Fig_2A-F.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ecdb234e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\astar\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n",
      "C:\\Users\\astar\\AppData\\Local\\Temp\\ipykernel_24848\\1722371918.py:8: DeprecationWarning: Please import `pearsonr` from the `scipy.stats` namespace; the `scipy.stats.stats` namespace is deprecated and will be removed in SciPy 2.0.0.\n",
      "  from scipy.stats.stats import pearsonr\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import re\n",
    "from scipy.stats.stats import pearsonr\n",
    "#import rpy2.robjects as robjects\n",
    "import random\n",
    "from statsmodels.stats.multitest import fdrcorrection\n",
    "import copy\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "from matplotlib.patches import PathPatch\n",
    "from matplotlib.path import Path\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68f6cf54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Colors\n",
    "mouse = \"#F2C911\"\n",
    "rat = \"#65B0AC\"\n",
    "intrinsic = \"#F55F00\"\n",
    "extrinsic = \"#7D9AF4\"\n",
    "reinforcing = \"#9B00F5\"\n",
    "opposing = \"#F50901\"\n",
    "interaction = \"#1E771A\"\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"white\")\n",
    "d_ct_abrev = {\"Forebrain glutamatergic progenitors\":\"brain.glut.prog\", \"Forebrain GABAergic progenitors\":\"brain.GABA.prog\", \"Intermediate progenitors\":\"inter.prog\", \"Forebrain glutamatergic neurons\":\"brain.glut.neu\", \"Forebrain GABAergic neurons\":\"brain.GABA.neu\", \"Spinal GABAergic neurons\":\"spine.GABA.neu\", \"Spinal glutamatergic neurons\":\"spine.glut.neu\", \"Chondrocytes\":\"chondrocyte\", \"Mesenchyme 0\":\"mesen.0\", \"Mesenchyme 2\":\"mesen.2\", \"Mesenchyme cycling\":\"mesen.cyc\", \"Forebrain GABAergic neurons 0\":\"Toss\"}\n",
    "\n",
    "#Function to convert file name to cell type\n",
    "def file_to_celltype(x):\n",
    "    x = x.split(\"/\")[-1]\n",
    "    x = x.replace(\"_GO_Molecular_Function_2023\", \"\").replace(\"_GO_Biological_Process_2023\", \"\").replace(\"Mesechyme\", \"Mesenchyme\").replace(\"Brain_Or_2010_Div_New4_NewNorm_\", \"\")\n",
    "    x = x.replace(\"Mesenchymal_Or_2010_Div_New4_NewNorm_\", \"\").replace(\"Chondrocyte_Or_2010_Div_New4_NewNorm_\", \"\").replace(\".csv\", \"\").replace(\"_\", \" \").replace(\" all\", \"\")\n",
    "    x = x.replace(\"Glutamatergic\", \"Forebrain glutamatergic\").replace(\"GABAergic\", \"Forebrain GABAergic\").replace(\"Spinal Forebrain\", \"Spinal\").replace(\"Chondrocytes\", \"Chondrocyte\")\n",
    "    return x\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Arial\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7fba3ca3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make a function to plot the various expression levels\n",
    "def bp(gene, file, extra_title = \"\"):\n",
    "    \n",
    "    #Read in the file of normalized counts and divergences\n",
    "    vv = pd.read_csv(file).set_index(\"Unnamed: 0\")\n",
    "    \n",
    "    #Restrict to only gene of interest\n",
    "    vp = vv.loc[gene]\n",
    "    \n",
    "    #Get the values of interest, renaming from my names to the ones in the paper that are more interpretable\n",
    "    r1 = [\"Rat-like env.\", \"Mouse\", vp[\"MR1_mi Norm CPM\"]]\n",
    "    r2 = [\"Rat-like env.\", \"Rat\", vp[\"MR1_ri Norm CPM\"]]\n",
    "    r3 = [\"Mouse-like env.\", \"Mouse\", vp[\"RM1_mi Norm CPM\"]]\n",
