{ "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 to construct cell type-specific PPI networks, analyze differentially expressed genes (DEGs), and evaluate how gene expression patterns vary across cell types in Alzheimer." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2025-02-27T09:37:56.583250Z", "start_time": "2025-02-27T09:37:48.632535Z" }, "pycharm": { "name": "#%%\n" } }, "source": [ "import scanpy as sc\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", "from adjustText import adjust_text\n", "from scipy.stats import zscore\n", "import random\n", "import glob\n", "import gseapy as gp\n", "import os\n", "import json\n", "\n", "import functions_proximity" ], "outputs": [], "execution_count": 599 }, { "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." ], "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('CellXGene/cross-dementia/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..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..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\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..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..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" } } }, { "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" } } }, { "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" } } }, { "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" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "sc.pp.normalize_total(adata, target_sum=1e4)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "sc.pp.log1p(adata)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 13, "outputs": [], "source": [ "adata.write('CellXGene/cross-dementia/preprocessed_data.h5ad')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "## 2.3. Dataset division\n", "\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 3, "outputs": [], "source": [ "adata = sc.read_h5ad('CellXGene/cross-dementia/preprocessed_data.h5ad')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "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" } } }, { "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" } } }, { "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", "\n", "\n", "**sc.tl.rank_genes_groups te da los genes más diferencialmente expresados entre los grupos comparados. El signo de logfoldchanges indica si el gen está más expresado en el grupo de interés o en el grupo de referencia. Si comparas \"normal\" vs \"Alzheimer\", un gen sobreexpresado en normal tendrá un logFC positivo para \"normal\" y un logFC negativo para \"Alzheimer\". Lo mismo al revés. El set de genes DEGs es el mismo, solo cambia el signo según qué grupo tomes como referencia.**" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "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", " # 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)} DEGs found for {type} for Alzheimer disease')\n", "\n", " degs_disease_filtered.to_csv(f'CellXGene/cross-dementia/complete/data/degs_{type}_total.csv', index=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 148, "outputs": [], "source": [ "gen = pd.read_csv('CellXGene/gen.tsv', sep = '\\t')\n", "gen_pro = pd.read_csv('CellXGene/gen_pro.tsv', sep = '\\t')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "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", " # Mapear los Gene Symbols a Entrez IDs\n", " #entrez_ids = {ensembl_id: symbol_to_entrez.get(gene_symbol, None) for ensembl_id, gene_symbol in gene_symbols.items()}\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" } } }, { "cell_type": "code", "execution_count": 151, "outputs": [ { "data": { "text/plain": "{'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'}" }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_type = 'pericyte'\n", "\n", "# Obtén el valor correspondiente a esa key\n", "data_for_sample_type = {sample_type: data_per_type[sample_type]}\n", "data_for_sample_type" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 152, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Mapping DEGs for pericyte...: 0%| | 0/1 [00:00 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'CellXGene/cross-dementia/complete/data/degs_{type}_mapped.csv', index=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "cell_type": "code", "execution_count": 164, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pericyte\n" ] } ], "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']\n", "keys = ['pericyte']\n", "\n", "for type in keys:\n", " print(type)\n", " mapped_file_path = f'CellXGene/cross-dementia/complete/data/degs_{type}_mapped.csv'\n", " add_protein_id(mapped_file_path)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### 4.2. Cell type-specific PPI construction and integration of DEGs expression values\n", "\n", "\n", "**Las PPI específicas de tipo celular y condición (sana/enferma) van a ser iguales en estructura (mismos nodos y edges) porque usas el mismo set de DEGs, solo que cambia el contexto de up/down-regulation. Se está filtrando el interactoma general (pro_pro.tsv) para quedarte con las interacciones entre DEGs.**\n", "\n", "Si los mismos genes aparecen como DEGs en ambas condiciones (solo cambia el signo del logFC), el set de proteínas mapeadas será el mismo.\n", "Eso implica que la topología (estructura) de la red será igual entre sano y enfermo.\n", "\n", "Problema: No estás capturando cambios estructurales reales entre las redes de condiciones sanas y enfermas. Solo podrías distinguirlas después (en análisis como enrichment o modularidad) considerando el estado up/down de los nodos, pero no en la conectividad." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 221, "outputs": [], "source": [ "gen_pro = pd.read_csv(\"CellXGene/gen_pro.tsv\", sep='\\t')\n", "pro_pro = pd.read_csv('CellXGene/pro_pro.tsv', sep = '\\t')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 222, "outputs": [], "source": [ "sample_type = 'astrocyte'\n", "\n", "# Obtén el valor correspondiente a esa key\n", "data_for_sample_type = {sample_type: data_per_type[sample_type]}" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 225, "outputs": [], "source": [ "def build_ppi_with_expression(cell_type, gen_pro, pro_pro):\n", " \"\"\"\n", " Construye la red PPI específica de un tipo celular, filtrada por DEGs,\n", " e integra los valores de expresión diferencial directamente en los nodos.\n", "\n", " Parámetros:\n", " - cell_type: str, nombre del tipo celular.\n", " - gen_pro: DataFrame, mapeo gene_id → protein_id.\n", " - pro_pro: DataFrame, interacciones proteína-proteína.\n", "\n", " Output:\n", " - G: Grafo NetworkX con expresión diferencial integrada.\n", " \"\"\"\n", " # Cargar los DEGs para este tipo celular\n", " degs = pd.read_csv(f\"CellXGene/cross-dementia/complete/data/degs_{cell_type}_mapped.csv\")\n", "\n", " # Mapear genes DEGs a proteínas (usando Entrez IDs)\n", " degs = degs.merge(gen_pro, on=\"gene_id\", how=\"left\")\n", "\n", " # Lista de proteínas DEGs\n", " proteins_degs_disease = degs[\"protein_id\"].dropna().unique()\n", "\n", " # Filtrar la red PPI para solo conservar interacciones entre proteínas DEGs y eliminar 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", " # Construir la red PPI\n", " G = nx.from_pandas_edgelist(ppi_filtered_disease, \"prA\", \"prB\")\n", "\n", " # Agregar la expresión diferencial a los nodos de la red\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", " # Guardar la red en un único archivo con expresión integrada\n", " output_path = f\"CellXGene/cross-dementia/complete/graphs/{cell_type}_network.graphml\"\n", " nx.write_graphml(G, output_path)\n", "\n", " return G" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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, gen_pro, 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" } } }, { "cell_type": "markdown", "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "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" } } }, { "cell_type": "code", "execution_count": 10, "outputs": [], "source": [ "dis_gen = pd.read_csv('CellXGene/dis_gen.tsv', sep = '\\t')\n", "gen_pro = pd.read_csv('CellXGene/gen_pro.tsv', sep = '\\t')\n", "pro_pro = pd.read_csv('CellXGene/pro_pro.tsv', sep = '\\t')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 11, "outputs": [], "source": [ "G_ppi = nx.from_pandas_edgelist(pro_pro, 'prA', 'prB')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "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" } } }, { "cell_type": "code", "execution_count": 14, "outputs": [ { "data": { "text/plain": "{'P52209',\n 'O43189',\n 'Q9NZQ7',\n 'Q9NZV6',\n 'P51843',\n 'Q16795',\n 'Q9HCE0',\n 'P35638',\n 'P04406',\n 'O15427',\n 'P03891',\n 'Q9ULV8',\n 'P35232',\n 'P00813',\n 'Q9UIK4',\n 'P63244',\n 'O75689',\n 'O15105',\n 'P51688',\n 'Q9BXY0',\n 'Q9BYF1',\n 'Q53EL6',\n 'P24387',\n 'P42768',\n 'P15529',\n 'P60228',\n 'Q9UHC9',\n 'Q9P2Y5',\n 'P12004',\n 'Q6ZW49',\n 'Q4V9L6',\n 'P49716',\n 'P04179',\n 'Q9Y2A7',\n 'Q13200',\n 'Q9NV58',\n 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Alzheimer module proteins filtering\n", "\n", "Keep only those proteins which are present in the Alzheimer disease module for subsequent analysis.\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "for cell_type in tqdm(cell_types, desc='Processing cell types...'):\n", "\n", " # Cargar red PPI específica para este tipo celular\n", " G = nx.read_graphml(f'CellXGene/cross-dementia/complete/graphs/{cell_type}_network.graphml')\n", " ppi_proteins = set(G.nodes())\n", "\n", " # Proteínas del módulo de Alzheimer presentes en la PPI de este tipo celular\n", " alz_in_ppi = lcc_alz.intersection(ppi_proteins)\n", "\n", " G_alz = G.subgraph(alz_in_ppi).copy()\n", " nx.write_graphml(G_alz, f'CellXGene/cross-dementia/filtered/graphs/{cell_type}_network.graphml')\n", "\n", " # Archivo filtrado de mapped genes\n", " df_complete = pd.read_csv(f'CellXGene/cross-dementia/complete/data/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'CellXGene/cross-dementia/filtered/data/degs_{cell_type}_mapped.csv', index=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 359, "outputs": [ { "data": { "text/plain": "2697" }, "execution_count": 359, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(lcc_alz)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "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" } } }, { "cell_type": "code", "execution_count": 232, "outputs": [], "source": [ "def calculate_lcc_for_cell_type(degs_cell_type, gen_pro, pro_pro, PPI):\n", "\n", " # Generar la subred para el tipo celular con sus DEGs\n", " prots = functions_proximity.pro_gen_dict(degs_cell_type, gen_pro) # Proteínas correspondientes a los DEGs\n", " prots_interactome = functions_proximity.gen_pro_PPI(prots, pro_pro)\n", " SG = PPI.subgraph(prots_interactome)\n", " lcc_cell_type = functions_proximity.lcc(SG)\n", "\n", " return lcc_cell_type\n", "\n", "# =================================================================================\n", "\n", "def calculate_lcc_from_prots(prots, pro_pro, PPI):\n", "\n", " prots_interactome = []\n", " for prot in prots:\n", " # Iterating over all proteins in the general prot:gen dictionary.\n", " if prot in pro_pro[\"prA\"].tolist() or prot in pro_pro[\"prB\"].tolist():\n", " # Selecting proteins that appear in the PPI network.\n", " prots_interactome.append(prot)\n", "\n", " SG = PPI.subgraph(prots_interactome)\n", " lcc_cell_type = functions_proximity.lcc(SG)\n", "\n", " return lcc_cell_type\n", "\n", "# =================================================================================\n", "\n", "def generate_log_bins(graph, num_bins):\n", " \"\"\"\n", " This function generates logarithmic bins to group nodes of a graph based on\n", " the degree distribution of the nodes.\n", " \"\"\"\n", " degrees = [degree for _, degree in graph.degree()]\n", " min_degree = max(min(degrees), 1) # Para evitar log(0)\n", " max_degree = max(degrees)\n", "\n", " return np.logspace(np.log10(min_degree), np.log10(max_degree), num_bins)\n", "\n", "# =================================================================================\n", "\n", "def group_nodes_by_bins(graph, log_bins):\n", " \"\"\"\n", " This function groups nodes of a graph in logarithmic bins based on its degree.\n", "\n", " Input:\n", " 1.graph\n", " 2. log_bins: logarithmic bins\n", " \"\"\"\n", " nodes_bins = {}\n", " for node, degree in graph.degree():\n", " bin_index = np.digitize(degree, log_bins) - 1 # Ajustar índice para Python (basado en 0)\n", " nodes_bins.setdefault(bin_index, []).append(node)\n", "\n", " return nodes_bins\n", "\n", "# =================================================================================\n", "\n", "def random_subset_generator(proteins, graph_ppi, num_iterations):\n", "\n", " # Generation of logarithmic bins\n", " num_bins = 10\n", " bin_edges = generate_log_bins(graph_ppi, num_bins)\n", "\n", " # Group nodes in logarithmic bins\n", " group_nodes_bins = group_nodes_by_bins(graph_ppi, bin_edges)\n", "\n", " results = []\n", "\n", " for _ in range(num_iterations): # For each iteration\n", "\n", " iteration_results = [] # list to append proteins for each disease\n", "\n", " for prot in proteins:\n", "\n", " # degree of the node\n", " degree_node = graph_ppi.degree(prot)\n", "\n", " # bin of the node based on its degree\n", " bin_index = np.digitize(degree_node, bin_edges) - 1\n", "\n", " # nodes of the same bin\n", " available_nodes = group_nodes_bins.get(bin_index, [])\n", "\n", " if available_nodes:\n", " random_node = np.random.choice(available_nodes) #choose randomly a node from available nodes\n", " # if len(available_nodes) == 1 and random_node == prot:\n", " # pass\n", " # else:\n", " while random_node == prot:\n", " random_node = np.random.choice(available_nodes)\n", "\n", " iteration_results.append(str(random_node))\n", "\n", " else:\n", " iteration_results.append(None)\n", "\n", " # results.append(results)\n", " if any(iteration_results):\n", " results.append(iteration_results)\n", "\n", " return results" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 243, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Processing cell types...: 0%| | 0/8 [00:00", "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": "
