SMG-bud-count-Itgb1-plate-i-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\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",
    "datasetPrefix = '20220530-plate-i-day4-ph-z-endpoint'\n",
    "datasetFolder = '../data/220509-30-for-bud-count-Kaz/count-all-planes/'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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     -9.2 0.0157 -16.8348 -1.5652   True\n",
      "sg-Control sg2-Itgb1    -11.6 0.0025 -19.2348 -3.9652   True\n",
      "sg-Control sg3-Itgb1     -9.8   0.01 -17.4348 -2.1652   True\n",
      " sg1-Itgb1 sg2-Itgb1     -2.4 0.7858 -10.0348  5.2348  False\n",
      " sg1-Itgb1 sg3-Itgb1     -0.6    0.9  -8.2348  7.0348  False\n",
      " sg2-Itgb1 sg3-Itgb1      1.8    0.9  -5.8348  9.4348  False\n",
      "------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.01572119 0.00253016 0.00997651 0.78578634 0.9        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": [
    "# - For Itgb1 KO, plates i set up on 5/26/22\n",
    "# - Plate i\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",
    "# Inspect raw data if curious about how they look\n",
    "ax = sns.swarmplot(df.groups, df.counts)\n",
    "# ax.set_ylim([0,50])\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n",
    "\n",
    "# Use different variables to hold counting data by Kaz or Rei\n",
    "df_Kaz = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the count data by Rei\n",
    "\n",
    "datasetPrefix = '20220530-plate-i-day4-ph-z-endpoint'\n",
    "datasetFolder = '../data/220509-30-for-bud-count-Rei/count-all-planes/'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\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",
      "sg-Control sg1-Itgb1     -7.2 0.1472 -16.2797  1.8797  False\n",
      "sg-Control sg2-Itgb1    -11.6 0.0103 -20.6797 -2.5203   True\n",
      "sg-Control sg3-Itgb1    -10.8 0.0172 -19.8797 -1.7203   True\n",
      " sg1-Itgb1 sg2-Itgb1     -4.4 0.5232 -13.4797  4.6797  False\n",
      " sg1-Itgb1 sg3-Itgb1     -3.6 0.6591 -12.6797  5.4797  False\n",
      " sg2-Itgb1 sg3-Itgb1      0.8    0.9  -8.2797  9.8797  False\n",
      "------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.14724008 0.01033211 0.01719212 0.52324742 0.65911974 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": [
    "# - For Itgb1 KO, plates i set up on 5/26/22\n",
    "# - Plate i\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",
    "# Inspect raw data if curious about how they look\n",
    "ax = sns.swarmplot(df.groups, df.counts)\n",
    "# ax.set_ylim([0,50])\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n",
    "\n",
    "# Use different variables to hold counting data by Kaz or Rei\n",
    "df_Rei = df"
   ]
  },
  {
   "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_Kaz</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-reta...</td>\n",
       "      <td>52cb8992486bc3e11910ba1b2c18a5fc.nd2</td>\n",
       "      <td>25</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plate07152021_WellD03_ChannelMono_Seq0001.nd2</td>\n",
       "      <td>5a893865076ef87475dffc85634777ca.nd2</td>\n",
       "      <td>13</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Plate07152021_WellD04_ChannelMono_Seq0000-reta...</td>\n",
       "      <td>02f37e5e19b113057f27bc7a92883f87.nd2</td>\n",
       "      <td>26</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Plate07152021_WellD05_ChannelMono_Seq0003.nd2</td>\n",
       "      <td>791bc431f12678cb46fa404fbe07f72b.nd2</td>\n",
       "      <td>25</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Plate07152021_WellD06_ChannelMono_Seq0004.nd2</td>\n",
       "      <td>3e220e8f458f2ae9d8c8e793a7bf9bca.nd2</td>\n",
       "      <td>20</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           file_name  \\\n",
       "0  Plate07152021_WellD02_ChannelMono_Seq0000-reta...   \n",
       "1      Plate07152021_WellD03_ChannelMono_Seq0001.nd2   \n",
       "2  Plate07152021_WellD04_ChannelMono_Seq0000-reta...   \n",
       "3      Plate07152021_WellD05_ChannelMono_Seq0003.nd2   \n",
       "4      Plate07152021_WellD06_ChannelMono_Seq0004.nd2   \n",
       "\n",
       "                    scrambled_file_name  counts_Kaz      groups  counts_Rei  \n",
       "0  52cb8992486bc3e11910ba1b2c18a5fc.nd2          25  sg-Control          31  \n",
       "1  5a893865076ef87475dffc85634777ca.nd2          13  sg-Control          16  \n",
       "2  02f37e5e19b113057f27bc7a92883f87.nd2          26  sg-Control          19  \n",
       "3  791bc431f12678cb46fa404fbe07f72b.nd2          25  sg-Control          20  \n",
       "4  3e220e8f458f2ae9d8c8e793a7bf9bca.nd2          20  sg-Control          14  "
      ]
     },
     "execution_count": 7,
     "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', 'scrambled_file_name', 'groups'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9a300b9ad0>]"
      ]
     },
     "execution_count": 8,
     "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.counts_Kaz, df.counts_Rei, 'ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['counts'] = (df['counts_Rei'] + df['counts_Kaz']) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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     -8.2 0.0341 -15.8709 -0.5291   True\n",
      "sg-Control sg2-Itgb1    -11.6 0.0026 -19.2709 -3.9291   True\n",
      "sg-Control sg3-Itgb1    -10.3 0.0071 -17.9709 -2.6291   True\n",
      " sg1-Itgb1 sg2-Itgb1     -3.4  0.587 -11.0709  4.2709  False\n",
      " sg1-Itgb1 sg3-Itgb1     -2.1 0.8484  -9.7709  5.5709  False\n",
      " sg2-Itgb1 sg3-Itgb1      1.3    0.9  -6.3709  8.9709  False\n",
      "------------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.03410873 0.00263857 0.00707177 0.58703271 0.84838166 0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f9a690138d0>"
      ]
     },
     "execution_count": 11,
     "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\n",
    "ax = 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": 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 = '220530-plate-i-day4-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",
    "\n",
    "# palette_dict = {'sg-Control': 'black', 'sg1-Itgb1': 'blue', 'sg2-Itgb1': 'red'}\n",
    "\n",
    "sns.swarmplot(data = df,\n",
    "              x = 'groups', y = 'counts',\n",
    "              size = 2\n",
    "             )\n",
    "sns.barplot(data = df,\n",
    "            x = 'groups', 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",
    "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": 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
}