls_freq)
+sum(enrichment_summary$n_enrich)
+
+#Check general statistics (per eQTL, combining all cell types)
+enriched_eqtls<-setdiff(unique(enrichment$snp_eGene),
+                        c("rs1131017_RPS26_positive","rs1131017_RPS26_negative"))
+length(enriched_eqtls)
+
+coegenes_counts_total<-as.data.frame(coegenes_counts_total)
+length(unique(coegenes_counts_total$snp_eqtlgene))
+setdiff(unique(coegenes_counts_total$snp_eqtlgene),enriched_eqtls)
diff --git a/05_coeqtl_interpretation/enrichment_TFs_Remap.R b/05_coeqtl_interpretation/enrichment_TFs_Remap.R
new file mode 100644
index 0000000..1683cae
--- /dev/null
+++ b/05_coeqtl_interpretation/enrichment_TFs_Remap.R
@@ -0,0 +1,258 @@
+# ------------------------------------------------------------------------------
+# Check enrichment of TFBS among co-eGenes using Remap 2022 annotations
+# - Check for each cell type and coeGene cluster the enrichment (FDR correction)
+# - Check if the enriched TF is itself part of the coeGenes
+# - Check if the SNP or a SNP in LD is part of the TF
+#
+# Input: TFBS from Remap2022 (filtered for blood cell types in script 
+#        enrichment_TFs_Remap_preprocessing.R), 
+#        gene annotation file,
+#        coeQTL results (complete result with all tested genes to 
+#        define also the background)
+# Output: signficant enrichment results with information about overlap 
+#         between TF and co-eQTL SNP
+# ------------------------------------------------------------------------------
+
+library(rtracklayer)
+library(data.table)
+
+source("snipe.R")
+
+peaks<-import("tfbs_enrichment_remap/remap2022_nr_macs2_hg19_v1_0_blood_related.bed")
+
+# Split name into TF and measured cell lines
+ann <- t(matrix(unlist(strsplit(values(peaks)[,"name"], ":", fixed=T)), nrow=2))
+colnames(ann) <- c("TF", "conditions")
+ann <- as.data.frame(ann,stringsAsFactors=FALSE)
+
+values(peaks)<-ann
+
+#Update seqlevel style to match between genotypes
+seqlevelsStyle(peaks)<-"NCBI"
+peaks<-keepStandardChromosomes(peaks, pruning.mode="coarse")
+
+#Get number of unique TFs
+tfs<-as.character(unique(ann$TF))
+
+################################################################################
+# Part 1: check if certain TFs are enriched within the co-eGenes
+################################################################################
+
+gene_annot<-import("tfbs_enrichment_remap/genes.gtf")
+gene_annot<-gene_annot[gene_annot$type =="gene",]
+gene_annot<-keepStandardChromosomes(gene_annot, pruning.mode="coarse")
+
+#Read coeQTL file (all tests)
+tf_enrichment_combined<-NULL
+total_set_tested_eqtls<-NULL
+for(ct in c("CD4T","CD8T","monocyte","NK","DC","B")){
+  
+  coeqtls<-fread(paste0("coeqtl_mapping/output/filtered_results/UT_",ct,
+                        "/coeqtls_fullresults_fixed.all.tsv.gz"))
+  coeqtls$gene1<-gsub(";.*","",coeqtls$Gene)
+  coeqtls$gene2<-gsub(".*;","",coeqtls$Gene)
+  coeqtls$eqtlgene<-gsub(".*_","",coeqtls$snp_eqtlgene)
+  coeqtls$gene2<-ifelse(coeqtls$gene1 == coeqtls$eqtlgen, coeqtls$gene2,
+                        coeqtls$gene1)
+  coeqtls$gene1<-NULL
+  tested_genes<-unique(c(coeqtls$eqtlgene,coeqtls$gene2))
+  
+  #Remove trailing .1 (R issue) of missing genes
+  missing_genes<-tested_genes[! tested_genes %in% gene_annot$gene_name]
+  tested_genes[! tested_genes %in% gene_annot$gene_name]<-gsub("\\.1$","",missing_genes)
+  tested_genes<-unique(tested_genes)
+  print(paste("Annotation found for x% of the genes:",
+              mean(tested_genes %in% gene_annot$gene_name)))
+  
+  #Read gene position file and determine TSS
+  gene_pos<-gene_annot[gene_annot$gene_name %in% tested_genes,]
+  mcols(gene_pos)<-data.frame(gene_name=gene_pos$gene_name)
+  
+  #Get TSS for the genes
+  gene_tss<-promoters(gene_pos,upstream=2000,downstream=2000)
+  
+  #Check for each TF in ReMap overlap with all gene TSS
+  tfbs_ann <- sapply(tfs, function(x) overlapsAny(gene_tss,
+                                                      peaks[peaks$TF == x]))
+  rownames(tfbs_ann)<-gene_tss$gene_name
+  
+  #Filter for TFs with at least one found binding
+  tfbs_ann<-tfbs_ann[,colSums(tfbs_ann)>0]
+  
+  #Collapse genes with multiple annotations (hit if at least a hit in one annotation)
+  tfbs_ann<- apply(tfbs_ann, 2, tapply, rownames(tfbs_ann),
+                     max, na.rm=T)
+  #Convert it back into a logical matrix
+  tfbs_ann<-matrix(as.logical(tfbs_ann),ncol=ncol(tfbs_ann),
+                       dimnames=list(rownames(tfbs_ann),colnames(tfbs_ann)))
+  
+  #Get significant coeQTLs
+  coeqtls_sign<-coeqtls[coeqtls$gene2_isSig,]
+  occ_eqtl<-as.data.frame(table(coeqtls_sign$snp_eqtlgene))
+  occ_eqtl<-occ_eqtl[occ_eqtl$Freq>=5,]
+  occ_eqtl$cell_type<-ct
+  total_set_tested_eqtls<-rbind(total_set_tested_eqtls,
+                                occ_eqtl)
+  
+  #Perform Fisher's test for the enrichment
+  fisher_all_eqtl<-NULL
+  for(eqtl in occ_eqtl$Var1){
+    
+    #Filter significant gene2s with existing promoter annotation
+    sign_gene2<-coeqtls_sign$gene2[coeqtls_sign$snp_eqtlgene==eqtl]
+    sign_gene2<-intersect(sign_gene2,rownames(tfbs_ann))
+    
+    #Iterate over each TF
+    fisher_res<-NULL
+    for(tf in colnames(tfbs_ann)){
+      
+      counts<-data.frame(tf_binding=c(sum(tfbs_ann[sign_gene2,tf]),sum(tfbs_ann[,tf])),
+                         tf_nonbinding=c(sum(!tfbs_ann[sign_gene2,tf]),sum(!tfbs_ann[,tf])))
+      
+      res_fisher<-fisher.test(counts,alternative="greater")
+      
+      fisher_res<-rbind(fisher_res,
+                        data.frame(celltype=ct,
+                                   eqtl,
+                                   tf,
+                                   is_coeGene = tf %in% sign_gene2,
+                                   fisher_pval=res_fisher$p.value,
+                                   tf_coeqtl=counts$tf_binding[1],
+                                   notf_coeqtl=counts$tf_nonbinding[1],
+                                   tf_background=counts$tf_binding[2],
+                                   notf_background=counts$tf_nonbinding[2]))
+    }
+    
+    #Multiple testing correction per eQTL
+    fisher_res$fisher_fdr<-p.adjust(fisher_res$fisher_pval,method="BH")
+    
+    fisher_all_eqtl<-rbind(fisher_all_eqtl,fisher_res)
+  }
+  
+  #Check for CD4T specificallly for RPS26 the positive & negative coeGenes separately
+  if(ct=="CD4T"){
+    eqtl<-"rs1131017_RPS26"
+    
+    #Test positive coeGenes (MAF not correctly flipped here)
+    sign_gene2<-coeqtls_sign$gene2[coeqtls_sign$snp_eqtlgene==eqtl
+                                   & coeqtls_sign$MetaPZ < 0]
+    sign_gene2<-intersect(sign_gene2,rownames(tfbs_ann))
+    
+    #Iterate over each TF
+    fisher_res<-NULL
+    for(tf in colnames(tfbs_ann)){
+      
+      counts<-data.frame(tf_binding=c(sum(tfbs_ann[sign_gene2,tf]),sum(tfbs_ann[,tf])),
+                         tf_nonbinding=c(sum(!tfbs_ann[sign_gene2,tf]),sum(!tfbs_ann[,tf])))
+      
+      res_fisher<-fisher.test(counts,alternative="greater")
+      
+      fisher_res<-rbind(fisher_res,
+                        data.frame(celltype=ct,
+                                   eqtl=paste0(eqtl,"_positive"),
+                                   tf,
+                                   is_coeGene = tf %in% sign_gene2,
+                                   fisher_pval=res_fisher$p.value,
+                                   tf_coeqtl=counts$tf_binding[1],
+                                   notf_coeqtl=counts$tf_nonbinding[1],
+                                   tf_background=counts$tf_binding[2],
+                                   notf_background=counts$tf_nonbinding[2]))
+    }
+    
+    #Multiple testing correction per eQTL
+    fisher_res$fisher_fdr<-p.adjust(fisher_res$fisher_pval,method="BH")
+    fisher_all_eqtl<-rbind(fisher_all_eqtl,fisher_res)
+    
+    #Test negative coeGenes (MAF not correctly flipped here)
+    sign_gene2<-coeqtls_sign$gene2[coeqtls_sign$snp_eqtlgene==eqtl
+                                   & coeqtls_sign$MetaPZ > 0]
+    sign_gene2<-intersect(sign_gene2,rownames(tfbs_ann))
+    
+    #Iterate over each TF
+    fisher_res<-NULL
+    for(tf in colnames(tfbs_ann)){
+      
+      counts<-data.frame(tf_binding=c(sum(tfbs_ann[sign_gene2,tf]),sum(tfbs_ann[,tf])),
+                         tf_nonbinding=c(sum(!tfbs_ann[sign_gene2,tf]),sum(!tfbs_ann[,tf])))
+      
+      res_fisher<-fisher.test(counts,alternative="greater")
+      
+      fisher_res<-rbind(fisher_res,
+                        data.frame(celltype=ct,
+                                   eqtl=paste0(eqtl,"_negative"),
+                                   tf,
+                                   is_coeGene = tf %in% sign_gene2,
+                                   fisher_pval=res_fisher$p.value,
+                                   tf_coeqtl=counts$tf_binding[1],
+                                   notf_coeqtl=counts$tf_nonbinding[1],
+                                   tf_background=counts$tf_binding[2],
