{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Sex-biased tissues per perturbagen analysis- Cancer drugs\n",
    "--------------------------------------------------------------------------------\n",
    "\n",
    "Author: Belén Otero Carrasco\n",
    "\n",
    "Last updated 19 March 2024\n",
    "\n",
    "\n",
    "--------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.0.1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pkg_resources\n",
    "# Print version of cmapPy being used in current conda environment \n",
    "pkg_resources.get_distribution(\"cmapPy\").version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame\n",
    "from cmapPy.pandasGEXpress.parse import parse\n",
    "from scipy.stats import hypergeom\n",
    "from scipy.stats import fisher_exact\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signatures_ids_perturbagen(drug):\n",
    "    sig_pertu_ids = final_sig_original_filter[\"sig_id\"][final_sig_original_filter[\"pert_iname\"] == drug]\n",
    "    print(\"number of samples treated with this perturbagen:\")\n",
    "    return sig_pertu_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perturbagen_significant_genes(sig_pertu_ids,drug):\n",
    "    perturbagen_genes_exp = parse(\"./Touchstone/GSE92742_Broad_LINCS_Level5_COMPZ.MODZ_n473647x12328.gctx\", cid=sig_pertu_ids)\n",
    "    df_perturbagen_genes_exp = perturbagen_genes_exp.data_df\n",
    "    df_filter_significant = df_perturbagen_genes_exp[(df_perturbagen_genes_exp >2.0) | (df_perturbagen_genes_exp <-2.0)]\n",
    "    df_bool_sign = df_filter_significant.notna()\n",
    "    df_bool_sign[\"count\"] = df_bool_sign.sum(axis=1)\n",
    "    df_bool_sign = df_bool_sign.reset_index()\n",
    "    df_gene_change = df_bool_sign[df_bool_sign[\"count\"]> 0]\n",
    "    df_gene_change_id = df_gene_change[[\"rid\",\"count\"]]\n",
    "    df_gene_change_id[\"perturbagen_name\"] = drug\n",
    "    df_gene_change_id[\"Signature_id\"] = sig_pertu_ids\n",
    "    return df_gene_change_id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_by_tissue(tissue_type):\n",
    "    data_gtex = pd.read_csv(\"signif.sbgenes.txt\", sep=\"\\t\")\n",
    "    filtered_df_tissue = data_gtex[data_gtex[\"tissue\"] == tissue_type]\n",
    "    return filtered_df_tissue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_by_ids_and_tissue(tissue_type):\n",
    "    data_total_genes_share = pd.read_csv(\"genes_hugo+ensembl.csv\", sep=\",\")\n",
    "    data_gtex_filter = data_gtex.merge(data_total_genes_share , on=\"gene\")\n",
    "    filtered_df_tissue_ids = data_gtex_filter[data_gtex_filter[\"tissue\"] == tissue_type]\n",
    "    return filtered_df_tissue_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocesing_plot(dataframe,sig_cell_info):\n",
    "  \n",
    "    \n",
    "    info_cell = sig_cell_info[[\"sig_id\",\"Signature_id\",\"cell_id\",\"primary_site\"]]\n",
    "    info_cell = info_cell.drop_duplicates()\n",
    "    clue_tiss_sig_gtex_ = dataframe.merge(info_tissue_cell, left_on=[\"Signature\"],right_on=[\"sig_id\"])\n",
    "    clue_tiss_sig_gtex_['Significant'] = clue_tiss_sig_gtex_.apply(lambda row: 'No' if row['P-value'] > 0.05 else 'Yes', axis=1)\n",
    "    \n",
    "    return clue_tiss_sig_gtex_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pivot_table_plot(df_general_plus_siggenes):\n",
    "    \n",
    "    clue_tiss_sig_gtex_pivot = df_general_plus_siggenes[[\"Signature_id\",\"Tissue_type\", \"Heatmap_values\"]]\n",
    "    clue_tiss_sig_gtex_pivot = clue_tiss_sig_gtex_pivot.drop_duplicates()\n",
    "    group_dis = clue_tiss_sig_gtex_pivot.pivot(index='Signature_id', columns='Tissue_type', values='Heatmap_values')\n",
    "    \n",
    "    return group_dis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heatmap(group_dis,drug):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(25, 25))\n",
    "    ax = sns.heatmap(group_dis,linewidth=.5,cmap=[\"#FDEBD0\",\"#82E0AA\",\"#e65caa\",\"#2EC7CE\",\"#117A65\",\"#910956\",\"#175296\"],vmin=1, vmax=7)\n",
    "    colorbar = ax.collections[0].colorbar\n",
    "    #colorbar.set_ticks([0, 1, 2])\n",
    "    plt.title(f'Sex-biased tissues on {drug} application', fontsize = 20) # title with fontsize 20\n",
    "    plt.xlabel('Tissues', fontsize = 12) # x-axis label with fontsize 15\n",
    "    plt.ylabel('Signatures', fontsize = 12) # y-axis label with fontsize 15\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"./images_sex-biased/heatmap_sig_tissue_{drug}.svg\")\n",
    "    # plt.show()\n",
    "    return None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter(group_dis,drug):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(25, 25))\n",
    "    ax = sns.heatmap(group_dis,linewidth=.5,cmap=[\"#FDEBD0\",\"#82E0AA\",\"#e65caa\",\"#2EC7CE\",\"#117A65\",\"#910956\",\"#175296\"],vmin=1, vmax=7)\n",
    "    colorbar = ax.collections[0].colorbar\n",
    "    #colorbar.set_ticks([0, 1, 2])\n",
    "    plt.title(f'Sex-biased tissues on {drug} application', fontsize = 20) # title with fontsize 20\n",
    "    plt.xlabel('Tissues', fontsize = 12) # x-axis label with fontsize 15\n",
    "    plt.ylabel('Signatures', fontsize = 12) # y-axis label with fontsize 15\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"./images_sex-biased/heatmap_sig_tissue_{drug}.svg\")\n",
    "    # plt.show()\n",
    "    return None "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Signature information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## signatures unique per cell line by TAS and exemplar = 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_id</th>\n",
       "      <th>base_cell_id</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>primary_site</th>\n",
       "      <th>tissue</th>\n",
       "      <th>abrev</th>\n",
       "      <th>subtype</th>\n",
       "      <th>sig_id</th>\n",
       "      <th>pert_iname</th>\n",
       "      <th>tas</th>\n",
       "      <th>is_exemplar</th>\n",
       "      <th>Signature_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC018_A375_6H:BRD-K06817181-001-01-5:10</td>\n",
       "      <td>1,2,3,4,5,6-hexabromocyclohexane</td>\n",
       "      <td>0.282354</td>\n",
       "      <td>1</td>\n",
       "      <td>1,2,3,4,5,6-hexabromocyclohexane_A375_skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC017_A375_6H:BRD-K74430258-001-01-2:10</td>\n",
       "      <td>1,2-dichlorobenzene</td>\n",
       "      <td>0.057646</td>\n",
       "      <td>1</td>\n",
       "      <td>1,2-dichlorobenzene_A375_skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC010_A375_6H:BRD-K32795028-001-10-9:10</td>\n",
       "      <td>1-benzylimidazole</td>\n",
       "      <td>0.221885</td>\n",
       "      <td>1</td>\n",
       "      <td>1-benzylimidazole_A375_skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC018_A375_6H:BRD-A80928489-001-01-0:10</td>\n",
       "      <td>1-monopalmitin</td>\n",
       "      <td>0.118443</td>\n",
       "      <td>1</td>\n",
       "      <td>1-monopalmitin_A375_skin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A375</td>\n",
       "      <td>A375</td>\n",
       "      <td>tumor</td>\n",
       "      <td>skin</td>\n",
       "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
       "      <td>SKINNS</td>\n",
       "      <td>malignant melanoma</td>\n",
       "      <td>CPC018_A375_6H:BRD-K31491153-001-01-2:10</td>\n",
       "      <td>1-phenylbiguanide</td>\n",
       "      <td>0.313940</td>\n",
       "      <td>1</td>\n",
       "      <td>1-phenylbiguanide_A375_skin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cell_id base_cell_id sample_type primary_site  \\\n",
       "0    A375         A375       tumor         skin   \n",
       "1    A375         A375       tumor         skin   \n",
       "2    A375         A375       tumor         skin   \n",
       "3    A375         A375       tumor         skin   \n",
