SMG-surface-cell-division-types.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob, warnings, logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.DEBUG)\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# plotting configuration\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 7}\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "# make a folder to store figures if not already there\n",
    "!mkdir ../jupyter_figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For contorl condition, combine 3 datasets of normal SMGs (e.g., not treated with collagenase)\n",
    "#\n",
    "# Each file is from one data set with a track_id column and an interior_number column,\n",
    "# which use number \"1\", \"2\" or \"0\" to indicate all three possible cell division outcomes\n",
    "\n",
    "df1 = pd.read_csv('../data/cell-division-tracking-data/180218-mTmGHisG-2photon-cell-division-outcome.csv')\n",
    "df1.track_id = ['180218_' + str(track_id) for track_id in df1.track_id]\n",
    "df2 = pd.read_csv('../data/cell-division-tracking-data/190227-mTmGHisG-2_E13-1_denoised-BCratio-cell-division-outcome.csv')\n",
    "df2.track_id = ['190227_' + str(track_id) for track_id in df2.track_id]\n",
    "df3 = pd.read_csv('../data/cell-division-tracking-data/190331-4_K14Rhet-HisG-1-1-denoised-BCratio-stacked-t1-120-cell-division-outcome.csv')\n",
    "df3.track_id = ['190331_' + str(track_id) for track_id in df3.track_id]\n",
    "\n",
    "df = pd.concat([df1, df2, df3])\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotate cell division types based on the number of interior daughter cells\n",
    "#\n",
    "# Type 1: 2 interior daughters\n",
    "# Type 2: 1 interior, 1 surface daughter\n",
    "# Type 3: 2 surface daughter\n",
    "# Type 4: 1 surface daughter, 1 extruded\n",
    "# Type 5: 2 extruded\n",
    "#\n",
    "# Note: we use negative numbers here to represent the number of daughters extruded\n",
    "# outward at anaphase onset. Only observed when basement membrane is disrupted.\n",
    "#\n",
    "division_type_dict = {'2': 'Type 1',\n",
    "                      '1': 'Type 2',\n",
    "                      '0': 'Type 3', \n",
    "                      '-1': 'Type 4',\n",
    "                      '-2': 'Type 5'}\n",
    "\n",
    "division_types = [ division_type_dict[str(df.number_interior.tolist()[i])]\n",
    "                      for i in range(len(df)) ]\n",
    "df['division_type'] = division_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total cell divisions: 289\n",
      "Ratio of 5 types: [0.92387543 0.07612457 0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "# Get summary stats\n",
    "\n",
    "division_types = []\n",
    "n_division_types = []\n",
    "for key, value in division_type_dict.items():\n",
    "    division_types.append(value)\n",
    "    df_temp = df[df.division_type==value]\n",
    "    n_division_types.append(df_temp.track_id.nunique())\n",
    "\n",
    "N = np.sum(n_division_types)\n",
    "print('Total cell divisions:', N)\n",
    "\n",
    "ratio_division_types = n_division_types / N\n",
    "print('Ratio of 5 types:', ratio_division_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make pie chart to represent the proportion of the two cases (types)\n",
    "\n",
    "fig_width, fig_height = 1, 1\n",
    "fig_dpi = 300\n",
    "fig = plt.figure(figsize=(fig_width, fig_height), dpi=fig_dpi)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = division_types[:2]\n",
    "sizes = ratio_division_types[:2]\n",
    "# explode = (0, 0.1)  # only \"explode\" the 2nd slice\n",
    "explode = [0.05]*len(labels)\n",
    "colors=['0.7', '0.5'] # gray values, the larger the lighter\n",
    "\n",
    "ax.pie(sizes, explode=explode, labels=labels,\n",
    "       autopct='%1.1f%%', colors=colors,\n",
    "       shadow=False, startangle=0)\n",
    "ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "# The following removes excessive clipping to facilitate detailing of line widths and colors etc in illustrator\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "plt.savefig('../jupyter_figures/control-cell-division-outcome-pie-chart.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>spot_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0in1ex</td>\n",
       "      <td>210124_SMG1_1</td>\n",