+                                   notf_background=counts$tf_nonbinding[2]))
+    }
+    
+    #Multiple testing correction per eQTL
+    fisher_res$fisher_fdr<-p.adjust(fisher_res$fisher_pval,method="BH")
+    
+    fisher_all_eqtl<-rbind(fisher_all_eqtl,fisher_res)
+  
+  }
+  
+  tf_enrichment_combined<-rbind(tf_enrichment_combined,
+                                fisher_all_eqtl[fisher_all_eqtl$fisher_fdr<0.05,])
+  
+}
+
+table(tf_enrichment_combined$eqtl,tf_enrichment_combined$celltype)
+tf_enrichment_combined[tf_enrichment_combined$is_coeGene,]
+
+################################################################################
+# Part 2: check if the SNP (a SNP in high LD) is in the TF peak
+################################################################################
+
+tf_enrichment_combined$eqtlsnp<-gsub("_.*","",tf_enrichment_combined$eqtl)
+
+#Get all SNPs in LD with the enriched eQTL SNP
+enriched_snps<-unique(tf_enrichment_combined$eqtlsnp)
+
+proxies <- snipa.get.ld.by.snp(enriched_snps,
+                               rsquare=0.9,
+                               population=c('eur'))
+
+snp_grange<-makeGRangesFromDataFrame(proxies,seqnames.field="CHR",
+                                     start.field = "POS2",end.field = "POS2")
+snp_grange$snp_name<-proxies$RSID
+snp_grange$eqtl_snp<-proxies$QRSID
+
+#Iterate over each SNP to check the overlap with the TF
+tf_enrichment_combined$snp_tf_overlap<-FALSE
+tf_enrichment_combined$snp_name_overlap<-""
+for(i in 1:nrow(tf_enrichment_combined)){
+  
+  #SNPs in high LD with the eQTL SNP
+  snp_subset<-snp_grange[snp_grange$eqtl_snp == tf_enrichment_combined$eqtlsnp[i]]
+  
+  #All peaks of the respective TF
+  peaks_tf<-peaks[peaks$TF == tf_enrichment_combined$tf[i]]
+  
+  overlap_snp<-overlapsAny(snp_subset,peaks_tf)
+  
+  tf_enrichment_combined$snp_tf_overlap[i]<-any(overlap_snp)
+  tf_enrichment_combined$snp_name_overlap[i]<-paste0(snp_subset$snp_name[overlap_snp],collapse=",")
+}
+
+#Save LD information and result table
+write.table(tf_enrichment_combined,
+            file="tfbs_enrichment_remap/tf_remap_enrichment_results.tsv",
+            sep="\t",quote=FALSE,row.names=FALSE)
+write.table(proxies,
+            file="tfbs_enrichment_remap/ld_proxies_with_position.tsv",
+            sep="\t",quote=FALSE,row.names=FALSE)
+
+#Filter for SNPs which overlap with the TF
+tf_enrichment_overlap<-tf_enrichment_combined[tf_enrichment_combined$snp_tf_overlap,]
+
+table(tf_enrichment_combined$eqtl,tf_enrichment_combined$celltype)
+table(tf_enrichment_overlap$eqtl,tf_enrichment_overlap$celltype)
+tfs_coegenes<-tf_enrichment_overlap[tf_enrichment_overlap$is_coeGene,c("celltype","eqtl","tf")]
+tfs_coegenes[,c("celltype","eqtl","tf")]
+
+
diff --git a/05_coeqtl_interpretation/enrichment_TFs_Remap_preprocessing.R b/05_coeqtl_interpretation/enrichment_TFs_Remap_preprocessing.R
new file mode 100644
index 0000000..3560fb5
--- /dev/null
+++ b/05_coeqtl_interpretation/enrichment_TFs_Remap_preprocessing.R
@@ -0,0 +1,54 @@
+# ------------------------------------------------------------------------------
+# Preparation of enrichment of TFBS among co-eGenes using Remap annotations:
+# filter Remap file for blood related cell lines 
+# (due to size of file this takes some time)
+# Input: bed file from Remap 2022 (from https://remap.univ-amu.fr/download_page)
+# Output: bed file filtered for blood cell lines 
+# ------------------------------------------------------------------------------
+
+library(rtracklayer)
+
+#Read peak file from Remap2022
+peaks<-import("tfbs_enrichment_remap/remap2022_nr_macs2_hg19_v1_0.bed.gz")
+
+# Filter blood related cell lines
+ann <- t(matrix(unlist(strsplit(values(peaks)[,"name"], ":", fixed=T)), nrow=2))
+colnames(ann) <- c("TF", "conditions")
+ann <- as.data.frame(ann,stringsAsFactors=FALSE)
+
+conditions<-data.frame(condition=unique(unlist(strsplit(ann$conditions,","))),
+                       blood_related=FALSE)
+conditions<-conditions[order(conditions$condition),]
+
+blood_related_terms<-c("ALL","AML",
+                       "B-cell","BJAB","BL41","blood",
+                       "CLL","DC","erythroid",
+                       "erythroid-progenitor","GM",
+                       "Jurkat","K-562","Kasumi",
+                       "LCL","leukemia","lymphoblast","lymphocyte",
+                       "macrophage","MM1-S","monocyte",
+                       "neutrophil","P493","peripheral-blood",
+                       "SEM","T-cell","Th1","Th17","THP-1","U-937",
+                       "monocyte")
+
+for(term in blood_related_terms){
+  conditions[grep(term, conditions$condition),"blood_related"] <- TRUE
+}
+
+write.table(conditions,file="tfbs_enrichment_remap/conditions_remap2022.tsv",
+            quote=FALSE,sep="\t")
+
+conditions<-conditions[conditions$blood_related,]
+
+ann$blood_related<-FALSE
+for(term in blood_related_terms){
+  ann[grep(term, ann$conditions),"blood_related"] <- TRUE
+}
+
+values(peaks)<-ann
+peaks<-peaks[peaks$blood_related]
+peaks$blood_related<-NULL
+
+#Put back into name column as only this column is exported from rtracklayer
+peaks$name<-paste0(peaks$TF,":",peaks$conditions)
+export(peaks,"tfbs_enrichment_remap/remap2022_nr_macs2_hg19_v1_0_blood_related.bed")
diff --git a/05_coeqtl_interpretation/general_stats.R b/05_coeqtl_interpretation/general_stats.R
new file mode 100644
index 0000000..1806307
--- /dev/null
+++ b/05_coeqtl_interpretation/general_stats.R
@@ -0,0 +1,51 @@
+# -----------------------------------------------------------------------------
+# Count number of second genes per eQTL (across all cell types)
+# And the distribution of effect sizes across cell types
+# Input: significnat coeQTL results for all cell types
+# Output: printed summary statistics
+# -----------------------------------------------------------------------------
+
+library(data.table)
+library(dplyr)
+
+coeqtl_dir<-"coeqtl_mapping/output/filtered_results/"
+
+all_counts<-NULL
+for(cell_type in c("CD4T","CD8T","monocyte","B","NK","DC")){
+  
+  # Read coeQTL results
+  coeqtls<-fread(paste0(coeqtl_dir,"UT_",
+                        cell_type,"/coeqtls_fullresults_fixed_withAF.sig.tsv"))
+  
+  # Correct MAF
+  coeqtls$MetaPZ<-ifelse(coeqtls$AF>0.5,(-1)*coeqtls$MetaPZ,coeqtls$MetaPZ)
+  
+  count_second_genes<-coeqtls%>%
+    group_by(snp_eqtlgene)%>%
+    summarise(num_second_genes=n(),
+              pos_dir=sum(MetaPZ>0),
+              neg_dir=sum(MetaPZ<0))
+  
+  count_second_genes$ct<-cell_type
+  all_counts<-rbind(all_counts,count_second_genes)
+}
+
+# Get some general statistics about number of coeGenes per eQTL
+# summary(all_counts$num_second_genes)
+# sum(all_counts$num_second_genes>4)
+# mean(all_counts$num_second_genes>4)
+unique_eqtls<-length(unique(all_counts$snp_eqtlgene))
+unique_eqtls_second<-length(unique(all_counts$snp_eqtlgene[all_counts$num_second_genes>=5]))
+unique_eqtls_second/unique_eqtls
+
+# Get general proportion of positive / negative co-eGenes
+count_direction<-all_counts%>%
+  group_by(ct)%>%
+  summarise(pos_dir=sum(pos_dir),
+            neg_dir=sum(neg_dir),
+            frac_pos=sum(pos_dir)/sum(num_second_genes))
+
+# Filter again for all genes with at least 5 genes
+all_counts_filtered<-all_counts[all_counts$num_second_genes >= 5,]
+all_counts_filtered$frac_pos<- all_counts_filtered$pos_dir / all_counts_filtered$num_second_genes
+
diff --git a/05_coeqtl_interpretation/magma_coeqtl.Rmd b/05_coeqtl_interpretation/magma_coeqtl.Rmd
new file mode 100644
index 0000000..80d014e
--- /dev/null
+++ b/05_coeqtl_interpretation/magma_coeqtl.Rmd
@@ -0,0 +1,591 @@
+---
+title: "GWAS signals in co-eQTL genes"
+author: "Matthias Heinig"
+date: "30.03.2022"
+output: html_document
+knit: (
+  function(inputFile, encoding) { 
+    outname <- gsub(".Rmd$", paste0("_", format(Sys.time(), "%Y%m%d"), ".html"), basename(inputFile));
+    print(outname);
+    rmarkdown::render( 
+      input       = inputFile, 
+      encoding    = encoding, 
+      output_file = file.path("../results/", outname)) }
+      )
+---
+
+```{r setup, include=FALSE, message=FALSE, warning=FALSE}
+
+################################################################################
+# Script to perform GWAS enrichment analysis for coeGenes that share the same
+# eQTL (for all eQTLs with at least 5 coeGenes)
+# Additionally to the R packages, the tool MAGMA needs to be downloaded
+# (https://ctg.cncr.nl/software/magma)
+#
+# Input: co-eQTL results (combined in one file with cell type column),
+#        file with all tested genes in co-eQTL analysis as background,
+#        GWAS summary statistics processed by the GTEx consortium
+#        (https://zenodo.org/record/3518299)
+# Output: file with MAGMA enrichment results
+###############################################################################
+
+.libPaths(c("~/packages/R/x86_64-redhat-linux-gnu-library/3.6/", .libPaths()))
+knitr::opts_chunk$set(echo = TRUE, fig.width=8, fig.height=7)
+knitr::opts_knit$set(root.dir=normalizePath(".."))
