SMG-a3integrin-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-a3int-IF'\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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       "<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>a3int_AUC</th>\n",
       "      <th>a3int_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>organoid_id</th>\n",
       "      <th>sgRNA</th>\n",
       "      <th>time_d</th>\n",
       "      <th>groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>80</th>\n",
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       "      <td>1614.4828</td>\n",
       "      <td>434.1611</td>\n",
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       "      <td>-183.6052</td>\n",
       "      <td>168.7956</td>\n",
       "      <td>161.0382</td>\n",
       "      <td>352.6743</td>\n",
       "      <td>1</td>\n",
       "      <td>319_4</td>\n",
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       "      <td>843.5868</td>\n",
       "      <td>295.6452</td>\n",
       "      <td>227.1358</td>\n",
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       "      <td>1</td>\n",
       "      <td>319_4</td>\n",
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       "      <td>319</td>\n",
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       "      <td>319_4</td>\n",
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       "      <td>235.3561</td>\n",
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       "      <td>4</td>\n",
       "      <td>High_High</td>\n",
       "      <td>5</td>\n",
       "      <td>4775.3020</td>\n",
       "      <td>761.2314</td>\n",
       "      <td>256.4160</td>\n",
       "      <td>226.0904</td>\n",
       "      <td>1</td>\n",
       "      <td>319_4</td>\n",
       "      <td>4</td>\n",
       "      <td>319</td>\n",
       "      <td>4</td>\n",
       "      <td>319_4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    file_name edge_category  id  a3int_AUC  a3int_peak_height  NLSmSL_cell_1  \\\n",
       "80          4     High_High   1  1614.4828           434.1611       100.2405   \n",
       "81          4     High_High   2  -183.6052           168.7956       161.0382   \n",
       "82          4     High_High   3   843.5868           295.6452       227.1358   \n",
       "83          4     High_High   4  1728.4787           235.3561       426.1702   \n",
       "84          4     High_High   5  4775.3020           761.2314       256.4160   \n",
       "\n",
       "    NLSmSL_cell_2  z_position sample_id organoid_id sgRNA  time_d groups  \n",
       "80       141.9268           1     319_4           4   319       4  319_4  \n",
       "81       352.6743           1     319_4           4   319       4  319_4  \n",
       "82       260.8445           1     319_4           4   319       4  319_4  \n",
       "83       309.2403           1     319_4           4   319       4  319_4  \n",
       "84       226.0904           1     319_4           4   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'].a3int_peak_height.mean()\n",
    "df['a3int_normed'] = df['a3int_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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-a3-integrin-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='a3int_normed', data=df,\n",
    "                   hue='organoid_id',\n",
    "#                    palette={'1': 'red', '2': 'green', '3': 'blue', '4': yellow', '5': 'purple'},\n",
    "                   # color=\"blue\",\n",
    "                   size=1.0, alpha=.5)\n",
    "\n",
    "ax = sns.barplot(x='sgRNA', y='a3int_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('ITGA3 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": 12,
   "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  317_4  -0.3314 0.0064 -0.5903 -0.0726   True\n",
      " 188_4  318_4  -0.1152 0.6321  -0.374  0.1437  False\n",
      " 188_4  319_4  -0.3517 0.0034 -0.6106 -0.0929   True\n",
      " 317_4  318_4   0.2163 0.1687 -0.0565  0.4891  False\n",
      " 317_4  319_4  -0.0203    0.9 -0.2931  0.2526  False\n",
      " 318_4  319_4  -0.2366 0.1127 -0.5094  0.0363  False\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00643324 0.63213169 0.00337872 0.16866405 0.9        0.11271531]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7ffbe00752b0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.a3int_normed, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 25\n",
      "317 n = 20\n",
      "318 n = 20\n",
      "319 n = 20\n"
     ]
    }
   ],
   "source": [
    "for i in df.sgRNA.unique():\n",
    "    print(i, 'n =', len(df[df.sgRNA==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../data/220606-a3int-IF/220606-188-a3int-line-scan-output/'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folders[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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_linescan_files = glob.glob(os.path.join(folders[0], 'raw-linescan-profile-record', '*a3int.txt'))\n",
    "i = 5\n",
    "df_example = pd.read_csv(raw_linescan_files[i])\n",
    "\n",
    "df_example['pixels'] = list(range(len(df_example)))\n",
    "\n",
    "outputPrefix = '220606-a3-integrin-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.a3int_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
}