{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Integrating scRNA-seq data with PPI networks to study cell type-specific expression patterns in Alzheimer\n",
    "\n",
    "This study aims toanalyze differentially expressed genes (DEGs), integrate the expression values in the PPI network and evaluate how gene expression patterns vary across cell types in Alzheimer."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "6e3db0831d32394b"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-05-07T13:08:52.533981Z",
     "start_time": "2025-05-07T13:08:47.590128Z"
    }
   },
   "source": [
    "import scanpy as sc\n",
    "import sys\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from pandas.plotting import table\n",
    "from scipy.stats import pearsonr\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from matplotlib.patches import Rectangle\n",
    "import glob\n",
    "\n",
    "sys.path.append('functions/')\n",
    "import functions"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "cell_type": "markdown",
   "source": [
    "## 1.  Load data\n",
    "\n",
    "The original dataset includes cells from multiple conditions (Normal, Alzheimer's, FTD, PSP). For this analysis, we are only interested in comparing Normal vs Alzheimer's, so these two conditions are filtered out.\n",
    "\n",
    "These dataset comes from CellXGene platform, and its available at: https://cellxgene.cziscience.com/collections/c53573b2-eff4-4c5e-9ad0-b24d422dfd9b"
   ],
   "id": "48bc73ea77a550de"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T15:40:36.960387Z",
     "start_time": "2025-02-26T15:36:57.430520Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "cell_type": "code",
   "source": [
    "adata = sc.read_h5ad('../data/cross-dementia.h5ad')"
   ],
   "id": "553338b8b68f7a23",
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[2], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m adata \u001B[38;5;241m=\u001B[39m \u001B[43msc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread_h5ad\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mCellXGene/cross-dementia/cross-dementia.h5ad\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\h5ad.py:258\u001B[0m, in \u001B[0;36mread_h5ad\u001B[1;34m(filename, backed, as_sparse, as_sparse_fmt, chunk_size)\u001B[0m\n\u001B[0;32m    255\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m read_dataframe(elem)\n\u001B[0;32m    256\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m func(elem)\n\u001B[1;32m--> 258\u001B[0m adata \u001B[38;5;241m=\u001B[39m \u001B[43mread_dispatched\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcallback\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcallback\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    260\u001B[0m \u001B[38;5;66;03m# Backwards compat (should figure out which version)\u001B[39;00m\n\u001B[0;32m    261\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraw.X\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m f:\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\experimental\\_dispatch_io.py:42\u001B[0m, in \u001B[0;36mread_dispatched\u001B[1;34m(elem, callback)\u001B[0m\n\u001B[0;32m     38\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21;01manndata\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_io\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mspecs\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m _REGISTRY, Reader\n\u001B[0;32m     40\u001B[0m reader \u001B[38;5;241m=\u001B[39m Reader(_REGISTRY, callback\u001B[38;5;241m=\u001B[39mcallback)\n\u001B[1;32m---> 42\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mreader\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread_elem\u001B[49m\u001B[43m(\u001B[49m\u001B[43melem\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\utils.py:211\u001B[0m, in \u001B[0;36mreport_read_key_on_error.<locals>.func_wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    209\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo element found in args.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    210\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 211\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    212\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    213\u001B[0m     path, key \u001B[38;5;241m=\u001B[39m _get_display_path(store)\u001B[38;5;241m.\u001B[39mrsplit(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;241m1\u001B[39m)\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\specs\\registry.py:275\u001B[0m, in \u001B[0;36mReader.read_elem\u001B[1;34m(self, elem, modifiers)\u001B[0m\n\u001B[0;32m    273\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcallback \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    274\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m read_func(elem)\n\u001B[1;32m--> 275\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcallback\u001B[49m\u001B[43m(\u001B[49m\u001B[43mread_func\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43melem\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43melem\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43miospec\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43miospec\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\h5ad.py:239\u001B[0m, in \u001B[0;36mread_h5ad.<locals>.callback\u001B[1;34m(func, elem_name, elem, iospec)\u001B[0m\n\u001B[0;32m    236\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21mcallback\u001B[39m(func, elem_name: \u001B[38;5;28mstr\u001B[39m, elem, iospec):\n\u001B[0;32m    237\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m iospec\u001B[38;5;241m.\u001B[39mencoding_type \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124manndata\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m elem_name\u001B[38;5;241m.\u001B[39mendswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m    238\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m AnnData(\n\u001B[1;32m--> 239\u001B[0m             \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m{\n\u001B[0;32m    240\u001B[0m                 \u001B[38;5;66;03m# This is covering up backwards compat in the anndata initializer\u001B[39;00m\n\u001B[0;32m    241\u001B[0m                 \u001B[38;5;66;03m# In most cases we should be able to call `func(elen[k])` instead\u001B[39;00m\n\u001B[0;32m    242\u001B[0m                 k: read_dispatched(elem[k], callback)\n\u001B[0;32m    243\u001B[0m                 \u001B[38;5;28;01mfor\u001B[39;00m k \u001B[38;5;129;01min\u001B[39;00m elem\u001B[38;5;241m.\u001B[39mkeys()\n\u001B[0;32m    244\u001B[0m                 \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m k\u001B[38;5;241m.\u001B[39mstartswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraw.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    245\u001B[0m             }\n\u001B[0;32m    246\u001B[0m         )\n\u001B[0;32m    247\u001B[0m     \u001B[38;5;28;01melif\u001B[39;00m elem_name\u001B[38;5;241m.\u001B[39mstartswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/raw.\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m    248\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\h5ad.py:242\u001B[0m, in \u001B[0;36m<dictcomp>\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m    236\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21mcallback\u001B[39m(func, elem_name: \u001B[38;5;28mstr\u001B[39m, elem, iospec):\n\u001B[0;32m    237\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m iospec\u001B[38;5;241m.\u001B[39mencoding_type \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124manndata\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m elem_name\u001B[38;5;241m.\u001B[39mendswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m    238\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m AnnData(\n\u001B[0;32m    239\u001B[0m             \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m{\n\u001B[0;32m    240\u001B[0m                 \u001B[38;5;66;03m# This is covering up backwards compat in the anndata initializer\u001B[39;00m\n\u001B[0;32m    241\u001B[0m                 \u001B[38;5;66;03m# In most cases we should be able to call `func(elen[k])` instead\u001B[39;00m\n\u001B[1;32m--> 242\u001B[0m                 k: \u001B[43mread_dispatched\u001B[49m\u001B[43m(\u001B[49m\u001B[43melem\u001B[49m\u001B[43m[\u001B[49m\u001B[43mk\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcallback\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    243\u001B[0m                 \u001B[38;5;28;01mfor\u001B[39;00m k \u001B[38;5;129;01min\u001B[39;00m elem\u001B[38;5;241m.\u001B[39mkeys()\n\u001B[0;32m    244\u001B[0m                 \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m k\u001B[38;5;241m.\u001B[39mstartswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraw.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    245\u001B[0m             }\n\u001B[0;32m    246\u001B[0m         )\n\u001B[0;32m    247\u001B[0m     \u001B[38;5;28;01melif\u001B[39;00m elem_name\u001B[38;5;241m.\u001B[39mstartswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/raw.\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m    248\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\experimental\\_dispatch_io.py:42\u001B[0m, in \u001B[0;36mread_dispatched\u001B[1;34m(elem, callback)\u001B[0m\n\u001B[0;32m     38\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21;01manndata\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01m_io\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mspecs\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m _REGISTRY, Reader\n\u001B[0;32m     40\u001B[0m reader \u001B[38;5;241m=\u001B[39m Reader(_REGISTRY, callback\u001B[38;5;241m=\u001B[39mcallback)\n\u001B[1;32m---> 42\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mreader\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread_elem\u001B[49m\u001B[43m(\u001B[49m\u001B[43melem\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\utils.py:211\u001B[0m, in \u001B[0;36mreport_read_key_on_error.<locals>.func_wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    209\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo element found in args.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    210\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 211\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    212\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    213\u001B[0m     path, key \u001B[38;5;241m=\u001B[39m _get_display_path(store)\u001B[38;5;241m.\u001B[39mrsplit(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;241m1\u001B[39m)\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\specs\\registry.py:275\u001B[0m, in \u001B[0;36mReader.read_elem\u001B[1;34m(self, elem, modifiers)\u001B[0m\n\u001B[0;32m    273\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcallback \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    274\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m read_func(elem)\n\u001B[1;32m--> 275\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcallback\u001B[49m\u001B[43m(\u001B[49m\u001B[43mread_func\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43melem\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43melem\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43miospec\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43miospec\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\h5ad.py:256\u001B[0m, in \u001B[0;36mread_h5ad.<locals>.callback\u001B[1;34m(func, elem_name, elem, iospec)\u001B[0m\n\u001B[0;32m    253\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m elem_name \u001B[38;5;129;01min\u001B[39;00m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/obs\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/var\u001B[39m\u001B[38;5;124m\"\u001B[39m}:\n\u001B[0;32m    254\u001B[0m     \u001B[38;5;66;03m# Backwards compat\u001B[39;00m\n\u001B[0;32m    255\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m read_dataframe(elem)\n\u001B[1;32m--> 256\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43melem\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_io\\specs\\methods.py:724\u001B[0m, in \u001B[0;36mread_sparse\u001B[1;34m(elem, _reader)\u001B[0m\n\u001B[0;32m    719\u001B[0m \u001B[38;5;129m@_REGISTRY\u001B[39m\u001B[38;5;241m.\u001B[39mregister_read(H5Group, IOSpec(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcsc_matrix\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m0.1.0\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n\u001B[0;32m    720\u001B[0m \u001B[38;5;129m@_REGISTRY\u001B[39m\u001B[38;5;241m.\u001B[39mregister_read(H5Group, IOSpec(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcsr_matrix\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m0.1.0\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n\u001B[0;32m    721\u001B[0m \u001B[38;5;129m@_REGISTRY\u001B[39m\u001B[38;5;241m.\u001B[39mregister_read(ZarrGroup, IOSpec(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcsc_matrix\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m0.1.0\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n\u001B[0;32m    722\u001B[0m \u001B[38;5;129m@_REGISTRY\u001B[39m\u001B[38;5;241m.\u001B[39mregister_read(ZarrGroup, IOSpec(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcsr_matrix\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m0.1.0\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n\u001B[0;32m    723\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21mread_sparse\u001B[39m(elem: GroupStorageType, \u001B[38;5;241m*\u001B[39m, _reader: Reader) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m sparse\u001B[38;5;241m.\u001B[39mspmatrix:\n\u001B[1;32m--> 724\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43msparse_dataset\u001B[49m\u001B[43m(\u001B[49m\u001B[43melem\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_memory\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\anndata\\_core\\sparse_dataset.py:575\u001B[0m, in \u001B[0;36mBaseCompressedSparseDataset.to_memory\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    573\u001B[0m format_class \u001B[38;5;241m=\u001B[39m get_memory_class(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mformat)\n\u001B[0;32m    574\u001B[0m mtx \u001B[38;5;241m=\u001B[39m format_class(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mshape, dtype\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdtype)\n\u001B[1;32m--> 575\u001B[0m mtx\u001B[38;5;241m.\u001B[39mdata \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroup\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mdata\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m    576\u001B[0m mtx\u001B[38;5;241m.\u001B[39mindices \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgroup[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mindices\u001B[39m\u001B[38;5;124m\"\u001B[39m][\u001B[38;5;241m.\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;241m.\u001B[39m]\n\u001B[0;32m    577\u001B[0m mtx\u001B[38;5;241m.\u001B[39mindptr \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mindptr\n",
      "File \u001B[1;32mh5py\\\\_objects.pyx:54\u001B[0m, in \u001B[0;36mh5py._objects.with_phil.wrapper\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mh5py\\\\_objects.pyx:55\u001B[0m, in \u001B[0;36mh5py._objects.with_phil.wrapper\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\h5py\\_hl\\dataset.py:781\u001B[0m, in \u001B[0;36mDataset.__getitem__\u001B[1;34m(self, args, new_dtype)\u001B[0m\n\u001B[0;32m    779\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fast_read_ok \u001B[38;5;129;01mand\u001B[39;00m (new_dtype \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[0;32m    780\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 781\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fast_reader\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    782\u001B[0m     \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m    783\u001B[0m         \u001B[38;5;28;01mpass\u001B[39;00m  \u001B[38;5;66;03m# Fall back to Python read pathway below\u001B[39;00m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Filter by Normal y Alzheimer\n",
    "adata = adata[adata.obs['disease'].isin(['normal', 'Alzheimer disease'])]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "7c9eb390f9654651"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-27T09:36:49.970112Z",
     "start_time": "2025-02-27T09:36:49.662224Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"\\n✅ Observations summary:\")\n",
    "print(f\"Total number of cells: {adata.n_obs}\")\n",
    "print(f\"Total number of cellular types: {adata.obs['cell_type'].nunique()}\")\n",
    "print(f\"Total number of unique tissues: {adata.obs['tissue'].nunique()}\")\n",
    "print(f\"Total number of states: {adata.obs['disease'].nunique()}\")\n",
    "\n",
    "print(\"\\n✅ Variables summary:\")\n",
    "print(f\"Total number of genes: {adata.n_vars}\")\n",
    "print(f\"Number of highly variable genes: {adata.var['vst.variable'].sum()}\")"
   ],
   "id": "69f8796db374964f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "✅ Observations summary:\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'adata' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[1], line 2\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m✅ Observations summary:\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m----> 2\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTotal number of cells: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00madata\u001B[38;5;241m.\u001B[39mn_obs\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m      3\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTotal number of cellular types: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00madata\u001B[38;5;241m.\u001B[39mobs[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcell_type\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m      4\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTotal number of unique tissues: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00madata\u001B[38;5;241m.\u001B[39mobs[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtissue\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'adata' is not defined"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "cell_type": "markdown",
   "source": [
    "## 2. Data preprocessing\n",
    "\n",
    "### 2.1. Filter genes\n",
    "\n",
    "Filter genes expressed in a minimum of 10 cells and filter cells which do not express at least 200 genes. These genes are often technical noise or of little biological relevance in the context of interest."
   ],
   "id": "81bc28121d2f73c1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T15:42:08.719989Z",
     "start_time": "2025-02-26T15:42:08.696187Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "cell_type": "code",
   "source": [
    "sc.pp.filter_cells(adata, min_genes=200)"
   ],
   "id": "e0b48324c0bf96e8",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "print(f\"Total number of cells after filtering: {adata.n_obs}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "b327b0c0db43aaee"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-26T15:42:18.243824Z",
     "start_time": "2025-02-26T15:42:12.759852Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "cell_type": "code",
   "source": [
    "sc.pp.filter_genes(adata, min_cells=10)"
   ],
   "id": "20aa135fce5a6f68",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "print(f\"Total number of genes after filtering: {adata.n_vars}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "1af23243eaf29f9d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2.2. Normalization and log transformation\n",
    "\n",
    "**Normalization** is performed because the raw RNA-seq counts depend on the sequencing depth. Therefore, the sum of counts per cell is normalised to a constant value (10,000). This allows comparison between cells with different sequencing depths.\n",
    "\n",
    "In addition, **log-transformation (log1p)** is applied to stabilise the variance and reduce the impact of genes with extreme high abundance."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "fdd1e2ccbd382133"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "sc.pp.normalize_total(adata, target_sum=1e4)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "43637bd25e62f2dd"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "sc.pp.log1p(adata)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "c9c7639db9679fd"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "adata.write('../data/preprocessed_data.h5ad')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "9867bba3fbce26b4"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2.3. Dataset division\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "1ea50780fc249bf"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "adata = sc.read_h5ad('../data/preprocessed_data.h5ad')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "bdeb091f50cba282"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell_type\n",
      "oligodendrocyte                      76181\n",
      "glutamatergic neuron                 60845\n",
      "astrocyte                            32390\n",
      "inhibitory interneuron               18662\n",
      "oligodendrocyte precursor cell       14054\n",
      "microglial cell                      10818\n",
      "endothelial cell of vascular tree     3017\n",
      "pericyte                               925\n",
      "T cell                                 136\n",
      "Name: count, dtype: int64\n",
      "disease\n",
      "Alzheimer disease    118234\n",
      "normal                98794\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(adata.obs['cell_type'].value_counts())\n",
    "print(adata.obs['disease'].value_counts())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "9aba2e6e2fcbf462"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Procesing cell types: 100%|██████████| 9/9 [03:05<00:00, 20.61s/it]\n"
     ]
    }
   ],
   "source": [
    "cell_types = adata.obs['cell_type'].unique()\n",
    "\n",
    "# Create a dictionary to save the data for each cell type\n",
    "data_per_type = {}\n",
    "for type in tqdm(cell_types, desc=\"Procesing cell types\"):\n",
    "    data_per_type[type] = adata[adata.obs['cell_type'] == type].copy()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "5b7875f61f3fa7f7"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "{'astrocyte': AnnData object with n_obs × n_vars = 32390 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap',\n 'glutamatergic neuron': AnnData object with n_obs × n_vars = 60845 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap',\n 'oligodendrocyte precursor cell': AnnData object with n_obs × n_vars = 14054 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap',\n 'oligodendrocyte': AnnData object with n_obs × n_vars = 76181 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap',\n 'inhibitory interneuron': AnnData object with n_obs × n_vars = 18662 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap',\n 'microglial cell': AnnData object with n_obs × n_vars = 10818 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap',\n 'endothelial cell of vascular tree': AnnData object with n_obs × n_vars = 3017 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap',\n 'pericyte': AnnData object with n_obs × n_vars = 925 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap',\n 'T cell': AnnData object with n_obs × n_vars = 136 × 28215\n     obs: 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'cell_type_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'suspension_type', 'ct_subcluster', 'library_id', 'tissue_type', 'is_primary_data', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'n_genes'\n     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'n_cells'\n     uns: 'citation', 'log1p', 'schema_reference', 'schema_version', 'title'\n     obsm: 'X_pca', 'X_tsne', 'X_umap'}"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_per_type"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "8383266313fe69e5"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3. Differential expression analysis\n",
    "\n",
    "Scanpy, when running sc.tl.rank_genes_groups(), automatically prioritises adata.raw if it exists. In this case, we saved the raw array in adata.raw to begin with (which is correct), but Scanpy is using that raw array instead of the normalised, log-transformed .X array.\n",
    "\n",
    "**sc.tl.rank_genes_groups()** performs a differential expression analysis between groups of cells defined in adata_type.obs['disease']. In this case, compares cells labelled as 'Alzheimer disease' against those labelled as 'normal', which is taken as reference group.\n",
    "\n",
    "**Wilcoxon rank-sum test** is a non-parametric test which finely adapts to scRNA-seq data. This test compares, for each gene, if its expression is significantly different in one group with respect to the other.\n",
    "- Null hyphotesis (H₀): the distribution of gene expression is equal in both groups (Alzheimer and normal).\n",
    "- Alternative hypothesis (H₁): the distributions are different (the test is bilateral by default).\n",
    "\n",
    "The test does not assume normality, only that the values are ordinal, which is ideal for scRNA-seq (where the data are usually noisy and with zeros).\n",
    "\n",
    "**sc.get.rank_genes_groups_df()** extracts the results in DataFrame format, one per case 'Alzheimer disease' vs 'normal'.\n",
    "\n",
    "Then, a threshold is applied:\n",
    "- **abs(logfoldchange) > 0.25** in order for the change to be biologically relevant.\n",
    "- **pvals_adj < 0.05** in order for the change to be statistically significant.\n",
    "\n",
    "This indicates that there is sufficient evidence to reject the null hypothesis that that gene is equally expressed between groups.The test does not assume normality, only that the values are ordinal, which is ideal for scRNA-seq (where the d\n",
    "\n",
    "Files **degs_{cell_type}_total.csv** stores all differentially expressed genes found per each cell type, identified by the ENSEMBL ID."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "2e30f0c5e3294df0"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...:  11%|█         | 1/9 [02:11<17:30, 131.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3712 DEGs found for astrocyte for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...:  22%|██▏       | 2/9 [08:22<31:45, 272.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5467 DEGs found for glutamatergic neuron for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...:  33%|███▎      | 3/9 [09:01<16:35, 165.95s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2193 DEGs found for oligodendrocyte precursor cell for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...:  44%|████▍     | 4/9 [16:45<23:37, 283.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4002 DEGs found for oligodendrocyte for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...:  56%|█████▌    | 5/9 [17:48<13:36, 204.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4878 DEGs found for inhibitory interneuron for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...:  67%|██████▋   | 6/9 [18:16<07:12, 144.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1722 DEGs found for microglial cell for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...:  78%|███████▊  | 7/9 [18:23<03:18, 99.43s/it] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "389 DEGs found for endothelial cell of vascular tree for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...:  89%|████████▉ | 8/9 [18:25<01:08, 68.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113 DEGs found for pericyte for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Analyzing cell types...: 100%|██████████| 9/9 [18:26<00:00, 122.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 DEGs found for T cell for Alzheimer disease\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for type, adata_type in tqdm(data_per_type.items(), desc= 'Analyzing cell types...'):\n",
    "\n",
    "    adata_normal = adata_type[adata_type.obs['disease'] == 'normal'].copy()\n",
    "    adata_disease = adata_type[adata_type.obs['disease'] == 'Alzheimer disease'].copy()\n",
    "\n",
    "    # IMPORTANT: forge use of log normalize X data.\n",
    "    adata_type.raw = None\n",
    "\n",
    "    # Differential expression analysis with wilcoxon, using the normal expression as baseline\n",
    "    sc.tl.rank_genes_groups(adata_type, groupby='disease', method='wilcoxon', reference='normal')\n",
    "\n",
    "    # Extract results of DEGs for \"disease\"\" condition (compared to normal)\n",
    "    degs_disease = sc.get.rank_genes_groups_df(adata_type, group='Alzheimer disease')\n",
    "\n",
    "    # All DEGs independetly of the logFC and p-adj\n",
    "    print(f'{len(degs_disease)} total DEGs found for {type} for Alzheimer disease')\n",
    "\n",
    "    # degs_disease.to_csv(f'../data/complete/degs_{type}.csv', index=False)\n",
    "\n",
    "    # Filter by logFC y p-adj\n",
    "    degs_disease_filtered = degs_disease[\n",
    "        (degs_disease['logfoldchanges'].abs() >= 0.25) &\n",
    "        (degs_disease['pvals_adj'] <= 0.05)\n",
    "    ]\n",
    "\n",
    "    print(f'{len(degs_disease_filtered)} significant DEGs found for {type} for Alzheimer disease')\n",
    "\n",
    "    # degs_disease_filtered.to_csv(f'../data/complete/degs_{type}_total.csv', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "d632f920ae57bec0"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### 3.1. Fraction expressed\n",
    "\n",
    "The percentage (or proportion) of cells within a group that express a given gene.\n",
    "\n",
    "For example, suppose you have a cell type (e.g., astrocytes) with 100 cells. For a specific gene (e.g., GEN1), you look at how many of those 100 cells have an expression greater than 0 for that gene. If GEN1 is expressed (i.e., has a value greater than 0) in 25 of those cells, then: fraction_expressed = 25 / 100 = 0.25."
