SMG-b1integrin-linescan-time-course.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": 45,
   "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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_folder = '../data/220608-b1int-IF-time-course'\n",
    "folders = glob.glob(os.path.join(input_folder, '*line-scan-output' + os.path.sep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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 = int(os.path.basename(folder).split('-')[4][0])\n",
    "    dfs.append(get_linescan_data(folder, sgRNA, time_d, n_edges=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dfs)\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     file_name edge_category  id  b1int_AUC  b1int_peak_height  NLSmSL_cell_1  \\\n",
       "420          6     High_High   1  2084.8348           232.6014         6.6626   \n",
       "421          6     High_High   2  6719.4988           606.4597         5.4863   \n",
       "422          6     High_High   3  3955.5658           538.2629         6.5926   \n",
       "423          6     High_High   4   192.4889            85.2923         5.5230   \n",
       "424          6     High_High   5  1170.4729            89.8526         4.8460   \n",
       "\n",
       "     NLSmSL_cell_2  z_position   sample_id     sgRNA  time_d      groups  \n",
       "420         5.9507           1  no_virus_6  no_virus       0  no_virus_0  \n",
       "421         5.9859           1  no_virus_6  no_virus       0  no_virus_0  \n",
       "422         5.9035           1  no_virus_6  no_virus       0  no_virus_0  \n",
       "423         5.4700           1  no_virus_6  no_virus       0  no_virus_0  \n",
       "424         5.8239           1  no_virus_6  no_virus       0  no_virus_0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_d1 = df[df.time_d==1]\n",
    "ctrl_average = df_d1[df_d1.sgRNA=='188'].b1int_peak_height.mean()\n",
    "df_d1['b1int_normed'] = df_d1['b1int_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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-d1-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_d1,\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_d1,\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": 69,
   "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_1  401_1   -0.231  0.037  -0.452   -0.01   True\n",
      " 188_1  402_1  -0.6348  0.001 -0.8324 -0.4372   True\n",
      " 188_1  403_1  -0.6918  0.001 -0.8895 -0.4942   True\n",
      " 401_1  402_1  -0.4038  0.001 -0.6014 -0.2061   True\n",
      " 401_1  403_1  -0.4608  0.001 -0.6584 -0.2632   True\n",
      " 402_1  403_1   -0.057 0.7944 -0.2282  0.1141  False\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.03699523 0.001      0.001      0.001      0.001      0.79443296]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f8ea8156c70>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_d1.b1int_normed, df_d1.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 15\n",
      "401 n = 15\n",
      "402 n = 25\n",
      "403 n = 25\n"
     ]
    }
   ],
   "source": [
    "for i in df_d1.sgRNA.unique():\n",
    "    print(i, 'n =', len(df_d1[df_d1.sgRNA==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_d2 = df[df.time_d==2]\n",
    "ctrl_average = df_d2[df_d2.sgRNA=='188'].b1int_peak_height.mean()\n",
    "df_d2['b1int_normed'] = df_d2['b1int_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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-d2-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_d2,\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_d2,\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": 71,
   "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_2  401_2  -0.5498  0.001 -0.7277 -0.3718   True\n",
      " 188_2  402_2   -0.766  0.001 -0.9548 -0.5772   True\n",
      " 188_2  403_2  -0.8095  0.001 -0.9875 -0.6315   True\n",
      " 401_2  402_2  -0.2162 0.0181  -0.405 -0.0275   True\n",
      " 401_2  403_2  -0.2597 0.0014 -0.4377 -0.0817   True\n",
      " 402_2  403_2  -0.0435    0.9 -0.2323  0.1453  False\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.001      0.001      0.01809277 0.00137611 0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f8e9862e100>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_d2.b1int_normed, df_d2.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 25\n",
      "401 n = 25\n",
      "402 n = 20\n",
      "403 n = 25\n"
     ]
    }
   ],
   "source": [
    "for i in df_d2.sgRNA.unique():\n",
    "    print(i, 'n =', len(df_d2[df_d2.sgRNA==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_d4 = df[df.time_d==4]\n",
    "ctrl_average = df_d4[df_d4.sgRNA=='188'].b1int_peak_height.mean()\n",
    "df_d4['b1int_normed'] = df_d4['b1int_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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-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_d4,\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_d4,\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": 77,
   "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   -0.756  0.001  -0.955  -0.557   True\n",
      " 188_4  402_4  -0.7082  0.001 -0.9071 -0.5092   True\n",
      " 188_4  403_4  -0.7016  0.001 -0.8771 -0.5262   True\n",
      " 401_4  402_4   0.0479    0.9 -0.1511  0.2468  False\n",
      " 401_4  403_4   0.0544 0.8321 -0.1211  0.2298  False\n",
      " 402_4  403_4   0.0065    0.9 -0.1689   0.182  False\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001     0.001     0.001     0.9       0.8321047 0.9      ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f8ee9a4f970>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_d4.b1int_normed, df_d4.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 25\n",
      "401 n = 25\n",
      "402 n = 25\n",
      "403 n = 45\n"
     ]
    }
   ],
   "source": [
    "for i in df_d4.sgRNA.unique():\n",
    "    print(i, 'n =', len(df_d4[df_d4.sgRNA==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_d7 = df[df.time_d==7]\n",
    "ctrl_average = df_d7[df_d7.sgRNA=='188'].b1int_peak_height.mean()\n",
    "df_d7['b1int_normed'] = df_d7['b1int_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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-d7-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_d7,\n",
    "                   # hue='sample_id',\n",
    "                   color=\"blue\",\n",
    "                   size=1.0, alpha=.6)\n",
    "\n",
    "ax = sns.barplot(x='groups', y='b1int_normed', data=df_d7,\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": 95,
   "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_7  401_7    -0.78 0.001 -0.9805 -0.5795   True\n",
      " 188_7  402_7  -0.7534 0.001 -0.9539 -0.5529   True\n",
      " 188_7  403_7  -0.7761 0.001 -0.9766 -0.5756   True\n",
      " 401_7  402_7   0.0266   0.9 -0.1739  0.2271  False\n",
      " 401_7  403_7   0.0039   0.9 -0.1966  0.2044  False\n",
      " 402_7  403_7  -0.0227   0.9 -0.2232  0.1778  False\n",
      "---------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001 0.001 0.001 0.9   0.9   0.9  ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f8eba24dc70>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df_d7.b1int_normed, df_d7.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 n = 25\n",
      "401 n = 25\n",
      "402 n = 25\n",
      "403 n = 25\n"
     ]
    }
   ],
   "source": [
    "for i in df_d7.sgRNA.unique():\n",
    "    print(i, 'n =', len(df_d7[df_d7.sgRNA==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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