Lasso_ADAS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: During startup - \n",
      "R[write to console]: Warning messages:\n",
      "\n",
      "R[write to console]: 1: Setting LC_CTYPE failed, using \"C\" \n",
      "\n",
      "R[write to console]: 2: Setting LC_COLLATE failed, using \"C\" \n",
      "\n",
      "R[write to console]: 3: Setting LC_TIME failed, using \"C\" \n",
      "\n",
      "R[write to console]: 4: Setting LC_MESSAGES failed, using \"C\" \n",
      "\n",
      "R[write to console]: 5: Setting LC_MONETARY failed, using \"C\" \n",
      "\n",
      "R[write to console]: 6: Setting LC_PAPER failed, using \"C\" \n",
      "\n",
      "R[write to console]: 7: Setting LC_MEASUREMENT failed, using \"C\" \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import rpy2\n",
    "# magic execute R script\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load genotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get genotype col names\n",
    "vcf_file = 'data/ADNI_JansenNG_33gene_unimputed.vcf'\n",
    "with open(vcf_file, 'r') as f_vcf:\n",
    "    while True:\n",
    "        line = f_vcf.readline()\n",
    "        if line[:2]!='##':\n",
    "            break\n",
    "\n",
    "    col_names = line[1:].split()\n",
    "        \n",
    "# print(col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>CHROM</th>\n",
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       "<p>1783 rows × 1585 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CHROM        POS          ID REF ALT QUAL FILTER INFO FORMAT  \\\n",
       "0         1  161111931  rs10908824   A   G    .      .   PR     GT   \n",
       "1         1  161116022  rs11589567   G   T    .      .   PR     GT   \n",
       "2         1  161122903   rs4656988   G   A    .      .   PR     GT   \n",
       "3         1  161135421      rs3402   C   T    .      .   PR     GT   \n",
       "4         1  161135559   rs2502806   G   A    .      .   PR     GT   \n",
       "...     ...        ...         ...  ..  ..  ...    ...  ...    ...   \n",
       "1778     20   55057904   rs6064401   G   A    .      .   PR     GT   \n",
       "1779     20   55062564   rs6092321   A   G    .      .   PR     GT   \n",
       "1780     20   55072472   rs6024911   G   A    .      .   PR     GT   \n",
       "1781     20   55078533   rs3827108   T   C    .      .   PR     GT   \n",
       "1782     20   55083478   rs6069767   G   T    .      .   PR     GT   \n",
       "\n",
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       "...           ...  ...          ...          ...          ...          ...   \n",
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       "\n",
       "     2_941_S_1363 2_002_S_4264 2_021_S_2077 2_024_S_4186 2_128_S_2057  \\\n",
       "0             0/1          0/1          0/0          0/0          0/1   \n",
       "1             0/1          0/1          0/0          0/0          0/1   \n",
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       "...           ...          ...          ...          ...          ...   \n",
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       "\n",
       "     2_941_S_4066  \n",
       "0             0/0  \n",
       "1             0/0  \n",
       "2             0/0  \n",
       "3             0/0  \n",
       "4             0/0  \n",
       "...           ...  \n",
       "1778          0/1  \n",
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       "1781          0/0  \n",
       "1782          0/1  \n",
       "\n",
       "[1783 rows x 1585 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load genotype\n",
    "vcf_file = 'data/ADNI_JansenNG_33gene_unimputed.vcf'\n",
    "vcf_genotype = pd.read_csv(\n",
    "    vcf_file,\n",
    "    sep='\\t',\n",
    "    header=None,\n",
    "    names=col_names,\n",
    "    comment='#')\n",
    "\n",
    "vcf_genotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>.</td>\n",
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       "      <td>GT</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>161122903</td>\n",
       "      <td>rs4656988</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>PR</td>\n",
       "      <td>GT</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>161135421</td>\n",
       "      <td>rs3402</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>PR</td>\n",
       "      <td>GT</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>161135559</td>\n",
       "      <td>rs2502806</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>PR</td>\n",
       "      <td>GT</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1778</th>\n",
       "      <td>20</td>\n",
       "      <td>55057904</td>\n",
       "      <td>rs6064401</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>PR</td>\n",
       "      <td>GT</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>20</td>\n",
       "      <td>55062564</td>\n",
       "      <td>rs6092321</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>PR</td>\n",
       "      <td>GT</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>20</td>\n",
       "      <td>55072472</td>\n",
       "      <td>rs6024911</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>PR</td>\n",
       "      <td>GT</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1781</th>\n",
       "      <td>20</td>\n",
       "      <td>55078533</td>\n",
       "      <td>rs3827108</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>PR</td>\n",