       "4    A375         A375       tumor         skin   \n",
       "\n",
       "                            tissue   abrev             subtype  \\\n",
       "0  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "1  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "2  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "3  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "4  Skin_Not_Sun_Exposed_Suprapubic  SKINNS  malignant melanoma   \n",
       "\n",
       "                                     sig_id                        pert_iname  \\\n",
       "0  CPC018_A375_6H:BRD-K06817181-001-01-5:10  1,2,3,4,5,6-hexabromocyclohexane   \n",
       "1  CPC017_A375_6H:BRD-K74430258-001-01-2:10               1,2-dichlorobenzene   \n",
       "2  CPC010_A375_6H:BRD-K32795028-001-10-9:10                 1-benzylimidazole   \n",
       "3  CPC018_A375_6H:BRD-A80928489-001-01-0:10                    1-monopalmitin   \n",
       "4  CPC018_A375_6H:BRD-K31491153-001-01-2:10                 1-phenylbiguanide   \n",
       "\n",
       "        tas  is_exemplar                                Signature_id  \n",
       "0  0.282354            1  1,2,3,4,5,6-hexabromocyclohexane_A375_skin  \n",
       "1  0.057646            1               1,2-dichlorobenzene_A375_skin  \n",
       "2  0.221885            1                 1-benzylimidazole_A375_skin  \n",
       "3  0.118443            1                    1-monopalmitin_A375_skin  \n",
       "4  0.313940            1                 1-phenylbiguanide_A375_skin  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig_cell_info = pd.read_excel((\"new_signame_tas_exemplar.xlsx\"),engine='openpyxl')\n",
    "sig_cell_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_cell_info_filter_site = sig_cell_info[(sig_cell_info[\"primary_site\"]!= \"ovary\")\n",
    "               & (sig_cell_info[\"primary_site\"]!= \"endometrium\") & (sig_cell_info[\"primary_site\"]!= \"prostate\")]\n",
    "## deja mama porque en los de GTEX y puede estar en hombres tambien"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_drugs = pd.read_csv(\"drugs_cancer_clue.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_ids = cancer_drugs[[\"pert_iname\"]].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_ids = drugs_ids.values.ravel().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping_tissue_GTEX_CLUE = pd.read_excel((\"mapping_tissues_GTEX_CLUE.xlsx\"),engine='openpyxl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gene (row) annotations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pr_gene_id', 'pr_gene_symbol', 'pr_gene_title', 'pr_is_lm',\n",
       "       'pr_is_bing'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_info = pd.read_csv(\"./GSE92742_Broad_LINCS_gene_info.txt\", sep=\"\\t\", dtype=str)\n",
    "gene_info.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pertub_id = pd.read_csv(\"./GSE92742_Broad_LINCS_pert_info.txt\", sep=\"\\t\", dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_metrics_id = pd.read_csv(\"./GSE92742_Broad_LINCS_pert_metrics.txt\", sep=\"\\t\", dtype=str)\n",
    "per_metrics_id = per_metrics_id.replace({\"-666\":\"Unknown\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_total_genes_share = pd.read_csv(\"genes_hugo+ensembl.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex = pd.read_csv(\"signif.sbgenes.txt\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex['Sex-bias'] = data_gtex.apply(lambda row: 'Female' if row['effsize'] >= 0 else 'Male', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex_filter = data_gtex.merge(data_total_genes_share , on=\"gene\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex_tissues = data_gtex[\"tissue\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENSEMBL_gene_id</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Sum of relative sex predictivity</th>\n",
       "      <th>Avg of relative sex predictivity (*100)</th>\n",
       "      <th>#Tissues predictivity</th>\n",
       "      <th>Female-biased</th>\n",
       "      <th>Male-biased</th>\n",
       "      <th>Female-biased (median)</th>\n",
       "      <th>Male-biased (median)</th>\n",
       "      <th>Reported Escapee?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>XIST</td>\n",
       "      <td>27.913224</td>\n",
       "      <td>63.439145</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>9.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000270641.1</td>\n",
       "      <td>TSIX</td>\n",
       "      <td>10.148605</td>\n",
       "      <td>23.601408</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000147050.14</td>\n",
       "      <td>KDM6A</td>\n",
       "      <td>0.953643</td>\n",
       "      <td>3.405867</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000005889.15</td>\n",
       "      <td>ZFX</td>\n",
       "      <td>0.571802</td>\n",
       "      <td>1.681771</td>\n",
       "      <td>34.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000198034.10</td>\n",
       "      <td>RPS4X</td>\n",
       "      <td>0.437481</td>\n",
       "      <td>2.083242</td>\n",
       "      <td>21.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ENSEMBL_gene_id HUGO_gene_id  Sum of relative sex predictivity  \\\n",
       "0  ENSG00000229807.10         XIST                         27.913224   \n",
       "1   ENSG00000270641.1         TSIX                         10.148605   \n",
       "2  ENSG00000147050.14        KDM6A                          0.953643   \n",
       "3  ENSG00000005889.15          ZFX                          0.571802   \n",
       "4  ENSG00000198034.10        RPS4X                          0.437481   \n",
       "\n",
       "   Avg of relative sex predictivity (*100)  #Tissues predictivity  \\\n",
       "0                                63.439145                   44.0   \n",
       "1                                23.601408                   43.0   \n",
       "2                                 3.405867                   28.0   \n",
       "3                                 1.681771                   34.0   \n",
       "4                                 2.083242                   21.0   \n",
       "\n",
       "   Female-biased  Male-biased  Female-biased (median)  Male-biased (median)  \\\n",
       "0             44            0                   9.514                   NaN   \n",
       "1              1            0                   7.480                   NaN   \n",
       "2             44            0                   0.636                   NaN   \n",
       "3             44            0                   0.581                   NaN   \n",
       "4             44            0                   0.485                   NaN   \n",
       "\n",
       "   Reported Escapee?  \n",
       "0                  1  \n",
       "1                  1  \n",
       "2                  1  \n",
       "3                  1  \n",
       "4                  1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escape_genes = pd.read_excel((\"escape_genes.xlsx\"),engine='openpyxl')\n",
    "escape_genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_genes_filter = escape_genes.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_genes_filter.loc[escape_genes_filter['Male-biased'] > escape_genes_filter['Female-biased'], 'Gene-bias'] = 2\n",
    "escape_genes_filter.loc[escape_genes_filter['Male-biased'] < escape_genes_filter['Female-biased'], 'Gene-bias'] = 3\n",
    "escape_genes_filter.loc[escape_genes_filter['Male-biased'] == escape_genes_filter['Female-biased'], 'Gene-bias'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "531"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(escape_genes_filter[\"HUGO_gene_id\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>ENSEMBL_gene_id</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Sum of relative sex predictivity</th>\n",
       "      <th>Avg of relative sex predictivity (*100)</th>\n",
       "      <th>#Tissues predictivity</th>\n",
