Enrichment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enrichment process\n",
    "\n",
    "In this process we will recived the affinity full calculations as input and then process it to establish the gene-side effect associations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Applying cut-off values (pI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Needed libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "#import plotly.express as px\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#opening the files\n",
    "#affinity_file  = '..\\affinity_full.csv'\n",
    "affinity_file  = 'affinity_full_toy.csv'\n",
    "\n",
    "affinity = pd.read_csv(affinity_file, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DrugID</th>\n",
       "      <th>GeneID</th>\n",
       "      <th>pI</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>pKd</th>\n",
       "      <th>filtered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>143</td>\n",
       "      <td>233</td>\n",
       "      <td>6.30</td>\n",
       "      <td>C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...</td>\n",
       "      <td>MGLTVSALFSRIFGKKQMRILMVGLDAAGKTTILYKLKLGEIVTTI...</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>143</td>\n",
       "      <td>412</td>\n",
       "      <td>5.57</td>\n",
       "      <td>C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...</td>\n",
       "      <td>MFPFYSCWRTGLLLLLLAVAVRESWQTEEKTCDLVGEKGKESEKEL...</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143</td>\n",
       "      <td>1008</td>\n",
       "      <td>5.35</td>\n",
       "      <td>C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...</td>\n",
       "      <td>MTAEEMKATESGAQSAPLPMEGVDISPKQDEGVLKVIKREGTGTEM...</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143</td>\n",
       "      <td>1146</td>\n",
       "      <td>8.68</td>\n",
       "      <td>C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...</td>\n",
       "      <td>MLFEGLDLVSALATLAACLVSVTLLLAVSQQLWQLRWAATRDKSCK...</td>\n",
       "      <td>5.43</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>143</td>\n",
       "      <td>2125</td>\n",
       "      <td>8.47</td>\n",
       "      <td>C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...</td>\n",
       "      <td>MSVLLRSGLGPLCAVARAAIPFIWRGKYFSSGNEPAENPVTPMLRH...</td>\n",
       "      <td>5.59</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6205</th>\n",
       "      <td>2955</td>\n",
       "      <td>233055</td>\n",
       "      <td>6.99</td>\n",
       "      <td>C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N</td>\n",
       "      <td>MAAEIHSRPQSSRPVLLSKIEGHQDAVTAALLIPKEDGVITASEDR...</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6206</th>\n",
       "      <td>2955</td>\n",
       "      <td>233057</td>\n",
       "      <td>8.58</td>\n",
       "      <td>C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N</td>\n",
       "      <td>MAGDLSAGFFMEELNTYRQKQGVVLKYQELPNSGPPHDRRFTFQVI...</td>\n",
       "      <td>5.07</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6207</th>\n",
       "      <td>2955</td>\n",
       "      <td>233078</td>\n",
       "      <td>8.73</td>\n",
       "      <td>C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N</td>\n",
       "      <td>MNNSGADEIGKLFVGGLDWSTTQETLRSYFSQYGEVVDCVIMKDKT...</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6208</th>\n",
       "      <td>2955</td>\n",
       "      <td>233099</td>\n",
       "      <td>6.77</td>\n",
       "      <td>C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N</td>\n",
       "      <td>MELAHSLLLNEEALAQITEAKRPVFIFEWLRFLDKVLVAANKTDVK...</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6209</th>\n",
       "      <td>2955</td>\n",
       "      <td>233121</td>\n",
       "      <td>5.33</td>\n",
       "      <td>C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N</td>\n",
       "      <td>MAVNVYSTSVTSENLSRHDMLAWVNDSLHLNYTKIEQLCSGAAYCQ...</td>\n",
       "      <td>4.12</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6210 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      DrugID  GeneID    pI                                             SMILES  \\\n",
       "0        143     233  6.30  C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...   \n",
       "1        143     412  5.57  C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...   \n",
       "2        143    1008  5.35  C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...   \n",
       "3        143    1146  8.68  C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...   \n",
       "4        143    2125  8.47  C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C...   \n",
       "...      ...     ...   ...                                                ...   \n",
       "6205    2955  233055  6.99              C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N   \n",
       "6206    2955  233057  8.58              C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N   \n",
       "6207    2955  233078  8.73              C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N   \n",
       "6208    2955  233099  6.77              C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N   \n",
       "6209    2955  233121  5.33              C1=CC(=CC=C1N)S(=O)(=O)C2=CC=C(C=C2)N   \n",
       "\n",
       "                                               Sequence   pKd  filtered  \n",
       "0     MGLTVSALFSRIFGKKQMRILMVGLDAAGKTTILYKLKLGEIVTTI...  5.33      0.00  \n",
       "1     MFPFYSCWRTGLLLLLLAVAVRESWQTEEKTCDLVGEKGKESEKEL...  5.32      5.32  \n",
       "2     MTAEEMKATESGAQSAPLPMEGVDISPKQDEGVLKVIKREGTGTEM...  4.84      4.84  \n",
       "3     MLFEGLDLVSALATLAACLVSVTLLLAVSQQLWQLRWAATRDKSCK...  5.43      0.00  \n",
       "4     MSVLLRSGLGPLCAVARAAIPFIWRGKYFSSGNEPAENPVTPMLRH...  5.59      0.00  \n",
       "...                                                 ...   ...       ...  \n",
       "6205  MAAEIHSRPQSSRPVLLSKIEGHQDAVTAALLIPKEDGVITASEDR...  4.04      0.00  \n",
       "6206  MAGDLSAGFFMEELNTYRQKQGVVLKYQELPNSGPPHDRRFTFQVI...  5.07      0.00  \n",
       "6207  MNNSGADEIGKLFVGGLDWSTTQETLRSYFSQYGEVVDCVIMKDKT...  4.54      0.00  \n",
       "6208  MELAHSLLLNEEALAQITEAKRPVFIFEWLRFLDKVLVAANKTDVK...  4.55      0.00  \n",
       "6209  MAVNVYSTSVTSENLSRHDMLAWVNDSLHLNYTKIEQLCSGAAYCQ...  4.12      0.00  \n",
       "\n",
       "[6210 rows x 7 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affinity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DrugID        int64\n",
       "GeneID        int64\n",
       "pI          float64\n",
       "SMILES       object\n",
       "Sequence     object\n",
       "pKd         float64\n",
       "filtered    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting the affinity values to numeric\n",
    "affinity['pKd'] = pd.to_numeric(affinity['pKd'])\n",
    "affinity.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Application\\Anaconda\\envs\\DeepPurpose\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "\n",
      "D:\\Application\\Anaconda\\envs\\DeepPurpose\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pI</th>\n",
       "      <th>pKd</th>\n",
       "      <th>filtered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.30</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.57</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.35</td>\n",
