{
 "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": 5,
   "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": 5,
     "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_drugs = pd.read_csv(\"drugs_cancer_clue.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_ids = cancer_drugs[[\"pert_iname\"]].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_ids = drugs_ids.values.ravel().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gtex_tissues = data_gtex[\"tissue\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Sex-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AC005082.12</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AC006946.16</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AC007278.2</td>\n",
       "      <td>No-dir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AC068279.3</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ACADL</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 HUGO_gene_id Sex-bias\n",
       "0           0  AC005082.12   Female\n",
       "1           1  AC006946.16     Male\n",
       "2           2   AC007278.2   No-dir\n",
       "3           3   AC068279.3   Female\n",
       "4           4        ACADL     Male"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top100 = pd.read_excel((\"df_top100_sexbias.xlsx\"),engine='openpyxl')\n",
    "top100.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(top100[\"HUGO_gene_id\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drugs_ids_check = [\"sirolimus\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 [00:16<25:25, 16.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rucaparib\n",
      "38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  2%|█▋                                                                                 | 2/95 [00:32<25:22, 16.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "afatinib\n",
      "15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  3%|██▌                                                                                | 3/95 [00:39<20:36, 13.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "celecoxib\n",
      "13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  4%|███▍                                                                               | 4/95 [00:45<16:51, 11.12s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exemestane\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  5%|████▎                                                                              | 5/95 [00:50<13:53,  9.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mercaptopurine\n",
      "5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  6%|█████▏                                                                             | 6/95 [00:52<10:34,  7.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cyclophosphamide\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  7%|██████                                                                             | 7/95 [00:55<08:29,  5.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decitabine\n",
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  8%|██████▉                                                                            | 8/95 [01:00<08:13,  5.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temsirolimus\n",
      "38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  9%|███████▊                                                                           | 9/95 [01:17<13:05,  9.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "goserelin\n",
      "7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 11%|████████▋                                                                         | 10/95 [01:20<10:23,  7.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vemurafenib\n",
      "48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 12%|█████████▍                                                                        | 11/95 [01:42<16:15, 11.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pirfenidone\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 13%|██████████▎                                                                       | 12/95 [01:47<13:18,  9.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nilutamide\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 14%|███████████▏                                                                      | 13/95 [01:52<11:13,  8.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tivozanib\n",
      "43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 15%|████████████                                                                      | 14/95 [02:11<15:28, 11.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "axitinib\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 16%|████████████▉                                                                     | 15/95 [02:16<12:42,  9.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "procarbazine\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 17%|█████████████▊                                                                    | 16/95 [02:21<10:48,  8.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "formestane\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 18%|██████████████▋                                                                   | 17/95 [02:26<09:33,  7.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorafenib\n",
      "18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 19%|███████████████▌                                                                  | 18/95 [02:35<09:58,  7.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fulvestrant\n",
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 20%|████████████████▍                                                                 | 19/95 [02:40<08:56,  7.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anastrozole\n",
      "7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 21%|█████████████████▎                                                                | 20/95 [02:44<07:21,  5.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dasatinib\n",
      "15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 22%|██████████████████▏                                                               | 21/95 [02:51<07:40,  6.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "letrozole\n",
      "5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 23%|██████████████████▉                                                               | 22/95 [02:53<06:08,  5.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "altretamine\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 24%|███████████████████▊                                                              | 23/95 [02:58<06:04,  5.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azacitidine\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 25%|████████████████████▋                                                             | 24/95 [03:01<05:11,  4.38s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azathioprine\n",
      "5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 26%|█████████████████████▌                                                            | 25/95 [03:03<04:22,  3.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dactinomycin\n",
      "10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 27%|██████████████████████▍                                                           | 26/95 [03:08<04:39,  4.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vinblastine\n",
      "13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 28%|███████████████████████▎                                                          | 27/95 [03:14<05:20,  4.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cladribine\n",