   ],
   "id": "92db8d7ff52f3e4b"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "fractions = []\n",
    "\n",
    "for type, adata_type in tqdm(data_per_type.items(), desc= 'Analyzing cell types...'):\n",
    "\n",
    "    gene_names = adata_type.var_names\n",
    "\n",
    "    # Compute fraction of cells expressing each gene in each group\n",
    "    fractions_type = {}\n",
    "\n",
    "    fraction = ((adata_type.X > 0).sum(axis=0) / adata_type.shape[0]) * 100\n",
    "    fraction = np.asarray(fraction).flatten()\n",
    "\n",
    "     # Create dataframe with gene names and fractions\n",
    "    df = pd.DataFrame({\n",
    "        'gene': gene_names,\n",
    "        'fraction_expressed': fraction,\n",
    "        'cell_type': type\n",
    "    })\n",
    "\n",
    "    fractions.append(df)\n",
    "\n",
    "# Convert to DataFrame for easy viewing\n",
    "fraction_df = pd.concat(fractions, ignore_index=True)\n",
    "\n",
    "print(fraction_df)"
   ],
   "id": "eec92c5171f93ed4"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "## Fraction expressed but instead of in a dataframe, in a matrix form with genes as columns and cell types as rows\n",
    "\n",
    "genes = adata.var_names\n",
    "\n",
    "# Esto te da los grupos (por ejemplo, tipos celulares)\n",
    "groupby = adata.obs['cell_type'].unique()\n",
    "\n",
    "# Creamos un diccionario para guardar la fracción expresada\n",
    "fraction_expressed = {}\n",
    "\n",
    "for group in tqdm(groupby):\n",
    "    # Subconjunto de células de este tipo\n",
    "    cells = adata[adata.obs['cell_type'] == group]\n",
    "\n",
    "    # Matriz de expresión para los genes de interés (X es la matriz de expresión)\n",
    "    X = cells[:, genes].X\n",
    "\n",
    "    # Convertimos a denso si es necesario (algunas veces es sparse)\n",
    "    if not isinstance(X, np.ndarray):\n",
    "        X = X.toarray()\n",
    "\n",
    "    # Cálculo: cuántas células tienen expresión > 0 para cada gen\n",
    "    frac = (X > 0).sum(axis=0) / X.shape[0]\n",
    "\n",
    "    fraction_expressed[group] = frac\n",
    "\n",
    "# Convertimos a DataFrame para visualizar\n",
    "fraction_expressed_df = pd.DataFrame(fraction_expressed, index=genes).T\n",
    "print(fraction_expressed_df)"
   ],
   "id": "a75080c359691c8d"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "sns.set_context(\"notebook\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "\n",
    "flierprops = dict(marker='o', markersize=3, linestyle='none')\n",
    "\n",
    "sns.boxplot(fraction_df, y = 'fraction_expressed', x = 'cell_type', flierprops=flierprops)\n",
    "\n",
    "plt.tick_params(axis='both', which='both', bottom=True, left=True)\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Fraction of cells for each cell type that express a given gene\", fontsize = 5)\n",
    "plt.xticks(rotation = 45, fontsize = 5)\n",
    "plt.yticks(fontsize = 5)\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "3dde199003b423bc"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Percentage of zeros in 'fraction_expressed' per 'cell_type'. Conditioned by the number of DEGs per cell type (less DEGs, more zeros)\n",
    "\n",
    "percentage_zeros = (\n",
    "    fraction_df.assign(is_zero=fraction_df['fraction_expressed'] == 0)\n",
    "      .groupby('cell_type')['is_zero']\n",
    "      .mean()\n",
    "      .multiply(100)\n",
    ")\n",
    "\n",
    "print(\"Percentage of zeros per cell_type:\\n\", percentage_zeros)"
   ],
   "id": "3b0374928af98c82"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 4. Individualized-PPI construction per cell type\n",
    "\n",
    "### 4.1. Gene-protein mapping\n",
    "\n",
    "In this case, scRNA-seq data uses ENSEMBL ID to identify genes, and our mapping file uses Entrez ID. First we perform a step in order to transform the identifiers using NCBI API.\n",
    "\n",
    "Files **degs_{cell_type}_mapped.csv** stores all the differentially expressed genes in each cell type, identified by Protein Accession Number, Gene Entrez ID, gene symbol, and ENSEMBL ID.\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "1a79dc025e43d3"
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "outputs": [],
   "source": [
    "gen = pd.read_csv('../data/disnet/gen.tsv', sep = '\\t')\n",
    "gen_pro = pd.read_csv('../data/disnet/gen_pro.tsv', sep = '\\t')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "cdf1a85b269314b5"
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "outputs": [],
   "source": [
    "def obtain_gene_symbol(ensembl_ids, type):\n",
    "    \"\"\"\n",
    "    Function that, given a list of ensembl ids, searches for the gene symbols in the ensembl api\n",
    "    \"\"\"\n",
    "    base_url = \"https://rest.ensembl.org/lookup/id/\"\n",
    "\n",
    "    gene_symbols = {}\n",
    "\n",
    "    for ensembl_id in tqdm(ensembl_ids, desc = f'Processing ensembl ids for cell type {type}...'):\n",
    "        url = f\"{base_url}{ensembl_id}?content-type=application/json\"\n",
    "        response = requests.get(url)\n",
    "\n",
    "        if response.status_code == 200:\n",
    "            data = response.json()\n",
    "            gene_symbol = data.get('display_name', None)\n",
    "            gene_symbols[ensembl_id] = gene_symbol\n",
    "        else:\n",
    "            print(f\"Error obtaining data for {ensembl_id}: {response.status_code}\")\n",
    "            gene_symbols[ensembl_id] = None\n",
    "\n",
    "    return gene_symbols"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "71f608e94690e6f1"
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "outputs": [],
   "source": [
    "def map_gene_symbols_to_entrez(gene_symbols, gen_file):\n",
    "    \"\"\"\n",
    "    Function to map gene symbols to Entrez IDs. You would replace this with your preferred method/API call.\n",
    "    \"\"\"\n",
    "    symbol_to_entrez = dict(zip(gen_file['gene_symbol'], gen_file['gene_id']))\n",
    "\n",
    "    entrez_ids = {}\n",
    "    for ensembl_id, gene_symbol in gene_symbols.items():\n",
    "        entrez_ids[ensembl_id] = (gene_symbol, symbol_to_entrez.get(gene_symbol, None))\n",
    "\n",
    "    return entrez_ids"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "320def8ad16af8cb"
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for type, adata_type in data_per_type.items():\n",
    "    # Load DEGs for the cellular type\n",
    "    degs_disease_filtered = pd.read_csv(f'../data/complete/degs_{type}_total.csv')\n",
    "\n",
    "    # Obtain ENSEMBL IDs from the DEGs\n",
    "    ensembl_ids_disease = degs_disease_filtered['names'].tolist()\n",
    "\n",
    "    # Map ENSEMBL -> Gene symbol\n",
    "    symbol_ids_disease = obtain_gene_symbol(ensembl_ids_disease, type)\n",
    "\n",
    "    # Map Gene symbol -> Entrez\n",
    "    gene_symbol_entrez = map_gene_symbols_to_entrez(symbol_ids_disease, gen)\n",
    "\n",
    "    # Map DEGs with respective Entrez IDs\n",
    "    #degs_disease_filtered['gene_id'] = degs_disease_filtered['names'].map(entrez_ids_disease)\n",
    "    degs_disease_filtered['gene_symbol'] = degs_disease_filtered['names'].map(lambda x: gene_symbol_entrez[x][0])\n",
    "    degs_disease_filtered['gene_id'] = degs_disease_filtered['names'].map(lambda x: gene_symbol_entrez[x][1])\n",
    "\n",
    "    # Reorder columns\n",
    "    column_order = ['gene_id', 'gene_symbol', 'names', 'logfoldchanges', 'pvals', 'pvals_adj', 'scores']\n",
    "    degs_disease_filtered = degs_disease_filtered[column_order]\n",
    "\n",
    "    degs_disease_filtered.to_csv(f'../data/complete/degs_{type}_mapped.csv', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "677c54fe169190da"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Add Protein Accession Number"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "4b2adc20d87588c9"
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [],
   "source": [
    "def add_protein_id(file_path):\n",
    "    df = pd.read_csv(file_path)\n",
    "    df['protein_id'] = df['gene_id'].map(gene_to_protein)\n",
    "    column_order = ['protein_id', 'gene_id', 'gene_symbol', 'names', 'logfoldchanges', 'pvals', 'pvals_adj', 'scores']\n",
    "    df = df[column_order]\n",
    "    df.to_csv(file_path, index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "de00e0b46cc4fd78"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "gene_to_protein = dict(zip(gen_pro['gene_id'], gen_pro['protein_id']))\n",
    "keys = ['astrocyte', 'glutamatergic neuron', 'oligodendrocyte precursor cell', 'oligodendrocyte', 'inhibitory interneuron', 'microglial cell']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "b58d0adb9e49f1df"
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pericyte\n"
     ]
    }
   ],
   "source": [
    "for type in keys:\n",
    "    print(type)\n",
    "    mapped_file_path = f'../data/complete/degs_{type}_mapped.csv'\n",
    "    add_protein_id(mapped_file_path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "320848226dd257"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4.2. Cell type-specific PPI construction and integration of DEGs expression values\n",
    "\n",
    "Values for logfoldchange, p-value and p-value_adj for each differentially expressed gene is going to keep stored as metadata in each node protein of the network. In this networks, all DEGs detected in the differential expression analysis are included\n",
    "\n",
    "**Important: there will be cases in which one gene encode more than one protein.**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "b8f5241a08d75a8d"
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "outputs": [],
   "source": [
    "pro_pro = pd.read_csv(\"../data/disnet/pro_pro.tsv\", sep = '\\t')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "e44ba2befa4ea4b2"
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "outputs": [],
   "source": [
    "def build_ppi_with_expression(cell_type, gen_pro, pro_pro):\n",
    "    \"\"\"\n",
    "    Constructs the cell type-specific PPI network, filtered by DEGs,  and integrates the differential expression values directly at the nodes.\n",
    "    \"\"\"\n",
    "    degs = pd.read_csv(f\"../data/complete/degs_{cell_type}_mapped.csv\")\n",
    "\n",
    "    proteins_degs_disease = degs[\"protein_id\"].dropna().unique()\n",
    "\n",
    "    # Filter the PPI network to retain only interactions between DEG proteins and remove self-interactions\n",
    "    ppi_filtered_disease = pro_pro[\n",
    "        (pro_pro[\"prA\"].isin(proteins_degs_disease)) &\n",
    "        (pro_pro[\"prB\"].isin(proteins_degs_disease)) &\n",
    "        (pro_pro[\"prA\"] != pro_pro[\"prB\"])\n",
    "    ]\n",
    "\n",
    "    G = nx.from_pandas_edgelist(ppi_filtered_disease, \"prA\", \"prB\")\n",
    "\n",
    "    # Adding the differential expression to the nodes of the network\n",
    "    for _, row in degs.iterrows():\n",
    "        if row[\"protein_id\"] in G:\n",
    "            G.nodes[row[\"protein_id\"]][\"gene_id\"] = row[\"gene_id\"]\n",
    "            G.nodes[row[\"protein_id\"]][\"gene_symbol\"] = row[\"gene_symbol\"]\n",
    "            G.nodes[row[\"protein_id\"]][\"logfoldchanges\"] = row[\"logfoldchanges\"]\n",
    "            G.nodes[row[\"protein_id\"]][\"pval\"] = row[\"pvals\"]\n",
    "            G.nodes[row[\"protein_id\"]][\"pval_adj\"] = row[\"pvals_adj\"]\n",
    "\n",
    "    nx.write_graphml(G, f\"../data/complete/graphs/{cell_type}_network.graphml\")\n",
    "\n",
    "    return G"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "389ae6f91a195fc2"
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building cell type-specific PPIs with expression values...: 100%|██████████| 9/9 [00:03<00:00,  2.44it/s]\n"
     ]
    }
   ],
   "source": [
    "cell_networks = {\n",
    "    cell: build_ppi_with_expression(cell, pro_pro)\n",
    "    for cell in tqdm(data_per_type.keys(), desc=\"Building cell type-specific PPIs with expression values...\")\n",
    "}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "83c782bed4b5b159"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 5. Integrated analysis of DEG overlap and presence in the alzheimer module by cell type\n",
    "\n",
    "### 5.1. Obtain Alzheimer disease module\n",
    "\n",
    "Calculate the disease module for Alzheimer's disease.\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "dad8b5ac45e9241b"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "dis_gen = pd.read_csv('../data/disnet/dis_gen.tsv', sep = '\\t')\n",
    "gen_pro = pd.read_csv('../data/disnet/gen_pro.tsv', sep = '\\t')\n",
    "pro_pro = pd.read_csv('../data/disnet/pro_pro.tsv', sep = '\\t')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "62418fa14954172e"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "G_ppi = nx.from_pandas_edgelist(pro_pro, 'prA', 'prB')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "486eff5f3bbfb63f"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "def get_disease_module(disease, dis_gen, gen_pro, pro_pro, PPI):\n",
    "\n",
    "    genes = functions_proximity.genes_dis(disease, dis_gen)\n",
    "    prots = functions_proximity.pro_gen_dict(genes, gen_pro)\n",
    "    prots_interactome = functions_proximity.gen_pro_PPI(prots, pro_pro)\n",
    "    SG = PPI.subgraph(prots_interactome)\n",
    "    lcc = functions_proximity.lcc(SG)\n",
    "\n",
    "    return lcc"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "da4460d1aae38f56"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "lcc_alz = get_disease_module('C0002395', dis_gen, gen_pro, pro_pro, G_ppi)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "d507efc9b2068b6a"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lcc_alz' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_1308\\1865455995.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlcc_alz\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m: name 'lcc_alz' is not defined"
     ]
    }
   ],
   "source": [
    "print(len(lcc_alz))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "1105abd8ce45459d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 5.2. Alzheimer module proteins filtering\n",
    "\n",
    "Next step consists on filtering the previously built cell-type-specific network by keeping only those nodes which belong to the general AD module.\n",
    "\n",
    "**{cell_type}_network-graphml** file store the PPI subgraphs of each cell type built only with those genes beloging to the AD module.\n",
    "\n",
    "**degs{cell_type}_mapped_filt.csv** files store all the data from the proteins and their corresponding DEGs which are present in the AD module."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "ba65d0b5e37d00"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "for cell_type in tqdm(cell_types, desc='Processing cell types...'):\n",
    "\n",
    "    G = nx.read_graphml(f\"../data/complete/graphs/{cell_type}_network.graphml\")\n",