       "      <td>852</td>\n",
       "      <td>74.090010</td>\n",
       "      <td>149.936688</td>\n",
       "      <td>42.0</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0in1ex</td>\n",
       "      <td>210124_SMG1_1</td>\n",
       "      <td>858</td>\n",
       "      <td>74.296677</td>\n",
       "      <td>153.036688</td>\n",
       "      <td>42.0</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0in1ex</td>\n",
       "      <td>210124_SMG1_4</td>\n",
       "      <td>880</td>\n",
       "      <td>51.976674</td>\n",
       "      <td>214.623363</td>\n",
       "      <td>40.0</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0in1ex</td>\n",
       "      <td>210124_SMG1_4</td>\n",
       "      <td>883</td>\n",
       "      <td>57.350008</td>\n",
       "      <td>215.243363</td>\n",
       "      <td>40.0</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0in1ex</td>\n",
       "      <td>210124_SMG1_8</td>\n",
       "      <td>921</td>\n",
       "      <td>271.663371</td>\n",
       "      <td>161.303356</td>\n",
       "      <td>56.0</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  track_name       track_id  spot_id           x           y     z    t\n",
       "0     0in1ex  210124_SMG1_1      852   74.090010  149.936688  42.0  153\n",
       "1     0in1ex  210124_SMG1_1      858   74.296677  153.036688  42.0  152\n",
       "2     0in1ex  210124_SMG1_4      880   51.976674  214.623363  40.0  213\n",
       "3     0in1ex  210124_SMG1_4      883   57.350008  215.243363  40.0  212\n",
       "4     0in1ex  210124_SMG1_8      921  271.663371  161.303356  56.0  171"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine 3 datasets of collagenase treated SMGs\n",
    "\n",
    "df1 = pd.read_csv('../data/cell-division-tracking-data/20210124-K14RhisG-2p-collagenase-SMG1-track-info.csv')\n",
    "df1.track_id = ['210124_SMG1_' + str(track_id) for track_id in df1.track_id]\n",
    "df2 = pd.read_csv('../data/cell-division-tracking-data/20210124-K14RhisG-2p-collagenase-SMG2-track-info.csv')\n",
    "df2.track_id = ['210124_SMG2_' + str(track_id) for track_id in df2.track_id]\n",
    "df3 = pd.read_csv('../data/cell-division-tracking-data/20210124-K14RhisG-2p-collagenase-SMG3-track-info.csv')\n",
    "df3.track_id = ['210124_SMG3_' + str(track_id) for track_id in df3.track_id]\n",
    "\n",
    "df = pd.concat([df1, df2, df3])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='track_name', ylabel='t'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(df.track_name, df.t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.t.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The track_name column contains the scored information of cell division types\n",
    "# As the name indicates, '2in0ex' means 2 interior daughters,\n",
    "# '0in2ex' means 2 extruded daughters etc.\n",
    "\n",
    "division_type_dict = {'2in0ex': 'Type 1',\n",
    "                      '1in0ex': 'Type 2',\n",
    "                      '0in0ex': 'Type 3', \n",
    "                      '0in1ex': 'Type 4',\n",
    "                      '0in2ex': 'Type 5'}\n",
    "\n",
    "division_types = [ division_type_dict[df.track_name.tolist()[i]]\n",
    "                      for i in range(len(df)) ]\n",
    "df['division_type'] = division_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total cell divisions: 130\n",
      "Ratio of 5 types: [0.43846154 0.28461538 0.03846154 0.13846154 0.1       ]\n"
     ]
    }
   ],
   "source": [
    "# Get summary stats\n",
    "\n",
    "division_types = []\n",
    "n_division_types = []\n",
    "for key, value in division_type_dict.items():\n",
    "    division_types.append(value)\n",
    "    df_temp = df[df.division_type==value]\n",
    "    n_division_types.append(df_temp.track_id.nunique())\n",
    "\n",
    "N = np.sum(n_division_types)\n",
    "print('Total cell divisions:', N)\n",
    "\n",
    "ratio_division_types = n_division_types / N\n",
    "print('Ratio of 5 types:', ratio_division_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make pie chart to represent the proportion of the two cases (types)\n",
    "\n",
    "fig_width, fig_height = 1, 1\n",
    "fig_dpi = 300\n",
    "fig = plt.figure(figsize=(fig_width, fig_height), dpi=fig_dpi)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = division_types\n",