      "7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 29%|████████████████████████▏                                                         | 28/95 [03:17<04:46,  4.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "toremifene\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 31%|█████████████████████████                                                         | 29/95 [03:22<04:55,  4.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mitotane\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 32%|█████████████████████████▉                                                        | 30/95 [03:27<05:05,  4.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clofarabine\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 33%|██████████████████████████▊                                                       | 31/95 [03:33<05:10,  4.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "capecitabine\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 34%|███████████████████████████▌                                                      | 32/95 [03:38<05:17,  5.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "daunorubicin\n",
      "17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 35%|████████████████████████████▍                                                     | 33/95 [03:46<06:11,  6.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ruxolitinib\n",
      "17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 36%|█████████████████████████████▎                                                    | 34/95 [03:55<06:45,  6.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neratinib\n",
      "44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 37%|██████████████████████████████▏                                                   | 35/95 [04:15<10:49, 10.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "palbociclib\n",
      "17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 38%|███████████████████████████████                                                   | 36/95 [04:23<09:47,  9.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "everolimus\n",
      "12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 39%|███████████████████████████████▉                                                  | 37/95 [04:29<08:24,  8.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tegafur\n",
      "5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 40%|████████████████████████████████▊                                                 | 38/95 [04:31<06:27,  6.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sonidegib\n",
      "38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 41%|█████████████████████████████████▋                                                | 39/95 [04:50<09:34, 10.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ixazomib\n",
      "40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 42%|██████████████████████████████████▌                                               | 40/95 [05:10<12:04, 13.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tofacitinib\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 43%|███████████████████████████████████▍                                              | 41/95 [05:15<09:45, 10.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raltitrexed\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 44%|████████████████████████████████████▎                                             | 42/95 [05:20<08:04,  9.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vindesine\n",
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 45%|█████████████████████████████████████                                             | 43/95 [05:22<06:02,  6.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abiraterone\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 46%|█████████████████████████████████████▉                                            | 44/95 [05:25<04:53,  5.75s/it]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "nilotinib\n",
      "17\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
      " 47%|██████████████████████████████████████▊                                           | 45/95 [05:33<05:26,  6.52s/it]"
     ]
    },
    {
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     "text": [
      "tacrolimus\n",
      "42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 48%|███████████████████████████████████████▋                                          | 46/95 [05:53<08:38, 10.59s/it]"
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    },
    {
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     "text": [
      "bosutinib\n",
      "13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 49%|████████████████████████████████████████▌                                         | 47/95 [05:59<07:24,  9.27s/it]"
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    {
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      "methotrexate\n",
      "6\n"
     ]
    },
    {
     "name": "stderr",
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      "\r",
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    },
    {
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     "text": [
      "docetaxel\n",
      "9\n"
     ]
    },
    {
     "name": "stderr",
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      "\r",
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    },
    {
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     "text": [
      "tretinoin\n",
      "46\n"
     ]
    },
    {
     "name": "stderr",
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      "\r",
      " 53%|███████████████████████████████████████████▏                                      | 50/95 [06:29<08:20, 11.12s/it]"
     ]
    },
    {
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      "belinostat\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
      " 54%|████████████████████████████████████████████                                      | 51/95 [06:34<06:51,  9.36s/it]"
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    {
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     "text": [
      "bicalutamide\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
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      "\r",
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    {
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    {
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   ],
   "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(top100,left_on= \"pr_gene_symbol\",right_on=\"HUGO_gene_id\")\n",
    "        overlap = overlap[[\"perturbagen_name\",\"Signature_id\",\"HUGO_gene_id\",\"Sex-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(top100) # 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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Signature</th>\n",
       "      <th>Perturbagen_name</th>\n",
       "      <th>P-value</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>0.764110</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>0.976826</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>1.000000</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>1.000000</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>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC020_A549_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_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>1.000000</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  0.764110\n",
       "0     CPC011_A549_6H:BRD-K69650333-003-11-6:10           idarubicin  0.976826\n",
