SMG-bud-count-plateI-E16-Itgb1-Rei-Kaz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
    "from sw_plotting import change_bar_width, plotCountBar\n",
    "from sw_utilities import tukeyTest, getCountDFbyPrefix, get_annotated_countDF\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",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.ERROR)\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 if it is not already there to store exported figures\n",
    "!mkdir ../jupyter_figures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the count data by Kaz\n",
    "\n",
    "datasetPrefix = '20220513-plateI-day0'\n",
    "datasetFolder = '../data/220509-30-for-bud-count-Kaz/sg-Itgb1-E16-blast/'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\n",
    "\n",
    "# - For Itgb1 KO\n",
    "# - Plate I, by Shaohe, add blasticidin\n",
    "# - D2-D6, LV-188\n",
    "# - D7-D11, LV-401\n",
    "# - E2-E6, LV-402\n",
    "# - E7-E11, LV-403\n",
    "\n",
    "groups = [\"sg-Control\"]*5 + [\"sg1-Itgb1\"]*5 + [\"sg2-Itgb1\"]*5 + [\"sg3-Itgb1\"]*5\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# This is day 0\n",
    "df.rename(columns={'counts': 'counts_d0'}, inplace=True)\n",
    "\n",
    "df0 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the count data by Kaz\n",
    "\n",
    "datasetPrefix = '20220517-plateI-day4'\n",
    "datasetFolder = '../data/220509-30-for-bud-count-Kaz/sg-Itgb1-E16-blast/'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\n",
    "\n",
    "# - For Itgb1 KO\n",
    "# - Plate I, by Shaohe, add blasticidin\n",
    "# - D2-D6, LV-188\n",
    "# - D7-D11, LV-401\n",
    "# - E2-E6, LV-402\n",
    "# - E7-E11, LV-403\n",
    "\n",
    "groups = [\"sg-Control\"]*5 + [\"sg1-Itgb1\"]*5 + [\"sg2-Itgb1\"]*5 + [\"sg3-Itgb1\"]*5\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# This is day 4\n",
    "df.rename(columns={'counts': 'counts_d4'}, inplace=True)\n",
    "\n",
    "df4 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
      "sg-Control sg1-Itgb1  -2.2255 0.0076  -3.899 -0.5519   True\n",
      "sg-Control sg2-Itgb1  -2.9836  0.001 -4.6572 -1.3101   True\n",
      "sg-Control sg3-Itgb1  -2.6088  0.002 -4.2823 -0.9353   True\n",
      " sg1-Itgb1 sg2-Itgb1  -0.7582 0.5719 -2.4317  0.9154  False\n",
      " sg1-Itgb1 sg3-Itgb1  -0.3833    0.9 -2.0569  1.2902  False\n",
      " sg2-Itgb1 sg3-Itgb1   0.3748    0.9 -1.2987  2.0484  False\n",
      "-----------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00762699 0.001      0.00201628 0.5719299  0.9        0.9       ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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bf66ISC4zs4XuXtzYa1pmEREJgMpcRCQAKnMRkQCozEVEAqAyFxEJgMpcRCQAKnMRkQCozEVEAqAyFxEJgMpcRCQAKnMRkQDEVuZmdoKZ/d7MHjGzQ+M6rohIJlQvX86Kb3+HpYeOZuV551OzZk3SkfZInDPzQuC7wA3AV2M8rohI7D758Y/ZWlqKV1VRsWABq6+5JulIeyS2Mnf3x4kK/T+AmXEdV0Qkbl5TQ+Vbi+rt21a6MKE08YhzmaU38Gvgp+6+tpHXp5hZqZmVlpWVxfWxkgs+XQIv/hwWPQw7apJOI4IVFNB55Mh6+7qMHp1MmJjEdj9zM/s9UASsB+a5+yNNvVf3M29HPngeHvgG