SMG-a6integrin-linescan-sg-Itga9-grouped.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": 47,
   "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": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = '../data/220606-a6int-IF'\n",
    "folders = glob.glob(os.path.join(input_folder, '*line-scan-output' + os.path.sep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dfs)\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>edge_category</th>\n",
       "      <th>id</th>\n",
       "      <th>a6int_AUC</th>\n",
       "      <th>a6int_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",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>5</td>\n",
       "      <td>High_High</td>\n",
       "      <td>1</td>\n",
       "      <td>1430.4343</td>\n",
       "      <td>143.0360</td>\n",
       "      <td>180.4431</td>\n",
       "      <td>153.2914</td>\n",
       "      <td>1</td>\n",
       "      <td>319_5</td>\n",
       "      <td>5</td>\n",
       "      <td>319</td>\n",
       "      <td>4</td>\n",
       "      <td>319_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5</td>\n",
       "      <td>High_High</td>\n",
       "      <td>2</td>\n",
       "      <td>400.8162</td>\n",
       "      <td>122.2846</td>\n",
       "      <td>257.9318</td>\n",
       "      <td>446.8839</td>\n",
       "      <td>1</td>\n",
       "      <td>319_5</td>\n",
       "      <td>5</td>\n",
       "      <td>319</td>\n",
       "      <td>4</td>\n",
       "      <td>319_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>5</td>\n",
       "      <td>High_High</td>\n",
       "      <td>3</td>\n",
       "      <td>1337.1548</td>\n",
       "      <td>175.0267</td>\n",
       "      <td>415.7933</td>\n",
       "      <td>402.5762</td>\n",
       "      <td>1</td>\n",
       "      <td>319_5</td>\n",
       "      <td>5</td>\n",
       "      <td>319</td>\n",
       "      <td>4</td>\n",
       "      <td>319_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>5</td>\n",
       "      <td>High_High</td>\n",
       "      <td>4</td>\n",
       "      <td>891.9148</td>\n",
       "      <td>226.9259</td>\n",
       "      <td>618.0205</td>\n",
       "      <td>401.0218</td>\n",
       "      <td>1</td>\n",
       "      <td>319_5</td>\n",
       "      <td>5</td>\n",
       "      <td>319</td>\n",
       "      <td>4</td>\n",
       "      <td>319_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5</td>\n",
       "      <td>High_High</td>\n",
       "      <td>5</td>\n",
       "      <td>1006.8580</td>\n",
       "      <td>228.8184</td>\n",
       "      <td>164.5885</td>\n",
       "      <td>99.7426</td>\n",
       "      <td>1</td>\n",
       "      <td>319_5</td>\n",
       "      <td>5</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  a6int_AUC  a6int_peak_height  NLSmSL_cell_1  \\\n",
       "85          5     High_High   1  1430.4343           143.0360       180.4431   \n",
       "86          5     High_High   2   400.8162           122.2846       257.9318   \n",
       "87          5     High_High   3  1337.1548           175.0267       415.7933   \n",
       "88          5     High_High   4   891.9148           226.9259       618.0205   \n",
       "89          5     High_High   5  1006.8580           228.8184       164.5885   \n",
       "\n",
       "    NLSmSL_cell_2  z_position sample_id organoid_id sgRNA  time_d groups  \n",
       "85       153.2914           1     319_5           5   319       4  319_4  \n",
       "86       446.8839           1     319_5           5   319       4  319_4  \n",
       "87       402.5762           1     319_5           5   319       4  319_4  \n",
       "88       401.0218           1     319_5           5   319       4  319_4  \n",
       "89        99.7426           1     319_5           5   319       4  319_4  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grouped = df.groupby('sample_id').describe()['a6int_peak_height'].reset_index()\n",
    "df_grouped['sgRNA'] = [i[:-2] for i in df_grouped.sample_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctrl_average = df_grouped[df.sgRNA=='188']['mean'].mean()\n",
    "df_grouped['a6int_normed'] = df_grouped['mean'] / ctrl_average "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 60,
   "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 = '220606-IF-a6-integrin-sg-Itga9-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='sgRNA', y='a6int_normed', data=df_grouped,\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=2.0 )\n",
    "\n",
    "ax = sns.barplot(x='sgRNA', y='a6int_normed', data=df_grouped,\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('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": 16,
   "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": 58,
   "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  -0.6617 0.6314 -2.2793 0.9558  False\n",
      "   188    318  -0.9253 0.3352 -2.4599 0.6092  False\n",
      "   188    319  -0.7675 0.4893  -2.302 0.7671  False\n",
      "   317    318  -0.2636    0.9 -1.7981  1.271  False\n",
      "   317    319  -0.1057    0.9 -1.6403 1.4288  False\n",
      "   318    319   0.1579    0.9 -1.2889 1.6047  False\n",
      "---------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.63139523 0.33517792 0.4893257  0.9        0.9        0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fc489721e80>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_grouped.a6int_normed, df_grouped.sgRNA)"
   ]
  },
  {
   "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
}