       "      <td>4.84</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.68</td>\n",
       "      <td>5.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.47</td>\n",
       "      <td>5.59</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6205</th>\n",
       "      <td>6.99</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6206</th>\n",
       "      <td>8.58</td>\n",
       "      <td>5.07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6207</th>\n",
       "      <td>8.73</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6208</th>\n",
       "      <td>6.77</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6209</th>\n",
       "      <td>5.33</td>\n",
       "      <td>4.12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6210 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        pI   pKd filtered\n",
       "0     6.30  5.33        0\n",
       "1     5.57  5.32     5.32\n",
       "2     5.35  4.84     4.84\n",
       "3     8.68  5.43        0\n",
       "4     8.47  5.59        0\n",
       "...    ...   ...      ...\n",
       "6205  6.99  4.04        0\n",
       "6206  8.58  5.07        0\n",
       "6207  8.73  4.54        0\n",
       "6208  6.77  4.55        0\n",
       "6209  5.33  4.12        0\n",
       "\n",
       "[6210 rows x 3 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define a new filterd column\n",
    "affinity['filtered'] = '-'\n",
    "\n",
    "#Filtering affinities with pI\n",
    "for i in range(len(affinity)):\n",
    "    \n",
    "    if affinity['pKd'][i] > affinity['pI'][i]*0.9:        # pKd > pI & 10% CI\n",
    "        affinity['filtered'][i] = affinity['pKd'][i]\n",
    "    else:\n",
    "        affinity['filtered'][i] = 0\n",
    "\n",
    "#print the results\n",
    "affinity[['pI','pKd','filtered']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The filteration rate is 66.553945% .\n"
     ]
    }
   ],
   "source": [
    "#Filteration rate\n",
    "filter_rate = len(affinity.loc[affinity['filtered'] == 0])/len(affinity)\n",
    "print('The filteration rate is {:%} .'.format(filter_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DrugID</th>\n",
       "      <th>GeneID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>143</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>143</td>\n",
       "      <td>2165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>143</td>\n",
       "      <td>3302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>143</td>\n",
       "      <td>5286</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6168</th>\n",
       "      <td>2955</td>\n",
       "      <td>231487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6184</th>\n",
       "      <td>2955</td>\n",
       "      <td>232165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6201</th>\n",
       "      <td>2955</td>\n",
       "      <td>232975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6202</th>\n",
       "      <td>2955</td>\n",
       "      <td>232978</td>\n",
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       "    <tr>\n",
       "      <th>6204</th>\n",
       "      <td>2955</td>\n",
       "      <td>233047</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2077 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      DrugID  GeneID\n",
       "1        143     412\n",
       "2        143    1008\n",
       "5        143    2165\n",
       "10       143    3302\n",
       "17       143    5286\n",
       "...      ...     ...\n",
       "6168    2955  231487\n",
       "6184    2955  232165\n",
       "6201    2955  232975\n",
       "6202    2955  232978\n",
       "6204    2955  233047\n",
       "\n",
       "[2077 rows x 2 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropes the pKd = 0\n",
    "affinity_cut = affinity.loc[affinity['filtered'] != 0]\n",
    "affinity_cut[['DrugID','GeneID']]\n",
    "#affinity_cut[['DrugID','GeneID']].to_csv('affinity_cut.csv', index=False, sep = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Visuizations DTIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DTI = affinity.pivot(index=\"GeneID\", columns=\"DrugID\", values=\"pKd\")\n",
    "DTI\n",
    "#sns.set_style('whitegrid')\n",
    "\n",
    "# Sample figsize in inches\n",
    "fig, ax = plt.subplots(figsize=(10,8))         \n",
    "\n",
    "# title\n",
    "plt.title('DTI affnities\\n'.upper(), loc='center')\n",
    "DTI_fig = sns.heatmap(DTI,  cmap='YlGnBu',xticklabels=False, \n",
    "                          yticklabels=False, vmin=3, vmax=9, ax=ax)\n",
    "DTI_fig.figure.savefig(\"DTI.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DTI_cut = affinity_cut.pivot(index=\"GeneID\", columns=\"DrugID\", values=\"pKd\")\n",
    "DTI_cut\n",
    "\n",
    "# Sample figsize in inches\n",
    "fig, ax = plt.subplots(figsize=(10,8))         \n",
    "\n",
    "# title\n",
    "plt.title('Filtered DTI affnities\\n'.upper(), loc='center')\n",
    "DTI_cut_fig = sns.heatmap(DTI_cut, cmap='YlGnBu',xticklabels=False, yticklabels=False, vmin=3, vmax=9, ax=ax)\n",
    "DTI_cut_fig.figure.savefig(\"DTI_cut\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>DrugID</th>\n",
       "      <th>143</th>\n",
       "      <th>2802</th>\n",
       "      <th>2914</th>\n",
       "      <th>2949</th>\n",
       "      <th>2951</th>\n",
       "      <th>2955</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GeneID</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>233</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>5.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.04</td>\n",
       "      <td>6.91</td>\n",
       "      <td>5.39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>4.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>5.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.29</td>\n",
       "      <td>5.54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3302</th>\n",
       "      <td>5.33</td>\n",
       "      <td>5.72</td>\n",
       "      <td>5.46</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.01</td>\n",
       "      <td>4.93</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232978</th>\n",
       "      <td>5.20</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.07</td>\n",
       "      <td>7.23</td>\n",
       "      <td>5.58</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233047</th>\n",
       "      <td>5.40</td>\n",
       "      <td>5.74</td>\n",
       "      <td>5.67</td>\n",
       "      <td>7.62</td>\n",
       "      <td>5.99</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233055</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233099</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233121</th>\n",
       "      <td>5.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.86</td>\n",
       "      <td>6.74</td>\n",
       "      <td>5.25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>658 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DrugID  143   2802  2914  2949  2951  2955\n",
       "GeneID                                    \n",
       "233      NaN   NaN   NaN  6.91   NaN   NaN\n",
       "412     5.32   NaN  5.04  6.91  5.39   NaN\n",
       "1008    4.84   NaN   NaN  5.19   NaN   NaN\n",
       "2165    5.43   NaN   NaN  7.29  5.54   NaN\n",
       "3302    5.33  5.72  5.46  7.50  6.01  4.93\n",
       "...      ...   ...   ...   ...   ...   ...\n",
       "232978  5.20  5.34  5.07  7.23  5.58  4.47\n",