    "sizes = ratio_division_types\n",
    "# explode = (0.1, 0.1, 0.1, 0.1, 0.1)\n",
    "explode = [0.03]*5\n",
    "colors=['0.8', '0.7', '0.6']\n",
    "\n",
    "ax.pie(sizes, explode=explode,\n",
    "       labels=labels, colors=colors,\n",
    "#        autopct='%1.1f%%',\n",
    "       shadow=False, startangle=0)\n",
    "ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "# The following removes excessive clipping to facilitate detailing of line widths and colors etc in illustrator\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "plt.savefig('../jupyter_figures/collagenase-cell-division-outcome-pie-chart.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 120x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stacked bar\n",
    "# [0.43846154 0.28461538 0.03846154 0.13846154 0.1       ]\n",
    "labels = ['control', 'collagenase']\n",
    "type1 = [92.4, 43.84]\n",
    "type2 = [7.6, 28.46]\n",
    "type3 = [0, 3.85]\n",
    "type4 = [0, 13.85]\n",
    "type5 = [0, 10]\n",
    "\n",
    "bottom1 = type1\n",
    "bottom2 = [type1[i]+type2[i] for i in range(len(type1))]\n",
    "bottom3 = [type1[i]+type2[i]+type3[i] for i in range(len(type1))]\n",
    "bottom4 = [type1[i]+type2[i]+type3[i]+type4[i] for i in range(len(type1))]\n",
    "\n",
    "fig_width, fig_height = .4, 1.2\n",
    "fig_dpi = 300\n",
    "fig = plt.figure(figsize=(fig_width, fig_height), dpi=fig_dpi)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "# change bar width\n",
    "width = 0.5\n",
    "\n",
    "ax.bar(labels, type1, width,\n",
    "       color = 'blue', alpha=1,\n",
    "       label='Type 1')\n",
    "\n",
    "ax.bar(labels, type2, width,\n",
    "#        color = '.7',\n",
    "       color = 'blue', alpha=.2,\n",
    "       bottom=bottom1, label='Type 2')\n",
    "\n",
    "ax.bar(labels, type3, width,\n",
    "       color = 'black',\n",
    "#        color = '.7',\n",
    "       bottom=bottom2, label='Type 3')\n",
    "\n",
    "ax.bar(labels, type4, width,\n",
    "#        color = '.7',\n",
    "       color = 'red', alpha=.2,\n",
    "       bottom=bottom3, label='Type 4')\n",
    "\n",
    "ax.bar(labels, type5, width,\n",
    "       color = 'red', alpha=1,\n",
    "       bottom=bottom4, label='Type 5')\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "# x_labels = ax.get_xticklabels()\n",
    "x_labels = ['control', 'collagenase']\n",
    "ax.set_xticklabels(labels=x_labels, rotation=30, ha=\"right\")\n",
    "\n",
    "# custome y label\n",
    "plt.ylabel(\"% Surface-derived\\ndivision types\")\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "outputFigPath = '../jupyter_figures/surface-division-types-control-collagenase-stacked-bar.svg'\n",
    "plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# single-cell culture\n",
    "#\n",
    "# Each file is from one data set with a track_id column and an interior_number column,\n",
    "# which use number \"1\", \"2\" or \"0\" to indicate all three possible cell division outcomes\n",
    "\n",
    "df = pd.read_csv('../data/cell-division-tracking-data/210201-mT-2p-2_single-cell-1d-mT.csv')\n",
    "df.columns = ['track_id', 'number_interior']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_id</th>\n",
       "      <th>number_interior</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   track_id  number_interior\n",
       "0         0                1\n",
       "1         1                2\n",
       "2         2                1\n",
       "3         3                2\n",
       "4         4                2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotate cell division types based on the number of interior daughter cells\n",
    "#\n",
    "# Type 1: 2 interior daughters\n",
    "# Type 2: 1 interior, 1 surface daughter\n",
    "# Type 3: 2 surface daughter\n",
    "# Type 4: 1 surface daughter, 1 extruded\n",
    "# Type 5: 2 extruded\n",
    "#\n",
    "# Note: we use negative numbers here to represent the number of daughters extruded\n",
    "# outward at anaphase onset. Only observed when basement membrane is disrupted.\n",
    "#\n",
    "division_type_dict = {'2': 'Type 1',\n",
    "                      '1': 'Type 2',\n",
    "                      '0': 'Type 3', \n",
    "                      '-1': 'Type 4',\n",
    "                      '-2': 'Type 5'}\n",
