{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of rpS6 235/236 immunolabeling in the olfactory nerve of larval *Xenopus laevis*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime, glob, multiprocessing\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from os import listdir\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import scikit_posthocs as sp\n",
    "from skimage.io import imread, imsave"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import custom scripts for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rps6_code import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original data was acquired with a Nikon A1R-MP multiphoton microscope and saved as nd2-files.\n",
    "\n",
    "Get a list of source files in a folder and in all contained subfolders. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base = './data/'  # source directory of raw nd2 files for import\n",
    "folders = listdir(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = []\n",
    "for folder in folders:\n",
    "    for i in sorted(glob.glob(base + folder + '/*.nd2')):\n",
    "        files.append(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process all image files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create metadata from file names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = []\n",
    "timepoint = []\n",
    "animal = []\n",
    "ob_side = []\n",
    "\n",
    "for file in files:\n",
    "    # get clean file name without extention\n",
    "    name = file.split('/')[-1][:-4]\n",
    "    # timepoint of experiment\n",
    "    t = name.split('_')[2]\n",
    "    # animal identification label\n",
    "    a = name.split('_')[-1][0:2]\n",
    "    # sample group: transected or non-transected side?\n",
    "    s = name.split('_')[-1][2:].rstrip('N')\n",
    "    \n",
    "    # Add data to output lists\n",
    "    filename.append(name)\n",
    "    timepoint.append(t)\n",
    "    animal.append(a)\n",
    "    ob_side.append(s)\n",
    "\n",
    "# increase readability of time point titles\n",
    "time_conversion = dict(zip(['ctrl', '24hat', '1hat',   '7dat',   '14dat',    '3wat', '6hat', '72hat', '12hat', '48hat', '3hat', '7wat'],\n",
    "                           ['ctrl', '24 h',   '1 h',   '7 d',    '14 d',     '21 d', '6 h' ,  '72 h',  '12 h',  '48 h', '3 h' , '49 d']))\n",
    "timepoint_new = [time_conversion[x] for x in timepoint]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processing of data:\n",
    "\n",
    "1. Autofluorescence correction (either subtract blue channel or all oversaturated pixels are set to 0)\n",
    "2. Crop image in y dimension (to exclude cellular signals from the olfactory bulb)\n",
    "3. Remove background (high sigma gaussian filtering of each plane)\n",
    "4. Median filtering 3 px radius (only in x,y dimension)\n",
    "5. Quantify cumulative rps6 signal intensity\n",
    "6. Create and save maximum intensity projection overviews for visual control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with multiprocessing.Pool() as pool:  # create a processing pool that uses all available cpus\n",
    "\tintensity_sum = pool.map(process_nd2, files)  # call the processing function for each item in parallel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataframe for the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(list(zip(filename, timepoint_new, animal, ob_side, intensity_sum)),\n",
    "                  columns = ['name', 'time after transection', 'animal', 'OB side', 'cumulative rpS6 235/236 intensity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>name</th>\n",
       "      <th>time after transection</th>\n",
       "      <th>animal</th>\n",
       "      <th>OB side</th>\n",
       "      <th>cumulative rpS6 235/236 intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>181213_wt51_1hat_pS6.235.36_594_Ele.A488_A3ntsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A3</td>\n",
       "      <td>nts</td>\n",
       "      <td>4.296463e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181213_wt51_1hat_pS6.235.36_594_Ele.A488_A3tsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A3</td>\n",
       "      <td>ts</td>\n",
       "      <td>1.766253e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190314_wt51_1hat_pS6.235.36_488_PI_A1ntsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A1</td>\n",
       "      <td>nts</td>\n",
       "      <td>6.343360e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190314_wt51_1hat_pS6.235.36_488_PI_A1tsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A1</td>\n",
       "      <td>ts</td>\n",
       "      <td>2.165628e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>190314_wt51_1hat_pS6.235.36_488_PI_A2ntsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A2</td>\n",
