qPCR-analysis-220525-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",
    "\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": 2,
   "id": "f561ed82-2192-4426-8adf-df14b0d35732",
   "metadata": {},
   "outputs": [],
   "source": [
    "def layout_to_annotation(plate_id,\n",
    "                         data_folder='../data/qPCR-data',\n",
    "                         template_layout_file=None,\n",
    "                         primer_layout_file=None):\n",
    "    '''\n",
    "    '''\n",
    "    if template_layout_file is None:\n",
    "        template_layout_files = glob.glob(os.path.join(data_folder, '*'+plate_id+'*template*layout*.csv'))\n",
    "        assert len(template_layout_files) == 1\n",
    "        template_layout_file = template_layout_files[0]\n",
    "    \n",
    "    if primer_layout_file is None:\n",
    "        primer_layout_files = glob.glob(os.path.join(data_folder, '*'+plate_id+'*primer*layout*.csv'))\n",
    "        assert len(primer_layout_files) == 1\n",
    "        primer_layout_file = primer_layout_files[0]\n",
    "    \n",
    "    # Extract template information\n",
    "    with open(template_layout_file, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    assert lines[0].startswith('template')\n",
    "    \n",
    "    template_list = []\n",
    "    for line in lines[1:]:\n",
    "        temp = line.strip().split(',')\n",
    "        assert len(temp) == 13\n",
    "        template_list = template_list + temp[1:]\n",
    "\n",
    "    assert len(template_list) == 96\n",
    "    \n",
    "    # Extract primer information\n",
    "    with open(primer_layout_file, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    assert lines[0].startswith('primer')\n",
    "    \n",
    "    primer_list = []\n",
    "    for line in lines[1:]:\n",
    "        temp = line.strip().split(',')\n",
    "        assert len(temp) == 13\n",
    "        primer_list = primer_list + temp[1:]\n",
    "\n",
    "    assert len(primer_list) == 96\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",
    "    annotation_file = os.path.join(data_folder, plate_id+'-annotation.csv')\n",
    "    with open(annotation_file, 'w') as f:\n",
    "        f.write(','.join(['Well', 'Sample', 'Primer']) + '\\n')\n",
    "        for i in range(96):\n",
    "            f.write(','.join([wells[i], template_list[i], primer_list[i]]) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29cf1fd1-af0b-460f-ab20-94cf83041b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_plate_data(plate_id, data_folder='../data/qPCR-data'):\n",
    "    ''' Given a plate id:\n",
    "          1. Locate the annotation csv file and the quantification Cq results csv file\n",
    "          2. Extract useful data, merge and return the data frame\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",
    "    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, '*Quantification Cq Results.csv'))\n",
    "    assert len(data_files) == 1\n",
    "    data_file = data_files[0]\n",
    "    \n",
    "    df = pd.read_csv(annotation_file)\n",
    "    df_annotation = df[['Well', 'Sample', 'Primer']]\n",
    "    \n",
    "    df = pd.read_csv(data_file)\n",
    "    df_data = df[['Well', 'Cq']]\n",
    "    \n",
    "    df = df_annotation.merge(df_data, on='Well')\n",
    "    df.dropna(inplace=True)\n",
    "    \n",
    "    return df\n",
    "\n",
    "def get_expression_data(df, test_primer, ref_primer='Rps29 v1'):\n",
    "    ''' From the data frame containing qPCR data and annotation, \n",
    "        calculate average values of replicates and deltaCq values of each sample\n",
    "        \n",
    "    '''\n",