", "image/png": 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" }, "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": "
", "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": "
", "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": "
", "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": "
", "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": "
", "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": "
", "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", " # Solapamiento DEGs en módulo de Alzheimer: número de proteínas DEGs que están en el módulo de Alzheimer.\n", " degs_cell_type = pd.read_csv(f'CellXGene/cross-dementia/complete/data/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'CellXGene/cross-dementia/complete/graphs/{cell_type}_network.graphml')\n", " ppi_proteins = set(G.nodes())\n", "\n", " # Proteínas del módulo de Alzheimer presentes en la PPI de este tipo celular\n", " alz_in_ppi = lcc_alz.intersection(ppi_proteins)\n", "\n", " # Calcular el LCC de la red de enfermedad para el tipo celular. Para ello utilizamos los DEGs que se encuentran en el LCC general de la enfermedad\n", " degs_cell_type_filt = pd.read_csv(f'CellXGene/cross-dementia/filtered/data/degs_{cell_type}_mapped.csv')\n", " lcc_cell_type = calculate_lcc_for_cell_type(degs_cell_type_filt['gene_id'], gen_pro, pro_pro, G_ppi)\n", "\n", " # Generar 1,000 redes aleatorias\n", " random_networks = random_subset_generator(lcc_cell_type, G_ppi, 1000)\n", "\n", " # Calcular el tamaño del LCC para cada red aleatoria\n", " random_lcc_sizes = [len(calculate_lcc_from_prots(network, pro_pro, G_ppi)) for network in random_networks]\n", "\n", " # Calcular estadísticas de los módulos aleatorios\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'CellXGene/cross-dementia/plots/significance/random_modules_{cell_type}.svg', format='svg', dpi=1200)\n", " plt.show()\n", "\n", " # Agregar la fila correspondiente\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", "# Crear el DataFrame de la tabla\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" } } }, { "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": "
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Cell TypeTotal DEGsDEGs mapped to proteinCell Type proteins in main LCCCell Type LCC sizeLCC meanLCC stdp_valueadjusted_p_valuez_score
0astrocyte3176287853218353.96313.7448040.00.09.388057
1microglial cell15851501334627.2183.6620320.00.014.959456
2oligodendrocyte3383305255419157.55314.3725850.00.09.284830
3glutamatergic neuron44363963733277120.87312.4255730.00.012.564974
4inhibitory interneuron4130376967325193.18614.1197520.00.011.176825
5endothelial cell of vascular tree3793677341.1150.3282910.00.08.787934
6oligodendrocyte precursor cell19631832320494.9962.2869160.00.019.241635
7pericyte1111072011.0000.0000001.01.0NaN
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" }, "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" } } }, { "cell_type": "code", "execution_count": 253, "outputs": [], "source": [ "table_df.to_csv('CellXGene/cross-dementia/filtered/results/cell_type_summary.tsv', sep='\\t', index=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### P-value and Adj P-value calculation" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 272, "outputs": [], "source": [ "df = pd.read_csv('CellXGene/cross-dementia/filtered/results/cell_type_summary.tsv', sep='\\t')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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": "
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Cell TypeTotal DEGsDEGs mapped to proteinCell Type proteins in main LCCCell Type LCC sizeLCC meanLCC stdz_scorep_valueadj_p_value
0astrocyte3176287853218353.96313.7448049.3880576.111731e-218.556424e-21
1microglial cell15851501334627.2183.66203214.9594561.351270e-504.729446e-50
2oligodendrocyte3383305255419157.55314.3725859.2848301.619661e-201.889605e-20
3glutamatergic neuron44363963733277120.87312.42557312.5649743.289849e-367.676313e-36
4inhibitory interneuron4130376967325193.18614.11975211.1768255.295062e-299.266358e-29
5endothelial cell of vascular tree3793677341.1150.3282918.7879341.523350e-181.523350e-18
6oligodendrocyte precursor cell19631832320494.9962.28691619.2416351.658917e-821.161242e-81
7pericyte1111072011.0000.000000NaNNaNNaN
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" }, "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'])) # p value bilateral porque tengo en cuenta que los modulos aleatorios pueden ser de mayor o menos tamaño que el real\n", "df = df.dropna(subset=['p_value'])\n", "df['p_value'] = pd.to_numeric(df['p_value'], errors='coerce') # Convertir a float\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 # Mantener el valor como NaN\n", "df = pd.concat([df, pericyte_row], ignore_index=True)\n", "\n", "df" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 275, "outputs": [], "source": [ "df.to_csv('CellXGene/cross-dementia/filtered/results/cell_type_summary_stats.tsv', sep='\\t', index=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### 6.2. Representation of common proteins with DEGs values" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 21, "outputs": [], "source": [ "def load_and_process(files):\n", " df_list = []\n", " for file in files:\n", " df = pd.read_csv(file)\n", " # Extraer el tipo celular del nombre del archivo (por ejemplo, \"degs_celltype_mapped.csv\")\n", " cell_type = file.split('_')[1] # Asumiendo que el tipo celular está en la segunda posición del nombre del archivo\n", " df['cell_type'] = cell_type # Agregar la columna 'cell_type' al DataFrame\n", " df_list.append(df)\n", " merged_df = pd.concat(df_list)\n", " return merged_df" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 22, "outputs": [], "source": [ "complete_files = glob.glob(\"CellXGene/cross-dementia/complete/data/degs_*_mapped.csv\")\n", "filtered_files = glob.glob(\"CellXGene/cross-dementia/filtered/data/degs_*_mapped.csv\")\n", "\n", "# Cargar datos\n", "complete_data = load_and_process(complete_files)\n", "filtered_data = load_and_process(filtered_files)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 23, "outputs": [ { "data": { "text/plain": " protein_id logfoldchange_mean std_logfoldchanges num_cell_types \\\n0 A0A087WUL8 0.485817 0.215743 4 \n1 A0A087WVF3 -3.359497 NaN 1 \n2 A0A0U1RRE5 0.342878 0.111791 3 \n3 A0A0U1RRL7 0.288252 NaN 1 \n4 A0A1B0GTU2 1.060499 NaN 1 \n... ... ... ... ... \n8387 Q9Y6X6 -0.007668 0.382404 2 \n8388 Q9Y6X8 0.516868 0.162532 4 \n8389 Q9Y6Y0 0.595799 0.244793 4 \n8390 Q9Y6Y8 -0.340800 0.078085 3 \n8391 Q9Y6Z7 -1.352683 NaN 1 \n\n gene_symbol \n0 NBPF19 \n1 TBC1D3D \n2 NBDY \n3 MMP24OS \n4 CTXND1 \n... ... \n8387 MYO16 \n8388 ZHX2 \n8389 IVNS1ABP \n8390 SEC23IP \n8391 COLEC10 \n\n[8392 rows x 5 columns]", "text/html": "
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protein_idlogfoldchange_meanstd_logfoldchangesnum_cell_typesgene_symbol
0A0A087WUL80.4858170.2157434NBPF19
1A0A087WVF3-3.359497NaN1TBC1D3D
2A0A0U1RRE50.3428780.1117913NBDY
3A0A0U1RRL70.288252NaN1MMP24OS
4A0A1B0GTU21.060499NaN1CTXND1
..................
8387Q9Y6X6-0.0076680.3824042MYO16
8388Q9Y6X80.5168680.1625324ZHX2
8389Q9Y6Y00.5957990.2447934IVNS1ABP
8390Q9Y6Y8-0.3408000.0780853SEC23IP
8391Q9Y6Z7-1.352683NaN1COLEC10
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8392 rows × 5 columns

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" }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calcular estadísticas por proteína\n", "aggregated = complete_data.groupby(\"protein_id\").agg(\n", " logfoldchange_mean=(\"logfoldchanges\", \"mean\"),\n", " std_logfoldchanges=(\"logfoldchanges\", \"std\"),\n", " num_cell_types=(\"cell_type\", \"nunique\"),\n", " gene_symbol=(\"gene_symbol\", \"first\")\n", ").reset_index()\n", "\n", "aggregated" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 24, "outputs": [ { "data": { "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Identificar proteínas comunes\n", "common_proteins = set(complete_data[\"protein_id\"]) & set(filtered_data[\"protein_id\"])\n", "aggregated[\"common\"] = aggregated[\"protein_id\"].isin(common_proteins)\n", "\n", "sns.set_context(\"paper\")\n", "sns.set_style(\"whitegrid\")\n", "\n", "# Crear el scatter plot\n", "plt.figure(figsize=(10, 7))\n", "sns.scatterplot(data=aggregated, x='logfoldchange_mean', y='std_logfoldchanges',\n", " size='num_cell_types', color='gray', alpha=0.7)\n", "\n", "# Resaltar proteínas comunes en azul\n", "sns.scatterplot(data=aggregated[aggregated[\"common\"]], x='logfoldchange_mean', y='std_logfoldchanges',\n", " color='blue', s=20, edgecolor='black', label='Common Proteins')\n", "\n", "plt.axvline(x=1, linestyle='--', color='gray') # Umbral de LFC > 1\n", "plt.axvline(x=-1, linestyle='--', color='gray') # Umbral de LFC < -1\n", "plt.axhline(y=0.5, linestyle='--', color='gray') # Umbral de alta variabilidad\n", "\n", "plt.title('Proteins with high variability in differential expression between cell types present in Alzheimer disease module')\n", "plt.xlabel('Average LFC')\n", "plt.ylabel('LFC Standard deviation')\n", "plt.legend(title='Number of cell types', loc='upper left')\n", "plt.tight_layout()\n", "\n", "plt.savefig('CellXGene/cross-dementia/plots/volcano_all.svg', format = 'svg', dpi=1200)\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### 6.3. Representation of each cell type differential expression values" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 226, "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" } } }, { "cell_type": "code", "execution_count": 350, "outputs": [ { "data": { "text/plain": "count 2.000000e+01\nmean 1.649116e-02\nstd 1.941289e-02\nmin 5.375182e-20\n25% 1.339488e-04\n50% 7.495072e-03\n75% 3.880477e-02\nmax 4.917496e-02\nName: pvals_adj, dtype: float64" }, "execution_count": 350, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['pvals_adj'].describe()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 528, "outputs": [ { "data": { "text/plain": "{'P02745', 'P04792', 'P08571', 'P21917', 'P46095'}" }, "execution_count": 528, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list = {'P21917', 'P46095', 'P02745', 'P04792', 'P08571'}\n", "list.intersection(lcc_alz)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "cell_type": "code", "execution_count": 615, "outputs": [ { "data": { "text/plain": " Id gene_id gene_symbol names logfoldchanges \\\n0 P07900 3320.0 HSP90AA1 ENSG00000080824 1.110109 \n1 O94875 8470.0 SORBS2 ENSG00000154556 0.827073 \n2 P60484 5728.0 PTEN ENSG00000171862 -0.782102 \n3 Q15303 2066.0 ERBB4 ENSG00000178568 1.107987 \n4 P04792 3315.0 HSPB1 ENSG00000106211 1.884719 \n5 P08238 3326.0 HSP90AB1 ENSG00000096384 0.861556 \n6 P54253 6310.0 ATXN1 ENSG00000124788 0.712304 \n7 P09619 5159.0 PDGFRB ENSG00000113721 -0.378450 \n8 Q92598 10808.0 HSPH1 ENSG00000120694 1.676103 \n9 O43524 2309.0 FOXO3 ENSG00000118689 0.854423 \n10 P61586 387.0 RHOA ENSG00000067560 0.785520 \n11 P0DMV8 3304.0 HSPA1B ENSG00000204388 1.975146 \n12 P17676 1051.0 CEBPB ENSG00000172216 1.105564 \n13 P02511 1410.0 CRYAB ENSG00000109846 1.525713 \n14 P13693 7178.0 TPT1 ENSG00000133112 0.802365 \n15 Q15185 10728.0 PTGES3 ENSG00000110958 0.866479 \n16 P08670 7431.0 VIM ENSG00000026025 1.117781 \n17 Q7Z6G8 56899.0 ANKS1B ENSG00000185046 0.978241 \n18 Q09666 79026.0 AHNAK ENSG00000124942 0.989039 \n19 P49716 1052.0 CEBPD ENSG00000221869 0.708934 \n\n pvals pvals_adj scores -log10(pvals_adj) flag \n0 2.761193e-13 1.112958e-09 7.305558 8.953521 False \n1 1.624354e-06 1.580385e-03 4.795295 2.801237 False \n2 9.017274e-12 2.826915e-08 -6.821376 7.548687 False \n3 1.496004e-04 4.292680e-02 3.791732 1.367271 False \n4 1.905079e-24 5.375182e-20 10.203879 19.269607 True \n5 2.566908e-06 2.336300e-03 4.702742 2.631471 False \n6 3.498667e-05 1.410213e-02 4.138314 1.850715 False \n7 1.948204e-04 4.917496e-02 -3.725640 1.308256 False \n8 1.338602e-05 7.097056e-03 4.353698 2.148922 True \n9 1.617127e-05 7.893088e-03 4.312098 2.102753 False \n10 1.640082e-04 4.407135e-02 3.768840 1.355844 False \n11 1.293792e-07 1.738302e-04 5.279723 3.759875 True \n12 9.147368e-06 5.491340e-03 4.436405 2.260322 False \n13 8.710331e-10 1.755443e-06 6.131411 5.755613 True \n14 1.618387e-04 4.390652e-02 3.772163 1.357471 False \n15 3.143596e-05 1.285458e-02 4.162807 1.890942 False \n16 8.111714e-09 1.430450e-05 5.766120 4.844527 True \n17 2.711835e-05 1.159309e-02 4.196406 1.935801 False \n18 1.952010e-04 4.917496e-02 3.725147 1.308256 False \n19 1.193964e-04 3.743076e-02 3.847362 1.426771 False ", "text/html": "
