{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting matplotlib display setting as inline plots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary packages\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.image as mpimg\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib_venn import venn3, venn2\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting working folder\n",
    "os.chdir('C:/Users/ooma1/OneDrive - UC San Diego/Guatelli Lab/Cell Fractionation/FractPaperFigures/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating lists for select proteins\n",
    "color_list = plt.cm.tab20(range(0,20))\n",
    "colorlist = [color_list[0], color_list[2], color_list[4], color_list[6], color_list[8], \n",
    "             color_list[10], color_list[12], color_list[5], '0.9', '0.4']\n",
    "compartments = ['ER', 'Endosome', 'Golgi', 'Large Protein Complex', 'Lysosome', \n",
    "                'Mitochondrion', 'Peroxisome', 'Plasma membrane', 'Unclassified', 'Undetected']\n",
    "\n",
    "## SERC5\n",
    "serc5_wt = [3, 2, 1]\n",
    "serc5_wtun_label = [colorlist[7], colorlist[0], colorlist[8]]\n",
    "serc5_wtind_label = [colorlist[8], colorlist[0], colorlist[4]]\n",
    "\n",
    "serc5_dnefun = [3, 1, 1, 1]\n",
    "serc5_dnefun_label = [colorlist[7], colorlist[1], colorlist[0], colorlist[3]]\n",
    "serc5_dnefind = [4, 1, 1]\n",
    "serc5_dnefind_label = [colorlist[8], colorlist[0], colorlist[7]]\n",
    "\n",
    "## Lck\n",
    "lck_wtun = [5, 1]\n",
    "lck_wtun_label = [colorlist[7], colorlist[8]]\n",
    "lck_wtind = [6]\n",
    "lck_wtind_label = [colorlist[7]]\n",
    "\n",
    "lck_dnefun = [3, 2, 1]\n",
    "lck_dnefun_label = [colorlist[7], colorlist[0], colorlist[8]]\n",
    "lck_dnefind = [4, 2]\n",
    "lck_dnefind_label = [colorlist[7], colorlist[8]]\n",
    "\n",
    "## CD28\n",
    "cd28_wtun = [5, 1]\n",
    "cd28_wtun_label = [colorlist[7], colorlist[3]]\n",
    "cd28_wtind = [3, 1, 1, 1]\n",
    "cd28_wtind_label = [colorlist[8], colorlist[1], colorlist[2], colorlist[3]]\n",
    "\n",
    "cd28_dnefun = [3, 2, 1]\n",
    "cd28_dnefun_label = [colorlist[8], colorlist[7], colorlist[0]]\n",
    "cd28_dnefind = [3, 1, 1, 1]\n",
    "cd28_dnefind_label = [colorlist[8], colorlist[0], colorlist[3], colorlist[4]]\n",
    "\n",
    "## Nef\n",
    "nef_wt = [3, 2, 1]\n",
    "nef_wt_label = [colorlist[8], colorlist[3], colorlist[1]]\n",
    "nef_dnef = [6]\n",
    "nef_dnef_label = [colorlist[9]]\n",
    "\n",
    "## Gag\n",
    "gag_wt = [6]\n",
    "gag_wt_label = [colorlist[1]]\n",
    "gag_dnef = [4, 1, 1]\n",
    "gag_dnef_label = [colorlist[8], colorlist[1], colorlist[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x792 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating figure\n",
    "fig = plt.figure(figsize=(8.5, 11))\n",
    "ax1 = plt.subplot2grid((9, 6), (0, 0))\n",
    "ax2 = plt.subplot2grid((9, 6), (0, 1))\n",
    "ax3 = plt.subplot2grid((9, 6), (1, 0))\n",
    "ax4 = plt.subplot2grid((9, 6), (1, 1))\n",
    "ax5 = plt.subplot2grid((9, 6), (0, 2))\n",
    "ax6 = plt.subplot2grid((9, 6), (0, 3))\n",
    "ax7 = plt.subplot2grid((9, 6), (1, 2))\n",
    "ax8 = plt.subplot2grid((9, 6), (1, 3))\n",
    "ax9 = plt.subplot2grid((9, 6), (2, 0))\n",
    "ax10 = plt.subplot2grid((9, 6), (2, 1))\n",
    "ax11 = plt.subplot2grid((9, 6), (3, 0))\n",
    "ax12 = plt.subplot2grid((9, 6), (3, 1))\n",
    "ax13 = plt.subplot2grid((9, 6), (2, 2))\n",
    "ax14 = plt.subplot2grid((9, 6), (2, 3))\n",
    "ax15 = plt.subplot2grid((9, 6), (3, 2))\n",
    "ax16 = plt.subplot2grid((9, 6), (3, 3))\n",
    "ax17 = plt.subplot2grid((9, 6), (0, 4), colspan=2, rowspan=9)\n",
    "ax18 = plt.subplot2grid((9, 6), (4, 0), colspan=6, rowspan=5)\n",