       "233047  5.40  5.74  5.67  7.62  5.99  4.84\n",
       "233055   NaN   NaN   NaN  6.96   NaN   NaN\n",
       "233099   NaN   NaN   NaN  7.13   NaN   NaN\n",
       "233121  5.23   NaN  4.86  6.74  5.25   NaN\n",
       "\n",
       "[658 rows x 6 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTI_cut\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Enrichment process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#opening the full files\n",
    "DTI_file  = '..\\data\\DTI_cut.csv'\n",
    "SE_file  = '..\\data\\se_all.csv'\n",
    "\n",
    "DTI_full_cut = pd.read_csv(DTI_file,index_col=0, sep=',')\n",
    "SE_all = pd.read_csv(SE_file, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
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       "      <th>146</th>\n",
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       "      <th>160</th>\n",
       "      <th>175</th>\n",
       "      <th>187</th>\n",
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       "      <th>70695640</th>\n",
       "      <th>71306834</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GeneID</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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>9606.ENSP00000485672</th>\n",
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       "    <tr>\n",
       "      <th>9606.ENSP00000485673</th>\n",
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       "    <tr>\n",
       "      <th>9606.ENSP00000485675</th>\n",
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       "      <td>4.294158</td>\n",
       "      <td>6.151701</td>\n",
       "      <td>5.726959</td>\n",
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       "      <td>3.542009</td>\n",
       "      <td>4.375259</td>\n",
       "      <td>5.104769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9606.ENSP00000485678</th>\n",
       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19555 rows × 1430 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            85       119       137       143       146  \\\n",
       "GeneID                                                                   \n",
       "9606.ENSP00000000233  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000000412  0.000000  0.000000  0.000000  5.155907  5.061961   \n",
       "9606.ENSP00000001008  0.000000  0.000000  0.000000  5.068107  4.986097   \n",
       "9606.ENSP00000001146  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000002125  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...                        ...       ...       ...       ...       ...   \n",
       "9606.ENSP00000485671  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000485672  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000485673  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000485675  3.609247  3.608212  3.948466  5.053681  4.956657   \n",
       "9606.ENSP00000485678  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "                           158       159       160      175       187  ...  \\\n",
       "GeneID                                                                 ...   \n",
       "9606.ENSP00000000233  0.000000  6.209226  0.000000  0.00000  0.000000  ...   \n",
       "9606.ENSP00000000412  0.000000  6.309342  5.378362  0.00000  5.513102  ...   \n",
       "9606.ENSP00000001008  0.000000  6.237625  5.342598  0.00000  5.419251  ...   \n",
       "9606.ENSP00000001146  0.000000  0.000000  0.000000  0.00000  0.000000  ...   \n",
       "9606.ENSP00000002125  0.000000  0.000000  0.000000  0.00000  0.000000  ...   \n",
       "...                        ...       ...       ...      ...       ...  ...   \n",
       "9606.ENSP00000485671  0.000000  6.134229  0.000000  0.00000  0.000000  ...   \n",
       "9606.ENSP00000485672  0.000000  0.000000  0.000000  0.00000  0.000000  ...   \n",
       "9606.ENSP00000485673  0.000000  0.000000  0.000000  0.00000  0.000000  ...   \n",
       "9606.ENSP00000485675  4.498624  6.222442  5.319235  4.10282  5.267531  ...   \n",
       "9606.ENSP00000485678  0.000000  0.000000  0.000000  0.00000  0.000000  ...   \n",
       "\n",
       "                      53627505  54677977  54681041  54682541  54687131  \\\n",
       "GeneID                                                                   \n",
       "9606.ENSP00000000233   0.00000  0.000000  0.000000  6.126608  5.709157   \n",
       "9606.ENSP00000000412   0.00000  5.248843  0.000000  6.218323  5.797246   \n",
       "9606.ENSP00000001008   0.00000  5.165845  0.000000  6.167012  5.707515   \n",
       "9606.ENSP00000001146   0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000002125   0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "...                        ...       ...       ...       ...       ...   \n",
       "9606.ENSP00000485671   0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000485672   0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000485673   0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "9606.ENSP00000485675   4.59863  5.184774  4.294158  6.151701  5.726959   \n",
       "9606.ENSP00000485678   0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "                      56603655  56842239  70683024  70695640  71306834  \n",
       "GeneID                                                                  \n",
       "9606.ENSP00000000233  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "9606.ENSP00000000412  0.000000  0.000000  0.000000  0.000000  5.158405  \n",
       "9606.ENSP00000001008  0.000000  0.000000  0.000000  0.000000  5.067790  \n",
       "9606.ENSP00000001146  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "9606.ENSP00000002125  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "...                        ...       ...       ...       ...       ...  \n",
       "9606.ENSP00000485671  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "9606.ENSP00000485672  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "9606.ENSP00000485673  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "9606.ENSP00000485675  4.757209  3.850921  3.542009  4.375259  5.104769  \n",
       "9606.ENSP00000485678  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "\n",
       "[19555 rows x 1430 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DTI_full_cut = DTI_full_cut.notnull().astype('int') #replaces non-NA with 1 and NA with 0\n",
    "DTI_full_cut = DTI_full_cut.fillna(0)\n",
    "DTI_full_cut.to_csv('DTI_matrix.csv', sep = ',')\n",
    "DTI_full_cut\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DrugID</th>\n",
       "      <th>SEID</th>\n",
       "      <th>SE name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CID100000085</td>\n",
       "      <td>C0000729</td>\n",
       "      <td>Abdominal cramps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CID100000085</td>\n",
       "      <td>C0000737</td>\n",
       "      <td>Abdominal pain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CID100000085</td>\n",
       "      <td>C0151736</td>\n",
       "      <td>Accidental injury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CID100000085</td>\n",
       "      <td>C0002418</td>\n",
       "      <td>Amblyopia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CID100000085</td>\n",
       "      <td>C0002871</td>\n",
       "      <td>Anaemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138894</th>\n",
