SMG-b1integrin-linescan-E16.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 scipy import stats\n",
    "from sw_plotting import change_bar_width\n",
    "from sw_utilities import tukeyTest\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",
    "\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": [
    "def get_linescan_data(folder, sgRNA, time_d, n_edges=5):\n",
    "    '''\n",
    "    Read in linescan data and annotate with meta data\n",
    "    \n",
    "    Inputs:\n",
    "    -------\n",
    "        - folder: the folder that contains the linescan output csv file\n",
    "        - sgRNA: which sgRNA was used for the treatment of this group\n",
    "        - time_d: how many days after culture the samples were collected\n",
    "        - n_edges: how many edges were quantified per sample\n",
    "        \n",
    "    Outputs:\n",
    "        - df: pandas data frame with linescan data and annotation data\n",
    "        \n",
    "    '''\n",
    "    \n",
    "    linescan_files = glob.glob(os.path.join(folder, '*.txt'))\n",
    "\n",
    "    if len(linescan_files) > 1:\n",
    "        print('There are more than 1 output file in', folder)\n",
    "        print('The newest one will be used.')\n",
    "        linescan_files = sorted(linescan_files,\n",
    "                                key = os.path.getmtime)\n",
    "        \n",
    "    df = pd.read_csv(linescan_files[-1])\n",
    "    \n",
    "    # annotate data\n",
    "    assert len(df) % 5 == 0\n",
    "    n_organoids = int(len(df) / 5)\n",
    "    \n",
    "    sample_ids = []\n",
    "    for i in range(n_organoids):\n",
    "        for j in range(n_edges):\n",
    "            sample_ids.append(sgRNA + '_' + str(i+1))\n",
    "    \n",
    "    df['sample_id'] = sample_ids\n",
    "    df['sgRNA'] = [sgRNA]*len(df)\n",
    "    df['time_d'] = time_d\n",
    "    df['groups'] = [sgRNA + '_' + str(time_d)]*len(df)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = '../data/220608-b1int-IF-E16/'\n",
    "folders = glob.glob(os.path.join(input_folder, '*line-scan-output' + os.path.sep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for folder in folders:\n",
    "    if folder.endswith(os.path.sep):\n",
    "        folder = folder[:-1]\n",
    "    sgRNA = os.path.basename(folder).split('-')[2]\n",
    "    time_d = 4\n",
    "    dfs.append(get_linescan_data(folder, sgRNA, time_d, n_edges=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dfs)\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "    }\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>edge_category</th>\n",
       "      <th>id</th>\n",
       "      <th>b1int_AUC</th>\n",
       "      <th>b1int_peak_height</th>\n",
       "      <th>NLSmSL_cell_1</th>\n",
       "      <th>NLSmSL_cell_2</th>\n",
       "      <th>z_position</th>\n",
       "      <th>sample_id</th>\n",
       "      <th>sgRNA</th>\n",
       "      <th>time_d</th>\n",
       "      <th>groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>3</td>\n",
       "      <td>High_High</td>\n",
       "      <td>1</td>\n",
       "      <td>169.3868</td>\n",
       "      <td>22.7746</td>\n",
       "      <td>1715.1177</td>\n",
       "      <td>1000.0299</td>\n",
       "      <td>1</td>\n",
       "      <td>403_3</td>\n",
       "      <td>403</td>\n",
       "      <td>4</td>\n",
       "      <td>403_4</td>\n",
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       "      <td>High_High</td>\n",
       "      <td>2</td>\n",
       "      <td>441.8480</td>\n",
       "      <td>40.0999</td>\n",
       "      <td>815.4210</td>\n",
       "      <td>1369.2257</td>\n",
       "      <td>1</td>\n",
       "      <td>403_3</td>\n",
       "      <td>403</td>\n",
       "      <td>4</td>\n",
       "      <td>403_4</td>\n",
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       "      <th>162</th>\n",
       "      <td>3</td>\n",
       "      <td>High_High</td>\n",
       "      <td>3</td>\n",
       "      <td>-103.7256</td>\n",
       "      <td>30.4196</td>\n",
       "      <td>4095.0000</td>\n",
       "      <td>1843.2200</td>\n",
       "      <td>1</td>\n",
       "      <td>403_3</td>\n",
       "      <td>403</td>\n",
       "      <td>4</td>\n",
       "      <td>403_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>3</td>\n",
       "      <td>High_High</td>\n",