    "    for i in ['Well', 'Sample', 'Primer', 'Cq']:\n",
    "        assert i in df.columns\n",
    "    \n",
    "    df_ref = df[df.Primer==ref_primer]\n",
    "    df_ref.rename(columns={'Cq': 'Cq_ref'}, inplace=True)\n",
    "    \n",
    "    df_test = df[df.Primer==test_primer]\n",
    "    df_test.rename(columns={'Cq': 'Cq_test'}, inplace=True)\n",
    "    \n",
    "    # calculate average values of technical replicates\n",
    "    df_ref_mean = df_ref.groupby('Sample').mean()\n",
    "    df_ref_mean.reset_index(inplace=True)\n",
    "    \n",
    "    df_test_mean = df_test.groupby('Sample').mean()\n",
    "    df_test_mean.reset_index(inplace=True)\n",
    "    \n",
    "    df = df_ref_mean.merge(df_test_mean, on='Sample')\n",
    "    df['deltaCq'] = df.Cq_test - df.Cq_ref\n",
    "    df['relExp'] = 2**(-df.deltaCq)\n",
    "    df['Group'] = [df.Sample[i][:-2] for i in range(len(df))]\n",
    "    \n",
    "    return df\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('20220525-plate3')\n",
    "df_plate4 = get_plate_data('20220525-plate4')\n",
    "df_plate5 = get_plate_data('20220525-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": {
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       "<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>1-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>19.870996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A02</td>\n",
       "      <td>1-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>19.804993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A03</td>\n",
       "      <td>1-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>19.605794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A04</td>\n",
       "      <td>1-188-1</td>\n",
       "      <td>Itgb1 sg1 v1</td>\n",
       "      <td>23.618603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A05</td>\n",
       "      <td>1-188-1</td>\n",
       "      <td>Itgb1 sg1 v1</td>\n",
       "      <td>23.529432</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>235</th>\n",
       "      <td>H02</td>\n",
       "      <td>2-403-8</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>22.945891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>H03</td>\n",
       "      <td>2-403-8</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>23.041317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>H04</td>\n",
       "      <td>2-403-8</td>\n",
       "      <td>Itgb1 sg3 v1</td>\n",
       "      <td>28.543950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>H05</td>\n",
       "      <td>2-403-8</td>\n",
       "      <td>Itgb1 sg3 v1</td>\n",
       "      <td>28.589372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>H06</td>\n",
       "      <td>2-403-8</td>\n",
       "      <td>Itgb1 sg3 v1</td>\n",
       "      <td>28.786691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Well   Sample        Primer         Cq\n",
       "0    A01  1-188-1      Rps29 v1  19.870996\n",
       "1    A02  1-188-1      Rps29 v1  19.804993\n",
       "2    A03  1-188-1      Rps29 v1  19.605794\n",
       "3    A04  1-188-1  Itgb1 sg1 v1  23.618603\n",
       "4    A05  1-188-1  Itgb1 sg1 v1  23.529432\n",
       "..   ...      ...           ...        ...\n",
       "235  H02  2-403-8      Rps29 v1  22.945891\n",
       "236  H03  2-403-8      Rps29 v1  23.041317\n",
       "237  H04  2-403-8  Itgb1 sg3 v1  28.543950\n",
       "238  H05  2-403-8  Itgb1 sg3 v1  28.589372\n",
       "239  H06  2-403-8  Itgb1 sg3 v1  28.786691\n",
       "\n",
       "[240 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": [