1G6Ptod/DSY9kGwmEaDqgw9Y9ZOrqHz7bboUFzPwphspGDgw6VjNapP7mbv7ue5+qrt/u7kil3bm1Tu/KHKApU/A+g+SyyOS0mn//en5zW+Q36sXlYsWsf6+mXhtbdKxWk2nJkpmWSN/xMzaPodIA1UffsSan17Djg0b8KoqPrv/fjb98Y9Jx2o1lblk1pEXQ17BF9sjJsDe+yWXJ0ct+2wZi9ctJonHPIaq8u1F0GA8tzX4oWgu0QOdJbO+NB4uegXeeyp6oPPwryWdKKfUei1Xvnglzyx/BoBRRaOYcdIMCgsKE06W+7qMGQN5ebDT0kph8WEJJtozmplL5vUZBmMvhZFfh3zNH3bHK6teqStygEVli5j7j7kJJgpHx8GDGTBtGgWDBpHfsye9p06l++mnJx2r1fQ3SySLfbr107T2Sev0OP1r9Dg9jH8tamYuksXGDxpP14Kuddv5ls+BvQ7k0fcf5ePNHyeYTLKNZuYiWaxPlz7cd8p9zFw8k8odlXQt6MpVL18FRMX+y2N+yQn7npBwSskGsV00tDt00ZDI7qveUc24OePYun1r3b4Dex3Io2c8mmAqaUttctGQiGTWDt9B9Y7qevu2bd+WUBrJNipzkRzRpUMXJhwwod6+SQdNSiiNZButmYvkkKtLrubQokNZumEpJf1LOG7IcUlHkiyhMhfJIfl5+Zw57MykY0gW0jKLiEgAVOYiIgFQmYuIBEBr5iLSbtWsXcvGOQ9RW7GFHmeeSefhw5OO1GoqcxFpl2q3bWP5pElsX7UagM9mz2HoHx7K2ULXMouItEtb5r9YV+QAXl3Nxrm5e0dKlblIDlmxeQUXPHsB4+aM40fzf8TGyo1JR8pZeV277rIvv5F9uUJlLpJDrph/Ba+ufpWNVRt5evnT3Pj6jUlHyll7HXUkhSUlddsd+ven56TcvaJWa+YiOWJz9WaWblhab98ba95IKE3us7w8htzzOyr+9gq1W8rpOn48eYW5+wQnlblIjuhW0I0h3Yawsnxl3b6RvUcmmCj3WV4eXY8em3SMWGiZRSRHmBk3jbuJod2HAjC6aDQ/OeInyYaSrKGZuUgOOaToEB4/83Eqt1fSuUPnpONIFtHMXCQHqcilIZW5iEgAVOYiIgFQmYuIBEBlLiISAJW5iEgAzN3b/kPNyoAVbf7Bu68PsC7pEAHReMZHYxmvXBnPfd29qLEXEinzXGFmpe5enHSOUGg846OxjFcI46llFhGRAKjMRUQCoDJv3vSkAwRG4xkfjWW8cn48tWYuIhIAzcxFRAKgMm8FM+thZnslnSPXmFl/M7Okc4RC4xmvXB/Pdl/mZtbZzO4ws3vM7EkzOyiNL7sM6N/MMb9nZifGFjJHmFm+md3Z8PduZv9jZgcAPwPyG/m6Wekcp70xsxvN7G4zu9fM8nbar/FsBTO7KvV3/boG+4MYz6DvZ25mE4HjgO7Ae0QXBhwBnOPuy1NvuxB4zN2fNbMioMTM9gYuIvpmdx3wn8AqYBjwP8DRQIWZDQMMuA+4GKgEnmyL31tSmhvT1P6mZjaFwBjg62bWGxhNNJ7/C3Q3s18DPYj+/5jUzHGC0sx4TgQWuPsTqbHpDZTt9KUaz0a08OfzLXf/mZk92MiX5vx4hj4zHwJsAO4G9nH3S4CGD00cDrwK4O5l7v44MBX4AXA5Ual3An4FzASOAhYA81L7rwS+DNzu7ucR/aEJWZNj6u73Aq818XVbgTeJxu1Id78QeCb1WgXRv3aeBE5t4TihaWo8PVXkhwPV7l7W4Os0no1r7s/nk2Z2NeCNLKfk/HgGPTMHngVqiWbXHVP7HBhgZjcBbwNLgBLg85n596n/Xffzb3hbgZrU9s6nAG1psC/rvmPHrKkxrcfMDgV+Avx1p92fv6+gwXa1u7uZbQd2xJ44uzU5nmZ2CtHk4UqNZ9qaG8+x7n69mf0QGGVmQY1n6DPzkcAPib6zrjSzXwLjgA/dfZK730B0fum/mtnvib6bP5baNx24Efh1I8f9kGj2/rnZwEVmdjswJ1O/mSzR1JhW7vwmd38rNcY7n7+7jOifqa+Z2S3AhNTXdTeza4AT+GI21F40N54zgP2A+4E1Gs+0NDeeZ5jZncAIYGlo49luzjM3s2uJlkXy3f1HSecJQWvH1MwuB/oC3YAfuntVhiLmFI1nvNrbeLabMhcRCVnoyywiIu2CylxEJAAqcxGRAKjMRUQCEPp55iIApC6+uRSoJnpk4VDgHqLrCiqBd4DFwAHu/lszu4/oyt7LgVeITkddDZwHdAYudfdP2/Q3IdIMzcylvbgA+C5wZ2r7FqJTz55NXe03romve8LdbwK+SnQ/nmqikt+Ywawiu00zc2kvGl6t2tiVu9uBDqlLvXvstB+iqwIXEc3gLyQ6f/mxDGcWSZvKXNqL36b+KwDOBmYRXfr9m9QSzPNEt3e4iuh+PRWpr5toZqOAuUQz8ylEl3QvbdP0Ii3QRUPSLpjZBKJ78PQGbnb3ZWl8zbGk1tAzm05kz6nMRUQCoB+AiogEQGUuIhIAlbmISABU5iIiAVCZi4gEQGUuIhKA/wdARnyR3KfYBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df0.merge(df4, on=['file_name', 'groups'])\n",
    "df['ratio_d4_to_d0'] = df['counts_d4'] / df['counts_d0']\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "ax = sns.swarmplot(df.groups, df.ratio_d4_to_d0)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.ratio_d4_to_d0, df.groups)\n",
    "\n",
    "# Use different variables to hold counting data by Kaz or Rei\n",
