0.[Prep GO]Download_and_parse_GO.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook for download GO and select 1000 terms for the analysis "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step1: Dowload and process GO_BP terms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### this function will download go-basic.obo and parse it filtering for biological process branch\n",
    "# it will output these two files that will be used in the next step:\n",
    "# collapsed_go.symbol (a network format of GO terms)\n",
    "# goID_2_name.tab (a mapping of GO terms to their names)\n",
    "\n",
    "outdir = 'data/GO_BP/'\n",
    "namespace = 'biological_process'\n",
    "go_basics = 'https://release.geneontology.org/2024-11-15/ontology/go-basic.obo'\n",
    "goahuman = 'https://release.geneontology.org/2024-11-15/annotations/goa_human_isoform.gaf.gz'\n",
    "\n",
    "%run process_the_gene_ontology.py $outdir --namespace $namespace --gobasic $go_basics --goahuman $goahuman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "# function specific to loading the collapsed_go.symbol type file\n",
    "def load_go(filename): \n",
    "    # Create an empty directed graph\n",
    "    G = nx.DiGraph()\n",
    "\n",
    "    with open(filename, 'r') as f:\n",
    "        for line in f:\n",
    "            columns = line.strip().split('\\t')  # tab-separated values\n",
    "            if len(columns) >= 3:\n",
    "                parent = columns[0] # based on collapsed_go.symbol, the first column is parent\n",
    "                child = columns[1] # second column is child\n",
    "                interaction = columns[2] # third column is interaction type\n",
    "                # Add an edge to the graph\n",
    "                G.add_edge(parent, child, interaction=interaction)\n",
    "\n",
    "    # Get the set of all genes\n",
    "    hiergeneset = set(n for n in G.nodes() if not n.startswith('GO:'))\n",
    "\n",
    "    # Get term statistics\n",
    "    df = get_termStats(G, hiergeneset)\n",
    "\n",
    "    return df\n",
    "\n",
    "def get_termStats(G, hiergeneset):\n",
    "    clusters = list(set(list(G.nodes())) - hiergeneset) #\n",
    "    tsize_list = []\n",
    "    cgene_list = []\n",
    "    for c in clusters:\n",
    "        infoset = nx.descendants(G, c) # get all the descendants of the cluster\n",
    "        cgeneset = infoset.intersection(hiergeneset) # get the genes that are in the cluster\n",
    "        tsize_list.append(len(cgeneset)) \n",
    "        cgene_list.append(list(cgeneset))\n",
    "\n",
    "    df = pd.DataFrame(index=clusters)\n",
    "    df['Genes'] = [' '.join(genes) for genes in cgene_list] # convert list to string separated by space\n",
    "    df['Gene_Count'] = tsize_list # number of genes in the cluster\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11943, 3)\n",
      "GO                  0\n",
      "Genes               0\n",
      "Gene_Count          0\n",
      "Term_Description    0\n",
      "dtype: int64\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>GO</th>\n",
       "      <th>Genes</th>\n",
       "      <th>Gene_Count</th>\n",
       "      <th>Term_Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GO:0006311</td>\n",
       "      <td>TEX11 PRDM9 RNF212</td>\n",
       "      <td>3</td>\n",
       "      <td>meiotic gene conversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO:0006855</td>\n",
       "      <td>ABCB11 ABCC4 ABCC3 SLC29A2 SLC37A3 SLC19A1 SLC...</td>\n",
       "      <td>16</td>\n",
       "      <td>xenobiotic transmembrane transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GO:0006890</td>\n",
       "      <td>GOLPH3L ARCN1 GBF1 KDELR1 BET1L PICK1 COPZ2 SC...</td>\n",
       "      <td>52</td>\n",
       "      <td>retrograde vesicle-mediated transport, Golgi t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO:1900372</td>\n",