       "      <th>Female-biased</th>\n",
       "      <th>Male-biased</th>\n",
       "      <th>Female-biased (median)</th>\n",
       "      <th>Male-biased (median)</th>\n",
       "      <th>Reported Escapee?</th>\n",
       "      <th>Gene-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>XIST</td>\n",
       "      <td>27.913224</td>\n",
       "      <td>63.439145</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>9.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000270641.1</td>\n",
       "      <td>TSIX</td>\n",
       "      <td>10.148605</td>\n",
       "      <td>23.601408</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000147050.14</td>\n",
       "      <td>KDM6A</td>\n",
       "      <td>0.953643</td>\n",
       "      <td>3.405867</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000005889.15</td>\n",
       "      <td>ZFX</td>\n",
       "      <td>0.571802</td>\n",
       "      <td>1.681771</td>\n",
       "      <td>34.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000198034.10</td>\n",
       "      <td>RPS4X</td>\n",
       "      <td>0.437481</td>\n",
       "      <td>2.083242</td>\n",
       "      <td>21.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>ENSG00000124333.15</td>\n",
       "      <td>VAMP7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>ENSG00000223571.6</td>\n",
       "      <td>DHRSX-IT1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.641</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>ENSG00000182508.13</td>\n",
       "      <td>LHFPL1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>ENSG00000189108.12</td>\n",
       "      <td>IL1RAPL2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>ENSG00000155622.6</td>\n",
       "      <td>XAGE2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.395</td>\n",
       "      <td>-0.357</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>531 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ENSEMBL_gene_id HUGO_gene_id  Sum of relative sex predictivity  \\\n",
       "0    ENSG00000229807.10         XIST                         27.913224   \n",
       "1     ENSG00000270641.1         TSIX                         10.148605   \n",
       "2    ENSG00000147050.14        KDM6A                          0.953643   \n",
       "3    ENSG00000005889.15          ZFX                          0.571802   \n",
       "4    ENSG00000198034.10        RPS4X                          0.437481   \n",
       "..                  ...          ...                               ...   \n",
       "526  ENSG00000124333.15        VAMP7                               NaN   \n",
       "527   ENSG00000223571.6    DHRSX-IT1                               NaN   \n",
       "528  ENSG00000182508.13       LHFPL1                               NaN   \n",
       "529  ENSG00000189108.12     IL1RAPL2                               NaN   \n",
       "530   ENSG00000155622.6        XAGE2                               NaN   \n",
       "\n",
       "     Avg of relative sex predictivity (*100)  #Tissues predictivity  \\\n",
       "0                                  63.439145                   44.0   \n",
       "1                                  23.601408                   43.0   \n",
       "2                                   3.405867                   28.0   \n",
       "3                                   1.681771                   34.0   \n",
       "4                                   2.083242                   21.0   \n",
       "..                                       ...                    ...   \n",
       "526                                      NaN                    NaN   \n",
       "527                                      NaN                    NaN   \n",
       "528                                      NaN                    NaN   \n",
       "529                                      NaN                    NaN   \n",
       "530                                      NaN                    NaN   \n",
       "\n",
       "     Female-biased  Male-biased  Female-biased (median)  Male-biased (median)  \\\n",
       "0               44            0                   9.514                   NaN   \n",
       "1                1            0                   7.480                   NaN   \n",
       "2               44            0                   0.636                   NaN   \n",
       "3               44            0                   0.581                   NaN   \n",
       "4               44            0                   0.485                   NaN   \n",
       "..             ...          ...                     ...                   ...   \n",
       "526              0            5                     NaN                -0.014   \n",
       "527              0            1                     NaN                -0.641   \n",
       "528              0            3                     NaN                -0.073   \n",
       "529              1            0                   0.381                   NaN   \n",
       "530              2            1                   0.395                -0.357   \n",
       "\n",
       "     Reported Escapee?  Gene-bias  \n",
       "0                    1        3.0  \n",
       "1                    1        3.0  \n",
       "2                    1        3.0  \n",
       "3                    1        3.0  \n",
       "4                    1        3.0  \n",
       "..                 ...        ...  \n",
       "526                  0        2.0  \n",
       "527                  0        2.0  \n",
       "528                  0        2.0  \n",
       "529                  1        3.0  \n",
       "530                  0        3.0  \n",
       "\n",
       "[531 rows x 11 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escape_genes_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drugs_ids_check = [\"sirolimus\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                           | 0/95 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idarubicin\n",
      "37\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  1%|▊                                                                                  | 1/95 [00:14<23:16, 14.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rucaparib\n",
      "38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  2%|█▋                                                                                 | 2/95 [00:30<23:19, 15.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "afatinib\n",
      "15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  3%|██▌                                                                                | 3/95 [00:36<18:57, 12.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "celecoxib\n",
      "13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  4%|███▍                                                                               | 4/95 [00:41<15:33, 10.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exemestane\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  5%|████▎                                                                              | 5/95 [00:46<12:47,  8.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mercaptopurine\n",
      "5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  6%|█████▏                                                                             | 6/95 [00:48<09:46,  6.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cyclophosphamide\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  7%|██████                                                                             | 7/95 [00:50<07:51,  5.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decitabine\n",
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  8%|██████▉                                                                            | 8/95 [00:55<07:35,  5.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temsirolimus\n",