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Idgene_idgene_symbolnameslogfoldchangespvalspvals_adjscores-log10(pvals_adj)flag
0P079003320.0HSP90AA1ENSG000000808241.1101092.761193e-131.112958e-097.3055588.953521False
1O948758470.0SORBS2ENSG000001545560.8270731.624354e-061.580385e-034.7952952.801237False
2P604845728.0PTENENSG00000171862-0.7821029.017274e-122.826915e-08-6.8213767.548687False
3Q153032066.0ERBB4ENSG000001785681.1079871.496004e-044.292680e-023.7917321.367271False
4P047923315.0HSPB1ENSG000001062111.8847191.905079e-245.375182e-2010.20387919.269607True
5P082383326.0HSP90AB1ENSG000000963840.8615562.566908e-062.336300e-034.7027422.631471False
6P542536310.0ATXN1ENSG000001247880.7123043.498667e-051.410213e-024.1383141.850715False
7P096195159.0PDGFRBENSG00000113721-0.3784501.948204e-044.917496e-02-3.7256401.308256False
8Q9259810808.0HSPH1ENSG000001206941.6761031.338602e-057.097056e-034.3536982.148922True
9O435242309.0FOXO3ENSG000001186890.8544231.617127e-057.893088e-034.3120982.102753False
10P61586387.0RHOAENSG000000675600.7855201.640082e-044.407135e-023.7688401.355844False
11P0DMV83304.0HSPA1BENSG000002043881.9751461.293792e-071.738302e-045.2797233.759875True
12P176761051.0CEBPBENSG000001722161.1055649.147368e-065.491340e-034.4364052.260322False
13P025111410.0CRYABENSG000001098461.5257138.710331e-101.755443e-066.1314115.755613True
14P136937178.0TPT1ENSG000001331120.8023651.618387e-044.390652e-023.7721631.357471False
15Q1518510728.0PTGES3ENSG000001109580.8664793.143596e-051.285458e-024.1628071.890942False
16P086707431.0VIMENSG000000260251.1177818.111714e-091.430450e-055.7661204.844527True
17Q7Z6G856899.0ANKS1BENSG000001850460.9782412.711835e-051.159309e-024.1964061.935801False
18Q0966679026.0AHNAKENSG000001249420.9890391.952010e-044.917496e-023.7251471.308256False
19P497161052.0CEBPDENSG000002218690.7089341.193964e-043.743076e-023.8473621.426771False
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" }, "execution_count": 615, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 685, "outputs": [ { "data": { "text/plain": "{'CRYAB', 'HSPA1B', 'HSPB1', 'HSPH1', 'VIM'}" }, "execution_count": 685, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(top_genes['gene_symbol'])" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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": "
", "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\"CellXGene/cross-dementia/filtered/data/degs_{cell_type}_mapped.csv\" # Ajustar el nombre del archivo según el formato\n", "\n", " try:\n", " # Cargar los datos\n", " df = pd.read_csv(file_path)\n", "\n", " df[\"-log10(pvals_adj)\"] = -np.log10(df[\"pvals_adj\"])\n", "\n", " # Obtener el menor pvals_adj no nulo y mayor a 0 para recalcular en caso de infinito\n", " min_pvals_adj = df.loc[df[\"pvals_adj\"] > 0, \"pvals_adj\"].min() * 0.1\n", "\n", " # Calcular los valores corregidos en caso de infinito\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", " # sns.scatterplot(data=top_genes, y=\"-log10(pvals_adj)\", x=\"logfoldchanges\", hue='color', legend=False, ax=axes[i])\n", " # texts = []\n", " for _, row in top_genes.iterrows():\n", " axes[i].scatter(row[\"logfoldchanges\"], row[\"-log10(pvals_adj)\"], color=row['color'])\n", " # axes[i].text(row[\"logfoldchanges\"] + 0.15, row[\"-log10(pvals_adj)\"] + 0.25, row[\"gene_symbol\"], fontsize=5, color=\"black\")\n", " #\n", " # adjust_text(texts, arrowprops=dict(arrowstyle=\"-\", color='k', lw=0.5))\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('CellXGene/cross-dementia/plots/volcano_per_type_notext.svg', format = 'svg', dpi=1200)\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 687, "outputs": [], "source": [ "top_genes_info = {'glutamatergic neuron': ['C1QA', 'HSPB1', 'GPR6', 'DRD4', 'CD14'], 'inhibitory neuron': ['PENK', 'GPR6', 'CRYZ', 'ARAP3', 'DRD4'], 'oligodendrocyte': ['RELN', 'CXCR4', 'BIRC3', 'HSPA1B'], 'astrocyte': ['CRYAB', 'ANXA1', 'EGR1', 'EGF', 'S100A10'], 'microglial cell': ['BAG3', 'HSPA1B', 'HSPB1', 'DNAJB1'], 'oligodendrocyte precursor cell': ['FOS', 'IGFBP3', 'EGR1', 'SOCS3', 'VGF'], 'endothelial cell of vascular tree': ['HSPA1B', 'CACYBP', 'RBM3', 'HSPB1', 'HSPH1'], 'pericyte': ['HSPA1B', 'CRYAB', 'HSPB1', 'VIM', 'HSPH1']}" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 531, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Id gene_id gene_symbol names logfoldchanges \\\n", "0 Q9Y276 617.0 BCS1L ENSG00000074582 0.837833 \n", "1 P11169 6515.0 SLC2A3 ENSG00000059804 0.387271 \n", "2 Q12904 9255.0 AIMP1 ENSG00000164022 0.431453 \n", "3 Q8N165 149420.0 PDIK1L ENSG00000175087 0.293635 \n", "4 P67809 4904.0 YBX1 ENSG00000065978 0.364904 \n", ".. ... ... ... ... ... \n", "728 Q9NQ75 57091.0 CASS4 ENSG00000087589 0.861684 \n", "729 Q92520 10447.0 FAM3C ENSG00000196937 0.350469 \n", "730 Q08117 166.0 TLE5 ENSG00000104964 0.452040 \n", "731 P35638 1649.0 DDIT3 ENSG00000175197 0.872030 \n", "732 O15121 8560.0 DEGS1 ENSG00000143753 0.813354 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) flag \n", "0 5.074668e-04 1.902495e-03 3.476784 2.720677 False \n", "1 1.823817e-26 2.245157e-25 10.645815 24.648753 False \n", "2 5.335923e-17 4.623866e-16 8.379045 15.334995 False \n", "3 9.947450e-08 5.149859e-07 5.327681 6.288205 False \n", "4 9.498815e-09 5.280967e-08 5.739443 7.277287 False \n", ".. ... ... ... ... ... \n", "728 9.660778e-04 3.508093e-03 3.300224 2.454929 False \n", "729 3.170170e-50 7.355784e-49 14.902596 48.133371 False \n", "730 3.025295e-101 1.574884e-99 21.361858 98.802751 False \n", "731 1.631244e-10 1.013556e-09 6.392566 8.994152 False \n", "732 7.507713e-32 1.091908e-30 11.744825 29.961814 False \n", "\n", "[733 rows x 10 columns]\n", " Id gene_id gene_symbol names logfoldchanges \\\n", "0 P11169 6515.0 SLC2A3 ENSG00000059804 0.449376 \n", "1 Q9Y2M5 27252.0 KLHL20 ENSG00000076321 0.354700 \n", "2 Q12904 9255.0 AIMP1 ENSG00000164022 0.341772 \n", "3 O60290 643641.0 ZNF862 ENSG00000106479 0.270450 \n", "4 P35613 682.0 BSG ENSG00000172270 0.289875 \n", ".. ... ... ... ... ... \n", "668 Q9NQ75 57091.0 CASS4 ENSG00000087589 1.062502 \n", "669 Q9Y6H5 9627.0 SNCAIP ENSG00000064692 0.450695 \n", "670 Q08117 166.0 TLE5 ENSG00000104964 0.396488 \n", "671 P49674 102800317.0 TPTEP2-CSNK1E ENSG00000283900 0.279767 \n", "672 O15121 8560.0 DEGS1 ENSG00000143753 0.455657 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) flag \n", "0 4.627132e-13 5.370404e-12 7.235823 11.269993 False \n", "1 6.490162e-20 1.268144e-18 9.135839 17.896832 False \n", "2 1.088346e-06 6.891310e-06 4.874953 5.161698 False \n", "3 5.461479e-11 5.306324e-10 6.557780 9.275206 False \n", "4 1.656321e-19 3.138555e-18 9.033918 17.503270 False \n", ".. ... ... ... ... ... \n", "668 6.727973e-03 2.591179e-02 2.710017 1.586503 False \n", "669 5.165977e-24 1.336004e-22 10.106573 21.874192 False \n", "670 1.013671e-34 5.345932e-33 12.290903 32.271977 False \n", "671 2.926702e-15 4.067827e-14 7.893992 13.390638 False \n", "672 5.582224e-07 3.651807e-06 5.005137 5.437492 False \n", "\n", "[673 rows x 10 columns]\n", " Id gene_id gene_symbol names logfoldchanges \\\n", "0 Q5JRX3 10531.0 PITRM1 ENSG00000107959 -0.416291 \n", "1 P35398 6095.0 RORA ENSG00000069667 -0.393192 \n", "2 Q14517 2195.0 FAT1 ENSG00000083857 0.278725 \n", "3 O60290 643641.0 ZNF862 ENSG00000106479 0.332163 \n", "4 P11274 613.0 BCR ENSG00000186716 0.266660 \n", ".. ... ... ... ... ... \n", "549 Q9HB63 59277.0 NTN4 ENSG00000074527 -0.683986 \n", "550 P42262 2891.0 GRIA2 ENSG00000120251 -0.435862 \n", "551 P49674 102800317.0 TPTEP2-CSNK1E ENSG00000283900 0.303965 \n", "552 P35638 1649.0 DDIT3 ENSG00000175197 0.385822 \n", "553 O15121 8560.0 DEGS1 ENSG00000143753 0.397353 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) flag \n", "0 5.756569e-11 5.997843e-10 -6.549926 9.222005 False \n", "1 3.596141e-48 1.100489e-46 -14.583136 45.958414 False \n", "2 4.717047e-03 2.333301e-02 2.825747 1.632029 False \n", "3 7.991042e-16 1.060523e-14 8.054338 13.974480 False \n", "4 7.822840e-04 4.393340e-03 3.358988 2.357205 False \n", ".. ... ... ... ... ... \n", "549 3.365789e-05 2.210047e-04 -4.147191 3.655598 False \n", "550 1.183541e-138 9.968240e-137 -25.065489 136.001382 False \n", "551 9.381694e-38 2.363433e-36 12.843278 35.626457 False \n", "552 9.698318e-04 5.378106e-03 3.299135 2.269371 False \n", "553 4.039385e-04 2.356191e-03 3.537497 2.627790 False \n", "\n", "[554 rows x 10 columns]\n", " Id gene_id gene_symbol names logfoldchanges \\\n", "0 P04626 2064.0 ERBB2 ENSG00000141736 -0.287940 \n", "1 Q8TCT8 84888.0 SPPL2A ENSG00000138600 -0.353458 \n", "2 Q14517 2195.0 FAT1 ENSG00000083857 0.343325 \n", "3 O60290 643641.0 ZNF862 ENSG00000106479 0.419059 \n", "4 P35613 682.0 BSG ENSG00000172270 -0.386828 \n", ".. ... ... ... ... ... \n", "527 P27338 4129.0 MAOB ENSG00000069535 -0.307064 \n", "528 P42262 2891.0 GRIA2 ENSG00000120251 -0.532803 \n", "529 Q09666 79026.0 AHNAK ENSG00000124942 0.630602 \n", "530 P49674 102800317.0 TPTEP2-CSNK1E ENSG00000283900 0.435953 \n", "531 Q8IZY2 10347.0 ABCA7 ENSG00000064687 0.761207 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) flag \n", "0 1.601280e-06 1.567121e-05 -4.798163 4.804897 False \n", "1 2.918588e-15 5.954299e-14 -7.894339 13.225169 False \n", "2 6.414122e-11 9.798292e-10 6.533754 9.008850 False \n", "3 1.014438e-15 2.139190e-14 8.025100 13.669751 False \n", "4 1.092795e-31 4.685898e-30 -11.713048 29.329207 False \n", ".. ... ... ... ... ... \n", "527 5.072092e-20 1.348813e-18 -9.162474 17.870048 False \n", "528 5.124494e-98 1.063144e-95 -21.011720 94.973408 False \n", "529 1.198747e-64 1.316056e-62 16.977812 61.880726 False \n", "530 1.172580e-25 4.015089e-24 10.471111 23.396305 False \n", "531 1.035701e-04 7.951646e-04 3.882072 3.099543 False \n", "\n", "[532 rows x 10 columns]\n", " Id gene_id gene_symbol names logfoldchanges \\\n", "0 P11169 6515.0 SLC2A3 ENSG00000059804 -0.779546 \n", "1 O60290 643641.0 ZNF862 ENSG00000106479 0.615092 \n", "2 Q02156 5581.0 PRKCE ENSG00000171132 0.317718 \n", "3 Q14393 2621.0 GAS6 ENSG00000183087 -0.490290 \n", "4 P04233 972.0 CD74 ENSG00000019582 0.365258 \n", ".. ... ... ... ... ... \n", "329 P20339 5868.0 RAB5A ENSG00000144566 -0.353601 \n", "330 P51790 1182.0 CLCN3 ENSG00000109572 0.440371 \n", "331 P13473 3920.0 LAMP2 ENSG00000005893 0.372019 \n", "332 Q9NQ75 57091.0 CASS4 ENSG00000087589 -0.592606 \n", "333 Q09666 79026.0 AHNAK ENSG00000124942 1.404643 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) flag \n", "0 1.832361e-15 1.416440e-13 -7.952195 12.848802 False \n", "1 1.569596e-05 3.727790e-04 4.318688 3.428549 False \n", "2 3.456984e-08 1.255326e-06 5.516561 5.901244 False \n", "3 5.320820e-11 2.666553e-09 -6.561671 8.574050 False \n", "4 4.915439e-17 4.280528e-15 8.388701 14.368503 False \n", ".. ... ... ... ... ... \n", "329 5.662569e-04 9.777808e-03 -3.447282 2.009758 False \n", "330 3.586279e-06 9.492201e-05 4.634023 4.022633 False \n", "331 3.486081e-05 7.757080e-04 4.139142 3.110302 False \n", "332 9.174418e-07 2.688019e-05 -4.908567 4.570568 False \n", "333 7.769801e-05 1.613134e-03 3.951392 2.792330 False \n", "\n", "[334 rows x 10 columns]\n", " Id gene_id gene_symbol names logfoldchanges \\\n", "0 P24821 3371.0 TNC ENSG00000041982 -1.644939 \n", "1 P13804 2108.0 ETFA ENSG00000140374 -0.412017 \n", "2 P35568 3667.0 IRS1 ENSG00000169047 0.499488 \n", "3 Q8IXJ6 22933.0 SIRT2 ENSG00000068903 0.371526 \n", "4 Q9UHD2 29110.0 TBK1 ENSG00000183735 -0.446271 \n", ".. ... ... ... ... ... \n", "315 P14735 3416.0 IDE ENSG00000119912 -0.288636 \n", "316 P24385 595.0 CCND1 ENSG00000110092 -0.548290 \n", "317 Q92520 10447.0 FAM3C ENSG00000196937 -0.293168 \n", "318 Q9Y570 51400.0 PPME1 ENSG00000214517 -0.307343 \n", "319 P49674 1454.0 CSNK1E ENSG00000213923 0.537802 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) flag \n", "0 1.198126e-12 5.161087e-11 -7.105587 10.287259 False \n", "1 2.253193e-05 3.919472e-04 -4.238200 3.406772 False \n", "2 2.773655e-03 2.916463e-02 