    "    r4 = [\"Mouse-like env.\", \"Rat\", vp[\"RM1_ri Norm CPM\"]]\n",
    "    r5 = [\"Mouse-like env.\", \"Mouse\", vp[\"RM2_mi Norm CPM\"]]\n",
    "    r6 = [\"Mouse-like env.\", \"Rat\", vp[\"RM2_ri Norm CPM\"]]\n",
    "    r7 = [\"Wildtype\", \"Mouse\", vp[\"WT_mi Norm CPM\"]]\n",
    "    r8 = [\"Wildype\", \"Rat\", vp[\"WT_ri Norm CPM\"]]\n",
    "    \n",
    "    #Depending on tissue, restrict to the rat-mouse chimera that had enough cells for computation\n",
    "    if \"Brain\" in file:\n",
    "        r21 = [\"Host\", \"Rat\", vp[\"MR1_ri Norm CPM\"]]\n",
    "        r31 = [\"Host\", \"Mouse\", vp[\"RM1_mi Norm CPM\"]]\n",
    "        tp = pd.DataFrame([r1, r2, r3, r4, r31, r21])\n",
    "    else:\n",
    "        r21 = [\"Host\", \"Rat\", vp[\"MR1_ri Norm CPM\"]]\n",
    "        r31 = [\"Host\", \"Mouse\", vp[\"RM2_mi Norm CPM\"]]\n",
    "        tp = pd.DataFrame([r31, r21, r1, r2, r6, r5])\n",
    "    \n",
    "    #Set column names\n",
    "    tp.columns = [\"Chimera (Donor-Host)\", \"Cell species\", \"Norm CPM\"]\n",
    "    #fig, ax = plt.subplots(figsize = (6, 4.5))\n",
    "    sns.set(font_scale=1.5)\n",
    "    sns.set_style(\"white\")\n",
    "    \n",
    "    #Make barplot and add labels\n",
    "    sns.barplot(data = tp, y = \"Norm CPM\", x = \"Chimera (Donor-Host)\", hue = \"Cell species\", palette = {\"Mouse\":mouse, \"Rat\":rat}, alpha = 1, order = [\"Host\", \"Rat-like env.\", \"Mouse-like env.\"])\n",
    "    plt.ylabel(\"Pseudobulked normalized counts\")\n",
    "    #plt.xlabel(\"Chimera (donor\" + r'$\\rightarrow$' + \"host)\")\n",
    "    plt.xlabel(\"Extrinsic environment\")\n",
    "    \n",
    "    #If we want a title add one\n",
    "    if not extra_title:\n",
    "        plt.title(\"Expression of $\\it{\" + gene + \"}$ in \" + file_to_celltype(file).lower())\n",
    "    else:\n",
    "        plt.title(extra_title + \"\\n$\\it{\" + gene + \"}$ in \" + file_to_celltype(file).lower())\n",
    "    xmin, xmax, ymin, ymax = plt.axis()\n",
    "    #plt.ylim(0, 1)\n",
    "    \n",
    "    #Add vertical line to demonstrate that we are showing data twice\n",
    "    plt.axvline(0.5, color = \"black\", linewidth = 2.5, alpha = 1, linestyle=\"dashed\")\n",
    "    #plt.legend(bbox_to_anchor=(1.375, 1.05))\n",
    "    plt.legend([],[], frameon=False)\n",
    "    plt.show()\n",
    "    return vp\n",
    "\n",
    "file = \"../Final/Div/Brain_Or_2010_Div_New4_NewNorm_Glutamatergic_neurons.csv\"\n",
    "#gene = \"Lsm6\"\n",
    "#vp = bp(gene, file, \"Example of purely intrinsic divergence:\")\n",
    "gene = \"Efnb3\"\n",
    "vp = bp(gene, file, \"Example of purely extrinsic divergence:\")\n",
    "#gene = \"Plcxd2\"\n",
    "#vp = bp(gene, file, \"Opposing intrinsic and extrinsic divergence:\")\n",
    "#gene = \"Pou3f2\"\n",
    "#vp = bp(gene, file, \"Interaction of intrinsic and extrinsic divergence:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e54abf54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make the plot to explain the plot of intrinsic/extrinsic divergence\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_aspect('equal')\n",
    "ax.set_xlim(-1.5, 1.5)\n",
    "ax.set_ylim(-1.5, 1.5)\n",
    "\n",
    "#Set the color order\n",
    "colors = [extrinsic, reinforcing, intrinsic, opposing, extrinsic, reinforcing, intrinsic, opposing]\n",
    "#colors = [extrinsic, opposing, intrinsic, reinforcing, extrinsic, opposing, intrinsic, reinforcing]\n",
    "# Create wedges of the circle\n",