    "    ppi_proteins = set(G.nodes())\n",
    "\n",
    "    # Alzheimer's module proteins present in the PPI of this cell type.\n",
    "    alz_in_ppi = lcc_alz.intersection(ppi_proteins)\n",
    "\n",
    "    # Built the filtered subgraph with only the proteins resulting form the intersection\n",
    "    G_alz = G.subgraph(alz_in_ppi).copy()\n",
    "    nx.write_graphml(G_alz, f\"../data/filtered/graphs/{cell_type}_network.graphml\")\n",
    "\n",
    "    # Extract all the data for these genes of the intersection in a separate file\n",
    "    df_complete = pd.read_csv(f\"../data/complete/degs_{cell_type}_mapped.csv\")\n",
    "\n",
    "    alz_data = [{\n",
    "        \"protein_id\": protein,\n",
    "        \"gene_id\": G.nodes[protein].get(\"gene_id\", \"NA\"),\n",
    "        \"gene_symbol\": G.nodes[protein].get(\"gene_symbol\", \"NA\")\n",
    "    } for protein in alz_in_ppi]\n",
    "\n",
    "    df_alz = pd.DataFrame(alz_data)\n",
    "\n",
    "    df_filtered = df_alz.merge(df_complete, on=[\"gene_id\"], how=\"left\")\n",
    "    df_filtered = df_filtered[df_filtered['protein_id'].isin(G_alz.nodes)]\n",
    "    df_filtered = df_filtered.drop(columns=[\"gene_symbol_x\"], errors='ignore')\n",
    "    df_filtered = df_filtered.rename(columns={\"gene_symbol_y\": \"gene_symbol\"})\n",
    "\n",
    "    df_filtered.to_csv(f\"../data/filtered/degs_{cell_type}_mapped_filt.csv\", index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "debe4e5f76e493f1"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 6. Cell type-specific network and Alzheimer module overlap\n",
    "\n",
    "This analysis investigates the relationship between differentially expressed genes (DEGs) and the Alzheimer disease module (defined as the largest connected component of a protein-protein interaction network associated with Alzheimer’s disease) across different cell types.\n",
    "\n",
    "- Cell Type\n",
    "- Total DEGs: numero total de DEGs en el dataset original\n",
    "- DEGs mapped to protein: numero de los DEGs totales codificados a proteinas\n",
    "- Cell Type proteins in main LCC: numero de proteinas que vienen de DEGs del tipo celular específico que se encuentran en el modulo del Alzheimer\n",
    "- Cell Type LCC size: tamaño del modulo de la enfermedad de la subred especifica del tipo celular\n",
    "- LCC mean: media del tamaño de los 1,000 modulos aleatorios generados\n",
    "- LCC std: desviación estándar del tamaño de los 1,000 modulos aleatorios generados\n",
    "- Zscore: calculado a partir del valor real del tamaño del LCC del subtipo celular (Cell Type LCC size), con la media (LCC mean) y la desviación estándar (LCC std) de los 1,000 modulos aleatorios\n",
    "- p_value: calculado a partir de la conversión del zscore a partir de la distribución Z\n",
    "- adjusted_p_value: calculado aplicando la corrección de Benjamini-Hochberg (FDR Correction) al p-value\n",
    "\n",
    "**Take into account that:** if a DEG can be mapped to protein only means that there is a known protein identifier for that gene. For it to be in the PPI, it must also be in the interaction network of the specific cell type. If there are no known interactions for that protein in that context, even if it has been mapped, it will not appear in the PPI network. This is why is why the value of ‘degs_in_alzheimer_module’ is higher than that of ‘alzheimer_proteins_in_specific_ppi’. This value can be higher because it is considering all DEGs proteins that have been mapped to proteins, without the need for all of them to necessarily be present in the cell type and condition specific PPI."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "14c6ef51514baa51"
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "outputs": [],
   "source": [
    "cell_types = ['astrocyte', 'microglial cell', 'oligodendrocyte', 'glutamatergic neuron', 'inhibitory interneuron', 'endothelial cell of vascular tree', 'oligodendrocyte precursor cell', 'pericyte']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "36ec09d23b5deac6"
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...:   0%|          | 0/8 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...:  12%|█▎        | 1/8 [18:01<2:06:12, 1081.80s/it]"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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cBVOY0T81/SNU3x41tekfnf5J6xu3+lcFj/ZUsNM39JEjR7rAouYdjRLUCU79VnJSKNQ3ZI2S0z90fYNXHyTVUgT/M9Xr6p+wvlEHN43qRKITnk62OumqFk6BSSEwUAMQTDUe6u+k19NJRO8lZ1OR6P0qhOkyYMAA18SnPlyBGgmFM033ohOrTrpHUvOpsivAtWrVyvX1E530A/2zdGLWvgweZatRgKr11IlQjw9QzYzeo2rKNNJVgbd3796uXKGi/lran4Hgqv2ikZ8HG22b8zhT85req0b5qua2WbNmWberf6U+Y/3UZ6xjasqUKe59K+gUFo0W1TGgcKmQrf57EydOdGFKbrrpJtffU323NMJYIVW1SKo9E4U8/d3oC1DA008/7b4kKRgphOh96lg6EvqSpefX567jWH3tNOJTIUe1eAqfOb/8FCb9rSkgaR/pmNZxqVCW19+emnk1AlYhSvtQtdc67vWeRH+TqvnXPlQtpvbZoebdFH0x/OWXX1yoVlOwwrQer/1yKPpbU8hUsA48Vv/7AiFc83nqWFRtsfanPl+N4AWiNKoi1IUAACASqAZQ4TlQ86hBNgqwgSlYgHBAsywAAEdINbeqAQwMNlJ/OtUGA+GEmjsAAI6QmkA16l39R9UHT82yGiilKWiAcEG4AwAA8BCaZQEAADyEcAcAAOAhhDsAAAAPIdwBAAB4SJGaxFizt2suIs2wruVbAAAAIoFWKdJobU1ofbhJwItUuFOw0yz+AAAAkUhLDWpVoEMpUuEusCaidkxgwXBoUk4tQG+maZpyrOoFAADC4ESqZSRVQXWo9Z2LZLgLNMUq2NWpUyfUxQkbCQlmPp9ZXJzWRg11aQAAiDAJx+9EeiTdyhhQAQAA4CGEOwAAAA8h3AEAAHgI4Q4AAMBDCHcAAAAeQrgDAADwEMIdAACAhxDuAAAAPKRITWKMvDVrZrZli1mlSqEuCQAAEahZeJ1ICXewqVNDXQIAACLY1PA6kdIsCwAA4CGEOwAAAA8h3AEAAHgI4Q7WvLlZnToHfgIAgMg+kTKgIgKkpqZaenp6ru3Fixe3mJiYY37+VavMfD6znTuP+akAACh6VoXXiZRwFwHBrkOHbubzbc91W1xcBUtImFggAQ8AAHgD4S7MqcZOwa5y5SkWHV06a3tGRrL5fF3c7YQ7AAAQQLiLEAp2weEOAAAgLwyoAAAA8BDCHQAAgIcQ7gAAADyEcAcAAOAhhDsAAAAPYbQs7JFHzPbsMStbNtQlAQAgAj0SXidSwh2se/dQlwAAgAjWPbxOpDTLAgAAeAjhDgAAwENC2iw7evRo27x5sz3xxBPu+rJly2zkyJGWkpJiZcqUsSeffNLOOOMMd9ukSZNsxowZlpGRYU2aNLFBgwZZiRIlQll8z0hK0nJmWgXDrFq1UJcGAIAIkxReJ9KQ1NytW7fOevfubVOmTMnatnHjRuvVq5cNHjzY5s6da23btrUhQ4a42xYtWmQzZ8504e7DDz+0bdu2ZXssjs1FF5mdcsqBnwAAILJPpCEJd9OnT7fGjRtbly5dsrYptF166aV2wQUXuOs33nijDR061P2+YMECa9OmjZUrV86io6OtY8eONnv27FAUHQAAIKyFJNz179/fOnXq5IJaQGJiomuK7devn91www3Wt2/frNuTkpKsWlA1Z9WqVd02AAAAhOmAivT0dPv0009dc61q5a666irr0aNH1u1RUVHZ7p/zOgAAAMIo3MXGxlr9+vXtrLPOctfbt29vf/75p+tfV716ddcnL0C/axsAAADCNNy1aNHCvv32Wxfo5KOPPrJTTz3VTjrpJHfbvHnzbNeuXZaZmWnTpk2zli1bhrrIAAAAYSdsVqioXbu2mxLlf/7nf1wTbdmyZW3MmDFWrFgxa9q0qa1Zs8YNpNBtDRs2tJ49e4a6yAAAAGEnpOFOgyaCxcfHu0teNLI2eHQtAAAAwrhZFgAAAMeOcAcAAOAhYdPnDqHz8ceaisasOEcDAAARfyINj1LAUlNT3WCRnJKTk83v9xfqa9eqVahPDwCAt9UKrxMp4S5Mgl2HDt3M59ue67bMzAxLTFxrsbGZbj1iAACAQyHchQHV2CnYVa48xaKjS2e7LTV1i61a1brQa+8AAIA3EO7CiIJdznCX83phePNNNf+alS5tduuthf5yAAB4y5vhdSIl3MEGDjTz+czi4sLimAQAILIMDK8TKVOhAAAAeAjhDgAAwEMIdwAAAB5CuAMAAPAQwh0AAICHEO4AAAA8hHAHAADgIYQ7AAAAD2ESY1jVqtl/AgCAyD2REu5gy5aFugQAAESwZeF1IqVZFgAAwEMIdwAAAB5CuAMAAPAQ+tzB7r7bbNs2s4oVzcaPD3VpAACIMHeH14mUcAebN8/M5zOLiwt1SQAAiEDzwutESrMsAACAhxDuAAAAPIRwBwAA4CGEOwAAAA8h3AEAAHgI4Q4AAMBDCHcAAAAeQrgDAADwECYxhnXsaLZ9u1mFCqEuCQAAEahjeJ1ICXewUaNCXQIAACLYqPA6kdIsCwAA4CGEOwAAAA8h3AEAAHhISMPd6NGjbfDgwbm2L1iwwOrWrZtt26RJk6x169bWokULGzp0qKWlpR3Hknpb7dpmJ5544CcAAIjsE2lIwt26deusd+/eNmXKlFy3rVmzxoYPH26ZmZlZ2xYtWmQzZ860GTNm2Icffmjbtm3L87HInz17zHbvPvATAABE9ok0JOFu+vTp1rhxY+vSpUu27Xv27LEBAwbkqs1TTV6bNm2sXLlyFh0dbR07drTZs2cf51IDAACEv5CEu/79+1unTp1cUAv20EMPWefOna1mzZrZticlJVm1atWyrletWtVtAwAAQJgOqBg/frxVqlTJ2rVrl+ftUVFRh7wOAACAMJrEWM2sJUqUcOFOgyUyMjLc72PGjLHq1avbxo0bs+6r37UNAAAAYRruNFAieMBFy5Ytbc6cOe66RsiOHDnSNeWWLVvWpk2b5m4HAABAmIa7Q2natKkbRauBFOnp6dawYUPr2bNnqIsFAAAQdkIa7vr27Zvn9ho1atjPP/+cbZtG1uYcXQsAAIAwHVABAACAItIsi8I1bpzZvn1mJ5wQ6pIAABCBxoXXiZRwB2vbNtQlAAAggrUNrxMpzbIAAAAeQrgDAADwEJplYcuXm6WmmsXEmF14YahLAwBAhFkeXidSwh1MK775fGZxcZpAOtSlAQAgwrQLrxMpzbIAAAAeQrgDAADwEMIdAACAh9DnzqNSU1PdOrw5FS9e3GLU4RMAAHgS4c6jwa5Dh27m823PdVtcXAVLSJhIwAMAwKMIdx6kGjsFu8qVp1h0dOms7RkZyebzdXG3E+4AAPAmwp2HKdgFhzsAAOB9DKgAAADwEMIdAACAh9AsC/vlFzO/3ywqKtQlAQAgAv0SXidSwh2sXLlQlwAAgAhWLrxOpDTLAgAAeAjhDgAAwENoloU9+6zZrl1mJ55o1q9fqEsDAECEeTa8TqSEO7hj0ufT6hVhcUwCABBZng2vEynNsgAAAB5CuAMAAPAQwh0AAICHEO4AAAA8hHAHAADgIYQ7AAAADyHcAQAAeAjhDgAAwEOYxBjWsKHZKaeYVa4c6pIAABCBGobXiZRwB5s7N9QlAAAggs0NrxMp4a6IyczMtOTk5DxvK168uMXExBz3MgEAgIJDuCtCMjNTLTFxjcXHd7ZixXJ3t4yLq2AJCRMJeAAARDDCXRHi96dbWloxq1RpssXElMt2W0ZGsvl8XSw9PZ1wBwBABCPcFUHR0aXdJWDpUrP9+0taZuaQkJYLAICIdN11Zps3HxhQEQb970Ia7kaPHm2bN2+2J554wl1/8cUX7YMPPnBNhieffLI9+uijdsYZZ7h+YqNGjbJPPvnEMjIy7Prrr7fevXtbVFRUKIvvGTt3mqWkRFuJEmeFuigAAESeFSvMfD71b7IiO8/dunXrXDibMmVK1rZ3333XPv30U5sxY4b7PT4+3gYOHOhumzZtmv34449u+3vvvWdffPGFvf/++6EoOgAAQFgLSbibPn26NW7c2Lp06ZK17bTTTrMhQ4ZY6dIHmgvr1atnPqVgM1uwYIF16NDB9QUrVaqU/fOf/7TZs2eHougAAABhLSTNsv3793c/x4wZk7VNYS5g//79rhn2H//4h7uelJRkVatWzbpdv2sbAAAAwnz5sU2bNlnnzp2tXLlyWc2yfr8/V/+6vKbyAAAAKOrCKiGtXLnSNb82atTIDa4ITMkRFxdnGzduzLqffq9WrVoISwoAABCewibc/fTTT9a1a1d78MEH7f77789WM9eiRQubNWuWpaamWkpKiiUkJFjLli1DWl4AAIBwFDbz3Kn/naY8mTBhgrsEzJkzx2666SY3wvaGG26wtLQ0N5JWNXwAAAAIo3DXt2/frN/HjRt30PtFR0fbgAED3AUAAAARUHOH0DnzTLPU1DTbt+8dM7s11MUBACCy9OtntmuX2YknWjgg3MGFu4yMNNuwYQ7hDgCA/IS7MBI2AyoAAABw7Ah3AAAAHkKzLCw9/cAlI+OEUBcFAIDIs3u3Vlww04IL5cqFujSEO5h99plZSkppK1Hi5VAXBQCAyHPuuWY+n1ZdMFu3LtSloVkWAADASwh3AAAAHkK4AwAA8BDCHQAAgIcQ7gAAADyEcAcAAOAhhDsAAAAPIdwBAAB4COEOAADAQ1ihAtaokZYfS7Ht2/9tZk+GujgAAESWOXPMUlPNYmIsHBDuYCedpHVlM23//jWhLgoAAJHnwgstnNAsCwAA4CGEOwAAAA+hWRa2caNZWlq07dp1UaiLAgBA5HnvPbN9+8xOOMGsbdtQl4ZwB7MffjBLSSlpJUr0CnVRAACIPD16mPl8ZnFxZuvWhbo0NMsCAAB4CeEOAADAQwh3AAAAHkK4AwAA8BDCHQAAgIcQ7gAAADyEcAcAAOAhhDsAAAAPIdzBoqN18VuxYimhLgoAAJGnbFmzcuUO/AwDrFABu+oqs4yMfbZhQ08zmx7q4gAAEFl+/dXCCTV3AAAAHkK4AwAA8BDCHQAAgIfQ5w72889mqakxlpraJdRFAQAg8gwYYLZ9u1mFCmajRoW6NIQ7mK1fb5aSUtxKlGga6qIAABB53nrLzOczi4sLi3AX0mbZ0aNH2+DBg7Ouz5kzx9q0aWOtWrWye++91/bs2ZN126RJk6x169bWokULGzp0qKWlpYWo1AAAAOErJOFu3bp11rt3b5syZUrWttWrV9uIESNs8uTJNn/+fKtataqNHDnS3bZo0SKbOXOmzZgxwz788EPbtm1btscCAAAghOFu+vTp1rhxY+vS5f/7eC1cuNCaNWtmsbGx7nqnTp3s3XfftczMTFuwYIGr0StXrpxFR0dbx44dbfbs2aEoOgAAgLfC3d69e4/5Rfv37+/Cm4JaQFJSklWrVi3rumrukpOTbceOHXnepm0AAAA4xnB3+eWXu3C2ZMkS8/v9VpCioqIOui3nbXndFwAAoKg76nCXkJBgNWrUsEcffdSuuOIK10/u1wJYdqN69eq2cePGrOv6vUyZMla+fPk8b9M2AAAAHGO4O+uss+y+++6zjz/+2J5//nlLT0+3Pn362HXXXecGOWzXPC/5cPXVV7uBE4EQN3XqVIuPj7dixYq5EbLz5s2zXbt2uT5406ZNs5YtW+brdQAAALws3wMqUlNTXZDTyFWFrtKlS9uPP/5o11xzjc2aNeuon++cc86xgQMHWrdu3dyUJ3/++WfWNClNmza1Dh06uIEUev5SpUpZz55a5B4AAADHNImx+tqpFk0jWE866SS79tpr3RQlp512mrtdtW/qk9e+ffvDPlffvn2zXddz6ZIXjawNHl2LglOlisJ6uqWlLVOUDnVxAACILG3amG3bZlaxokVkuFOTrGrWxo0bZ40aNcp1e82aNe3mm28uqPLhOKhXzywjI9U2bHiJcAcAwNEaP97CyVGHuy+++MLWrFljp5xyiru+fPly1yR77rnnuuuasmSA1lgDAABA+Pe5U5Psrbfe6laZkFWrVtm//vUvt3IEAAAAIizcjR071v7zn/9Y7dq13XUNcpg4caJbJxYAAAAR1iy7efNmq1u3brZt559/vm3ZsqUgy4XjaPFis5SUUlas2LOhLgoAAJGnUSOzDRu0hJbZMg1OjLCau1q1atlbb72VbZtGy2ogBSLT/v26FLP09AqhLgoAAJFnwwYzn+/Az0isuXvggQfs7rvvdpMMa5WIDRs22NatW13TLAAAACIs3DVo0MA++ugj++yzz2zTpk1WtWpVa9asmVsmDAAAABEW7kRTn1x66aVuKTDZu3evu7DeKwAAQISFO02F8thjj9mePXuytvn9fouKirJffvmloMsHAACAwgx3L7zwgutz165dOytePF8VfwAAAAinqVC6du1qxYod9UBbAAAAFLKjTmhNmjSxxZoYDQAAAJFfc6em2F69elmdOnWsYsWK2W4bN25cQZYNAAAAhR3uzjzzTOvZs+fRPgxh7NxzzdLS9tvu3f8xs16hLg4AAJFl5Eiz5GRNJ2IRGe769OlTOCVByMTFmWVkZNiGDYsIdwAAHK1bb7WI7nOnaU9ef/11a9u2rV1yySW2fv166927t+3evbtwSggAAIDCC3fqVzdz5kzX706TGJcrV86Sk5Nt2LBhR/tUAAAACHW4U7AbO3as/eMf/3ATFyvcPffcc4ygjWCaj3r37ihLSYkLdVEAAIg8v/1m9tNPB35GYp871dJVqlQpq4k2sByZgh4i09dfm6WknGAlSvw71EUBACDyXH21mc93oBP7unWRV3N30UUX2dNPP+064AcCnZpqGzZsWBjlAwAAQGHW3A0ePNh69OhhjRo1sv3799vll1/u5rubMGHC0T4VAAAAQh3uYmNjLSEhwX744Qc3UlbX69WrxzqzAAAAYeCoE5kCnVSuXNldZNOmTe5n9erVC7p8AAAAKMxw17x581yDJ3S9VKlStmLFiqN9OgAAAIQy3H311VfZrm/fvt1eeeUV1zQLAACACAt3FSpUyHX9kUcesWuuucY6duxYkGUDAABAYU+FkpctW7a4kbMAAACIsJo7TYMSLC0tzVauXGmtW7cuyHIBAADgeIS7unXrZrterFgx69Chg7Vo0SI/r48w0KSJWXr6PtuypZ+mpA51cQAAiCxLl5plZJhFR1tEhrs+ffoUTkkQMqVK6Zj0W4kS20NdFAAAIk+1ahZOjjrcPfTQQ0d0v6eeeio/5QEAAMDxHFBRunRpmzdvnqWnp1tcXJyVKFHC5s+f76ZE0W2BCwAAACKg5u63336zl19+2Zqoo9b/ad++vY0aNcoefvjhgi4fjoO//tLAmOK2d2+rUBcFAIDIM2GC2Z49ZmXLmnXvHnnh7ueff7ZLL70027bzzjvPfv/994IsF46j1avNUlJirESJW0JdFAAAIs+wYWY+n1lcXFiEu6Nulq1Zs6a99NJLlqFRIaZRlun23HPPWf369QujfAAAACjMmrvHHnvMevbsaa+99ppVqlTJNm/ebKeffrqNHTvWCsLChQvt+eefd1OslC1b1oYNG2ZnnXWWTZo0yWbMmOFCpZqEBw0a5Pr7AQAA4BjCXe3ate2jjz6y5cuX27Zt26xGjRp2/vnnW1RUlB2rlJQU69evn82aNcvOPvtse/311+3RRx+1u+66y2bOnOnCnQZr6D5Tpkyx7mFQ9QkAABDxy49lZmbazp07bePGjXbOOefYL7/8UiCFUa2cQqKeW5KTk61UqVK2YMECa9OmjZUrV86io6PdGrazZ88ukNcEAAAo0jV3a9assa5du7oatA0bNljz5s3tpptusmeeecZatTq20ZZlypSxoUOHWufOna1ixYpuvVo1/44cOdIaNGiQdb+qVataUlLSMb0WAACAFx11zZ36wKk59IMPPrDixYvbaaedZqNHj7YXX3zxmAujaVZeeOEFmzt3rn3++ec2ZMgQ1ySrmsKczb4F0QwMAABgRT3cqQn2lltuyRaw4uPjbf369cdcmCVLlrj+e2eeeaa7fu2117qmWl3UBByg36tXr37Mr4fc1BSe85KamhrqYgEAgMJqlj355JNt9erVbmBFgK5XqVLFjlWdOnXcQAk196rpdenSpW6qlTvuuMOeffZZ69SpkxtBO23aNGvZsuUxvx6y076Oj7/TjVQOFhdXwRISJlpMTEzIygYAAAop3Km/Xbdu3ey2226ztLQ0e/PNN10g07ZjpcmRe/fubV26dHHTnKhfn6ZYadiwof31119uIIUCiK5rOhYUjDJlzKKj023fvj+tUqXJFhNTLuu2jIxk8/m6uP1OuAMAIA81a5qVL28WG2sRGe5uvPFGK1++vJuWRE2jmpfu3nvvdaNZC4ICnC45KfDpgoJ32WVmqak7bPHirhYd/Y1FR7M2MAAAR+yTTyycHHW4Gz58uPXp04dmUQAAAC8MqND8cpp7DgAAAOHnqGvuNIL1gQcesLZt21rlypWzTUmiAREAAACIoHD3xhtvuJ/z5s3Ltl0hr6BWqsDxtWKF2f79J1pq6shQFwUAgMjTqZPZli1mlSqZTZ0aOeFOkxSrr92vv/5auCXCcbdtm9b11UjYRqEuCgAAkWfRIjOfT3OHWUT1uZs8eXK263mNaAUAAECEhDu/35/t+u+//14Y5QEAAMDxCHes7QoAAODBqVAAAAAQvo54QEVGRoYtWLAgq3lWS48FXxcmNgYAAIiQcHfyySfbU089lXW9QoUK2a6rmZZwBwAAECHh7pMwWzcNAAAAudHnDgAAoCivUAHvOfVUrVCxz3y+mWbWOdTFAQAgstx1l9nOnWbly1s4INzBatY0S03da5s2vUy4AwDgaD36qIUTmmUBAAA8hHAHAADgIYQ7AAAAD6HPHWzhQrOUlEqa8CbURQEAIPLUqGHm85nFxZmtWxfq0lBzBwAA4CWEOwAAAA+hWfY4S01NtfT09GzbkpOTs63RCwAAkF+Eu+Mc7Dp06GY+3/Zs2zMzMywxca3FxmZadHTIigcAADyAcHccqcZOwa5y5SkWHV06a3tq6hZbtao1tXcAAOCYEe5CQMEuONwF/w4AAHAsGFABAADgIYQ7AAAADyHcAQAAeAh97mD162tQx0778ccHzOzFUBcHAIDI8sYbZvv3m5UsaeGAcAerVEnhLs2io5eGuigAAESeK6+0cEKzLAAAgIcQ7gAAADyEZlnYli1qli1hGRkXhbooAABEns8++/8+d2HQREu4g333nVlKSnkzGxHqogAAEHluu83M5zOLizNbty7UpaFZFgAAwEsIdwAAAB4SduFu9erVdvvtt9v1119v7du3t+/UZmhmc+bMsTZt2lirVq3s3nvvtT179oS6qAAAAGEnrMJdSkqKde3a1YW7d955x+655x677777XOAbMWKETZ482ebPn29Vq1a1kSNHhrq4AAAAYSeswt2SJUuscuXK1rJlS3e9WbNmNnbsWFu4cKH7PTY21m3v1KmTvfvuu5aZmRniEgMAAISXsAp3iYmJVqVKFRsyZIhrku3cubOlpqZaUlKSVatWLet+qrlLTk62HTt2hLS8AAAA4Saswl16erp98cUXrr/drFmz7M4777Tu3bu77VFRUbnun9c2AACAoiys5rlTs+vpp59ujRo1ctfVFFu8eHGrXr26bdiwIet+GzdutDJlylj58pqbDaGkmlWF75z0ucXExISkTAAAFGVhFe6aNm1qTz75pBshW79+fVu+fLkLD/Hx8XbXXXe5UKcAOHXqVLetWLGwqngscvTZdOjQzXy+7blui4urYAkJEwl4AAAU5XBXqVIlGz9+vAt46lMXHR1tY8aMsdq1a9vAgQOtW7dulpaWZmeccYYNHz481MX1jPh4BbUttnhxczP75ogfpxo7BbvKladYdHTprO0ZGcnm83VxtxPuAACety70q1KEbbiTCy+80N5+++1c26+99lp3QfhRsAsOdwAAIHRo1wQAAPAQwh0AAICHhF2zLI6/VavM9u8vY2lpvUJdFAAAIs/QoWY7d5ppFo9HHw11aQh3MPv7by39doKZ/TPURQEAIPK88oqZz6epIsIi3NEsCwAA4CGEOwAAAA8h3AEAAHgI4Q4AAMBDCHcAAAAeQrgDAADwEMIdAACAhxDuAAAAPIRJjGEVK2qFilTbsWOZmTULdXEAAIgszZqZbdliVqmShQPCHaxhQ7PU1F22ePFAM/sm1MUBACCyTJ1q4YRmWQAAAA8h3AEAAHgI4Q4AAMBD6HMH++ors5SUk2z//smhLgoAAJGneXOzjRvNYmPNPvkk1KUh3MFs716FOx0Kp4e6KAAARJ5Vq8x8PrOdOy0c0CwLAADgIYQ7AAAADyHcAQAAeAjhDgAAwEMIdwAAAB5CuAMAAPAQwh0AAICHEO4AAAA8hEmMYeecY7Z//x5LTBxrZveHujgAAESWRx4x27PHrGxZCweEO9hpp5mlpqbY2rUzCizcZWZmWnJycp63FS9e3GJiYgrkdQAACLnu3S2cEO5Q4DIzUy0xcY3Fx3e2YsVyt/zHxVWwhISJBDwAAAoB4Q4Fzu9Pt7S0Ylap0mSLiSmX7baMjGTz+bpYeno64Q4AgEJAuIOlpKhZtpj5/ZUK9Hmjo0u7CwAAnpaUpNoLnfjMqlULdWkIdzBbskQBr6KZvR3qogAAEHkuusjM51O/I7N160JdGqZCAQAA8BLCHQAAgIcQ7gAAADwkbMPdypUrrW7durZhwwZ3fc6cOdamTRtr1aqV3XvvvbZHkwUCAAAg/MPd1q1b7dFHH7W0tDR3ffXq1TZixAibPHmyzZ8/36pWrWojR44MdTEBAADCTtiFO81/1q9fPxswYEDWtoULF1qzZs0sNjbWXe/UqZO9++67bhUEAAAAhHG4U43cJZdcYpdffnnWtqSkJKsWNG+Mau60tNWOHTtCVEoAAIDwFFbh7r333rO///7bevbsmeu2qKioI9oGAABQlIXVJMYJCQm2ceNGu/7667O23XnnnXbNNddkDawQ3adMmTJWvnz5EJUUAAAgPIVVuJsyZUq267Vq1bJJkybZ7t27rWvXri7Uqd/d1KlTLT4+Ps9F6XH0Lr1Uy49tt+XLu5rZ9FAXBwCAyPLxxxo0YFY8PGJVeJTiMM455xwbOHCgdevWzY2gPeOMM2z48OGhLpZnlC2rcJdhxYr9GeqiAAAQeWrVsnAS1uHut99+y/r92muvdRcAAAAcHO2aAAAAHhLWNXc4Pnw+s/37S1p6eptQFwUAgMjz5ptmyclmpUub3XprqEtDuIPZL7+YpaSUM7P+oS4KAACRZ+DAAzUlcXGEOxRNWllEk1DnVLx4cYuJiQlJmQAA8ArCHY6rzMxUS0xcY/HxnXNNZRMXV8ESEiYS8AAAOAaEOxxXfn+6paUVs0qVJltMjJqCD8jISDafr4tbW5hwBwBA/hHuEBLR0aXdBQAAFCymQgEAAPAQwh0AAICHEO4AAAA8hHAHAADgIQyogJUsqVGsGZaausXMyoS6OAAARJaqVbP/DDHCHeyKK8xSU7fb4sU3mdk3oS4OAACRZdkyCyc0ywIAAHgI4Q4AAMBDCHcAAAAeQp872Pffm+3fX85SUx+zcJWamuqWJstL8eLFWbIMABA6d99ttm2bWcWKZuPHh7o0hDuYbdpklpJS0syaWrgGuw4dupnPtz3P2+PiKlhCwkQCHgAgNObNM/P5dEKycEC4Q9hTjZ2CXeXKU3KtR5uRkWw+Xxd3H8IdAACEO0QQBbuc4Q4AAGTHgAoAAAAPIdwBAAB4COEOAADAQwh3AAAAHkK4AwAA8BDCHQAAgIcwFQqsenWtUJFiGze+b2Y3hro4AABElo4dzbZvN6tQwcIB4Q523nlaBWKPbdv2NOEOAICjNWqUhRPCHSJeZmamJScn59rOmrMAgKKIcIeIlpmZaomJayw+vrMVK5a9CylrzgIAiiLCHSKa359uaWnFrFKlyRYTUy5rO2vOAgCKKsId7NNPzVJSKlpm5nsWqVh3FgAQMrVrm61ff2CE4q+/hro0hDuolksXNWkSjgAAOGp79pjt3n3gZxhgnjsAAAAPoeYORW4UrTCSFgDgVYQ7FLlRtMJIWgCAV4VduHvrrbfszTfftKioKDvhhBNs8ODBVq9ePZs0aZLNmDHDMjIyrEmTJjZo0CArUaJEqIuLCBtFK4ykBQB4WVj1uVuxYoVNmDDBXn31VZs7d6716NHDevXqZYsWLbKZM2e6cPfhhx/atm3bbMqUKaEuLiJoFG3OCwAAXhVW4a58+fL2+OOPW8WKFd111dht3brVFixYYG3atLFy5cpZdHS0dezY0WbPnh3q4gIAAISdsGqWPeuss9wl0Bn+ySeftCuvvNKSkpKsQYMGWferWrWq2wYAAIAwrrkL2LNnj/Xp08d8Pp+NGDHCbVMfvGA5rwMAACDMau4kMTHRevbs6Zpkn3vuOStZsqRVr17dNm7cmHUf/a5tKBjnn2+2f/8u+/XXoWY2MtTFQRjy+/1ZF+BY6ct54AJ4wrhxZvv2mZ1wgoWDsAp369evt06dOtmdd97pLgEtWrSwkSNHutvKli1r06ZNs5YtW4a0rF4SG2uWmppqq1cvCnVREGbUPWLTpk22Y8cOgh0KlILdSSedZFWqVMlzuiIgorRta+EkrMKdpjvZtWuXGymrS4BG0Hbo0MENpND0FQ0bNnS1ewAK119//eVOvKeffjpTD6FApaWluVYYHWNnnHFGqIsDeEpYhbuHH37YXfLSpUsXdwFw/GrtUlJS7JxzznEregAFSTMfxMXF2erVq92xRu0dUHD4jw3bsUPNssUtM/O8UBcFYSTQDEu/KBSWwLFFkz8i3vLlOpGaaWL8Cy8MdWkIdzBbtswsJeUkM3sx1EUBACDytGtn5vNpbUuzdetCXRrCHYDItW7dOje46ueff87z9r1799rLL79sn3/+uWv20/KFGqCllW8CfQg1UfoLL7xgy5cvd02FqkVq37693XHHHYVW7jFjxtiGDRvsiSeeKLTXAFB00ckBgCdpBLgCmvoNvv322zZnzhw30v6nn36yRx55xN1n586ddtNNN7m+X1r1RvfR0oYa0PXii9RkA4hM1NwB8CStQ71lyxYbNGiQq5ETTaWkJQ5Xrlzprr/55ptWuXJl6969e9bjTj75ZHv66afzXAVn1qxZLihqpKfo9yVLlthLL73ktul1+vbta82aNXM1gJrCadmyZS5g7tu3zx588EGLj48/bvsAQNFEuANwdJ599sDlcBo2NAua0si57jqzFSsO/9h+/Q5cjsH333/vJkMPBLuA2NjYrHkyFfI0tVJOZ555prvk5ffff7eFCxe6Odo0jYdW0Xn99dddKFQz8S233OJCn4Lln3/+aW+99ZYbbfzOO+/Y888/T7gDUOgIdwCOzq5dBzoOH84pp+TetnnzkT1Wr1EADjcKU6M1j3ak5tlnn+2CnajWTiGua9euWbcryP3222921VVXuZq6mTNn2tq1a+3bb791fQABoLAR7gAcnRNPPDAi7HAqV85725E8Vq9xjFQjp1o1TXwePE+f1qweOnSoq0XTfVQLl5OC2NSpU23UqFG5poIpVapU1u+an+3CCy+0sWPHZm3TxLwVK1a0RYsWudfR/JxNmza1Bg0a2JNPPnnM7wsADocBFQCOjppLNdT/cJecTbKibUfy2GNskhWNilXI+ve//+36vAUGUDz22GNWoUIFO+GEE+zmm292o1YVzgL96BTO1C/v1FNPPewcf40bN7b//ve/9uuvv2Y1BavJd/Pmza5Wr0mTJnb77be7ALhgwQI3WhcAChs1dwAimgKTasWCqVZOtWUa+frss8/a9ddfbzExMe6+rVq1ylq+8MQTT3R94jSAom3btlayZEm3Xcsddu7c+bCvfdZZZ9nw4cPtoYcecs+tJt7nnnvOqlev7pZLvP/++93zqt+fgqDW6N29e3ch7QkAOIBwByBi1ahRw/VvOxj1jRs2bNghn0NBTAHwSGj+O11y1hDqkpMGZGh0bbAHHnjA/dSIWgAoLIQ72JVXak6wrfbll23N7NNQFwcAgMjyyy8awaVRWhYOCHcw9TXPzPRbVFSyFRXqCJ+cnPv9quO9mu8AADhi5cpZOCHcocjJzEy1xMQ1Fh/f2S1JFSwuroIlJEwk4AEAIhbhDkWO359uaWnFrFKlyRYT8//ftjIyks3n6+KmziDcAQAiFeEO9scfZikppS0t7fCjA70kOrq0uwAAcEw0KEuTr2uOzgKYyulYEe6QFe7Mila4y89C9KrVy4l+egBQxD377IHVdzRJO+EOiJxg16FDN/P5tue6jX56AIBwQrgDjoBq7BTsKleekq0pl356AIBwQ7gDjgL99MJPrVq17Oyzz85aP1ZLjSloDxw40K644ooCe5077rjDrrvuulyTGBeEMWPGuGXQnnjiiTxv//nnn9191q5d61a70NJpvXr1cqtwBHz11Vc2YcIE27p1q7uupdf69etn9erVK/DyAghvhDsAEW/SpElWtWrVbNe1JJjWd410K1eutO7du9vIkSOtWbNmbtuPP/5od955p40ePdouu+wyt27t0KFD7eWXX84Kc59//rl17drVpk+f7pZJA1B0EO6AQpoQWRhscfypiTwpKckqVKjgrmu9VwWjZcuWuVq9ffv22YMPPmjx8fGuNuyvv/6ynTt3uloxLVempci0JNkff/xhgwYNsr1791pcXJxt3/7//S1XrFjhnlO3RUVFuaDVrl07W7dund1+++0uhOn1dFxoybEPPvjAVq9ebWXLlrWxY8e6WrUjpQB3yy23ZAU7qVu3riu7yiujRo2ye+65J1stnWr1nnnmmTyPP5VRz/H111+7mj6tf6vn+vTTT10Not63llRTX1O9vmoFdZyfdtpp9vDDD1vlypXd/nrsscfcPti0aZNbCk731Xtr3ry52x///e9/bf369Xb11VfbkCFD8v2ZAjg6hDugkCZE9upgCw0KO5KlWBs2NJs7N/u2665TMDr8YzXY7GgGnClcqblSAaxEiRKuOXbcuHHuth9++MH+/PNPe+utt1zYfuedd+z555934U6WLl1qc+fOtfLly1ufPn3sjTfecE26AwYMcE2wnTp1st9//z2rOVav0bt3bxdkLrnkEhckb7zxRhcAVXuoMHPxxRe74PPiiy+6GsR58+ZZtWrVrEuXLu611MR7NDV3ed1fryE7duxwAbWhdngOwYEwpzVr1lhCQoIrv8KYyql9pPIpOCrcTZw40QU83U/H93/+8x8bPHiwa/6dMWOGtW7d2r13BT99Btq3qi2UXbt22dSpU23z5s3uuRRQ1XwOoPAR7o7jlBn6Fq9aBHh/QuTAYIu1azu7k1zp0qU9U6OnqZw04v9wTjkl97bNm4/ssXqN/DTL/vbbb9atWzdXg6WwJfpdNXUzZ850tU3ffvutq20KaNSokQt2cu6557raNwUmNX2+/vrrbrtCSSBMff/99y6oKdiJflfIUa1Xx44d3baWLVv+3z44xWrWrOnuE7geXAN4JFQzqPB0qNvlUPfJiwKXApv2k4LxlVpk2sxOPfVU9/7lk08+cbWaCsCB11DNp9x7772uRk/7XuFSYfHCCy/Men4FRlEtX6VKlWzbtm1HVT4A+Ue4O45TZmRmZlhi4lqLjc206OiQFA/HcaCFV5c50xyd/5ebDqly5by3Hclj9Rr5HVzx6KOPuuBRp04dq127ti1atMj1R1OtmZoqGzRoYE8++WTWY0qVKpUtKAV/AQv+PTBgI68vaNqWkZHhfldQCtxXVJN4LFQjp2bgq666Ktt21YqpmVk1ZupTp/soSAZTDaWaUq+//vpcz5vz2Asuc4DCnGoxFQRFr7d79273+/333+++sLZp08YaN27sQl/wvilZsuRB9yuAwkW4O05TZkhq6hZbtap12P2TU6VFyZJptnv3z2Z2UaiL4xleXebsaJtMg+Vspi0Mam5Vc6RC3rRp09ygiiZNmrh+ZtrnalYMBLGDUf+z+vXr29tvv+1CoWr8VHulGrkLLrjAfD6fffPNN1nNsvPnz7fhw4cXyvtRE7ACnGoYA82s3333nWs6Vf9AUX+7xx9/3NU8qnyBWrc333wzq/YxP9S8refQT4Xgp59+2hITE11tnQZsqNlW+0m1narFC9SWAkVOw4YHmivy+lYbAoS741iTE65TaFx0kYLnTlu8uI+ZfRPq4ngO06ccfxoQoBolhTs1laqWSYMGVKumWiY1OwZqoA5GQUbPo/5msbGxLjiJBmq89NJL7nY176p2SwFMAVIhJ7/UX+3999/Puq7XUUBTeNIgDPXf0yAO1QJrYIYGUei9iJqFtV01koHuH1WqVLFXXnklV23e0dB0K3qdDh06uOdUk20gxGqf9u/f3zVpq3ZSQVfNs0CRNPc4fHM9CoQ7ABFN/exyUg2SarcCZs2ale12jWCVvn375gozAeofd7BaL9WiKTjmpBGjmpMuQCNGdQkYNmxYns+ncuQsSzD193vttdfsUFSrGOjrdzg531dwmRUmVSsXaFo92ChXDZDQJS8KpcE0VQuA4yf38D4AAABELMIdAACAh9AsC9MsBykp5W3//hdDXRQcYiqdSJ4+BQA87brrDsz1pAEVYdD/jnAH27lT4U7TNZwX6qIUeYeaSud4T58SmD8t3EZ3wzsCx1bgWAMi1ooVBybxDJMR44Q7IAKm0imM6VMOV0Oo0ZcaXaq5zTQ6EyhoOrZ0jOW1uguA/CPcAUVw+pQjrSHU6gKa002jTzXPGTUsKKgaOwU7HVuasgVAwSLcAUXQoWoIg5dM01QYmsdM87dp4l+FOwIeCoJq7BTsNJ8fgIJFuAOKsJw1hIdaMq1EieKuVu/NN18+5iW1ULTpCwJNsUDhiahwt3jxYjcr/P79+91C3CNGjKBKHzgOS6YFavX+/LOLW5FBtS4AgPAUMeFu27ZtbrkbzdKuxcH186GHHnJrHAJFgUKVlpY62G151YTkd/qUg/X5O1gZDvb6x1IGAIDHw50W/1ao00W07I3WWdy8ebPr9A142aGaSxWskpL+turVT8vVH64gp085WBkO9foFXQYAgIfC3YYNG1xTbIBOFOqIm5SUdMThTs25smbNmkIrp0aAZWSkWHLytxYdXSrbbWlpO6xECb/t2/etpaeXOez2Q92m11Az2bfffutGMeYuw75cZTjYc5UoEWN+v07WW23fPiu01yno9xPO+7QwXscsysqUuddKlMheo5aWttNSUvpb6dL/k+22zMz99vffT9jSpUsL7P3kVYaDvf7/l+Ep++GHH3KVAQA8o1gxLcZ84OdPPxXKSwSySyDLHEqUP0JmKB0/frwlJiba8OHDs7ZdccUVNnbsWKtbt+4RPcfcuXNtwIABhVhKAACAwjNq1Ci7TitieKHmrnr16vb1119nm6dr+/btbvuRatKkidspNWrUcFM8AAAARALV2GlaKmUZz9TcaUDFNddcY6+//rrVrFnTpk6dau+//777CQAAgAgLd/Lll1+6mjel15NPPtk10WrmfAAAAERguAMAAMChMUU4AACAhxDuAAAAPIRwBwAA4CGEOwAAAA8h3AEAAHgI4Q4AAMBDCHcAAAAeQrj7P4sXL7Z27dpZ69atrUuXLrZp06ZQFymijR492gYPHpx1fc6cOdamTRtr1aqV3XvvvbZnz56Qli+SvPXWW3bttde6tQRvvvlm+/777932SZMmueO1RYsWNnToUEtLSwt1USNGQkKC26e6/Otf/3LrVgv79NitXLnSrfe9YcMGd52//fx76KGHrHnz5u7cpMs999zjtnOc5t/q1avt9ttvt+uvv97at29v3333nTePU01iXNRt3brVf/HFF/t//fVXd/3VV1/1d+3aNdTFikhr16719+rVy1+vXj3/oEGD3LZVq1b5L7vsMv+GDRvc9aeeesr/8MMPh7ikkWH58uX+K6+80h2j8sknn/gvv/xy/2effeZv3bq1f9euXf709HT/Pffc4x8/fnyoixsR1qxZ447HLVu2uOuvvfaa/7bbbmOfFgDt03bt2vlr1qzpT0pK4m//GLVo0cL/22+/ZdvGcZp/+/bt8zdp0sQ/f/58d/3TTz91/1+9eJxSc2dmS5YssVq1armL3HLLLfbNN9/Y5s2bQ120iDN9+nRr3Lixq/0MWLhwoTVr1sxiY2Pd9U6dOtm7775rmZmZISxpZChfvrw9/vjjVrFiRXe9Xr16tnXrVluwYIH7llmuXDmLjo62jh072uzZs0Nd3Ihw5pln2qJFi9wShunp6bZ+/XqrUKEC+/QYaV/269fPBgwYkLWNv/3827Jlizs2X3jhBVfD3LdvX/P5fBynx3iur1y5srVs2dJd17E5duxYTx6nhDsz13xQrVq1rOsxMTHun31SUlJIyxWJ+vfv7/4w9E8nQPsxeP9WrVrVkpOTbceOHSEqZeQ466yzrEmTJu53/aN58skn7corr8xzn3K8HrkSJUrYsmXL3D90fSHRlxH26bEZOXKkXXLJJXb55ZdnbeNvP//UNUh/+2qanTt3rvti16NHDxfwOE7zJzEx0apUqWJDhgxxTbKdO3e21NRUTx6nhLsDTdMWFRWVa3uxYuyegpLX/s1rG/Km/h99+vRx/9hHjBiR5/5jfx6dRo0a2RdffOFCSffu3d029mn+vPfee/b3339bz549c93G337+nHfeeTZu3DiLi4tz+6tbt27u71/nJY7T/Ncuf/HFF66/3axZs+zOO+90f/va7rXjlPRiZtWrV7eNGzdmXVeS3759u9uOgt+/+r1MmTKuyRFH9m3zn//8p5UtW9ZeffVVO/HEE/PcpxyvR2bdunX21VdfZV2Pj493NXmqGWWf5n+AisKdTprq+C86caqWhL/9/Pn222/tww8/zFURocETHKf5Exsba6effrr7YiequS9evLgnz1GEOzPXjPDzzz/bqlWr3PUZM2bYBRdckNXPCcfm6quvdn2cAn88U6dOdSdUakYPT31u1Mx94403uhqmkiVLuu0aJTdv3jzbtWuXCyXTpk3L6keCQ9u5c6cbDRcYzaljU8fiHXfcwT7NpylTptj777/vRhzqEhjRqZGH/O3njyoZ1N82MHPD66+/7rppKDRznOZP06ZNXRNsYITs8uXL3X7WMem147R4qAsQDhTinnvuOXvggQds//79rqO1TqQoGOecc44NHDjQNSvoW+cZZ5xhw4cPD3WxIoJOkPonrj43ugRMmDDBOnTo4DpTq0mhYcOGeTaJIbc6deq4v/W77rrL/fNWTegrr7xi5557rv3xxx/s0wLE337+qf9ir169XH/QjIwMV7v0/PPPu2ZajtP8qVSpko0fP971XVafOvUNHzNmjNWuXdtzx2mUhsyGuhAAAAAoGJFb5wgAAIBcCHcAAAAeQrgDAADwEMIdAACAhxDuAAAAPIRwBwAA4CGEOwCeV6tWLfvhhx8Oevu2bdvchLFXXXWVNWjQwE1gqrkvNcFpsPnz59ttt91mF110kZuHTHOQrVix4qjLo3Vtg9dgBYCCRLgDUKRt3rzZLZulJci0Oo2WfdKanosXL7b77rsv634vvfSSm9xca/xq+TLNaH/llVda165dbeXKlfla1xYACgPhDkCRpln/tWrFgw8+6Gawl7PPPtueeeYZN4O9livTkkUvvviim83+0ksvdetRlipVyjp37uyWLfv999/zfG4tGdW8eXNX06cVRRQI5ZtvvnE1hDJs2DD3e+BSt25dt1pGSkqKW5lAq5GoJlE1hVqJILBsGgAcDOEOQJH2+eefW+vWrXNt1xJEL7zwgls8fMmSJVajRg0777zzct1P69QquOX0119/uZq+yZMn29KlS+2WW26xwYMHu8AW7JFHHnG1hbp8/PHHVq1aNevfv78Lj6+99prNnj3bLUOncqpMWpKKhYUAHArhDkCRpv52gRq7Q91Ha04fDdXuaWH36dOn2/fff2/t27d3Tb2qDcyL+vepyVdNtlrjUt5++23r0aOHnXbaaVayZEnr16+fJSYmHrL/IAAQ7gAUaVWqVHH97vKyZcsW97Ny5coHvc+uXbtyDbwQLfCuWjst8q6mWw2g0KLlB6t1GzRokEVFRdnQoUOztq1fv94ee+wxF/h0UZOwAqPP58vnuwVQFBQPdQEAIJSaNm1qH3zwgRtUEUw1ZNdcc40bZKFgNmTIEFdjdv7552e7n8LYnj17XHDLWdsXExPjtqelpdmXX37pauYCfe2CqS+fmmX1WnpMQGxsrAt9GrgRsGbNGtdEDAAHQ80dgCJBYUuDEQKXTZs2ue3qw6Zm0+HDh9vWrVtdzZpC3D333GNXX321C3MKWd27d3f96zRSVv3mFOjGjh1rCxcudAMdclLtmkbSaqoUjcRVDaGcdNJJ2e43d+5c17dOIbBixYrZblNfPg3k0HOpxm7q1Kl2ww032I4dOwp1XwGIbFF+euYCKALz3OWkkKVRq7J27Vo3avbrr7+2vXv3umbYa6+91u6+++5sNWnqPzdt2jR3f/Wd08jWvn37Wv369fN8Xd134sSJLjSqz56e78Ybb3Svq750qq1TgFTzrwZQBDfvvvLKK+55FfpmzZpl27dvdwMq7r//frvssssKZT8B8AbCHQAAgIfQLAsAAOAhhDsAAAAPIdwBAAB4COEOAADAQwh3AAAAHkK4AwAA8BDCHQAAgIcQ7gAAADyEcAcAAOAhhDsAAAAPIdwBAAB4COEOAADAvON/AT/BFl49g+oBAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...:  25%|██▌       | 2/8 [23:59<1:05:34, 655.83s/it] "
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...:  38%|███▊      | 3/8 [43:44<1:14:47, 897.45s/it]"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...:  50%|█████     | 4/8 [1:11:51<1:20:37, 1209.26s/it]"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...:  62%|██████▎   | 5/8 [1:37:00<1:05:52, 1317.36s/it]"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...:  75%|███████▌  | 6/8 [1:37:23<29:14, 877.04s/it]   "
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...:  88%|████████▊ | 7/8 [1:41:58<11:20, 680.37s/it]"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing cell types...: 100%|██████████| 8/8 [1:42:04<00:00, 765.58s/it]\n"
     ]
    }
   ],
   "source": [
    "table = []\n",
    "\n",
    "for cell_type in tqdm(cell_types, desc=\"Processing cell types...\"):\n",
    "\n",
    "    # Overlapping DEGs in Alzheimer's module: number of DEG proteins that are in the Alzheimer's module.\n",
    "    degs_cell_type = pd.read_csv(f'../data/complete/degs_{cell_type}_mapped.csv')\n",
    "    degs_total = degs_cell_type.dropna(subset=['gene_id'])['gene_id']\n",
    "    degs_protein = degs_cell_type.dropna(subset=['protein_id'])['protein_id']\n",
    "\n",
    "    G = nx.read_graphml(f'../data/complete/graphs/{cell_type}_network.graphml')\n",
    "    ppi_proteins = set(G.nodes())\n",
    "\n",
    "    # Alzheimer's module proteins present in the PPI of this cell type.\n",
    "    alz_in_ppi = lcc_alz.intersection(ppi_proteins)\n",
    "\n",
    "    # Calculate the LCC of the disease network for the cell type. For this we use the DEGs found in the overall disease LCC.\n",
    "    degs_cell_type_filt = pd.read_csv(f'../data/filtered/degs_{cell_type}_mapped_filt.csv')\n",
    "    lcc_cell_type = functions.calculate_lcc_for_cell_type(degs_cell_type_filt['gene_id'], gen_pro, pro_pro, G_ppi)\n",
    "\n",
    "    # Generate 1,000 random networks\n",
    "    random_networks = functions.random_subset_generator(lcc_cell_type, G_ppi, 1000)\n",
    "\n",
    "    # Calculate the size of the LCC for each random network\n",
    "    random_lcc_sizes = [len(functions.calculate_lcc_from_prots(network, pro_pro, G_ppi)) for network in random_networks]\n",
    "\n",
    "    # Calculate statistics of random modules\n",
    "    random_lcc_mean = np.mean(random_lcc_sizes)\n",
    "    random_lcc_std = np.std(random_lcc_sizes)\n",
    "\n",
    "    plt.hist(random_lcc_sizes, bins=30, alpha=0.7, color='blue', edgecolor='black')\n",
    "    plt.axvline(len(lcc_cell_type), color='red', linestyle='dashed', linewidth=2, label=\"LCC real\")\n",
    "    plt.axvline(random_lcc_mean, color='blue', linestyle='dashed', linewidth=2, label=\"Random LCC mean\")\n",
    "    plt.xlabel(\"LCC size\")\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.title(f\"LCC size distribution in random networks - {cell_type}\")\n",
    "    plt.legend(loc = 'best')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'../figures/significance/random_modules_{cell_type}.svg', format='svg', dpi=1200)\n",
    "    plt.show()\n",
    "\n",
    "    table.append([cell_type, len(degs_total), len(degs_protein), len(alz_in_ppi), len(lcc_cell_type), random_lcc_mean, random_lcc_std])\n",
    "\n",
    "table_df = pd.DataFrame(table, columns=[\"Cell Type\", \"Total DEGs\", \"DEGs mapped to protein\", \"Cell Type proteins in main LCC\", \"Cell Type LCC size\", \"LCC mean\", \"LCC std\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "26ab0c73b66197fb"
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "outputs": [
    {
     "data": {
      "text/plain": "                           Cell Type  Total DEGs  DEGs mapped to protein  \\\n0                          astrocyte        3176                    2878   \n1                    microglial cell        1585                    1501   \n2                    oligodendrocyte        3383                    3052   \n3               glutamatergic neuron        4436                    3963   \n4             inhibitory interneuron        4130                    3769   \n5  endothelial cell of vascular tree         379                     367   \n6     oligodendrocyte precursor cell        1963                    1832   \n7                           pericyte         111                     107   \n\n   Cell Type proteins in main LCC  Cell Type LCC size  LCC mean    LCC std  \\\n0                             532                 183    53.963  13.744804   \n1                             334                  62     7.218   3.662032   \n2                             554                 191    57.553  14.372585   \n3                             733                 277   120.873  12.425573   \n4                             673                 251    93.186  14.119752   \n5                              73                   4     1.115   0.328291   \n6                             320                  49     4.996   2.286916   \n7                              20                   1     1.000   0.000000   \n\n   p_value  adjusted_p_value    z_score  \n0      0.0               0.0   9.388057  \n1      0.0               0.0  14.959456  \n2      0.0               0.0   9.284830  \n3      0.0               0.0  12.564974  \n4      0.0               0.0  11.176825  \n5      0.0               0.0   8.787934  \n6      0.0               0.0  19.241635  \n7      1.0               1.0        NaN  ",