2.991770 1.535143 False \n", "3 8.949580e-08 2.287250e-06 5.346855 5.640686 False \n", "4 2.094059e-06 4.350800e-05 -4.744129 4.361431 False \n", ".. ... ... ... ... ... \n", "315 3.064300e-03 3.168165e-02 -2.961213 1.499192 False \n", "316 1.989628e-04 2.842398e-03 -3.720330 2.546315 False \n", "317 1.881041e-03 2.077711e-02 -3.108399 1.682415 False \n", "318 5.603309e-04 7.147259e-03 -3.450123 2.145860 False \n", "319 1.961435e-08 5.473974e-07 5.615369 6.261697 False \n", "\n", "[320 rows x 10 columns]\n", " Id gene_id gene_symbol names logfoldchanges \\\n", "0 Q8TEW0 56288.0 PARD3 ENSG00000148498 0.536861 \n", "1 P05362 3383.0 ICAM1 ENSG00000090339 -1.358272 \n", "2 P46934 4734.0 NEDD4 ENSG00000069869 0.635650 \n", "3 P01130 3949.0 LDLR ENSG00000130164 0.673549 \n", "4 P07900 3320.0 HSP90AA1 ENSG00000080824 1.269916 \n", ".. ... ... ... ... ... \n", "68 P04439 3105.0 HLA-A ENSG00000206503 -0.385615 \n", "69 Q13627 1859.0 DYRK1A ENSG00000157540 0.508074 \n", "70 Q9NX09 54541.0 DDIT4 ENSG00000168209 1.310385 \n", "71 P22694 5567.0 PRKACB ENSG00000142875 0.540728 \n", "72 Q13464 6093.0 ROCK1 ENSG00000067900 0.449656 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) flag \n", "0 1.576170e-06 3.532825e-04 4.801328 3.451878 False \n", "1 1.738858e-06 3.803247e-04 -4.781623 3.419845 False \n", "2 8.374939e-07 2.054773e-04 4.926421 3.687236 False \n", "3 3.863434e-04 3.113288e-02 3.549241 1.506781 False \n", "4 2.942664e-42 2.767576e-38 13.622513 37.557900 False \n", ".. ... ... ... ... ... \n", "68 9.379684e-06 1.584717e-03 -4.431001 2.800048 False \n", "69 4.812537e-06 8.648773e-04 4.572798 3.063045 False \n", "70 7.300147e-12 5.281375e-09 6.851653 8.277253 False \n", "71 4.519031e-05 5.448908e-03 4.079203 2.263691 False \n", "72 2.866264e-04 2.458104e-02 3.627096 1.609400 False \n", "\n", "[73 rows x 10 columns]\n", " Id gene_id gene_symbol names logfoldchanges \\\n", "0 P07900 3320.0 HSP90AA1 ENSG00000080824 1.110109 \n", "1 O94875 8470.0 SORBS2 ENSG00000154556 0.827073 \n", "2 P60484 5728.0 PTEN ENSG00000171862 -0.782102 \n", "3 Q15303 2066.0 ERBB4 ENSG00000178568 1.107987 \n", "4 P04792 3315.0 HSPB1 ENSG00000106211 1.884719 \n", "5 P08238 3326.0 HSP90AB1 ENSG00000096384 0.861556 \n", "6 P54253 6310.0 ATXN1 ENSG00000124788 0.712304 \n", "7 P09619 5159.0 PDGFRB ENSG00000113721 -0.378450 \n", "8 Q92598 10808.0 HSPH1 ENSG00000120694 1.676103 \n", "9 O43524 2309.0 FOXO3 ENSG00000118689 0.854423 \n", "10 P61586 387.0 RHOA ENSG00000067560 0.785520 \n", "11 P0DMV8 3304.0 HSPA1B ENSG00000204388 1.975146 \n", "12 P17676 1051.0 CEBPB ENSG00000172216 1.105564 \n", "13 P02511 1410.0 CRYAB ENSG00000109846 1.525713 \n", "14 P13693 7178.0 TPT1 ENSG00000133112 0.802365 \n", "15 Q15185 10728.0 PTGES3 ENSG00000110958 0.866479 \n", "16 P08670 7431.0 VIM ENSG00000026025 1.117781 \n", "17 Q7Z6G8 56899.0 ANKS1B ENSG00000185046 0.978241 \n", "18 Q09666 79026.0 AHNAK ENSG00000124942 0.989039 \n", "19 P49716 1052.0 CEBPD ENSG00000221869 0.708934 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) flag \n", "0 2.761193e-13 1.112958e-09 7.305558 8.953521 False \n", "1 1.624354e-06 1.580385e-03 4.795295 2.801237 False \n", "2 9.017274e-12 2.826915e-08 -6.821376 7.548687 False \n", "3 1.496004e-04 4.292680e-02 3.791732 1.367271 False \n", "4 1.905079e-24 5.375182e-20 10.203879 19.269607 True \n", "5 2.566908e-06 2.336300e-03 4.702742 2.631471 False \n", "6 3.498667e-05 1.410213e-02 4.138314 1.850715 False \n", "7 1.948204e-04 4.917496e-02 -3.725640 1.308256 False \n", "8 1.338602e-05 7.097056e-03 4.353698 2.148922 True \n", "9 1.617127e-05 7.893088e-03 4.312098 2.102753 False \n", "10 1.640082e-04 4.407135e-02 3.768840 1.355844 False \n", "11 1.293792e-07 1.738302e-04 5.279723 3.759875 True \n", "12 9.147368e-06 5.491340e-03 4.436405 2.260322 False \n", "13 8.710331e-10 1.755443e-06 6.131411 5.755613 True \n", "14 1.618387e-04 4.390652e-02 3.772163 1.357471 False \n", "15 3.143596e-05 1.285458e-02 4.162807 1.890942 False \n", "16 8.111714e-09 1.430450e-05 5.766120 4.844527 True \n", "17 2.711835e-05 1.159309e-02 4.196406 1.935801 False \n", "18 1.952010e-04 4.917496e-02 3.725147 1.308256 False \n", "19 1.193964e-04 3.743076e-02 3.847362 1.426771 False \n" ] }, { "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" ] } ], "source": [ "for i, cell_type in enumerate(cell_types):\n", " file_path = f\"CellXGene/cross-dementia/filtered/graphs/gephi/degs_{cell_type}_mapped_gephi.csv\" # Ajustar el nombre del archivo según el formato\n", "\n", " try:\n", " # Cargar los datos\n", " df = pd.read_csv(file_path)\n", "\n", " df[\"-log10(pvals_adj)\"] = -np.log10(df[\"pvals_adj\"])\n", "\n", " top_genes = df.loc[df[\"logfoldchanges\"].abs().nlargest(5).index]\n", "\n", " # Crear la columna \"flag\" con False por defecto\n", " df[\"flag\"] = False\n", "\n", " # Marcar los top 5 genes como True\n", " df.loc[top_genes.index, \"flag\"] = True\n", " print(df)\n", " df.to_csv(f\"CellXGene/cross-dementia/filtered/graphs/gephi/degs_{cell_type}_mapped_gephi.csv\", index=False)\n", " # print(f\"Archivo guardado: {output_file}\")\n", "\n", " except FileNotFoundError:\n", " print(f\"No data for {cell_type}\")\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 480, "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ " protein_id gene_id gene_symbol names logfoldchanges \\\n", "0 Q9Y276 617.0 BCS1L ENSG00000074582 0.837833 \n", "1 P11169 6515.0 SLC2A3 ENSG00000059804 0.387271 \n", "2 Q12904 9255.0 AIMP1 ENSG00000164022 0.431453 \n", "3 Q8N165 149420.0 PDIK1L ENSG00000175087 0.293635 \n", "4 P67809 4904.0 YBX1 ENSG00000065978 0.364904 \n", ".. ... ... ... ... ... \n", "728 Q9NQ75 57091.0 CASS4 ENSG00000087589 0.861684 \n", "729 Q92520 10447.0 FAM3C ENSG00000196937 0.350469 \n", "730 Q08117 166.0 TLE5 ENSG00000104964 0.452040 \n", "731 P35638 1649.0 DDIT3 ENSG00000175197 0.872030 \n", "732 O15121 8560.0 DEGS1 ENSG00000143753 0.813354 \n", "\n", " pvals pvals_adj scores -log10(pvals_adj) color \n", "0 5.074668e-04 1.902495e-03 3.476784 2.720677 grey \n", "1 1.823817e-26 2.245157e-25 10.645815 24.648753 grey \n", "2 5.335923e-17 4.623866e-16 8.379045 15.334995 grey \n", "3 9.947450e-08 5.149859e-07 5.327681 6.288205 grey \n", "4 9.498815e-09 5.280967e-08 5.739443 7.277287 grey \n", ".. ... ... ... ... ... \n", "728 9.660778e-04 3.508093e-03 3.300224 2.454929 grey \n", "729 3.170170e-50 7.355784e-49 14.902596 48.133371 grey \n", "730 3.025295e-101 1.574884e-99 21.361858 98.802751 grey \n", "731 1.631244e-10 1.013556e-09 6.392566 8.994152 grey \n", "732 7.507713e-32 1.091908e-30 11.744825 29.961814 grey \n", "\n", "[733 rows x 10 columns]\n" ] }, { "ename": "AttributeError", "evalue": "PathCollection.set() got an unexpected keyword argument 'order'", "output_type": "error", "traceback": [ "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[1;31mAttributeError\u001B[0m Traceback (most recent call last)", "Cell \u001B[1;32mIn[480], line 32\u001B[0m\n\u001B[0;32m 27\u001B[0m df\u001B[38;5;241m.\u001B[39mloc[top_genes\u001B[38;5;241m.\u001B[39mindex, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcolor\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mloc[top_genes\u001B[38;5;241m.\u001B[39mindex, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlogfoldchanges\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mapply(\n\u001B[0;32m 28\u001B[0m \u001B[38;5;28;01mlambda\u001B[39;00m x: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mred\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mblue\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 29\u001B[0m )\n\u001B[0;32m 30\u001B[0m \u001B[38;5;28mprint\u001B[39m(df)\n\u001B[1;32m---> 32\u001B[0m \u001B[43msns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mscatterplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m-log10(pvals_adj)\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mlogfoldchanges\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlegend\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43max\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxes\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morder\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morder\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 34\u001B[0m \u001B[38;5;66;03m# for _, row in top_genes.iterrows():\u001B[39;00m\n\u001B[0;32m 35\u001B[0m \u001B[38;5;66;03m# # axes[i].scatter(row[\"logfoldchanges\"], row[\"-log10(pvals_adj)\"])\u001B[39;00m\n\u001B[0;32m 36\u001B[0m \u001B[38;5;66;03m# axes[i].text(row[\"logfoldchanges\"] + 0.05, row[\"-log10(pvals_adj)\"] + 0.1, row[\"gene_symbol\"], fontsize=5, color=\"black\")\u001B[39;00m\n\u001B[0;32m 37\u001B[0m \n\u001B[0;32m 38\u001B[0m \u001B[38;5;66;03m# Etiquetas y título\u001B[39;00m\n\u001B[0;32m 39\u001B[0m axes[i]\u001B[38;5;241m.\u001B[39mset_ylabel(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m-log10(pvals_adj)\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\seaborn\\relational.py:634\u001B[0m, in \u001B[0;36mscatterplot\u001B[1;34m(data, x, y, hue, size, style, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, style_order, legend, ax, **kwargs)\u001B[0m\n\u001B[0;32m 631\u001B[0m p\u001B[38;5;241m.\u001B[39m_attach(ax)\n\u001B[0;32m 633\u001B[0m color \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcolor\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m--> 634\u001B[0m kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcolor\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43m_default_color\u001B[49m\u001B[43m(\u001B[49m\u001B[43max\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mscatter\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 636\u001B[0m p\u001B[38;5;241m.\u001B[39mplot(ax, kwargs)\n\u001B[0;32m 638\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m ax\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\seaborn\\utils.py:105\u001B[0m, in \u001B[0;36m_default_color\u001B[1;34m(method, hue, color, kws, saturation)\u001B[0m\n\u001B[0;32m 99\u001B[0m scout_size \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mmax\u001B[39m(\n\u001B[0;32m 100\u001B[0m np\u001B[38;5;241m.\u001B[39matleast_1d(kws\u001B[38;5;241m.\u001B[39mget(key, []))\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m 101\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m key \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124ms\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mc\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfc\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfacecolor\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfacecolors\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m 102\u001B[0m )\n\u001B[0;32m 103\u001B[0m scout_x \u001B[38;5;241m=\u001B[39m scout_y \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mfull(scout_size, np\u001B[38;5;241m.\u001B[39mnan)\n\u001B[1;32m--> 105\u001B[0m scout \u001B[38;5;241m=\u001B[39m method(scout_x, scout_y, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkws)\n\u001B[0;32m 106\u001B[0m facecolors \u001B[38;5;241m=\u001B[39m scout\u001B[38;5;241m.\u001B[39mget_facecolors()\n\u001B[0;32m 108\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(facecolors):\n\u001B[0;32m 109\u001B[0m \u001B[38;5;66;03m# Handle bug in matplotlib <= 3.2 (I think)\u001B[39;00m\n\u001B[0;32m 110\u001B[0m \u001B[38;5;66;03m# This will limit the ability to use non color= kwargs to specify\u001B[39;00m\n\u001B[0;32m 111\u001B[0m \u001B[38;5;66;03m# a color in versions of matplotlib with the bug, but trying to\u001B[39;00m\n\u001B[0;32m 112\u001B[0m \u001B[38;5;66;03m# work out what the user wanted by re-implementing the broken logic\u001B[39;00m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;66;03m# of inspecting the kwargs is probably too brittle.\u001B[39;00m\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\__init__.py:1476\u001B[0m, in \u001B[0;36m_preprocess_data..inner\u001B[1;34m(ax, data, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1473\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m 1474\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21minner\u001B[39m(ax, \u001B[38;5;241m*\u001B[39margs, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 1475\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1476\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\n\u001B[0;32m 1477\u001B[0m ax,\n\u001B[0;32m 1478\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;28mmap\u001B[39m(sanitize_sequence, args),\n\u001B[0;32m 1479\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m{k: sanitize_sequence(v) \u001B[38;5;28;01mfor\u001B[39;00m k, v \u001B[38;5;129;01min\u001B[39;00m kwargs\u001B[38;5;241m.\u001B[39mitems()})\n\u001B[0;32m 1481\u001B[0m bound \u001B[38;5;241m=\u001B[39m new_sig\u001B[38;5;241m.\u001B[39mbind(ax, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1482\u001B[0m auto_label \u001B[38;5;241m=\u001B[39m (bound\u001B[38;5;241m.