       "0   CPC006_A673_6H:BRD-A71390734-001-01-7:0.08           idarubicin  1.000000\n",
       "0    CPC006_AGS_6H:BRD-A71390734-001-01-7:0.08           idarubicin  1.000000\n",
       "0   CPC006_CL34_6H:BRD-A71390734-001-01-7:0.08           idarubicin  1.000000\n",
       "..                                         ...                  ...       ...\n",
       "0     CPC020_A549_6H:BRD-K82216340-001-19-7:10  medroxyprogesterone  1.000000\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  1.000000\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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perturbagen_name</th>\n",
       "      <th>Signature_id</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Sex-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>WISP2</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>PRSS16</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>PPFIA3</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>SALL1</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A549_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <td>CACNA2D2</td>\n",
       "      <td>Female</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>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>CDKN2A</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>F13A1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>WISP2</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>SHH</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5-aminolevulinic-acid</td>\n",
       "      <td>CPC020_HCC515_6H:BRD-K57631554-001-07-4:10</td>\n",
       "      <td>NLRP2</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2425 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",
       "0              idarubicin    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "1              idarubicin    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "..                    ...                                         ...   \n",
       "0   5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "1   5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "2   5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "3   5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "4   5-aminolevulinic-acid  CPC020_HCC515_6H:BRD-K57631554-001-07-4:10   \n",
       "\n",
       "   HUGO_gene_id Sex-bias  \n",
       "0         WISP2     Male  \n",
       "1        PRSS16   Female  \n",
       "2        PPFIA3   Female  \n",
       "0         SALL1   Female  \n",
       "1      CACNA2D2   Female  \n",
       "..          ...      ...  \n",
       "0        CDKN2A   Female  \n",
       "1         F13A1     Male  \n",
       "2         WISP2     Male  \n",
       "3           SHH   Female  \n",
       "4         NLRP2   Female  \n",
       "\n",
       "[2425 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.764110</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>0.976826</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>No</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>1.000000</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>1.000000</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>1.000000</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>1.000000</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>1.000000</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     0.764110    CPC011_A375_6H:BRD-K69650333-003-11-6:10   \n",
       "1     0.976826    CPC011_A549_6H:BRD-K69650333-003-11-6:10   \n",
       "2     1.000000  CPC006_A673_6H:BRD-A71390734-001-01-7:0.08   \n",
       "3     1.000000   CPC006_AGS_6H:BRD-A71390734-001-01-7:0.08   \n",
       "4     1.000000  CPC006_CL34_6H:BRD-A71390734-001-01-7:0.08   \n",
       "...        ...                                         ...   \n",
       "1658  1.000000    CPC020_A549_6H:BRD-K82216340-001-19-7:10   \n",
       "1659  1.000000    CPC020_HA1E_6H:BRD-K82216340-001-19-7:10   \n",
       "1660  1.000000  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          No  \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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clue_tiss_sig_gtex_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sig = clue_tiss_sig_gtex_[clue_tiss_sig_gtex_[\"Significant\"]==\"Yes\"]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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</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>128</th>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>0.014447</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10</td>\n",
       "      <td>temozolomide</td>\n",
       "      <td>0.039682</td>\n",
       "      <td>CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10</td>\n",
       "      <td>temozolomide_SKLU1_lung</td>\n",
       "      <td>SKLU1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>busulfan</td>\n",
       "      <td>0.031420</td>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>busulfan_MCF7_breast</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Signature Perturbagen_name   P-value  \\\n",
       "128   CPC013_HCC515_6H:BRD-K79254416-001-08-5:10       decitabine  0.014447   \n",
       "1365   CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10     temozolomide  0.039682   \n",
       "1386    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10         busulfan  0.031420   \n",
       "\n",
       "                                          sig_id             Signature_id  \\\n",
       "128   CPC013_HCC515_6H:BRD-K79254416-001-08-5:10   decitabine_HCC515_lung   \n",
       "1365   CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10  temozolomide_SKLU1_lung   \n",
       "1386    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10     busulfan_MCF7_breast   \n",
       "\n",
       "     cell_id Significant  \n",
       "128   HCC515         Yes  \n",
       "1365   SKLU1         Yes  \n",
       "1386    MCF7         Yes  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>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>Sex-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>0.014447</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>HSD11B1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>0.014447</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>SALL1</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>0.014447</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>HOXD11</td>\n",
       "      <td>No-dir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>0.014447</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>PPFIA3</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>0.014447</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>HCC515</td>\n",
       "      <td>Yes</td>\n",
       "      <td>decitabine</td>\n",
       "      <td>CPC013_HCC515_6H:BRD-K79254416-001-08-5:10</td>\n",
       "      <td>CD1E</td>\n",
       "      <td>No-dir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10</td>\n",
       "      <td>temozolomide</td>\n",
       "      <td>0.039682</td>\n",
       "      <td>CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10</td>\n",
       "      <td>temozolomide_SKLU1_lung</td>\n",
       "      <td>SKLU1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>temozolomide</td>\n",
       "      <td>CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10</td>\n",
       "      <td>CDKN2A</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10</td>\n",
       "      <td>temozolomide</td>\n",
       "      <td>0.039682</td>\n",
       "      <td>CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10</td>\n",
       "      <td>temozolomide_SKLU1_lung</td>\n",
       "      <td>SKLU1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>temozolomide</td>\n",