    "\n",
    "## SERC5\n",
    "ax1.annotate('A)', (-0.35, 1.15), xycoords='axes fraction', size=12)\n",
    "ax1.pie(serc5_wt, colors=serc5_wtun_label)\n",
    "ax1.set_title('Uninduced', fontsize=9, y=0.9)\n",
    "ax1.annotate('WT', (0, 0.5), xycoords='axes fraction', size=9, horizontalalignment='right')\n",
    "ax1.annotate('SERINC5', (1.225, -0.2), xycoords='axes fraction', size=9, horizontalalignment='center')\n",
    "\n",
    "ax2.pie(serc5_wt, colors=serc5_wtind_label)\n",
    "ax2.set_title('Induced', fontsize=9, y=0.9)\n",
    "\n",
    "ax3.pie(serc5_dnefun, colors=serc5_dnefun_label)\n",
    "ax3.annotate('dNef', (0, 0.5), xycoords='axes fraction', size=9, horizontalalignment='right')\n",
    "\n",
    "ax4.pie(serc5_dnefind, colors=serc5_dnefind_label)\n",
    "\n",
    "## Lck\n",
    "ax5.annotate('B)', (-0.35, 1.15), xycoords='axes fraction', size=12)\n",
    "ax5.pie(lck_wtun, colors=lck_wtun_label)\n",
    "ax5.set_title('Uninduced', fontsize=9, y=0.9)\n",
    "ax5.annotate('WT', (0, 0.5), xycoords='axes fraction', size=9, horizontalalignment='right')\n",
    "ax5.annotate('Lck', (1.225, -0.2), xycoords='axes fraction', size=9, horizontalalignment='center')\n",
    "\n",
    "ax6.pie(lck_wtind, colors=lck_wtind_label)\n",
    "ax6.set_title('Induced', fontsize=9, y=0.9)\n",
    "\n",
    "ax7.pie(lck_dnefun, colors=lck_dnefun_label)\n",
    "ax7.annotate('dNef', (0, 0.5), xycoords='axes fraction', size=9, horizontalalignment='right')\n",
    "\n",
    "ax8.pie(lck_dnefind, colors=lck_dnefind_label)\n",
    "\n",
    "## CD28\n",
    "ax9.annotate('C)', (-0.35, 1.15), xycoords='axes fraction', size=12)\n",
    "ax9.pie(cd28_wtun, colors=cd28_wtun_label)\n",
    "ax9.set_title('Uninduced', fontsize=9, y=0.9)\n",
    "ax9.annotate('WT', (0, 0.5), xycoords='axes fraction', size=9, horizontalalignment='right')\n",
    "ax9.annotate('CD28', (1.225, -0.2), xycoords='axes fraction', size=9, horizontalalignment='center')\n",
    "\n",
    "ax10.pie(cd28_wtind, colors=cd28_wtind_label)\n",
    "ax10.set_title('Induced', fontsize=9, y=0.9)\n",
    "\n",
    "ax11.pie(cd28_dnefun, colors=cd28_dnefun_label)\n",
    "ax11.annotate('dNef', (0, 0.5), xycoords='axes fraction', size=9, horizontalalignment='right')\n",
    "\n",
    "legendpatch = []\n",
    "for comp, colorcode in zip(compartments, colorlist):\n",
    "    legendpatch.append(Patch(facecolor=colorcode, label=comp))\n",
    "\n",
    "ax11.legend(handles=legendpatch, ncol=3, fontsize=8, loc=(-0.25, -1))\n",
    "    \n",
    "ax12.pie(cd28_dnefind, colors=cd28_dnefind_label)\n",
    "\n",
    "## Nef\n",
    "ax13.annotate('D)', (-0.35, 1.15), xycoords='axes fraction', size=12)\n",
    "ax13.pie(nef_wt, colors=nef_wt_label)\n",
    "ax13.set_title('WT', fontsize=9, y=0.9)\n",
    "ax13.annotate('Nef', (1.225, 0.5), xycoords='axes fraction', size=9, horizontalalignment='center')\n",
    "\n",
    "ax14.pie(nef_dnef, colors=nef_dnef_label)\n",
    "ax14.set_title('dNef', fontsize=9, y=0.9)\n",
    "\n",
    "## Gag\n",
    "ax15.annotate('E)', (-0.35, 1.15), xycoords='axes fraction', size=12)\n",
    "ax15.pie(gag_wt, colors=gag_wt_label)\n",
    "ax15.set_title('WT', fontsize=9, y=0.9)\n",
    "ax15.annotate('Gag', (1.225, 0.5), xycoords='axes fraction', size=9, horizontalalignment='center')\n",
    "\n",
    "ax16.pie(gag_dnef, colors=gag_dnef_label)\n",
    "ax16.set_title('dNef', fontsize=9, y=0.9)\n",
    "\n",
    "ax17.axis('off')\n",
    "ax18.axis('off')\n",
    "\n",
    "fig.suptitle('Figure 4', x=0, y=1, fontsize=12)\n",
    "\n",
    "plt.savefig('Figure4.pdf', dpi=500, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  }
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