       "      <td>GT</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782</th>\n",
       "      <td>20</td>\n",
       "      <td>55083478</td>\n",
       "      <td>rs6069767</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>PR</td>\n",
       "      <td>GT</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1783 rows × 1585 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CHROM        POS          ID REF ALT QUAL FILTER INFO FORMAT  \\\n",
       "0         1  161111931  rs10908824   A   G    .      .   PR     GT   \n",
       "1         1  161116022  rs11589567   G   T    .      .   PR     GT   \n",
       "2         1  161122903   rs4656988   G   A    .      .   PR     GT   \n",
       "3         1  161135421      rs3402   C   T    .      .   PR     GT   \n",
       "4         1  161135559   rs2502806   G   A    .      .   PR     GT   \n",
       "...     ...        ...         ...  ..  ..  ...    ...  ...    ...   \n",
       "1778     20   55057904   rs6064401   G   A    .      .   PR     GT   \n",
       "1779     20   55062564   rs6092321   A   G    .      .   PR     GT   \n",
       "1780     20   55072472   rs6024911   G   A    .      .   PR     GT   \n",
       "1781     20   55078533   rs3827108   T   C    .      .   PR     GT   \n",
       "1782     20   55083478   rs6069767   G   T    .      .   PR     GT   \n",
       "\n",
       "      2_123_S_0108  ...  2_141_S_1245  2_941_S_1194  2_941_S_1295  \\\n",
       "0                0  ...             1             2             0   \n",
       "1                0  ...             1             2             0   \n",
       "2                1  ...             1             2             1   \n",
       "3                0  ...             0             0             0   \n",
       "4                0  ...             1             0             1   \n",
       "...            ...  ...           ...           ...           ...   \n",
       "1778             1  ...             1             1             0   \n",
       "1779             0  ...             0             1             2   \n",
       "1780             0  ...             0             1             2   \n",
       "1781             1  ...             1             0             0   \n",
       "1782             1  ...             1             1             0   \n",
       "\n",
       "      2_941_S_1311  2_941_S_1363  2_002_S_4264  2_021_S_2077  2_024_S_4186  \\\n",
       "0                0             1             1             0             0   \n",
       "1                0             1             1             0             0   \n",
       "2                0             1             1             1             1   \n",
       "3                0             0             0             0             0   \n",
       "4                0             1             1             1             1   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1778             1             1             1             1             2   \n",
       "1779             1             0             0             0             0   \n",
       "1780             1             0             0             0             0   \n",
       "1781             0             1             1             1             0   \n",
       "1782             1             1             1             1             2   \n",
       "\n",
       "      2_128_S_2057  2_941_S_4066  \n",
       "0                1             0  \n",
       "1                1             0  \n",
       "2                1             0  \n",
       "3                0             0  \n",
       "4                1             0  \n",
       "...            ...           ...  \n",
       "1778             1             1  \n",
       "1779             1             1  \n",
       "1780             1             1  \n",
       "1781             0             0  \n",
       "1782             1             1  \n",
       "\n",
       "[1783 rows x 1585 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace genotype with integers\n",
    "vcf_int = vcf_genotype.replace({\n",
    "    './.': np.NaN,\n",
    "    '0/0': 0,\n",
    "    '0/1': 1,\n",
    "    '1/0': 1,\n",
    "    '1/1': 2\n",
    "})\n",
    "\n",
    "vcf_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "      ...  2_141_S_1245  2_941_S_1194  2_941_S_1295  2_941_S_1311  \\\n",
       "0     ...             1             2             0             0   \n",
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       "\n",
       "      2_941_S_1363  2_002_S_4264  2_021_S_2077  2_024_S_4186  2_128_S_2057  \\\n",
       "0                1             1             0             0             1   \n",
       "1                1             1             0             0             1   \n",
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       "3                0             0             0             0             0   \n",
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       "...            ...           ...           ...           ...           ...   \n",
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       "1781             1             1             1             0             0   \n",
       "1782             1             1             1             2             1   \n",
       "\n",
       "      2_941_S_4066  \n",
       "0                0  \n",
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       "1779             1  \n",
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       "1782             1  \n",