    "df_Kaz = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the count data by Rei\n",
    "\n",
    "datasetPrefix = '20220513-plateI-day0'\n",
    "datasetFolder = '../data/220509-30-for-bud-count-Rei/sg-Itgb1-E16-blast/'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\n",
    "\n",
    "# - For Itgb1 KO\n",
    "# - Plate C, mouse 1, by Shaohe\n",
    "# - D2-D6, LV-188\n",
    "# - D7-D11, LV-401\n",
    "# - E2-E6, LV-402\n",
    "# - E7-E11, LV-403\n",
    "\n",
    "groups = [\"sg-Control\"]*5 + [\"sg1-Itgb1\"]*5 + [\"sg2-Itgb1\"]*5 + [\"sg3-Itgb1\"]*5\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# This is day 0\n",
    "df.rename(columns={'counts': 'counts_d0'}, inplace=True)\n",
    "\n",
    "df0 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the count data by Rei\n",
    "\n",
    "datasetPrefix = '20220517-plateI-day4'\n",
    "datasetFolder = '../data/220509-30-for-bud-count-Rei/sg-Itgb1-E16-blast/'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\n",
    "\n",
    "# - For Itgb1 KO\n",
    "# - Plate C, mouse 1, by Shaohe\n",
    "# - D2-D6, LV-188\n",
    "# - D7-D11, LV-401\n",
    "# - E2-E6, LV-402\n",
    "# - E7-E11, LV-403\n",
    "\n",
    "groups = [\"sg-Control\"]*5 + [\"sg1-Itgb1\"]*5 + [\"sg2-Itgb1\"]*5 + [\"sg3-Itgb1\"]*5\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# This is day 4\n",
    "df.rename(columns={'counts': 'counts_d4'}, inplace=True)\n",
    "\n",
    "df4 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
      "sg-Control sg1-Itgb1  -5.6765 0.0508 -11.3685  0.0155  False\n",
      "sg-Control sg2-Itgb1  -7.0152 0.0134 -12.7073 -1.3232   True\n",
      "sg-Control sg3-Itgb1  -7.3127 0.0099 -13.0048 -1.6207   True\n",
      " sg1-Itgb1 sg2-Itgb1  -1.3387    0.9  -7.0308  4.3533  False\n",
      " sg1-Itgb1 sg3-Itgb1  -1.6362 0.8273  -7.3282  4.0558  False\n",
      " sg2-Itgb1 sg3-Itgb1  -0.2975    0.9  -5.9895  5.3945  False\n",
      "------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.05075264 0.01341764 0.00991211 0.9        0.82726046 0.9       ]\n"
     ]
    },
    {
     "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 = df0.merge(df4, on=['file_name', 'groups'])\n",
    "df['ratio_d4_to_d0'] = df['counts_d4'] / df['counts_d0']\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "ax = sns.swarmplot(df.groups, df.ratio_d4_to_d0)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.ratio_d4_to_d0, df.groups)\n",
    "\n",
    "# Use different variables to hold counting data by Kaz or Rei\n",
    "df_Rei = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>file_name</th>\n",
       "      <th>scrambled_file_name_x_x</th>\n",
       "      <th>counts_d0_Kaz</th>\n",
       "      <th>groups</th>\n",
       "      <th>scrambled_file_name_y_x</th>\n",
       "      <th>counts_d4_Kaz</th>\n",
       "      <th>ratio_d4_to_d0_Kaz</th>\n",
       "      <th>scrambled_file_name_x_y</th>\n",
       "      <th>counts_d0_Rei</th>\n",
       "      <th>scrambled_file_name_y_y</th>\n",
       "      <th>counts_d4_Rei</th>\n",
       "      <th>ratio_d4_to_d0_Rei</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Plate07152021_WellD02_ChannelMono_Seq0000.nd2</td>\n",
       "      <td>346f30cabbdda2ab706a80f5d3d12407.nd2</td>\n",
       "      <td>3</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>069261f8006cfcbb20320bdff2aba1ec.nd2</td>\n",
       "      <td>21</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>346f30cabbdda2ab706a80f5d3d12407.nd2</td>\n",
       "      <td>1</td>\n",
       "      <td>069261f8006cfcbb20320bdff2aba1ec.nd2</td>\n",
       "      <td>20</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plate07152021_WellD03_ChannelMono_Seq0001.nd2</td>\n",
       "      <td>1475877d3d7146aaf4789f735c1e504d.nd2</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>268f675ba180336022880af8c7629f3b.nd2</td>\n",
       "      <td>21</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1475877d3d7146aaf4789f735c1e504d.nd2</td>\n",
       "      <td>3</td>\n",
       "      <td>268f675ba180336022880af8c7629f3b.nd2</td>\n",
       "      <td>17</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Plate07152021_WellD04_ChannelMono_Seq0002.nd2</td>\n",
       "      <td>227a5cc1e88ac7380136473bf9f6b8df.nd2</td>\n",
       "      <td>7</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>c90ce29d4ecf6d98ba9d47b329548fb5.nd2</td>\n",
       "      <td>35</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>227a5cc1e88ac7380136473bf9f6b8df.nd2</td>\n",
       "      <td>4</td>\n",
       "      <td>c90ce29d4ecf6d98ba9d47b329548fb5.nd2</td>\n",
       "      <td>34</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Plate07152021_WellD05_ChannelMono_Seq0003.nd2</td>\n",
       "      <td>284ced130d0cf9ff3537a14be689072b.nd2</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>47cfb993d1e26d3aec78875faf1fa2f3.nd2</td>\n",
       "      <td>17</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>284ced130d0cf9ff3537a14be689072b.nd2</td>\n",
       "      <td>2</td>\n",
       "      <td>47cfb993d1e26d3aec78875faf1fa2f3.nd2</td>\n",
       "      <td>18</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plate07152021_WellD06_ChannelMono_Seq0004.nd2</td>\n",
       "      <td>d55b016654394aed8fb7d54ed8e35cec.nd2</td>\n",
       "      <td>7</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>197e829b537eb7c76f67b7bb70e0d347.nd2</td>\n",
       "      <td>23</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>d55b016654394aed8fb7d54ed8e35cec.nd2</td>\n",
       "      <td>5</td>\n",
       "      <td>197e829b537eb7c76f67b7bb70e0d347.nd2</td>\n",
       "      <td>23</td>\n",
       "      <td>4.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       file_name  \\\n",
       "0  Plate07152021_WellD02_ChannelMono_Seq0000.nd2   \n",
       "1  Plate07152021_WellD03_ChannelMono_Seq0001.nd2   \n",
       "2  Plate07152021_WellD04_ChannelMono_Seq0002.nd2   \n",
       "3  Plate07152021_WellD05_ChannelMono_Seq0003.nd2   \n",
       "4  Plate07152021_WellD06_ChannelMono_Seq0004.nd2   \n",
       "\n",
       "                scrambled_file_name_x_x  counts_d0_Kaz      groups  \\\n",
       "0  346f30cabbdda2ab706a80f5d3d12407.nd2              3  sg-Control   \n",
       "1  1475877d3d7146aaf4789f735c1e504d.nd2              4  sg-Control   \n",
       "2  227a5cc1e88ac7380136473bf9f6b8df.nd2              7  sg-Control   \n",
       "3  284ced130d0cf9ff3537a14be689072b.nd2              4  sg-Control   \n",
       "4  d55b016654394aed8fb7d54ed8e35cec.nd2              7  sg-Control   \n",
       "\n",
       "                scrambled_file_name_y_x  counts_d4_Kaz  ratio_d4_to_d0_Kaz  \\\n",
       "0  069261f8006cfcbb20320bdff2aba1ec.nd2             21            7.000000   \n",
       "1  268f675ba180336022880af8c7629f3b.nd2             21            5.250000   \n",
       "2  c90ce29d4ecf6d98ba9d47b329548fb5.nd2             35            5.000000   \n",
       "3  47cfb993d1e26d3aec78875faf1fa2f3.nd2             17            4.250000   \n",
       "4  197e829b537eb7c76f67b7bb70e0d347.nd2             23            3.285714   \n",
       "\n",
       "                scrambled_file_name_x_y  counts_d0_Rei  \\\n",
       "0  346f30cabbdda2ab706a80f5d3d12407.nd2              1   \n",
       "1  1475877d3d7146aaf4789f735c1e504d.nd2              3   \n",
       "2  227a5cc1e88ac7380136473bf9f6b8df.nd2              4   \n",
       "3  284ced130d0cf9ff3537a14be689072b.nd2              2   \n",
       "4  d55b016654394aed8fb7d54ed8e35cec.nd2              5   \n",
       "\n",
       "                scrambled_file_name_y_y  counts_d4_Rei  ratio_d4_to_d0_Rei  \n",
       "0  069261f8006cfcbb20320bdff2aba1ec.nd2             20           20.000000  \n",
       "1  268f675ba180336022880af8c7629f3b.nd2             17            5.666667  \n",
       "2  c90ce29d4ecf6d98ba9d47b329548fb5.nd2             34            8.500000  \n",
       "3  47cfb993d1e26d3aec78875faf1fa2f3.nd2             18            9.000000  \n",
       "4  197e829b537eb7c76f67b7bb70e0d347.nd2             23            4.600000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two data set and compare the countings of Rei and Kaz\n",
    "df_Kaz.rename(columns={'counts_d0': 'counts_d0_Kaz'}, inplace=True)\n",
    "df_Kaz.rename(columns={'counts_d4': 'counts_d4_Kaz'}, inplace=True)\n",
    "df_Kaz.rename(columns={'ratio_d4_to_d0': 'ratio_d4_to_d0_Kaz'}, inplace=True)\n",
    "\n",
    "df_Rei.rename(columns={'counts_d0': 'counts_d0_Rei'}, inplace=True)\n",
    "df_Rei.rename(columns={'counts_d4': 'counts_d4_Rei'}, inplace=True)\n",
    "df_Rei.rename(columns={'ratio_d4_to_d0': 'ratio_d4_to_d0_Rei'}, inplace=True)\n",
    "\n",
    "df = df_Kaz.merge(df_Rei, on=['file_name', 'groups'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7faf503dc910>]"
      ]
     },
     "execution_count": 22,
     "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": [
    "# plt.plot(df.ratio_d4_to_d0_Kaz, df.ratio_d4_to_d0_Rei, 'ok')\n",
    "plt.plot(df.counts_d4_Kaz, df.counts_d4_Rei, 'ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7faf200645e0>]"
      ]
     },
     "execution_count": 23,
     "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": [
    "# plt.plot(df.ratio_d4_to_d0_Kaz, df.ratio_d4_to_d0_Rei, 'ok')\n",
    "plt.plot(df.counts_d0_Kaz, df.counts_d0_Rei, 'ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['counts_d0'] = (df['counts_d0_Rei'] + df['counts_d0_Kaz']) / 2\n",
    "df['counts_d4'] = (df['counts_d4_Rei'] + df['counts_d4_Kaz']) / 2\n",
    "df['ratio_d4_to_d0'] = df['counts_d4'] / df['counts_d0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
      "sg-Control sg1-Itgb1  -3.1633 0.0066 -5.4997 -0.8268   True\n",
      "sg-Control sg2-Itgb1  -4.1438  0.001 -6.4803 -1.8073   True\n",
      "sg-Control sg3-Itgb1   -4.029  0.001 -6.3655 -1.6926   True\n",
      " sg1-Itgb1 sg2-Itgb1  -0.9805 0.6234  -3.317  1.3559  False\n",
      " sg1-Itgb1 sg3-Itgb1  -0.8658 0.6991 -3.2023  1.4707  False\n",
      " sg2-Itgb1 sg3-Itgb1   0.1148    0.9 -2.2217  2.4512  False\n",
      "-----------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00662687 0.001      0.001      0.62338528 0.69912088 0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7faf20089b50>"
      ]
     },
     "execution_count": 25,
     "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": [
    "# Inspect raw data if curious about how they look\n",
    "ax = sns.swarmplot(df.groups, df.ratio_d4_to_d0)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.ratio_d4_to_d0, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 1.2, 2.0 # inches\n",
    "\n",
    "line_width = .8\n",
    "output_prefix = '220517-plateI-E16-d4-d0-ratio-Itgb1'\n",
    "outputFigPath = '../jupyter_figures/' + output_prefix + '.svg'\n",
    "# outputFigPath = '../jupyter_figures/' + output_prefix + '.tif'\n",
    "\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "# ax = fig.add_axes([0.28, 0.35, 0.67, 0.6])\n",
    "ax = fig.add_axes([0.35, 0.4, 0.6, 0.55])\n",
    "\n",
    "# palette_dict = {'sg-Control': 'black', 'sg1-Itgb1': 'blue', 'sg2-Itgb1': 'red'}\n",
    "\n",
    "sns.swarmplot(data = df,\n",
    "              x = 'groups', y = 'ratio_d4_to_d0',\n",
    "              color = 'blue',\n",
    "              alpha = 0.3,\n",
    "              size = 1.5\n",
    "             )\n",
    "sns.barplot(data = df,\n",
    "            x = 'groups', y = 'ratio_d4_to_d0',\n",
    "            ci = 'sd',\n",
    "            color=\".7\", # This a nicer looking lighter gray to my own taste\n",
    "            errwidth=.7, errcolor=\"k\", capsize=.2)\n",
    "\n",
    "change_bar_width(ax, .5)\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "x_labels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "ax.set_yticks([0,5,10])\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Bud ratio\")\n",
    "\n",
    "# ax.get_legend().remove()\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",
    "plt.savefig(outputFigPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}