       "      <td>PARP1 FLCN RD3 PID1</td>\n",
       "      <td>4</td>\n",
       "      <td>negative regulation of purine nucleotide biosy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GO:0031645</td>\n",
       "      <td>LPIN1 HCRT GPR35 EIF2AK3 GLRA1 MTNR1B CCN3 MTM...</td>\n",
       "      <td>16</td>\n",
       "      <td>negative regulation of nervous system process</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           GO                                              Genes  Gene_Count  \\\n",
       "0  GO:0006311                                 TEX11 PRDM9 RNF212           3   \n",
       "1  GO:0006855  ABCB11 ABCC4 ABCC3 SLC29A2 SLC37A3 SLC19A1 SLC...          16   \n",
       "2  GO:0006890  GOLPH3L ARCN1 GBF1 KDELR1 BET1L PICK1 COPZ2 SC...          52   \n",
       "3  GO:1900372                                PARP1 FLCN RD3 PID1           4   \n",
       "4  GO:0031645  LPIN1 HCRT GPR35 EIF2AK3 GLRA1 MTNR1B CCN3 MTM...          16   \n",
       "\n",
       "                                    Term_Description  \n",
       "0                            meiotic gene conversion  \n",
       "1                 xenobiotic transmembrane transport  \n",
       "2  retrograde vesicle-mediated transport, Golgi t...  \n",
       "3  negative regulation of purine nucleotide biosy...  \n",
       "4      negative regulation of nervous system process  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## use case\n",
    "## GO version: latest 2023-11-15\n",
    "\n",
    "go_terms = load_go('./data/GO_BP/collapsed_go.symbol')\n",
    "go_terms = go_terms.reset_index().rename(columns={'index': 'GO'}) # rename the index column to column name GO\n",
    "print(go_terms.shape)\n",
    "\n",
    "# add the go term description\n",
    "term_des = pd.read_table('./data/GO_BP/goID_2_name.tab',\n",
    "                                        header=None,\n",
    "                                        names=['Term', 'Term_Description'],\n",
    "                                        index_col=0)\n",
    "go_terms_des = go_terms.merge(term_des, left_on='GO', right_index=True, how='left')\n",
    "\n",
    "# check if there are any missing values\n",
    "print(go_terms_des.isnull().sum())\n",
    "\n",
    "go_terms_des.to_csv('data/go_terms.csv')\n",
    "go_terms_des.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1677, 3)\n",
      "GO                  0\n",
      "Genes               0\n",
      "Gene_Count          0\n",
      "Term_Description    0\n",
      "dtype: int64\n",
      "(3399, 3)\n",
      "GO                  0\n",
      "Genes               0\n",
      "Gene_Count          0\n",
      "Term_Description    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## use case\n",
    "## GO version: latest 2023-11-15\n",
    "for branch in ['CC', 'MF']:\n",
    "    \n",
    "    go_terms = load_go(f'./data/GO_{branch}/collapsed_go.symbol')\n",
    "    go_terms = go_terms.reset_index().rename(columns={'index': 'GO'}) # rename the index column to column name GO\n",
    "    print(go_terms.shape)\n",
    "\n",
    "    # add the go term description\n",
    "    term_des = pd.read_table(f'./data/GO_{branch}/goID_2_name.tab',\n",
    "                                            header=None,\n",
    "                                            names=['Term', 'Term_Description'],\n",
    "                                            index_col=0)\n",
    "    go_terms_des = go_terms.merge(term_des, left_on='GO', right_index=True, how='left')\n",
    "\n",
    "    # check if there are any missing values\n",
    "    print(go_terms_des.isnull().sum())\n",
    "\n",
    "    go_terms_des.to_csv(f'data/GO_term_analysis/CC_MF_branch/{branch}_go_terms.csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Understand GO size distribution and select 1000 GO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "go_terms = pd.read_csv('data/go_terms.csv', sep = ',',index_col=0)\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.hist(go_terms['Gene_Count'], bins=50)\n",
