{
 "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 26 february 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": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sex_biased_dir(dataframe):\n",
    "    \n",
    "    data_sig = clue_tiss_sig_gtex_[clue_tiss_sig_gtex_[\"Significant\"]==\"Yes\"]\n",
    "    df_genes_tiss_sb = data_sig.merge(df_all, left_on= [\"sig_id\",\"Perturbagen_name\"], right_on=[\"Signature_id\",\"perturbagen_name\"], how =\"inner\")\n",
    "    \n",
    "    genes_female = df_all[df_all[\"Gene-bias\"]== 3.0 ]\n",
    "    genes_male = df_all[df_all[\"Gene-bias\"]== 2.0]\n",
    "    threshold = 0.05\n",
    "\n",
    "    df_genes_sig = pd.DataFrame(columns = [\"Signature\",\"P-value\",\"Significance\",\"odds_ratio\",\"Sex-biased\",\"Number genes Male\",\n",
    "                                       \"Number genes Female\"])\n",
    "\n",
    "    for sig_id in df_genes_tiss_sb[\"Signature\"]:\n",
    "        #print (sig_id)\n",
    "        df_sig = df_genes_tiss_sb[df_genes_tiss_sb[\"Signature\"] == sig_id]\n",
    "    \n",
    "       \n",
    "        \n",
    "        df_male_genes = df_tissue[df_tissue[\"Sex-bias\"] == \"Male\"]\n",
    "        df_female_genes = df_tissue[df_tissue[\"Sex-bias\"] == \"Female\"]\n",
    "        df_female_total = genes_female[genes_female[\"tissue\"]== tissue]\n",
    "        df_male_total = genes_male[genes_male[\"tissue\"]== tissue]\n",
    "\n",
    "        # Contar la frecuencia del suceso en cada caso\n",
    "        frecuencia_c = len(df_male_genes)\n",
    "        frecuencia_a = len(df_female_genes)\n",
    "        frecuencia_d = len(df_male_total) - len(df_male_genes)\n",
    "        frecuencia_b = len(df_female_total) - len(df_female_genes)\n",
    " \n",
    "        # Create the contingency table\n",
    "        contingency_table = [[frecuencia_a , frecuencia_b], [frecuencia_c , frecuencia_d]]\n",
    " \n",
    "        # Perform Fisher's exact test\n",
    "        odds_ratio, p_value = fisher_exact(contingency_table)\n",
    " \n",
    "        \n",
    "            # Make a decision based on the p-value\n",
    "            if p_value < threshold:\n",
    "                #print('Reject the null hypothesis. There is a significant association.')\n",
    "                p_value_label = \"Yes\" \n",
    "            else:\n",
    "                #print('Insufficient evidence to reject the null hypothesis.')\n",
    "                p_value_label = \"No\"\n",
    "            #sex-biased \n",
    "            if p_value < threshold and frecuencia_c > frecuencia_a:\n",
    "                #print(\"sex-biased male\")\n",
    "                sex_biased  = \"sex-biased male\"\n",
    "            elif p_value < threshold and frecuencia_c < frecuencia_a:\n",
    "                #print(\"sex-biased female\")\n",
    "                sex_biased  = \"sex-biased female\"\n",
    "            else:\n",
    "                #print(\"no sex-biased\")\n",
    "                sex_biased  = \"no sex-biased differences\"\n",
    "            \n",
    "            row = pd.DataFrame({\"Signature\": [sig_id],\"Tissue_type\":[tissue],\"P-value\": [p_value],\"Significance\": [p_value_label],\"odds_ratio\":[odds_ratio]\n",
    "                            ,\"Sex-biased\": [sex_biased], \"Number genes Male\": [frecuencia_c],\n",
    "                            \"Number genes Female\": [frecuencia_a]})\n",
    "            df_genes_sig = pd.concat([df_genes_sig, row])\n",
    "            \n",
    "        df_genes_sig = df_genes_sig.drop_duplicates()\n",
    "        df_general_plus_siggenes = clue_tiss_sig_gtex_.merge(df_genes_sig,on =[\"Signature\",\"Tissue_type\"], how=\"left\" )\n",
    "    \n",
    "    \n",
    "    return df_general_plus_siggenes\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_heatmap_value(row):\n",
    "    if row[\"Significant\"] == \"Yes\":\n",
    "        if row[\"Equal tissue\"]:\n",
    "            if row[\"Sex-biased\"] == \"sex-biased male\":\n",
    "                return 7\n",
    "            elif row[\"Sex-biased\"] == \"sex-biased female\":\n",
    "                return 6\n",
    "            elif row[\"Sex-biased\"] == \"no sex-biased differences\":\n",
    "                return 5\n",
    "        else:\n",
    "            if row[\"Sex-biased\"] == \"sex-biased male\":\n",
    "                return 4\n",
    "            elif row[\"Sex-biased\"] == \"sex-biased female\":\n",
    "                return 3\n",
    "            elif row[\"Sex-biased\"] == \"no sex-biased differences\":\n",
    "                return 2\n",
    "    return 1"
   ]
  },
  {
   "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": "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",
       "    .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>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": {
      "text/html": [
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       "    }\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",
       "    </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",