+knitr::opts_knit$set(tidy=TRUE)
+
+## knitr::opts_chunk$set(dev='pdf')
+library(ggplot2)
+library(ggpubr)
+library(dplyr)
+library(tidyverse)
+library(DT) # interactive html tables
+library(knitr)
+library(kableExtra)
+library(Rgraphviz) # plot networks
+library(Homo.sapiens) # annotation data
+library(scales)
+library(RColorBrewer)
+
+theme_set(theme_bw())
+```
+
+
+
+## Implementation of the MAGMA gene set approach
+
+```{r, echo=FALSE}
+## define a wrapper function to call magma
+magma.run <- function(...) { #function
+    args <- list(...)
+    cmd <- "./packages/magma/magma"
+    for (arg in names(args)) {
+      val <- args[[arg]]
+      if (!is.null(val)) {
+        cmd = paste0(cmd, " --", arg, " ", val)
+      }
+    }
+    cat(cmd, "\n")
+    system(cmd, ignore.stdout = TRUE)
+}
+
+## define a wrapper for preprocessing
+preprocess.for.magma <- function(input.file, prefix, gene.loc.file, p.column, snp.column, chr.column, pos.column, sample.size) {
+  ## files are very big, so we will do the parsing and reformating with awk and sed
+  outdir <- dirname(prefix)
+  dir.create(outdir, recursive=TRUE, showWarnings = FALSE)
+  
+  cat.cmd <- "cat"
+  sed.cmd <- "sed" ## gsed needed on mac os
+  if (length(grep(".gz$", input.file)) > 0) {
+    cat.cmd <- "zcat" ## gzcat needed on mac os will fix later
+  }
+  
+  system("pwd")
+  cat(input.file, "\n")
+  
+  ## Rename the header column to P and SNP (from p and snptestid)
+  cmd <- paste0(cat.cmd, " ", input.file, " | head -n 1 ")
+  if (snp.column != "SNP") { 
+    cmd <- paste0(cmd, " | ", 
+                  sed.cmd, " 's/\\bSNP\\b/__old__SNP/g' | ", ## rename old SNP column (if exists)
+                  sed.cmd, " 's/\\b", snp.column, "\\b/SNP/g' ") ## rename new SNP column 
+  }
+  if (p.column != "P") {
+    cmd <- paste0(cmd, " |",
+                  sed.cmd, " 's/\\bP\\s+/__old__P/g' | ", ## rename old Pvalue column (if exists)
+                  sed.cmd, " 's/\\b", p.column, "\\b/P/g'") ## rename P val col
+  }
+  # cmd <- paste0(cmd, " > ", prefix, "_P.txt")
+  # cat(cmd, "\n")
+  # system(cmd)
+  # cmd <- paste0(cat.cmd, " ", input.file, "| tail -n +2 >> ", prefix, "_P.txt")
+  # cat(cmd, "\n")
+  # system(cmd)
+  
+  ## Annotate the SNPs of our GWAS to genes. SNP locations are formated:
+  ## rsid, chrom, bp
+  ## find the indices of the rsid, chrom and bp
+  cn <- colnames(read.csv(input.file, sep="\t", nrow=2))
+  col.idx <- match(c(snp.column, chr.column, pos.column), cn)
+  if (any(is.na(col.idx))) {
+    cat("colname(s)", c(snp.column, chr.column, pos.column)[is.na(col.idx)], "not found in", cn, "\n")
+    stop()
+  }
+  col.idx <- paste0("$", col.idx)
+  cmd <- paste0(cat.cmd, " ", input.file, " | tail -n +2 ", " | awk 'BEGIN{OFS=\"\t\"}{gsub(/chr/, \"\", ", col.idx[2], "); print ", 
+                paste(col.idx, collapse=", "), "}' | sed 's/$chr//g' > ", prefix, "_snppos.txt")
+  cat(cmd, "\n")
+  system(cmd)
+  
+  ## annotate SNPs to genes
+  magma.run(annotate="", `snp-loc`=paste0(prefix, "_snppos.txt"), `gene-loc`=gene.loc.file, out=prefix)
+  
+  ## unzip the file if needed
+  if (length(grep(".gz$", input.file)) > 0) {
+    new.input <- paste0(prefix, "_P.txt")
+    cmd <- paste(cat.cmd, input.file, ">", new.input)
+    system(cmd)
+    input.file <- new.input
+  }
+  
+  ## run the gene level analysis
+  if (is.na(as.numeric(sample.size))) {
+    sample.size <- paste0("ncol=", sample.size)
+  } else {
+    sample.size <- paste0("N=", sample.size)
+  }
+  pval.arg <- paste0(input.file, " ", sample.size, " use=", paste(snp.column, p.column, sep=","))
+  magma.run(bfile="data/current/references/magma/g1000_eur",  pval=pval.arg, `gene-annot`=paste0(prefix, ".genes.annot"), out=prefix)
+}
+```
+
+
+Gene annotations for magma are based on entrez ids. So we check if all symbols present in the PPI data can be mapped to entrez ids.
+```{r, message=FALSE}
+symbol2entrez <- select(Homo.sapiens, columns=c("SYMBOL","ENTREZID"), keys=keys(Homo.sapiens, keytype="SYMBOL"), keytype="SYMBOL")
+alias2entrez <- select(Homo.sapiens, columns=c("ALIAS","ENTREZID"), keys=keys(Homo.sapiens, keytype="ALIAS"), keytype="ALIAS")
+colnames(alias2entrez)[1] <- "SYMBOL"
+name2entrez <- unique(rbind(symbol2entrez, alias2entrez))
+
+write.table(name2entrez, file="results/current/name2entrez.txt", sep="\t", quote=F, row.names=F)
+```
+
+
+
+
+## Run the analysis systematically on many GWAS
+
+```{r, echo=FALSE}
+run.magma.on.gwas.list <- function(magma.params, set.file, out.suffix, rerun=TRUE, ...) {
+  magma.res <- NULL
+  for (i in 1:nrow(magma.params)) {
+    ## print(magma.params[i,])
+    prefix <- magma.params[i,"prefix"]
+    gsa.file <- paste0(prefix, out.suffix, ".gsa.out")
+    if (rerun || !file.exists(gsa.file)) {
+      with(magma.params[i,], {
+        print(genome.build)
+        if (genome.build == "hg19") {
+          gene.loc.file <- "data/current/references/magma/NCBI37.3.gene.loc"
+        } else if (genome.build == "hg38") {
+          gene.loc.file <- "data/current/references/magma/NCBI38.gene.loc"
+        } else {
+          cat("Only genome build hg19 and hg38 currently available!!\n")
+          next  
+        }
+        gene.level.file <- paste0(prefix, ".genes.raw")
+        if (!file.exists(gene.level.file)) {
+          preprocess.for.magma(input.file, prefix, gene.loc.file, p.column, snp.column, chrom.column, pos.column, sample.size)
+        }
+        magma.run(`gene-results`=gene.level.file, `set-annot`=set.file, out=paste0(prefix, out.suffix), ...) # model="condition-hide=Average direction=greater",
+        })
+    }
+    enrichment <- read.table(gsa.file, stringsAsFactors=F, header=TRUE, comment="#")
+    enrichment <- data.frame(enrichment, 
+                      prefix,
+                      trait=basename(prefix),
+                      FDR=p.adjust(enrichment$P, "BH"),
+                      stringsAsFactors=FALSE)
+    magma.res <- rbind(magma.res, enrichment)
+  }
+  write.table(magma.res, file=paste0("magma_enrichment", out.suffix, ".txt"), sep="\t", quote=F)
+  return(magma.res)
+}
+```
+
+Overview of GWAS input data:
+
+```{r, message=FALSE, echo=FALSE}
+gwas.info <- read_tsv("data/current/gtex_gwas_data/gwas_metadata.txt")
+
+gwas.magma.params <- dplyr::rename(gwas.info[,c("new_abbreviation", "Sample_Size", "Tag")], prefix=new_abbreviation, sample.size=Sample_Size)
+gwas.magma.params$input.file <- paste0("data/current/gtex_gwas_data/imputed_gwas_hg38_1.1/imputed_", gwas.magma.params$Tag, ".txt.gz")
+gwas.magma.params$prefix <- paste0("results/current/magma/", gwas.magma.params$prefix)
+## add genome build,  "snp.column"   "p.column"     "chrom.column" "pos.column"
+gwas.magma.params <- data.frame(gwas.magma.params, genome.build="hg38", snp.column="variant_id", p.column="pvalue", chrom.column="chromosome", pos.column="position", stringsAsFactors = FALSE)
+
+## also add the T1D GWAS
+t1d <- data.frame(prefix="results/current/magma/T1D_Onengut", sample.size=18932, Tag="T1D_Onengut", input.file="data/current/other_gwas/25751624-GCST005536-EFO_0001359.h.tsv.gz",  genome.build="hg38", snp.column="hm_rsid", p.column="p_value", chrom.column="chromosome", pos.column="base_pair_location", stringsAsFactors = FALSE)
+
+gwas.magma.params <- rbind(gwas.magma.params, t1d)
+
+datatable(gwas.magma.params)                             
+```
+
+
+
+```{r, echo=FALSE}
+grep.cmd <- "grep"
+## on my mac need to use gnu grep (faster)
+if (system("hostname", intern=TRUE) == "MB080512") {
+  grep.cmd <- "/opt/homebrew/bin/ggrep"
+}
+```
+
+
+```{r, echo=FALSE}
+
+get.gene.pvalues <- function(prefix, sets, name2entrez=NULL) {
+  af.zscores <- read.table(paste0(prefix, ".genes.out"), header=T, stringsAsFactors=FALSE)
+  af.zscores <- dplyr::rename(af.zscores, Nsamples=N)
+  expr.cl <- sapply(sets, function(set) af.zscores$GENE %in% set)
+  merged <- cbind(af.zscores, expr.cl)
+  
+  merged <- cbind(merged, leverage=NA, FDR=p.adjust(merged$P, "BH"))
+  if (!is.null(name2entrez)) {
+    merged <- merge(merged, name2entrez, by.x="GENE", by.y="ENTREZID")
+  }
+  return(merged)
+}
+
+## To get back to the SNP level we use the annotation files of magma. We select all genes in the interaction network. In a first step we reduce the size of the GWAS data.