       "      <td>CID171306834</td>\n",
       "      <td>C0042963</td>\n",
       "      <td>Vomiting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138895</th>\n",
       "      <td>CID171306834</td>\n",
       "      <td>C3665596</td>\n",
       "      <td>Warts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138896</th>\n",
       "      <td>CID171306834</td>\n",
       "      <td>C0043096</td>\n",
       "      <td>Weight decreased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138897</th>\n",
       "      <td>CID171306834</td>\n",
       "      <td>C0043094</td>\n",
       "      <td>Weight increased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138898</th>\n",
       "      <td>CID171306834</td>\n",
       "      <td>C0043144</td>\n",
       "      <td>Wheezing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138899 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              DrugID      SEID            SE name\n",
       "0       CID100000085  C0000729   Abdominal cramps\n",
       "1       CID100000085  C0000737     Abdominal pain\n",
       "2       CID100000085  C0151736  Accidental injury\n",
       "3       CID100000085  C0002418          Amblyopia\n",
       "4       CID100000085  C0002871            Anaemia\n",
       "...              ...       ...                ...\n",
       "138894  CID171306834  C0042963           Vomiting\n",
       "138895  CID171306834  C3665596              Warts\n",
       "138896  CID171306834  C0043096   Weight decreased\n",
       "138897  CID171306834  C0043094   Weight increased\n",
       "138898  CID171306834  C0043144           Wheezing\n",
       "\n",
       "[138899 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SE_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>SE name</th>\n",
       "      <th>5'nucleotidase increased</th>\n",
       "      <th>AGEP</th>\n",
       "      <th>AIDS dementia complex</th>\n",
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       "      <th>Abdominal aortic aneurysm</th>\n",
       "      <th>...</th>\n",
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       "      <th>Xerosis</th>\n",
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       "      <th>Yeast infection</th>\n",
       "      <th>Yellow nail syndrome</th>\n",
       "      <th>Yellow skin</th>\n",
       "      <th>Zinc deficiency</th>\n",
       "      <th>Zollinger-Ellison syndrome</th>\n",
       "      <th>pH urine decreased</th>\n",
       "      <th>pH urine increased</th>\n",
       "    </tr>\n",
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       "      <th>DrugID</th>\n",
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       "      <th>CID100000085</th>\n",
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       "      <th>CID100000137</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>CID100000143</th>\n",
       "      <td>0</td>\n",
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       "      <th>CID100000146</th>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>CID156603655</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CID156842239</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>CID170683024</th>\n",
       "      <td>0</td>\n",
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       "      <th>CID170695640</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>CID171306834</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1430 rows × 5805 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "SE name       5'nucleotidase increased  AGEP  AIDS dementia complex  AION  \\\n",
       "DrugID                                                                      \n",
       "CID100000085                         0     0                      0     0   \n",
       "CID100000119                         0     0                      0     0   \n",
       "CID100000137                         0     0                      0     0   \n",
       "CID100000143                         0     0                      0     0   \n",
       "CID100000146                         0     0                      0     0   \n",
       "...                                ...   ...                    ...   ...   \n",
       "CID156603655                         0     0                      0     0   \n",
       "CID156842239                         0     0                      0     0   \n",
       "CID170683024                         0     0                      0     0   \n",
       "CID170695640                         0     0                      0     0   \n",
       "CID171306834                         0     0                      0     0   \n",
       "\n",
       "SE name       AML progression  ANA increased  Abasia  Abdominal abscess  \\\n",
       "DrugID                                                                    \n",
       "CID100000085                0              0       0                  0   \n",
       "CID100000119                0              0       0                  0   \n",
       "CID100000137                0              0       0                  0   \n",
       "CID100000143                0              0       0                  0   \n",
       "CID100000146                0              0       0                  0   \n",
       "...                       ...            ...     ...                ...   \n",
       "CID156603655                0              0       0                  0   \n",
       "CID156842239                0              0       0                  0   \n",
       "CID170683024                0              0       0                  0   \n",
       "CID170695640                0              0       0                  0   \n",
       "CID171306834                0              0       0                  0   \n",
       "\n",
       "SE name       Abdominal adhesions  Abdominal aortic aneurysm  ...  \\\n",
       "DrugID                                                        ...   \n",
       "CID100000085                    0                          0  ...   \n",
       "CID100000119                    0                          0  ...   \n",
       "CID100000137                    0                          0  ...   \n",
       "CID100000143                    0                          0  ...   \n",
       "CID100000146                    0                          0  ...   \n",
       "...                           ...                        ...  ...   \n",
       "CID156603655                    0                          0  ...   \n",
       "CID156842239                    0                          0  ...   \n",
       "CID170683024                    0                          0  ...   \n",
       "CID170695640                    0                          0  ...   \n",
       "CID171306834                    0                          0  ...   \n",
       "\n",
       "SE name       Xerophthalmia  Xerosis  Yawning  Yeast infection  \\\n",
       "DrugID                                                           \n",
       "CID100000085              0        0        0                0   \n",
       "CID100000119              0        0        0                0   \n",
       "CID100000137              0        0        0                0   \n",
       "CID100000143              0        0        0                0   \n",
       "CID100000146              0        0        0                0   \n",