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       "<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",
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      "\r",
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      "vincristine\n",
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      "vorinostat\n",
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     "output_type": "stream",
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      "streptozotocin\n",
      "6\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "output_type": "stream",
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      "homoharringtonine\n",
      "11\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
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    {
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     "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": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\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",
       "    </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",
       "      <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>0.443174</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>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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>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",
       "    </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>PHKA1</td>\n",
       "      <td>3.0</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>IL13RA1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <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",
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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>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",
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       "\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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       "\n",
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       "    }\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>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": {
      "text/html": [
       "<div>\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",
       "      <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",
       "    .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>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",
       "    }\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>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": 98,
   "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": [
    "# Define the colors for each category\n",
    "category_colors = {\n",
    "    0: 'yellow',             # No significant\n",
    "    1: 'green',            # No direction\n",
    "    2: 'blue',           # Male sex-biased\n",
    "    3: 'red'               # Female sex-biased\n",
    "}\n",
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "# Plot the scatter plot with colors based on category\n",
    "plt.scatter(result_df['Gene-bias'], result_df['Signature_id_x'], c=result_df['Gene-bias'].map(category_colors))\n",
    "plt.xlabel('Sex-bias',fontsize = 12)\n",
    "plt.ylabel('Signatures',fontsize = 12)\n",
    "plt.xticks(range(4), ['No significant', 'No direction', 'Male sex-biased', 'Female sex-biased'])  # Set x-axis labels\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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",
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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>IDs</th>\n",
       "      <th>Categorias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IDs  Categorias\n",
       "0  101           0\n",
       "1  102           1\n",
       "2  103           2\n",
       "3  104           0\n",
       "4  105           3"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clue_tiss_sig_gtex_pivot = df_genes_tiss_sb[[\"Signature_id_x\",\"Tissue_type\", \"Heatmap_values\"]]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes_female = df_all[df_all[\"Gene-bias\"]== 3.0]\n",
    "genes_male = df_all[df_all[\"Gene-bias\"]== 2.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "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",
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       "        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>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",
       "    </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>PHKA1</td>\n",
       "      <td>3.0</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>IL13RA1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>idarubicin</td>\n",
       "      <td>CPC011_A375_6H:BRD-K69650333-003-11-6:10</td>\n",
       "      <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>5</th>\n",
       "      <td>afatinib</td>\n",
       "      <td>CPC013_SKB_24H:BRD-K66175015-001-01-7:10</td>\n",
       "      <td>IGBP1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>afatinib</td>\n",
       "      <td>CPC013_SKB_24H:BRD-K66175015-001-01-7:10</td>\n",