    "\n",
    "# subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,8))\n",
    "ax1.hist(go_terms[(go_terms['Gene_Count'] <= 150)]['Gene_Count'], bins=50)\n",
    "ax2.hist(go_terms[(go_terms['Gene_Count'] > 150)]['Gene_Count'], bins=50)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.746043707611153"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if we randomly select GO terms between 3-100, what percentage of terms will be in the pool?\n",
    "go_terms[(go_terms['Gene_Count'] >= 3) & (go_terms['Gene_Count'] <= 100)].shape[0]/go_terms.shape[0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene_Count\n",
      "count  8910.000000\n",
      "mean     20.143098\n",
      "std      21.781439\n",
      "min       3.000000\n",
      "25%       5.000000\n",
      "50%      11.000000\n",
      "75%      26.000000\n",
      "max     100.000000\n",
      "(8910, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([195., 116., 103.,  64.,  70.,  43.,  35.,  30.,  23.,  27.,  13.,\n",
       "         22.,  16.,  14.,  14.,  14.,   7.,  14.,  19.,  11.,  14.,   7.,\n",
       "          8.,   9.,   6.,   5.,   5.,   8.,   5.,   7.,   5.,   3.,   5.,\n",
       "          3.,   5.,   5.,   7.,   3.,   4.,   3.,   3.,   3.,   4.,   7.,\n",
       "          4.,   2.,   3.,   2.,   3.,   2.]),\n",
       " array([  3.  ,   4.94,   6.88,   8.82,  10.76,  12.7 ,  14.64,  16.58,\n",
       "         18.52,  20.46,  22.4 ,  24.34,  26.28,  28.22,  30.16,  32.1 ,\n",
       "         34.04,  35.98,  37.92,  39.86,  41.8 ,  43.74,  45.68,  47.62,\n",
       "         49.56,  51.5 ,  53.44,  55.38,  57.32,  59.26,  61.2 ,  63.14,\n",
       "         65.08,  67.02,  68.96,  70.9 ,  72.84,  74.78,  76.72,  78.66,\n",
       "         80.6 ,  82.54,  84.48,  86.42,  88.36,  90.3 ,  92.24,  94.18,\n",
       "         96.12,  98.06, 100.  ]),\n",
       " <BarContainer object of 50 artists>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## randomly select 1000 GO terms from the pool between 3-100\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "SEED = 42\n",
    "min = 3\n",
    "max = 100\n",
    "\n",
    "go_terms = pd.read_csv('data/go_terms.csv', sep = ',',index_col=0)\n",
    "go_filtered = go_terms[(go_terms['Gene_Count'] >= min) & (go_terms['Gene_Count'] <= max)]\n",
    "print(go_filtered.describe())\n",
    "print(go_filtered.shape)\n",
    "\n",
    "# randomly select 1000 GO terms from the pool between min and max\n",
    "\n",
    "selected_go = go_filtered.sample(n=1000, random_state=SEED)\n",
    "\n",
    "selected_go.to_csv('data/GO_term_analysis/1000_selected_go_terms.csv', index=False)\n",
    "\n",
    "# quick check of the distribution of the selected GO terms\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,8))\n",
    "ax1.hist(go_filtered['Gene_Count'], bins=50)\n",
    "ax2.hist(selected_go['Gene_Count'], bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene_Count\n",
      "count  1000.000000\n",
      "mean     20.413000\n",
      "std      21.789613\n",
      "min       3.000000\n",
      "25%       6.000000\n",
      "50%      11.000000\n",
      "75%      28.000000\n",
      "max     100.000000\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>GO</th>\n",
       "      <th>Genes</th>\n",
       "      <th>Gene_Count</th>\n",
       "      <th>Term_Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6107</th>\n",
       "      <td>GO:0048627</td>\n",
       "      <td>MEGF10 SDC1 WNT10B SOX15</td>\n",
       "      <td>4</td>\n",
       "      <td>myoblast development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>GO:1904888</td>\n",
       "      <td>CPLANE2 NEUROG1 GRHL2 TGFB3 EXT1 TGFBR2 TWIST1...</td>\n",
       "      <td>72</td>\n",