      "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 Type</th>\n      <th>Total DEGs</th>\n      <th>DEGs mapped to protein</th>\n      <th>Cell Type proteins in main LCC</th>\n      <th>Cell Type LCC size</th>\n      <th>LCC mean</th>\n      <th>LCC std</th>\n      <th>p_value</th>\n      <th>adjusted_p_value</th>\n      <th>z_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>astrocyte</td>\n      <td>3176</td>\n      <td>2878</td>\n      <td>532</td>\n      <td>183</td>\n      <td>53.963</td>\n      <td>13.744804</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.388057</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>microglial cell</td>\n      <td>1585</td>\n      <td>1501</td>\n      <td>334</td>\n      <td>62</td>\n      <td>7.218</td>\n      <td>3.662032</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>14.959456</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>oligodendrocyte</td>\n      <td>3383</td>\n      <td>3052</td>\n      <td>554</td>\n      <td>191</td>\n      <td>57.553</td>\n      <td>14.372585</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.284830</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>glutamatergic neuron</td>\n      <td>4436</td>\n      <td>3963</td>\n      <td>733</td>\n      <td>277</td>\n      <td>120.873</td>\n      <td>12.425573</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.564974</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>inhibitory interneuron</td>\n      <td>4130</td>\n      <td>3769</td>\n      <td>673</td>\n      <td>251</td>\n      <td>93.186</td>\n      <td>14.119752</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.176825</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>endothelial cell of vascular tree</td>\n      <td>379</td>\n      <td>367</td>\n      <td>73</td>\n      <td>4</td>\n      <td>1.115</td>\n      <td>0.328291</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.787934</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>oligodendrocyte precursor cell</td>\n      <td>1963</td>\n      <td>1832</td>\n      <td>320</td>\n      <td>49</td>\n      <td>4.996</td>\n      <td>2.286916</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>19.241635</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>pericyte</td>\n      <td>111</td>\n      <td>107</td>\n      <td>20</td>\n      <td>1</td>\n      <td>1.000</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_df['z_score'] = (table_df['Cell Type LCC size'] - table_df['LCC mean']) / table_df['LCC std']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "fab2e3fc09d91232"
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "outputs": [],
   "source": [
    "table_df.to_csv('../data/results/cell_type_summary_stats.tsv', sep='\\t', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "aad0d6cb90a2a129"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### P-value and Adj P-value calculation"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "73076752d5a07029"
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/results/cell_type_summary_stats.tsv', sep='\\t')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "24ffb1d66118d53d"
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\868517119.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['p_value'] = pd.to_numeric(df['p_value'], errors='coerce')  # Convertir a float\n",
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\868517119.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['p_value'] = df['p_value'].replace(0, np.nextafter(0, 1))\n",
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\868517119.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['adj_p_value'] = multipletests(df['p_value'], method='fdr_bh')[1]\n"
     ]
    },
    {
     "data": {
      "text/plain": "                           Cell Type  Total DEGs  DEGs mapped to protein  \\\n0                          astrocyte        3176                    2878   \n1                    microglial cell        1585                    1501   \n2                    oligodendrocyte        3383                    3052   \n3               glutamatergic neuron        4436                    3963   \n4             inhibitory interneuron        4130                    3769   \n5  endothelial cell of vascular tree         379                     367   \n6     oligodendrocyte precursor cell        1963                    1832   \n7                           pericyte         111                     107   \n\n   Cell Type proteins in main LCC  Cell Type LCC size  LCC mean    LCC std  \\\n0                             532                 183    53.963  13.744804   \n1                             334                  62     7.218   3.662032   \n2                             554                 191    57.553  14.372585   \n3                             733                 277   120.873  12.425573   \n4                             673                 251    93.186  14.119752   \n5                              73                   4     1.115   0.328291   \n6                             320                  49     4.996   2.286916   \n7                              20                   1     1.000   0.000000   \n\n     z_score       p_value   adj_p_value  \n0   9.388057  6.111731e-21  8.556424e-21  \n1  14.959456  1.351270e-50  4.729446e-50  \n2   9.284830  1.619661e-20  1.889605e-20  \n3  12.564974  3.289849e-36  7.676313e-36  \n4  11.176825  5.295062e-29  9.266358e-29  \n5   8.787934  1.523350e-18  1.523350e-18  \n6  19.241635  1.658917e-82  1.161242e-81  \n7        NaN           NaN           NaN  ",
      "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 Type</th>\n      <th>Total DEGs</th>\n      <th>DEGs mapped to protein</th>\n      <th>Cell Type proteins in main LCC</th>\n      <th>Cell Type LCC size</th>\n      <th>LCC mean</th>\n      <th>LCC std</th>\n      <th>z_score</th>\n      <th>p_value</th>\n      <th>adj_p_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>astrocyte</td>\n      <td>3176</td>\n      <td>2878</td>\n      <td>532</td>\n      <td>183</td>\n      <td>53.963</td>\n      <td>13.744804</td>\n      <td>9.388057</td>\n      <td>6.111731e-21</td>\n      <td>8.556424e-21</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>microglial cell</td>\n      <td>1585</td>\n      <td>1501</td>\n      <td>334</td>\n      <td>62</td>\n      <td>7.218</td>\n      <td>3.662032</td>\n      <td>14.959456</td>\n      <td>1.351270e-50</td>\n      <td>4.729446e-50</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>oligodendrocyte</td>\n      <td>3383</td>\n      <td>3052</td>\n      <td>554</td>\n      <td>191</td>\n      <td>57.553</td>\n      <td>14.372585</td>\n      <td>9.284830</td>\n      <td>1.619661e-20</td>\n      <td>1.889605e-20</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>glutamatergic neuron</td>\n      <td>4436</td>\n      <td>3963</td>\n      <td>733</td>\n      <td>277</td>\n      <td>120.873</td>\n      <td>12.425573</td>\n      <td>12.564974</td>\n      <td>3.289849e-36</td>\n      <td>7.676313e-36</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>inhibitory interneuron</td>\n      <td>4130</td>\n      <td>3769</td>\n      <td>673</td>\n      <td>251</td>\n      <td>93.186</td>\n      <td>14.119752</td>\n      <td>11.176825</td>\n      <td>5.295062e-29</td>\n      <td>9.266358e-29</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>endothelial cell of vascular tree</td>\n      <td>379</td>\n      <td>367</td>\n      <td>73</td>\n      <td>4</td>\n      <td>1.115</td>\n      <td>0.328291</td>\n      <td>8.787934</td>\n      <td>1.523350e-18</td>\n      <td>1.523350e-18</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>oligodendrocyte precursor cell</td>\n      <td>1963</td>\n      <td>1832</td>\n      <td>320</td>\n      <td>49</td>\n      <td>4.996</td>\n      <td>2.286916</td>\n      <td>19.241635</td>\n      <td>1.658917e-82</td>\n      <td>1.161242e-81</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>pericyte</td>\n      <td>111</td>\n      <td>107</td>\n      <td>20</td>\n      <td>1</td>\n      <td>1.000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pericyte_row = df[df['Cell Type'] == 'pericyte'].copy()\n",
    "\n",
    "df['p_value'] = 2 * norm.sf(abs(df['z_score'])) # bilateral p-value because I take into account that the random modules can be larger or smaller than the real one.\n",
    "df = df.dropna(subset=['p_value'])\n",
    "df['p_value'] = pd.to_numeric(df['p_value'], errors='coerce')\n",
    "df['p_value'] = df['p_value'].replace(0, np.nextafter(0, 1))\n",
    "df['adj_p_value'] = multipletests(df['p_value'], method='fdr_bh')[1]\n",
    "\n",
    "pericyte_row['adj_p_value'] = np.nan\n",
    "df = pd.concat([df, pericyte_row], ignore_index=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "9ec6f81c3535120b"
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "outputs": [],
   "source": [
    "df.to_csv('../data/results/cell_type_summary_stats.tsv', sep='\\t', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "756ed2c087c6a80b"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 6.2. Representation of each cell type differential expression values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "c00d209c275d105"
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "outputs": [],
   "source": [
    "cell_types = ['glutamatergic neuron', 'inhibitory interneuron', 'oligodendrocyte', 'astrocyte', 'microglial cell', 'oligodendrocyte precursor cell', 'endothelial cell of vascular tree', 'pericyte']\n",
    "titles = ['Glutamatergic neuron', 'Inhibitory interneuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial cell', 'OPC', 'Endothelial cell', 'Pericyte']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "3650bf9c9f06e457"
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Andrea\\Downloads\\single-cell\\lib\\site-packages\\pandas\\core\\arraylike.py:399: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "C:\\Users\\Andrea\\Downloads\\single-cell\\lib\\site-packages\\pandas\\core\\arraylike.py:399: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "C:\\Users\\Andrea\\Downloads\\single-cell\\lib\\site-packages\\pandas\\core\\arraylike.py:399: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glutamatergic neuron : {'C1QA', 'HSPB1', 'GPR6', 'DRD4', 'CD14'}\n",
      "inhibitory interneuron : {'PENK', 'GPR6', 'CRYZ', 'ARAP3', 'DRD4'}\n",
      "oligodendrocyte : {'RELN', 'CXCR4', 'BIRC3', 'HSPA1B'}\n",
      "astrocyte : {'CRYAB', 'ANXA1', 'EGR1', 'EGF', 'S100A10'}\n",
      "microglial cell : {'BAG3', 'HSPA1B', 'HSPB1', 'DNAJB1'}\n",
      "oligodendrocyte precursor cell : {'FOS', 'IGFBP3', 'EGR1', 'SOCS3', 'VGF'}\n",
      "endothelial cell of vascular tree : {'HSPA1B', 'CACYBP', 'RBM3', 'HSPB1', 'HSPH1'}\n",
      "pericyte : {'HSPA1B', 'CRYAB', 'HSPB1', 'VIM', 'HSPH1'}\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2200x300 with 8 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"paper\")\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 8, figsize=(22,3))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, cell_type in enumerate(cell_types):\n",
    "    file_path = f\"../data/filtered/degs_{cell_type}_mapped.csv\"\n",
    "\n",
    "    try:\n",
    "        df = pd.read_csv(file_path)\n",
    "\n",
    "        df[\"-log10(pvals_adj)\"] = -np.log10(df[\"pvals_adj\"])\n",
    "\n",
    "        # Obtain the smallest non-zero pvals_adj greater than 0 to recalculate in case of infinity\n",
    "        min_pvals_adj = df.loc[df[\"pvals_adj\"] > 0, \"pvals_adj\"].min() * 0.1\n",
    "\n",
    "        # Calculate the corrected values in case of infinity\n",
    "        df.loc[np.isinf(df[\"-log10(pvals_adj)\"]), \"-log10(pvals_adj)\"] = -np.log10(min_pvals_adj)\n",
    "\n",
    "        top_genes = df.loc[df[\"logfoldchanges\"].abs().nlargest(5).index]\n",
    "\n",
    "        top_genes.loc[top_genes.index, \"color\"] = top_genes.loc[top_genes.index, \"logfoldchanges\"].apply(\n",
    "            lambda x: \"red\" if x > 0 else \"blue\"\n",
    "        )\n",
    "\n",
    "        print(cell_type, \":\", set(top_genes[\"gene_symbol\"]))\n",
    "\n",
    "        sns.scatterplot(data=df, y=\"-log10(pvals_adj)\", x=\"logfoldchanges\", color='grey', legend=False, ax=axes[i])\n",
    "\n",
    "        for _, row in top_genes.iterrows():\n",
    "            axes[i].scatter(row[\"logfoldchanges\"], row[\"-log10(pvals_adj)\"], color=row['color'])\n",
    "\n",
    "        axes[i].set_ylabel(\"-log10(pvals_adj)\")\n",
    "        axes[i].set_xlabel(\"log2 Fold Change\")\n",
    "        axes[i].set_title(f\"{titles[i]}\")\n",
    "\n",
    "    except FileNotFoundError:\n",
    "        axes[i].set_title(f\"No data for {cell_type}\")\n",
    "        axes[i].axis(\"off\")\n",
    "\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "plt.savefig('../figures/volcano_per_type_notext.svg', format = 'svg', dpi=1200)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "8ff326052036394c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 6.3. Representation of top 20 most up- and down-regulated DEGs and DEGs expressed in all cell types"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "811f2c043899cde8"
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "gen_pro = pd.read_csv('../data/disnet/gen_pro.tsv', sep='\\t')\n",
    "gen = pd.read_csv(\"../data/disnet/gen.tsv\", sep=\"\\t\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "35b6e0963296b42"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "cell_types = ['astrocyte', 'microglial cell', 'oligodendrocyte', 'glutamatergic neuron', 'inhibitory interneuron',\n",
    "              'endothelial cell of vascular tree', 'oligodendrocyte precursor cell', 'pericyte']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "cc2ecf08f2268100"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Heatmap 1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "5161ee29eae2c753"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "file_pattern = '../data/filtered/degs_*_mapped.csv'\n",
    "\n",
    "files = glob.glob(file_pattern)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "cd0c8bf7020ff320"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "dfs = []\n",
    "\n",
    "for file in files:\n",
    "    df = pd.read_csv(file)\n",
    "\n",
    "    df_filtered = df[['protein_id', 'gene_symbol', 'logfoldchanges']]\n",
    "\n",