       "      <td>RBM3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>afatinib</td>\n",
       "      <td>CPC013_SKB_24H:BRD-K66175015-001-01-7:10</td>\n",
       "      <td>DIAPH2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>afatinib</td>\n",
       "      <td>CPC013_SKB_24H:BRD-K66175015-001-01-7:10</td>\n",
       "      <td>RENBP</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>afatinib</td>\n",
       "      <td>CPC013_SKB_24H:BRD-K66175015-001-01-7:10</td>\n",
       "      <td>GRIA3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   perturbagen_name                              Signature_id HUGO_gene_id  \\\n",
       "0        idarubicin  CPC011_A375_6H:BRD-K69650333-003-11-6:10      SUV39H1   \n",
       "1        idarubicin  CPC011_A375_6H:BRD-K69650333-003-11-6:10        PHKA1   \n",
       "2        idarubicin  CPC011_A375_6H:BRD-K69650333-003-11-6:10      IL13RA1   \n",
       "3        idarubicin  CPC011_A375_6H:BRD-K69650333-003-11-6:10         SSR4   \n",
       "4        idarubicin  CPC011_A375_6H:BRD-K69650333-003-11-6:10       TSPAN7   \n",
       "..              ...                                       ...          ...   \n",
       "5          afatinib  CPC013_SKB_24H:BRD-K66175015-001-01-7:10        IGBP1   \n",
       "7          afatinib  CPC013_SKB_24H:BRD-K66175015-001-01-7:10         RBM3   \n",
       "9          afatinib  CPC013_SKB_24H:BRD-K66175015-001-01-7:10       DIAPH2   \n",
       "10         afatinib  CPC013_SKB_24H:BRD-K66175015-001-01-7:10        RENBP   \n",
       "11         afatinib  CPC013_SKB_24H:BRD-K66175015-001-01-7:10        GRIA3   \n",
       "\n",
       "    Gene-bias  \n",
       "0         3.0  \n",
       "1         3.0  \n",
       "2         3.0  \n",
       "3         3.0  \n",
       "4         3.0  \n",
       "..        ...  \n",
       "5         3.0  \n",
       "7         3.0  \n",
       "9         3.0  \n",
       "10        3.0  \n",
       "11        3.0  \n",
       "\n",
       "[503 rows x 4 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genes_sig = pd.DataFrame(columns = [\"Signature\",\"P-value\",\"Significance\",\"odds_ratio\",\"Sex-biased\",\"Number genes Male\",\n",
    "                                       \"Number genes Female\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>P-value</th>\n",
       "      <th>Significance</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>Sex-biased</th>\n",
       "      <th>Number genes Male</th>\n",
       "      <th>Number genes Female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Signature, P-value, Significance, odds_ratio, Sex-biased, Number genes Male, Number genes Female]\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genes_sig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "  for sig_id in df_genes_tiss_sb[\"Signature\"]:\n",
    "        #print (sig_id)\n",
    "        df_sig = df_genes_tiss_sb[df_genes_tiss_sb[\"Signature\"] == sig_id]\n",
    "    \n",
    "       \n",
    "        \n",
    "        df_male_genes = df_tissue[df_tissue[\"Sex-bias\"] == \"Male\"]\n",
    "        df_female_genes = df_tissue[df_tissue[\"Sex-bias\"] == \"Female\"]\n",
    "        df_female_total = genes_female[genes_female[\"tissue\"]== tissue]\n",
    "        df_male_total = genes_male[genes_male[\"tissue\"]== tissue]\n",
    "\n",
    "        # Contar la frecuencia del suceso en cada caso\n",
    "        frecuencia_c = len(df_male_genes)\n",
    "        frecuencia_a = len(df_female_genes)\n",
    "        frecuencia_d = len(df_male_total) - len(df_male_genes)\n",
    "        frecuencia_b = len(df_female_total) - len(df_female_genes)\n",
    " \n",
    "        # Create the contingency table\n",
    "        contingency_table = [[frecuencia_a , frecuencia_b], [frecuencia_c , frecuencia_d]]\n",
    " \n",
    "        # Perform Fisher's exact test\n",
    "        odds_ratio, p_value = fisher_exact(contingency_table)\n",
    " \n",
    "        \n",
    "            # Make a decision based on the p-value\n",
    "            if p_value < threshold:\n",
    "                #print('Reject the null hypothesis. There is a significant association.')\n",
    "                p_value_label = \"Yes\" \n",
    "            else:\n",
    "                #print('Insufficient evidence to reject the null hypothesis.')\n",
    "                p_value_label = \"No\"\n",
    "            #sex-biased \n",
    "            if p_value < threshold and frecuencia_c > frecuencia_a:\n",
    "                #print(\"sex-biased male\")\n",
    "                sex_biased  = \"sex-biased male\"\n",
    "            elif p_value < threshold and frecuencia_c < frecuencia_a:\n",
    "                #print(\"sex-biased female\")\n",
    "                sex_biased  = \"sex-biased female\"\n",
    "            else:\n",
    "                #print(\"no sex-biased\")\n",
    "                sex_biased  = \"no sex-biased differences\"\n",
    "            \n",
    "            row = pd.DataFrame({\"Signature\": [sig_id],\"Tissue_type\":[tissue],\"P-value\": [p_value],\"Significance\": [p_value_label],\"odds_ratio\":[odds_ratio]\n",