\u001B[39marguments\u001B[38;5;241m.\u001B[39mget(label_namer)\n\u001B[0;32m 1483\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m bound\u001B[38;5;241m.\u001B[39mkwargs\u001B[38;5;241m.\u001B[39mget(label_namer))\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\axes\\_axes.py:4901\u001B[0m, in \u001B[0;36mAxes.scatter\u001B[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001B[0m\n\u001B[0;32m 4897\u001B[0m keys_str \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mk\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m k \u001B[38;5;129;01min\u001B[39;00m extra_keys)\n\u001B[0;32m 4898\u001B[0m _api\u001B[38;5;241m.\u001B[39mwarn_external(\n\u001B[0;32m 4899\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo data for colormapping provided via \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mc\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 4900\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mParameters \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mkeys_str\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m will be ignored\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m-> 4901\u001B[0m \u001B[43mcollection\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_internal_update\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 4903\u001B[0m \u001B[38;5;66;03m# Classic mode only:\u001B[39;00m\n\u001B[0;32m 4904\u001B[0m \u001B[38;5;66;03m# ensure there are margins to allow for the\u001B[39;00m\n\u001B[0;32m 4905\u001B[0m \u001B[38;5;66;03m# finite size of the symbols. In v2.x, margins\u001B[39;00m\n\u001B[0;32m 4906\u001B[0m \u001B[38;5;66;03m# are present by default, so we disable this\u001B[39;00m\n\u001B[0;32m 4907\u001B[0m \u001B[38;5;66;03m# scatter-specific override.\u001B[39;00m\n\u001B[0;32m 4908\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m mpl\u001B[38;5;241m.\u001B[39mrcParams[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m_internal.classic_mode\u001B[39m\u001B[38;5;124m'\u001B[39m]:\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\artist.py:1216\u001B[0m, in \u001B[0;36mArtist._internal_update\u001B[1;34m(self, kwargs)\u001B[0m\n\u001B[0;32m 1209\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21m_internal_update\u001B[39m(\u001B[38;5;28mself\u001B[39m, kwargs):\n\u001B[0;32m 1210\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 1211\u001B[0m \u001B[38;5;124;03m Update artist properties without prenormalizing them, but generating\u001B[39;00m\n\u001B[0;32m 1212\u001B[0m \u001B[38;5;124;03m errors as if calling `set`.\u001B[39;00m\n\u001B[0;32m 1213\u001B[0m \n\u001B[0;32m 1214\u001B[0m \u001B[38;5;124;03m The lack of prenormalization is to maintain backcompatibility.\u001B[39;00m\n\u001B[0;32m 1215\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m-> 1216\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_update_props\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 1217\u001B[0m \u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;132;43;01m{cls.__name__}\u001B[39;49;00m\u001B[38;5;124;43m.set() got an unexpected keyword argument \u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\n\u001B[0;32m 1218\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;132;43;01m{prop_name!r}\u001B[39;49;00m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\artist.py:1190\u001B[0m, in \u001B[0;36mArtist._update_props\u001B[1;34m(self, props, errfmt)\u001B[0m\n\u001B[0;32m 1188\u001B[0m func \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mset_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mk\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m 1189\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mcallable\u001B[39m(func):\n\u001B[1;32m-> 1190\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m(\n\u001B[0;32m 1191\u001B[0m errfmt\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mtype\u001B[39m(\u001B[38;5;28mself\u001B[39m), prop_name\u001B[38;5;241m=\u001B[39mk))\n\u001B[0;32m 1192\u001B[0m ret\u001B[38;5;241m.\u001B[39mappend(func(v))\n\u001B[0;32m 1193\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ret:\n", "\u001B[1;31mAttributeError\u001B[0m: PathCollection.set() got an unexpected keyword argument 'order'" ] }, { "data": { "text/plain": "
", "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(2, 4, figsize=(20,10))\n", "# fig, axes = plt.subplots(2, 4, figsize=(12,6))\n", "axes = axes.flatten()\n", "\n", "order = ['glutamatergic neuron', 'inhibitory interneuron',\n", " 'oligodendrocyte', 'astrocyte', 'microglial cell',\n", " 'oligodendrocyte precursor cell', 'endothelial cell of vascular tree', 'pericyte']\n", "\n", "# Iterar sobre cada tipo celular y graficar su volcano plot\n", "for i, cell_type in enumerate(order):\n", " file_path = f\"CellXGene/cross-dementia/filtered/data/degs_{cell_type}_mapped.csv\" # Ajustar el nombre del archivo según el formato\n", "\n", " try:\n", " # Cargar los datos\n", " df = pd.read_csv(file_path)\n", "\n", " # Calcular el eje x: -log10(pvals_adj)\n", " df[\"-log10(pvals_adj)\"] = -np.log10(df[\"pvals_adj\"])\n", "\n", " df[\"color\"] = \"grey\" # Color predeterminado para genes no significativos\n", " top_genes = df.loc[df[\"logfoldchanges\"].abs().nlargest(5).index]\n", "\n", " # Asignar el color según el signo de logfoldchanges\n", " df.loc[top_genes.index, \"color\"] = df.loc[top_genes.index, \"logfoldchanges\"].apply(\n", " lambda x: \"red\" if x > 0 else \"blue\"\n", " )\n", " print(df)\n", "\n", " sns.scatterplot(data=df, y=\"-log10(pvals_adj)\", x=\"logfoldchanges\", legend=False, ax=axes[i])\n", "\n", " # for _, row in top_genes.iterrows():\n", " # # axes[i].scatter(row[\"logfoldchanges\"], row[\"-log10(pvals_adj)\"])\n", " # axes[i].text(row[\"logfoldchanges\"] + 0.05, row[\"-log10(pvals_adj)\"] + 0.1, row[\"gene_symbol\"], fontsize=5, color=\"black\")\n", "\n", " # Etiquetas y título\n", " axes[i].set_ylabel(\"-log10(pvals_adj)\")\n", " axes[i].set_xlabel(\"log2 Fold Change\")\n", " axes[i].set_title(f\"{cell_type}\")\n", " # axes[i].axhline(y=0, color='black', linestyle='--', linewidth=1)\n", " # axes[i].grid(True, linestyle='--', alpha=0.7)\n", " except FileNotFoundError:\n", " axes[i].set_title(f\"No data for {cell_type}\")\n", " axes[i].axis(\"off\")\n", "\n", "# Ajustar el diseño\n", "plt.tight_layout()\n", "sns.despine()\n", "# plt.savefig('CellXGene/cross-dementia/plots/volcano_per_type.svg', format = 'svg', dpi=1200)\n", "# plt.savefig('CellXGene/cross-dementia/plots/volcano_per_type.png', format = 'png', dpi=500)\n", "plt.show()\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### 6.4. Representation of top 20 most up- and down-regulated differentially expressed genes" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 276, "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" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "file_pattern = 'CellXGene/cross-dementia/filtered/data/degs_*_mapped.csv'\n", "files = glob.glob(file_pattern)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 281, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " protein_id gene_symbol logfoldchanges cell_type\n", "0 P04626 ERBB2 -0.287940 astrocyte\n", "1 Q8TCT8 SPPL2A -0.353458 astrocyte\n", "2 Q14517 FAT1 0.343325 astrocyte\n", "3 O60290 ZNF862 0.419059 astrocyte\n", "4 P35613 BSG -0.386828 astrocyte\n", "... ... ... ... ...\n", "3234 Q15185 PTGES3 0.866479 pericyte\n", "3235 P08670 VIM 1.117781 pericyte\n", "3236 Q7Z6G8 ANKS1B 0.978241 pericyte\n", "3237 Q09666 AHNAK 0.989039 pericyte\n", "3238 P49716 CEBPD 0.708934 pericyte\n", "\n", "[3239 rows x 4 columns]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\72016613.py:16: 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_filtered['cell_type'] = cell_type\n", "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\72016613.py:16: 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_filtered['cell_type'] = cell_type\n", "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\72016613.py:16: 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_filtered['cell_type'] = cell_type\n", "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\72016613.py:16: 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_filtered['cell_type'] = cell_type\n", "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\72016613.py:16: 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_filtered['cell_type'] = cell_type\n", "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\72016613.py:16: 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_filtered['cell_type'] = cell_type\n", "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\72016613.py:16: 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_filtered['cell_type'] = cell_type\n", "C:\\Users\\Andrea\\AppData\\Local\\Temp\\ipykernel_21772\\72016613.py:16: 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_filtered['cell_type'] = cell_type\n" ] } ], "source": [ "dfs = []\n", "\n", "# Leer cada archivo y seleccionar las columnas relevantes\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", " # Añadir el DataFrame a la lista\n", " dfs.append(df_filtered)\n", "\n", "# Concatenar todos los DataFrames en uno solo\n", "lfc_df = pd.concat(dfs, ignore_index=True)\n", "print(lfc_df)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 436, "outputs": [], "source": [ "top_high = lfc_df.nlargest(20, \"logfoldchanges\")[\"protein_id\"]\n", "top_low = lfc_df.nsmallest(20, \"logfoldchanges\")[\"protein_id\"]\n", "top_proteins = pd.concat([top_high, top_low])" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 437, "outputs": [], "source": [ "lfc_filtered = lfc_df[lfc_df[\"protein_id\"].isin(top_proteins)]" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 286, "outputs": [], "source": [ "heatmap_df = lfc_filtered.pivot(index=\"protein_id\", columns=\"cell_type\", values=\"logfoldchanges\")\n", "\n", "# Agregar anotación de gene_symbol en las filas\n", "gene_symbols = lfc_filtered.set_index(\"protein_id\")[\"gene_symbol\"].to_dict()\n", "heatmap_df.index = heatmap_df.index.map(lambda x: f\"{gene_symbols.get(x, x)} ({x})\") # Formato: gene_symbol (protein_id)\n", "\n", "heatmap_df[\"max_lfc\"] = heatmap_df.max(axis=1) # Calcular la media de logfoldchange por proteína\n", "heatmap_df = heatmap_df.sort_values(by=\"max_lfc\", ascending=False) # Ordenar de mayor a menor\n", "heatmap_df = heatmap_df.drop(columns=[\"max_lfc\"]) # Eliminar la columna auxiliar" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 287, "outputs": [ { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_context(\"paper\")\n", "sns.set_style(\"white\")\n", "\n", "# Crear heatmap\n", "plt.figure(figsize=(9, 8))\n", "sns.heatmap(heatmap_df, cmap=\"coolwarm\", center=0, linewidths=0.5, annot=False, linecolor='grey')\n", "plt.title(\"Top 20 proteins with highest and top 20 proteins with lowest LFC by cell type\")\n", "plt.xlabel(\"Cell type\")\n", "plt.ylabel(\"Gene Symbol (Protein ID)\")\n", "# plt.xticks(rotation=45, ha='right')\n", "plt.yticks(rotation=0)\n", "plt.tight_layout()\n", "# plt.savefig('CellXGene/cross-dementia/plots/heatmap_top20genes.svg', format = 'svg', dpi=1200)\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### 6.5. Representation of differentially expressed genes expressed in all cell types" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 60, "outputs": [], "source": [ "gen_pro = pd.read_csv('CellXGene/gen_pro.tsv', sep='\\t')\n", "gen = pd.read_csv(\"CellXGene/gen.tsv\", sep=\"\\t\")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 61, "outputs": [ { "data": { "text/plain": "{'astrocyte': ,\n 'microglial cell': ,\n 'oligodendrocyte': ,\n 'glutamatergic neuron': ,\n 'inhibitory interneuron': ,\n 'endothelial cell of vascular tree': ,\n 'oligodendrocyte precursor cell': ,\n 'pericyte': }" }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cell_types = ['astrocyte', 'microglial cell', 'oligodendrocyte', 'glutamatergic neuron', 'inhibitory interneuron', 'endothelial cell of vascular tree', 'oligodendrocyte precursor cell', 'pericyte']\n", "\n", "cell_networks = {cell: nx.read_graphml(f\"CellXGene/cross-dementia/filtered/graphs/{cell}_network.graphml\") for cell in cell_types}\n", "cell_networks" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 62, "outputs": [], "source": [ "combined_degs = [\n", " {\"protein_id\": node, \"gene_symbol\": G.nodes[node]['gene_symbol'], \"gene_id\": G.nodes[node]['gene_id'], \"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", "]" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 69, "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[3239 rows x 5 columns]", "text/html": "
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protein_idgene_symbolgene_idcell_typelogfoldchanges
0P04626ERBB22064.0astrocyte-0.287940
1Q14517FAT12195.0astrocyte0.343325
2Q8TCT8SPPL2A84888.0astrocyte-0.353458
3O60290ZNF862643641.0astrocyte0.419059
4P35613BSG682.0astrocyte-0.386828
..................