       "...                     ...      ...      ...              ...   \n",
       "CID156603655              0        0        0                0   \n",
       "CID156842239              0        0        0                0   \n",
       "CID170683024              0        0        0                0   \n",
       "CID170695640              0        0        0                0   \n",
       "CID171306834              0        0        0                0   \n",
       "\n",
       "SE name       Yellow nail syndrome  Yellow skin  Zinc deficiency  \\\n",
       "DrugID                                                             \n",
       "CID100000085                     0            0                0   \n",
       "CID100000119                     0            0                0   \n",
       "CID100000137                     0            0                0   \n",
       "CID100000143                     0            0                0   \n",
       "CID100000146                     0            0                0   \n",
       "...                            ...          ...              ...   \n",
       "CID156603655                     0            0                0   \n",
       "CID156842239                     0            0                0   \n",
       "CID170683024                     0            0                0   \n",
       "CID170695640                     0            0                0   \n",
       "CID171306834                     0            0                0   \n",
       "\n",
       "SE name       Zollinger-Ellison syndrome  pH urine decreased  \\\n",
       "DrugID                                                         \n",
       "CID100000085                           0                   0   \n",
       "CID100000119                           0                   0   \n",
       "CID100000137                           0                   0   \n",
       "CID100000143                           0                   0   \n",
       "CID100000146                           0                   0   \n",
       "...                                  ...                 ...   \n",
       "CID156603655                           0                   0   \n",
       "CID156842239                           0                   0   \n",
       "CID170683024                           0                   0   \n",
       "CID170695640                           0                   0   \n",
       "CID171306834                           0                   0   \n",
       "\n",
       "SE name       pH urine increased  \n",
       "DrugID                            \n",
       "CID100000085                   0  \n",
       "CID100000119                   0  \n",
       "CID100000137                   0  \n",
       "CID100000143                   0  \n",
       "CID100000146                   0  \n",
       "...                          ...  \n",
       "CID156603655                   0  \n",
       "CID156842239                   0  \n",
       "CID170683024                   0  \n",
       "CID170695640                   0  \n",
       "CID171306834                   0  \n",
       "\n",
       "[1430 rows x 5805 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SE = SE_all.pivot(index=\"DrugID\", columns=\"SE name\", values=\"SEID\")\n",
    "SE = SE.notnull().astype('int') #replaces non-NA with 1 and NA with 0\n",
    "SE.to_csv('SE_matrix.csv', sep = ',')\n",
    "SE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making matrices\n",
    "SE_matrix = SE.to_numpy()\n",
    "DTI_matrix = DTI_full_cut.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Making the gene-side effect (GSE) matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.56 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# GSE = DTI x SE\n",
    "# 19555*5085 = 19555*1430 x 1430*5085\n",
    "GSE_matrix = np.matmul(DTI_matrix,SE_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>7.090710</td>\n",
       "      <td>18.387997</td>\n",
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       "      <td>201.049668</td>\n",
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       "      <td>5.057459</td>\n",
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       "    <tr>\n",
       "      <th>9606.ENSP00000001146</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>9606.ENSP00000485671</th>\n",
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       "      <td>50.723423</td>\n",
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       "    <tr>\n",
       "      <th>9606.ENSP00000485672</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>9606.ENSP00000485673</th>\n",
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       "    <tr>\n",
       "      <th>9606.ENSP00000485675</th>\n",
       "      <td>4.530163</td>\n",
       "      <td>16.544797</td>\n",
       "      <td>7.041358</td>\n",
       "      <td>22.901625</td>\n",
       "      <td>5.061875</td>\n",
       "      <td>9.310631</td>\n",
       "      <td>5.746259</td>\n",
       "      <td>9.261227</td>\n",
       "      <td>4.317516</td>\n",
       "      <td>4.737044</td>\n",
       "      <td>...</td>\n",
       "      <td>139.083987</td>\n",
       "      <td>31.440770</td>\n",
       "      <td>241.032683</td>\n",
       "      <td>7.898255</td>\n",
       "      <td>3.689083</td>\n",
       "      <td>22.843521</td>\n",
       "      <td>4.459551</td>\n",
       "      <td>4.536626</td>\n",
       "      <td>4.767276</td>\n",
       "      <td>5.015444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9606.ENSP00000485678</th>\n",
       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19555 rows × 5805 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      5'nucleotidase increased       AGEP  \\\n",
       "9606.ENSP00000000233                  0.000000   6.725073   \n",
       "9606.ENSP00000000412                  0.000000  12.470183   \n",
       "9606.ENSP00000001008                  0.000000  12.307338   \n",
       "9606.ENSP00000001146                  0.000000   0.000000   \n",
       "9606.ENSP00000002125                  0.000000   0.000000   \n",
       "...                                        ...        ...   \n",
       "9606.ENSP00000485671                  0.000000   6.616918   \n",
       "9606.ENSP00000485672                  0.000000   0.000000   \n",
       "9606.ENSP00000485673                  0.000000   0.000000   \n",
       "9606.ENSP00000485675                  4.530163  16.544797   \n",
       "9606.ENSP00000485678                  0.000000   0.000000   \n",
       "\n",
       "                      AIDS dementia complex       AION  AML progression  \\\n",
       "9606.ENSP00000000233               7.024553   7.453834         0.000000   \n",
       "9606.ENSP00000000412               7.132782  18.775259         5.096781   \n",
       "9606.ENSP00000001008               7.090710  18.387997         5.037927   \n",
       "9606.ENSP00000001146               0.000000   0.000000         0.000000   \n",
       "9606.ENSP00000002125               0.000000   7.692634         0.000000   \n",
       "...                                     ...        ...              ...   \n",
       "9606.ENSP00000485671               6.929232   7.357901         0.000000   \n",
       "9606.ENSP00000485672               0.000000   0.000000         0.000000   \n",
       "9606.ENSP00000485673               0.000000   0.000000         0.000000   \n",