    "                            ,\"Sex-biased\": [sex_biased], \"Number genes Male\": [frecuencia_c],\n",
    "                            \"Number genes Female\": [frecuencia_a]})\n",
    "            df_genes_sig = pd.concat([df_genes_sig, row])\n",
    "            \n",
    "        df_genes_sig = df_genes_sig.drop_duplicates()\n",
    "        df_general_plus_siggenes = clue_tiss_sig_gtex_.merge(df_genes_sig,on =[\"Signature\",\"Tissue_type\"], how=\"left\" )\n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Repurposing data\n",
    "\n",
    "DB00675 (DBID: tamoxifen)\tMalignant epithelial neoplasm of female breast (CUI: C3163805)\tApproved\t\n",
    "DB00675 (DBID: tamoxifen)\tInvasive Ductal Breast Carcinoma (CUI: C1134719)\tApproved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## estrategies for repurposing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#escape genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_genes = pd.read_excel((\"escape_genes_Oliva2020.xlsx\"),engine='openpyxl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_sex_bias_tamoxifen = escape_genes.merge(genes_all_tissues, left_on=\"HUGO_gene_id\", right_on=\"gene_symbol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": true
   },
   "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>ENSEMBL_gene_id</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Genetype</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_symbol</th>\n",
       "      <th>perturbagen_name</th>\n",
       "      <th>tissue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000229807.10</td>\n",
       "      <td>XIST</td>\n",
       "      <td>lincRNA</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>XIST</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000005889.15</td>\n",
       "      <td>ZFX</td>\n",
       "      <td>protein_coding</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>ZFX</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000198034.10</td>\n",
       "      <td>RPS4X</td>\n",
       "      <td>protein_coding</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>RPS4X</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000006757.11</td>\n",
       "      <td>PNPLA4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.284667</td>\n",
       "      <td>1.293940</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>PNPLA4</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000126012.11</td>\n",
       "      <td>KDM5C</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.282845</td>\n",
       "      <td>1.131381</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>KDM5C</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ENSG00000130021.13</td>\n",
       "      <td>PUDP</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.244160</td>\n",
       "      <td>0.739878</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>PUDP</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENSG00000169249.12</td>\n",
       "      <td>ZRSR2</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.202390</td>\n",
       "      <td>1.065212</td>\n",
       "      <td>19.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>ZRSR2</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENSG00000173674.10</td>\n",
       "      <td>EIF1AX</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.123401</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>15.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>EIF1AX</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENSG00000215301.9</td>\n",
       "      <td>DDX3X</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.033712</td>\n",
       "      <td>0.421399</td>\n",
       "      <td>8.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>DDX3X</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENSG00000130741.10</td>\n",
       "      <td>EIF2S3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.002818</td>\n",
       "      <td>0.040263</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>EIF2S3</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ENSG00000186312.10</td>\n",
       "      <td>CA5BP1</td>\n",
       "      <td>transcribed_unprocessed_pseudogene</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.069600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>CA5BP1</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ENSG00000072501.17</td>\n",
       "      <td>SMC1A</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.062003</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>SMC1A</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ENSG00000130985.16</td>\n",
       "      <td>UBA1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.083839</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>UBA1</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ENSG00000086712.12</td>\n",
       "      <td>TXLNG</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.001319</td>\n",
       "      <td>0.032986</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>TXLNG</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ENSG00000124486.12</td>\n",