       "      <td>nts</td>\n",
       "      <td>1.177601e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              name time after transection  \\\n",
       "0  181213_wt51_1hat_pS6.235.36_594_Ele.A488_A3ntsN                    1 h   \n",
       "1   181213_wt51_1hat_pS6.235.36_594_Ele.A488_A3tsN                    1 h   \n",
       "2        190314_wt51_1hat_pS6.235.36_488_PI_A1ntsN                    1 h   \n",
       "3         190314_wt51_1hat_pS6.235.36_488_PI_A1tsN                    1 h   \n",
       "4        190314_wt51_1hat_pS6.235.36_488_PI_A2ntsN                    1 h   \n",
       "\n",
       "  animal OB side  cumulative rpS6 235/236 intensity  \n",
       "0     A3     nts                       4.296463e+07  \n",
       "1     A3      ts                       1.766253e+08  \n",
       "2     A1     nts                       6.343360e+07  \n",
       "3     A1      ts                       2.165628e+08  \n",
       "4     A2     nts                       1.177601e+08  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save dataframe as csv-file for later reuse without reprocessing\n",
    "today = str(datetime.datetime.now().strftime(\"%Y%m%d\"))\n",
    "df.to_csv(today + '_rps6_ON_cropped_gaussianbg35_median3_cumsum.csv', header=True, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading of saved dataframe for replotting\n",
    "#df = pd.read_csv(base + '20241011_rps6_ON_cropped_gaussianbg35_median3_cumsum.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract separate lists of cumulative rpS6 intensity for non-transected and transected samples. Calculate the ratio $\\frac{transected}{non-transected}$ for all samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = df[df['OB side'] == 'ts']['cumulative rpS6 235/236 intensity'].tolist()\n",
    "ts = np.array(ts)\n",
    "nts = df[df['OB side'] == 'nts']['cumulative rpS6 235/236 intensity'].tolist()\n",
    "nts = np.array(nts)\n",
    "ratio = ts / nts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new dataframe containing this ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio = df[df['OB side'] == 'ts']\n",
    "df_ratio = df_ratio.drop(columns=['cumulative rpS6 235/236 intensity'])\n",
    "df_ratio['relative rpS6 235/236 intensity (ts/nts)'] = ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>name</th>\n",
       "      <th>time after transection</th>\n",
       "      <th>animal</th>\n",
       "      <th>OB side</th>\n",
       "      <th>relative rpS6 235/236 intensity (ts/nts)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>181213_wt51_1hat_pS6.235.36_594_Ele.A488_A3tsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A3</td>\n",
       "      <td>ts</td>\n",
       "      <td>4.110947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190314_wt51_1hat_pS6.235.36_488_PI_A1tsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A1</td>\n",
       "      <td>ts</td>\n",
       "      <td>3.414008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>190314_wt51_1hat_pS6.235.36_488_PI_A2tsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A2</td>\n",
       "      <td>ts</td>\n",
       "      <td>1.959637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>190314_wt51_1hat_pS6.235.36_488_PI_A4tsN</td>\n",
       "      <td>1 h</td>\n",
       "      <td>A4</td>\n",
       "      <td>ts</td>\n",
       "      <td>1.437575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>190315_wt51_7dat_pS6.235.36_488_PI_A1tsN</td>\n",
       "      <td>7 d</td>\n",
       "      <td>A1</td>\n",
       "      <td>ts</td>\n",
       "      <td>0.790704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             name time after transection  \\\n",
       "1  181213_wt51_1hat_pS6.235.36_594_Ele.A488_A3tsN                    1 h   \n",
       "3        190314_wt51_1hat_pS6.235.36_488_PI_A1tsN                    1 h   \n",
       "5        190314_wt51_1hat_pS6.235.36_488_PI_A2tsN                    1 h   \n",
       "7        190314_wt51_1hat_pS6.235.36_488_PI_A4tsN                    1 h   \n",
       "9        190315_wt51_7dat_pS6.235.36_488_PI_A1tsN                    7 d   \n",
       "\n",
       "  animal OB side  relative rpS6 235/236 intensity (ts/nts)  \n",
       "1     A3      ts                                  4.110947  \n",
       "3     A1      ts                                  3.414008  \n",
       "5     A2      ts                                  1.959637  \n",
       "7     A4      ts                                  1.437575  \n",