       "      <td>CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10</td>\n",
       "      <td>MYLK3</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>busulfan</td>\n",
       "      <td>0.031420</td>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>busulfan_MCF7_breast</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>busulfan</td>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>LMF1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>busulfan</td>\n",
       "      <td>0.031420</td>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>busulfan_MCF7_breast</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>busulfan</td>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>DDX43</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>busulfan</td>\n",
       "      <td>0.031420</td>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>busulfan_MCF7_breast</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>busulfan</td>\n",
       "      <td>CPD001_MCF7_6H:BRD-K23204545-001-09-9:10</td>\n",
       "      <td>PRX</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Signature Perturbagen_name   P-value  \\\n",
       "0  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10       decitabine  0.014447   \n",
       "1  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10       decitabine  0.014447   \n",
       "2  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10       decitabine  0.014447   \n",
       "3  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10       decitabine  0.014447   \n",
       "4  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10       decitabine  0.014447   \n",
       "5   CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10     temozolomide  0.039682   \n",
       "6   CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10     temozolomide  0.039682   \n",
       "7    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10         busulfan  0.031420   \n",
       "8    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10         busulfan  0.031420   \n",
       "9    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10         busulfan  0.031420   \n",
       "\n",
       "                                       sig_id           Signature_id_x  \\\n",
       "0  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10   decitabine_HCC515_lung   \n",
       "1  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10   decitabine_HCC515_lung   \n",
       "2  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10   decitabine_HCC515_lung   \n",
       "3  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10   decitabine_HCC515_lung   \n",
       "4  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10   decitabine_HCC515_lung   \n",
       "5   CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10  temozolomide_SKLU1_lung   \n",
       "6   CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10  temozolomide_SKLU1_lung   \n",
       "7    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10     busulfan_MCF7_breast   \n",
       "8    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10     busulfan_MCF7_breast   \n",
       "9    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10     busulfan_MCF7_breast   \n",
       "\n",
       "  cell_id Significant perturbagen_name  \\\n",
       "0  HCC515         Yes       decitabine   \n",
       "1  HCC515         Yes       decitabine   \n",
       "2  HCC515         Yes       decitabine   \n",
       "3  HCC515         Yes       decitabine   \n",
       "4  HCC515         Yes       decitabine   \n",
       "5   SKLU1         Yes     temozolomide   \n",
       "6   SKLU1         Yes     temozolomide   \n",
       "7    MCF7         Yes         busulfan   \n",
       "8    MCF7         Yes         busulfan   \n",
       "9    MCF7         Yes         busulfan   \n",
       "\n",
       "                               Signature_id_y HUGO_gene_id Sex-bias  \n",
       "0  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10      HSD11B1     Male  \n",
       "1  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10        SALL1   Female  \n",
       "2  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10       HOXD11   No-dir  \n",
       "3  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10       PPFIA3   Female  \n",
       "4  CPC013_HCC515_6H:BRD-K79254416-001-08-5:10         CD1E   No-dir  \n",
       "5   CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10       CDKN2A   Female  \n",
       "6   CPC006_SKLU1_6H:BRD-K32107296-001-03-7:10        MYLK3     Male  \n",
       "7    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10         LMF1     Male  \n",
       "8    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10        DDX43     Male  \n",
       "9    CPD001_MCF7_6H:BRD-K23204545-001-09-9:10          PRX   Female  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genes_tiss_sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genes_tiss_sb_fil = df_genes_tiss_sb[[\"Signature_id_x\",\"Sex-bias\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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_id_x</th>\n",
       "      <th>Sex-bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>No-dir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>No-dir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>temozolomide_SKLU1_lung</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>temozolomide_SKLU1_lung</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>busulfan_MCF7_breast</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>busulfan_MCF7_breast</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>busulfan_MCF7_breast</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Signature_id_x Sex-bias\n",
       "0   decitabine_HCC515_lung     Male\n",
       "1   decitabine_HCC515_lung   Female\n",
       "2   decitabine_HCC515_lung   No-dir\n",
       "3   decitabine_HCC515_lung   Female\n",
       "4   decitabine_HCC515_lung   No-dir\n",
       "5  temozolomide_SKLU1_lung   Female\n",
       "6  temozolomide_SKLU1_lung     Male\n",
       "7     busulfan_MCF7_breast     Male\n",
       "8     busulfan_MCF7_breast     Male\n",
       "9     busulfan_MCF7_breast   Female"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genes_tiss_sb_fil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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')['Sex-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='Sex-bias')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df[\"Plot\"]= [2,1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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_id_x</th>\n",
       "      <th>Sex-bias</th>\n",
       "      <th>Plot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>busulfan_MCF7_breast</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>decitabine_HCC515_lung</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>temozolomide_SKLU1_lung</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Signature_id_x Sex-bias  Plot\n",
       "0     busulfan_MCF7_breast     Male     2\n",
       "1   decitabine_HCC515_lung   Female     1\n",
       "2  temozolomide_SKLU1_lung   Female     1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df\n",
    "#altero el valor porque tienen valores igualas las categorias por ello se decide que no hay direccion del sex-bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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['Plot'], result_df['Signature_id_x'], c=result_df['Plot'].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_top100.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
}