       "      <td>USP9X</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.034813</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>USP9X</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ENSG00000102309.12</td>\n",
       "      <td>PIN4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.016223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>PIN4</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ENSEMBL_gene_id HUGO_gene_id                            Genetype  \\\n",
       "0   ENSG00000229807.10         XIST                             lincRNA   \n",
       "1   ENSG00000005889.15          ZFX                      protein_coding   \n",
       "2   ENSG00000198034.10        RPS4X                      protein_coding   \n",
       "3   ENSG00000006757.11       PNPLA4                      protein_coding   \n",
       "4   ENSG00000126012.11        KDM5C                      protein_coding   \n",
       "5   ENSG00000130021.13         PUDP                      protein_coding   \n",
       "6   ENSG00000169249.12        ZRSR2                      protein_coding   \n",
       "7   ENSG00000173674.10       EIF1AX                      protein_coding   \n",
       "8    ENSG00000215301.9        DDX3X                      protein_coding   \n",
       "9   ENSG00000130741.10       EIF2S3                      protein_coding   \n",
       "10  ENSG00000186312.10       CA5BP1  transcribed_unprocessed_pseudogene   \n",
       "11  ENSG00000072501.17        SMC1A                      protein_coding   \n",
       "12  ENSG00000130985.16         UBA1                      protein_coding   \n",
       "13  ENSG00000086712.12        TXLNG                      protein_coding   \n",
       "14  ENSG00000124486.12        USP9X                      protein_coding   \n",
       "15  ENSG00000102309.12         PIN4                      protein_coding   \n",
       "\n",
       "    Sum of relative sex predictivity  Avg of relative sex predictivity (*100)  \\\n",
       "0                          27.913224                                63.439145   \n",
       "1                           0.571802                                 1.681771   \n",
       "2                           0.437481                                 2.083242   \n",
       "3                           0.284667                                 1.293940   \n",
       "4                           0.282845                                 1.131381   \n",
       "5                           0.244160                                 0.739878   \n",
       "6                           0.202390                                 1.065212   \n",
       "7                           0.123401                                 0.822676   \n",
       "8                           0.033712                                 0.421399   \n",
       "9                           0.002818                                 0.040263   \n",
       "10                          0.002784                                 0.069600   \n",
       "11                          0.001860                                 0.062003   \n",
       "12                          0.001677                                 0.083839   \n",
       "13                          0.001319                                 0.032986   \n",
       "14                          0.000348                                 0.034813   \n",
       "15                          0.000162                                 0.016223   \n",
       "\n",
       "    #Tissues predictivity  Female-biased  Male-biased  Female-biased (median)  \\\n",
       "0                    44.0             44            0                   9.514   \n",
       "1                    34.0             44            0                   0.581   \n",
       "2                    21.0             44            0                   0.485   \n",
       "3                    22.0             44            0                   0.419   \n",
       "4                    25.0             44            0                   0.448   \n",
       "5                    33.0             44            0                   0.562   \n",
       "6                    19.0             44            0                   0.419   \n",
       "7                    15.0             44            0                   0.460   \n",
       "8                     8.0             44            0                   0.419   \n",
       "9                     7.0             44            0                   0.288   \n",
       "10                    4.0             44            0                   0.282   \n",
       "11                    3.0             44            0                   0.239   \n",
       "12                    2.0             44            0                   0.168   \n",
       "13                    4.0             44            0                   0.305   \n",
       "14                    1.0             44            0                   0.159   \n",
       "15                    1.0             44            0                   0.070   \n",
       "\n",
       "    Male-biased (median)  Reported Escapee? gene_symbol  perturbagen_name  \\\n",
