SMG-post-division-return-time.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\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",
    "from sw_plotting import change_bar_width, plot_cum_dist, plot_dist\n",
    "from sw_utilities import tukeyTest\n",
    "\n",
    "# make a folder to store figures if not already there\n",
    "!mkdir ../jupyter_figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>group_id</th>\n",
       "      <th>AO_to_SR_minutes</th>\n",
       "      <th>AO_to_SR_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>control</td>\n",
       "      <td>225.0</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>control</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>control</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>control</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>control</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group_id  AO_to_SR_minutes  AO_to_SR_hours\n",
       "0  control             225.0        3.750000\n",
       "1  control              50.0        0.833333\n",
       "2  control              90.0        1.500000\n",
       "3  control              85.0        1.416667\n",
       "4  control              75.0        1.250000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contorl\n",
    "df = pd.read_csv('../data/SMG-post-division-return-time/180218-mTmGHisG-2photon-cell-division-returning-time.csv')\n",
    "df.rename(columns={'anaphase_to_surface_return':'AO_to_SR_minutes'}, inplace=True)\n",
    "df['AO_to_SR_frames'] = df.AO_to_SR_minutes / 5\n",
    "df['AO_to_SR_hours'] = df.AO_to_SR_minutes / 60\n",
    "df['group_id'] = ['control']*len(df)\n",
    "df1 = df[['group_id', 'AO_to_SR_minutes', 'AO_to_SR_hours']]\n",
    "\n",
    "# collagenase\n",
    "df = pd.read_csv('../data/SMG-post-division-return-time/20210124-K14RhisG-2p-collagenase-post-division-return.csv')\n",
    "df['AO_to_SR_minutes'] = df.AO_to_SR_frames * 5\n",
    "df['AO_to_SR_hours'] = df.AO_to_SR_minutes / 60\n",
    "df['group_id'] = ['collagenase']*len(df)\n",
    "df2 = df[['group_id', 'AO_to_SR_minutes', 'AO_to_SR_hours']]\n",
    "\n",
    "df = pd.concat([df1, df2])\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.333333333333334"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slowest return time\n",
    "df1.AO_to_SR_hours.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>group_id</th>\n",
       "      <th>AO_to_SR_minutes</th>\n",
       "      <th>AO_to_SR_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>control</td>\n",
       "      <td>740</td>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group_id  AO_to_SR_minutes  AO_to_SR_hours\n",
       "67  control               740       12.333333"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of cells returned after >12 hours\n",
    "df1[df1.AO_to_SR_hours>12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x7fb315069450>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_prefix = '180218-post-division-return-time'\n",
    "output_fig_path = '../jupyter_figures/' + dataset_prefix + '-CDF.svg'\n",
    "plot_cum_dist(df1.AO_to_SR_hours, n_bins=15, output_fig_path=None, x_ticks=[0,4,8,12])\n",
    "\n",
    "# output_fig_path = '../jupyter_figures/' + dataset_prefix + '-distribution.svg'\n",
    "# plot_dist(df.anaphase_to_surface_return, n_bins=12, output_fig_path=output_fig_path, x_ticks=[0,400,800])\n",
    "# plot_dist(return_time_data, n_bins=12, x_ticks=[0,300,600,900])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      "      Multiple Comparison of Means - Tukey HSD, FWER=0.05     \n",
      "=============================================================\n",
      "   group1    group2 meandiff p-adj    lower    upper   reject\n",
      "-------------------------------------------------------------\n",
      "collagenase control -187.316 0.0014 -299.9276 -74.7044   True\n",
      "-------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00135951]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fb3153987d0>"
      ]
     },
     "execution_count": 6,
     "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": [
    "# df.dropna(inplace=True)\n",
    "sns.swarmplot(data=df, x='group_id', y='AO_to_SR_minutes')\n",
    "sns.barplot(data=df, x='group_id', y='AO_to_SR_minutes')\n",
    "tukeyTest(df.dropna().AO_to_SR_minutes, df.dropna().group_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.30314617081286, pvalue=0.001358002642491236)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "ttest_ind(df1.AO_to_SR_hours, df2.dropna().AO_to_SR_hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 150x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Composite wwarm and bar plot of different collagenase concentrations\n",
    "\n",
    "outputPrefix = 'SMG-collagenase-post-division-return-time-bar'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width, fig_height = 0.5, 1.2\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "xCol, yCol = 'group_id', 'AO_to_SR_hours'\n",
    "\n",
    "# ax = sns.swarmplot(x=xCol, y=yCol, data=df,\n",
    "#                    color=\"blue\",\n",
    "#                    size=1.0,\n",
    "#                    alpha=.4)\n",
    "\n",
    "ax = sns.barplot(x=xCol, y=yCol, data=df,\n",
    "                 color=\".7\", alpha=1.0,\n",
    "                 errwidth=.6, errcolor=\".4\", capsize=.2, ci=95)\n",
    "\n",
    "plt.ylim(0, 10.5)\n",
    "plt.yticks([0, 5, 10])\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Anaphase onset to\\nsurface return (h)\")\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "x_labels = [\"Control\", \"Collagenase\"]\n",