+get.snp.pvalues <- function(prefix, selected.entrez, grep.cmd="grep", snp.col="variant_id", redo=FALSE) {
+  efile <- paste0(prefix, "_entrez_with_ppi.txt")
+  sfile <- paste0(prefix, "_snps_with_ppi.txt")
+  pfile <- paste0(prefix, "_P_with_ppi.txt")
+  if (!file.exists(pfile) || redo) {
+    cat(selected.entrez, file=efile, sep="\n")
+    cmd <- paste0(grep.cmd, " -F -w -f ", efile, " ", prefix, ".genes.annot | cut -d '\t' -f 2- | tr '\\t' '\\n' | grep -v NA | sort -u > ", sfile)
+    print(cmd)
+    system(cmd)
+    cmd <- paste0(grep.cmd, " -F -w -f ", sfile, " ", prefix, "_P.txt > ", pfile)
+    system(cmd)
+    print(cmd)
+    snp_pval <- read.table(pfile, sep="\t", stringsAsFactors = FALSE)
+    colnames(snp_pval) <- colnames(read.csv(paste0(prefix, "_P.txt"), sep="\t", nrows=3))
+    
+    if (snp.col != "variant_id") {
+      colnames(snp_pval) <- gsub(snp.col, "variant_id", colnames(snp_pval))
+    }
+    
+    #In the next step we read in the mapping of genes to SNPs
+    ann <- readLines(paste0(prefix, ".genes.annot"))
+    ann <- bind_rows(lapply(strsplit(ann, "\t"), function(x) {
+      if (length(x) > 1) {
+        snp <- x[-1]
+      } else {
+        snp <- NA
+      }
+      data.frame(gene=x[1], snp, stringsAsFactors = FALSE)
+    }))
+    ann <- ann[!is.na(ann$snp),]
+    ann <- ann[ann$snp != "NA",]
+    ann <- ann[ann$snp != ".",]
+    
+    # Annotate SNPs with genes and gene symbols
+    snp_pval <- merge(snp_pval, ann, by.x="variant_id", by.y="snp")
+    write.table(snp_pval, file=pfile, sep="\t", quote=F, row.names=F)
+  } else {
+    snp_pval <- read.csv(pfile, sep="\t", stringsAsFactors = FALSE)
+  }
+  return(snp_pval)
+}
+
+get.drivers <- function(prefix, sets, name2entrez=NULL, grep.cmd="grep", redo=FALSE, pval.col="pvalue", snp.col="variant_id") {
+  selected.entrez <- unique(unlist(sets))
+  gene_pvals <- get.gene.pvalues(prefix, sets, name2entrez)
+  snp_pval <- get.snp.pvalues(prefix, selected.entrez, grep.cmd=grep.cmd, snp.col=snp.col)
+  if (pval.col != "pvalue") {
+    colnames(snp_pval) <- gsub(pval.col, "pvalue", colnames(snp_pval))
+  }
+  browser()
+  minp <- group_by(snp_pval, gene) %>% summarise(variant_id=variant_id[which.min(pvalue)], minp=min(pvalue))
+  pvals <- merge(gene_pvals, minp, by.x="GENE", by.y="gene")
+  return(pvals)
+}
+```
+
+```{r, echo=FALSE}
+get_gwas_region <- function(prefix, chrom, start, end, awk.cmd="awk", cat.cmd="cat", redo=FALSE, chr.column="chromosome", pos.column="position") {
+  out.file <- paste0(prefix, "_P_", chrom, "_", start, "_", end, ".txt")
+  if (!file.exists(out.file) || redo) {
+    input.file <- paste0(prefix, "_P.txt")
+    ## find the indices of the chrom and bp
+    cn <- colnames(read.csv(input.file, sep="\t", nrow=2))
+    col.idx <- match(c(chr.column, pos.column), cn)
+    if (any(is.na(col.idx))) {
+      cat("colname(s)", c(chr.column, pos.column)[is.na(col.idx)], "not found in", cn, "\n")
+      stop()
+    }
+    col.idx <- paste0("$", col.idx)
+    ## build an awk script to filter the file
+    cmd <- paste0(cat.cmd, " ", input.file, " | tail -n +2 ", " | ", awk.cmd, 
+                  " 'BEGIN{OFS=\"\t\"}{gsub(/chr/, \"\", ", col.idx[1], "); ", ## replace chr prefix of chrom names
+                  "if (", col.idx[1], ' == "', chrom, '" && ', ## match chrom name
+                          col.idx[2], " > ", start, " && ", col.idx[2], " < ", end, ")", ## match position
+                  "{print $0;}}' >> ", out.file)
+    cat(cmd, "\n")
+    ## write header to out file
+    system(paste0("head -n 1 ", input.file, " > ", out.file))
+    ## then extract the region
+    system(cmd)
+  }
+  
+  gwas_pvals <- read_tsv(out.file)
+  return(gwas_pvals)
+}
+
+## convenience function that works with the gwas input parameter table
+get_gwas_region_for_study <- function(prefix, magma.params, chrom, start, end, ...) {
+  idx <- which(magma.params$prefix == prefix)
+  get_gwas_region(prefix, chrom, start, end, chr.column=magma.params[idx,"chrom.column"], pos.column=magma.params[idx,"pos.column"])
+}
+```
+
+## Run for co-eQTLs
+
+Define new sets
+
+```{r, message=FALSE, eval=FALSE}
+## these are the new communities
+coeqtl <- read_tsv("data/current/PPI/coeqtls_supptable6.tsv")
+pval_cols <- c("MetaP_CD4T", "MetaP_CD8T", "MetaP_monocyte", "MetaP_DC", "MetaP_NK", "MetaP_B")
+x <- coeqtl %>% separate(`SNP-eGene-co-eGene`, into=c("SNP", "eGene", "coeGene"), sep="_")
+
+coeqtl <- coeqtl %>% 
+  pivot_longer(names_to="celltype", cols=pval_cols, values_to="pvalue") %>%
+  filter(!is.na(pvalue)) %>%
+  mutate(celltype=gsub("MetaP_", "", celltype)) %>%
+  separate(`SNP-eGene-co-eGene`, into=c("SNP", "eGene", "coeGene"), sep="_") %>% 
+  mutate(set_id=paste(celltype, SNP, eGene, sep="_"))
+```
+
+
+```{r, message=FALSE}
+coeqtl <- read_tsv("data/current/PPI/coeqtls_merged.txt")
+coeqtl <- coeqtl %>% 
+  dplyr::rename(eGene=eqtlgene, coeGene=gene2) %>%
+  mutate(set_id=paste(celltype, SNP, eGene, sep="_"))
+coeqtl <- coeqtl %>% inner_join(name2entrez, by=c("coeGene"="SYMBOL"))
+```
+
+
+Define the gene sets
+
+```{r}
+csets <- with(coeqtl, tapply(ENTREZID, set_id, as.list))
+```
+
+
+Also add the gene set of Tcell specific negative coeqtl of RPS26
+```{r}
+RPS26_CD4T <- coeqtl %>% filter(eGene == "RPS26" & MetaPZ< 0 & celltype == "CD4T") %>% distinct()
+csets[["CD4T_rs1131017_RPS26downCD4T"]] <- as.list(unique(RPS26_CD4T$ENTREZID))
+```
+
+Remove sets that are smaller than 5 genes
+```{r}
+csets <- csets[sapply(csets, length) >= 5]
+```
+
+
+```{r}
+comm.setfile <- "results/current/coeqtl_gene_sets.txt"
+for (set in names(csets)) {
+  cat(set, "\t", paste(csets[[set]], collapse="\t"), "\n", sep="", file=comm.setfile, append=(set != names(csets)[1]))
+}
+```
+
+
+Run the GWAS enrichments on this set
+```{r}
+magma.res.coeqtl <- run.magma.on.gwas.list(gwas.magma.params, set.file=comm.setfile, "_gwas_by_coeqtl", rerun=FALSE)
+write_tsv(magma.res.coeqtl, "results/current/magma_gtex_gwas_by_coeqtl.txt")
+```
+
+```{r}
+magma.res.coeqtl %>% filter(FDR < 0.1)
+```
+
+
+```{r}
+info_cols <- c("Tag", "PUBMED_Paper_Link", "new_abbreviation", "Phenotype")
+magma.res.coeqtl %>% filter(FDR < 0.1 & VARIABLE == "CD4T_rs1131017_RPS26downCD4T") %>% inner_join(dplyr::select(gwas.info, !!info_cols), by=c("trait"="new_abbreviation"))
+```
+
+
+Also get the gwas P-values for all of the co-eQTL SNPs
+
+```{r}
+cat(unique(coeqtl$SNP), sep="\n", file="results/current/coeqtl_snp_list.txt")
+```
+
+Check which SNPs are missing from the GWAS
+```{r}
+infile <- paste0(gwas.magma.params$prefix[1], "_P.txt")
+cmd <- paste0("awk 'NR==FNR {key[$1]; next} !($1 in key)' ", infile, " results/current/coeqtl_snp_list.txt")
+missing_snps <- system(cmd, intern=TRUE)
+```
+
+Actually it is not clear if all GWAS always contain the same SNPs. So we extract LD proxxies for all co-eQTL to be on the safe side.
