SMG-bud-count-Serpinh1-Rei-Kaz.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, 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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the count data by Kaz\n",
    "\n",
    "datasetFolder = '../data/220509-30-for-bud-count-Kaz/sg-Serpinh1/'\n",
    "\n",
    "files = glob.glob(os.path.join(datasetFolder, '*fileNameRecord.txt'))\n",
    "\n",
    "datasetPrefixes = []\n",
    "for f in files:\n",
    "    datasetPrefixes.append('-'.join(os.path.basename(f).split('-')[:3]))\n",
    "\n",
    "sgRNAs = [\"sg-Control\"]*5 + [\"sg1-Serpinh1\"]*5 + [\"sg2-Serpinh1\"]*5 + [\"sg3-Serpinh1\"]*5\n",
    "dfs = []\n",
    "for datasetPrefix in datasetPrefixes:\n",
    "    dfs.append( get_annotated_countDF(datasetPrefix, datasetFolder, sgRNAs) )\n",
    "\n",
    "df = pd.concat(dfs)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df_Kaz = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the count data by Rei\n",
    "\n",
    "datasetFolder = '../data/220509-30-for-bud-count-Rei/sg-Serpinh1/'\n",
    "\n",
    "files = glob.glob(os.path.join(datasetFolder, '*fileNameRecord.txt'))\n",
    "\n",
    "datasetPrefixes = []\n",
    "for f in files:\n",
    "    datasetPrefixes.append('-'.join(os.path.basename(f).split('-')[:3]))\n",
    "\n",
    "sgRNAs = [\"sg-Control\"]*5 + [\"sg1-Serpinh1\"]*5 + [\"sg2-Serpinh1\"]*5 + [\"sg3-Serpinh1\"]*5\n",
    "dfs = []\n",
    "for datasetPrefix in datasetPrefixes:\n",
    "    dfs.append( get_annotated_countDF(datasetPrefix, datasetFolder, sgRNAs) )\n",
    "\n",
    "df = pd.concat(dfs)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df_Rei = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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_Kaz</th>\n",
       "      <th>sample_id</th>\n",
       "      <th>sgRNA</th>\n",
       "      <th>time_d</th>\n",
       "      <th>groups</th>\n",
       "      <th>counts_Rei</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Plate07152021_WellD02_ChannelMono_Seq0000.nd2</td>\n",
       "      <td>669d18242fcdaf1429a422fc4e401af6.nd2</td>\n",
       "      <td>39</td>\n",
       "      <td>plateA_4_01</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control_4</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plate07152021_WellD03_ChannelMono_Seq0001.nd2</td>\n",
       "      <td>bf1d157d911c44a0642fbc41c639b031.nd2</td>\n",
       "      <td>37</td>\n",
       "      <td>plateA_4_02</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control_4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Plate07152021_WellD04_ChannelMono_Seq0002.nd2</td>\n",
       "      <td>5fce83b0645448351e9b0488c1e3c166.nd2</td>\n",
       "      <td>41</td>\n",
       "      <td>plateA_4_03</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control_4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Plate07152021_WellD05_ChannelMono_Seq0003.nd2</td>\n",
       "      <td>7768efe88f11214422878ef25babcce4.nd2</td>\n",
       "      <td>32</td>\n",
       "      <td>plateA_4_04</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control_4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plate07152021_WellD06_ChannelMono_Seq0004.nd2</td>\n",
       "      <td>dc148d84f899a1559723595be1c2b7e4.nd2</td>\n",
       "      <td>34</td>\n",
       "      <td>plateA_4_05</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>4</td>\n",
       "      <td>sg-Control_4</td>\n",
       "      <td>37</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  counts_Kaz    sample_id       sgRNA  \\\n",
       "0  669d18242fcdaf1429a422fc4e401af6.nd2          39  plateA_4_01  sg-Control   \n",
       "1  bf1d157d911c44a0642fbc41c639b031.nd2          37  plateA_4_02  sg-Control   \n",
       "2  5fce83b0645448351e9b0488c1e3c166.nd2          41  plateA_4_03  sg-Control   \n",
       "3  7768efe88f11214422878ef25babcce4.nd2          32  plateA_4_04  sg-Control   \n",
       "4  dc148d84f899a1559723595be1c2b7e4.nd2          34  plateA_4_05  sg-Control   \n",
       "\n",
       "   time_d        groups  counts_Rei  \n",
       "0       4  sg-Control_4          43  \n",
       "1       4  sg-Control_4          35  \n",
       "2       4  sg-Control_4          42  \n",
       "3       4  sg-Control_4          33  \n",
       "4       4  sg-Control_4          37  "
      ]
     },
     "execution_count": 5,
     "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': 'counts_Kaz'}, inplace=True)\n",
    "df_Rei.rename(columns={'counts': 'counts_Rei'}, inplace=True)\n",
    "df = df_Kaz.merge(df_Rei, on=['file_name',\n",
    "                              'scrambled_file_name',\n",
    "                              'sample_id',\n",
    "                              'sgRNA',\n",
    "                              'time_d',\n",
    "                              'groups'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate average counting data\n",
    "df['counts'] = (df['counts_Rei'] + df['counts_Kaz']) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 0.8, 1.2 # inches\n",
    "\n",
    "line_width = .8\n",
    "output_prefix = '220516-plateAD-E13-d4-sg-Serpinh1'\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([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "sns.swarmplot(data = df,\n",
    "              x = 'sgRNA', y = 'counts',\n",
    "              color = 'blue',\n",
    "              alpha = 0.3,\n",
    "              size = 1.5\n",
    "             )\n",
    "sns.barplot(data = df,\n",
    "            x = 'sgRNA', y = 'counts',\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",
    "plt.ylim(0, 72)\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 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": 10,
   "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-Serpinh1     -8.9 0.0995 -18.9758  1.1758  False\n",
      "  sg-Control sg2-Serpinh1    -4.55 0.6083 -14.6258  5.5258  False\n",
      "  sg-Control sg3-Serpinh1     -6.2 0.3613 -16.2758  3.8758  False\n",
      "sg1-Serpinh1 sg2-Serpinh1     4.35 0.6378  -5.7258 14.4258  False\n",
      "sg1-Serpinh1 sg3-Serpinh1      2.7 0.8814  -7.3758 12.7758  False\n",
      "sg2-Serpinh1 sg3-Serpinh1    -1.65    0.9 -11.7258  8.4258  False\n",
      "-----------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.09949495 0.60830769 0.36127151 0.63782576 0.88135394 0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f96189fd160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.counts, df.sgRNA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sg-Control n = 10\n",
      "sg1-Serpinh1 n = 10\n",
      "sg2-Serpinh1 n = 10\n",
      "sg3-Serpinh1 n = 10\n"
     ]
    }
   ],
   "source": [
    "for i in df.sgRNA.unique():\n",
    "    print(i, 'n =', len(df[df.sgRNA==i]))"
   ]
  },
  {
   "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
}