    "    cell_type = file.split('/')[-1].split('_')[1]\n",
    "    df_filtered['cell_type'] = cell_type\n",
    "    dfs.append(df_filtered)\n",
    "\n",
    "lfc_df = pd.concat(dfs, ignore_index=True)\n",
    "print(lfc_df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "eb51c1f6f73d00ef"
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\1253390478.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  lfc_filtered[\"max_abs_LFC\"] = lfc_filtered[\"logfoldchanges\"].abs()\n",
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\1253390478.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  lfc_filtered['cell_type'] = lfc_filtered[\"cell_type\"].replace(\"endothelial cell of vascular tree\", \"endothelial cell\")\n"
     ]
    },
    {
     "data": {
      "text/plain": "     protein_id gene_symbol  logfoldchanges        cell_type  max_abs_LFC\n14       P43490       NAMPT       -0.900505        astrocyte     0.900505\n22       P06703      S100A6        1.067540        astrocyte     1.067540\n50       Q2I0M5       RSPO4       -1.032063        astrocyte     1.032063\n118      O60603        TLR2       -1.532612        astrocyte     1.532612\n133      P17275        JUNB       -0.300338        astrocyte     0.300338\n...         ...         ...             ...              ...          ...\n3194     P04792       HSPB1        2.069515  oligodendrocyte     2.069515\n3223     P04792       HSPB1        1.884719         pericyte     1.884719\n3227     Q92598       HSPH1        1.676103         pericyte     1.676103\n3230     P0DMV8      HSPA1B        1.975146         pericyte     1.975146\n3232     P02511       CRYAB        1.525713         pericyte     1.525713\n\n[123 rows x 5 columns]",
      "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>protein_id</th>\n      <th>gene_symbol</th>\n      <th>logfoldchanges</th>\n      <th>cell_type</th>\n      <th>max_abs_LFC</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14</th>\n      <td>P43490</td>\n      <td>NAMPT</td>\n      <td>-0.900505</td>\n      <td>astrocyte</td>\n      <td>0.900505</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>P06703</td>\n      <td>S100A6</td>\n      <td>1.067540</td>\n      <td>astrocyte</td>\n      <td>1.067540</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>Q2I0M5</td>\n      <td>RSPO4</td>\n      <td>-1.032063</td>\n      <td>astrocyte</td>\n      <td>1.032063</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>O60603</td>\n      <td>TLR2</td>\n      <td>-1.532612</td>\n      <td>astrocyte</td>\n      <td>1.532612</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>P17275</td>\n      <td>JUNB</td>\n      <td>-0.300338</td>\n      <td>astrocyte</td>\n      <td>0.300338</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3194</th>\n      <td>P04792</td>\n      <td>HSPB1</td>\n      <td>2.069515</td>\n      <td>oligodendrocyte</td>\n      <td>2.069515</td>\n    </tr>\n    <tr>\n      <th>3223</th>\n      <td>P04792</td>\n      <td>HSPB1</td>\n      <td>1.884719</td>\n      <td>pericyte</td>\n      <td>1.884719</td>\n    </tr>\n    <tr>\n      <th>3227</th>\n      <td>Q92598</td>\n      <td>HSPH1</td>\n      <td>1.676103</td>\n      <td>pericyte</td>\n      <td>1.676103</td>\n    </tr>\n    <tr>\n      <th>3230</th>\n      <td>P0DMV8</td>\n      <td>HSPA1B</td>\n      <td>1.975146</td>\n      <td>pericyte</td>\n      <td>1.975146</td>\n    </tr>\n    <tr>\n      <th>3232</th>\n      <td>P02511</td>\n      <td>CRYAB</td>\n      <td>1.525713</td>\n      <td>pericyte</td>\n      <td>1.525713</td>\n    </tr>\n  </tbody>\n</table>\n<p>123 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_high = lfc_df.sort_values(by=\"logfoldchanges\", ascending=False)[\"protein_id\"].drop_duplicates().head(20)\n",
    "top_low = lfc_df.sort_values(by=\"logfoldchanges\", ascending=True)[\"protein_id\"].drop_duplicates().head(20)\n",
    "top_proteins = pd.concat([top_high, top_low])\n",
    "\n",
    "lfc_filtered = lfc_df[lfc_df[\"protein_id\"].isin(top_proteins)]\n",
    "\n",
    "lfc_filtered[\"max_abs_LFC\"] = lfc_filtered[\"logfoldchanges\"].abs()\n",
    "lfc_filtered['cell_type'] = lfc_filtered[\"cell_type\"].replace(\"endothelial cell of vascular tree\", \"endothelial cell\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "7c9916fcf5f41771"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Heatmap 2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "fc90b950ca3b48da"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "cell_networks = {cell: nx.read_graphml(f\"../data/filtered/graphs/{cell}_network.graphml\") for cell in\n",
    "                 cell_types}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "2baf164cd35de867"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "combined_degs = [\n",
    "    {\"protein_id\": node, \"gene_symbol\": G.nodes[node]['gene_symbol'], \"gene_id\": G.nodes[node]['gene_id'],\n",
    "     \"cell_type\": cell_type, \"logfoldchanges\": G.nodes[node]['logfoldchanges']}\n",
    "    for cell_type, G in cell_networks.items()\n",
    "    for node in G.nodes()\n",
    "    if \"logfoldchanges\" in G.nodes[node]\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(combined_degs)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "2316a71810b114b0"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "count_cells = df.groupby(\"protein_id\")[\"cell_type\"].nunique().reset_index()\n",
    "count_cells.columns = [\"protein_id\", \"num_cell_types\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "1ae100391c3b3b2b"
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "outputs": [
    {
     "data": {
      "text/plain": "     protein_id gene_symbol   gene_id  cell_type  logfoldchanges  \\\n0        P04626       ERBB2    2064.0  astrocyte       -0.287940   \n1        Q14517        FAT1    2195.0  astrocyte        0.343325   \n2        Q8TCT8      SPPL2A   84888.0  astrocyte       -0.353458   \n3        O60290      ZNF862  643641.0  astrocyte        0.419059   \n4        P35613         BSG     682.0  astrocyte       -0.386828   \n...         ...         ...       ...        ...             ...   \n3234     P60484        PTEN    5728.0   pericyte       -0.782102   \n3235     P17676       CEBPB    1051.0   pericyte        1.105564   \n3236     P49716       CEBPD    1052.0   pericyte        0.708934   \n3237     Q09666       AHNAK   79026.0   pericyte        0.989039   \n3238     Q7Z6G8      ANKS1B   56899.0   pericyte        0.978241   \n\n      num_cell_types  \n0                  1  \n1                  2  \n2                  1  \n3                  4  \n4                  3  \n...              ...  \n3234               2  \n3235               4  \n3236               5  \n3237               3  \n3238               4  \n\n[3239 rows x 6 columns]",
      "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>protein_id</th>\n      <th>gene_symbol</th>\n      <th>gene_id</th>\n      <th>cell_type</th>\n      <th>logfoldchanges</th>\n      <th>num_cell_types</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>P04626</td>\n      <td>ERBB2</td>\n      <td>2064.0</td>\n      <td>astrocyte</td>\n      <td>-0.287940</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Q14517</td>\n      <td>FAT1</td>\n      <td>2195.0</td>\n      <td>astrocyte</td>\n      <td>0.343325</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Q8TCT8</td>\n      <td>SPPL2A</td>\n      <td>84888.0</td>\n      <td>astrocyte</td>\n      <td>-0.353458</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>O60290</td>\n      <td>ZNF862</td>\n      <td>643641.0</td>\n      <td>astrocyte</td>\n      <td>0.419059</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P35613</td>\n      <td>BSG</td>\n      <td>682.0</td>\n      <td>astrocyte</td>\n      <td>-0.386828</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3234</th>\n      <td>P60484</td>\n      <td>PTEN</td>\n      <td>5728.0</td>\n      <td>pericyte</td>\n      <td>-0.782102</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3235</th>\n      <td>P17676</td>\n      <td>CEBPB</td>\n      <td>1051.0</td>\n      <td>pericyte</td>\n      <td>1.105564</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3236</th>\n      <td>P49716</td>\n      <td>CEBPD</td>\n      <td>1052.0</td>\n      <td>pericyte</td>\n      <td>0.708934</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>3237</th>\n      <td>Q09666</td>\n      <td>AHNAK</td>\n      <td>79026.0</td>\n      <td>pericyte</td>\n      <td>0.989039</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3238</th>\n      <td>Q7Z6G8</td>\n      <td>ANKS1B</td>\n      <td>56899.0</td>\n      <td>pericyte</td>\n      <td>0.978241</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>3239 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.merge(count_cells, on=\"protein_id\")\n",
    "df2['cell_type'] = df2[\"cell_type\"].replace(\"endothelial cell of vascular tree\", \"endothelial cell\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "79248fec0321c6e6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Representation"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "1568a88bb60744d7"
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "outputs": [],
   "source": [
    "titles = ['Glutamatergic neuron', 'Inhibitory interneuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial cell', 'OPC', 'Endothelial cell', 'Pericyte']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "ed3f1cc6e47a628b"
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x1000 with 3 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap_df = lfc_filtered.pivot(index=\"protein_id\", columns=\"cell_type\", values=\"logfoldchanges\")\n",
    "\n",
    "# Annotate gene_symbol\n",
    "gene_symbols = lfc_filtered.set_index(\"protein_id\")[\"gene_symbol\"].to_dict()\n",
    "heatmap_df.index = [f\"{gene_symbols.get(x, x)} ({x})\" for x in heatmap_df.index]\n",
    "\n",
    "heatmap_df[\"max_lfc\"] = heatmap_df.max(axis=1)\n",
    "heatmap_df = heatmap_df.sort_values(by=\"max_lfc\", ascending=False).drop(columns=[\"max_lfc\"])\n",
    "\n",
    "# Blank space between up- and down-regulated genes\n",
    "half = len(heatmap_df) // 2\n",
    "gap = pd.DataFrame(np.nan, index=[\"\"], columns=heatmap_df.columns)\n",
    "heatmap_df = pd.concat([heatmap_df.iloc[:half], gap, heatmap_df.iloc[half:]])\n",
    "\n",
    "# ===============================================================================\n",
    "\n",
    "df_filtered_proteins = df2[df2[\"num_cell_types\"] == 8]\n",
    "gene_symbol_mapping = df_filtered_proteins.set_index(\"protein_id\")[\"gene_symbol\"].drop_duplicates()\n",
    "df_pivot = df_filtered_proteins.pivot_table(index=\"protein_id\", columns=\"cell_type\", values=\"logfoldchanges\")\n",
    "\n",
    "# Annotate gene_symbol\n",
    "df_pivot.index = [f\"{gene_symbol_mapping.get(x, x)}\\n({x})\" for x in df_pivot.index]\n",
    "\n",
    "df_pivot[\"max_lfc\"] = df_pivot.max(axis=1)\n",
    "df_pivot = df_pivot.sort_values(by=\"max_lfc\", ascending=False).drop(columns=[\"max_lfc\"])\n",
    "\n",
    "# Define column order\n",
    "order = ['glutamatergic neuron', 'inhibitory interneuron', 'oligodendrocyte', 'astrocyte', 'microglial cell', 'oligodendrocyte precursor cell', 'endothelial cell', 'pericyte']\n",
    "\n",
    "heatmap_df = heatmap_df[order]\n",
    "df_pivot = df_pivot[order]\n",
    "\n",
    "# Define shared color palette\n",
    "vmin = min(heatmap_df.min().min(), df_pivot.min().min())\n",
    "vmax = max(heatmap_df.max().max(), df_pivot.max().max())\n",
    "\n",
    "# ===============================================================================\n",
    "\n",
    "sns.set_context(\"paper\")\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 10), gridspec_kw={'width_ratios': [1, 1]})\n",
    "\n",
    "# Heatmap 1\n",
    "sns.heatmap(\n",
    "    heatmap_df, cmap=\"coolwarm\", center=0, linewidths=0.5, linecolor=\"grey\",\n",
    "    vmin=vmin, vmax=vmax, annot=False, ax=axes[0], cbar=False\n",
    ")\n",
    "axes[0].add_patch(Rectangle((0, 20), 8, 1, fill=True, facecolor= 'white', edgecolor='gray', lw=1))\n",
    "axes[0].set_title(\"Proteins with highest and lowest LFC values by cell type\", fontsize=18)\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[0].set_ylabel(\"Gene Symbol (Protein Accession Number)\", fontsize=15)\n",
    "axes[0].set_xticklabels(titles, rotation=45, ha='right', fontsize=15)\n",
    "axes[0].set_yticklabels(axes[0].get_yticklabels(), fontsize=14)\n",
    "\n",
    "# Heatmap 2\n",
    "sns.heatmap(\n",
    "    df_pivot, cmap=\"coolwarm\", linewidths=0.5, linecolor='grey',\n",
    "    vmin=vmin, vmax=vmax, annot=False, ax=axes[1]\n",
    ")\n",
    "axes[1].set_title(\"Differentially expressed genes in all cell types\", fontsize=18)\n",
    "axes[1].set_xlabel(\"\")\n",
    "axes[1].set_ylabel(\"\")\n",
    "axes[1].set_xticklabels(titles, rotation=45, ha='right', fontsize=15)\n",
    "axes[1].set_yticklabels(axes[1].get_yticklabels(), rotation = 0, fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('../figures/combined_heatmaps.pdf', format='pdf', dpi=1200)\n",
    "plt.savefig('../figures/combined_heatmaps_pdf.svg', format='svg', dpi=1200)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "972930a0f2d2d237"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 8. Network metrics analysis\n",
    "\n",
    "Calculation of degrees, betweenness and closeness centralities and clustering coefficient for each of the cell type-specific DEGs inside the main interactome.\n",
    "\n",
    "Considering the entire PPI, I want to know how the cell type-specific DEGs are located within the interactome. For the DEGs in each cell type, I draw the metrics (from the subnetworks I only need the genes that are in them) and plot the distributions that follow these metrics, comparing them with the distribution that the Alzheimer's module follows.\n",
    "\n",
    "### 8.1. Alzheimer module calculation"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "fb4bab50846dc8ed"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "dis_gen = pd.read_csv('../data/disnet/dis_gen.tsv', sep='\\t')\n",
    "gen_pro = pd.read_csv('../data/disnet/gen_pro.tsv', sep='\\t')\n",
    "pro_pro = pd.read_csv('../data/disnet/pro_pro.tsv', sep='\\t')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "729f9cc8a9cb0da2"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "G_ppi = nx.from_pandas_edgelist(pro_pro, 'prA', 'prB')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "ec240b52f8abf701"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def get_disease_module(disease, dis_gen, gen_pro, pro_pro, PPI):\n",
    "    genes = functions.genes_dis(disease, dis_gen)\n",
    "    prots = functions.pro_gen_dict(genes, gen_pro)\n",
    "    prots_interactome = functions.gen_pro_PPI(prots, pro_pro)\n",
    "    SG = PPI.subgraph(prots_interactome)\n",
    "    lcc = functions.lcc(SG)\n",
    "\n",