       "      <td>cranial skeletal system development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6624</th>\n",
       "      <td>GO:0019585</td>\n",
       "      <td>DCXR UGT1A9 UGT2B7 PRKCE UGT1A7 UGT2A3 SORD UG...</td>\n",
       "      <td>26</td>\n",
       "      <td>glucuronate metabolic process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10759</th>\n",
       "      <td>GO:1902267</td>\n",
       "      <td>AZIN1 OAZ2 OAZ1 AZIN2 OAZ3</td>\n",
       "      <td>5</td>\n",
       "      <td>regulation of polyamine transmembrane transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4053</th>\n",
       "      <td>GO:0019748</td>\n",
       "      <td>BDH2 CYP2A7 AKR1C1 ACMSD ATP7A ASIP DDT CYP3A4...</td>\n",
       "      <td>56</td>\n",
       "      <td>secondary metabolic process</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               GO                                              Genes  \\\n",
       "6107   GO:0048627                           MEGF10 SDC1 WNT10B SOX15   \n",
       "1986   GO:1904888  CPLANE2 NEUROG1 GRHL2 TGFB3 EXT1 TGFBR2 TWIST1...   \n",
       "6624   GO:0019585  DCXR UGT1A9 UGT2B7 PRKCE UGT1A7 UGT2A3 SORD UG...   \n",
       "10759  GO:1902267                         AZIN1 OAZ2 OAZ1 AZIN2 OAZ3   \n",
       "4053   GO:0019748  BDH2 CYP2A7 AKR1C1 ACMSD ATP7A ASIP DDT CYP3A4...   \n",
       "\n",
       "       Gene_Count                                 Term_Description  \n",
       "6107            4                             myoblast development  \n",
       "1986           72              cranial skeletal system development  \n",
       "6624           26                    glucuronate metabolic process  \n",
       "10759           5  regulation of polyamine transmembrane transport  \n",
       "4053           56                      secondary metabolic process  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(selected_go.describe())\n",
    "selected_go.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gene_Count\n",
      "count  1160.000000\n",
      "mean     18.095690\n",
      "std      21.995928\n",
      "min       3.000000\n",
      "25%       5.000000\n",
      "50%       9.000000\n",
      "75%      20.000000\n",
      "max     100.000000\n",
      "CC has 1160 of GO terms between 3 and 100\n",
      "        Gene_Count\n",
      "count  2414.000000\n",
      "mean     15.081607\n",
      "std      17.861081\n",
      "min       3.000000\n",
      "25%       4.000000\n",
      "50%       8.000000\n",
      "75%      18.000000\n",
      "max     100.000000\n",
      "MF has 2414 of GO terms between 3 and 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## randomly select 1000 GO terms from the pool between 3-100 for CC and MF\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "SEED = 42\n",
    "min = 3\n",
    "max = 100\n",
    "for branch in ['CC', 'MF']:\n",
    "    go_terms = pd.read_csv(f'data/GO_term_analysis/CC_MF_branch/{branch}_go_terms.csv', sep = ',', index_col=0)\n",
    "    go_filtered = go_terms[(go_terms['Gene_Count'] >= min) & (go_terms['Gene_Count'] <= max)]\n",
    "    print(go_filtered.describe())\n",
    "    print(f'{branch} has {go_filtered.shape[0]} of GO terms between {min} and {max}')\n",
    "\n",
    "    # randomly select 1000 GO terms from the pool between min and max\n",
    "\n",
    "    selected_go = go_filtered.sample(n=1000, random_state=SEED)\n",
    "    assert selected_go.shape[0] == 1000\n",
    "    selected_go.to_csv(f'data/GO_term_analysis/CC_MF_branch/{branch}_1000_selected_go_terms.csv', index=False)\n",
    "\n",
    "    # quick check of the distribution of the selected GO terms\n",
    "    fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,4))\n",
    "    ax1.hist(go_filtered['Gene_Count'], bins=50)\n",
    "    ax2.hist(selected_go['Gene_Count'], bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "llm",
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}