SMG-a6integrin-BM-linescan-sg-Itga9.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/220606-a6int-IF/across-BM'\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('-')[1]\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": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_name</th>\n",
       "      <th>id</th>\n",
       "      <th>a6int_AUC</th>\n",
       "      <th>a6int_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",
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      ],
      "text/plain": [
       "    file_name  id  a6int_AUC  a6int_peak_height  z_position sample_id  \\\n",
       "85          5   1   506.4681           915.8687           1     319_5   \n",
       "86          5   2 -1657.7425           595.1022           1     319_5   \n",
       "87          5   3 -2866.7707           656.3918           1     319_5   \n",
       "88          5   4   369.4836           672.6330           1     319_5   \n",
       "89          5   5  1897.1260           502.9492           1     319_5   \n",
       "\n",
       "   organoid_id sgRNA  time_d groups  \n",
       "85           5   319       4  319_4  \n",
       "86           5   319       4  319_4  \n",
       "87           5   319       4  319_4  \n",
       "88           5   319       4  319_4  \n",
       "89           5   319       4  319_4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize to the average of control\n",
    "ctrl_average = df[df.sgRNA=='188'].a6int_peak_height.mean()\n",
    "df['a6int_normed'] = df['a6int_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 240x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swarm plot of 4 categories\n",
    "\n",
    "outputPrefix = '220606-IF-a6-integrin-across-BM-sg-Itga9-d4-bar-swarm'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width = 0.8\n",
    "fig_height = 1.0\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='a6int_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='a6int_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, 3.5)\n",
    "# plt.yticks([0, 3, 6])\n",
    "\n",
    "plt.xlabel(None)\n",
    "# plt.ylabel(r\"$\\beta$1-integrin intensity\")\n",
    "plt.ylabel('ITGA6 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-Itga9', 'sg2-Itga9', 'sg3-Itga9']\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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 20\n",
      "317 n = 20\n",
      "318 n = 25\n",
      "319 n = 25\n"
     ]
    }
   ],
   "source": [
    "for i in df.sgRNA.unique():\n",
    "    print(i, 'n =', len(df[df.sgRNA==i]))"
   ]
  },
  {
   "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    317  -0.0581    0.9  -0.413 0.2969  False\n",
      "   188    318  -0.3287 0.0584 -0.6655  0.008  False\n",
      "   188    319    -0.14 0.6752 -0.4768 0.1967  False\n",
      "   317    318  -0.2707 0.1596 -0.6074 0.0661  False\n",
      "   317    319   -0.082    0.9 -0.4187 0.2548  False\n",
      "   318    319   0.1887 0.4097 -0.1288 0.5062  False\n",
      "---------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.05837206 0.67522405 0.15955509 0.9        0.40974176]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7ff940dda790>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.a6int_normed, df.sgRNA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../data/220606-a6int-IF/across-BM/220606-188-a6int-BM-line-scan-output/'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folders[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
    "\n",
    "raw_BM_linescan_files = glob.glob(os.path.join(folders[0], 'raw-BM-linescan-profile-record', '*a6int.txt'))\n",
    "i = 0\n",
    "df_example = pd.read_csv(raw_BM_linescan_files[i])\n",
    "\n",
    "df_example['pixels'] = list(range(len(df_example)))\n",
    "\n",
    "outputPrefix = '220606-a6-integrin-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.a6int_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": 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
}