    "    return lcc"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "e0f294118c7c8b50"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "lcc_alz = get_disease_module('C0002395', dis_gen, gen_pro, pro_pro, G_ppi)\n",
    "len(lcc_alz)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "a14c03fb7c7f181b"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 8.2. Metrics calculation for all the nodes in the interactome"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "cebc1f904da014e8"
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "outputs": [],
   "source": [
    "def calculate_metrics(graph):\n",
    "    \"\"\"\n",
    "    Calculates network metrics at node level. Returns a DataFrame with the metrics.\n",
    "    \"\"\"\n",
    "    # Betweenness centrality\n",
    "    betweenness = nx.betweenness_centrality(graph)\n",
    "\n",
    "    # Closeness centrality\n",
    "    closeness = nx.closeness_centrality(graph)\n",
    "\n",
    "    # Clustering coefficient\n",
    "    clustering = nx.clustering(graph)\n",
    "\n",
    "    # Identificar hubs (proteínas con mayor grado)\n",
    "    degree = dict(graph.degree())\n",
    "    degree_values = list(degree.values())\n",
    "    percentile_90 = np.percentile(degree_values, 90)\n",
    "    is_hub = {node: degree[node] >= percentile_90 for node in degree}\n",
    "\n",
    "    return betweenness, closeness, clustering, degree, is_hub"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "9d2d785a496d0b74"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Metrics calculation for all proteins in the PPI\n",
    "betweenness, closeness, clustering, degree, is_hub = calculate_metrics(G_ppi)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "91d4e2e356a3135a"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Convert metrics of whole interactome to a dataframe\n",
    "df = pd.DataFrame({\n",
    "    'protein_id': list(degree.keys()),\n",
    "    'degree': list(degree.values()),\n",
    "    'betweenness_centrality': list(betweenness.values()),\n",
    "    'closeness_centrality': list(closeness.values()),\n",
    "    'is_hub': list(is_hub.values())\n",
    "})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "7b0416a26b27e657"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df['is_in_LCC'] = df['protein_id'].isin(lcc_alz)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "d6685ea1ded8af48"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df.to_csv('../data/results/G_ppi_analysis.csv', index = False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "1566902d75d5d3c5"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 8.3. Metrics calculation for the DEGs nodes for each cell type"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "82770eeeea3b2858"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "cell_types = ['astrocyte', 'microglial cell', 'oligodendrocyte', 'glutamatergic neuron', 'inhibitory interneuron', 'endothelial cell of vascular tree', 'oligodendrocyte precursor cell', 'pericyte']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "4af0f8f342eb46f1"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "results_list = []\n",
    "\n",
    "for cell_type in tqdm(cell_types, desc=\"Processing cell types...\"):\n",
    "\n",
    "    graph = nx.read_graphml(f'../data/filtered/graphs/{cell_type}_network.graphml')\n",
    "\n",
    "    ppi_proteins = set(graph.nodes())\n",
    "    print(ppi_proteins)\n",
    "\n",
    "    # Añadir las métricas al DataFrame\n",
    "    for protein in ppi_proteins:\n",
    "        protein_id = protein\n",
    "        gene_id = graph.nodes[protein].get(\"gene_id\", \"NA\")\n",
    "        gene_symbol = graph.nodes[protein].get(\"gene_symbol\", \"NA\")\n",
    "        logfoldchanges = graph.nodes[protein].get(\"logfoldchanges\", \"NA\")\n",
    "        pvals = graph.nodes[protein].get(\"pval\", \"NA\")\n",
    "        pvals_adj = graph.nodes[protein].get(\"pval_adj\", \"NA\")\n",
    "\n",
    "        # Obtener las métricas para la proteína\n",
    "        betweenness_val = betweenness.get(protein_id, None)\n",
    "        closeness_val = closeness.get(protein_id, None)\n",
    "        clustering_val = clustering.get(protein_id, None)\n",
    "        degree_val = degree.get(protein_id, None)\n",
    "        is_hub_val = is_hub.get(protein_id, False)\n",
    "\n",
    "        # Añadir la fila al DataFrame de resultados\n",
    "        result_row = {\n",
    "            \"protein_id\": protein_id,\n",
    "            \"gene_id\": gene_id,\n",
    "            \"gene_symbol\": gene_symbol,\n",
    "            \"cell_type\": cell_type,\n",
    "            \"logfoldchanges\": logfoldchanges,\n",
    "            \"pvals\": pvals,\n",
    "            \"pvals_adj\": pvals_adj,\n",
    "            \"degree\": degree_val,\n",
    "            \"betweenness_centrality\": betweenness_val,\n",
    "            \"closeness_centrality\": closeness_val,\n",
    "            \"clustering_coefficient\": clustering_val,\n",
    "            \"is_hub\": is_hub_val\n",
    "        }\n",
    "\n",
    "        results_list.append(result_row)\n",
    "\n",
    "results_df = pd.DataFrame(results_list)\n",
    "\n",
    "results_df.to_csv('../data/results/network_analysis.csv', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "d98d305963c7d4ee"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 8.4. Analysis of the metrics distributions across cell types"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "3d0220c29266cc6a"
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "outputs": [],
   "source": [
    "cell_data = pd.read_csv('../data/results/network_analysis.csv')\n",
    "module_data = pd.read_csv('../data/results/G_ppi_analysis.csv')\n",
    "\n",
    "module_data_lcc = module_data[module_data['is_in_LCC'] == True]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "3237a956b8eb2b7c"
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\602881201.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  module_data_lcc[\"dataset\"] = \"General module for AD\"\n"
     ]
    },
    {
     "data": {
      "text/plain": "      protein_id  degree  betweenness_centrality  closeness_centrality  \\\n0     A0A087WXM9       8                0.000003              0.305735   \n2         P05783      91                0.000429              0.367223   \n4         P08670     266                0.003726              0.398942   \n6         P53350     221                0.001902              0.381054   \n32        Q96CV9      92                0.000621              0.362136   \n...          ...     ...                     ...                   ...   \n3234      P17676      27                0.000056              0.346096   \n3235      P07900     237                0.001698              0.398471   \n3236      P13693     124                0.000313              0.374662   \n3237      P0DMV8      63                0.000533              0.359315   \n3238      P08238     553                0.013363              0.420794   \n\n      is_hub is_in_LCC                dataset  gene_id gene_symbol cell_type  \\\n0      False      True  General module for AD      NaN         NaN       NaN   \n2       True      True  General module for AD      NaN         NaN       NaN   \n4       True      True  General module for AD      NaN         NaN       NaN   \n6       True      True  General module for AD      NaN         NaN       NaN   \n32      True      True  General module for AD      NaN         NaN       NaN   \n...      ...       ...                    ...      ...         ...       ...   \n3234   False       NaN               pericyte   1051.0       CEBPB  pericyte   \n3235    True       NaN               pericyte   3320.0    HSP90AA1  pericyte   \n3236    True       NaN               pericyte   7178.0        TPT1  pericyte   \n3237   False       NaN               pericyte   3304.0      HSPA1B  pericyte   \n3238    True       NaN               pericyte   3326.0    HSP90AB1  pericyte   \n\n      logfoldchanges         pvals     pvals_adj  clustering_coefficient  \n0                NaN           NaN           NaN                     NaN  \n2                NaN           NaN           NaN                     NaN  \n4                NaN           NaN           NaN                     NaN  \n6                NaN           NaN           NaN                     NaN  \n32               NaN           NaN           NaN                     NaN  \n...              ...           ...           ...                     ...  \n3234        1.105564  9.147368e-06  5.491340e-03                0.150000  \n3235        1.110109  2.761193e-13  1.112958e-09                0.061975  \n3236        0.802365  1.618387e-04  4.390652e-02                0.056517  \n3237        1.975146  1.293792e-07  1.738302e-04                0.045059  \n3238        0.861556  2.566908e-06  2.336300e-03                0.020023  \n\n[5936 rows x 14 columns]",
      "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>protein_id</th>\n      <th>degree</th>\n      <th>betweenness_centrality</th>\n      <th>closeness_centrality</th>\n      <th>is_hub</th>\n      <th>is_in_LCC</th>\n      <th>dataset</th>\n      <th>gene_id</th>\n      <th>gene_symbol</th>\n      <th>cell_type</th>\n      <th>logfoldchanges</th>\n      <th>pvals</th>\n      <th>pvals_adj</th>\n      <th>clustering_coefficient</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A0A087WXM9</td>\n      <td>8</td>\n      <td>0.000003</td>\n      <td>0.305735</td>\n      <td>False</td>\n      <td>True</td>\n      <td>General module for AD</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>P05783</td>\n      <td>91</td>\n      <td>0.000429</td>\n      <td>0.367223</td>\n      <td>True</td>\n      <td>True</td>\n      <td>General module for AD</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>P08670</td>\n      <td>266</td>\n      <td>0.003726</td>\n      <td>0.398942</td>\n      <td>True</td>\n      <td>True</td>\n      <td>General module for AD</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>P53350</td>\n      <td>221</td>\n      <td>0.001902</td>\n      <td>0.381054</td>\n      <td>True</td>\n      <td>True</td>\n      <td>General module for AD</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Q96CV9</td>\n      <td>92</td>\n      <td>0.000621</td>\n      <td>0.362136</td>\n      <td>True</td>\n      <td>True</td>\n      <td>General module for AD</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</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    </tr>\n    <tr>\n      <th>3234</th>\n      <td>P17676</td>\n      <td>27</td>\n      <td>0.000056</td>\n      <td>0.346096</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>pericyte</td>\n      <td>1051.0</td>\n      <td>CEBPB</td>\n      <td>pericyte</td>\n      <td>1.105564</td>\n      <td>9.147368e-06</td>\n      <td>5.491340e-03</td>\n      <td>0.150000</td>\n    </tr>\n    <tr>\n      <th>3235</th>\n      <td>P07900</td>\n      <td>237</td>\n      <td>0.001698</td>\n      <td>0.398471</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>pericyte</td>\n      <td>3320.0</td>\n      <td>HSP90AA1</td>\n      <td>pericyte</td>\n      <td>1.110109</td>\n      <td>2.761193e-13</td>\n      <td>1.112958e-09</td>\n      <td>0.061975</td>\n    </tr>\n    <tr>\n      <th>3236</th>\n      <td>P13693</td>\n      <td>124</td>\n      <td>0.000313</td>\n      <td>0.374662</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>pericyte</td>\n      <td>7178.0</td>\n      <td>TPT1</td>\n      <td>pericyte</td>\n      <td>0.802365</td>\n      <td>1.618387e-04</td>\n      <td>4.390652e-02</td>\n      <td>0.056517</td>\n    </tr>\n    <tr>\n      <th>3237</th>\n      <td>P0DMV8</td>\n      <td>63</td>\n      <td>0.000533</td>\n      <td>0.359315</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>pericyte</td>\n      <td>3304.0</td>\n      <td>HSPA1B</td>\n      <td>pericyte</td>\n      <td>1.975146</td>\n      <td>1.293792e-07</td>\n      <td>1.738302e-04</td>\n      <td>0.045059</td>\n    </tr>\n    <tr>\n      <th>3238</th>\n      <td>P08238</td>\n      <td>553</td>\n      <td>0.013363</td>\n      <td>0.420794</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>pericyte</td>\n      <td>3326.0</td>\n      <td>HSP90AB1</td>\n      <td>pericyte</td>\n      <td>0.861556</td>\n      <td>2.566908e-06</td>\n      <td>2.336300e-03</td>\n      <td>0.020023</td>\n    </tr>\n  </tbody>\n</table>\n<p>5936 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 575,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "module_data_lcc[\"dataset\"] = \"General module for AD\"\n",
    "cell_data[\"dataset\"] = cell_data[\"cell_type\"]\n",
    "\n",
    "combined_data = pd.concat([module_data_lcc, cell_data])\n",
    "combined_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "950b80af953c2896"
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "outputs": [],
   "source": [
    "titles = ['General module for AD', 'Glutamatergic neuron', 'Inhibitory interneuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial cell', 'OPC', 'Endothelial cell', 'Pericyte']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "251c73b3b42201a1"
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\2142562447.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  subset_sizes = combined_data.groupby(\"dataset\")[\"protein_id\"].nunique()\n",
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\2142562447.py:18: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=combined_data, x=\"dataset\", y=\"degree\", ax=ax, palette=palette, order=ordered_datasets,dodge=False)\n",
      "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\2142562447.py:25: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([f\"{titles[i]}\\nn = {subset_sizes[dataset]}\" for i, dataset in enumerate(ordered_datasets)],\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 900x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Subset sizes calculation\n",
    "subset_sizes = combined_data.groupby(\"dataset\")[\"protein_id\"].nunique()\n",
    "\n",
    "# Color palette\n",
    "ordered_datasets = subset_sizes.sort_values(ascending=False).index\n",
    "combined_data[\"dataset\"] = pd.Categorical(combined_data[\"dataset\"], categories=ordered_datasets, ordered=True)\n",
    "palette = {dataset: '#79C4FF' for dataset in ordered_datasets}\n",
    "\n",
    "# Assign light grey to general module for AD\n",
    "palette[\"General module for AD\"] = \"#D3D3D3\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "\n",
    "sns.set_context(\"paper\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "sns.boxplot(data=combined_data, x=\"dataset\", y=\"degree\", ax=ax, palette=palette, order=ordered_datasets,dodge=False)\n",
    "\n",
    "ax.tick_params(axis='both', which='both', bottom=True, left=True)\n",
    "\n",
    "plt.title('Degree distribution for the Alzheimer disease module and each cell type', fontsize=16)\n",
    "plt.xlabel('')\n",