    "df_sg1 = get_expression_data(df, 'Itgb1 sg1 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "697fd19e-be65-4465-847c-fe2eed124674",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>1-188-1</td>\n",
       "      <td>19.760594</td>\n",
       "      <td>23.503611</td>\n",
       "      <td>3.743017</td>\n",
       "      <td>0.074686</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-188-2</td>\n",
       "      <td>20.973606</td>\n",
       "      <td>25.302116</td>\n",
       "      <td>4.328509</td>\n",
       "      <td>0.049772</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-188-3</td>\n",
       "      <td>19.399780</td>\n",
       "      <td>23.625935</td>\n",
       "      <td>4.226155</td>\n",
       "      <td>0.053432</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-188-4</td>\n",
       "      <td>20.766016</td>\n",
       "      <td>24.974736</td>\n",
       "      <td>4.208720</td>\n",
       "      <td>0.054082</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-188-5</td>\n",
       "      <td>24.878703</td>\n",
       "      <td>29.384501</td>\n",
       "      <td>4.505798</td>\n",
       "      <td>0.044017</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1-188-6</td>\n",
       "      <td>21.794087</td>\n",
       "      <td>25.636496</td>\n",
       "      <td>3.842409</td>\n",
       "      <td>0.069714</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1-188-7</td>\n",
       "      <td>20.799839</td>\n",
       "      <td>25.311534</td>\n",
       "      <td>4.511695</td>\n",
       "      <td>0.043837</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1-188-8</td>\n",
       "      <td>20.836073</td>\n",
       "      <td>24.483958</td>\n",
       "      <td>3.647885</td>\n",
       "      <td>0.079777</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1-401-1</td>\n",
       "      <td>20.254450</td>\n",
       "      <td>25.330177</td>\n",
       "      <td>5.075727</td>\n",
       "      <td>0.029652</td>\n",
       "      <td>1-401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1-401-2</td>\n",
       "      <td>19.663666</td>\n",
       "      <td>24.744556</td>\n",
       "      <td>5.080890</td>\n",
       "      <td>0.029546</td>\n",
       "      <td>1-401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1-401-3</td>\n",
       "      <td>19.343486</td>\n",
       "      <td>24.437431</td>\n",
       "      <td>5.093944</td>\n",
       "      <td>0.029280</td>\n",
       "      <td>1-401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1-401-4</td>\n",
       "      <td>20.253024</td>\n",
       "      <td>24.788365</td>\n",
       "      <td>4.535340</td>\n",
       "      <td>0.043125</td>\n",
       "      <td>1-401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1-401-5</td>\n",
       "      <td>21.087898</td>\n",
       "      <td>25.971990</td>\n",
       "      <td>4.884092</td>\n",
       "      <td>0.033864</td>\n",
       "      <td>1-401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1-401-6</td>\n",
       "      <td>22.010307</td>\n",
       "      <td>27.089591</td>\n",
       "      <td>5.079284</td>\n",
       "      <td>0.029579</td>\n",
       "      <td>1-401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1-401-7</td>\n",
       "      <td>19.456925</td>\n",
       "      <td>24.049150</td>\n",
       "      <td>4.592225</td>\n",
       "      <td>0.041457</td>\n",
       "      <td>1-401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1-401-8</td>\n",
       "      <td>20.567791</td>\n",
       "      <td>25.365366</td>\n",
       "      <td>4.797576</td>\n",
       "      <td>0.035957</td>\n",
       "      <td>1-401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample     Cq_ref    Cq_test   deltaCq    relExp  Group\n",