    "\n",
    "division_types = [ division_type_dict[str(df.number_interior.tolist()[i])]\n",
    "                      for i in range(len(df)) ]\n",
    "df['division_type'] = division_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total cell divisions: 23\n",
      "Ratio of 5 types: [0.56521739 0.43478261 0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "# Get summary stats\n",
    "\n",
    "division_types = []\n",
    "n_division_types = []\n",
    "for key, value in division_type_dict.items():\n",
    "    division_types.append(value)\n",
    "    df_temp = df[df.division_type==value]\n",
    "    n_division_types.append(df_temp.track_id.nunique())\n",
    "\n",
    "N = np.sum(n_division_types)\n",
    "print('Total cell divisions:', N)\n",
    "\n",
    "ratio_division_types = n_division_types / N\n",
    "print('Ratio of 5 types:', ratio_division_types)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# single-bud culture\n",
    "#\n",
    "# Each file is from one data set with a track_id column and an interior_number column,\n",
    "# which use number \"1\", \"2\" or \"0\" to indicate all three possible cell division outcomes\n",
    "\n",
    "df = pd.read_csv('../data/cell-division-tracking-data/210201-mT-2p-3_single-bud-0d-mT.csv')\n",
    "df.columns = ['track_id', 'number_interior']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotate cell division types based on the number of interior daughter cells\n",
    "#\n",
    "# Type 1: 2 interior daughters\n",
    "# Type 2: 1 interior, 1 surface daughter\n",
    "# Type 3: 2 surface daughter\n",
    "# Type 4: 1 surface daughter, 1 extruded\n",
    "# Type 5: 2 extruded\n",
    "#\n",
    "# Note: we use negative numbers here to represent the number of daughters extruded\n",
    "# outward at anaphase onset. Only observed when basement membrane is disrupted.\n",
    "#\n",
    "division_type_dict = {'2': 'Type 1',\n",
    "                      '1': 'Type 2',\n",
    "                      '0': 'Type 3', \n",
    "                      '-1': 'Type 4',\n",
    "                      '-2': 'Type 5'}\n",
    "\n",
    "division_types = [ division_type_dict[str(df.number_interior.tolist()[i])]\n",
    "                      for i in range(len(df)) ]\n",
    "df['division_type'] = division_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total cell divisions: 24\n",
      "Ratio of 5 types: [0.70833333 0.29166667 0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "# Get summary stats\n",
    "\n",
    "division_types = []\n",
    "n_division_types = []\n",
    "for key, value in division_type_dict.items():\n",
    "    division_types.append(value)\n",
    "    df_temp = df[df.division_type==value]\n",
    "    n_division_types.append(df_temp.track_id.nunique())\n",
    "\n",
    "N = np.sum(n_division_types)\n",
    "print('Total cell divisions:', N)\n",
    "\n",
    "ratio_division_types = n_division_types / N\n",
    "print('Ratio of 5 types:', ratio_division_types)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 150x240 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stacked bar\n",
    "\n",
    "labels = ['intact culture', 'single bud', 'single cell']\n",
    "type1 = [92.4, 70.8, 56.5]\n",
    "type2 = [7.6, 29.2, 43.5]\n",
    "\n",
    "fig_width, fig_height = .5, .8\n",
    "fig_dpi = 300\n",
    "fig = plt.figure(figsize=(fig_width, fig_height), dpi=fig_dpi)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "# change bar width\n",
    "width = 0.5\n",
    "\n",
    "ax.bar(labels, type1, width,\n",
    "       color = 'blue', alpha=1,\n",
    "       label='Type 1')\n",
    "ax.bar(labels, type2, width,\n",
    "#        color = '.7',\n",
    "       color = 'blue', alpha=.2,\n",
    "       bottom=type1, label='Type 2')\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "# x_labels = ax.get_xticklabels()\n",
    "x_labels = ['Intact culture', 'Single bud', 'Single cell']\n",
    "ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "# custome y label\n",
    "plt.ylabel(\"% Surface-derived\\ncell division types\")\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "outputFigPath = '../jupyter_figures/surface-division-types-intact-culture-single-bud-single-cell-stacked-bar.svg'\n",
    "plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}