       "\n",
       "[1783 rows x 1576 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# snp matrix with shape of (SNP loci, individual)\n",
    "snp_ind_matrix = vcf_int.iloc[:, 9:]\n",
    "snp_ind_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "              0     1     2     3     4     5     6     7     8     9     ...  \\\n",
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       "...            ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
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       "2_024_S_4186     0     0     1     0     1     1     0     1     1     0  ...   \n",
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       "\n",
       "              1773  1774  1775  1776  1777  1778  1779  1780  1781  1782  \n",
       "2_123_S_0108     1     1     1     1     1     1     0     0     1     1  \n",
       "2_024_S_0985     0     0     0     0     0     2     0     0     0     2  \n",
       "2_131_S_0123     1     1     1     1     1     1     0     0     1     1  \n",
       "2_098_S_0160     2     2     2     2     2     0     0     0     2     0  \n",
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       "...            ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "2_002_S_4264     0     0     0     1     1     1     0     0     1     1  \n",
       "2_021_S_2077     1     1     1     1     1     1     0     0     1     1  \n",
       "2_024_S_4186     0     0     0     0     0     2     0     0     0     2  \n",
       "2_128_S_2057     0     0     0     0     0     1     1     1     0     1  \n",
       "2_941_S_4066     0     0     0     0     0     1     1     1     0     1  \n",
       "\n",
       "[1576 rows x 1783 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transposition is necessary.\n",
    "ind_snp_matrix = snp_ind_matrix.transpose()\n",
    "ind_snp_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load phenotype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>5283</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>5287</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>5288</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>5290</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>5295</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>930 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RID  TOTSCORE  TOTAL13\n",
       "0      31       5.0     10.0\n",
       "1      35       6.0     13.0\n",
       "2      47       8.0     14.0\n",
       "3      51       9.0     20.0\n",
       "4      58       5.0     10.0\n",
       "..    ...       ...      ...\n",
       "930  5283       3.0      7.0\n",
       "931  5287       3.0      4.0\n",
       "932  5288       6.0      8.0\n",
       "933  5290       5.0      6.0\n",
       "934  5295      11.0     18.0\n",
       "\n",
       "[930 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load phenotype\n",
    "ADAS_file = 'data/ADAS_selected.csv'\n",
    "ADAS_data = pd.read_csv(ADAS_file, sep=',', header=0)\n",
    "ADAS_data = ADAS_data.dropna()\n",
    "ADAS_data"
   ]
  },
  {
   "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>TOTSCORE</th>\n",
       "      <th>TOTAL13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>930.000000</td>\n",
       "      <td>930.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.259140</td>\n",
       "      <td>16.009677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.352463</td>\n",
       "      <td>10.353541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         TOTSCORE     TOTAL13\n",
       "count  930.000000  930.000000\n",
       "mean    10.259140   16.009677\n",
       "std      7.352463   10.353541\n",
       "min      0.000000    0.000000\n",
       "25%      5.000000    9.000000\n",
       "50%      8.000000   13.000000\n",
       "75%     13.000000   21.000000\n",
       "max     52.000000   67.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ADAS_data[['TOTSCORE','TOTAL13']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5e9b43c860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5e9b3f7c88>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ADAS_data[['TOTSCORE','TOTAL13']].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.asarray(ADAS_data['TOTSCORE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normlization\n",
    "Y = (Y - Y.min()) / (Y.max() - Y.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intersection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "genetic_id = [x[-4:] for x in ind_snp_matrix.index]\n",
    "rid = ['{:04d}'.format(x) for x in ADAS_data['RID']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_id = [genetic_id.index(x) for x in rid]"
   ]
  },
  {
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1773</th>\n",
       "      <th>1774</th>\n",
       "      <th>1775</th>\n",
       "      <th>1776</th>\n",
       "      <th>1777</th>\n",
       "      <th>1778</th>\n",
       "      <th>1779</th>\n",
       "      <th>1780</th>\n",
       "      <th>1781</th>\n",
       "      <th>1782</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2_023_S_0031</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_100_S_0035</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_100_S_0047</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_099_S_0051</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_023_S_0058</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>2_036_S_5283</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_053_S_5287</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_027_S_5288</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_024_S_5290</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_057_S_5295</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>930 rows × 1783 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0     1     2     3     4     5     6     7     8     9     ...  \\\n",