+
+```{r}
+proxies <- read_tsv("data/current/PPI/proxySearch.results.csv")
+cat(unique(proxies$RSID), sep="\n", file="results/current/coeqtl_snp_list_with_proxies.txt")
+```
+
+```{r}
+for (prefix in gwas.magma.params$prefix) {
+  outfile <- paste0(prefix, "_coeqtl_snps_P.txt")
+  cmd <- paste0("head -n 1 ", prefix, "_P.txt > ", outfile)
+  system(cmd)
+  cmd <- paste0("fgrep -w -f results/current/coeqtl_snp_list_with_proxies.txt ", prefix, "_P.txt >> ", outfile)
+  print(cmd)
+  system(cmd)
+}
+```
+
+```{r, message=FALSE, warning=FALSE}
+coeqtl_gwas <- bind_rows(lapply(1:nrow(gwas.magma.params), function(x) {
+  cnames <- c(variant_id="snp.column", chromosome="chrom.column", position="pos.column", pvalue="p.column")
+  cols <- gwas.magma.params[x,cnames]
+  more_cols <- c("prefix")
+  tab <- read_tsv(paste0(gwas.magma.params$prefix[x], "_coeqtl_snps_P.txt")) %>% mutate(prefix=gwas.magma.params$prefix[x])
+  tab <- tab[,c(as.character(cols), more_cols)]
+  colnames(tab) <- c(names(cnames), more_cols)
+  if (is.numeric(tab$chromosome))  {
+    tab$chromosome <- as.character(tab$chromosome)
+  }
+  return(tab)
+  }))
+```
+
+
+
+
+Check if there is enrichment and association for the same trait - for that: combine the gwas and the magma data using the best proxy (the snp itself if included)
+```{r}
+coeqtl_with_gwas_and_magma <- magma.res.coeqtl %>% 
+  separate(col=VARIABLE, into=c("celltype", "SNP", "gene"), sep="_", remove=FALSE) %>%
+  inner_join(dplyr::select(gwas.info, !!info_cols), by=c("trait"="new_abbreviation")) %>%
+  inner_join(proxies, by=c("SNP"="QRSID")) %>%
+  inner_join(coeqtl_gwas, by=c(prefix="prefix", RSID="variant_id")) %>%
+  group_by(VARIABLE, trait) %>% 
+  arrange(-R2, abs(DIST)) %>% dplyr::slice(1)
+write_tsv(coeqtl_with_gwas_and_magma, "results/current/coeqtl_with_gwas_and_magma.tsv")
+```
+
+
+
+Summarise the findings globally
+```{r}
+coeqtl_with_gwas_and_magma %>% ungroup() %>% filter(FDR < 0.05 & pvalue < 5e-8) %>% group_by(celltype, SNP, gene) %>% summarise(ntraits=length(unique(trait)))
+```
+
+Look at the RPS26 locus in more detail: CD4T specific coeQTL with negative effect size
+```{r}
+coeqtl_with_gwas_and_magma %>% ungroup() %>% filter(FDR < 0.05 & VARIABLE == "CD4T_rs1131017_RPS26downCD4T" & pvalue < 5e-8) %>% dplyr::select(VARIABLE, magma_FDR=FDR, Tag, RSID, R2, pvalue)
+```
+
+Look at the RPS26 locus in more detail: CD4T coeQTL with any effect size
+```{r}
+coeqtl_with_gwas_and_magma %>% ungroup() %>% filter(FDR < 0.05 & VARIABLE == "CD4T_rs1131017_RPS26" & pvalue < 5e-8) %>% dplyr::select(VARIABLE, magma_FDR=FDR, Tag, RSID, R2, pvalue)
+```
+
+
+Also run with a restricted background set of genes (all tested for coeQTLs)
+```{r}
+bg_file_symbols <- "data/current/PPI/coeqtls_tested_genes.txt"
+bg_file <- "results/current/coeqtls_tested_genes.txt"
+background <- scan(bg_file, what=character())
+background <- filter(symbol2entrez, SYMBOL %in% background)
+cat(background$ENTREZID, sep="\n", file=bg_file)
+```
+
+
+```{r}
+magma.res.coeqtl <- run.magma.on.gwas.list(gwas.magma.params, set.file=comm.setfile, "_gwas_by_coeqtl_with_bg", rerun=FALSE, settings=paste0(" gene-include=", bg_file))
+write_tsv(magma.res.coeqtl, "results/current/magma_gtex_gwas_by_coeqtl_with_bg.txt")
+```
+
+
+Check if there is enrichment and association for the same trait - for that: combine the gwas and the magma data using the best proxy (the snp itself if included)
+```{r}
+coeqtl_with_gwas_and_magma <- magma.res.coeqtl %>% 
+  separate(col=VARIABLE, into=c("celltype", "SNP", "gene"), sep="_", remove=FALSE) %>%
+  inner_join(dplyr::select(gwas.info, !!info_cols), by=c("trait"="new_abbreviation")) %>%
+  inner_join(proxies, by=c("SNP"="QRSID")) %>%
+  inner_join(coeqtl_gwas, by=c(prefix="prefix", RSID="variant_id")) %>%
+  group_by(VARIABLE, trait) %>% 
+  arrange(-R2, abs(DIST)) %>% dplyr::slice(1)
+write_tsv(coeqtl_with_gwas_and_magma, "results/current/coeqtl_with_gwas_and_magma_with_bg.tsv")
+```
+
+
+
+Summarise the findings globally
+```{r}
+coeqtl_with_gwas_and_magma %>% ungroup() %>% filter(FDR < 0.05 & pvalue < 5e-8) %>% group_by(celltype, SNP, gene) %>% summarise(ntraits=length(unique(trait)))
+```
+
+Look at the RPS26 locus in more detail: CD4T specific coeQTL with negative effect size
+```{r}
+coeqtl_with_gwas_and_magma %>% ungroup() %>% filter(FDR < 0.05 & VARIABLE == "CD4T_rs1131017_RPS26downCD4T" & pvalue < 5e-8) %>% dplyr::select(VARIABLE, magma_FDR=FDR, Tag, RSID, R2, pvalue)
+```
+
+
+
+  
+Also run the same analysis without the HLA genes
+```{r}
+csets <- with(coeqtl %>% filter(!str_detect(coeGene, "HLA") ), tapply(ENTREZID, set_id, as.list))
+```
+
+
+Also add the gene set of Tcell specific negative coeqtl of RPS26
+```{r}
+RPS26_CD4T <- coeqtl %>% filter(eGene == "RPS26" & MetaPZ< 0 & celltype == "CD4T" & !str_detect(coeGene, "HLA")) %>% distinct()
+csets[["CD4T_rs1131017_RPS26downCD4T"]] <- as.list(unique(RPS26_CD4T$ENTREZID))
+```
+
+Remove sets that are smaller than 5 genes
+```{r}
+csets <- csets[sapply(csets, length) >= 5]
+```
+
+
+```{r}
+comm.setfile <- "results/current/coeqtl_gene_sets_no_HLA.txt"
+for (set in names(csets)) {
+  cat(set, "\t", paste(csets[[set]], collapse="\t"), "\n", sep="", file=comm.setfile, append=(set != names(csets)[1]))
+}
+```
+
+
+Run the GWAS enrichments on this set
+```{r}
+magma.res.coeqtl <- run.magma.on.gwas.list(gwas.magma.params, set.file=comm.setfile, "_gwas_by_coeqtl_no_HLA", rerun=FALSE)
+write_tsv(magma.res.coeqtl, "results/current/magma_gtex_gwas_by_coeqtl_no_HLA.txt")
+```
+  
+  
+Check if there is enrichment and association for the same trait - for that: combine the gwas and the magma data using the best proxy (the snp itself if included)
+```{r}
+coeqtl_with_gwas_and_magma <- magma.res.coeqtl %>% 
+  separate(col=VARIABLE, into=c("celltype", "SNP", "gene"), sep="_", remove=FALSE) %>%
+  inner_join(dplyr::select(gwas.info, !!info_cols), by=c("trait"="new_abbreviation")) %>%
+  inner_join(proxies, by=c("SNP"="QRSID")) %>%
+  inner_join(coeqtl_gwas, by=c(prefix="prefix", RSID="variant_id")) %>%
+  group_by(VARIABLE, trait) %>% 
+  arrange(-R2, abs(DIST)) %>% dplyr::slice(1)
+write_tsv(coeqtl_with_gwas_and_magma, "results/current/coeqtl_with_gwas_and_magma_no_HLA.tsv")
+```
+
+
+
+Summarise the findings globally
+```{r}
+coeqtl_with_gwas_and_magma %>% ungroup() %>% filter(FDR < 0.1 & pvalue < 5e-8) %>% group_by(celltype, SNP, gene) %>% summarise(ntraits=length(unique(trait)))
+```
+
+Look at the RPS26 locus in more detail: CD4T specific coeQTL with negative effect size
+```{r}
+coeqtl_with_gwas_and_magma %>% ungroup() %>% filter(FDR < 0.1 & VARIABLE == "CD4T_rs1131017_RPS26downCD4T" & pvalue < 5e-8) %>% dplyr::select(VARIABLE, magma_FDR=FDR, Tag, RSID, R2, effect_allele, pvalue)
+```
+
+  
+
+  
+
+
diff --git a/05_coeqtl_interpretation/plot_CD4T_mono_RPS26_subnetwork.R b/05_coeqtl_interpretation/plot_CD4T_mono_RPS26_subnetwork.R
new file mode 100644
index 0000000..0705549
--- /dev/null
+++ b/05_coeqtl_interpretation/plot_CD4T_mono_RPS26_subnetwork.R
@@ -0,0 +1,139 @@
+# ------------------------------------------------------------------------------
+# Create network of co-eQTLs connected with rs1131017-RPS26 in CD4+ T cells
+# and/or Monocytes,  color edges by direction of effect,
