SMG-Col4-BM-linescan-sg-Serpinh1.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",
    "    organoid_ids = []\n",
    "    for i in range(n_organoids):\n",
    "        for j in range(n_edges):\n",
    "            sample_ids.append(sgRNA + '_' + str(i+1))\n",
    "            organoid_ids.append( str(i+1) )\n",
    "    \n",
    "    df['sample_id'] = sample_ids\n",
    "    df['organoid_id'] = organoid_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/190503-Serpinh1-KO-Col4-IF/line-scans-across-BM/'\n",
    "folders = glob.glob(os.path.join(input_folder, '*line-scan-output' + os.path.sep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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('-')[4]\n",
    "    time_d = 4\n",
    "    dfs.append(get_linescan_data(folder, sgRNA, time_d, n_edges=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dfs)\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_name</th>\n",
       "      <th>id</th>\n",
       "      <th>Col4_AUC</th>\n",
       "      <th>Col4_peak_height</th>\n",
       "      <th>z_position</th>\n",
       "      <th>sample_id</th>\n",
       "      <th>organoid_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>140</th>\n",
       "      <td>6-z-20</td>\n",
       "      <td>1</td>\n",
       "      <td>4412.8010</td>\n",
       "      <td>264.1396</td>\n",
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       "      <td>230</td>\n",
       "      <td>4</td>\n",
       "      <td>230_4</td>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6-z-20</td>\n",
       "      <td>3</td>\n",
       "      <td>10439.8835</td>\n",
       "      <td>747.3499</td>\n",
       "      <td>1</td>\n",
       "      <td>230_6</td>\n",
       "      <td>6</td>\n",
       "      <td>230</td>\n",
       "      <td>4</td>\n",
       "      <td>230_4</td>\n",
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       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6-z-20</td>\n",
       "      <td>4</td>\n",
       "      <td>5193.4890</td>\n",
       "      <td>560.5394</td>\n",
       "      <td>1</td>\n",
       "      <td>230_6</td>\n",
       "      <td>6</td>\n",
       "      <td>230</td>\n",
       "      <td>4</td>\n",
       "      <td>230_4</td>\n",
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       "      <td>16010.0167</td>\n",
       "      <td>1289.1151</td>\n",
       "      <td>1</td>\n",
       "      <td>230_6</td>\n",
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       "      <td>230_4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    file_name  id    Col4_AUC  Col4_peak_height  z_position sample_id  \\\n",
       "140    6-z-20   1   4412.8010          264.1396           1     230_6   \n",
       "141    6-z-20   2   -352.2645          149.3963           1     230_6   \n",
       "142    6-z-20   3  10439.8835          747.3499           1     230_6   \n",
       "143    6-z-20   4   5193.4890          560.5394           1     230_6   \n",
       "144    6-z-20   5  16010.0167         1289.1151           1     230_6   \n",
       "\n",
       "    organoid_id sgRNA  time_d groups  \n",
       "140           6   230       4  230_4  \n",
       "141           6   230       4  230_4  \n",
       "142           6   230       4  230_4  \n",
       "143           6   230       4  230_4  \n",
       "144           6   230       4  230_4  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize to the average of control\n",
    "ctrl_average = df[df.sgRNA=='188'].Col4_peak_height.mean()\n",
    "df['Col4_normed'] = df['Col4_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 240x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swarm plot of 4 categories\n",
    "\n",
    "outputPrefix = '190503-IF-Col4-across-BM-sg-Serpinh1-bar-swarm'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width = 0.8\n",
    "fig_height = 1.2\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='sgRNA', y='Col4_normed', data=df,\n",
    "                   hue='organoid_id',\n",
    "#                    palette={'1': 'red', '2': 'green', '3': 'blue', '4': yellow', '5': 'purple'},\n",
    "                   # color=\"blue\",\n",
    "                   alpha=0.5,\n",
    "                   size=1.0 )\n",
    "\n",
    "ax = sns.barplot(x='sgRNA', y='Col4_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, 4.5)\n",
    "# plt.yticks([0, 3, 6])\n",
    "\n",
    "plt.xlabel(None)\n",
    "# plt.ylabel(r\"$\\beta$1-integrin intensity\")\n",
    "plt.ylabel('Collagen IV 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-Serpinh1', 'sg2-Serpinh1', 'sg3-Serpinh1']\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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 45\n",
      "228 n = 20\n",
      "229 n = 50\n",
      "230 n = 30\n"
     ]
    }
   ],
   "source": [
    "for i in df.sgRNA.unique():\n",
    "    print(i, 'n =', len(df[df.sgRNA==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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    228   0.0793    0.9 -0.3308 0.4894  False\n",
      "   188    229   0.0246    0.9  -0.289 0.3381  False\n",
      "   188    230  -0.1449 0.6983 -0.5046 0.2148  False\n",
      "   228    229  -0.0547    0.9 -0.4585  0.349  False\n",
      "   228    230  -0.2242 0.5441 -0.6647 0.2164  False\n",
      "   229    230  -0.1694  0.585 -0.5219  0.183  False\n",
      "---------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.9        0.6983394  0.9        0.5441151  0.58498304]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fcad81d9550>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.Col4_normed, df.sgRNA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../data/190503-Serpinh1-KO-Col4-IF/line-scans-across-BM/190503-Cas9-E13p5epi-LV-188-antiHSP47-405-anti-Col4-647-temp-BM-line-scan-output/'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folders[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 150x150 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example linescan profile\n",
    "\n",
    "raw_BM_linescan_files = glob.glob(os.path.join(folders[0], 'raw-BM-linescan-profile-record', '*a6int.txt'))\n",
    "i = 5\n",
    "df_example = pd.read_csv(raw_BM_linescan_files[i])\n",
    "\n",
    "df_example['pixels'] = list(range(len(df_example)))\n",
    "\n",
    "outputPrefix = '190503-Col4-across-BM-line-scan-example'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width=0.5\n",
    "fig_height=0.5\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",
    "\n",
    "plt.plot(df_example.pixels, df_example.Col4_intensity, color='#00d000')\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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 45\n",
      "228 n = 20\n",
      "229 n = 50\n",
      "230 n = 30\n"
     ]
    }
   ],
   "source": [
    "for i in df.sgRNA.unique():\n",
    "    print(i, 'n =', len(df[df.sgRNA==i]))"
   ]
  },
  {
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