       "0   1-188-1  19.760594  23.503611  3.743017  0.074686  1-188\n",
       "1   1-188-2  20.973606  25.302116  4.328509  0.049772  1-188\n",
       "2   1-188-3  19.399780  23.625935  4.226155  0.053432  1-188\n",
       "3   1-188-4  20.766016  24.974736  4.208720  0.054082  1-188\n",
       "4   1-188-5  24.878703  29.384501  4.505798  0.044017  1-188\n",
       "5   1-188-6  21.794087  25.636496  3.842409  0.069714  1-188\n",
       "6   1-188-7  20.799839  25.311534  4.511695  0.043837  1-188\n",
       "7   1-188-8  20.836073  24.483958  3.647885  0.079777  1-188\n",
       "8   1-401-1  20.254450  25.330177  5.075727  0.029652  1-401\n",
       "9   1-401-2  19.663666  24.744556  5.080890  0.029546  1-401\n",
       "10  1-401-3  19.343486  24.437431  5.093944  0.029280  1-401\n",
       "11  1-401-4  20.253024  24.788365  4.535340  0.043125  1-401\n",
       "12  1-401-5  21.087898  25.971990  4.884092  0.033864  1-401\n",
       "13  1-401-6  22.010307  27.089591  5.079284  0.029579  1-401\n",
       "14  1-401-7  19.456925  24.049150  4.592225  0.041457  1-401\n",
       "15  1-401-8  20.567791  25.365366  4.797576  0.035957  1-401"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sg1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b470502-bb83-4815-97e9-a9dd204d42ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 9,
     "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=df_sg1, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b96876ad-46e2-446c-8b58-5947c1b37e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 10,
     "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=df_sg1, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6c69dab-e451-4279-9b6f-bf2f39fc6943",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sg2 = get_expression_data(df, 'Itgb1 sg2 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>1-188-1</td>\n",
       "      <td>19.760594</td>\n",
       "      <td>23.256513</td>\n",
       "      <td>3.495919</td>\n",
       "      <td>0.088639</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1-188-2</td>\n",
       "      <td>20.973606</td>\n",
       "      <td>24.970328</td>\n",
       "      <td>3.996722</td>\n",
       "      <td>0.062642</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1-188-3</td>\n",
       "      <td>19.399780</td>\n",
       "      <td>23.291593</td>\n",
       "      <td>3.891813</td>\n",
       "      <td>0.067367</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1-188-4</td>\n",
       "      <td>20.766016</td>\n",
       "      <td>24.677269</td>\n",
       "      <td>3.911253</td>\n",
       "      <td>0.066465</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1-188-5</td>\n",
       "      <td>24.878703</td>\n",
       "      <td>28.771838</td>\n",
       "      <td>3.893136</td>\n",
       "      <td>0.067305</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1-188-6</td>\n",
       "      <td>21.794087</td>\n",
       "      <td>25.226742</td>\n",
       "      <td>3.432656</td>\n",
       "      <td>0.092612</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1-188-7</td>\n",
       "      <td>20.799839</td>\n",
       "      <td>25.114199</td>\n",
       "      <td>4.314361</td>\n",
       "      <td>0.050263</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1-188-8</td>\n",
       "      <td>20.836073</td>\n",
       "      <td>24.104698</td>\n",
       "      <td>3.268625</td>\n",
       "      <td>0.103764</td>\n",
       "      <td>1-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2-402-1</td>\n",
       "      <td>21.064817</td>\n",