      "38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  9%|███████▊                                                                           | 9/95 [01:11<11:56,  8.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "goserelin\n",
      "7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 11%|████████▋                                                                         | 10/95 [01:14<09:30,  6.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vemurafenib\n",
      "48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 12%|█████████▍                                                                        | 11/95 [01:34<14:57, 10.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pirfenidone\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 13%|██████████▎                                                                       | 12/95 [01:38<12:18,  8.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nilutamide\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 14%|███████████▏                                                                      | 13/95 [01:43<10:24,  7.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tivozanib\n",
      "43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 15%|████████████                                                                      | 14/95 [02:02<14:41, 10.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "axitinib\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 16%|████████████▉                                                                     | 15/95 [02:06<12:03,  9.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "procarbazine\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 17%|█████████████▊                                                                    | 16/95 [02:11<10:11,  7.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "formestane\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 18%|██████████████▋                                                                   | 17/95 [02:16<08:54,  6.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "sorafenib\n",
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      "crizotinib\n",
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      "midostaurin\n",
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      "cytarabine\n",
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      "flutamide\n",
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      "temozolomide\n",
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      "busulfan\n",
      "6\n"
     ]
    },
    {
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      "\r",
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      "tamoxifen\n",
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    },
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      "\r",
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      "topotecan\n",
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    {
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      "\r",
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      "lenalidomide\n",
      "43\n"
     ]
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    {
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      "\r",
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      "gemcitabine\n",
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    },
    {
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      "\r",
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     "text": [
      "vincristine\n",
      "10\n"
     ]
    },
    {
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      "\r",
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      "floxuridine\n",
      "10\n"
     ]
    },
    {
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      "gefitinib\n",
      "17\n"
     ]
    },
    {
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      "imatinib\n",
      "17\n"
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    {
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      "leflunomide\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
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     "text": [
      "vorinostat\n",
      "48\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "streptozotocin\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "homoharringtonine\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 98%|████████████████████████████████████████████████████████████████████████████████▎ | 93/95 [13:45<00:19,  9.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-aminolevulinic-acid\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "medroxyprogesterone\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 95/95 [13:51<00:00,  8.76s/it]\n"
     ]
    }
   ],
   "source": [
    "signatures_per_perturbagen = []\n",
    "genes_significant_per_perturbagen = []\n",
    "p_values_per_tissue_perturbagen = []\n",
    "df = pd.DataFrame(columns=[\"Signature\",\"Perturbagen_name\", \"P-value\" ])\n",
    "overlap_genes = []\n",
    "\n",
    "for drug in tqdm(drugs_ids):\n",
    "    print(drug)\n",
    "    sig_pertu_ids = sig_cell_info_filter_site[\"sig_id\"][sig_cell_info_filter_site[\"pert_iname\"] == drug]\n",
    "    sig_pertu_ids = sig_pertu_ids.drop_duplicates()\n",
    "    print (len(sig_pertu_ids))\n",
    "    signatures_per_perturbagen.append(sig_pertu_ids)\n",
    "    \n",
    "    for num_sig, sig in enumerate(sig_pertu_ids): \n",
    "        #print (num_sig, sig)\n",
    "        genes_significant = perturbagen_significant_genes(sig,drug)\n",
    "        gene_info = pd.read_csv(\"./GSE92742_Broad_LINCS_gene_info.txt\", sep=\"\\t\", dtype=str)\n",
    "        genes_sig_per_pertur = genes_significant.merge(gene_info, left_on=\"rid\", right_on=\"pr_gene_id\")\n",
    "        genes_sig_per_pertur = genes_sig_per_pertur[[\"perturbagen_name\",\"Signature_id\",\"pr_gene_id\",\"pr_gene_symbol\"]]\n",
    "        genes_significant_per_perturbagen.append(genes_sig_per_pertur)\n",
    "\n",
    "        \n",
    "        overlap = genes_sig_per_pertur.merge(escape_genes_filter,left_on= \"pr_gene_symbol\",right_on=\"HUGO_gene_id\")\n",
    "        overlap = overlap[[\"perturbagen_name\",\"Signature_id\",\"HUGO_gene_id\",\"Gene-bias\"]]\n",
    "        overlap_genes.append(overlap)\n",
    "        df_all = pd.concat(overlap_genes)\n",
    "\n",
    "        # parameters\n",
    "        population_total = 20000 \n",
    "        num_genes_change_expression_perturbagen = len(genes_sig_per_pertur)  # Number of genes clue significant\n",
    "        num_genes_escap = len(escape_genes_filter) # GTEX tissue type n-genes\n",
    "        overlap_len = len(overlap)  #overlap\n",
    "        #test \n",
    "        #resultado = hypergeom.sf(overlap - 1, population_total, num_genes_change_expression_perturbagen, num_genes_gtex_tissue)\n",
    "        result = hypergeom.sf(overlap_len - 1, population_total,num_genes_escap,num_genes_change_expression_perturbagen)\n",
    "        p_values_per_tissue_perturbagen.append(result)\n",
    "        #print(\"P-value:\", resultado, tissue, drug)\n",
    "        row = pd.DataFrame({\"P-value\": [result], \"Perturbagen_name\": [drug],\"Signature\":[sig]})\n",
    "        df = pd.concat([df, row])\n",
    "        \n",
    "    \n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.899871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.012220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC006_A673_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.442725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC006_AGS_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.855082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC006_CL34_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.831179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC020_A549_6H:BRD-K82216340-001-19-7:10</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC020_HA1E_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC020_HCC515_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>0.580988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC020_HT29_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC020_MCF7_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1663 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Signature     Perturbagen_name   P-value\n",