+# annotate them with GO terms for transcription initiation
+# and lympocyte activity and transfer it to cytoscape for final layouting
+# Input: coeQTL results from CD4+ T cells and Monocytes
+# Output: cytoscape graph
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(igraph) #to plot the graph
+library(dplyr)
+library(ggplot2)
+library(AnnotationHub) # for GO annotation
+library(GO.db) # for GO annotation
+library(RCy3) # to move igraph object to cytoscape
+
+theme_set(theme_bw())
+
+# Load current set of coeQTL
+coeqtls_CD4T<-fread("coeqtl_mapping/CD4T/coeqtls_fullresults_fixed_withAF.sig.tsv")
+coeqtls_mono<-fread("coeqtl_mapping/monocyte/coeqtls_fullresults_fixed_withAF.sig.tsv")
+
+#Correct for the direction of effect
+coeqtls_CD4T$MetaPZ<-ifelse(coeqtls_CD4T$AF>0.5,(-1)*coeqtls_CD4T$MetaPZ,
+                            coeqtls_CD4T$MetaPZ)
+coeqtls_mono$MetaPZ<-ifelse(coeqtls_mono$AF>0.5,(-1)*coeqtls_mono$MetaPZ,
+                            coeqtls_mono$MetaPZ)
+
+coeqtls<-rbind(coeqtls_CD4T,coeqtls_mono)
+
+#Get gene1 and gene2 correctly sorted
+coeqtls$gene1<-gsub(";.*","",coeqtls$Gene)
+coeqtls$gene2<-gsub(".*;","",coeqtls$Gene)
+coeqtls$eqtlgene<-gsub(".*_","",coeqtls$snp_eqtlgene)
+coeqtls$gene2<-ifelse(coeqtls$gene1 == coeqtls$eqtlgen, coeqtls$gene2,
+                      coeqtls$gene1)
+coeqtls$gene1<-coeqtls$eqtlgene
+coeqtls$direction<-ifelse(coeqtls$MetaPZ>0,"positive","negative")
+
+#Set the direction is NA for the ones without matching direction
+coeqtls<-unique(coeqtls[,c("snp_genepair","snp_eqtlgene","gene2","direction")])
+non_matching_dir<-intersect(coeqtls$snp_genepair[coeqtls$direction=="positive"],
+                            coeqtls$snp_genepair[coeqtls$direction=="negative"])
+coeqtls$direction[coeqtls$snp_genepair %in% non_matching_dir]<-"not_maching"
+
+#Remove duplicate entries for non-matching directions
+coeqtls<-unique(coeqtls[,c("snp_genepair","snp_eqtlgene","gene2","direction")])
+coeqtls$type<-ifelse(coeqtls$snp_genepair %in% coeqtls_CD4T$snp_genepair,
+                     ifelse(coeqtls$snp_genepair %in% coeqtls_mono$snp_genepair,
+                            "both","CD4T"),"mono")
+# Filter for RPS26
+coeqtls<-coeqtls[coeqtls$snp_eqtlgene=="rs1131017_RPS26",]
+
+#Check how much the correlation structure matches for overlapping examples
+corr_RPS26_cd4t<-fread("coeqtl_interpretation/correlation_structure_coeqtl_rps26.tsv")
+corr_RPS26_mono<-fread("coeqtl_interpretation/correlation_structure_coeqtl_rps26monocyte.tsv")
+corr_comp<-merge(corr_RPS26_cd4t,corr_RPS26_mono,by=c("Var1","Var2"))
+
+g<-ggplot(corr_comp,aes(x=value.x,y=value.y))+
+  geom_point()+geom_abline()+
+  xlab("Correlation CD4T")+ylab("Correlation Monocytes")
+print(g)
+ggsave("coeqtl_interpretation/plots_filtered/correlation_structure_RPS26_compare_cts.png")
+
+#Load interaction structure
+corr_RPS26<-rbind(corr_RPS26_cd4t,corr_RPS26_mono)
+corr_RPS26<-corr_RPS26%>%
+  group_by(Var1,Var2)%>%
+  summarise(value=max(value))%>%
+  as.data.frame()
+
+#First thing, show only strong associations
+corr_RPS26<-corr_RPS26[corr_RPS26$value>0.2,]
+corr_RPS26$value<-NULL
+colnames(corr_RPS26)<-c("snp_eqtlgene","gene2")
+corr_RPS26$direction<-"correlation"
+
+#Edges
+# edges_combined<-rbind(coeqtls[,c("snp_eqtlgene","gene2","direction")],
+#                   corr_RPS26)
+edges_combined<-coeqtls[,c("snp_eqtlgene","gene2","direction")]
+
+# Graph object
+graph_object <- graph_from_edgelist(as.matrix(edges_combined[,1:2]),
+                                    directed=FALSE)
+
+#Color SNPs and genes differently
+V(graph_object)$node_type<-ifelse(startsWith(names(V(graph_object)),"rs"),
+                                  "eQTL","gene2")
+
+#Get for nodes also to which type of network they belong
+nodes_cd4t<-unique(unlist(coeqtls[coeqtls$type %in% c("CD4T","both"),
+                                  c("snp_eqtlgene","gene2")]))
+nodes_mono<-unique(unlist(coeqtls[coeqtls$type %in% c("mono","both"),
+                                  c("snp_eqtlgene","gene2")]))
+nodes_both<-intersect(nodes_cd4t,nodes_mono)
+
+V(graph_object)$coeqtl<-ifelse(names(V(graph_object)) %in% nodes_both,
+                               "both",
+                               ifelse(names(V(graph_object)) %in% nodes_cd4t,
+                                      "CD4T","mono"))
+
+#Color edges
+E(graph_object)$direction<-edges_combined$direction
+
+# Check how many are associated with transcription initiation (GO:0006413)
+# and T cell activiation (GO:0042110) / lympocyte activity (GO:0046649)
+# or any of its offspring
+
+ah <- AnnotationHub()
+orgs <- subset(ah, ah$rdataclass == "OrgDb")
+orgdb <- query(orgs, "Homo sapiens")[[1]]
+
+go_transcriptinit <- AnnotationDbi::select(orgdb, c("GO:0006413",GOBPOFFSPRING[["GO:0006413"]]), 
+                                  "SYMBOL", "GO")
+go_tcell <- AnnotationDbi::select(orgdb, c("GO:0046649",GOBPOFFSPRING[["GO:0046649"]]), 
+                                  "SYMBOL", "GO")
+
+#intersect(go_transcriptinit$hgnc_symbol,go_tcell$hgnc_symbol)
+V(graph_object)$GO<-ifelse(names(V(graph_object)) %in% go_transcriptinit$SYMBOL,
+                           ifelse(names(V(graph_object)) %in% go_tcell$SYMBOL,
+                                  "both","transcript_init"),
+                           ifelse(names(V(graph_object)) %in% go_tcell$SYMBOL,
+                                  "tcell_active","none"))
+
+coeqtls$enrichment<-ifelse(coeqtls$gene2 %in% go_transcriptinit$SYMBOL,
+                           ifelse(coeqtls$gene2 %in% go_tcell$SYMBOL,
+                                  "both","transcript_init"),
+                           ifelse(coeqtls$gene2 %in% go_tcell$SYMBOL,
+                                  "tcell_active","none"))
+table(coeqtls$enrichment,coeqtls$direction)
+
+plot(graph_object,vertex.label=NA, vertex.size=3)
+
+# Import into cytoscape
+cytoscapePing()
+createNetworkFromIgraph(graph_object,"RPS26_network_function")
+
diff --git a/05_coeqtl_interpretation/plot_CD4T_mono_network.R b/05_coeqtl_interpretation/plot_CD4T_mono_network.R
new file mode 100644
index 0000000..9db284b
--- /dev/null
+++ b/05_coeqtl_interpretation/plot_CD4T_mono_network.R
@@ -0,0 +1,77 @@
+# ------------------------------------------------------------------------------
+# Create network of all co-eQTLs from CD4+ T cells
+# and/or Monocytes (displaying only the large connected component) 
+# and color edges by direction of effect
+# Input: coeQTL results from CD4+ T cells and Monocytes
+# Output: cytoscape graph
+# ------------------------------------------------------------------------------
+
+library(data.table)
+library(igraph) #to plot the graph
+library(RCy3) # to transfer to cytoscape
+
+# Load current set of coeQTL
+coeqtls_CD4T<-fread("coeqtl_mapping/CD4T/coeqtls_fullresults_fixed_withAF.sig.tsv")
+coeqtls_mono<-fread("coeqtl_mapping/monocyte/coeqtls_fullresults_fixed_withAF.sig.tsv")
+
+#Correct for the direction of effect
+coeqtls_CD4T$MetaPZ<-ifelse(coeqtls_CD4T$AF>0.5,(-1)*coeqtls_CD4T$MetaPZ,
+                            coeqtls_CD4T$MetaPZ)
+coeqtls_mono$MetaPZ<-ifelse(coeqtls_mono$AF>0.5,(-1)*coeqtls_mono$MetaPZ,
+                            coeqtls_mono$MetaPZ)
+
+coeqtls<-rbind(coeqtls_CD4T,coeqtls_mono)
+
+#Get gene1 and gene2 correctly sorted