       "0                    NaN                  1        XIST                44   \n",
       "1                    NaN                  1         ZFX                44   \n",
       "2                    NaN                  1       RPS4X                44   \n",
       "3                    NaN                  1      PNPLA4                44   \n",
       "4                    NaN                  1       KDM5C                44   \n",
       "5                    NaN                  1        PUDP                44   \n",
       "6                    NaN                  1       ZRSR2                44   \n",
       "7                    NaN                  1      EIF1AX                44   \n",
       "8                    NaN                  1       DDX3X                44   \n",
       "9                    NaN                  1      EIF2S3                44   \n",
       "10                   NaN                  1      CA5BP1                44   \n",
       "11                   NaN                  1       SMC1A                44   \n",
       "12                   NaN                  1        UBA1                44   \n",
       "13                   NaN                  1       TXLNG                44   \n",
       "14                   NaN                  1       USP9X                44   \n",
       "15                   NaN                  1        PIN4                44   \n",
       "\n",
       "    tissue  \n",
       "0       44  \n",
       "1       44  \n",
       "2       44  \n",
       "3       44  \n",
       "4       44  \n",
       "5       44  \n",
       "6       44  \n",
       "7       44  \n",
       "8       44  \n",
       "9       44  \n",
       "10      44  \n",
       "11      44  \n",
       "12      44  \n",
       "13      44  \n",
       "14      44  \n",
       "15      44  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escape_sex_bias_tamoxifen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 16/17 son escape genes, all have female sex-bias, present female-biased in 44 tissues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_sex_bias_tamoxifen_all_genes = escape_genes.merge(df_more_impot_genes, left_on=\"HUGO_gene_id\", right_on=\"gene_symbol\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "escape_genes_filter = escape_sex_bias_tamoxifen_all_genes[escape_sex_bias_tamoxifen_all_genes[\"Reported Escapee?\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "escape_genes_filter.loc[escape_genes_filter['Male-biased'] > escape_genes_filter['Female-biased'], 'Gene-bias'] = 1 \n",
    "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'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "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>ENSEMBL_gene_id</th>\n",
       "      <th>HUGO_gene_id</th>\n",
       "      <th>Genetype</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_symbol</th>\n",
       "      <th>perturbagen_name</th>\n",
       "      <th>tissue</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>lincRNA</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>XIST</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000005889.15</td>\n",
       "      <td>ZFX</td>\n",
       "      <td>protein_coding</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>ZFX</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000198034.10</td>\n",
       "      <td>RPS4X</td>\n",
       "      <td>protein_coding</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>RPS4X</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000006757.11</td>\n",
       "      <td>PNPLA4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.284667</td>\n",
       "      <td>1.293940</td>\n",
       "      <td>22.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>PNPLA4</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000126012.11</td>\n",
       "      <td>KDM5C</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>0.282845</td>\n",
       "      <td>1.131381</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>KDM5C</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>ENSG00000186310.9</td>\n",
       "      <td>NAP1L3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NAP1L3</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>ENSG00000189221.9</td>\n",
       "      <td>MAOA</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>1</td>\n",
       "      <td>MAOA</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>ENSG00000089820.15</td>\n",
       "      <td>ARHGAP4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>1</td>\n",
       "      <td>ARHGAP4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>ENSG00000102178.12</td>\n",
       "      <td>UBL4A</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>1</td>\n",
       "      <td>UBL4A</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>ENSG00000189108.12</td>\n",
       "      <td>IL1RAPL2</td>\n",
       "      <td>protein_coding</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>IL1RAPL2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ENSEMBL_gene_id HUGO_gene_id        Genetype  \\\n",