3234P60484PTEN5728.0pericyte-0.782102
3235P17676CEBPB1051.0pericyte1.105564
3236P49716CEBPD1052.0pericyte0.708934
3237Q09666AHNAK79026.0pericyte0.989039
3238Q7Z6G8ANKS1B56899.0pericyte0.978241
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3239 rows × 5 columns

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" }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(combined_degs)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 74, "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": "
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protein_idgene_symbolgene_idcell_typelogfoldchangesnum_cell_types
0P04626ERBB22064.0astrocyte-0.2879401
1Q14517FAT12195.0astrocyte0.3433252
2Q8TCT8SPPL2A84888.0astrocyte-0.3534581
3O60290ZNF862643641.0astrocyte0.4190594
4P35613BSG682.0astrocyte-0.3868283
.....................
3234P60484PTEN5728.0pericyte-0.7821022
3235P17676CEBPB1051.0pericyte1.1055644
3236P49716CEBPD1052.0pericyte0.7089345
3237Q09666AHNAK79026.0pericyte0.9890393
3238Q7Z6G8ANKS1B56899.0pericyte0.9782414
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3239 rows × 6 columns

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" }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_cells = df.groupby(\"protein_id\")[\"cell_type\"].nunique().reset_index()\n", "count_cells.columns = [\"protein_id\", \"num_cell_types\"]\n", "\n", "df2 = df.merge(count_cells, on=\"protein_id\")\n", "df2" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 73, "outputs": [ { "data": { "text/plain": "num_cell_types\n2 932\n3 690\n1 610\n4 500\n5 240\n6 114\n7 105\n8 48\nName: count, dtype: int64" }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2['num_cell_types'].value_counts()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 75, "outputs": [ { "data": { "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Heatmap con valores de expresión agrupados por proteinas y anotación por el gen que codifica dicha proteína.\n", "\n", "df_filtered_proteins = df2[df2[\"num_cell_types\"] == 8]\n", "\n", "# Crear un diccionario de mapeo de protein_id a gene_symbol\n", "gene_symbol_mapping = df_filtered_proteins.set_index(\"protein_id\")[\"gene_symbol\"].drop_duplicates()\n", "\n", "# Crear la tabla pivote\n", "df_pivot = df_filtered_proteins.pivot_table(index=\"protein_id\", columns=\"cell_type\", values=\"logfoldchanges\")\n", "\n", "sns.set_context(\"paper\")\n", "sns.set_style(\"white\")\n", "\n", "# Crear la figura y el heatmap\n", "fig, ax = plt.subplots(figsize=(6,6))\n", "sns.heatmap(df_pivot, cmap=\"coolwarm\", linewidths=0.5, fmt=\".2f\", linecolor='grey', cbar=True, square=False, ax=ax)\n", "\n", "# Ajustar título y etiquetas\n", "title = \"LFC heatmap for proteins in the 90th percentile across cell types\"\n", "plt.title(title, fontsize=10)\n", "plt.xlabel(\"Cell type\", fontsize=8)\n", "plt.ylabel(\"Gene Symbol\", fontsize=8)\n", "plt.xticks(rotation=45, ha='right', fontsize=8)\n", "ax.set_yticklabels(gene_symbol_mapping.loc[df_pivot.index].values, rotation=0, fontsize=8)\n", "plt.tight_layout()\n", "\n", "# plt.savefig('CellXGene/cross-dementia/plots/commonproteins.svg', format = 'svg', dpi=1200)\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "#### Combination of 6.4 and 6.5 plots" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "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": "
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protein_idgene_symbollogfoldchangescell_typemax_abs_LFC
14P43490NAMPT-0.900505astrocyte0.900505
22P06703S100A61.067540astrocyte1.067540
50Q2I0M5RSPO4-1.032063astrocyte1.032063
118O60603TLR2-1.532612astrocyte1.532612
133P17275JUNB-0.300338astrocyte0.300338
..................
3194P04792HSPB12.069515oligodendrocyte2.069515
3223P04792HSPB11.884719pericyte1.884719
3227Q92598HSPH11.676103pericyte1.676103
3230P0DMV8HSPA1B1.975146pericyte1.975146
3232P02511CRYAB1.525713pericyte1.525713
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123 rows × 5 columns

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" }, "execution_count": 445, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## HEATMAP 1: Top 20 más ↑ y ↓ logfoldchange\n", "\n", "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\")\n", "lfc_filtered" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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": "
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protein_idgene_symbolgene_idcell_typelogfoldchangesnum_cell_types
0P04626ERBB22064.0astrocyte-0.2879401
1Q14517FAT12195.0astrocyte0.3433252
2Q8TCT8SPPL2A84888.0astrocyte-0.3534581
3O60290ZNF862643641.0astrocyte0.4190594
4P35613BSG682.0astrocyte-0.3868283
.....................
3234P60484PTEN5728.0pericyte-0.7821022
3235P17676CEBPB1051.0pericyte1.1055644
3236P49716CEBPD1052.0pericyte0.7089345
3237Q09666AHNAK79026.0pericyte0.9890393
3238Q7Z6G8ANKS1B56899.0pericyte0.9782414
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3239 rows × 6 columns

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" }, "execution_count": 448, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## HEATMAP 2: Proteínas con expresión en 8 tipos celulares\n", "\n", "cell_types = ['astrocyte', 'microglial cell', 'oligodendrocyte', 'glutamatergic neuron', 'inhibitory interneuron',\n", " 'endothelial cell of vascular tree', 'oligodendrocyte precursor cell', 'pericyte']\n", "\n", "cell_networks = {cell: nx.read_graphml(f\"CellXGene/cross-dementia/filtered/graphs/{cell}_network.graphml\") for cell in\n", " cell_types}\n", "cell_networks\n", "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", "df = pd.DataFrame(combined_degs)\n", "\n", "count_cells = df.groupby(\"protein_id\")[\"cell_type\"].nunique().reset_index()\n", "count_cells.columns = [\"protein_id\", \"num_cell_types\"]\n", "\n", "df2 = df.merge(count_cells, on=\"protein_id\")\n", "df2['cell_type'] = df2[\"cell_type\"].replace(\"endothelial cell of vascular tree\", \"endothelial cell\")\n", "df2" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "cell_type": "code", "execution_count": 680, "outputs": [ { "data": { "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 🔹\n", "# 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", "#\n", "# top_proteins = pd.concat([top_high, top_low])\n", "#\n", "# lfc_filtered = lfc_df[lfc_df[\"protein_id\"].isin(top_proteins)].copy()\n", "\n", "# lfc_filtered = lfc_filtered[\"cell_type\"].replace(\"endothelial cell of vascular tree\", \"endothelial cell\")\n", "\n", "heatmap_df = lfc_filtered.pivot(index=\"protein_id\", columns=\"cell_type\", values=\"logfoldchanges\")\n", "\n", "# Agregar anotación de 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", "# 🔹 Espacio en blanco entre los genes más up y down regulados\n", "half = len(heatmap_df) // 2\n", "gap = pd.DataFrame(np.nan, index=[\"\"], columns=heatmap_df.columns) # Dos filas vacías como separador\n", "heatmap_df = pd.concat([heatmap_df.iloc[:half], gap, heatmap_df.iloc[half:]])\n", "\n", "# 🔹 HEATMAP 2: Proteínas con expresión en 8 tipos celulares\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", "# Agregar anotación de gene_symbol\n", "# df_pivot.index = [f\"{gene_symbol_mapping.get(x, x)} ({x})\" for x in df_pivot.index]\n", "# Agregar anotación de gene_symbol con salto de línea\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", "# Definir el orden deseado de las columnas\n", "order = ['glutamatergic neuron', 'inhibitory interneuron',\n", " 'oligodendrocyte', 'astrocyte', 'microglial cell',\n", " 'oligodendrocyte precursor cell', 'endothelial cell', 'pericyte']\n", "\n", "# Reordenar las columnas de los DataFrame antes de graficar\n", "heatmap_df = heatmap_df[order]\n", "df_pivot = df_pivot[order]\n", "\n", "# 🔹 Definir escala de colores compartida\n", "vmin = min(heatmap_df.min().min(), df_pivot.min().min())\n", "vmax = max(heatmap_df.max().max(), df_pivot.max().max())\n", "\n", "# 🔹 Configurar estilo y figura con 2 subplots\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: Top 20 ↑↓ logfoldchange\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: High variability proteins\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(\"\") # Evita duplicar etiqueta\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", "# Ajustar distribución y guardar\n", "plt.tight_layout()\n", "plt.savefig('CellXGene/cross-dementia/plots/combined_heatmaps.pdf', format='pdf', dpi=1200)\n", "# plt.savefig('CellXGene/cross-dementia/plots/combined_heatmaps_pdf.svg', format='svg', dpi=1200)\n", "plt.show()\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "dis_gen = pd.read_csv('CellXGene/dis_gen.tsv', sep='\\t')\n", "gen_pro = pd.read_csv('CellXGene/gen_pro.tsv', sep='\\t')\n", "pro_pro = pd.read_csv('CellXGene/pro_pro.tsv', sep='\\t')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "G_ppi = nx.from_pandas_edgelist(pro_pro, 'prA', 'prB')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def get_disease_module(disease, dis_gen, gen_pro, pro_pro, PPI):\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" } } }, { "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" } } }, { "cell_type": "markdown", "source": [ "### 8.2. Metrics calculation for all the nodes in the interactome" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 605, "outputs": [], "source": [ "def calculate_metrics(graph):\n", " \"\"\"\n", " Calcula métricas de red a nivel de nodo. Devuelve un DataFrame con las métricas.\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" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Calculo de las metricas de red para cada una de las proteinas de la PPI\n", "betweenness, closeness, clustering, degree, is_hub = calculate_metrics(G_ppi)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "df['is_in_LCC'] = df['protein_id'].isin(lcc_alz)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "df.to_csv('CellXGene/cross-dementia/filtered/results/G_ppi_analysis.csv', index = False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### 8.3. Metrics calculation for the DEGs nodes for each cell type" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "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" } } }, { "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'CellXGene/cross-dementia/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('CellXGene/cross-dementia/filtered/results/network_analysis.csv', index=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### 8.4. Analysis of the metrics distributions across cell types" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 386, "outputs": [], "source": [ "cell_data = pd.read_csv('CellXGene/cross-dementia/filtered/results/network_analysis.csv')\n", "module_data = pd.read_csv('CellXGene/cross-dementia/filtered/results/G_ppi_analysis.csv')\n", "\n", "module_data_lcc = module_data[module_data['is_in_LCC'] == True]" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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": "
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protein_iddegreebetweenness_centralitycloseness_centralityis_hubis_in_LCCdatasetgene_idgene_symbolcell_typelogfoldchangespvalspvals_adjclustering_coefficient
0A0A087WXM980.0000030.305735FalseTrueGeneral module for ADNaNNaNNaNNaNNaNNaNNaN
2P05783910.0004290.367223TrueTrueGeneral module for ADNaNNaNNaNNaNNaNNaNNaN
4P086702660.0037260.398942TrueTrueGeneral module for ADNaNNaNNaNNaNNaNNaNNaN
6P533502210.0019020.381054TrueTrueGeneral module for ADNaNNaNNaNNaNNaNNaNNaN
32Q96CV9920.0006210.362136TrueTrueGeneral module for ADNaNNaNNaNNaNNaNNaNNaN
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3234P17676270.0000560.346096FalseNaNpericyte1051.0CEBPBpericyte1.1055649.147368e-065.491340e-030.150000
3235P079002370.0016980.398471TrueNaNpericyte3320.0HSP90AA1pericyte1.1101092.761193e-131.112958e-090.061975
3236P136931240.0003130.374662TrueNaNpericyte7178.0TPT1pericyte0.8023651.618387e-044.390652e-020.056517
3237P0DMV8630.0005330.359315FalseNaNpericyte3304.0HSPA1Bpericyte1.9751461.293792e-071.738302e-040.045059
3238P082385530.0133630.420794TrueNaNpericyte3326.0HSP90AB1pericyte0.8615562.566908e-062.336300e-030.020023
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5936 rows × 14 columns