    "# x_labels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(labels=x_labels, rotation=60, ha=\"right\")\n",
    "\n",
    "# make the bar width narrower\n",
    "change_bar_width(ax, .5)\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",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# Composite wwarm and bar plot of different collagenase concentrations\n",
    "\n",
    "outputPrefix = 'SMG-collagenase-post-division-return-time-swarm'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width, fig_height = 0.4, 1.2\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "xCol, yCol = 'group_id', 'AO_to_SR_hours'\n",
    "\n",
    "# Customize color of each group\n",
    "paletteDict = {'control': 'black', 'collagenase': 'red'}\n",
    "\n",
    "ax = sns.swarmplot(x=xCol, y=yCol, data=df,\n",
    "                   hue='group_id', palette=paletteDict,\n",
    "#                    color='Gray',\n",
    "#                    color=\"blue\",\n",
    "                   alpha=.5,\n",
    "                   size=1.0)\n",
    "\n",
    "# ax = sns.barplot(x=xCol, y=yCol, data=df,\n",
    "#                  color=\".7\", alpha=1.0,\n",
    "#                  errwidth=.7, errcolor=\"k\", capsize=.2, ci=95)\n",
    "\n",
    "plt.ylim(0, 15.5)\n",
    "plt.yticks([0, 5, 10, 15])\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Anaphase onset to\\nsurface return (h)\")\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "x_labels = [\"Control\", \"Collagenase\"]\n",
    "# x_labels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(labels=x_labels, rotation=60, ha=\"right\")\n",
    "\n",
    "# make the bar width narrower\n",
    "change_bar_width(ax, .6)\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",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 90x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Composite wwarm and bar plot of different collagenase concentrations\n",
    "\n",
    "outputPrefix = 'SMG-collagenase-post-division-return-time-bar-swarm'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width, fig_height = 0.3, 1.2\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "xCol, yCol = 'group_id', 'AO_to_SR_hours'\n",
    "\n",
    "# Customize color of each group\n",
    "paletteDict = {'control': 'black', 'collagenase': 'red'}\n",
    "\n",
    "ax = sns.swarmplot(x=xCol, y=yCol, data=df,\n",
    "                   hue='group_id', palette=paletteDict,\n",
    "#                    color='Gray',\n",
    "#                    color=\"blue\",\n",
    "                   alpha=.5,\n",
    "                   size=1.0)\n",
    "\n",
    "ax = sns.barplot(x=xCol, y=yCol, data=df,\n",
    "                 color=\".7\", alpha=1.0,\n",
    "                 errwidth=.7, errcolor=\"k\", capsize=.2, ci=95)\n",
    "\n",
    "plt.ylim(0, 15.5)\n",
    "plt.yticks([0, 5, 10, 15])\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Anaphase onset to\\nsurface return (h)\")\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "x_labels = [\"Control\", \"Collagenase\"]\n",
    "# x_labels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(labels=x_labels, rotation=60, ha=\"right\")\n",
    "\n",
    "# make the bar width narrower\n",
    "change_bar_width(ax, .6)\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",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>group_id</th>\n",
       "      <th>AO_to_SR_minutes</th>\n",
       "      <th>AO_to_SR_hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>collagenase</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>collagenase</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>collagenase</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>collagenase</td>\n",
       "      <td>610.0</td>\n",
       "      <td>10.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>collagenase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group_id  AO_to_SR_minutes  AO_to_SR_hours\n",
       "0  collagenase             120.0        2.000000\n",
       "1  collagenase             215.0        3.583333\n",
       "2  collagenase              75.0        1.250000\n",
       "3  collagenase             610.0       10.166667\n",
       "4  collagenase               NaN             NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group_id            0\n",
       "AO_to_SR_minutes    9\n",
       "AO_to_SR_hours      9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)"
   ]
  },
  {
   "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>cell_division_id</th>\n",
       "      <th>daughter_cell_id</th>\n",
       "      <th>track_id</th>\n",
       "      <th>track_start</th>\n",
       "      <th>track_end</th>\n",
       "      <th>start_on_surface</th>\n",
       "      <th>surface_return</th>\n",
       "      <th>AO_to_SR_frames</th>\n",
       "      <th>experimental_group</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>AO_to_SR_minutes</th>\n",
       "      <th>AO_to_SR_hours</th>\n",
       "      <th>group_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>24.0</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1B</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>43.0</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2A</td>\n",
       "      <td>6.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>15.0</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2B</td>\n",
       "      <td>8.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>122.0</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>610.0</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>collagenase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>collagenase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>collagenase</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cell_division_id daughter_cell_id  track_id  track_start  track_end  \\\n",