       "0    ENSG00000229807.10         XIST         lincRNA   \n",
       "1    ENSG00000005889.15          ZFX  protein_coding   \n",
       "2    ENSG00000198034.10        RPS4X  protein_coding   \n",
       "3    ENSG00000006757.11       PNPLA4  protein_coding   \n",
       "4    ENSG00000126012.11        KDM5C  protein_coding   \n",
       "..                  ...          ...             ...   \n",
       "241   ENSG00000186310.9       NAP1L3  protein_coding   \n",
       "243   ENSG00000189221.9         MAOA  protein_coding   \n",
       "244  ENSG00000089820.15      ARHGAP4  protein_coding   \n",
       "246  ENSG00000102178.12        UBL4A  protein_coding   \n",
       "248  ENSG00000189108.12     IL1RAPL2  protein_coding   \n",
       "\n",
       "     Sum of relative sex predictivity  \\\n",
       "0                           27.913224   \n",
       "1                            0.571802   \n",
       "2                            0.437481   \n",
       "3                            0.284667   \n",
       "4                            0.282845   \n",
       "..                                ...   \n",
       "241                               NaN   \n",
       "243                               NaN   \n",
       "244                               NaN   \n",
       "246                               NaN   \n",
       "248                               NaN   \n",
       "\n",
       "     Avg of relative sex predictivity (*100)  #Tissues predictivity  \\\n",
       "0                                  63.439145                   44.0   \n",
       "1                                   1.681771                   34.0   \n",
       "2                                   2.083242                   21.0   \n",
       "3                                   1.293940                   22.0   \n",
       "4                                   1.131381                   25.0   \n",
       "..                                       ...                    ...   \n",
       "241                                      NaN                    NaN   \n",
       "243                                      NaN                    NaN   \n",
       "244                                      NaN                    NaN   \n",
       "246                                      NaN                    NaN   \n",
       "248                                      NaN                    NaN   \n",
       "\n",
       "     Female-biased  Male-biased  Female-biased (median)  Male-biased (median)  \\\n",
       "0               44            0                   9.514                   NaN   \n",
       "1               44            0                   0.581                   NaN   \n",
       "2               44            0                   0.485                   NaN   \n",
       "3               44            0                   0.419                   NaN   \n",
       "4               44            0                   0.448                   NaN   \n",
       "..             ...          ...                     ...                   ...   \n",
       "241             26            0                   0.105                   NaN   \n",
       "243              1            2                   0.049                -0.070   \n",
       "244              0            2                     NaN                -0.030   \n",
       "246              1           18                   0.065                -0.021   \n",
       "248              1            0                   0.381                   NaN   \n",
       "\n",
       "     Reported Escapee? gene_symbol  perturbagen_name  tissue  Gene-bias  \n",
       "0                    1        XIST                44      44        2.0  \n",
       "1                    1         ZFX                44      44        2.0  \n",
       "2                    1       RPS4X                44      44        2.0  \n",
       "3                    1      PNPLA4                44      44        2.0  \n",
       "4                    1       KDM5C                44      44        2.0  \n",
       "..                 ...         ...               ...     ...        ...  \n",
       "241                  1      NAP1L3                26      26        2.0  \n",
       "243                  1        MAOA                 3       3        1.0  \n",
       "244                  1     ARHGAP4                 2       2        1.0  \n",
       "246                  1       UBL4A                19      19        1.0  \n",
       "248                  1    IL1RAPL2                 1       1        2.0  \n",
       "\n",
       "[161 rows x 15 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "escape_genes_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gene-bias\n",
       "2.0    99\n",
       "1.0    56\n",
       "0.0     6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(escape_genes_filter[\"Gene-bias\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 161/5070 escape genes, more female-biased "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check tissues CLUE + GTEX"
   ]
  }
 ],
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