       "0     CPC011_A375_6H:BRD-K69650333-003-11-6:10           idarubicin  0.899871\n",
       "0     CPC011_A549_6H:BRD-K69650333-003-11-6:10           idarubicin  0.012220\n",
       "0   CPC006_A673_6H:BRD-A71390734-001-01-7:0.08           idarubicin  0.442725\n",
       "0    CPC006_AGS_6H:BRD-A71390734-001-01-7:0.08           idarubicin  0.855082\n",
       "0   CPC006_CL34_6H:BRD-A71390734-001-01-7:0.08           idarubicin  0.831179\n",
       "..                                         ...                  ...       ...\n",
       "0     CPC020_A549_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone  0.443174\n",
       "0     CPC020_HA1E_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone  1.000000\n",
       "0   CPC020_HCC515_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone  0.580988\n",
       "0     CPC020_HT29_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone  1.000000\n",
       "0     CPC020_MCF7_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone  1.000000\n",
       "\n",
       "[1663 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perturbagen_name</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Gene-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>SUV39H1</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>idarubicin</td>\n",
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       "      <td>PHKA1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>IL13RA1</td>\n",
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       "      <td>SSR4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>TSPAN7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>CPC020_A549_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>TSC22D3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>CPC020_A549_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>MSN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>CPC020_A549_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>SLC25A6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>TSC22D3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>TIMP1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23921 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       perturbagen_name                                Signature_id  \\\n",
       "0            idarubicin    CPC011_A375_6H:BRD-K69650333-003-11-6:10   \n",
       "1            idarubicin    CPC011_A375_6H:BRD-K69650333-003-11-6:10   \n",
       "2            idarubicin    CPC011_A375_6H:BRD-K69650333-003-11-6:10   \n",
       "3            idarubicin    CPC011_A375_6H:BRD-K69650333-003-11-6:10   \n",
       "4            idarubicin    CPC011_A375_6H:BRD-K69650333-003-11-6:10   \n",
       "..                  ...                                         ...   \n",
       "0   medroxyprogesterone    CPC020_A549_6H:BRD-K82216340-001-19-7:10   \n",
       "1   medroxyprogesterone    CPC020_A549_6H:BRD-K82216340-001-19-7:10   \n",
       "2   medroxyprogesterone    CPC020_A549_6H:BRD-K82216340-001-19-7:10   \n",
       "0   medroxyprogesterone  CPC020_HCC515_6H:BRD-K82216340-001-19-7:10   \n",
       "1   medroxyprogesterone  CPC020_HCC515_6H:BRD-K82216340-001-19-7:10   \n",
       "\n",
       "   HUGO_gene_id  Gene-bias  \n",
       "0       SUV39H1        3.0  \n",
       "1         PHKA1        3.0  \n",
       "2       IL13RA1        3.0  \n",
       "3          SSR4        3.0  \n",
       "4        TSPAN7        3.0  \n",
       "..          ...        ...  \n",
       "0       TSC22D3        3.0  \n",
       "1           MSN        2.0  \n",
       "2       SLC25A6        2.0  \n",
       "0       TSC22D3        3.0  \n",
       "1         TIMP1        2.0  \n",
       "\n",
       "[23921 rows x 4 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "info_cell = sig_cell_info[[\"sig_id\",\"Signature_id\",\"cell_id\"]]\n",
    "info_cell = info_cell.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "      <th>sig_id</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>cell_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC018_A375_6H:BRD-K06817181-001-01-5:10</td>\n",
       "      <td>1,2,3,4,5,6-hexabromocyclohexane_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPC017_A375_6H:BRD-K74430258-001-01-2:10</td>\n",
       "      <td>1,2-dichlorobenzene_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPC010_A375_6H:BRD-K32795028-001-10-9:10</td>\n",
       "      <td>1-benzylimidazole_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPC018_A375_6H:BRD-A80928489-001-01-0:10</td>\n",
       "      <td>1-monopalmitin_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC018_A375_6H:BRD-K31491153-001-01-2:10</td>\n",
       "      <td>1-phenylbiguanide_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258599</th>\n",
       "      <td>KDB008_SKL_96H:TRCN0000020197:-666</td>\n",
       "      <td>ZNF589_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258600</th>\n",
       "      <td>KDB002_SKL_96H:TRCN0000236645:-666</td>\n",
       "      <td>ZNF629_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258601</th>\n",
       "      <td>KDB001_SKL_96H:TRCN0000150938:-666</td>\n",
       "      <td>ZW10_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258602</th>\n",
       "      <td>KDB007_SKL_96H:TRCN0000072242:-666</td>\n",
       "      <td>lacZ_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258603</th>\n",
       "      <td>KDB008_SKL_96H:TRCN0000145620:-666</td>\n",
       "      <td>pgw_SKL_muscle</td>\n",
       "      <td>SKL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173142 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          sig_id  \\\n",
       "0       CPC018_A375_6H:BRD-K06817181-001-01-5:10   \n",
       "1       CPC017_A375_6H:BRD-K74430258-001-01-2:10   \n",
       "2       CPC010_A375_6H:BRD-K32795028-001-10-9:10   \n",
       "3       CPC018_A375_6H:BRD-A80928489-001-01-0:10   \n",
       "4       CPC018_A375_6H:BRD-K31491153-001-01-2:10   \n",
       "...                                          ...   \n",
       "258599        KDB008_SKL_96H:TRCN0000020197:-666   \n",
       "258600        KDB002_SKL_96H:TRCN0000236645:-666   \n",
       "258601        KDB001_SKL_96H:TRCN0000150938:-666   \n",
       "258602        KDB007_SKL_96H:TRCN0000072242:-666   \n",
       "258603        KDB008_SKL_96H:TRCN0000145620:-666   \n",
       "\n",
       "                                      Signature_id cell_id  \n",
       "0       1,2,3,4,5,6-hexabromocyclohexane_A375_skin    A375  \n",
       "1                    1,2-dichlorobenzene_A375_skin    A375  \n",
       "2                      1-benzylimidazole_A375_skin    A375  \n",
       "3                         1-monopalmitin_A375_skin    A375  \n",
       "4                      1-phenylbiguanide_A375_skin    A375  \n",
       "...                                            ...     ...  \n",
       "258599                           ZNF589_SKL_muscle     SKL  \n",
       "258600                           ZNF629_SKL_muscle     SKL  \n",
       "258601                             ZW10_SKL_muscle     SKL  \n",
       "258602                             lacZ_SKL_muscle     SKL  \n",
       "258603                              pgw_SKL_muscle     SKL  \n",
       "\n",
       "[173142 rows x 3 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex_ = df.merge(info_cell, left_on=[\"Signature\"],right_on=[\"sig_id\"])\n",
    "clue_tiss_sig_gtex_['Significant'] = clue_tiss_sig_gtex_.apply(lambda row: 'No' if row['P-value'] > 0.05 else 'Yes', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