+coeqtls$gene1<-gsub(";.*","",coeqtls$Gene)
+coeqtls$gene2<-gsub(".*;","",coeqtls$Gene)
+coeqtls$eqtlgene<-gsub(".*_","",coeqtls$snp_eqtlgene)
+coeqtls$gene2<-ifelse(coeqtls$gene1 == coeqtls$eqtlgen, coeqtls$gene2,
+                      coeqtls$gene1)
+coeqtls$gene1<-coeqtls$eqtlgene
+coeqtls$direction<-ifelse(coeqtls$MetaPZ>0,"positive","negative")
+
+#Set the direction is NA for the ones without matching direction
+coeqtls<-unique(coeqtls[,c("snp_genepair","snp_eqtlgene","gene2","direction")])
+non_matching_dir<-intersect(coeqtls$snp_genepair[coeqtls$direction=="positive"],
+                            coeqtls$snp_genepair[coeqtls$direction=="negative"])
+coeqtls$direction[coeqtls$snp_genepair %in% non_matching_dir]<-"not_maching"
+
+#Remove duplicate entries for non-matching directions
+coeqtls<-unique(coeqtls[,c("snp_genepair","snp_eqtlgene","gene2","direction")])
+coeqtls$type<-ifelse(coeqtls$snp_genepair %in% coeqtls_CD4T$snp_genepair,
+                     ifelse(coeqtls$snp_genepair %in% coeqtls_mono$snp_genepair,
+                            "both","CD4T"),"mono")
+
+# Graph object
+graph_object <- graph_from_edgelist(as.matrix(coeqtls[,c("snp_eqtlgene","gene2")]),
+                                    directed=FALSE)
+
+#Color SNPs and genes differently
+V(graph_object)$node_type<-ifelse(startsWith(names(V(graph_object)),"rs"),
+                                  "eQTL","gene2")
+
+#Get for nodes also to which type of network they belong
+nodes_cd4t<-unique(unlist(coeqtls[coeqtls$type %in% c("CD4T","both"),
+                                  c("snp_eqtlgene","gene2")]))
+nodes_mono<-unique(unlist(coeqtls[coeqtls$type %in% c("mono","both"),
+                                  c("snp_eqtlgene","gene2")]))
+nodes_both<-intersect(nodes_cd4t,nodes_mono)
+
+V(graph_object)$coeqtl<-ifelse(names(V(graph_object))%in% nodes_both,
+                               "both",
+                               ifelse(names(V(graph_object)) %in% nodes_cd4t,
+                                      "CD4T","mono"))
+
+#Color edges
+E(graph_object)$direction<-coeqtls$direction
+
+#Remove all the small components
+comps<-components(graph_object)
+subgraph<-induced_subgraph(graph_object,vids=names(which(comps$membership==1)))
+plot(subgraph,vertex.label=NA, vertex.size=3)
+
+# Transfer results to cytoscape
+cytoscapePing()
+createNetworkFromIgraph(subgraph,"eqtl_network")
+
diff --git a/05_coeqtl_interpretation/snipe.R b/05_coeqtl_interpretation/snipe.R
new file mode 100644
index 0000000..04b8139
--- /dev/null
+++ b/05_coeqtl_interpretation/snipe.R
@@ -0,0 +1,248 @@
+# -----------------------------------------------------------------------------
+# Help functions to query the SNiPA website to get SNPs in LD with the query SNPs
+# Applied in the script enrichment_TFs_Remap.R
+# -----------------------------------------------------------------------------
+
+library(rvest) #web scraper by hadley wickham
+library(httr) #http package to send GET/POST http methods
+library(magrittr) #useful pipe operator
+
+# SNiPA URIs --------------------------------------------------------------
+
+`%+%` <- paste0 #poor man's homemade concat operator
+
+#define URI's that will be used for HTTP requests
+snipa.base              <-"https://snipa.helmholtz-muenchen.de/snipa3"
+snipa.pairwise.page.uri <- snipa.base %+% "/index.php?task=pairwise_ld"
+snipa.ld.form.uri       <- snipa.base %+% "/backend/snipaProxySearch.php"
+snipa.rand.id.uri       <- snipa.base %+% "/backend/snipaTempdir.php"
+snipa.get.status.uri    <- function(i) snipa.base %+% "/tmpdata/" %+% i %+% "/status.txt"
+snipa.get.report.uri    <- function(i) snipa.base %+% "/tmpdata/" %+% i %+% "/report.txt"
+snipa.get.result.uri    <- function(i) snipa.base %+% "/tmpdata/" %+% i %+% "/proxySearch.results.csv"
+user.agent <- "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.50 Safari/537.36"
+
+#split list of snps/genes/regions and query each separately if its length exceeds
+#this threshold
+paging.length.threshold <- 1000
+
+
+# Helper Functions --------------------------------------------------------
+
+#generate a random ID through Snipa server method
+#this is used throughout all snipa operations
+snipa.get.rand <- function() {
+  s <- html_session(snipa.rand.id.uri,
+                    user_agent(user.agent))
+  stop_for_status(s)
+  #Updated due to deprecation of html()
+  #s %>% html() %>% html_node('p') %>% html_text()
+  s %>% read_html() %>% html_node('p') %>% html_text()
+}
+
+wait.processing.steps <- function(id, wait=0.5, tryout=10) {
+  uri <- snipa.get.status.uri(id)
+  i <- 0
+  got <- FALSE
+
+  while (!got || (i < tryout)) {
+    res <- GET(uri, user_agent(user.agent))
+
+    stop_for_status(res)
+    res <- httr::content(res, type='application/json')
+    if(res$errmessage != '')
+      stop(res$errmessage)
+
+    if(res$stepnum == res$totalstepnum)
+      got <- TRUE
+
+    Sys.sleep(wait)
+    i <- i + 1
+  }
+
+  return(got)
+}
+
+#generate a list which is converted to urlencoded POST() parameters
+snipa.generate.ld.form.params <- function(snps_sentinels=NULL,
+                                          id=NULL,
+                                          genomerelease='grch37',
+                                          referenceset='1kgpp3v5',
+                                          population='eur',
+                                          annotation='ensembl80',
+                                          snps_input_type='snps',
+                                          snps_gene=NULL,
+                                          snps_region_chr=NULL,
+                                          snps_region_begin=NULL,
+                                          snps_region_end=NULL,
+                                          rsquare=0.8,
+                                          incl_sentinel=1,
+                                          incl_funcann=0,
+                                          download=1,
+                                          dyn_tables=0,
+                                          pairwise=1){
+  mget(names(formals()))
+}
+
+#submits given query through HTTP POST method to the given URI
+snipa.submit.query <- function(params, uri, paging=T) {
+
+  s <- html_session(snipa.pairwise.page.uri)
+  stop_for_status(s)
+  scook <- cookies(s)
+
+  #perform "paging" to split long vectors into smaller chunks
+  if (params$snps_input_type == 'snps') {
+    if(paging) {
+      #split vector
+      long.vec <- params$snps_sentinels
+      long.vec.split <- split(long.vec, ceiling(seq_along(long.vec)/paging.length.threshold))
+      #clone params.list
+      params$snps_sentinels <- ''
+      params.list <- replicate(length(long.vec.split), params, simplify = F)
+      #replace long vectors with split vector chunks
+      params.list <- Map(function(param, lv){
+        param$id <- snipa.get.rand()
+        snps <- paste(lv, collapse = '\n')
+        param$snps_sentinels <- snps
+        param
+      }, params.list, long.vec.split)
+
+    } else {
+      #if(params$snps_sentinels > paging.length.threshold) {
+      if(length(params$snps_sentinels)> paging.length.threshold){
+        warning('Number of SNPs is above the threshold! Disable pairwise or decrease the number...')