       "      <td>26.177930</td>\n",
       "      <td>5.113113</td>\n",
       "      <td>0.028893</td>\n",
       "      <td>2-402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2-402-2</td>\n",
       "      <td>20.133946</td>\n",
       "      <td>25.040474</td>\n",
       "      <td>4.906528</td>\n",
       "      <td>0.033342</td>\n",
       "      <td>2-402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2-402-3</td>\n",
       "      <td>20.513455</td>\n",
       "      <td>25.758629</td>\n",
       "      <td>5.245174</td>\n",
       "      <td>0.026366</td>\n",
       "      <td>2-402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2-402-4</td>\n",
       "      <td>22.439045</td>\n",
       "      <td>27.699840</td>\n",
       "      <td>5.260796</td>\n",
       "      <td>0.026082</td>\n",
       "      <td>2-402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2-402-5</td>\n",
       "      <td>22.179948</td>\n",
       "      <td>27.798803</td>\n",
       "      <td>5.618854</td>\n",
       "      <td>0.020350</td>\n",
       "      <td>2-402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2-402-6</td>\n",
       "      <td>21.738101</td>\n",
       "      <td>27.153316</td>\n",
       "      <td>5.415215</td>\n",
       "      <td>0.023435</td>\n",
       "      <td>2-402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2-402-7</td>\n",
       "      <td>19.407327</td>\n",
       "      <td>24.333140</td>\n",
       "      <td>4.925813</td>\n",
       "      <td>0.032899</td>\n",
       "      <td>2-402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2-402-8</td>\n",
       "      <td>22.096200</td>\n",
       "      <td>27.378234</td>\n",
       "      <td>5.282034</td>\n",
       "      <td>0.025701</td>\n",
       "      <td>2-402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sample     Cq_ref    Cq_test   deltaCq    relExp  Group\n",
       "0   1-188-1  19.760594  23.256513  3.495919  0.088639  1-188\n",
       "1   1-188-2  20.973606  24.970328  3.996722  0.062642  1-188\n",
       "2   1-188-3  19.399780  23.291593  3.891813  0.067367  1-188\n",
       "3   1-188-4  20.766016  24.677269  3.911253  0.066465  1-188\n",
       "4   1-188-5  24.878703  28.771838  3.893136  0.067305  1-188\n",
       "5   1-188-6  21.794087  25.226742  3.432656  0.092612  1-188\n",
       "6   1-188-7  20.799839  25.114199  4.314361  0.050263  1-188\n",
       "7   1-188-8  20.836073  24.104698  3.268625  0.103764  1-188\n",
       "8   2-402-1  21.064817  26.177930  5.113113  0.028893  2-402\n",
       "9   2-402-2  20.133946  25.040474  4.906528  0.033342  2-402\n",
       "10  2-402-3  20.513455  25.758629  5.245174  0.026366  2-402\n",
       "11  2-402-4  22.439045  27.699840  5.260796  0.026082  2-402\n",
       "12  2-402-5  22.179948  27.798803  5.618854  0.020350  2-402\n",
       "13  2-402-6  21.738101  27.153316  5.415215  0.023435  2-402\n",
       "14  2-402-7  19.407327  24.333140  4.925813  0.032899  2-402\n",
       "15  2-402-8  22.096200  27.378234  5.282034  0.025701  2-402"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67053bf4-dc70-423b-beab-9025898c0df9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD+CAYAAADbNKXUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMb0lEQVR4nO3dXYxU93mA8ec1EFxXNGlhK1ZObNLYqrxu4kbd+IIqlWoai6qVIiEFktqKqaou2HJMbRXUKl+tEzUt+Wgu0iQsECmpkS/IRZSIiKrqhWmVq12rVhvaRq4VO0KMs9RCso0pH35zcQ7Kslm8QzgzA7zPT0Jnz39257xC62ePD3N2IjORJF3fbhj1AJKkwTP2klSAsZekAoy9JBVg7CWpgOWjHuBS1qxZk+vWrRv1GJJ0TZmdnT2RmWML16/a2K9bt46ZmZlRjyFJ15SIeH6xdS/jSFIBxl6SCjD2klSAsZekAoy9JBVg7CWpAGMvSQUYe0kq4Kq9qUrd2bVrF71ej7Vr17J79+5RjyNpBIx9Ab1ej2PHjo16DEkj5GUcSSrA2EtSAcZekgow9pJUgLGXpAKMvSQVYOwlqQBjL0kFGHtJKsDYS1IBxl6SCjD2klSAsZekAjr5rZcRsR7Y1u7uyMyTEbEJ2EjzA2U78GFgAngr8MeZ+VoXx5YkLa2rM/spmtjvB7a0a5szcwo4AmwA7gXeBiw39JI0XF3FfllmngaOA+Pt2tl2e2HtS5m5BXg2Im5d7EkiYioiZiJiZm5urqPRJEldxf5URKykiXqvXTvfbi+sPdruzwGrF3uSzJzOzMnMnBwbG+toNElSV+9UNQ3sAVYAhyNiAjgYEXuBoLnE89sR8RXgXGY+3dFxJUl96CT2mTkLbF2wfBQ4NG//410cS5J0+XzppSQVYOwlqQBjL0kFGHtJKsDYS1IBxl6SCjD2klSAsZekArq6g/aq9Fs7vzHqEa4Kq068zDLghRMvl/87mf3sh0c9gjQSntlLUgHGXpIKMPaSVICxl6QCjL0kFWDsJakAYy9JBRh7SSrA2EtSAcZekgow9pJUgLGXpAKMvSQVYOwlqQBjL0kFGHtJKsDYS1IBxl6SCjD2klSAsZekAoy9JBVg7CWpAGMvSQUYe0kqwNhLUgHGXpIKMPaSVICxl6QCjL0kFbB81ANo8F5/0y9etJVUj7Ev4NXb7x31CJJGzMs4klSAsZekAjq5jBMR64Ft7e6OzDwZEZuAjTQ/ULYDm4D1wJuBhzLztS6OLUlaWldn9lM0sd8PbGnXNmfmFHAE2AC8H0jgOUMvScPVVeyXZeZp4Dgw3q6dbbcX1m4BHgNWRsTEYk8SEVMRMRMRM3Nzcx2NJknqKvanImIlTdR77dr5dnth7fnMTOClSx03M6czczIzJ8fGxjoaTZLU1Usvp4E9wArgcHvmfjAi9gJBc4nnrRHxFeAM8P2OjitJ6kMnsc/MWWDrguWjwKF5+/vaP5KkIfOll5JUgLGXpAKMvSQVYOwlqQBjL0kFGHtJKsDYS1IBxl6SCjD2klSAsZekAoy9JBVg7CWpAGMvSQUYe0kqwNhLUgHGXpIKMPaSVICxl6QCjL0kFWDsJakAYy9JBRh7SSrA2EtSAcuX+oSIuA3YDpwF9mTmDwc9lCSpW0vGHngE+CjwC8AXgPsHOpEkqXP9XMY5A/wq8GbgTET82mBHkiR1rZ/Yv0xzNv9HwPN4Zi9J15x+LuMsAx4HVgGfycztgx1JktS1fmJ/CPgn4DTw8GDHkSQNQj+XcXYATwLfaz+WJF1j+jmzfzQzXwSIiN8Y8DySpAF4wzP7iNiXmS9GxEfbpV1DmEmS1LGlLuO8qd2+o93GAGeRJA3IUrGPiLhh/nYIM0mSOrZU7M8B+9qP99HcYCVJusYs9Q+0/zaUKSRJA7VU7J8FchiDSJIG5w0v42TmU8B7gIeAm4HJYQwlSepWPzdVrQOey8wngbcPdhxJ0iD0c1PVncD/RcQ2YPWA55EkDUA/sT8O/CewAvjkYMeRJA1CP5dxfkBzc9Vq4EODHUeSNAhLntln5l8v9TkRsR7Y1u7uyMyTEbEJ2EjzA2V7Zp6LiHuABzLzgSsZWpJ0ebp6w/EpmtjvB7a0a5szcwo4AmyIiNXAPTS/H39RETEVETMRMTM3N9fRaJKkrmK/LDNP01zfH2/XzrbbC2t/CfzdGz1JZk5n5mRmTo6NjXU0miSpq9ifioiVNFHvtWvn2+04cBNwO/BZ4O6IuKuj40qS+tDPq3H6MQ3soXnFzuGImAAORsReml+eti0zvwwQEU9k5jMdHVeS1IdOYp+Zs8DWBctHad7ScOHn+oblkjRkXV3GkSRdxYy9JBVg7CWpAGMvSQUYe0kqwNhLUgHGXpIKMPaSVICxl6QCjL0kFWDsJakAYy9JBRh7SSrA2EtSAcZekgow9pJUgLGXpAKMvSQV0NV70ErSZdu1axe9Xo+1a9eye/fuUY9zXTP2kkam1+tx7NixUY9RgpdxJKkAYy9JBRh7SSrA2EtSAcZekgow9pJUgC+9lEbghcffOeoRrgrnXvoVYDnnXnrevxPglk/8x8Ce2zN7SSrA2EtSAcZekgow9pJUgLGXpAKMvSQVYOwlqQBfZy9pZNbc+Dpwrt1qkIy9pJH583edHPUIZXgZR5IKMPaSVICxl6QCjL0kFWDsJamATl6NExHrgW3t7o7MPBkRm4CNND9QtgMfAW4FxoBtmflKF8eWJC2tqzP7KZrY7we2tGubM3MKOAJsAP43M/8MmAHu6Oi4kqQ+dBX7ZZl5GjgOjLdrZ9vtcWA8M78dEbcBE8DsYk8SEVMRMRMRM3Nzcx2NJknqKvanImIlTeh77dr5djsO9CLibuAR4OHMXPR2ucyczszJzJwcGxvraDRJUlexnwb20FzKeTUiJoCDEbEX+B3gn4EDwGpgf0Tc2dFxJUl96OQfaDNzFti6YPkocGje/u1dHEuSdPl86aUkFWDsJakAYy9JBRh7SSrA2EtSAcZekgow9pJUgLGXpAKMvSQVYOwlqQBjL0kFGHtJKsDYS1IBxl6SCjD2klSAsZekAoy9JBVg7CWpAGMvSQUYe0kqwNhLUgHGXpIKMPaSVICxl6QCjL0kFWDsJakAYy9JBRh7SSrA2EtSAcZekgow9pJUgLGXpAKMvSQVYOwlqQBjL0kFGHtJKsDYS1IBxl6SCjD2klSAsZekAoy9JBVg7CWpgOVdPElErAe2tbs7MvNkRGwCNtL8QNnePn4HcCYzH+viuJKk/nR1Zj9FE/P9wJZ2bXNmTgFHgA3AezPzYeBkRPx6R8eVJPWhkzN7YFlmno6I48A97drZdnscuHnB/jjwPwufJCKmaH5wALwSET/zOfq5rQFOjHqIUYvPPTDqEfSz/N684JPRxbPcuthiV7E/FREraSLea9fOt9sLa6/P2//XxZ4kM6eB6Y5m0jwRMZOZk6OeQ1rI783h6Cr208AeYAVwOCImgIMRsRcImks8N0fEPwCnM/O/OzquJKkPkZmjnkFD4NmTrlZ+bw6HL72sw8tjulr5vTkEntlLUgGe2UtSAcZekgow9teJiLgnIvYtWHtbRDzVfnxrROyNiP0R8fsR8Z6I+HJEPBER7x7N1LreRcSG9vvuQETcNW/9hoj4l4i4rf1zICK+ERFvv9TX6Mp09dJLjVBE3Aa8G7hxwUOPACfbj+8AJtv954DfBSbax3408CFV1U00N0r+JnAv8Ey7/jA/vZHqT4CP0dyb8yDwvUt8ja6AZ/bXgcx8NjM/v8j6TuDldvdHNHc3Pwh8EHia5j+kvwX+YEijqpjM/A5N8B8Bvg4QEZPAa8B/tZ+2hubO+uPA+GJfoyvnmf11JiK+CqzKzPsWPPQg8GlgDlgNPAbc3+5PIA1ARKwBdgOfAB6PiFXAC8Av0/yf5k3AMWAtzV32vflfk5k/Hsng1yFjf53JzO2XeOgA8CXgVeBvaC7r/CNwBtg5nOlU0BeAMeAzwLcy85sXHoiIvwKeoPm9WZ+m6dHO+V8TERd9jX5+vs5ekgrwmr0kFWDsJakAYy9JBRh7SSrAV+NI80TECuDzwCqa/z6+m5lPjnYq6coZe+lif0oT+MMAEfF7EXEUOERzZ+cfAiuBR4G/z8z7I2Ir8EOau0KfoXk3tscy8/Twx5cWZ+yli90FPNne2PM54BbgB5m5MyIOZOZ9EXE3zQ1pC/1/Zn4qIt4PvA/4zvDGlt6Y1+yli30feF9mnsjMrcCLwCvtY/PfDfqGeftvWfD4jcC5wY4pXR7P7KWLfRX4YkR8oN1/Griz/fibEbEHWAb8BXA2Ir4G/BLw78CqiPgizfX+h4Y5tLQU76CVOhIRT2TmYpd3pJEz9pJUgNfsJakAYy9JBRh7SSrA2EtSAcZekgr4CRhSibq0zd9wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df_sg2, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c68a898-c7f6-47ab-9684-32ff9fc44529",
