2. Processed data DREGE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sqlalchemy import create_engine\n",
    "from sklearn import preprocessing\n",
    "import mysql.connector\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. DATA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "duples_repodb = pd.read_csv(\"./Data/Input/Drug Repurposing/repoDB_all_disdru.tsv\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "duples_csbj = pd.read_csv(\"./Data/Input/Drug Repurposing/duplas_CSBJ.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "duples_csbj = duples_csbj.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "duples_csbj = duples_csbj.rename(columns={\"Disease CUI\": \"disease_id\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>disease_id</th>\n",
       "      <th>pathway_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0020538</td>\n",
       "      <td>WP554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0018799</td>\n",
       "      <td>WP1544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0018799</td>\n",
       "      <td>WP1528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0027947</td>\n",
       "      <td>WP229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0013369</td>\n",
       "      <td>WP229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>C0268274</td>\n",
       "      <td>WP4153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>C0085131</td>\n",
       "      <td>WP4153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>C0036161</td>\n",
       "      <td>WP4153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>C0268275</td>\n",
       "      <td>WP4153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>C0162666</td>\n",
       "      <td>WP4236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>664 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    disease_id pathway_id\n",
       "0     C0020538      WP554\n",
       "1     C0018799     WP1544\n",
       "2     C0018799     WP1528\n",
       "3     C0027947      WP229\n",
       "4     C0013369      WP229\n",
       "..         ...        ...\n",
       "659   C0268274     WP4153\n",
       "660   C0085131     WP4153\n",
       "661   C0036161     WP4153\n",
       "662   C0268275     WP4153\n",
       "663   C0162666     WP4236\n",
       "\n",
       "[664 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_path_direct = pd.read_csv('./Data/Input/DISNET/disease_pathway.tsv', sep='\\t')\n",
    "dis_path_direct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_gen = pd.read_csv('./Data/Input/DISNET/drug_gen.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_gen = drug_gen.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis_gen = pd.read_csv('./Data/Input/DISNET/dis_genes_gda.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis_gen = dis_gen.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis_gen_sinfil = pd.read_csv('./Data/Input/DISNET/dis_genes.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis_gen_sinfil = dis_gen_sinfil.drop([\"Unnamed: 0\"],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JOIN REPODB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dis_drug_gen_sinfil = dis_gen_sinfil.merge(drug_gen, how =\"inner\", on = \"gene_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplas_target_repodb = dis_drug_path_fil.merge(dis_drug_gen_sinfil, how = \"inner\", on =[\"disease_id\",\"drug_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_repodb_target = duples_repodb.merge(duplas_target_repodb,how = \"inner\",on = [\"disease_id\",\"drug_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_repodb_target.to_csv(\"cases_repodb_target.tsv\", sep =\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27445783132529894"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_repodb_target[\"score\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JOIN CSBJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplas_target_csbj = dis_drug_path_csbj_fil.merge(dis_drug_gen_sinfil, how = \"inner\", on =[\"disease_id\",\"drug_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_csbj_target = duples_csbj.merge(duplas_target_csbj,how = \"inner\",on = [\"disease_id\",\"drug_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_csbj_target.to_csv(\"cases_csbj_target.tsv\", sep =\"\\t\")"
   ]
  }
 ],
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