       "0                 1               1A       0.0         12.0       36.0   \n",
       "1                 1               1B       5.0         12.0       55.0   \n",
       "2                 2               2A       6.0        133.0      148.0   \n",
       "3                 2               2B       8.0        133.0      255.0   \n",
       "4                 3               3A       NaN          NaN        NaN   \n",
       "\n",
       "  start_on_surface surface_return  AO_to_SR_frames experimental_group  \\\n",
       "0               no            yes             24.0        collagenase   \n",
       "1               no            yes             43.0        collagenase   \n",
       "2               no            yes             15.0        collagenase   \n",
       "3               no            yes            122.0        collagenase   \n",
       "4              yes            NaN              NaN        collagenase   \n",
       "\n",
       "   Unnamed: 9  Unnamed: 10  AO_to_SR_minutes  AO_to_SR_hours     group_id  \n",
       "0         NaN          NaN             120.0        2.000000  collagenase  \n",
       "1         NaN          NaN             215.0        3.583333  collagenase  \n",
       "2         NaN          NaN              75.0        1.250000  collagenase  \n",
       "3         NaN          NaN             610.0       10.166667  collagenase  \n",
       "4         NaN          NaN               NaN             NaN  collagenase  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/SMG-post-division-return-time/20210124-K14RhisG-2p-collagenase-post-division-return.csv')\n",
    "df['AO_to_SR_minutes'] = df.AO_to_SR_frames * 5\n",
    "df['AO_to_SR_hours'] = df.AO_to_SR_minutes / 60\n",
    "df['group_id'] = ['collagenase']*len(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_division_id       0\n",
       "daughter_cell_id       0\n",
       "track_id               9\n",
       "track_start            8\n",
       "track_end              8\n",
       "start_on_surface       0\n",
       "surface_return         8\n",
       "AO_to_SR_frames        9\n",
       "experimental_group     0\n",
       "Unnamed: 9            20\n",
       "Unnamed: 10           20\n",
       "AO_to_SR_minutes       9\n",
       "AO_to_SR_hours         9\n",
       "group_id               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">cell_division_id</th>\n",
       "      <th colspan=\"2\" halign=\"left\">track_id</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">AO_to_SR_minutes</th>\n",
       "      <th colspan=\"8\" halign=\"left\">AO_to_SR_hours</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_on_surface</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>12.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.876235</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.25</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.090909</td>\n",
       "      <td>...</td>\n",
       "      <td>537.5</td>\n",
       "      <td>865.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.189394</td>\n",
       "      <td>4.351419</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>2.791667</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>8.958333</td>\n",
       "      <td>14.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.507133</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cell_division_id                                            \\\n",
       "                            count mean       std  min  25%  50%   75%   max   \n",
       "start_on_surface                                                              \n",
       "no                           12.0  4.5  2.876235  1.0  2.0  4.5  6.25  10.0   \n",
       "yes                           8.0  7.0  2.507133  3.0  5.5  7.5  9.00  10.0   \n",
       "\n",
       "                 track_id             ... AO_to_SR_minutes         \\\n",
       "                    count       mean  ...              75%    max   \n",
       "start_on_surface                      ...                           \n",
       "no                   11.0  30.090909  ...            537.5  865.0   \n",
       "yes                   0.0        NaN  ...              NaN    NaN   \n",
       "\n",
       "                 AO_to_SR_hours                                          \\\n",
       "                          count      mean       std       min       25%   \n",
       "start_on_surface                                                          \n",
       "no                         11.0  6.189394  4.351419  1.083333  2.791667   \n",
       "yes                         0.0       NaN       NaN       NaN       NaN   \n",
       "\n",
       "                                                 \n",
       "                       50%       75%        max  \n",
       "start_on_surface                                 \n",
       "no                5.833333  8.958333  14.416667  \n",
       "yes                    NaN       NaN        NaN  \n",
       "\n",
       "[2 rows x 72 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('start_on_surface').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What if we do not consider the really fast returning cells in control?\n",
    "df1_filtered = df1[df1.AO_to_SR_hours<=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.9341364294958345, pvalue=0.010882192827607811)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(df1_filtered.AO_to_SR_hours, df2.dropna().AO_to_SR_hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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