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" }, "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", "# Reemplazar el nombre de \"endothelial cell of vascular tree\" por \"endothelial cell\"\n", "combined_data = pd.concat([module_data_lcc, cell_data])\n", "# combined_data[\"dataset\"] = combined_data[\"dataset\"].replace(\"endothelial cell of vascular tree\", \"Endothelial cell\")\n", "# combined_data[\"dataset\"] = combined_data[\"dataset\"].replace(\"oligodendrocyte precursor cell\", \"OPC\")\n", "# combined_data[\"dataset\"] = combined_data[\"dataset\"].replace(\"astrocyte\", \"Astrocyte\")\n", "combined_data" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 543, "outputs": [ { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(data=combined_data, x=\"dataset\", y=\"degree\")\n", "\n", "plt.yscale('log')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "cell_type": "code", "execution_count": 656, "outputs": [ { "data": { "text/plain": "10" }, "execution_count": 656, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max(10, len(ordered_datasets) * 0.5)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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": "
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calcular el tamaño de cada subset\n", "subset_sizes = combined_data.groupby(\"dataset\")[\"protein_id\"].nunique()\n", "\n", "# Generar una paleta de colores\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", "# Asignar rojo al \"Module\"\n", "palette[\"General module for AD\"] = \"#D3D3D3\"\n", "\n", "# fig, ax = plt.subplots(figsize=(max(10, len(ordered_datasets) * 0.5), 5))\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(titles, fontsize=9)\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.xticks(rotation=45)\n", "plt.yticks(fontsize=12)\n", "plt.yscale('log')\n", "plt.ylabel('log(Degree)', fontsize=12)\n", "sns.despine()\n", "# Añadir leyenda y mostrar la gráfica\n", "plt.tight_layout()\n", "plt.savefig('CellXGene/cross-dementia/plots/degree_boxplot.pdf', format='pdf', dpi=1200)\n", "# plt.savefig('CellXGene/cross-dementia/plots/degree_boxplot.svg', format='svg', dpi=1200)\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 595, "outputs": [ { "ename": "AttributeError", "evalue": "Figure.set() got an unexpected keyword argument 'bottom'", "output_type": "error", "traceback": [ "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[1;31mAttributeError\u001B[0m Traceback (most recent call last)", "Cell \u001B[1;32mIn[595], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m fig, ax \u001B[38;5;241m=\u001B[39m \u001B[43mplt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msubplots\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfigsize\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m13\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m6\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbottom\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.25\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3\u001B[0m sns\u001B[38;5;241m.\u001B[39mset_context(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpaper\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 4\u001B[0m sns\u001B[38;5;241m.\u001B[39mset_style(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mwhitegrid\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\pyplot.py:1759\u001B[0m, in \u001B[0;36msubplots\u001B[1;34m(nrows, ncols, sharex, sharey, squeeze, width_ratios, height_ratios, subplot_kw, gridspec_kw, **fig_kw)\u001B[0m\n\u001B[0;32m 1604\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21msubplots\u001B[39m(\n\u001B[0;32m 1605\u001B[0m nrows: \u001B[38;5;28mint\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m, ncols: \u001B[38;5;28mint\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m*\u001B[39m,\n\u001B[0;32m 1606\u001B[0m sharex: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m|\u001B[39m Literal[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnone\u001B[39m\u001B[38;5;124m\"\u001B[39m, 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figure(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfig_kw)\n\u001B[0;32m 1760\u001B[0m axs \u001B[38;5;241m=\u001B[39m fig\u001B[38;5;241m.\u001B[39msubplots(nrows\u001B[38;5;241m=\u001B[39mnrows, ncols\u001B[38;5;241m=\u001B[39mncols, sharex\u001B[38;5;241m=\u001B[39msharex, sharey\u001B[38;5;241m=\u001B[39msharey,\n\u001B[0;32m 1761\u001B[0m squeeze\u001B[38;5;241m=\u001B[39msqueeze, subplot_kw\u001B[38;5;241m=\u001B[39msubplot_kw,\n\u001B[0;32m 1762\u001B[0m gridspec_kw\u001B[38;5;241m=\u001B[39mgridspec_kw, height_ratios\u001B[38;5;241m=\u001B[39mheight_ratios,\n\u001B[0;32m 1763\u001B[0m width_ratios\u001B[38;5;241m=\u001B[39mwidth_ratios)\n\u001B[0;32m 1764\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m fig, axs\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\pyplot.py:1027\u001B[0m, in \u001B[0;36mfigure\u001B[1;34m(num, figsize, dpi, facecolor, edgecolor, frameon, FigureClass, clear, **kwargs)\u001B[0m\n\u001B[0;32m 1017\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(allnums) \u001B[38;5;241m==\u001B[39m max_open_warning \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m 1018\u001B[0m _api\u001B[38;5;241m.\u001B[39mwarn_external(\n\u001B[0;32m 1019\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMore than \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmax_open_warning\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m figures have been opened. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 1020\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFigures created through the pyplot interface \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 1024\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mConsider using `matplotlib.pyplot.close()`.\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 1025\u001B[0m 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\u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\artist.py:147\u001B[0m, in \u001B[0;36mArtist.__init_subclass__..\u001B[1;34m(self, **kwargs)\u001B[0m\n\u001B[0;32m 139\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39mset, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m_autogenerated_signature\u001B[39m\u001B[38;5;124m'\u001B[39m):\n\u001B[0;32m 140\u001B[0m \u001B[38;5;66;03m# Don't overwrite cls.set if the subclass or one of its parents\u001B[39;00m\n\u001B[0;32m 141\u001B[0m \u001B[38;5;66;03m# has defined a set method set itself.\u001B[39;00m\n\u001B[0;32m 142\u001B[0m \u001B[38;5;66;03m# If there was no explicit definition, cls.set is inherited from\u001B[39;00m\n\u001B[0;32m 143\u001B[0m \u001B[38;5;66;03m# the hierarchy of auto-generated set methods, which hold the\u001B[39;00m\n\u001B[0;32m 144\u001B[0m \u001B[38;5;66;03m# flag _autogenerated_signature.\u001B[39;00m\n\u001B[0;32m 145\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[1;32m--> 147\u001B[0m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39mset \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mlambda\u001B[39;00m \u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs: Artist\u001B[38;5;241m.\u001B[39mset(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 148\u001B[0m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39mset\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mset\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 149\u001B[0m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39mset\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__qualname__\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__qualname__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.set\u001B[39m\u001B[38;5;124m\"\u001B[39m\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\artist.py:1224\u001B[0m, in \u001B[0;36mArtist.set\u001B[1;34m(self, **kwargs)\u001B[0m\n\u001B[0;32m 1220\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21mset\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 1221\u001B[0m \u001B[38;5;66;03m# docstring and signature are auto-generated via\u001B[39;00m\n\u001B[0;32m 1222\u001B[0m \u001B[38;5;66;03m# Artist._update_set_signature_and_docstring() at the end of the\u001B[39;00m\n\u001B[0;32m 1223\u001B[0m \u001B[38;5;66;03m# module.\u001B[39;00m\n\u001B[1;32m-> 1224\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_internal_update\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcbook\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mnormalize_kwargs\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\artist.py:1216\u001B[0m, in \u001B[0;36mArtist._internal_update\u001B[1;34m(self, kwargs)\u001B[0m\n\u001B[0;32m 1209\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21m_internal_update\u001B[39m(\u001B[38;5;28mself\u001B[39m, kwargs):\n\u001B[0;32m 1210\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 1211\u001B[0m \u001B[38;5;124;03m Update artist properties without prenormalizing them, but generating\u001B[39;00m\n\u001B[0;32m 1212\u001B[0m \u001B[38;5;124;03m errors as if calling `set`.\u001B[39;00m\n\u001B[0;32m 1213\u001B[0m \n\u001B[0;32m 1214\u001B[0m \u001B[38;5;124;03m The lack of prenormalization is to maintain backcompatibility.\u001B[39;00m\n\u001B[0;32m 1215\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m-> 1216\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_update_props\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 1217\u001B[0m \u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;132;43;01m{cls.__name__}\u001B[39;49;00m\u001B[38;5;124;43m.set() got an unexpected keyword argument \u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\n\u001B[0;32m 1218\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;132;43;01m{prop_name!r}\u001B[39;49;00m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n", "File \u001B[1;32m~\\Downloads\\single-cell\\lib\\site-packages\\matplotlib\\artist.py:1190\u001B[0m, in \u001B[0;36mArtist._update_props\u001B[1;34m(self, props, errfmt)\u001B[0m\n\u001B[0;32m 1188\u001B[0m func \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mgetattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mset_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mk\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m 1189\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mcallable\u001B[39m(func):\n\u001B[1;32m-> 1190\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m(\n\u001B[0;32m 1191\u001B[0m errfmt\u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mtype\u001B[39m(\u001B[38;5;28mself\u001B[39m), prop_name\u001B[38;5;241m=\u001B[39mk))\n\u001B[0;32m 1192\u001B[0m ret\u001B[38;5;241m.\u001B[39mappend(func(v))\n\u001B[0;32m 1193\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ret:\n", "\u001B[1;31mAttributeError\u001B[0m: Figure.set() got an unexpected keyword argument 'bottom'" ] } ], "source": [ "fig, ax = plt.subplots(figsize=(13, 6))\n", "\n", "sns.set_context(\"paper\")\n", "sns.set_style(\"whitegrid\")\n", "\n", "# Calcular el tamaño de cada subset\n", "subset_sizes = combined_data.groupby(\"dataset\")[\"protein_id\"].nunique()\n", "\n", "# Generar una paleta de colores\n", "unique_datasets = combined_data[\"dataset\"].unique()\n", "palette = {dataset: '#79C4FF' for dataset in unique_datasets}\n", "\n", "# Asignar rojo al \"Module\"\n", "palette[\"Alzheimer LCC\"] = \"grey\"\n", "sns.violinplot(data=combined_data, x=\"dataset\", y=np.log10(combined_data[\"degree\"]), ax=ax, palette=palette)\n", "\n", "# Añadir etiquetas con el tamaño de cada subset\n", "for i, dataset in enumerate(combined_data[\"dataset\"].unique()):\n", " ax.text(i, ax.get_ylim()[0], f\"n = {subset_sizes.get(dataset, 0)}\", ha='center', va='bottom', fontsize=10, color='black')\n", "\n", "plt.title('Degree distribution for the Alzheimer disease module and each cell type')\n", "plt.ylabel('log(Degree)')\n", "plt.xlabel('Dataset / Cell type')\n", "\n", "# Añadir leyenda y mostrar la gráfica\n", "plt.tight_layout()\n", "# plt.legend(title='Cell Types', loc='upper right')\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 420, "outputs": [ { "data": { "text/plain": "
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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(13, 6))\n", "\n", "sns.set_context(\"paper\")\n", "sns.set_style(\"whitegrid\")\n", "\n", "# Calcular el tamaño de cada subset\n", "subset_sizes = combined_data.groupby(\"dataset\")[\"protein_id\"].nunique()\n", "\n", "# Generar una paleta de colores\n", "unique_datasets = combined_data[\"dataset\"].unique()\n", "palette = {dataset: '#79C4FF' for dataset in unique_datasets}\n", "\n", "# Asignar rojo al \"Module\"\n", "palette[\"Alzheimer LCC\"] = \"grey\"\n", "sns.histplot(data=combined_data, x=np.log10(combined_data[\"degree\"]), hue = 'dataset', ax=ax, palette=palette)\n", "\n", "# Añadir etiquetas con el tamaño de cada subset\n", "# for i, dataset in enumerate(combined_data[\"dataset\"].unique()):\n", "# ax.text(i, ax.get_ylim()[0], f\"n = {subset_sizes.get(dataset, 0)}\", ha='center', va='bottom', fontsize=10, color='black')\n", "\n", "plt.title('Degree distribution for the Alzheimer disease module and each cell type')\n", "plt.xlabel('log(Degree)')\n", "# plt.ylabel('log(Protein Count)')\n", "\n", "# Añadir leyenda y mostrar la gráfica\n", "plt.tight_layout()\n", "# plt.legend(title='Cell Types', loc='upper right')\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 367, "outputs": [], "source": [ "plot_data = cell_data.groupby([\"degree\", \"cell_type\"]).count()[\"protein_id\"].reset_index()\n", "plot_data.rename(columns={\"protein_id\": \"protein_count\"}, inplace=True)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 455, "outputs": [ { "data": { "text/plain": "