       "2_023_S_0031     1     1     1     0     1     1     1     0     0     1  ...   \n",
       "2_100_S_0035     0     0     1     0     1     1     0     1     1     0  ...   \n",
       "2_100_S_0047     0     0     0     0     1     0     0     0     0     0  ...   \n",
       "2_099_S_0051     2     2     2     1     0     2     2     0     0     2  ...   \n",
       "2_023_S_0058     0     0     0     0     2     0     0     0     0     0  ...   \n",
       "...            ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "2_036_S_5283     2     2     2     1     0     2     2     0     0     2  ...   \n",
       "2_053_S_5287     0     0     0     0     2     0     0     0     0     0  ...   \n",
       "2_027_S_5288     0     0     0     0     2     0     0     0     0     0  ...   \n",
       "2_024_S_5290     1     1     1     1     1     1     1     0     0     1  ...   \n",
       "2_057_S_5295     0     0     0     0     1     0     0     0     0     0  ...   \n",
       "\n",
       "              1773  1774  1775  1776  1777  1778  1779  1780  1781  1782  \n",
       "2_023_S_0031     2     2     2     2     2     0     0     0     2     0  \n",
       "2_100_S_0035     1     1     1     1     1     1     0     0     1     1  \n",
       "2_100_S_0047     0     0     0     0     0     1     1     1     0     1  \n",
       "2_099_S_0051     0     1     1     1     1     1     0     0     1     1  \n",
       "2_023_S_0058     1     0     0     0     0     1     1     1     0     1  \n",
       "...            ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "2_036_S_5283     1     2     2     2     2     0     0     0     2     0  \n",
       "2_053_S_5287     0     0     0     0     0     2     0     0     0     1  \n",
       "2_027_S_5288     1     1     1     1     1     1     0     0     1     1  \n",
       "2_024_S_5290     1     1     1     1     1     1     0     0     1     1  \n",
       "2_057_S_5295     2     2     2     2     2     0     0     0     2     0  \n",
       "\n",
       "[930 rows x 1783 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = ind_snp_matrix.iloc[row_id, :]\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((837, 1783), (93, 1783), (837,), (93,))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split df to train and valid\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    X, Y, test_size=0.1)\n",
    "\n",
    "X_train.shape, X_test.shape, Y_train.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((753, 1783), (84, 1783), (753,), (84,))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split df to train and valid\n",
    "X_train, X_valid, Y_train, Y_valid = train_test_split(\n",
    "    X_train, Y_train, test_size=0.1)\n",
    "\n",
    "X_train.shape, X_valid.shape, Y_train.shape, Y_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 0.01406496\n"
     ]
    }
   ],
   "source": [
    "%%R -i X_train,Y_train,X_valid,Y_valid,X_test,Y_test\n",
    "# imported variables are seperated by ',', no space\n",
    "library('glmnet')\n",
    "\n",
    "# cross validation to optimize lasso\n",
    "cv_main <- cv.glmnet(as.matrix(X_train), as.matrix(Y_train), nfolds=5)\n",
    "\n",
    "cv_main$lambda.min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      s0 \n",
      "0.186429 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "blup_main <- glmnet(\n",
    "  as.matrix(X_train),\n",
    "  as.matrix(Y_train),\n",
    "  alpha = 1,\n",
    "  lambda = cv_main$lambda.min,\n",
    "  intercept = TRUE\n",
    ")\n",
    "\n",
    "blup_main\n",
    "blup_main$a0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R -o Y_predict\n",
    "# -o: R to Python\n",
    "# predict\n",
    "\n",
    "# using cv.glmnet model\n",
    "# predicted_results <- predict(cv_main, type = \"class\", newx = as.matrix(test_X))\n",
    "\n",
    "# using glmnet model\n",
    "Y_predict <- predict(blup_main, type = \"class\", newx = as.matrix(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.036006156116200394"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(Y_test, Y_predict[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.026047693622009597"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(Y_test, Y_predict[:, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.plot(range(len(Y_test)), Y_predict, 'bo', alpha=0.8,label='Predict score')\n",
    "plt.plot(range(len(Y_test)), Y_test, 'ro', alpha=0.8,label='Real score')\n",
    "plt.legend(fontsize=20)\n",
    "plt.tick_params(labelsize=20)\n",
    "\n",
    "plt.title('Predict and real score',fontsize=30)\n",
    "plt.xlabel('Individual', fontsize=30)\n",
    "plt.ylabel('ADAS total score', fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5e97239e48>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 37,
     "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": [
    "# difference histgram\n",
    "diff=(Y_predict[:,0]-Y_test)\n",
    "pd.DataFrame(diff).hist()"
   ]
  },
  {
   "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": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}