+      }
+      params$id <- snipa.get.rand()
+      params$snps_sentinels <- paste(params$snps_sentinels, collapse = '\n')
+      params.list <- list(params)
+    }
+  } else {
+    params$id <- snipa.get.rand()
+    params.list <- list(params)
+  }
+
+  #submit each query separately and merge results with rbind
+  res.list <- lapply(seq_along(params.list), function(i){
+    params <- params.list[[i]]
+
+    if (length(params.list) > 1) {
+      cat(sprintf("Group %d/%d is being processed...", i, length(params.list)))
+    }
+    
+    res <- POST(uri,
+                body=params,
+                encode='form',
+                user_agent(user.agent),
+                do.call(set_cookies, scook))
+
+    stop_for_status(res)
+    res <- httr::content(res, type='application/json')
+    if(res$errmessage != '')
+      stop(res$errmessage)
+
+    stopifnot(wait.processing.steps(params$id))
+
+    res <- GET(snipa.get.result.uri(params$id),
+               user_agent(user.agent),
+               do.call(set_cookies, scook))
+
+    if (res$status_code == 404) {
+      return(NULL) #probably query returned nothing
+    }
+
+    stop_for_status(res)
+    res <- httr::content(res, as='text')
+    read.delim(text=res, stringsAsFactors = F)
+  })
+  do.call(rbind, res.list)
+}
+
+# Main Functions ----------------------------------------------------------
+
+#get LD information for given SNPs
+snipa.get.ld.by.snp <- function(snps, #vector of sentinel SNPs
+                                rsquare=0.8, #rsquared threshold
+                                pairwise=F,  #pairwise=T returns pairwise LD values for given sentinels
+                                annotation=F, #whether functional annotation of SNPs will be returned
+                                population=c('eur', 'afr', 'amr', 'eas', 'sas'),
+                                ...){
+
+  if(any(substr(snps, 1, 2) != 'rs'))
+    stop('All SNPs must have a valid rs ID!')
+
+  #translate arguments to real form variables
+  params <- snipa.generate.ld.form.params(snps_sentinels = snps,
+                                          rsquare=rsquare,
+                                          pairwise=as.integer(pairwise),
+                                          incl_funcann = as.integer(annotation),
+                                          population=match.arg(population),
+                                          ...)
+  
+  #disable paging if pairwise=T
+  result <- snipa.submit.query(params, snipa.ld.form.uri, paging = !pairwise)
+  excluded <- setdiff(snps, result$QRSID)
+
+  if (length(excluded) > 0) {
+    warning(cat(sprintf('These SNPs are excluded from the result table: %s\n',
+                        paste(excluded, collapse = ','))))
+  }
+  return(result)
+}
+
+snipa.get.ld.by.region <- function(chr, begin, end,
+                                   rsquare=0.8, #rsquared threshold
+                                   annotation=F, #whether functional annotation of SNPs will be returned
+                                   population=c('eur', 'afr', 'amr', 'eas', 'sas'),
+                                   ...){
+  #strip 'chr' prefix off, if exists
+  if(substr(chr, 1, 3)=='chr') chr <- substr(chr, 4, nchar(chr))
+
+  #translate arguments to real form variables
+  params <- snipa.generate.ld.form.params(snps_region_chr = chr,
+                                          snps_region_begin = begin,
+                                          snps_region_end = end,
+                                          snps_input_type='region',
+                                          rsquare=rsquare,
+                                          pairwise=0,
+                                          incl_funcann = as.integer(annotation),
+                                          population=match.arg(population),
+                                          ...)
+  snipa.submit.query(params, snipa.ld.form.uri)
+}
+
+#get LD information for given gene
+snipa.get.ld.by.gene <- function(gene,
+                                 rsquare=0.8,
+                                 annotation=F, #whether functional annotation of SNPs will be returned
+                                 gene.id=c('ENSEMBL', 'SYMBOL', 'ENTREZID'),
+                                 population=c('eur', 'afr', 'amr', 'eas', 'sas'),
+                                 ...){
+
+  gene.id <- match.arg(gene.id)
+
+  #convert symbol/entrezid to ensembl id
+  convertIDs <- function(ids, from) {
+
+    #packages needed to convert gene symbols to ensembl ids
+    library(AnnotationDbi)
+    library(org.Hs.eg.db)
+
+    suppressWarnings(selRes <- AnnotationDbi::select(org.Hs.eg.db,
+                                                     keys=ids,
+                                                     keytype=from,
+                                                     columns=c(from,'ENSEMBL')))
+    return(selRes[match(ids, selRes[, 1] ), 2])
+  }
+
+  if(gene.id != 'ENSEMBL') gene <- convertIDs(gene, gene.id)
+
+  #translate arguments to real form variables
+  params <- snipa.generate.ld.form.params(snps_gene = gene,
+                                          snps_input_type = 'gene',
+                                          rsquare=rsquare,
+                                          pairwise=0,
+                                          incl_funcann = as.integer(annotation),
+                                          population=match.arg(population),
+                                          ...)
+  snipa.submit.query(params, snipa.ld.form.uri)
+}
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..594e071
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,25 @@
+BSD 2-Clause License
+
+Copyright (c) 2022, shuang1330
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..839e504
--- /dev/null
+++ b/README.md
@@ -0,0 +1,51 @@
+# Single cell co-expression QTL analysis
+
+This repository contains the code to generate the results and figures from
+
+**Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data**
+
+Shuang Li *, Katharina T. Schmid *, Dylan de Vries *\*, Maryna Korshevniuk *\*, Corinna Losert, Roy Oelen, Irene van Blokland, BIOS Consortium, sc-eQTLgen Consortium, Hilde E. Groot, Morris A. Swertz, Pim van der Harst, Harm-Jan Westra, Monique van der Wijst, Matthias Heinig †, Lude Franke †
+
+\* These authors contributed equally<br>
+** These authors contributed equally<br>
+† These authors contributed equally
+
+Preprint: https://www.biorxiv.org/content/10.1101/2022.04.20.488925v1
+
+## Overview
+
+The code for the analysis is separated in different steps, each in its own subdirectory including a README file:
+* Exploring different different association metrics and other GRN construction tools, including pseudotemporal based ones and combing cells to meta cells [01_association_metrics/](01_association_metrics/)
+* Comparing correlation between the different single cell data sets, with bulk data and CRISPR knock-out data; testing potential occurence of Simpson's paradox [02_correlation_evaluation/](02_correlation_evaluation/)
+* Comparing correlation between different cell types and between different individuals within one cell type [03_celltype_individual_comparison/](03_celltype_individual_comparison/)
+* Running eQTL and coeQTL mapping pipelines followed by replication in bulk and technical evaluation of the co-eQTLs, such as correlation distribution, sub cell type effects and effects of subsampling cells or donors [04_coeqtl_mapping/](04_coeqtl_mapping/)
+* Interpretation of co-eQTL results based on GWAS annotation and different enrichment analyses (GO enrichment, TFBS enrichment using Remap database and GWAS enrichment using MAGMA) [05_coeqtl_interpretation/](05_coeqtl_interpretation/)
+
+## Software requirements
+
+Most code is implemented in R or python, the required packages are documented in the respective yaml files (`conda_env_R.yml` and `conda_env_python.yml`) and can be used to setup the respective [conda environments](https://docs.conda.io/en/latest/):
+
+```
+conda env create -f conda_env_R.yml
+conda activate r_env
+```
+
+The following R packages are not part of conda and need to be added afterwards (if the respective code part should be run):
+
+```
+if (!require("BiocManager")) install.packages('BiocManager') 
+BiocManager::install("tanaylab/metacell")
+
+devtools::install_github("heiniglab/scPower")
+```
+
+Further external tools were used:
+
+* To calculate the eQTLs See the documentation [here](https://github.com/molgenis/systemsgenetics/wiki/eQTL-mapping-analysis-cookbook-for-RNA-seq-data).
+* To calculate the co-eQTLs: [mbQTL](https://github.com/molgenis/systemsgenetics/tree/master/mbQTL).
+* To perform GWAS enrichment analysis on the co-eQTL genes: [MAGMA](https://ctg.cncr.nl/software/magma).
+
+
+
+
+
diff --git a/conda_env_R.yml b/conda_env_R.yml
new file mode 100644
index 0000000..61dcfa0
--- /dev/null
+++ b/conda_env_R.yml
@@ -0,0 +1,46 @@
+name: r_env
+channels:
+  - anaconda
+  - bioconda
+  - conda-forge
+  - defaults
+dependencies:
+  - bioconductor-annotationhub
+  - bioconductor-clusterprofiler
+  - bioconductor-edger
+  - bioconductor-enrichplot
+  - bioconductor-go.db
+  - bioconductor-homo.sapiens
+  - bioconductor-rcy3
+  - bioconductor-rgraphviz
+  - bioconductor-rtracklayer 
+  - bioconductor-singlecellexperiment
+  - bioconductor-sva
+  - r-base=4.0.2
+  - r-corrplot
+  - r-data.table
+  - r-dplyr
+  - r-dt
+  - r-ggally
+  - r-ggplot2
+  - r-ggpubr
+  - r-gtools
+  - r-hmisc
+  - r-httr
+  - r-igraph
+  - r-kableextra
+  - r-knitr
+  - r-magrittr
+  - r-matrix
+  - r-optparse
+  - r-propr
+  - r-rcolorbrewer
+  - r-reshape2
+  - r-reticulate
+  - r-rvest
+  - r-scales
+  - r-seurat
+  - r-stringr
+  - r-tidyverse
+  - r-upsetr
+  - r-viridis
diff --git a/conda_env_python.yml b/conda_env_python.yml
new file mode 100644
index 0000000..408bbea
--- /dev/null
+++ b/conda_env_python.yml
@@ -0,0 +1,20 @@
+name: python_env
+channels:
+  - bioconda
+  - conda-forge
+  - defaults
+dependencies:
+  - matplotlib=3.2.1
+  - numpy=1.18.4
+  - pandas=1.0.3
+  - pathlib
+  - python=3.6.10
+  - scipy=1.4.1
+  - time
+  - tqdm=4.46.0
+  - pip:
+    - anndata==0.7.3
+    - scanpy==1.5.1
+    - scvelo==0.2.1
+    - seaborn==0.10.1
+    - umap-learn==0.4.4