       "      <th>P-value</th>\n",
       "      <th>sig_id</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>Significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.899871</td>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin_A375_skin</td>\n",
       "      <td>A375</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.012220</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>A549</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPC006_A673_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.442725</td>\n",
       "      <td>CPC006_A673_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin_A673_bone</td>\n",
       "      <td>A673</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPC006_AGS_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.855082</td>\n",
       "      <td>CPC006_AGS_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin_AGS_stomach</td>\n",
       "      <td>AGS</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC006_CL34_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.831179</td>\n",
       "      <td>CPC006_CL34_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin_CL34_large intestine</td>\n",
       "      <td>CL34</td>\n",
       "      <td>No</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>1658</th>\n",
       "      <td>CPC020_A549_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>0.443174</td>\n",
       "      <td>CPC020_A549_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone_A549_lung</td>\n",
       "      <td>A549</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>CPC020_HA1E_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>CPC020_HA1E_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone_HA1E_kidney</td>\n",
       "      <td>HA1E</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>CPC020_HCC515_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>0.580988</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>CPC020_HT29_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>CPC020_HT29_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone_HT29_large intestine</td>\n",
       "      <td>HT29</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>CPC020_MCF7_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>CPC020_MCF7_6H:BRD-K82216340-001-19-7:10</td>\n",
       "      <td>medroxyprogesterone_MCF7_breast</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1663 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Signature     Perturbagen_name  \\\n",
       "0       CPC011_A375_6H:BRD-K69650333-003-11-6:10           idarubicin   \n",
       "1       CPC011_A549_6H:BRD-K69650333-003-11-6:10           idarubicin   \n",
       "2     CPC006_A673_6H:BRD-A71390734-001-01-7:0.08           idarubicin   \n",
       "3      CPC006_AGS_6H:BRD-A71390734-001-01-7:0.08           idarubicin   \n",
       "4     CPC006_CL34_6H:BRD-A71390734-001-01-7:0.08           idarubicin   \n",
       "...                                          ...                  ...   \n",
       "1658    CPC020_A549_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone   \n",
       "1659    CPC020_HA1E_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone   \n",
       "1660  CPC020_HCC515_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone   \n",
       "1661    CPC020_HT29_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone   \n",
       "1662    CPC020_MCF7_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone   \n",
       "\n",
       "       P-value                                      sig_id  \\\n",
       "0     0.899871    CPC011_A375_6H:BRD-K69650333-003-11-6:10   \n",
       "1     0.012220    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "2     0.442725  CPC006_A673_6H:BRD-A71390734-001-01-7:0.08   \n",
       "3     0.855082   CPC006_AGS_6H:BRD-A71390734-001-01-7:0.08   \n",
       "4     0.831179  CPC006_CL34_6H:BRD-A71390734-001-01-7:0.08   \n",
       "...        ...                                         ...   \n",
       "1658  0.443174    CPC020_A549_6H:BRD-K82216340-001-19-7:10   \n",
       "1659  1.000000    CPC020_HA1E_6H:BRD-K82216340-001-19-7:10   \n",
       "1660  0.580988  CPC020_HCC515_6H:BRD-K82216340-001-19-7:10   \n",
       "1661  1.000000    CPC020_HT29_6H:BRD-K82216340-001-19-7:10   \n",
       "1662  1.000000    CPC020_MCF7_6H:BRD-K82216340-001-19-7:10   \n",
       "\n",
       "                                  Signature_id cell_id Significant  \n",
       "0                         idarubicin_A375_skin    A375          No  \n",
       "1                         idarubicin_A549_lung    A549         Yes  \n",
       "2                         idarubicin_A673_bone    A673          No  \n",
       "3                       idarubicin_AGS_stomach     AGS          No  \n",
       "4              idarubicin_CL34_large intestine    CL34          No  \n",
       "...                                        ...     ...         ...  \n",
       "1658             medroxyprogesterone_A549_lung    A549          No  \n",
       "1659           medroxyprogesterone_HA1E_kidney    HA1E          No  \n",
       "1660           medroxyprogesterone_HCC515_lung  HCC515          No  \n",
       "1661  medroxyprogesterone_HT29_large intestine    HT29          No  \n",
       "1662           medroxyprogesterone_MCF7_breast    MCF7          No  \n",
       "\n",
       "[1663 rows x 7 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clue_tiss_sig_gtex_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sig = clue_tiss_sig_gtex_[clue_tiss_sig_gtex_[\"Significant\"]==\"Yes\"]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
       "      <th>P-value</th>\n",
       "      <th>sig_id</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>Significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.012220</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>A549</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CPC006_T3M10_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.026053</td>\n",
       "      <td>CPC006_T3M10_6H:BRD-A71390734-001-01-7:0.08</td>\n",
       "      <td>idarubicin_T3M10_lung</td>\n",
       "      <td>T3M10</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CPC006_CORL23_6H:BRD-K88560311-011-02-2:10</td>\n",
       "      <td>rucaparib</td>\n",
       "      <td>0.017453</td>\n",
       "      <td>CPC006_CORL23_6H:BRD-K88560311-011-02-2:10</td>\n",
       "      <td>rucaparib_CORL23_lung</td>\n",
       "      <td>CORL23</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>CPC006_NOMO1_6H:BRD-K88560311-011-02-2:10</td>\n",
       "      <td>rucaparib</td>\n",
       "      <td>0.027374</td>\n",
       "      <td>CPC006_NOMO1_6H:BRD-K88560311-011-02-2:10</td>\n",
       "      <td>rucaparib_NOMO1_haematopoietic and lymphoid ti...</td>\n",
       "      <td>NOMO1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>LJP002_MCF10A_24H:BRD-A58767537-001-01-2:10</td>\n",
       "      <td>afatinib</td>\n",
       "      <td>0.042561</td>\n",
       "      <td>LJP002_MCF10A_24H:BRD-A58767537-001-01-2:10</td>\n",
       "      <td>afatinib_MCF10A_breast</td>\n",
       "      <td>MCF10A</td>\n",
       "      <td>Yes</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>1469</th>\n",
       "      <td>CPC006_SKLU1_6H:BRD-A17883755-001-02-0:177.6</td>\n",
       "      <td>lenalidomide</td>\n",
       "      <td>0.048612</td>\n",
       "      <td>CPC006_SKLU1_6H:BRD-A17883755-001-02-0:177.6</td>\n",
       "      <td>lenalidomide_SKLU1_lung</td>\n",
       "      <td>SKLU1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>LJP002_BT20_6H:BRD-K64052750-001-08-4:10</td>\n",
       "      <td>gefitinib</td>\n",
       "      <td>0.043654</td>\n",
       "      <td>LJP002_BT20_6H:BRD-K64052750-001-08-4:10</td>\n",
       "      <td>gefitinib_BT20_breast</td>\n",
       "      <td>BT20</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572</th>\n",