       "9606.ENSP00000485675               7.041358  22.901625         5.061875   \n",
       "9606.ENSP00000485678               0.000000   7.497735         0.000000   \n",
       "\n",
       "                      ANA increased    Abasia  Abdominal abscess  \\\n",
       "9606.ENSP00000000233       0.000000  5.718798           0.000000   \n",
       "9606.ENSP00000000412       5.313812  5.799043           5.371833   \n",
       "9606.ENSP00000001008       5.211054  5.708994           5.271610   \n",
       "9606.ENSP00000001146       0.000000  0.000000           0.000000   \n",
       "9606.ENSP00000002125       0.000000  0.000000           0.000000   \n",
       "...                             ...       ...                ...   \n",
       "9606.ENSP00000485671       0.000000  0.000000           0.000000   \n",
       "9606.ENSP00000485672       0.000000  0.000000           0.000000   \n",
       "9606.ENSP00000485673       0.000000  0.000000           0.000000   \n",
       "9606.ENSP00000485675       9.310631  5.746259           9.261227   \n",
       "9606.ENSP00000485678       0.000000  0.000000           0.000000   \n",
       "\n",
       "                      Abdominal adhesions  Abdominal aortic aneurysm  ...  \\\n",
       "9606.ENSP00000000233             0.000000                   0.000000  ...   \n",
       "9606.ENSP00000000412             0.000000                   0.000000  ...   \n",
       "9606.ENSP00000001008             0.000000                   0.000000  ...   \n",
       "9606.ENSP00000001146             0.000000                   0.000000  ...   \n",
       "9606.ENSP00000002125             0.000000                   0.000000  ...   \n",
       "...                                   ...                        ...  ...   \n",
       "9606.ENSP00000485671             0.000000                   0.000000  ...   \n",
       "9606.ENSP00000485672             0.000000                   0.000000  ...   \n",
       "9606.ENSP00000485673             0.000000                   0.000000  ...   \n",
       "9606.ENSP00000485675             4.317516                   4.737044  ...   \n",
       "9606.ENSP00000485678             0.000000                   0.000000  ...   \n",
       "\n",
       "                      Xerophthalmia    Xerosis     Yawning  Yeast infection  \\\n",
       "9606.ENSP00000000233      68.786461  12.542385  127.915960         0.000000   \n",
       "9606.ENSP00000000412      80.642611  23.518950  199.942375         0.000000   \n",
       "9606.ENSP00000001008      98.489266  23.155396  201.049668         0.000000   \n",
       "9606.ENSP00000001146       8.856247   0.000000   24.220131         0.000000   \n",
       "9606.ENSP00000002125      24.874148   0.000000   48.194148         0.000000   \n",
       "...                             ...        ...         ...              ...   \n",
       "9606.ENSP00000485671      50.723423   6.721102   97.381403         0.000000   \n",
       "9606.ENSP00000485672       8.923135   0.000000    0.000000         0.000000   \n",
       "9606.ENSP00000485673       8.984249   0.000000    8.688242         0.000000   \n",
       "9606.ENSP00000485675     139.083987  31.440770  241.032683         7.898255   \n",
       "9606.ENSP00000485678      16.695431   0.000000   31.952471         0.000000   \n",
       "\n",
       "                      Yellow nail syndrome  Yellow skin  Zinc deficiency  \\\n",
       "9606.ENSP00000000233              0.000000    12.006820         0.000000   \n",
       "9606.ENSP00000000412              0.000000    23.110771         0.000000   \n",
       "9606.ENSP00000001008              0.000000    22.761878         0.000000   \n",
       "9606.ENSP00000001146              0.000000     0.000000         0.000000   \n",
       "9606.ENSP00000002125              0.000000     0.000000         0.000000   \n",
       "...                                    ...          ...              ...   \n",
       "9606.ENSP00000485671              0.000000     6.207889         0.000000   \n",
       "9606.ENSP00000485672              0.000000     0.000000         0.000000   \n",
       "9606.ENSP00000485673              0.000000     0.000000         0.000000   \n",
       "9606.ENSP00000485675              3.689083    22.843521         4.459551   \n",
       "9606.ENSP00000485678              0.000000     0.000000         0.000000   \n",
       "\n",
       "                      Zollinger-Ellison syndrome  pH urine decreased  \\\n",
       "9606.ENSP00000000233                    0.000000            0.000000   \n",
       "9606.ENSP00000000412                    0.000000            0.000000   \n",
       "9606.ENSP00000001008                    0.000000            0.000000   \n",
       "9606.ENSP00000001146                    0.000000            0.000000   \n",
       "9606.ENSP00000002125                    0.000000            0.000000   \n",
       "...                                          ...                 ...   \n",
       "9606.ENSP00000485671                    0.000000            0.000000   \n",
       "9606.ENSP00000485672                    0.000000            0.000000   \n",
       "9606.ENSP00000485673                    0.000000            0.000000   \n",
       "9606.ENSP00000485675                    4.536626            4.767276   \n",
       "9606.ENSP00000485678                    0.000000            0.000000   \n",
       "\n",
       "                      pH urine increased  \n",
       "9606.ENSP00000000233            0.000000  \n",
       "9606.ENSP00000000412            5.098909  \n",
       "9606.ENSP00000001008            5.057459  \n",
       "9606.ENSP00000001146            0.000000  \n",
       "9606.ENSP00000002125            0.000000  \n",
       "...                                  ...  \n",
       "9606.ENSP00000485671            0.000000  \n",
       "9606.ENSP00000485672            0.000000  \n",
       "9606.ENSP00000485673            0.000000  \n",
       "9606.ENSP00000485675            5.015444  \n",
       "9606.ENSP00000485678            0.000000  \n",
       "\n",
       "[19555 rows x 5805 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSE_full = pd.DataFrame(GSE_matrix, columns=list(SE.columns), index=list(DTI_full_cut.index))\n",
    "GSE_full.to_csv('../data/GSE_full_pkd.csv', sep = ',')\n",
    "GSE_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5'nucleotidase increased</th>\n",
       "      <th>AGEP</th>\n",
       "      <th>AIDS dementia complex</th>\n",
       "      <th>AION</th>\n",
       "      <th>AML progression</th>\n",
       "      <th>ANA increased</th>\n",
       "      <th>Abasia</th>\n",
       "      <th>Abdominal abscess</th>\n",
       "      <th>Abdominal adhesions</th>\n",
       "      <th>Abdominal aortic aneurysm</th>\n",
       "      <th>...</th>\n",
       "      <th>Xerophthalmia</th>\n",
       "      <th>Xerosis</th>\n",
       "      <th>Yawning</th>\n",
       "      <th>Yeast infection</th>\n",
       "      <th>Yellow nail syndrome</th>\n",
       "      <th>Yellow skin</th>\n",
       "      <th>Zinc deficiency</th>\n",
       "      <th>Zollinger-Ellison syndrome</th>\n",
       "      <th>pH urine decreased</th>\n",
       "      <th>pH urine increased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.00000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "      <td>19555.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.156277</td>\n",