       "9     A1      ts                                  0.790704  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate descriptive statistics of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time after transection</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 h</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.730542</td>\n",
       "      <td>1.243477</td>\n",
       "      <td>1.437575</td>\n",
       "      <td>1.829122</td>\n",
       "      <td>2.686823</td>\n",
       "      <td>3.588243</td>\n",
       "      <td>4.110947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 h</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.256141</td>\n",
       "      <td>0.871321</td>\n",
       "      <td>2.397303</td>\n",
       "      <td>2.814497</td>\n",
       "      <td>3.231691</td>\n",
       "      <td>3.685560</td>\n",
       "      <td>4.139430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14 d</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.631391</td>\n",
       "      <td>0.487623</td>\n",
       "      <td>0.207052</td>\n",
       "      <td>0.304291</td>\n",
       "      <td>0.510537</td>\n",
       "      <td>0.837637</td>\n",
       "      <td>1.297438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21 d</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.554840</td>\n",
       "      <td>0.325598</td>\n",
       "      <td>0.238424</td>\n",
       "      <td>0.349819</td>\n",
       "      <td>0.477288</td>\n",
       "      <td>0.635825</td>\n",
       "      <td>1.140037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24 h</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.205557</td>\n",
       "      <td>2.860296</td>\n",
       "      <td>0.888134</td>\n",
       "      <td>3.719345</td>\n",
       "      <td>5.212012</td>\n",
       "      <td>7.115326</td>\n",
       "      <td>10.189614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 h</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.233688</td>\n",
       "      <td>0.753437</td>\n",
       "      <td>1.200730</td>\n",
       "      <td>1.984102</td>\n",
       "      <td>2.375325</td>\n",
       "      <td>2.624911</td>\n",
       "      <td>2.983371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48 h</th>\n",
       "      <td>12.0</td>\n",
       "      <td>1.416480</td>\n",
       "      <td>0.561118</td>\n",
       "      <td>0.747105</td>\n",
       "      <td>0.972005</td>\n",
       "      <td>1.327488</td>\n",
       "      <td>1.621380</td>\n",
       "      <td>2.475338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49 d</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.935071</td>\n",
       "      <td>0.328991</td>\n",
       "      <td>0.633520</td>\n",
       "      <td>0.759640</td>\n",
       "      <td>0.885759</td>\n",
       "      <td>1.085847</td>\n",
       "      <td>1.285935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 h</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.599818</td>\n",
       "      <td>1.040815</td>\n",
       "      <td>0.758347</td>\n",
       "      <td>0.951118</td>\n",
       "      <td>1.233250</td>\n",
       "      <td>1.833052</td>\n",
       "      <td>3.638793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 d</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.742492</td>\n",
       "      <td>0.183355</td>\n",
       "      <td>0.492877</td>\n",
       "      <td>0.595167</td>\n",
       "      <td>0.746296</td>\n",
       "      <td>0.847894</td>\n",
       "      <td>1.015292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72 h</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.074387</td>\n",
       "      <td>0.602884</td>\n",
       "      <td>0.306156</td>\n",
       "      <td>0.816497</td>\n",
       "      <td>1.118926</td>\n",
       "      <td>1.376816</td>\n",
       "      <td>1.753538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctrl</th>\n",
       "      <td>11.0</td>\n",
       "      <td>1.045554</td>\n",
       "      <td>0.330200</td>\n",
       "      <td>0.650476</td>\n",
       "      <td>0.821330</td>\n",
       "      <td>0.979723</td>\n",
       "      <td>1.277739</td>\n",
       "      <td>1.660166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count      mean       std       min       25%  \\\n",
       "time after transection                                                  \n",
       "1 h                       4.0  2.730542  1.243477  1.437575  1.829122   \n",
       "12 h                      3.0  3.256141  0.871321  2.397303  2.814497   \n",
       "14 d                      4.0  0.631391  0.487623  0.207052  0.304291   \n",
       "21 d                      6.0  0.554840  0.325598  0.238424  0.349819   \n",
       "24 h                     10.0  5.205557  2.860296  0.888134  3.719345   \n",