    "wedges, texts = ax.pie([0.05, 0.2, 0.05, 0.2, 0.05, 0.2, 0.05, 0.2], colors=colors, startangle=81, wedgeprops={'alpha':0.8})\n",
    "\n",
    "#Add vertical and horizontal lines to separate things\n",
    "plt.axvline(0, color = \"black\", linewidth = 2, alpha = 1)\n",
    "plt.axhline(0, color = \"black\", linewidth = 2, alpha = 1)\n",
    "plt.legend(handles = wedges, labels = [\"Extrinsic\", \"Opposing\", \"Intrinsic\", \"Reinforcing\"])\n",
    "\n",
    "#Relabel to get the proper labels\n",
    "handles = wedges\n",
    "labels = [\"Extrinsic\", \"Opposing\", \"Intrinsic\", \"Reinforcing\"]\n",
    "order = [2, 0, 3, 1]\n",
    "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order], bbox_to_anchor=(1, 0.8), frameon = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c31bdf17",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding percent variance explained by intrinsic for all divergence\n",
    "out = []\n",
    "sns.set_style('white')\n",
    "\n",
    "#Iterate through the cell types\n",
    "for file in os.listdir(\"Final/Div/\"):\n",
    "    print(file)\n",
    "    vv = pd.read_csv(\"Final/Div/\" + file).set_index(\"Unnamed: 0\")\n",
    "    \n",
    "    #Require at least 500 genes in a cell type\n",
    "    if vv.shape[0] >= 500:\n",
    "        \n",
    "        #Get the average proportion intrinsic, extrinsic, and interaction\n",
    "        prop_ins = np.sum(vv['Proportion intrinsic'])/vv.shape[0]\n",
    "        prop_extr = np.sum(vv['Proportion extrinsic'])/vv.shape[0]\n",
    "        prop_inter = np.sum(vv['Proportion interaction'])/vv.shape[0]\n",
    "\n",
    "        fig, ax = plt.subplots(figsize = (6, 6))\n",
    "        ax = plt.vlines(0, -7, 7, color = extrinsic, linewidths = 2.5, alpha = 0.65)\n",
    "        ax = plt.hlines(0, -7, 7, color = intrinsic, linewidths = 2.5, alpha = 0.65)\n",
    "        \n",
    "        #Plot extrinsic and compare\n",
    "        ax = sns.scatterplot(x=vv[\"Intrinsic\"], y=vv[\"Extrinsic\"], hue = vv[\"Proportion interaction\"], palette = \"viridis\")\n",
    "        \n",
    "        #Set various plot things\n",
    "        ax = plt.xlim(-7, 7)\n",
    "        ax = plt.ylim(-7, 7)\n",
    "        ax = plt.xlabel(\"Intrinsic log$_{2}$ fold-change\")\n",
    "        ax = plt.ylabel(\"Extrinsic log$_{2}$ fold-change\")\n",
    "        plt.xticks([-7, -5, -3, -1, 1, 3, 5, 7], [-7, -5, -3, -1, 1, 3, 5, 7])\n",
    "        plt.yticks([-7, -5, -3, -1, 1, 3, 5, 7], [-7, -5, -3, -1, 1, 3, 5, 7])\n",
    "        ct = file_to_celltype(file)\n",
    "        if ct != \"Chondrocyte\":\n",
    "            plt.title(ct)\n",
    "        else:\n",
    "            plt.title(ct + \"s\")\n",
    "        #plt.legend(bbox_to_anchor=(1.325, 1))\n",
    "        plt.legend([],[], frameon=False)\n",
    "        #Add a title for the plot we will show\n",
    "        if \"Brain_Or_2010_Div_New4_NewNorm_Glutamatergic_neurons.csv\" == file:\n",
    "            plt.title(\"Forebrain glutamatergic neurons\")\n",
    "        plt.show()\n",
    "        out.append([file, prop_ins, prop_extr, prop_inter])\n",
    "df = pd.DataFrame(out)\n",
    "df.columns = [\"File\", \"Proportion intrinsic\", \"Proportion extrinsic\", \"Proportion interaction\"]\n",
    "#df.to_csv(\"Final/Proportion_IntExtInter.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a9cb4c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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