    "ax.set_xticklabels([f\"{titles[i]}\\nn = {subset_sizes[dataset]}\" for i, dataset in enumerate(ordered_datasets)],\n",
    "                   rotation=45, ha=\"center\", fontsize=12)\n",
    "\n",
    "plt.yticks(fontsize=12)\n",
    "plt.yscale('log')\n",
    "plt.ylabel('log(Degree)', fontsize=12)\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "# plt.savefig('../figures/degree_boxplot.pdf', format='pdf', dpi=1200)\n",
    "plt.savefig('../figures/degree_boxplot.svg', format='svg', dpi=1200)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "9f37530477a84199"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Subset sizes calculation\n",
    "subset_sizes = combined_data.groupby(\"dataset\")[\"protein_id\"].nunique()\n",
    "\n",
    "# Generate color palette\n",
    "ordered_datasets = subset_sizes.sort_values(ascending=False).index\n",
    "combined_data[\"dataset\"] = pd.Categorical(combined_data[\"dataset\"], categories=ordered_datasets, ordered=True)\n",
    "palette = {dataset: '#79C4FF' for dataset in ordered_datasets}\n",
    "\n",
    "# Assign different color to module\n",
    "palette[\"General module for AD\"] = \"#D3D3D3\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "\n",
    "sns.set_context(\"paper\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "sns.boxplot(data=combined_data, x=\"dataset\", y=\"betweenness_centrality\", ax=ax, palette=palette, order=ordered_datasets,dodge=False)\n",
    "\n",
    "ax.tick_params(axis='both', which='both', bottom=True, left=True)\n",
    "\n",
    "plt.title('Betweenness centrality distribution for the Alzheimer disease module and each cell type', fontsize=16)\n",
    "plt.xlabel('')\n",
    "ax.set_xticklabels([f\"{titles[i]}\\nn = {subset_sizes[dataset]}\" for i, dataset in enumerate(ordered_datasets)],\n",
    "                   rotation=45, ha=\"center\", fontsize=12)\n",
    "\n",
    "plt.yticks(fontsize=12)\n",
    "plt.yscale('log')\n",
    "plt.ylabel('log(Betweenness centrality)', fontsize=12)\n",
    "sns.despine()\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('../figures/betweenness_boxplot.pdf', format='pdf', dpi=1200)\n",
    "plt.savefig('../figures/betweenness_boxplot.svg', format='svg', dpi=1200)\n",
    "plt.show()"
   ],
   "id": "a75971402585ab7"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Subset sizes calculation\n",
    "subset_sizes = combined_data.groupby(\"dataset\")[\"protein_id\"].nunique()\n",
    "\n",
    "# Generate color palette\n",
    "ordered_datasets = subset_sizes.sort_values(ascending=False).index\n",
    "combined_data[\"dataset\"] = pd.Categorical(combined_data[\"dataset\"], categories=ordered_datasets, ordered=True)\n",
    "palette = {dataset: '#79C4FF' for dataset in ordered_datasets}\n",
    "\n",
    "# Assign different color to module\n",
    "palette[\"General module for AD\"] = \"#D3D3D3\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "\n",
    "sns.set_context(\"paper\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "sns.boxplot(data=combined_data, x=\"dataset\", y=\"closeness_centrality\", ax=ax, palette=palette, order=ordered_datasets,dodge=False)\n",
    "\n",
    "ax.tick_params(axis='both', which='both', bottom=True, left=True)\n",
    "\n",
    "plt.title('Closeness centrality distribution for the Alzheimer disease module and each cell type', fontsize=16)\n",
    "plt.xlabel('')\n",
    "ax.set_xticklabels([f\"{titles[i]}\\nn = {subset_sizes[dataset]}\" for i, dataset in enumerate(ordered_datasets)],\n",
    "                   rotation=45, ha=\"center\", fontsize=12)\n",
    "\n",
    "plt.yticks(fontsize=12)\n",
    "plt.ylabel('Closeness centrality', fontsize=12)\n",
    "sns.despine()\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('../figures/closeness_boxplot.pdf', format='pdf', dpi=1200)\n",
    "plt.savefig('../figures/closeness_boxplot.svg', format='svg', dpi=1200)\n",
    "plt.show()"
   ],
   "id": "b3f8b51fb337d705"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Subset sizes calculation\n",
    "subset_sizes = combined_data.groupby(\"dataset\")[\"protein_id\"].nunique()\n",
    "\n",
    "# Generate color palette\n",
    "ordered_datasets = subset_sizes.sort_values(ascending=False).index\n",
    "combined_data[\"dataset\"] = pd.Categorical(combined_data[\"dataset\"], categories=ordered_datasets, ordered=True)\n",
    "palette = {dataset: '#79C4FF' for dataset in ordered_datasets}\n",
    "\n",
    "# Assign different color to module\n",
    "palette[\"General module for AD\"] = \"#D3D3D3\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "\n",
    "sns.set_context(\"paper\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "sns.boxplot(data=combined_data, x=\"dataset\", y=\"clustering_coefficient\", ax=ax, palette=palette, order=ordered_datasets,dodge=False)\n",
    "\n",
    "ax.tick_params(axis='both', which='both', bottom=True, left=True)\n",
    "\n",
    "plt.title('Clustering coefficient distribution for the Alzheimer disease module and each cell type', fontsize=16)\n",
    "plt.xlabel('')\n",
    "ax.set_xticklabels([f\"{titles[i]}\\nn = {subset_sizes[dataset]}\" for i, dataset in enumerate(ordered_datasets)],\n",
    "                   rotation=45, ha=\"center\", fontsize=12)\n",
    "\n",
    "plt.yticks(fontsize=12)\n",
    "plt.ylabel('Clustering coefficient', fontsize=12)\n",
    "sns.despine()\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('../figures/clustering_boxplot.pdf', format='pdf', dpi=1200)\n",
    "plt.savefig('../figures/clustering_boxplot.svg', format='svg', dpi=1200)\n",
    "plt.show()"
   ],
   "id": "f5044677900aa100"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 8.5. Network metrics analysis\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "e6f887b5b178def1"
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "outputs": [],
   "source": [
    "cell_data = pd.read_csv('../data/results/network_analysis.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "ab727ee10b040538"
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         mean         std  median  min   max  \\\n",
      "cell_type                                                                      \n",
      "astrocyte                           57.486842   97.024051    29.0    1  1379   \n",
      "endothelial cell of vascular tree   98.767123  119.863872    55.0    2   553   \n",
      "glutamatergic neuron                61.219645   98.005476    30.0    1  1379   \n",
      "inhibitory interneuron              61.210996   99.727546    31.0    1  1381   \n",
      "microglial cell                     66.434132  129.637865    35.5    2  1381   \n",
      "oligodendrocyte                     56.301444   97.276815    29.0    2  1379   \n",
      "oligodendrocyte precursor cell      65.284375  105.131312    38.0    2  1379   \n",
      "pericyte                           132.300000  138.173080    74.0   15   553   \n",
      "\n",
      "                                   count  \n",
      "cell_type                                 \n",
      "astrocyte                            532  \n",
      "endothelial cell of vascular tree     73  \n",
      "glutamatergic neuron                 733  \n",
      "inhibitory interneuron               673  \n",
      "microglial cell                      334  \n",
      "oligodendrocyte                      554  \n",
      "oligodendrocyte precursor cell       320  \n",
      "pericyte                              20  \n"
     ]
    }
   ],
   "source": [
    "cols_of_interest = [\"degree\"]\n",
    "\n",
    "stats = cell_data.groupby(\"cell_type\")[\"degree\"].agg([\"mean\", \"std\", \"median\", \"min\", \"max\"])\n",
    "stats[\"count\"] = cell_data.groupby(\"cell_type\").size()\n",
    "\n",
    "print(stats)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "6b57bbe54b5b28"
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean        45.292918\n",
      "std         84.963373\n",
      "median      23.000000\n",
      "min          1.000000\n",
      "max       1901.000000\n",
      "count     2697.000000\n",
      "Name: degree, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "module_data = pd.read_csv('../data/results/G_ppi_analysis.csv')\n",
    "\n",
    "module_data_lcc = module_data[module_data['is_in_LCC'] == True]\n",
    "stats = module_data_lcc[\"degree\"].agg([\"mean\", \"std\", \"median\", \"min\", \"max\", \"count\"])\n",
    "\n",
    "print(stats)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "2581372c52955a56"
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C1QA', 'HSPB1', 'GPR6', 'DRD4', 'CD14', 'PENK', 'GPR6', 'CRYZ', 'ARAP3', 'DRD4', 'RELN', 'CXCR4', 'BIRC3', 'HSPA1B', 'CRYAB', 'ANXA1', 'EGR1', 'EGF', 'S100A10', 'BAG3', 'HSPA1B', 'HSPB1', 'DNAJB1', 'FOS', 'IGFBP3', 'EGR1', 'SOCS3', 'VGF', 'HSPA1B', 'CACYBP', 'RBM3', 'HSPB1', 'HSPH1', 'HSPA1B', 'CRYAB', 'HSPB1', 'VIM', 'HSPH1']\n"
     ]
    }
   ],
   "source": [
    "gene_info = []\n",
    "\n",
    "for type, gene in top_genes.items():\n",
    "    for g in gene:\n",
    "        gene_info.append(g)\n",
    "\n",
    "print(gene_info)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "e139b54ba0f81255"
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "outputs": [
    {
     "data": {
      "text/plain": "   gene_symbol  degree\n30        RELN       2\n56        GPR6       2\n32         VGF       3\n58       ARAP3       4\n59        DRD4       5\n62        PENK       8\n5          EGF       9\n35        CRYZ      11\n25        C1QA      19\n81      IGFBP3      21\n49        CD14      24\n6         EGR1      25\n39      HSPA1B      31\n9         RBM3      35\n27       CXCR4      37\n42       BIRC3      41\n1      S100A10      43\n89       SOCS3      47\n0        HSPH1      48\n65       CRYAB      49\n46      DNAJB1      51\n11      HSPA1B      63\n51       ANXA1      80\n45      CACYBP     104\n75        BAG3     118\n80         FOS     140\n57         VIM     266\n26       HSPB1     320",
      "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>gene_symbol</th>\n      <th>degree</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>30</th>\n      <td>RELN</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>GPR6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>VGF</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>ARAP3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>DRD4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>PENK</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>EGF</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>CRYZ</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>C1QA</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>IGFBP3</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>CD14</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>EGR1</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>HSPA1B</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>RBM3</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>CXCR4</td>\n      <td>37</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>BIRC3</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>S100A10</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>SOCS3</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>HSPH1</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>CRYAB</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>DNAJB1</td>\n      <td>51</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>HSPA1B</td>\n      <td>63</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>ANXA1</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>CACYBP</td>\n      <td>104</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>BAG3</td>\n      <td>118</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>FOS</td>\n      <td>140</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>VIM</td>\n      <td>266</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>HSPB1</td>\n      <td>320</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 710,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_genes.sort_values('degree')[['gene_symbol', 'degree']].drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "cdce48893bc12655"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 8.5. Gephi file preparation\n",
    "\n",
    "Quiero tener un archivo con la red representada por aquellos genes que están en el LCC del Alzheimer y además en al menos un tipo celular."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "id": "8bd41103417ab828"
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "outputs": [],
   "source": [
    "pro_pro = pd.read_csv(\"../data/disnet/pro_pro.tsv\", sep='\\t')\n",
    "\n",
    "cell_types = ['astrocyte', 'microglial cell', 'oligodendrocyte', 'glutamatergic neuron', 'inhibitory interneuron',\n",
    "              'endothelial cell of vascular tree', 'oligodendrocyte precursor cell', 'pericyte']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "ff63fb10fa5b4b3b"
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "outputs": [],
   "source": [
    "filtered_files = glob.glob(\"../data/filtered/degs_*_mapped.csv\")\n",
    "\n",
    "filtered_data = functions.load_and_process(filtered_files)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "1023e511329af34a"
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "outputs": [],
   "source": [
    "unique_proteins = filtered_data['protein_id'].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "800b8523df769f3"
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "\n",
    "G.add_nodes_from(unique_proteins)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "54f44484637389"
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph with 1519 nodes and 0 edges\n"
     ]
    }
   ],
   "source": [
    "print(G)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "f9b8f634f6a63750"
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph with 1519 nodes and 8331 edges\n"
     ]
    }
   ],
   "source": [
    "for _, row in pro_pro.iterrows():\n",
    "    protein_A = row['prA']\n",
    "    protein_B = row['prB']\n",
    "\n",
    "    if protein_A in unique_proteins and protein_B in unique_proteins:\n",
    "        # Avoid self-interactions\n",
    "        if protein_A != protein_B:\n",
    "            G.add_edge(protein_A, protein_B)\n",
    "\n",
    "print(G)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "153d1bce0152d14c"
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph with 1519 nodes and 8331 edges\n"
     ]
    }
   ],
   "source": [
    "for _, row in filtered_data.iterrows():\n",
    "    protein_id = row['protein_id']\n",
    "\n",
    "    if protein_id in G.nodes:\n",
    "        G.nodes[protein_id]['gene_id'] = row['gene_id']\n",
    "        G.nodes[protein_id]['gene_symbol'] = row['gene_symbol']\n",
    "\n",
    "print(G)\n",
    "\n",
    "nx.write_graphml(G, \"../data/filtered/graphs/gephi/proteins_network.graphml\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "id": "7a6486a2755f8775"
  }
 ],
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