       "3 h                       4.0  2.233688  0.753437  1.200730  1.984102   \n",
       "48 h                     12.0  1.416480  0.561118  0.747105  0.972005   \n",
       "49 d                      3.0  0.935071  0.328991  0.633520  0.759640   \n",
       "6 h                       7.0  1.599818  1.040815  0.758347  0.951118   \n",
       "7 d                       8.0  0.742492  0.183355  0.492877  0.595167   \n",
       "72 h                      4.0  1.074387  0.602884  0.306156  0.816497   \n",
       "ctrl                     11.0  1.045554  0.330200  0.650476  0.821330   \n",
       "\n",
       "                             50%       75%        max  \n",
       "time after transection                                 \n",
       "1 h                     2.686823  3.588243   4.110947  \n",
       "12 h                    3.231691  3.685560   4.139430  \n",
       "14 d                    0.510537  0.837637   1.297438  \n",
       "21 d                    0.477288  0.635825   1.140037  \n",
       "24 h                    5.212012  7.115326  10.189614  \n",
       "3 h                     2.375325  2.624911   2.983371  \n",
       "48 h                    1.327488  1.621380   2.475338  \n",
       "49 d                    0.885759  1.085847   1.285935  \n",
       "6 h                     1.233250  1.833052   3.638793  \n",
       "7 d                     0.746296  0.847894   1.015292  \n",
       "72 h                    1.118926  1.376816   1.753538  \n",
       "ctrl                    0.979723  1.277739   1.660166  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio.groupby(['time after transection'])['relative rpS6 235/236 intensity (ts/nts)'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a list with the desired order of timepoints to adjust the order of display."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "o = ['ctrl', '1 h', '3 h', '6 h', '12 h', '24 h', '48 h', '72 h', '7 d', '14 d', '21 d', '49 d']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Kruskal-Wallis** test to check if all samples originate from the same distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=np.float64(47.796508686119125), pvalue=np.float64(1.5520122716269033e-06))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.kruskal(*[group['relative rpS6 235/236 intensity (ts/nts)'].values for name, group in df_ratio.groupby('time after transection')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dunn's** posthoc statistical test for multiple sample groups with compensation for multiple comparisons using the **Bonferroni-Holm** method.\n",
    "\n",
    "Display significances as heatmap for overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = sp.posthoc_dunn(df_ratio, val_col='relative rpS6 235/236 intensity (ts/nts)', group_col='time after transection', p_adjust='holm')\n",
    "\n",
    "heatmap_args = {'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}\n",
    "sp.sign_plot(pc.reindex(index=o, columns=o), **heatmap_args);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show corresponding pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmatrix = pc.reindex(index=o, columns=o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>ctrl</th>\n",
       "      <th>1 h</th>\n",
       "      <th>3 h</th>\n",
       "      <th>6 h</th>\n",
       "      <th>12 h</th>\n",
       "      <th>24 h</th>\n",
       "      <th>48 h</th>\n",
       "      <th>72 h</th>\n",
       "      <th>7 d</th>\n",
       "      <th>14 d</th>\n",
       "      <th>21 d</th>\n",
       "      <th>49 d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ctrl</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025412</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 h</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132582</td>\n",
       "      <td>0.299059</td>\n",
       "      <td>0.049333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 h</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.317383</td>\n",
       "      <td>0.593814</td>\n",
       "      <td>0.126643</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 h</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.826717</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 h</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116450</td>\n",
       "      <td>0.226318</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24 h</th>\n",
       "      <td>0.025412</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.663341</td>\n",
       "      <td>0.593814</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.008966</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.348916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48 h</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.663341</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.826717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.308470</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72 h</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.593814</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 d</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132582</td>\n",
       "      <td>0.317383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116450</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.826717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14 d</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299059</td>\n",
       "      <td>0.593814</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226318</td>\n",
       "      <td>0.008966</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21 d</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049333</td>\n",
       "      <td>0.126643</td>\n",
       "      <td>0.826717</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.308470</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49 d</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.348916</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ctrl       1 h       3 h       6 h      12 h      24 h      48 h  \\\n",
       "ctrl  1.000000  1.000000  1.000000  1.000000  1.000000  0.025412  1.000000   \n",
       "1 h   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "3 h   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "6 h   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "12 h  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "24 h  0.025412  1.000000  1.000000  1.000000  1.000000  1.000000  0.663341   \n",
       "48 h  1.000000  1.000000  1.000000  1.000000  1.000000  0.663341  1.000000   \n",
       "72 h  1.000000  1.000000  1.000000  1.000000  1.000000  0.593814  1.000000   \n",
       "7 d   1.000000  0.132582  0.317383  1.000000  0.116450  0.000375  0.826717   \n",
       "14 d  1.000000  0.299059  0.593814  1.000000  0.226318  0.008966  1.000000   \n",
       "21 d  1.000000  0.049333  0.126643  0.826717  0.044378  0.000144  0.308470   \n",
       "49 d  1.000000  1.000000  1.000000  1.000000  1.000000  0.348916  1.000000   \n",
       "\n",
       "          72 h       7 d      14 d      21 d      49 d  \n",
       "ctrl  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "1 h   1.000000  0.132582  0.299059  0.049333  1.000000  \n",
       "3 h   1.000000  0.317383  0.593814  0.126643  1.000000  \n",
       "6 h   1.000000  1.000000  1.000000  0.826717  1.000000  \n",
       "12 h  1.000000  0.116450  0.226318  0.044378  1.000000  \n",
       "24 h  0.593814  0.000375  0.008966  0.000144  0.348916  \n",
       "48 h  1.000000  0.826717  1.000000  0.308470  1.000000  \n",
       "72 h  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "7 d   1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "14 d  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "21 d  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "49 d  1.000000  1.000000  1.000000  1.000000  1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmatrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember significant pvalues for annotation in plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcoord = np.argwhere(pmatrix < 0.05)  # Get all significant pvalue coordinates\n",
    "pcoord_clean = remove_mirrored_duplicates(pcoord)  # remove mirrored duplicates from matrix\n",
    "pvalues = [pmatrix.to_numpy()[y,x] for y,x in pcoord_clean]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create box plot with annotations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(context='paper', style='ticks', font_scale=0.75)\n",
    "\n",
    "fig = plt.figure(figsize=(4,4), dpi=300)\n",
    "ax = sns.stripplot(x='time after transection', y='relative rpS6 235/236 intensity (ts/nts)', data=df_ratio, \n",
    "                   edgecolor=[0.41,0.41,0.41], facecolor=\"none\", linewidth=0.5, order=o)\n",
    "ax = sns.boxplot(  x='time after transection', y='relative rpS6 235/236 intensity (ts/nts)', data=df_ratio, color=[0,1,0], fliersize=0, order=o)\n",
    "sns.despine()\n",
    "\n",
    "# annotation of statistical significance\n",
    "annotate_significance(ax, pcoord_clean, pvalues)\n",
    "\n",
    "# manual y axis limits\n",
    "ax.yaxis.set_ticks(np.arange(0, 12, 1))\n",
    "\n",
    "# Save plot\n",
    "today = str(datetime.datetime.now().strftime(\"%Y%m%d\"))\n",
    "fname = today + '_rps6_boxplot_gaussianbg35_median3'\n",
    "plt.rcParams['svg.fonttype'] = 'none' # render text as text and not paths when exporting to svg\n",
    "plt.savefig(fname + '.svg', format='svg')"
   ]
  },
  {
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}