       "      <td>1.082434</td>\n",
       "      <td>0.623984</td>\n",
       "      <td>1.638762</td>\n",
       "      <td>0.251036</td>\n",
       "      <td>0.399693</td>\n",
       "      <td>0.461161</td>\n",
       "      <td>0.401125</td>\n",
       "      <td>0.101457</td>\n",
       "      <td>0.181181</td>\n",
       "      <td>...</td>\n",
       "      <td>8.592636</td>\n",
       "      <td>1.960675</td>\n",
       "      <td>17.51102</td>\n",
       "      <td>0.108208</td>\n",
       "      <td>0.042905</td>\n",
       "      <td>1.788750</td>\n",
       "      <td>0.157300</td>\n",
       "      <td>0.173511</td>\n",
       "      <td>0.172897</td>\n",
       "      <td>0.280593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.363127</td>\n",
       "      <td>1.037871</td>\n",
       "      <td>0.484397</td>\n",
       "      <td>1.453838</td>\n",
       "      <td>0.433620</td>\n",
       "      <td>0.607646</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.592458</td>\n",
       "      <td>0.301941</td>\n",
       "      <td>0.385178</td>\n",
       "      <td>...</td>\n",
       "      <td>7.094134</td>\n",
       "      <td>2.044693</td>\n",
       "      <td>14.58109</td>\n",
       "      <td>0.443783</td>\n",
       "      <td>0.202647</td>\n",
       "      <td>1.814254</td>\n",
       "      <td>0.364093</td>\n",
       "      <td>0.378698</td>\n",
       "      <td>0.378168</td>\n",
       "      <td>0.449301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>42.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 5805 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       5'nucleotidase increased          AGEP  AIDS dementia complex  \\\n",
       "count              19555.000000  19555.000000           19555.000000   \n",
       "mean                   0.156277      1.082434               0.623984   \n",
       "std                    0.363127      1.037871               0.484397   \n",
       "min                    0.000000      0.000000               0.000000   \n",
       "25%                    0.000000      0.000000               0.000000   \n",
       "50%                    0.000000      1.000000               1.000000   \n",
       "75%                    0.000000      2.000000               1.000000   \n",
       "max                    1.000000      3.000000               1.000000   \n",
       "\n",
       "               AION  AML progression  ANA increased        Abasia  \\\n",
       "count  19555.000000     19555.000000   19555.000000  19555.000000   \n",
       "mean       1.638762         0.251036       0.399693      0.461161   \n",
       "std        1.453838         0.433620       0.607646      0.498502   \n",
       "min        0.000000         0.000000       0.000000      0.000000   \n",
       "25%        0.000000         0.000000       0.000000      0.000000   \n",
       "50%        1.000000         0.000000       0.000000      0.000000   \n",
       "75%        3.000000         1.000000       1.000000      1.000000   \n",
       "max        4.000000         1.000000       2.000000      1.000000   \n",
       "\n",
       "       Abdominal abscess  Abdominal adhesions  Abdominal aortic aneurysm  ...  \\\n",
       "count       19555.000000         19555.000000               19555.000000  ...   \n",
       "mean            0.401125             0.101457                   0.181181  ...   \n",
       "std             0.592458             0.301941                   0.385178  ...   \n",
       "min             0.000000             0.000000                   0.000000  ...   \n",
       "25%             0.000000             0.000000                   0.000000  ...   \n",
       "50%             0.000000             0.000000                   0.000000  ...   \n",
       "75%             1.000000             0.000000                   0.000000  ...   \n",
       "max             2.000000             1.000000                   1.000000  ...   \n",
       "\n",
       "       Xerophthalmia       Xerosis      Yawning  Yeast infection  \\\n",
       "count   19555.000000  19555.000000  19555.00000     19555.000000   \n",
       "mean        8.592636      1.960675     17.51102         0.108208   \n",
       "std         7.094134      2.044693     14.58109         0.443783   \n",
       "min         0.000000      0.000000      0.00000         0.000000   \n",
       "25%         1.000000      0.000000      3.00000         0.000000   \n",
       "50%         8.000000      1.000000     18.00000         0.000000   \n",
       "75%        13.000000      4.000000     32.00000         0.000000   \n",
       "max        26.000000      6.000000     42.00000         2.000000   \n",
       "\n",
       "       Yellow nail syndrome   Yellow skin  Zinc deficiency  \\\n",
       "count          19555.000000  19555.000000     19555.000000   \n",
       "mean               0.042905      1.788750         0.157300   \n",
       "std                0.202647      1.814254         0.364093   \n",
       "min                0.000000      0.000000         0.000000   \n",
       "25%                0.000000      0.000000         0.000000   \n",
       "50%                0.000000      1.000000         0.000000   \n",
       "75%                0.000000      4.000000         0.000000   \n",
       "max                1.000000      4.000000         1.000000   \n",
       "\n",
       "       Zollinger-Ellison syndrome  pH urine decreased  pH urine increased  \n",
       "count                19555.000000        19555.000000        19555.000000  \n",
       "mean                     0.173511            0.172897            0.280593  \n",
       "std                      0.378698            0.378168            0.449301  \n",
       "min                      0.000000            0.000000            0.000000  \n",
       "25%                      0.000000            0.000000            0.000000  \n",
       "50%                      0.000000            0.000000            0.000000  \n",
       "75%                      0.000000            0.000000            1.000000  \n",
       "max                      1.000000            1.000000            1.000000  \n",
       "\n",
       "[8 rows x 5805 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSE_info = GSE_full.describe()\n",
    "#GSE_info.to_csv('GSE_info.csv', sep = ',')\n",
    "GSE_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>9606.ENSP00000000233</th>\n",
       "      <th>9606.ENSP00000000412</th>\n",
       "      <th>9606.ENSP00000001008</th>\n",
       "      <th>9606.ENSP00000001146</th>\n",
       "      <th>9606.ENSP00000002125</th>\n",
       "      <th>9606.ENSP00000002165</th>\n",
       "      <th>9606.ENSP00000002596</th>\n",
       "      <th>9606.ENSP00000002829</th>\n",
       "      <th>9606.ENSP00000003084</th>\n",
       "      <th>9606.ENSP00000003100</th>\n",
       "      <th>...</th>\n",
       "      <th>9606.ENSP00000485656</th>\n",
       "      <th>9606.ENSP00000485659</th>\n",
       "      <th>9606.ENSP00000485663</th>\n",
       "      <th>9606.ENSP00000485664</th>\n",
       "      <th>9606.ENSP00000485668</th>\n",
       "      <th>9606.ENSP00000485671</th>\n",
       "      <th>9606.ENSP00000485672</th>\n",
       "      <th>9606.ENSP00000485673</th>\n",
       "      <th>9606.ENSP00000485675</th>\n",
       "      <th>9606.ENSP00000485678</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "      <td>5805.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.447890</td>\n",