       "      <td>4</td>\n",
       "      <td>939.8510</td>\n",
       "      <td>68.4402</td>\n",
       "      <td>3008.0131</td>\n",
       "      <td>3025.0636</td>\n",
       "      <td>1</td>\n",
       "      <td>403_3</td>\n",
       "      <td>403</td>\n",
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       "      <th>164</th>\n",
       "      <td>3</td>\n",
       "      <td>High_High</td>\n",
       "      <td>5</td>\n",
       "      <td>221.6023</td>\n",
       "      <td>25.0188</td>\n",
       "      <td>1400.8460</td>\n",
       "      <td>1428.5251</td>\n",
       "      <td>1</td>\n",
       "      <td>403_3</td>\n",
       "      <td>403</td>\n",
       "      <td>4</td>\n",
       "      <td>403_4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     file_name edge_category  id  b1int_AUC  b1int_peak_height  NLSmSL_cell_1  \\\n",
       "160          3     High_High   1   169.3868            22.7746      1715.1177   \n",
       "161          3     High_High   2   441.8480            40.0999       815.4210   \n",
       "162          3     High_High   3  -103.7256            30.4196      4095.0000   \n",
       "163          3     High_High   4   939.8510            68.4402      3008.0131   \n",
       "164          3     High_High   5   221.6023            25.0188      1400.8460   \n",
       "\n",
       "     NLSmSL_cell_2  z_position sample_id sgRNA  time_d groups  \n",
       "160      1000.0299           1     403_3   403       4  403_4  \n",
       "161      1369.2257           1     403_3   403       4  403_4  \n",
       "162      1843.2200           1     403_3   403       4  403_4  \n",
       "163      3025.0636           1     403_3   403       4  403_4  \n",
       "164      1428.5251           1     403_3   403       4  403_4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctrl_average = df[df.sgRNA=='188'].b1int_peak_height.mean()\n",
    "df['b1int_normed'] = df['b1int_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swarm plot of 4 categories\n",
    "\n",
    "outputPrefix = '220608-IF-b1-integrin-E16-d4-bar-swarm'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width = 1.2\n",
    "fig_height = 1.8\n",
    "\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",
    "ax = fig.add_axes([0.35, 0.4, 0.6, 0.55])\n",
    "\n",
    "ax = sns.swarmplot(x='groups', y='b1int_normed', data=df,\n",
    "                   # hue='sample_id',\n",
    "#                    palette={'1': 'red', '2': 'green', '3': 'blue', '4': yellow', '5': 'purple'},\n",
    "                   color=\"blue\",\n",
    "                   size=1.0, alpha=.6)\n",
    "\n",
    "ax = sns.barplot(x='groups', y='b1int_normed', data=df,\n",
    "                 color=\".7\", alpha=1.0,\n",
    "                 errwidth=.7, errcolor=\"k\", capsize=.2, ci='sd')\n",
    "\n",
    "# plt.ylim(0, 1.7)\n",
    "# plt.ylim(0, 2.7)\n",
    "# plt.yticks([0, 3, 6])\n",
    "\n",
    "plt.xlabel(None)\n",
    "# plt.ylabel(r\"$\\beta$1-integrin intensity\")\n",
    "plt.ylabel('ITGB1 intensity')\n",
    "\n",
    "# make the bar width narrower\n",
    "change_bar_width(ax, .5)\n",
    "\n",
    "# rotate x tick labels\n",
    "# x_labels = ax.get_xticklabels()\n",
    "x_labels = ['sg-Control', 'sg1-Itgb1', 'sg2-Itgb1', 'sg3-Itgb1']\n",
    "ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\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",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "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",
      " 188_4  401_4  -0.6693  0.001 -0.8653 -0.4733   True\n",
      " 188_4  402_4  -0.6995  0.001 -0.9198 -0.4793   True\n",
      " 188_4  403_4   -0.729  0.001 -0.9313 -0.5267   True\n",
      " 401_4  402_4  -0.0302    0.9  -0.255  0.1946  False\n",
      " 401_4  403_4  -0.0597 0.8656  -0.267  0.1475  False\n",
      " 402_4  403_4  -0.0295    0.9 -0.2598  0.2009  False\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.001      0.001      0.9        0.86560462 0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f9c38277af0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.b1int_normed, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 50\n",
      "401 n = 45\n",
      "402 n = 30\n",
      "403 n = 40\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": []
  }
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