qPCR-analysis-220627-plate3to5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec1f6d73-3ad7-4908-89d5-2f7ca6735fbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob, warnings, logging\n",
    "import string\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",
    "from sw_qPCR import *\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.ERROR)\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.DEBUG)\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": 4,
   "id": "efcf0422-23ad-4b71-b55b-26a6719ed5a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data\n",
    "df_plate3 = get_plate_data('220627-plate3')\n",
    "df_plate4 = get_plate_data('220627-plate4')\n",
    "df_plate5 = get_plate_data('220627-plate5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a33278c-da84-40cb-aec5-68f7e58deaa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate the data (stacking up and down)\n",
    "df = pd.concat([df_plate3, df_plate4, df_plate5])\n",
    "df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9906e3e-cd0e-4837-aba8-23a2c5d37064",
   "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>Well</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Primer</th>\n",
       "      <th>Cq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A01</td>\n",
       "      <td>C-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>19.147778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A02</td>\n",
       "      <td>C-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>19.153126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A03</td>\n",
       "      <td>C-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>19.190614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A04</td>\n",
       "      <td>C-317-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>24.040855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A05</td>\n",
       "      <td>C-317-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>23.564872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>E08</td>\n",
       "      <td>C-318-5</td>\n",
       "      <td>Itga6 v1</td>\n",
       "      <td>25.255417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>E09</td>\n",
       "      <td>C-318-5</td>\n",
       "      <td>Itga6 v1</td>\n",
       "      <td>25.073371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>E10</td>\n",
       "      <td>C-319-5</td>\n",
       "      <td>Itga6 v1</td>\n",
       "      <td>31.078961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>E11</td>\n",
       "      <td>C-319-5</td>\n",
       "      <td>Itga6 v1</td>\n",
       "      <td>31.152747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>E12</td>\n",
       "      <td>C-319-5</td>\n",
       "      <td>Itga6 v1</td>\n",
       "      <td>30.829341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Well   Sample    Primer         Cq\n",
       "0    A01  C-188-1  Rps29 v1  19.147778\n",
       "1    A02  C-188-1  Rps29 v1  19.153126\n",
       "2    A03  C-188-1  Rps29 v1  19.190614\n",
       "3    A04  C-317-1  Rps29 v1  24.040855\n",
       "4    A05  C-317-1  Rps29 v1  23.564872\n",
       "..   ...      ...       ...        ...\n",
       "172  E08  C-318-5  Itga6 v1  25.255417\n",
       "173  E09  C-318-5  Itga6 v1  25.073371\n",
       "174  E10  C-319-5  Itga6 v1  31.078961\n",
       "175  E11  C-319-5  Itga6 v1  31.152747\n",
       "176  E12  C-319-5  Itga6 v1  30.829341\n",
       "\n",
       "[177 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4c82282-37d7-48e3-870c-7a0f420f172f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = get_expression_data(df, 'Itga3 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "697fd19e-be65-4465-847c-fe2eed124674",
   "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>Sample</th>\n",
       "      <th>Cq_ref</th>\n",
       "      <th>Cq_test</th>\n",
       "      <th>deltaCq</th>\n",
       "      <th>relExp</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C-188-1</td>\n",
       "      <td>19.163839</td>\n",
       "      <td>27.568621</td>\n",
       "      <td>8.404782</td>\n",
       "      <td>0.002951</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C-188-2</td>\n",
       "      <td>25.860871</td>\n",
       "      <td>36.459749</td>\n",
       "      <td>10.598878</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C-188-3</td>\n",
       "      <td>20.509570</td>\n",
       "      <td>29.961668</td>\n",
       "      <td>9.452097</td>\n",
       "      <td>0.001428</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C-188-4</td>\n",
       "      <td>20.949640</td>\n",
       "      <td>29.623080</td>\n",
       "      <td>8.673440</td>\n",
       "      <td>0.002449</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-188-5</td>\n",
       "      <td>20.861994</td>\n",
       "      <td>29.800349</td>\n",
       "      <td>8.938355</td>\n",
       "      <td>0.002038</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C-317-1</td>\n",
       "      <td>23.827209</td>\n",
       "      <td>33.712199</td>\n",
       "      <td>9.884990</td>\n",
       "      <td>0.001058</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C-317-2</td>\n",
       "      <td>20.755214</td>\n",
       "      <td>30.114298</td>\n",
       "      <td>9.359084</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C-317-3</td>\n",
       "      <td>25.432740</td>\n",
       "      <td>29.941388</td>\n",
       "      <td>4.508648</td>\n",
       "      <td>0.043930</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C-317-4</td>\n",
       "      <td>21.337782</td>\n",
       "      <td>30.582441</td>\n",
       "      <td>9.244660</td>\n",
       "      <td>0.001648</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C-317-5</td>\n",
       "      <td>22.973691</td>\n",
       "      <td>33.199052</td>\n",
       "      <td>10.225361</td>\n",
       "      <td>0.000835</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C-318-1</td>\n",
       "      <td>21.402879</td>\n",
       "      <td>29.881230</td>\n",
       "      <td>8.478351</td>\n",
       "      <td>0.002804</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C-318-2</td>\n",
       "      <td>20.649215</td>\n",
       "      <td>27.967254</td>\n",
       "      <td>7.318039</td>\n",
       "      <td>0.006267</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>C-318-3</td>\n",
       "      <td>21.896448</td>\n",
       "      <td>29.487451</td>\n",
       "      <td>7.591003</td>\n",
       "      <td>0.005187</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C-318-4</td>\n",
       "      <td>23.004116</td>\n",
       "      <td>30.990595</td>\n",
       "      <td>7.986479</td>\n",
       "      <td>0.003943</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C-318-5</td>\n",
       "      <td>21.326323</td>\n",
       "      <td>30.012928</td>\n",
       "      <td>8.686605</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>C-319-1</td>\n",
       "      <td>20.206760</td>\n",
       "      <td>26.638230</td>\n",
       "      <td>6.431470</td>\n",
       "      <td>0.011586</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>C-319-2</td>\n",
       "      <td>21.800616</td>\n",
       "      <td>31.706994</td>\n",
       "      <td>9.906377</td>\n",
       "      <td>0.001042</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C-319-3</td>\n",
       "      <td>26.888771</td>\n",
       "      <td>36.121380</td>\n",
       "      <td>9.232609</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>C-319-4</td>\n",
       "      <td>21.243001</td>\n",
       "      <td>29.609746</td>\n",
       "      <td>8.366745</td>\n",
       "      <td>0.003029</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>C-319-5</td>\n",
       "      <td>24.920650</td>\n",
       "      <td>37.178930</td>\n",
       "      <td>12.258281</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample     Cq_ref    Cq_test    deltaCq    relExp  Group\n",
       "0   C-188-1  19.163839  27.568621   8.404782  0.002951  C-188\n",
       "1   C-188-2  25.860871  36.459749  10.598878  0.000645  C-188\n",
       "2   C-188-3  20.509570  29.961668   9.452097  0.001428  C-188\n",
       "3   C-188-4  20.949640  29.623080   8.673440  0.002449  C-188\n",
       "4   C-188-5  20.861994  29.800349   8.938355  0.002038  C-188\n",
       "5   C-317-1  23.827209  33.712199   9.884990  0.001058  C-317\n",
       "6   C-317-2  20.755214  30.114298   9.359084  0.001523  C-317\n",
       "7   C-317-3  25.432740  29.941388   4.508648  0.043930  C-317\n",
       "8   C-317-4  21.337782  30.582441   9.244660  0.001648  C-317\n",
       "9   C-317-5  22.973691  33.199052  10.225361  0.000835  C-317\n",
       "10  C-318-1  21.402879  29.881230   8.478351  0.002804  C-318\n",
       "11  C-318-2  20.649215  27.967254   7.318039  0.006267  C-318\n",
       "12  C-318-3  21.896448  29.487451   7.591003  0.005187  C-318\n",
       "13  C-318-4  23.004116  30.990595   7.986479  0.003943  C-318\n",
       "14  C-318-5  21.326323  30.012928   8.686605  0.002427  C-318\n",
       "15  C-319-1  20.206760  26.638230   6.431470  0.011586  C-319\n",
       "16  C-319-2  21.800616  31.706994   9.906377  0.001042  C-319\n",
       "17  C-319-3  26.888771  36.121380   9.232609  0.001662  C-319\n",
       "18  C-319-4  21.243001  29.609746   8.366745  0.003029  C-319\n",
       "19  C-319-5  24.920650  37.178930  12.258281  0.000204  C-319"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ea888eb-85d4-4efa-80b9-d39f2fede3f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_filtered = df1[df1.Cq_ref<23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b470502-bb83-4815-97e9-a9dd204d42ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df1_filtered, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b96876ad-46e2-446c-8b58-5947c1b37e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df1_filtered, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b6c69dab-e451-4279-9b6f-bf2f39fc6943",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = get_expression_data(df, 'Itga6 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2be6a5c6-2e38-43d4-860e-c0236f6253de",
   "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>Sample</th>\n",
       "      <th>Cq_ref</th>\n",
       "      <th>Cq_test</th>\n",
       "      <th>deltaCq</th>\n",
       "      <th>relExp</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C-188-1</td>\n",
       "      <td>19.163839</td>\n",
       "      <td>22.680868</td>\n",
       "      <td>3.517028</td>\n",
       "      <td>0.087351</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C-188-2</td>\n",
       "      <td>25.860871</td>\n",
       "      <td>33.004432</td>\n",
       "      <td>7.143562</td>\n",
       "      <td>0.007073</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C-188-3</td>\n",
       "      <td>20.509570</td>\n",
       "      <td>24.906753</td>\n",
       "      <td>4.397183</td>\n",
       "      <td>0.047459</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C-188-4</td>\n",
       "      <td>20.949640</td>\n",
       "      <td>25.057104</td>\n",
       "      <td>4.107464</td>\n",
       "      <td>0.058014</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C-188-5</td>\n",
       "      <td>20.861994</td>\n",
       "      <td>25.133488</td>\n",
       "      <td>4.271494</td>\n",
       "      <td>0.051779</td>\n",
       "      <td>C-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C-317-1</td>\n",
       "      <td>23.827209</td>\n",
       "      <td>28.535763</td>\n",
       "      <td>4.708554</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C-317-2</td>\n",
       "      <td>20.755214</td>\n",
       "      <td>25.588652</td>\n",
       "      <td>4.833439</td>\n",
       "      <td>0.035074</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C-317-3</td>\n",
       "      <td>25.432740</td>\n",
       "      <td>29.850001</td>\n",
       "      <td>4.417262</td>\n",
       "      <td>0.046803</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C-317-4</td>\n",
       "      <td>21.337782</td>\n",
       "      <td>26.170904</td>\n",
       "      <td>4.833123</td>\n",
       "      <td>0.035082</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C-317-5</td>\n",
       "      <td>22.973691</td>\n",
       "      <td>28.017033</td>\n",
       "      <td>5.043342</td>\n",
       "      <td>0.030325</td>\n",
       "      <td>C-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C-318-1</td>\n",
       "      <td>21.402879</td>\n",
       "      <td>24.975039</td>\n",
       "      <td>3.572160</td>\n",
       "      <td>0.084076</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C-318-2</td>\n",
       "      <td>20.649215</td>\n",
       "      <td>23.956866</td>\n",
       "      <td>3.307651</td>\n",
       "      <td>0.100995</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>C-318-3</td>\n",
       "      <td>21.896448</td>\n",
       "      <td>24.967408</td>\n",
       "      <td>3.070960</td>\n",
       "      <td>0.119001</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C-318-4</td>\n",
       "      <td>23.004116</td>\n",
       "      <td>26.657967</td>\n",
       "      <td>3.653851</td>\n",
       "      <td>0.079448</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C-318-5</td>\n",
       "      <td>21.326323</td>\n",
       "      <td>25.178832</td>\n",
       "      <td>3.852509</td>\n",
       "      <td>0.069228</td>\n",
       "      <td>C-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>C-319-1</td>\n",
       "      <td>20.206760</td>\n",
       "      <td>23.025471</td>\n",
       "      <td>2.818711</td>\n",
       "      <td>0.141737</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>C-319-2</td>\n",
       "      <td>21.800616</td>\n",
       "      <td>27.079716</td>\n",
       "      <td>5.279099</td>\n",
       "      <td>0.025753</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C-319-3</td>\n",
       "      <td>26.888771</td>\n",
       "      <td>31.243042</td>\n",
       "      <td>4.354271</td>\n",
       "      <td>0.048892</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>C-319-4</td>\n",
       "      <td>21.243001</td>\n",
       "      <td>25.401473</td>\n",
       "      <td>4.158472</td>\n",
       "      <td>0.055998</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>C-319-5</td>\n",
       "      <td>24.920650</td>\n",
       "      <td>31.020350</td>\n",
       "      <td>6.099700</td>\n",
       "      <td>0.014582</td>\n",
       "      <td>C-319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample     Cq_ref    Cq_test   deltaCq    relExp  Group\n",
       "0   C-188-1  19.163839  22.680868  3.517028  0.087351  C-188\n",
       "1   C-188-2  25.860871  33.004432  7.143562  0.007073  C-188\n",
       "2   C-188-3  20.509570  24.906753  4.397183  0.047459  C-188\n",
       "3   C-188-4  20.949640  25.057104  4.107464  0.058014  C-188\n",
       "4   C-188-5  20.861994  25.133488  4.271494  0.051779  C-188\n",
       "5   C-317-1  23.827209  28.535763  4.708554  0.038246  C-317\n",
       "6   C-317-2  20.755214  25.588652  4.833439  0.035074  C-317\n",
       "7   C-317-3  25.432740  29.850001  4.417262  0.046803  C-317\n",
       "8   C-317-4  21.337782  26.170904  4.833123  0.035082  C-317\n",
       "9   C-317-5  22.973691  28.017033  5.043342  0.030325  C-317\n",
       "10  C-318-1  21.402879  24.975039  3.572160  0.084076  C-318\n",
       "11  C-318-2  20.649215  23.956866  3.307651  0.100995  C-318\n",
       "12  C-318-3  21.896448  24.967408  3.070960  0.119001  C-318\n",
       "13  C-318-4  23.004116  26.657967  3.653851  0.079448  C-318\n",
       "14  C-318-5  21.326323  25.178832  3.852509  0.069228  C-318\n",
       "15  C-319-1  20.206760  23.025471  2.818711  0.141737  C-319\n",
       "16  C-319-2  21.800616  27.079716  5.279099  0.025753  C-319\n",
       "17  C-319-3  26.888771  31.243042  4.354271  0.048892  C-319\n",
       "18  C-319-4  21.243001  25.401473  4.158472  0.055998  C-319\n",
       "19  C-319-5  24.920650  31.020350  6.099700  0.014582  C-319"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "67053bf4-dc70-423b-beab-9025898c0df9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df2, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9c68a898-c7f6-47ab-9684-32ff9fc44529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df2, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1ac3d259-5160-4644-9b05-fd27042cc42e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing plotting melting curves\n",
    "def get_melting_curves_data(plate_id, data_folder='../data/qPCR-data'):\n",
    "    ''' Given a plate id:\n",
    "          1. Locate the annotation csv file and the melting curves csv file\n",
    "          2. Extract useful data, merge and return the data frame\n",
    "    '''\n",
    "    \n",
    "    exported_csv_folders = glob.glob(os.path.join(data_folder, '*'+plate_id+'*'+os.path.sep))\n",
    "    assert len(exported_csv_folders) == 1\n",
    "    exported_csv_folder = exported_csv_folders[0]\n",
    "    \n",
    "    data_files = glob.glob(os.path.join(exported_csv_folder, '*Melt Curve Derivative Results_SYBR.csv'))\n",
    "    assert len(data_files) == 1\n",
    "    data_file = data_files[0]\n",
    "    \n",
    "    df = pd.read_csv(data_file)\n",
    "    \n",
    "    # drop the first column (empty)\n",
    "    df = df.iloc[:, 1:]\n",
    "    \n",
    "    wells = []\n",
    "    for i in string.ascii_uppercase[:8]:\n",
    "        for j in range(1,13):\n",
    "            wells.append(i+'{:02d}'.format(j))\n",
    "    \n",
    "    # rename columns to keep the well names consistent\n",
    "    df.columns = ['Temperature'] + wells\n",
    "    \n",
    "    return df\n",
    "\n",
    "def plot_melting_curves(plate_id, primer,\n",
    "                        exclude_wells=None,\n",
    "                        sample=None,\n",
    "                        data_folder='../data/qPCR-data'):\n",
    "    '''\n",
    "    '''\n",
    "    layout_to_annotation(plate_id)\n",
    "    annotation_files = glob.glob(os.path.join(data_folder, '*'+plate_id+'*annotation.csv'))\n",
    "    assert len(annotation_files) == 1\n",
    "    annotation_file = annotation_files[0]\n",
    "\n",
    "    df = pd.read_csv(annotation_file)\n",
    "    df_annotation = df[['Well', 'Sample', 'Primer']]\n",
    "    \n",
    "    # select the wells based on the query of primer and sample\n",
    "    if type(primer) is str:\n",
    "        df_selected = df_annotation[df_annotation.Primer==primer]\n",
    "    elif type(primer) is list:\n",
    "        df_selected = df_annotation[df_annotation.Primer.isin(primer)]\n",
    "        \n",
    "    if sample is not None:\n",
    "        if type(sample) is str:\n",
    "            df_selected = df_selected[df_selected.Sample==sample]\n",
    "        elif type(sample) is list:\n",
    "            df_selected = df_selected[df_selected.Sample.isin(sample)]\n",
    "    \n",
    "    df = get_melting_curves_data(plate_id, data_folder)\n",
    "    for well in df_selected.Well.tolist():\n",
    "        if exclude_wells is None:\n",
    "            plt.plot(df.Temperature, df[well])\n",
    "        elif well not in exclude_wells:\n",
    "            plt.plot(df.Temperature, df[well])\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8c6b52ed-d3a4-417c-b109-dfb73f60daa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_melting_curves_data('220627-plate2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "69b05d8d-257f-4482-a3f9-bff4ed26ed87",
   "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>Temperature</th>\n",
       "      <th>A01</th>\n",
       "      <th>A02</th>\n",
       "      <th>A03</th>\n",
       "      <th>A04</th>\n",
       "      <th>A05</th>\n",
       "      <th>A06</th>\n",
       "      <th>A07</th>\n",
       "      <th>A08</th>\n",
       "      <th>A09</th>\n",
       "      <th>...</th>\n",
       "      <th>H03</th>\n",
       "      <th>H04</th>\n",
       "      <th>H05</th>\n",
       "      <th>H06</th>\n",
       "      <th>H07</th>\n",
       "      <th>H08</th>\n",
       "      <th>H09</th>\n",
       "      <th>H10</th>\n",
       "      <th>H11</th>\n",
       "      <th>H12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1.454877</td>\n",
       "      <td>-3.312484</td>\n",
       "      <td>-3.867710</td>\n",
       "      <td>-11.887255</td>\n",
       "      <td>-5.299782</td>\n",
       "      <td>-8.662307</td>\n",
       "      <td>-5.613364</td>\n",
       "      <td>-5.107449</td>\n",
       "      <td>-6.908295</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.601167</td>\n",
       "      <td>-4.336527</td>\n",
       "      <td>-5.138667</td>\n",
       "      <td>0.563135</td>\n",
       "      <td>-4.889344</td>\n",
       "      <td>-7.431574</td>\n",
       "      <td>-4.902143</td>\n",
       "      <td>-4.600001</td>\n",
       "      <td>-5.455169</td>\n",
       "      <td>0.600081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.5</td>\n",
       "      <td>3.152232</td>\n",
       "      <td>-7.177048</td>\n",
       "      <td>-8.380039</td>\n",
       "      <td>-25.755720</td>\n",
       "      <td>-11.482861</td>\n",
       "      <td>-18.768333</td>\n",
       "      <td>-12.162290</td>\n",
       "      <td>-11.066140</td>\n",
       "      <td>-14.967971</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.469196</td>\n",
       "      <td>-9.395808</td>\n",
       "      <td>-11.133778</td>\n",
       "      <td>1.220126</td>\n",
       "      <td>-10.593579</td>\n",
       "      <td>-16.101744</td>\n",
       "      <td>-10.621310</td>\n",
       "      <td>-9.966669</td>\n",
       "      <td>-11.819533</td>\n",
       "      <td>1.300175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2.909753</td>\n",
       "      <td>-6.624967</td>\n",
       "      <td>-7.735421</td>\n",
       "      <td>-23.774510</td>\n",
       "      <td>-10.599564</td>\n",
       "      <td>-17.324615</td>\n",
       "      <td>-11.226729</td>\n",
       "      <td>-10.214898</td>\n",
       "      <td>-13.816589</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.202335</td>\n",
       "      <td>-8.673054</td>\n",
       "      <td>-10.277334</td>\n",
       "      <td>1.126270</td>\n",
       "      <td>-9.778688</td>\n",
       "      <td>-14.863149</td>\n",
       "      <td>-9.804286</td>\n",
       "      <td>-9.200002</td>\n",
       "      <td>-10.910338</td>\n",
       "      <td>1.200161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66.5</td>\n",
       "      <td>2.909753</td>\n",
       "      <td>-6.624967</td>\n",
       "      <td>-7.735421</td>\n",
       "      <td>-23.774510</td>\n",
       "      <td>-10.599564</td>\n",
       "      <td>-17.324615</td>\n",
       "      <td>-11.226729</td>\n",
       "      <td>-10.214898</td>\n",
       "      <td>-13.816589</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.202335</td>\n",
       "      <td>-8.673054</td>\n",
       "      <td>-10.277334</td>\n",
       "      <td>1.126270</td>\n",
       "      <td>-9.778688</td>\n",
       "      <td>-14.863149</td>\n",
       "      <td>-9.804286</td>\n",
       "      <td>-9.200002</td>\n",
       "      <td>-10.910338</td>\n",
       "      <td>1.200161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67.0</td>\n",
       "      <td>2.909753</td>\n",
       "      <td>-6.624967</td>\n",
       "      <td>-7.735421</td>\n",
       "      <td>-23.774510</td>\n",
       "      <td>-10.599564</td>\n",
       "      <td>-17.324615</td>\n",
       "      <td>-11.226729</td>\n",
       "      <td>-10.214898</td>\n",
       "      <td>-13.816589</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.202335</td>\n",
       "      <td>-8.673054</td>\n",
       "      <td>-10.277334</td>\n",
       "      <td>1.126270</td>\n",
       "      <td>-9.778688</td>\n",
       "      <td>-14.863149</td>\n",
       "      <td>-9.804286</td>\n",
       "      <td>-9.200002</td>\n",
       "      <td>-10.910338</td>\n",
       "      <td>1.200161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature       A01       A02       A03        A04        A05        A06  \\\n",
       "0         65.0  1.454877 -3.312484 -3.867710 -11.887255  -5.299782  -8.662307   \n",
       "1         65.5  3.152232 -7.177048 -8.380039 -25.755720 -11.482861 -18.768333   \n",
       "2         66.0  2.909753 -6.624967 -7.735421 -23.774510 -10.599564 -17.324615   \n",
       "3         66.5  2.909753 -6.624967 -7.735421 -23.774510 -10.599564 -17.324615   \n",
       "4         67.0  2.909753 -6.624967 -7.735421 -23.774510 -10.599564 -17.324615   \n",
       "\n",
       "         A07        A08        A09  ...       H03       H04        H05  \\\n",
       "0  -5.613364  -5.107449  -6.908295  ... -1.601167 -4.336527  -5.138667   \n",
       "1 -12.162290 -11.066140 -14.967971  ... -3.469196 -9.395808 -11.133778   \n",
       "2 -11.226729 -10.214898 -13.816589  ... -3.202335 -8.673054 -10.277334   \n",
       "3 -11.226729 -10.214898 -13.816589  ... -3.202335 -8.673054 -10.277334   \n",
       "4 -11.226729 -10.214898 -13.816589  ... -3.202335 -8.673054 -10.277334   \n",
       "\n",
       "        H06        H07        H08        H09       H10        H11       H12  \n",
       "0  0.563135  -4.889344  -7.431574  -4.902143 -4.600001  -5.455169  0.600081  \n",
       "1  1.220126 -10.593579 -16.101744 -10.621310 -9.966669 -11.819533  1.300175  \n",
       "2  1.126270  -9.778688 -14.863149  -9.804286 -9.200002 -10.910338  1.200161  \n",
       "3  1.126270  -9.778688 -14.863149  -9.804286 -9.200002 -10.910338  1.200161  \n",
       "4  1.126270  -9.778688 -14.863149  -9.804286 -9.200002 -10.910338  1.200161  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a8063cda-6724-4a2a-b307-6739ea74c7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_melting_curves('220627-plate2', 'Serpinh1 sg1 v2')\n",
    "# plot_melting_curves('220627-plate2', 'Serpinh1 sg1 v2', ['B01', 'B02', 'B03'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d2cf948c-e392-43cb-aef0-5abcc5916e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_melting_curves('220627-plate2', 'Serpinh1 sg2 v1')\n",
    "# plot_melting_curves('220627-plate2', 'Serpinh1 sg2 v1', ['B07', 'B08', 'B09'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ba2f0202-e33d-4336-9031-369c6da497d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot_melting_curves('220627-plate2', 'Serpinh1 sg3 v1')\n",
    "plot_melting_curves('220627-plate2', 'Serpinh1 sg3 v1', ['F02', 'G02', 'H02'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e1ce400b-d65a-4803-86e5-43ab985cc603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_melting_curves('220627-plate3', 'Rps29 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ff6a511f-9d62-4b84-af4d-7201a50e29f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_melting_curves('220627-plate4', 'Itga3 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "28a5944b-96cc-4bcd-bd41-afcb8df565d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_melting_curves('220627-plate5', 'Itga6 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "daa1e522-5d50-4ec4-8a8d-9233c9a2352c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_melting_curves_data('220627-plate4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "433ae024-6ac7-4acb-91f9-c40e49297ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_des = df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1223cb0a-6735-4429-a75d-a68f4a5c1f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_des_selected = df_des[df_des['max']<100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9d0b0879-9510-4b92-aa95-a5d2126e7e78",
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>61.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.876467</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B01</th>\n",
       "      <td>61.0</td>\n",
       "      <td>1.711060</td>\n",
       "      <td>1.937336</td>\n",
       "      <td>-1.837209</td>\n",
       "      <td>0.456968</td>\n",
       "      <td>2.039379</td>\n",
       "      <td>2.938694</td>\n",
       "      <td>6.497265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B03</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.653266</td>\n",
       "      <td>1.759784</td>\n",
       "      <td>-3.066064</td>\n",
       "      <td>0.462292</td>\n",
       "      <td>1.162438</td>\n",
       "      <td>1.664942</td>\n",
       "      <td>5.157548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C11</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.654247</td>\n",
       "      <td>2.254326</td>\n",
       "      <td>-3.962630</td>\n",
       "      <td>-1.144113</td>\n",
       "      <td>1.373300</td>\n",
       "      <td>2.261796</td>\n",
       "      <td>4.987240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E12</th>\n",
       "      <td>61.0</td>\n",
       "      <td>12.255502</td>\n",
       "      <td>13.628308</td>\n",
       "      <td>0.289052</td>\n",
       "      <td>2.003677</td>\n",
       "      <td>7.033940</td>\n",
       "      <td>17.340363</td>\n",
       "      <td>54.845063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F01</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.720347</td>\n",
       "      <td>0.724656</td>\n",
       "      <td>-0.424999</td>\n",
       "      <td>0.261397</td>\n",
       "      <td>0.619465</td>\n",
       "      <td>1.076909</td>\n",
       "      <td>3.946777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F02</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.686501</td>\n",
       "      <td>0.671821</td>\n",
       "      <td>-0.818053</td>\n",
       "      <td>0.293229</td>\n",
       "      <td>0.702768</td>\n",
       "      <td>1.061714</td>\n",
       "      <td>2.946124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F03</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.787472</td>\n",
       "      <td>0.697868</td>\n",
       "      <td>-0.496704</td>\n",
       "      <td>0.258107</td>\n",
       "      <td>0.793034</td>\n",
       "      <td>1.430756</td>\n",
       "      <td>2.423318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F04</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.566426</td>\n",
       "      <td>0.640075</td>\n",
       "      <td>-2.043711</td>\n",
       "      <td>0.338719</td>\n",
       "      <td>0.731239</td>\n",
       "      <td>0.934882</td>\n",
       "      <td>1.645098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F05</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.700107</td>\n",
       "      <td>0.738857</td>\n",
       "      <td>-0.988836</td>\n",
       "      <td>0.247864</td>\n",
       "      <td>0.597376</td>\n",
       "      <td>1.114427</td>\n",
       "      <td>2.233724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F06</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.645915</td>\n",
       "      <td>0.631967</td>\n",
       "      <td>-1.185026</td>\n",
       "      <td>0.296818</td>\n",
       "      <td>0.700638</td>\n",
       "      <td>1.024416</td>\n",
       "      <td>1.878511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F07</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.661001</td>\n",
       "      <td>0.559007</td>\n",
       "      <td>-0.428107</td>\n",
       "      <td>0.308442</td>\n",
       "      <td>0.638809</td>\n",
       "      <td>1.119692</td>\n",
       "      <td>2.404188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F08</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.709368</td>\n",
       "      <td>0.566463</td>\n",
       "      <td>-0.296655</td>\n",
       "      <td>0.311918</td>\n",
       "      <td>0.689973</td>\n",
       "      <td>1.062953</td>\n",
       "      <td>2.305359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F09</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.652824</td>\n",
       "      <td>0.423475</td>\n",
       "      <td>-0.217698</td>\n",
       "      <td>0.400527</td>\n",
       "      <td>0.641842</td>\n",
       "      <td>0.891159</td>\n",
       "      <td>1.667869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F10</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.715905</td>\n",
       "      <td>0.908506</td>\n",
       "      <td>-0.482830</td>\n",
       "      <td>0.155310</td>\n",
       "      <td>0.822236</td>\n",
       "      <td>1.093509</td>\n",
       "      <td>4.855205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F11</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.685454</td>\n",
       "      <td>1.235386</td>\n",
       "      <td>-5.457471</td>\n",
       "      <td>0.445558</td>\n",
       "      <td>0.856413</td>\n",
       "      <td>1.468267</td>\n",
       "      <td>2.009888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F12</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.835220</td>\n",
       "      <td>0.861696</td>\n",
       "      <td>-2.524921</td>\n",
       "      <td>0.553880</td>\n",
       "      <td>0.912030</td>\n",
       "      <td>1.296520</td>\n",
       "      <td>2.573024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G01</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.758042</td>\n",
       "      <td>0.347035</td>\n",
       "      <td>-0.139960</td>\n",
       "      <td>0.469767</td>\n",
       "      <td>0.861287</td>\n",
       "      <td>1.033911</td>\n",
       "      <td>1.471087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G02</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.613273</td>\n",
       "      <td>0.532701</td>\n",
       "      <td>-0.435503</td>\n",
       "      <td>0.244567</td>\n",
       "      <td>0.614514</td>\n",
       "      <td>0.960408</td>\n",
       "      <td>1.728149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G03</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.516093</td>\n",
       "      <td>0.470159</td>\n",
       "      <td>-0.628537</td>\n",
       "      <td>0.198821</td>\n",
       "      <td>0.618464</td>\n",
       "      <td>0.816709</td>\n",
       "      <td>1.348571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G04</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.517548</td>\n",
       "      <td>0.886668</td>\n",
       "      <td>-3.239528</td>\n",
       "      <td>0.165114</td>\n",
       "      <td>0.601408</td>\n",
       "      <td>1.084753</td>\n",
       "      <td>1.901055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G05</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.597570</td>\n",
       "      <td>0.690431</td>\n",
       "      <td>-1.562021</td>\n",
       "      <td>0.171254</td>\n",
       "      <td>0.691200</td>\n",
       "      <td>1.013423</td>\n",
       "      <td>1.983881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G06</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.443007</td>\n",
       "      <td>0.491313</td>\n",
       "      <td>-0.458518</td>\n",
       "      <td>-0.046861</td>\n",
       "      <td>0.448467</td>\n",
       "      <td>0.762108</td>\n",
       "      <td>1.311960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G07</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.287976</td>\n",
       "      <td>1.205645</td>\n",
       "      <td>-5.237489</td>\n",
       "      <td>0.071513</td>\n",
       "      <td>0.468979</td>\n",
       "      <td>1.010182</td>\n",
       "      <td>1.802396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G08</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.584962</td>\n",
       "      <td>1.030383</td>\n",
       "      <td>-3.920855</td>\n",
       "      <td>0.187295</td>\n",
       "      <td>0.718914</td>\n",
       "      <td>1.175555</td>\n",
       "      <td>1.851304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G09</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.659778</td>\n",
       "      <td>0.810683</td>\n",
       "      <td>-0.975190</td>\n",
       "      <td>0.165918</td>\n",
       "      <td>0.606456</td>\n",
       "      <td>1.182099</td>\n",
       "      <td>2.054731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G10</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.550179</td>\n",
       "      <td>0.829769</td>\n",
       "      <td>-1.083386</td>\n",
       "      <td>0.112486</td>\n",
       "      <td>0.656129</td>\n",
       "      <td>1.048805</td>\n",
       "      <td>2.384124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G11</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.699939</td>\n",
       "      <td>0.871886</td>\n",
       "      <td>-1.639893</td>\n",
       "      <td>0.242779</td>\n",
       "      <td>0.780021</td>\n",
       "      <td>1.284336</td>\n",
       "      <td>2.399340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G12</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.576238</td>\n",
       "      <td>0.822877</td>\n",
       "      <td>-1.401971</td>\n",
       "      <td>-0.012739</td>\n",
       "      <td>0.490007</td>\n",
       "      <td>1.226479</td>\n",
       "      <td>2.452490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H01</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.939633</td>\n",
       "      <td>0.427010</td>\n",
       "      <td>-0.200164</td>\n",
       "      <td>0.725733</td>\n",
       "      <td>0.945920</td>\n",
       "      <td>1.214892</td>\n",
       "      <td>1.745699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H02</th>\n",
       "      <td>61.0</td>\n",
       "      <td>1.745947</td>\n",
       "      <td>0.617763</td>\n",
       "      <td>0.365053</td>\n",
       "      <td>1.326390</td>\n",
       "      <td>1.629878</td>\n",
       "      <td>2.236646</td>\n",
       "      <td>3.116514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H03</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.893254</td>\n",
       "      <td>0.699929</td>\n",
       "      <td>-0.578460</td>\n",
       "      <td>0.343626</td>\n",
       "      <td>0.718207</td>\n",
       "      <td>1.375719</td>\n",
       "      <td>2.778764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H04</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.684774</td>\n",
       "      <td>0.660998</td>\n",
       "      <td>-0.395916</td>\n",
       "      <td>0.140406</td>\n",
       "      <td>0.628930</td>\n",
       "      <td>1.029197</td>\n",
       "      <td>2.610137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H05</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.755953</td>\n",
       "      <td>0.558692</td>\n",
       "      <td>-0.504695</td>\n",
       "      <td>0.369753</td>\n",
       "      <td>0.827155</td>\n",
       "      <td>1.209354</td>\n",
       "      <td>2.095513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H06</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.756519</td>\n",
       "      <td>0.492493</td>\n",
       "      <td>-0.271147</td>\n",
       "      <td>0.483957</td>\n",
       "      <td>0.718693</td>\n",
       "      <td>1.057273</td>\n",
       "      <td>1.974465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H07</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.620802</td>\n",
       "      <td>0.751569</td>\n",
       "      <td>-2.172218</td>\n",
       "      <td>0.296902</td>\n",
       "      <td>0.617101</td>\n",
       "      <td>0.993449</td>\n",
       "      <td>2.343114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H08</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.775985</td>\n",
       "      <td>0.690881</td>\n",
       "      <td>-0.540049</td>\n",
       "      <td>0.244361</td>\n",
       "      <td>0.731324</td>\n",
       "      <td>1.328394</td>\n",
       "      <td>2.218823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H09</th>\n",
       "      <td>61.0</td>\n",
       "      <td>1.164374</td>\n",
       "      <td>0.641673</td>\n",
       "      <td>-0.176426</td>\n",
       "      <td>0.654305</td>\n",
       "      <td>0.965485</td>\n",
       "      <td>1.835723</td>\n",
       "      <td>2.518866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H10</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.852879</td>\n",
       "      <td>0.775337</td>\n",
       "      <td>-1.181931</td>\n",
       "      <td>0.460297</td>\n",
       "      <td>0.807925</td>\n",
       "      <td>1.347451</td>\n",
       "      <td>2.442515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H11</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.660852</td>\n",
       "      <td>0.841555</td>\n",
       "      <td>-0.977706</td>\n",
       "      <td>0.216480</td>\n",
       "      <td>0.858768</td>\n",
       "      <td>1.237510</td>\n",
       "      <td>2.168764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H12</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.770440</td>\n",
       "      <td>0.969274</td>\n",
       "      <td>-0.800661</td>\n",
       "      <td>0.032524</td>\n",
       "      <td>0.839839</td>\n",
       "      <td>1.465548</td>\n",
       "      <td>2.867457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count       mean        std        min        25%        50%  \\\n",
       "Temperature   61.0  80.000000   8.876467  65.000000  72.500000  80.000000   \n",
       "B01           61.0   1.711060   1.937336  -1.837209   0.456968   2.039379   \n",
       "B03           61.0   0.653266   1.759784  -3.066064   0.462292   1.162438   \n",
       "C11           61.0   0.654247   2.254326  -3.962630  -1.144113   1.373300   \n",
       "E12           61.0  12.255502  13.628308   0.289052   2.003677   7.033940   \n",
       "F01           61.0   0.720347   0.724656  -0.424999   0.261397   0.619465   \n",
       "F02           61.0   0.686501   0.671821  -0.818053   0.293229   0.702768   \n",
       "F03           61.0   0.787472   0.697868  -0.496704   0.258107   0.793034   \n",
       "F04           61.0   0.566426   0.640075  -2.043711   0.338719   0.731239   \n",
       "F05           61.0   0.700107   0.738857  -0.988836   0.247864   0.597376   \n",
       "F06           61.0   0.645915   0.631967  -1.185026   0.296818   0.700638   \n",
       "F07           61.0   0.661001   0.559007  -0.428107   0.308442   0.638809   \n",
       "F08           61.0   0.709368   0.566463  -0.296655   0.311918   0.689973   \n",
       "F09           61.0   0.652824   0.423475  -0.217698   0.400527   0.641842   \n",
       "F10           61.0   0.715905   0.908506  -0.482830   0.155310   0.822236   \n",
       "F11           61.0   0.685454   1.235386  -5.457471   0.445558   0.856413   \n",
       "F12           61.0   0.835220   0.861696  -2.524921   0.553880   0.912030   \n",
       "G01           61.0   0.758042   0.347035  -0.139960   0.469767   0.861287   \n",
       "G02           61.0   0.613273   0.532701  -0.435503   0.244567   0.614514   \n",
       "G03           61.0   0.516093   0.470159  -0.628537   0.198821   0.618464   \n",
       "G04           61.0   0.517548   0.886668  -3.239528   0.165114   0.601408   \n",
       "G05           61.0   0.597570   0.690431  -1.562021   0.171254   0.691200   \n",
       "G06           61.0   0.443007   0.491313  -0.458518  -0.046861   0.448467   \n",
       "G07           61.0   0.287976   1.205645  -5.237489   0.071513   0.468979   \n",
       "G08           61.0   0.584962   1.030383  -3.920855   0.187295   0.718914   \n",
       "G09           61.0   0.659778   0.810683  -0.975190   0.165918   0.606456   \n",
       "G10           61.0   0.550179   0.829769  -1.083386   0.112486   0.656129   \n",
       "G11           61.0   0.699939   0.871886  -1.639893   0.242779   0.780021   \n",
       "G12           61.0   0.576238   0.822877  -1.401971  -0.012739   0.490007   \n",
       "H01           61.0   0.939633   0.427010  -0.200164   0.725733   0.945920   \n",
       "H02           61.0   1.745947   0.617763   0.365053   1.326390   1.629878   \n",
       "H03           61.0   0.893254   0.699929  -0.578460   0.343626   0.718207   \n",
       "H04           61.0   0.684774   0.660998  -0.395916   0.140406   0.628930   \n",
       "H05           61.0   0.755953   0.558692  -0.504695   0.369753   0.827155   \n",
       "H06           61.0   0.756519   0.492493  -0.271147   0.483957   0.718693   \n",
       "H07           61.0   0.620802   0.751569  -2.172218   0.296902   0.617101   \n",
       "H08           61.0   0.775985   0.690881  -0.540049   0.244361   0.731324   \n",
       "H09           61.0   1.164374   0.641673  -0.176426   0.654305   0.965485   \n",
       "H10           61.0   0.852879   0.775337  -1.181931   0.460297   0.807925   \n",
       "H11           61.0   0.660852   0.841555  -0.977706   0.216480   0.858768   \n",
       "H12           61.0   0.770440   0.969274  -0.800661   0.032524   0.839839   \n",
       "\n",
       "                   75%        max  \n",
       "Temperature  87.500000  95.000000  \n",
       "B01           2.938694   6.497265  \n",
       "B03           1.664942   5.157548  \n",
       "C11           2.261796   4.987240  \n",
       "E12          17.340363  54.845063  \n",
       "F01           1.076909   3.946777  \n",
       "F02           1.061714   2.946124  \n",
       "F03           1.430756   2.423318  \n",
       "F04           0.934882   1.645098  \n",
       "F05           1.114427   2.233724  \n",
       "F06           1.024416   1.878511  \n",
       "F07           1.119692   2.404188  \n",
       "F08           1.062953   2.305359  \n",
       "F09           0.891159   1.667869  \n",
       "F10           1.093509   4.855205  \n",
       "F11           1.468267   2.009888  \n",
       "F12           1.296520   2.573024  \n",
       "G01           1.033911   1.471087  \n",
       "G02           0.960408   1.728149  \n",
       "G03           0.816709   1.348571  \n",
       "G04           1.084753   1.901055  \n",
       "G05           1.013423   1.983881  \n",
       "G06           0.762108   1.311960  \n",
       "G07           1.010182   1.802396  \n",
       "G08           1.175555   1.851304  \n",
       "G09           1.182099   2.054731  \n",
       "G10           1.048805   2.384124  \n",
       "G11           1.284336   2.399340  \n",
       "G12           1.226479   2.452490  \n",
       "H01           1.214892   1.745699  \n",
       "H02           2.236646   3.116514  \n",
       "H03           1.375719   2.778764  \n",
       "H04           1.029197   2.610137  \n",
       "H05           1.209354   2.095513  \n",
       "H06           1.057273   1.974465  \n",
       "H07           0.993449   2.343114  \n",
       "H08           1.328394   2.218823  \n",
       "H09           1.835723   2.518866  \n",
       "H10           1.347451   2.442515  \n",
       "H11           1.237510   2.168764  \n",
       "H12           1.465548   2.867457  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_des_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e1910ca-1688-4752-9594-90f7ff9c5f0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}