       "      <td>14.094746</td>\n",
       "      <td>15.025151</td>\n",
       "      <td>0.918691</td>\n",
       "      <td>1.774505</td>\n",
       "      <td>12.178295</td>\n",
       "      <td>0.591731</td>\n",
       "      <td>1.364513</td>\n",
       "      <td>0.885271</td>\n",
       "      <td>1.159001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.507838</td>\n",
       "      <td>8.920413</td>\n",
       "      <td>12.061843</td>\n",
       "      <td>1.987769</td>\n",
       "      <td>0.505943</td>\n",
       "      <td>5.497158</td>\n",
       "      <td>0.112145</td>\n",
       "      <td>0.274246</td>\n",
       "      <td>23.927476</td>\n",
       "      <td>1.506460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.987322</td>\n",
       "      <td>45.400446</td>\n",
       "      <td>48.264780</td>\n",
       "      <td>2.693754</td>\n",
       "      <td>5.239078</td>\n",
       "      <td>39.379383</td>\n",
       "      <td>1.957526</td>\n",
       "      <td>4.035778</td>\n",
       "      <td>2.697779</td>\n",
       "      <td>3.439350</td>\n",
       "      <td>...</td>\n",
       "      <td>1.657475</td>\n",
       "      <td>28.747682</td>\n",
       "      <td>39.004649</td>\n",
       "      <td>5.893643</td>\n",
       "      <td>1.650400</td>\n",
       "      <td>17.668775</td>\n",
       "      <td>0.523729</td>\n",
       "      <td>0.961474</td>\n",
       "      <td>78.159453</td>\n",
       "      <td>4.527826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>399.000000</td>\n",
       "      <td>684.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>588.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1207.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 19555 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       9606.ENSP00000000233  9606.ENSP00000000412  9606.ENSP00000001008  \\\n",
       "count           5805.000000           5805.000000           5805.000000   \n",
       "mean               8.447890             14.094746             15.025151   \n",
       "std               26.987322             45.400446             48.264780   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                0.000000              1.000000              1.000000   \n",
       "50%                1.000000              2.000000              2.000000   \n",
       "75%                4.000000              7.000000              7.000000   \n",
       "max              399.000000            684.000000            730.000000   \n",
       "\n",
       "       9606.ENSP00000001146  9606.ENSP00000002125  9606.ENSP00000002165  \\\n",
       "count           5805.000000           5805.000000           5805.000000   \n",
       "mean               0.918691              1.774505             12.178295   \n",
       "std                2.693754              5.239078             39.379383   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                0.000000              0.000000              1.000000   \n",
       "50%                0.000000              0.000000              2.000000   \n",
       "75%                1.000000              1.000000              6.000000   \n",
       "max               33.000000             70.000000            592.000000   \n",
       "\n",
       "       9606.ENSP00000002596  9606.ENSP00000002829  9606.ENSP00000003084  \\\n",
       "count           5805.000000           5805.000000           5805.000000   \n",
       "mean               0.591731              1.364513              0.885271   \n",
       "std                1.957526              4.035778              2.697779   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                0.000000              0.000000              0.000000   \n",
       "50%                0.000000              0.000000              0.000000   \n",
       "75%                0.000000              1.000000              1.000000   \n",
       "max               26.000000             52.000000             34.000000   \n",
       "\n",
       "       9606.ENSP00000003100  ...  9606.ENSP00000485656  9606.ENSP00000485659  \\\n",
       "count           5805.000000  ...           5805.000000           5805.000000   \n",
       "mean               1.159001  ...              0.507838              8.920413   \n",
       "std                3.439350  ...              1.657475             28.747682   \n",
       "min                0.000000  ...              0.000000              0.000000   \n",
       "25%                0.000000  ...              0.000000              0.000000   \n",
       "50%                0.000000  ...              0.000000              1.000000   \n",
       "75%                1.000000  ...              0.000000              4.000000   \n",
       "max               41.000000  ...             21.000000            431.000000   \n",
       "\n",
       "       9606.ENSP00000485663  9606.ENSP00000485664  9606.ENSP00000485668  \\\n",
       "count           5805.000000           5805.000000           5805.000000   \n",
       "mean              12.061843              1.987769              0.505943   \n",
       "std               39.004649              5.893643              1.650400   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                1.000000              0.000000              0.000000   \n",
       "50%                2.000000              0.000000              0.000000   \n",
       "75%                6.000000              1.000000              0.000000   \n",
       "max              588.000000             83.000000             21.000000   \n",
       "\n",
       "       9606.ENSP00000485671  9606.ENSP00000485672  9606.ENSP00000485673  \\\n",
       "count           5805.000000           5805.000000           5805.000000   \n",
       "mean               5.497158              0.112145              0.274246   \n",
       "std               17.668775              0.523729              0.961474   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                0.000000              0.000000              0.000000   \n",
       "50%                1.000000              0.000000              0.000000   \n",
       "75%                3.000000              0.000000              0.000000   \n",
       "max              254.000000              7.000000             15.000000   \n",
       "\n",
       "       9606.ENSP00000485675  9606.ENSP00000485678  \n",
       "count           5805.000000           5805.000000  \n",
       "mean              23.927476              1.506460  \n",
       "std               78.159453              4.527826  \n",
       "min                1.000000              0.000000  \n",
       "25%                1.000000              0.000000  \n",
       "50%                3.000000              0.000000  \n",
       "75%               11.000000              1.000000  \n",
       "max             1207.000000             60.000000  \n",
       "\n",
       "[8 rows x 19555 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSE_transposed = GSE_full.transpose()\n",
    "GSE_info2 = GSE_transposed.describe()\n",
    "#GSE_info.to_csv('GSE_info.csv', sep = ',')\n",
    "GSE_info2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "GSE_info2.T.to_csv('GSE_info2.csv', sep = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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