       "      <td>NMH002_FIBRNPC_6H:BRD-K92723993-066-14-4:10</td>\n",
       "      <td>imatinib</td>\n",
       "      <td>0.033941</td>\n",
       "      <td>NMH002_FIBRNPC_6H:BRD-K92723993-066-14-4:10</td>\n",
       "      <td>imatinib_FIBRNPC_skin</td>\n",
       "      <td>FIBRNPC</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>HDAC002_MCF7_24H:BRD-K81418486-001-24-4:10</td>\n",
       "      <td>vorinostat</td>\n",
       "      <td>0.028829</td>\n",
       "      <td>HDAC002_MCF7_24H:BRD-K81418486-001-24-4:10</td>\n",
       "      <td>vorinostat_MCF7_breast</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1654</th>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>0.027782</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Signature       Perturbagen_name  \\\n",
       "1         CPC011_A549_6H:BRD-K69650333-003-11-6:10             idarubicin   \n",
       "33     CPC006_T3M10_6H:BRD-A71390734-001-01-7:0.08             idarubicin   \n",
       "42      CPC006_CORL23_6H:BRD-K88560311-011-02-2:10              rucaparib   \n",
       "59       CPC006_NOMO1_6H:BRD-K88560311-011-02-2:10              rucaparib   \n",
       "83     LJP002_MCF10A_24H:BRD-A58767537-001-01-2:10               afatinib   \n",
       "...                                            ...                    ...   \n",
       "1469  CPC006_SKLU1_6H:BRD-A17883755-001-02-0:177.6           lenalidomide   \n",
       "1543      LJP002_BT20_6H:BRD-K64052750-001-08-4:10              gefitinib   \n",
       "1572   NMH002_FIBRNPC_6H:BRD-K92723993-066-14-4:10               imatinib   \n",
       "1604    HDAC002_MCF7_24H:BRD-K81418486-001-24-4:10             vorinostat   \n",
       "1654    CPC020_HCC515_6H:BRD-K57631554-001-07-4:10  5-aminolevulinic-acid   \n",
       "\n",
       "       P-value                                        sig_id  \\\n",
       "1     0.012220      CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "33    0.026053   CPC006_T3M10_6H:BRD-A71390734-001-01-7:0.08   \n",
       "42    0.017453    CPC006_CORL23_6H:BRD-K88560311-011-02-2:10   \n",
       "59    0.027374     CPC006_NOMO1_6H:BRD-K88560311-011-02-2:10   \n",
       "83    0.042561   LJP002_MCF10A_24H:BRD-A58767537-001-01-2:10   \n",
       "...        ...                                           ...   \n",
       "1469  0.048612  CPC006_SKLU1_6H:BRD-A17883755-001-02-0:177.6   \n",
       "1543  0.043654      LJP002_BT20_6H:BRD-K64052750-001-08-4:10   \n",
       "1572  0.033941   NMH002_FIBRNPC_6H:BRD-K92723993-066-14-4:10   \n",
       "1604  0.028829    HDAC002_MCF7_24H:BRD-K81418486-001-24-4:10   \n",
       "1654  0.027782    CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "\n",
       "                                           Signature_id  cell_id Significant  \n",
       "1                                  idarubicin_A549_lung     A549         Yes  \n",
       "33                                idarubicin_T3M10_lung    T3M10         Yes  \n",
       "42                                rucaparib_CORL23_lung   CORL23         Yes  \n",
       "59    rucaparib_NOMO1_haematopoietic and lymphoid ti...    NOMO1         Yes  \n",
       "83                               afatinib_MCF10A_breast   MCF10A         Yes  \n",
       "...                                                 ...      ...         ...  \n",
       "1469                            lenalidomide_SKLU1_lung    SKLU1         Yes  \n",
       "1543                              gefitinib_BT20_breast     BT20         Yes  \n",
       "1572                              imatinib_FIBRNPC_skin  FIBRNPC         Yes  \n",
       "1604                             vorinostat_MCF7_breast     MCF7         Yes  \n",
       "1654                  5-aminolevulinic-acid_HCC515_lung   HCC515         Yes  \n",
       "\n",
       "[86 rows x 7 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genes_tiss_sb = data_sig.merge(df_all, left_on= [\"sig_id\",\"Perturbagen_name\"], right_on=[\"Signature_id\",\"perturbagen_name\"], how =\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
       "      <th>P-value</th>\n",
       "      <th>sig_id</th>\n",
       "      <th>Signature_id_x</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>Significant</th>\n",
       "      <th>perturbagen_name</th>\n",
       "      <th>Signature_id_y</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Gene-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.012220</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>A549</td>\n",
       "      <td>Yes</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>PRKX</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.012220</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>A549</td>\n",
       "      <td>Yes</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>FOXO4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.012220</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>A549</td>\n",
       "      <td>Yes</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>DYNLT3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.012220</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>A549</td>\n",
       "      <td>Yes</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>PGK1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>0.012220</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>A549</td>\n",
       "      <td>Yes</td>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>BCAP31</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4106</th>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>0.027782</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>NXF3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4107</th>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>0.027782</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>GDPD2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4108</th>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>0.027782</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>PRRG3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4109</th>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>0.027782</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>SAGE1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4110</th>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>0.027782</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>SYN1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4111 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Signature       Perturbagen_name  \\\n",
       "0       CPC011_A549_6H:BRD-K69650333-003-11-6:10             idarubicin   \n",
       "1       CPC011_A549_6H:BRD-K69650333-003-11-6:10             idarubicin   \n",
       "2       CPC011_A549_6H:BRD-K69650333-003-11-6:10             idarubicin   \n",
       "3       CPC011_A549_6H:BRD-K69650333-003-11-6:10             idarubicin   \n",
       "4       CPC011_A549_6H:BRD-K69650333-003-11-6:10             idarubicin   \n",
       "...                                          ...                    ...   \n",
       "4106  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10  5-aminolevulinic-acid   \n",
       "4107  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10  5-aminolevulinic-acid   \n",
       "4108  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10  5-aminolevulinic-acid   \n",
       "4109  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10  5-aminolevulinic-acid   \n",
       "4110  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10  5-aminolevulinic-acid   \n",
       "\n",
       "       P-value                                      sig_id  \\\n",
       "0     0.012220    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "1     0.012220    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "2     0.012220    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "3     0.012220    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "4     0.012220    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "...        ...                                         ...   \n",