", "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "plt.figure(figsize=(8, 6))\n", "# sns.barplot(data=plot_data, x=\"degree\", y=\"protein_count\", hue=\"cell_type\", alpha=0.7)\n", "sns.lineplot(data=plot_data, x=\"degree\", y=\"protein_count\", hue=\"cell_type\")\n", "# sns.boxplot(data = plot_data, x = \"degree\", y = \"protein_count\", hue = \"cell_type\")\n", "# Configurar escala logarítmica en ambos ejes\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\")\n", "\n", "# Etiquetas\n", "plt.xlabel(\"log(Degree)\")\n", "plt.ylabel(\"log(Protein Count)\")\n", "plt.title(\"Distribution of Degree per Protein\")\n", "plt.legend(title=\"Cell Type\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "# Mostrar el gráfico\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 301, "outputs": [ { "data": { "text/plain": "
", "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.FacetGrid(cell_data, col=\"cell_type\", col_wrap=4, sharex=True, sharey=True)\n", "g.map(sns.histplot, \"degree\", bins=300)\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "#### Numeric analysis\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 659, "outputs": [], "source": [ "cell_data = pd.read_csv('CellXGene/cross-dementia/filtered/results/network_analysis.csv')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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", "# Calcular estadísticas por 'cell_type'\n", "stats = cell_data.groupby(\"cell_type\")[\"degree\"].agg([\"mean\", \"std\", \"median\", \"min\", \"max\"])\n", "\n", "stats[\"count\"] = cell_data.groupby(\"cell_type\").size()\n", "\n", "# Mostrar resultados\n", "print(stats)\n", "\n", "# Si deseas guardar los resultados en un archivo CSV:\n", "stats.to_csv(\"CellXGene/cross-dementia/filtered/results/cell_type_statistics.csv\")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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('CellXGene/cross-dementia/filtered/results/G_ppi_analysis.csv')\n", "\n", "module_data_lcc = module_data[module_data['is_in_LCC'] == True]\n", "\n", "# Calcular estadísticas por 'cell_type'\n", "stats = module_data_lcc[\"degree\"].agg([\"mean\", \"std\", \"median\", \"min\", \"max\", \"count\"])\n", "\n", "# stats[\"count\"] = module_data_lcc.groupby(\"cell_type\").size()\n", "\n", "# Mostrar resultados\n", "print(stats)\n", "\n", "# Si deseas guardar los resultados en un archivo CSV:\n", "# stats.to_csv(\"CellXGene/cross-dementia/filtered/results/general_module_statistics.csv\")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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_info.items():\n", " for g in gene:\n", " gene_info.append(g)\n", " # gene_info = gene_info.append(cell_data[cell_data['gene_symbol'] == g])\n", "\n", "\n", "# gene_info = pd.DataFrame(gene_info)\n", "print(gene_info)\n", "\n", "cell_data[cell_data['gene_symbol'].isin(gene_info)].to_csv('CellXGene/cross-dementia/filtered/results/top_gene_info.csv', index = False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 705, "outputs": [], "source": [ "top_genes = pd.read_csv('CellXGene/cross-dementia/filtered/results/top_gene_info.csv')" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 714, "outputs": [ { "data": { "text/plain": " protein_id gene_id gene_symbol cell_type logfoldchanges \\\n30 P78509 5649.0 RELN oligodendrocyte 2.069801 \n\n pvals pvals_adj degree betweenness_centrality \\\n30 4.778383e-177 5.414541e-175 2 1.279763e-08 \n\n closeness_centrality clustering_coefficient is_hub \n30 0.255853 0.0 False ", "text/html": "
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protein_idgene_idgene_symbolcell_typelogfoldchangespvalspvals_adjdegreebetweenness_centralitycloseness_centralityclustering_coefficientis_hub
30P785095649.0RELNoligodendrocyte2.0698014.778383e-1775.414541e-17521.279763e-080.2558530.0False
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" }, "execution_count": 714, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_genes[top_genes['gene_symbol'] == 'RELN']" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 716, "outputs": [ { "data": { "text/plain": " protein_id gene_id gene_symbol cell_type \\\n72 P04792 3315.0 HSPB1 endothelial cell of vascular tree \n73 P25685 3337.0 DNAJB1 endothelial cell of vascular tree \n74 P02511 1410.0 CRYAB endothelial cell of vascular tree \n75 O95817 9531.0 BAG3 endothelial cell of vascular tree \n76 Q92598 10808.0 HSPH1 endothelial cell of vascular tree \n77 P98179 5935.0 RBM3 endothelial cell of vascular tree \n78 P0DMV8 3304.0 HSPA1B endothelial cell of vascular tree \n79 Q9HB71 27101.0 CACYBP endothelial cell of vascular tree \n\n logfoldchanges pvals pvals_adj degree \\\n72 2.352189 9.755175e-62 1.376211e-57 320 \n73 1.947837 8.083351e-14 8.447102e-11 51 \n74 1.457500 3.843713e-10 2.046233e-07 49 \n75 1.000120 5.294115e-05 6.187393e-03 118 \n76 2.194557 2.518458e-33 1.015118e-29 48 \n77 -1.982801 4.016365e-09 1.770652e-06 35 \n78 2.800699 9.817635e-38 6.925114e-34 63 \n79 2.015665 3.298393e-12 2.658976e-09 104 \n\n betweenness_centrality closeness_centrality clustering_coefficient \\\n72 0.005160 0.402288 0.021804 \n73 0.000148 0.366387 0.092549 \n74 0.000346 0.347922 0.035153 \n75 0.000841 0.369861 0.025786 \n76 0.000107 0.370486 0.117908 \n77 0.000041 0.348381 0.147899 \n78 0.000533 0.359315 0.045059 \n79 0.000331 0.363172 0.041636 \n\n is_hub \n72 True \n73 False \n74 False \n75 True \n76 False \n77 False \n78 False \n79 True ", "text/html": "
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protein_idgene_idgene_symbolcell_typelogfoldchangespvalspvals_adjdegreebetweenness_centralitycloseness_centralityclustering_coefficientis_hub
72P047923315.0HSPB1endothelial cell of vascular tree2.3521899.755175e-621.376211e-573200.0051600.4022880.021804True
73P256853337.0DNAJB1endothelial cell of vascular tree1.9478378.083351e-148.447102e-11510.0001480.3663870.092549False
74P025111410.0CRYABendothelial cell of vascular tree1.4575003.843713e-102.046233e-07490.0003460.3479220.035153False
75O958179531.0BAG3endothelial cell of vascular tree1.0001205.294115e-056.187393e-031180.0008410.3698610.025786True
76Q9259810808.0HSPH1endothelial cell of vascular tree2.1945572.518458e-331.015118e-29480.0001070.3704860.117908False
77P981795935.0RBM3endothelial cell of vascular tree-1.9828014.016365e-091.770652e-06350.0000410.3483810.147899False
78P0DMV83304.0HSPA1Bendothelial cell of vascular tree2.8006999.817635e-386.925114e-34630.0005330.3593150.045059False
79Q9HB7127101.0CACYBPendothelial cell of vascular tree2.0156653.298393e-122.658976e-091040.0003310.3631720.041636True
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" }, "execution_count": 716, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_genes[top_genes['cell_type'] == 'endothelial cell of vascular tree']" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
gene_symboldegree
30RELN2
56GPR62
32VGF3
58ARAP34
59DRD45
62PENK8
5EGF9
35CRYZ11
25C1QA19
81IGFBP321
49CD1424
6EGR125
39HSPA1B31
9RBM335
27CXCR437
42BIRC341
1S100A1043
89SOCS347
0HSPH148
65CRYAB49
46DNAJB151
11HSPA1B63
51ANXA180
45CACYBP104
75BAG3118
80FOS140
57VIM266
26HSPB1320
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" }, "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" } } }, { "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" } } }, { "cell_type": "code", "execution_count": 333, "outputs": [], "source": [ "pro_pro = pd.read_csv(\"CellXGene/pro_pro.tsv\", sep='\\t')\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" } } }, { "cell_type": "code", "execution_count": 334, "outputs": [], "source": [ "filtered_files = glob.glob(\"CellXGene/cross-dementia/filtered/data/degs_*_mapped.csv\")\n", "\n", "filtered_data = load_and_process(filtered_files)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 335, "outputs": [], "source": [ "unique_proteins = filtered_data['protein_id'].unique()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "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" } } }, { "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" } } }, { "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", " # Asegurarse de que las proteínas estén en la lista de interés\n", " if protein_A in unique_proteins and protein_B in unique_proteins:\n", " # Evitar 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" } } }, { "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, \"CellXGene/cross-dementia/filtered/graphs/gephi/proteins_network.graphml\")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 344, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Red para astrocyte exportada: Graph with 1519 nodes and 8331 edges\n", "Red para microglial cell exportada: Graph with 1519 nodes and 8331 edges\n", "Red para oligodendrocyte exportada: Graph with 1519 nodes and 8331 edges\n", "Red para glutamatergic neuron exportada: Graph with 1519 nodes and 8331 edges\n", "Red para inhibitory interneuron exportada: Graph with 1519 nodes and 8331 edges\n", "Red para endothelial cell of vascular tree exportada: Graph with 1519 nodes and 8331 edges\n", "Red para oligodendrocyte precursor cell exportada: Graph with 1519 nodes and 8331 edges\n", "Red para pericyte exportada: Graph with 1519 nodes and 8331 edges\n" ] } ], "source": [ "## Anotaciones para cada tipo celular\n", "\n", "for cell_type in cell_types:\n", " # Crear una copia de la red grande para trabajar con ella\n", " G_cell_type = G.copy()\n", "\n", " # Cargar el archivo de sobreexpresión para el tipo celular\n", " degs_cell_type = pd.read_csv(f\"CellXGene/cross-dementia/filtered/data/degs_{cell_type}_mapped.csv\")\n", "\n", " # Añadir los datos de sobreexpresión a los nodos correspondientes para este tipo celular\n", " for _, row in degs_cell_type.iterrows():\n", " protein_id = row['protein_id']\n", " if protein_id in G_cell_type.nodes:\n", " # Añadir atributos específicos para cada tipo celular\n", " G_cell_type.nodes[protein_id]['logfoldchanges'] = row['logfoldchanges']\n", " G_cell_type.nodes[protein_id]['pvals_'] = row['pvals']\n", " G_cell_type.nodes[protein_id]['pvals_adj_'] = row['pvals_adj']\n", " G_cell_type.nodes[protein_id]['scores_'] = row['scores']\n", "\n", " # Guardar la red con los atributos específicos para este tipo celular\n", " print(f\"Red para {cell_type} exportada: {G}\")\n", " nx.write_graphml(G_cell_type, f\"CellXGene/cross-dementia/filtered/graphs/gephi/protein_network_{cell_type}.graphml\")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "# CONSIDERACIONES A LLEVAR A CABO EN LA ESTANCIA A RAIZ DE ESTE ANALISIS\n", "\n", "1. Trabajar con coexpresión diferencial (recomendado)\n", "En lugar de construir las redes solo con DEGs (listas de genes diferencialmente expresados), podrías construir redes de coexpresión génica por condición.\n", "\n", "- Calculas redes de coexpresión (Spearman o Pearson) para cada tipo celular, una en sano y otra en enfermo.\n", "- Filtras esas redes usando un umbral (por ejemplo, correlación > 0.7).\n", "- Mapeas esas redes a proteínas y filtrarlas usando el interactoma general, conservando solo edges entre genes que también interaccionan a nivel de proteínas.\n", "\n", "De esta manera, la estructura de la red sí va a cambiar entre sano y enfermo, porque la coexpresión (y sus patrones) realmente cambia con la enfermedad.\n", "\n", "2. Peso en las aristas basado en coexpresión o logFC\n", "Si quieres mantener el enfoque de PPI usando solo DEGs, puedes añadir pesos a las aristas que reflejen:\n", "\n", "- Correlación de expresión entre los genes conectados (si tienes datos de expresión para cada célula).\n", "- Media del logFC de los genes conectados (refleja qué tan diferencial es la interacción).Así, aunque las redes tengan la misma topología (mismo set de nodos y aristas), al menos el análisis estructural posterior (centralidad, modularidad, etc.) podría diferenciarse usando pesos.\n", "\n", "3. Redes basadas en \"differential interactomes\"\n", "Si tienes una red PPI general, puedes crear una red diferencial donde:\n", "\n", "- Un edge existe solo si hay evidencia de interacción alterada en la condición enferma. Por ejemplo, podrías integrar información de interacción diferencial (differential coexpression + PPI)." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }