SMG-bud-count-decoding-plotting-211022-25.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",
    "from sw_plotting import change_bar_width, plotCountBar\n",
    "from sw_utilities import tukeyTest, getCountDFbyPrefix\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the raw data sets to plot\n",
    "\n",
    "datasetPrefix = \"phase-z-to-split-splitPositions\"\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder='../data/211022-25-E13-Cas9-bud-LV-188-401-402-phase-contrast/')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotate the data set\n",
    "#\n",
    "# sg-Control: LV-188\n",
    "# sg1-Itgb1: LV-401\n",
    "# sg2-Itgb1: LV-402\n",
    "\n",
    "# groups = [\"1\"]*10 + [\"2\"]*10 + [\"3\"]*10 + \\\n",
    "#          [\"4\"]*10 + [\"5\"]*10 + [\"6\"]*10 + \\\n",
    "#          [\"7\"]*9 + [\"8\"]*9 + [\"9\"]*9\n",
    "# df[\"groups\"] = groups\n",
    "\n",
    "sgRNA = [\"sg-Control\"]*30 + [\"sg1-Itgb1\"]*30 + [\"sg2-Itgb1\"]*27\n",
    "df[\"sgRNA\"] = sgRNA\n",
    "\n",
    "treatment = ([\"NA\"]*7 + [\"blasticidin\"]*3)*3 + \\\n",
    "            ([\"NA\"]*7 + [\"blasticidin\"]*3)*3 + \\\n",
    "            ([\"NA\"]*6 + [\"blasticidin\"]*3)*3\n",
    "df[\"treatment\"] = treatment\n",
    "\n",
    "time_h = [\"23\"]*10 + [\"43\"]*10 + [\"71\"]*10 + \\\n",
    "         [\"23\"]*10 + [\"43\"]*10 + [\"71\"]*10 + \\\n",
    "         [\"23\"]*9 + [\"43\"]*9 + [\"71\"]*9\n",
    "df[\"time_h\"] = time_h\n",
    "\n",
    "# change time_h to integer\n",
    "df = df.astype({'time_h': 'int64'})\n",
    "\n",
    "sample_id = list(range(1,11))*3 + list(range(11,21))*3 + list(range(21,30))*3\n",
    "df[\"sample_id\"] = sample_id\n",
    "\n",
    "groups = [sgRNA[i]+'_'+time_h[i]+'_'+treatment[i] for i in range(len(sgRNA))]\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "sg_treatment = [sgRNA[i]+'_'+treatment[i] for i in range(len(sgRNA))]\n",
    "df[\"sg_treatment\"] = sg_treatment\n",
    "\n",
    "# # Inspect raw data if curious about how they look\n",
    "# sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# # Perform a Tukey test across groups\n",
    "# tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_NA = df[df.treatment=='NA']\n",
    "df_blast = df[df.treatment=='blasticidin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 1.0, 1.2 # inches\n",
    "line_width = .8\n",
    "output_prefix = '211022-25-E13-Cas9-bud-no-blast'\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([0.35, 0.3, 0.6, 0.65])\n",
    "\n",
    "palette_dict = {'sg-Control': 'black', 'sg1-Itgb1': 'blue', 'sg2-Itgb1': 'red'}\n",
    "\n",
    "sns.lineplot(data = df_NA,\n",
    "             x = 'time_h', y = 'counts', hue = 'sgRNA',\n",
    "             size = line_width+.2,\n",
    "             palette = palette_dict,\n",
    "             ci = 'sd',\n",
    "             err_style = \"bars\",\n",
    "             err_kws = {'capsize': 2,\n",
    "                        'elinewidth': line_width,\n",
    "                        'capthick': line_width}\n",
    "            )\n",
    "\n",
    "y_max = 22\n",
    "plt.ylim([0,y_max])\n",
    "plt.yticks(np.arange(0,y_max,5))\n",
    "\n",
    "plt.xlim([12,80])\n",
    "plt.xticks([23,43,71])\n",
    "\n",
    "plt.xlabel(\"Time (h)\")\n",
    "plt.ylabel(\"Bud count\")\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 1.0, 1.2 # inches\n",
    "line_width = .8\n",
    "output_prefix = '211022-25-E13-Cas9-bud-with-blast'\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([0.35, 0.3, 0.6, 0.65])\n",
    "\n",
    "palette_dict = {'sg-Control': 'black', 'sg1-Itgb1': 'blue', 'sg2-Itgb1': 'red'}\n",
    "\n",
    "sns.lineplot(data = df_blast,\n",
    "             x = 'time_h', y = 'counts', hue = 'sgRNA',\n",
    "             size = line_width+.2,\n",
    "             palette = palette_dict,\n",
    "#              err_style = 'band',\n",
    "             ci = 'sd',\n",
    "             err_style = \"bars\",\n",
    "             err_kws = {'capsize': 2,\n",
    "                        'elinewidth': line_width,\n",
    "                        'capthick': line_width}\n",
    "            )\n",
    "\n",
    "y_max = 32\n",
    "plt.ylim([0,y_max])\n",
    "plt.yticks(np.arange(0,y_max,10))\n",
    "\n",
    "plt.xlim([12,80])\n",
    "plt.xticks([23,43,71])\n",
    "\n",
    "plt.xlabel(\"Time (h)\")\n",
    "plt.ylabel(\"Bud count\")\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": 7,
   "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>file_name</th>\n",
       "      <th>scrambled_file_name</th>\n",
       "      <th>counts</th>\n",
       "      <th>sgRNA</th>\n",
       "      <th>treatment</th>\n",
       "      <th>time_h</th>\n",
       "      <th>sample_id</th>\n",
       "      <th>groups</th>\n",
       "      <th>sg_treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LV-188-D02-D11-23h-z-Ph-00.tif</td>\n",
       "      <td>4ea9ca61f133ff2eeb6d4a95be8e8725.tif</td>\n",
       "      <td>7</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>NA</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_23_NA</td>\n",
       "      <td>sg-Control_NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LV-188-D02-D11-23h-z-Ph-01.tif</td>\n",
       "      <td>d19cf2132198299b70047b211c6559a5.tif</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>NA</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>sg-Control_23_NA</td>\n",
       "      <td>sg-Control_NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LV-188-D02-D11-23h-z-Ph-02.tif</td>\n",
       "      <td>7de296dfa4ae0b494225773bce4a55f8.tif</td>\n",
       "      <td>6</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>NA</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>sg-Control_23_NA</td>\n",
       "      <td>sg-Control_NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LV-188-D02-D11-23h-z-Ph-03.tif</td>\n",
       "      <td>4e9c3e22a59c02d7e7fbc7b5e8b37991.tif</td>\n",
       "      <td>7</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>NA</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control_23_NA</td>\n",
       "      <td>sg-Control_NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LV-188-D02-D11-23h-z-Ph-04.tif</td>\n",
       "      <td>3225048ff43f29e2defce0a771e5ef8f.tif</td>\n",
       "      <td>8</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>NA</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>sg-Control_23_NA</td>\n",
       "      <td>sg-Control_NA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        file_name                   scrambled_file_name  \\\n",
       "0  LV-188-D02-D11-23h-z-Ph-00.tif  4ea9ca61f133ff2eeb6d4a95be8e8725.tif   \n",
       "1  LV-188-D02-D11-23h-z-Ph-01.tif  d19cf2132198299b70047b211c6559a5.tif   \n",
       "2  LV-188-D02-D11-23h-z-Ph-02.tif  7de296dfa4ae0b494225773bce4a55f8.tif   \n",
       "3  LV-188-D02-D11-23h-z-Ph-03.tif  4e9c3e22a59c02d7e7fbc7b5e8b37991.tif   \n",
       "4  LV-188-D02-D11-23h-z-Ph-04.tif  3225048ff43f29e2defce0a771e5ef8f.tif   \n",
       "\n",
       "   counts       sgRNA treatment  time_h  sample_id            groups  \\\n",
       "0       7  sg-Control        NA      23          1  sg-Control_23_NA   \n",
       "1       4  sg-Control        NA      23          2  sg-Control_23_NA   \n",
       "2       6  sg-Control        NA      23          3  sg-Control_23_NA   \n",
       "3       7  sg-Control        NA      23          4  sg-Control_23_NA   \n",
       "4       8  sg-Control        NA      23          5  sg-Control_23_NA   \n",
       "\n",
       "    sg_treatment  \n",
       "0  sg-Control_NA  \n",
       "1  sg-Control_NA  \n",
       "2  sg-Control_NA  \n",
       "3  sg-Control_NA  \n",
       "4  sg-Control_NA  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the bud ratios\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_23h = df[df.time_h==23]\n",
    "df_23h.reset_index(inplace=True)\n",
    "\n",
    "df_43h = df[df.time_h==43]\n",
    "df_43h.reset_index(inplace=True)\n",
    "\n",
    "df_71h = df[df.time_h==71]\n",
    "df_71h.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratios = df_43h.counts / df_23h.counts\n",
    "df_71h['ratios_d2_to_d1'] = ratios\n",
    "\n",
    "ratios = df_71h.counts / df_43h.counts\n",
    "df_71h['ratios_d3_to_d2'] = ratios\n",
    "\n",
    "ratios = df_71h.counts / df_23h.counts\n",
    "df_71h['ratios_d3_to_d1'] = ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_71h_NA = df_71h[df_71h.treatment=='NA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 1.2, 1.5 # inches\n",
    "line_width = .8\n",
    "output_prefix = '211022-25-E13-Cas9-bud-ratios-d3-d2-no-blast'\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",
    "\n",
    "palette_dict = {'sg-Control': 'black', 'sg1-Itgb1': 'blue', 'sg2-Itgb1': 'red'}\n",
    "\n",
    "sns.swarmplot(data = df_71h_NA,\n",
    "              x = 'sgRNA', y = 'ratios_d3_to_d2', hue = 'sgRNA',\n",
    "              size = 2,\n",
    "              palette = palette_dict,\n",
    "             )\n",
    "sns.barplot(data = df_71h_NA,\n",
    "            x = 'sgRNA', y = 'ratios_d3_to_d2',\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",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Bud ratio\")\n",
    "\n",
    "ax.get_legend().remove()\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",
    "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 1.2, 1.5 # inches\n",
    "line_width = .8\n",
    "output_prefix = '211022-25-E13-Cas9-bud-ratios-d3-d1-no-blast'\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",
    "\n",
    "palette_dict = {'sg-Control': 'black', 'sg1-Itgb1': 'blue', 'sg2-Itgb1': 'red'}\n",
    "\n",
    "sns.swarmplot(data = df_71h_NA,\n",
    "              x = 'sgRNA', y = 'ratios_d3_to_d1', hue = 'sgRNA',\n",
    "              size = 2,\n",
    "              palette = palette_dict,\n",
    "             )\n",
    "sns.barplot(data = df_71h_NA,\n",
    "            x = 'sgRNA', y = 'ratios_d3_to_d1',\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",
    "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": 13,
   "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  -0.5228 0.2478 -1.3292  0.2836  False\n",
      "sg-Control sg2-Itgb1  -1.0408 0.0143 -1.8802 -0.2015   True\n",
      " sg1-Itgb1 sg2-Itgb1  -0.5181 0.2798 -1.3574  0.3213  False\n",
      "-----------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.24784029 0.01432459 0.27979362]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7ff1d007c040>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_71h_NA.ratios_d3_to_d1, df_71h_NA.sgRNA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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_23_NA sg-Control_43_NA   3.1429 0.0542   -0.031  6.3167  False\n",
      "sg-Control_23_NA sg-Control_71_NA   7.8571  0.001   4.6833  11.031   True\n",
      "sg-Control_23_NA  sg1-Itgb1_23_NA   0.7143    0.9  -2.4596  3.8881  False\n",
      "sg-Control_23_NA  sg1-Itgb1_43_NA   2.8571 0.1092  -0.3167   6.031  False\n",
      "sg-Control_23_NA  sg1-Itgb1_71_NA      6.0  0.001   2.8262  9.1738   True\n",
      "sg-Control_23_NA  sg2-Itgb1_23_NA  -0.0238    0.9  -3.3272  3.2796  False\n",
      "sg-Control_23_NA  sg2-Itgb1_43_NA   1.3095    0.9  -1.9939   4.613  False\n",
      "sg-Control_23_NA  sg2-Itgb1_71_NA   1.6429 0.7696  -1.6606  4.9463  False\n",
      "sg-Control_43_NA sg-Control_71_NA   4.7143  0.001   1.5404  7.8881   True\n",
      "sg-Control_43_NA  sg1-Itgb1_23_NA  -2.4286 0.2668  -5.6024  0.7453  False\n",
      "sg-Control_43_NA  sg1-Itgb1_43_NA  -0.2857    0.9  -3.4596  2.8881  False\n",
      "sg-Control_43_NA  sg1-Itgb1_71_NA   2.8571 0.1092  -0.3167   6.031  False\n",
      "sg-Control_43_NA  sg2-Itgb1_23_NA  -3.1667 0.0701  -6.4701  0.1368  False\n",
      "sg-Control_43_NA  sg2-Itgb1_43_NA  -1.8333 0.6615  -5.1368  1.4701  False\n",
      "sg-Control_43_NA  sg2-Itgb1_71_NA     -1.5 0.8508  -4.8034  1.8034  False\n",
      "sg-Control_71_NA  sg1-Itgb1_23_NA  -7.1429  0.001 -10.3167  -3.969   True\n",
      "sg-Control_71_NA  sg1-Itgb1_43_NA     -5.0  0.001  -8.1738 -1.8262   True\n",
      "sg-Control_71_NA  sg1-Itgb1_71_NA  -1.8571 0.6049   -5.031  1.3167  False\n",
      "sg-Control_71_NA  sg2-Itgb1_23_NA   -7.881  0.001 -11.1844 -4.5775   True\n",
      "sg-Control_71_NA  sg2-Itgb1_43_NA  -6.5476  0.001  -9.8511 -3.2442   True\n",
      "sg-Control_71_NA  sg2-Itgb1_71_NA  -6.2143  0.001  -9.5177 -2.9109   True\n",
      " sg1-Itgb1_23_NA  sg1-Itgb1_43_NA   2.1429 0.4324   -1.031  5.3167  False\n",
      " sg1-Itgb1_23_NA  sg1-Itgb1_71_NA   5.2857  0.001   2.1119  8.4596   True\n",
      " sg1-Itgb1_23_NA  sg2-Itgb1_23_NA  -0.7381    0.9  -4.0415  2.5653  False\n",
      " sg1-Itgb1_23_NA  sg2-Itgb1_43_NA   0.5952    0.9  -2.7082  3.8987  False\n",
      " sg1-Itgb1_23_NA  sg2-Itgb1_71_NA   0.9286    0.9  -2.3749   4.232  False\n",
      " sg1-Itgb1_43_NA  sg1-Itgb1_71_NA   3.1429 0.0542   -0.031  6.3167  False\n",
      " sg1-Itgb1_43_NA  sg2-Itgb1_23_NA   -2.881 0.1331  -6.1844  0.4225  False\n",
      " sg1-Itgb1_43_NA  sg2-Itgb1_43_NA  -1.5476 0.8237  -4.8511  1.7558  False\n",
      " sg1-Itgb1_43_NA  sg2-Itgb1_71_NA  -1.2143    0.9  -4.5177  2.0891  False\n",
      " sg1-Itgb1_71_NA  sg2-Itgb1_23_NA  -6.0238  0.001  -9.3272 -2.7204   True\n",
      " sg1-Itgb1_71_NA  sg2-Itgb1_43_NA  -4.6905  0.001  -7.9939  -1.387   True\n",
      " sg1-Itgb1_71_NA  sg2-Itgb1_71_NA  -4.3571 0.0025  -7.6606 -1.0537   True\n",
      " sg2-Itgb1_23_NA  sg2-Itgb1_43_NA   1.3333    0.9  -2.0948  4.7615  False\n",
      " sg2-Itgb1_23_NA  sg2-Itgb1_71_NA   1.6667 0.7905  -1.7615  5.0948  False\n",
      " sg2-Itgb1_43_NA  sg2-Itgb1_71_NA   0.3333    0.9  -3.0948  3.7615  False\n",
      "-------------------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.0542145  0.001      0.9        0.1091716  0.001      0.9\n",
      " 0.9        0.76963792 0.001      0.26684379 0.9        0.1091716\n",
      " 0.0700703  0.66147335 0.85075759 0.001      0.001      0.604891\n",
      " 0.001      0.001      0.001      0.43244761 0.001      0.9\n",
      " 0.9        0.9        0.0542145  0.13311147 0.82371792 0.9\n",
      " 0.001      0.001      0.00254542 0.9        0.79054456 0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7ff21025da60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_NA.counts, df_NA.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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_23_blasticidin sg-Control_43_blasticidin   3.6667 0.6505  -3.3841 10.7175  False\n",
      "sg-Control_23_blasticidin sg-Control_71_blasticidin  12.6667  0.001   5.6159 19.7175   True\n",
      "sg-Control_23_blasticidin  sg1-Itgb1_23_blasticidin  -1.6667    0.9  -8.7175  5.3841  False\n",
      "sg-Control_23_blasticidin  sg1-Itgb1_43_blasticidin      0.0    0.9  -7.0508  7.0508  False\n",
      "sg-Control_23_blasticidin  sg1-Itgb1_71_blasticidin      3.0 0.8276  -4.0508 10.0508  False\n",
      "sg-Control_23_blasticidin  sg2-Itgb1_23_blasticidin  -1.6667    0.9  -8.7175  5.3841  False\n",
      "sg-Control_23_blasticidin  sg2-Itgb1_43_blasticidin      0.0    0.9  -7.0508  7.0508  False\n",
      "sg-Control_23_blasticidin  sg2-Itgb1_71_blasticidin  -0.6667    0.9  -7.7175  6.3841  False\n",
      "sg-Control_43_blasticidin sg-Control_71_blasticidin      9.0  0.007   1.9492 16.0508   True\n",
      "sg-Control_43_blasticidin  sg1-Itgb1_23_blasticidin  -5.3333 0.2329 -12.3841  1.7175  False\n",
      "sg-Control_43_blasticidin  sg1-Itgb1_43_blasticidin  -3.6667 0.6505 -10.7175  3.3841  False\n",
      "sg-Control_43_blasticidin  sg1-Itgb1_71_blasticidin  -0.6667    0.9  -7.7175  6.3841  False\n",
      "sg-Control_43_blasticidin  sg2-Itgb1_23_blasticidin  -5.3333 0.2329 -12.3841  1.7175  False\n",
      "sg-Control_43_blasticidin  sg2-Itgb1_43_blasticidin  -3.6667 0.6505 -10.7175  3.3841  False\n",
      "sg-Control_43_blasticidin  sg2-Itgb1_71_blasticidin  -4.3333  0.473 -11.3841  2.7175  False\n",
      "sg-Control_71_blasticidin  sg1-Itgb1_23_blasticidin -14.3333  0.001 -21.3841 -7.2825   True\n",
      "sg-Control_71_blasticidin  sg1-Itgb1_43_blasticidin -12.6667  0.001 -19.7175 -5.6159   True\n",
      "sg-Control_71_blasticidin  sg1-Itgb1_71_blasticidin  -9.6667 0.0035 -16.7175 -2.6159   True\n",
      "sg-Control_71_blasticidin  sg2-Itgb1_23_blasticidin -14.3333  0.001 -21.3841 -7.2825   True\n",
      "sg-Control_71_blasticidin  sg2-Itgb1_43_blasticidin -12.6667  0.001 -19.7175 -5.6159   True\n",
      "sg-Control_71_blasticidin  sg2-Itgb1_71_blasticidin -13.3333  0.001 -20.3841 -6.2825   True\n",
      " sg1-Itgb1_23_blasticidin  sg1-Itgb1_43_blasticidin   1.6667    0.9  -5.3841  8.7175  False\n",
      " sg1-Itgb1_23_blasticidin  sg1-Itgb1_71_blasticidin   4.6667 0.3823  -2.3841 11.7175  False\n",
      " sg1-Itgb1_23_blasticidin  sg2-Itgb1_23_blasticidin      0.0    0.9  -7.0508  7.0508  False\n",
      " sg1-Itgb1_23_blasticidin  sg2-Itgb1_43_blasticidin   1.6667    0.9  -5.3841  8.7175  False\n",
      " sg1-Itgb1_23_blasticidin  sg2-Itgb1_71_blasticidin      1.0    0.9  -6.0508  8.0508  False\n",
      " sg1-Itgb1_43_blasticidin  sg1-Itgb1_71_blasticidin      3.0 0.8276  -4.0508 10.0508  False\n",
      " sg1-Itgb1_43_blasticidin  sg2-Itgb1_23_blasticidin  -1.6667    0.9  -8.7175  5.3841  False\n",
      " sg1-Itgb1_43_blasticidin  sg2-Itgb1_43_blasticidin      0.0    0.9  -7.0508  7.0508  False\n",
      " sg1-Itgb1_43_blasticidin  sg2-Itgb1_71_blasticidin  -0.6667    0.9  -7.7175  6.3841  False\n",
      " sg1-Itgb1_71_blasticidin  sg2-Itgb1_23_blasticidin  -4.6667 0.3823 -11.7175  2.3841  False\n",
      " sg1-Itgb1_71_blasticidin  sg2-Itgb1_43_blasticidin     -3.0 0.8276 -10.0508  4.0508  False\n",
      " sg1-Itgb1_71_blasticidin  sg2-Itgb1_71_blasticidin  -3.6667 0.6505 -10.7175  3.3841  False\n",
      " sg2-Itgb1_23_blasticidin  sg2-Itgb1_43_blasticidin   1.6667    0.9  -5.3841  8.7175  False\n",
      " sg2-Itgb1_23_blasticidin  sg2-Itgb1_71_blasticidin      1.0    0.9  -6.0508  8.0508  False\n",
      " sg2-Itgb1_43_blasticidin  sg2-Itgb1_71_blasticidin  -0.6667    0.9  -7.7175  6.3841  False\n",
      "-------------------------------------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.6504584  0.001      0.9        0.9        0.82764555 0.9\n",
      " 0.9        0.9        0.00699635 0.23290921 0.6504584  0.9\n",
      " 0.23290921 0.6504584  0.47297169 0.001      0.001      0.00352237\n",
      " 0.001      0.001      0.001      0.9        0.38229887 0.9\n",
      " 0.9        0.9        0.82764555 0.9        0.9        0.9\n",
      " 0.38229887 0.82764555 0.6504584  0.9        0.9        0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7ff242776700>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_blast.counts, df_blast.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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