       "4106  0.027782  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4107  0.027782  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4108  0.027782  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4109  0.027782  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4110  0.027782  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "\n",
       "                         Signature_id_x cell_id Significant  \\\n",
       "0                  idarubicin_A549_lung    A549         Yes   \n",
       "1                  idarubicin_A549_lung    A549         Yes   \n",
       "2                  idarubicin_A549_lung    A549         Yes   \n",
       "3                  idarubicin_A549_lung    A549         Yes   \n",
       "4                  idarubicin_A549_lung    A549         Yes   \n",
       "...                                 ...     ...         ...   \n",
       "4106  5-aminolevulinic-acid_HCC515_lung  HCC515         Yes   \n",
       "4107  5-aminolevulinic-acid_HCC515_lung  HCC515         Yes   \n",
       "4108  5-aminolevulinic-acid_HCC515_lung  HCC515         Yes   \n",
       "4109  5-aminolevulinic-acid_HCC515_lung  HCC515         Yes   \n",
       "4110  5-aminolevulinic-acid_HCC515_lung  HCC515         Yes   \n",
       "\n",
       "           perturbagen_name                              Signature_id_y  \\\n",
       "0                idarubicin    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "1                idarubicin    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "2                idarubicin    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "3                idarubicin    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "4                idarubicin    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "...                     ...                                         ...   \n",
       "4106  5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4107  5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4108  5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4109  5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4110  5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "\n",
       "     HUGO_gene_id  Gene-bias  \n",
       "0            PRKX        3.0  \n",
       "1           FOXO4        1.0  \n",
       "2          DYNLT3        2.0  \n",
       "3            PGK1        3.0  \n",
       "4          BCAP31        3.0  \n",
       "...           ...        ...  \n",
       "4106         NXF3        2.0  \n",
       "4107        GDPD2        2.0  \n",
       "4108        PRRG3        3.0  \n",
       "4109        SAGE1        3.0  \n",
       "4110         SYN1        1.0  \n",
       "\n",
       "[4111 rows x 11 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genes_tiss_sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genes_tiss_sb_fil = df_genes_tiss_sb[[\"Signature_id_x\",\"Gene-bias\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Signature_id_x</th>\n",
       "      <th>Gene-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>idarubicin_A549_lung</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4106</th>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4107</th>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4108</th>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4109</th>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4110</th>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4111 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Signature_id_x  Gene-bias\n",
       "0                  idarubicin_A549_lung        3.0\n",
       "1                  idarubicin_A549_lung        1.0\n",
       "2                  idarubicin_A549_lung        2.0\n",
       "3                  idarubicin_A549_lung        3.0\n",
       "4                  idarubicin_A549_lung        3.0\n",
       "...                                 ...        ...\n",
       "4106  5-aminolevulinic-acid_HCC515_lung        2.0\n",
       "4107  5-aminolevulinic-acid_HCC515_lung        2.0\n",
       "4108  5-aminolevulinic-acid_HCC515_lung        3.0\n",
       "4109  5-aminolevulinic-acid_HCC515_lung        3.0\n",
       "4110  5-aminolevulinic-acid_HCC515_lung        1.0\n",
       "\n",
       "[4111 rows x 2 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genes_tiss_sb_fil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agrupar por ID y obtener la categoría con el mayor conteo para cada ID\n",
    "max_category_by_id = df_genes_tiss_sb_fil.groupby('Signature_id_x')['Gene-bias'].apply(lambda x: x.value_counts().idxmax())\n",
    "\n",
    "# Convertir la serie resultante en un DataFrame\n",
    "result_df = max_category_by_id.reset_index(name='Gene-bias')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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",
       "      <th>Signature_id_x</th>\n",
       "      <th>Gene-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5-aminolevulinic-acid_HCC515_lung</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>afatinib_MCF10A_breast</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aminoglutethimide_HCC515_lung</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anagrelide_A549_lung</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>axitinib_NEU_Unknown</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>topotecan_NPC_central nervous system</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>tretinoin_A549_lung</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>vemurafenib_HT29_large intestine</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>vemurafenib_SKMEL1_skin</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>vorinostat_MCF7_breast</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Signature_id_x  Gene-bias\n",
       "0      5-aminolevulinic-acid_HCC515_lung        3.0\n",
       "1                 afatinib_MCF10A_breast        3.0\n",
       "2          aminoglutethimide_HCC515_lung        2.0\n",
       "3                   anagrelide_A549_lung        3.0\n",
       "4                   axitinib_NEU_Unknown        3.0\n",
       "..                                   ...        ...\n",
       "81  topotecan_NPC_central nervous system        3.0\n",
       "82                   tretinoin_A549_lung        3.0\n",
       "83      vemurafenib_HT29_large intestine        3.0\n",
       "84               vemurafenib_SKMEL1_skin        2.0\n",
       "85                vorinostat_MCF7_breast        3.0\n",
       "\n",
       "[86 rows x 2 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the colors for each category\n",
    "category_colors = {\n",
    "    1: 'green',            # No direction\n",
    "    2: 'blue',           # Male sex-biased\n",
    "    3: 'red'               # Female sex-biased\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "\n",
    "# Plot the scatter plot with colors based on category\n",
    "scatter = plt.scatter(result_df['Gene-bias'], result_df['Signature_id_x'], c=result_df['Gene-bias'].map(category_colors), s=100, zorder=2)\n",
    "\n",
    "# Set labels and x-axis ticks\n",
    "#plt.xlabel('Sex-bias', fontsize=12)\n",
    "plt.ylabel('Cancer drug + Signature', fontsize=12)\n",
    "plt.xticks(range(4), [\" \",'No direction', 'Male sex-biased', 'Female sex-biased'])\n",
    "\n",
    "# Add grid\n",
    "plt.grid(color='grey', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# Customize font for axes labels\n",
    "plt.setp(ax.get_xticklabels(), fontsize=12, color=\"black\", fontweight=\"normal\")\n",
    "plt.setp(ax.get_yticklabels(), fontsize=12, color=\"black\", fontweight=\"normal\")\n",
    "\n",
    "# Save the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./images_genes_overlap/enrichment_x_linked.svg\")\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}