   "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=df_sg2, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e33bb47a-22d2-4c9b-b4a7-82f96edaf06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sg3 = get_expression_data(df, 'Itgb1 sg3 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fa52bedc-2de0-4bfd-84c6-311510d8ea91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 19,
     "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=df_sg3, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30609e94-6208-4e91-8aad-f173f72cf360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD+CAYAAADbNKXUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAATNklEQVR4nO3df4xdZ17f8fd37nhmJ86UHU0mrqWBDSUrg0FakO9GXaeq1HVBQbTdrlFjCpYJLR6bsCRkYRGI0h9QdYta6KpsQzyTrFg30UobpPCjQUEIwUYsf6A7q67KBgLLigivbOeucSRn/ON65n77xzkz9UxnYnt8xvfHeb+k0Z3z3Lnn+dqSP/P4Oc85T2QmkqThNtLrAiRJO8+wl6QaMOwlqQYMe0mqAcNekmpgtNcFbOW+++7LBx54oNdlSNJAWVxc/Fpmzmxs79uwf+CBB2i1Wr0uQ5IGSkS8sVm70ziSVAOGvSTVgGEvSTVg2EtSDRj2klQDfbsaZzu63eTCUofO8gpjow2md48xMhK9LkuSem5owr7bTV4/f4njp1ucuXiF2akJFo412bdn0sCXVHtDM41zYamzFvQAZy5e4fjpFheWOj2uTJJ6r7KRfUQcBE6Uh09m5lsRcRh4hOKXykngGLAfmAV+KDOvVNV/Z3llLehXnbl4hc7ySlVdSNLAqnJkP0cR9s8BR8q2RzNzDngVOAR8F/D1wGiVQQ8wNtpgdmpiXdvs1ARjo40qu5GkgVRl2Dcy8ypwFthbtl0vX1fbPpmZR4AvR8R7Np4gIuYiohURrXa7fVudT+8eY+FYcy3wV+fsp3ePbfOPI0nDo8oLtJcjYpwi1M+VbatzKKttTwF/BLSBaWDdMxwycx6YB2g2m7e1X+LISLBvzyQvPf6wq3EkaYMqw34eOAXsAl6JiP3AixGxAATFFM/DEfGrwHJmfqHCvoEi8Gcmx6s+rSQNvMrCPjMXgcc2NL8GvHzD8c9V1Z8k6dYNzdJLSdLWDHtJqgHDXpJqwLCXpBow7CWpBgx7SaoBw16SasCwl6QaMOwlqQYMe0mqAcNekmrAsJekGjDsJakGDHtJqgHDXpJqoMrNS3qu200uLHXcqUqSNhiasO92k9fPX+L46RZnLl5Z24N2355JA19S7Q3NNM6Fpc5a0AOcuXiF46dbXFjq9LgySeq9oQn7zvLKWtCvOnPxCp3llS0+IUn1MTRhPzbaYHZqYl3b7NQEY6ONHlUkSf1jaMJ+evcYC8eaa4G/Omc/vXusx5VJUu8NzQXakZFg355JXnr8YVfjSNIGlYV9RBwETpSHT2bmWxFxGHiE4n8QJ4FPAu8CDgLfm5n/p6r+oQj8mcnxKk8pSUOhypH9HEXYPwQcAU4Bj2bm90XEMeBQZp6MiAeBH6g66CVJW6tyzr6RmVeBs8Desu16+Xpj21PAf9/sBBExFxGtiGi12+0KS5Okeqsy7C9HxDhFqJ8r21bXPe4FzkXEBBCZeXGzE2TmfGY2M7M5MzNz2wV0u0n70jW+evEy7UvX6HZzG38MSRo+VU7jzFNM3ewCXomI/cCLEbEABMUUz7cDf1Vhn2u8g1aSthaZ/Tn6bTab2Wq1bvnn25eu8eGnP7/uxqrZqQleevxhL9pKqo2IWMzM5sb2oVln7x20krS1oQl776CVpK0NTdh7B60kbc07aCWpBoYm7ME7aCVpK0MzjSNJ2pphL0k1YNhLUg0Y9pJUA4a9JNWAYS9JNWDYS1INGPaSVAOGvSTVgGEvSTVg2EtSDRj2klQDhr0k1YBhL0k1YNhLUg0Y9pJUA4a9JNWAYS9JNVDZtoQRcRA4UR4+mZlvRcRh4BGKXyongcPAQeDrgMcz80pV/UuStlblyH6OIuyfA46UbY9m5hzwKnAI+BCQwFc2C/qImIuIVkS02u12haVJUr1VGfaNzLwKnAX2lm3Xy9fVtm8APgqMR8T+jSfIzPnMbGZmc2ZmpsLSJKneqgz7yxExThHq58q2lfJ1te2NzEzgbyvuW5L0DiqbswfmgVPALuCVcuT+YkQsAEExxTMbEb8KdIAvVdi3JOkdRDHQ7j/NZjNbrVavy5CkgRIRi5nZ3NjuVIok1YBhL0k1YNhLUg0Y9pJUA4a9JNWAYS9JNWDYS1INGPaSVAOGvSTVgGEvSTVg2EtSDRj2klQDhr0k1YBhL0k1YNhLUg1UuXlJz3W7yYWlDp3lFcZGG0zvHmNkJHpdliT13NCEfbebvH7+EsdPtzhz8QqzUxMsHGuyb8+kgS+p9oZmGufCUmct6AHOXLzC8dMtLix1elyZJPXe0IR9Z3llLehXnbl4hc7yyhafkKT6GJqwHxttMDs1sa5tdmqCsdFGjyqSpP4xNHP207vHOP2vHuKNC5e5Z6zB5c4K75m+h+ndY70uTZJ6bmjCvttNLndW+Lnf/NO1C7TPHD1At5teoJVUe5VN40TEwYj4dPn17rLtcETMR8SzETEaEU9GxGcj4tciYm9VfQO8+fY1Tj6/uO4C7cnnF3nz7WtVdiNJA6nKOfs54ATwHHCkbHs0M+eAV4FDwAHga8CbwPkK++b6SnfTC7TLK90qu5GkgVRl2Dcy8ypwFlgdtV8vX1fbngZ+FPgy8MjGE0TEXES0IqLVbrdvq/NdjZFNL9CONobmGrQkbVuVSXg5IsYpQv1c2ba67nG17UBmJnCRTa4XZOZ8ZjYzszkzM3Nbnd9/7zjPHD2wFvirc/b33zu+vT+NJA2RKi/QzgOngF3AKxGxH3gxIhaAoJjieTAini6Pn6iwb0ZHR/jmPZN89sQHWF7pMtoY4f57xxkddWQvSVEMtPtPs9nMVqvV6zIkaaBExGJmNje2O+yVpBq46TRORDwInKS42HoqM/96p4uSJFXrVubsnwB+FpgAfhk4uqMVSZIqdyvTOB3gfuDrgE5E/L2dLUmSVLVbCftLFKP57wfewJG9JA2cW5nGaQA/D0wCH8/MkztbkiSparcS9i8DvwtcBT6ys+VIknbCrUzjPAl8Bvjj8ntJ0oC5lZH9U5l5HiAivm2H65Ek7YB3HNlHxLOZeT4ifrZs+qm7UNO2dbtJ+9I1vnrxMu1L1+h2+/PuYEm62242sl/d5umbyte+3QWk201eP39pbdPx2akJFo412bdn0s1LJNXezebsIyJGbny9CzVty4WlzlrQQ/Es++OnW1xY6vS4MknqvZuF/TLwbPn9sxQ3WPWlzvLKppuXdJZXtviEJNXHzaZx/uiuVFGBsdEGs1MT6wJ/dmqCsdFGD6uSpP5ws5H9l4G/3PDVl6Z3j7FwrLlu85KFY02md4/d5JOSNPzecWSfmZ+LiJ8A3g/8JsWOU6/ejcJu18hI8N6Ze/nsiQ9wfaXLrnLzEi/OStKtrbN/APhKZn4mIn5lh+vZtm43+cv2267GkaRN3ModtN8KvDciTgDTO1zPtrkaR5K2disj+7PAn1LsLfvvdrac7XM1jiRt7VZG9n9BcXPVNPAvd7ac7VtdjXMjV+NIUuGmI/vM/A93o5A7tboaZ+OcvatxJOnWpnEGwshIsG/PJC89/jCd5RXGRhtM7x7z4qwkUWHYR8RB4ER5+GRmvhURh4FHKKaLTmbmckR8EPjBzPzBqvpeNTISzEyOV31aSRp4tzJnf6vmKML+OeBI2fZoZs5RrM0/FBHTwAcpdr+SJN0lVYZ9IzOvUqze2Vu2XS9fV9t+BvjFrU4QEXMR0YqIVrvdrrA0Saq3KsP+ckSMU4T6ubJtdd3jXuAe4L3AfwEeioj3bTxBZs5nZjMzmzMzMxWWJkn1VuUF2nngFMV6/FciYj/wYkQsUDwa+URmPg0QEc9n5hcr7FuS9A4qC/vMXAQe29D8GsWG5Rt/9mhV/UqSbq7KaRxJUp8y7CWpBgx7SaoBw16SasCwl6QaMOwlqQYMe0mqAcNekmrAsJekGjDsJakGDHtJqgHDXpJqwLCXpBow7CWpBgx7SaoBw16SasCwl6QaMOwlqQYMe0mqAcNekmrAsJekGjDsJakGDHtJqoHRKk4SEQeBE+Xhk5n5VkQcBh6h+IVyEngMeB8wkpk/WkW/kjRMut3kwlKHzvIKY6MNpnePMTISlZy7qpH9HEXYPwccKdsezcw54FXgEHA2M38MICKmNjtJRMxFRCsiWu12u6LSJKn/dbvJ6+cv8eGnP8/Dv/gHfPjpz/P6+Ut0u1nJ+asK+0ZmXgXOAnvLtuvl61lgb2a+HBE/DHwTcHmzk2TmfGY2M7M5MzNTUWmS1P8uLHU4frrFmYtXADhz8QrHT7e4sNSp5PxVhf3liBinCPpzZdtK+boXOBcR/yAznwUWgO+pqF9JGgqd5ZW1oF915uIVOssrW3zi9lQV9vPAKYqpnKWI2A+8GBELwD8Efg/4+xHxP4B/Cnyuon4laSiMjTaYnZpY1zY7NcHYaKOS80dmNfNBVWs2m9lqtXpdhiTdFatz9qtTObNTEywca7Jvz+RtXaSNiMXMbG5sr2Q1jiTpzoyMBPv2TPLS4w/vyGocw16S+sTISDAzOb4z596Rs0qS+ophL0k1YNhLUg04Zy9JfWInH5dg2EtSH6hq6eVWnMaRpD4wKI9LkCTdgUF5XIIk6Q7s9OMSDHtJ6gPTu8dYONZcC/zVOfvp3WOVnN8LtJLUB3xcgiTVxE4+LsGwl6Q+4Tp7SRpyrrOXpBpwnb0k1YDr7CWpBlxnL0k14Dp7SaqBnV5n78hekmrAkb0k9YGBWXoZEQcj4tPl17vLtsMRMR8Rz0bEaEQ8FRGfiIgXIuLeqvqWpEE3SEsv54ATwHPAkbLt0cycA14FDgF/lZk/DrSAb9l4goiYi4hWRLTa7XaFpUlSfxukpZeNzLwKnAX2lm3Xy9ezwN7M/K2IeBDYDyxuPEFmzmdmMzObMzMzFZYmSf1tkJZeXo6IcYqgP1e2rf5K2guci4iHgCeAj2Rmt8K+JWmg7fTSy8jMak4UcQD4MWAX8ArFyP0bgX8OBMUUz58DfwIk8PHM/NJW52s2m9lqtSqpTZIGQRUPQouIxcxsbmyvbDVOZi4Cj21ofg14+Ybj91bVnyQNm518xLHr7CWpBlxnL0l9wufZS9KQG5ibqiRJ2zdIN1VJkrZpkG6qkiRt0yDdVCVJ2iafZy9JNbDTz7M37CWpT3hTlSTpjhj2klQDhr0k1YBhL0k1YNhLUg24GkeS+sTycpc3377G9ZUuuxoj3H/vOKOj1YzJDXtJ6gPLy13+/PwlTj6/uPYgtGeOHuCb90xWEvhO40hSH3jz7WtrQQ/Fc3FOPr/Im29fq+T8hr0k9YHrK91NH4S2vFLNdt2GvST1gV2NkU0fhDbaqCamDXtJ6gP33zvOM0cPrHsQ2jNHD3D/vdU8PsELtJLUB0ZGgnvGGvzCh76Ne8YaXO6scM9YwwehSdIwubDU4din/mTdvP3s1AQvPf5wJQ9Hq2waJyIORsSny693l22HI2I+Ip6NiNGy7Wci4oer6leShsEg7VQ1B5wAngOOlG2PZuYc8CpwKCL+GTBVYZ+SNBQGaaeqRmZeBc4Ce8u26+XrWWBvZv4W8DtbnSAi5iKiFRGtdrtdYWmS1N8GaaeqyxExThH058q21f9/3Ni2pcycB+YBms1mVlibJPW1Qdqpah44BewCXomI/cCLEbEABMUUjyRpCzu5U1VlYZ+Zi8BjG5pfA17e8HN/CPxhVf1Kkm7Om6okqQYMe0mqAcNekmrAsJekGojM/lzhGBFt4I1tfvw+4GsVliNJd9OdZNh7MnNmY2Pfhv2diIhWZjZ7XYckbcdOZJjTOJJUA4a9JNXAsIb9fK8LkKQ7UHmGDeWcvSRpvWEd2UuSbmDYS1INDGzYR8QHI+LZDW1fHxGfK79/T0QsRMRzEfHdEfH+iHg6Ip6PiO/oTdWS6i4iDpXZ9EJEvO+G9pGI+P2IeLD8eiEiTkfEN0bEgYj4tXInwD3b6Xcg96CNiAeB7wDeteGtJ4C3yu+/BWiWx18B/hGwv3zvb3a8SEna3D0UO/t9O/BdwBfL9o/w/26k+tfAv6HYE+RHgP8F/Hj58x8AfuN2Ox3IkX1mfjkzf2mT9o8Bl8rDvwE+SPEX9X3AFyj+ov4z8D13qVRJWiczf5si8J8APg0QEU3gCvBn5Y/dR7HD3+ouf5+nGMD+JPC/t9PvQIb9jSLimYh4YZO3fgQYB9rANPBRoHvDsSTddRFxH/ArwL8Ffr7Mr+8FDgD/hGLU/1Xg71Lu8hcR7wcWge+myLLbNpDTODfKzJNbvPUC8ElgCfhPFL8V/yfQAT52d6qTpP/PLwMzwMeB38jMX199IyL+PfA8xf7d/5Eioz8G7AM+RZFf21qD7zp7SaqBgZ/GkSTdnGEvSTVg2EtSDRj2klQDA78aR6pSROwCfgmYpPj38TuZ+ZneViXdOcNeWu84RcC/AhAR/zgiXgNeBv6YYh30OPAU8N8y82hEPAb8NcUdkF+kWBv90cy8evfLlzZn2EvrvQ/4THnjy38FvgH4i8z8WES8kJk/EBEPAUc3+ey1zPyFiPgQ8J3Ab9+9sqV35py9tN6XgO/MzK9l5mPAeeDt8r244edGbjh+94b33wUs72yZ0u1xZC+t9wzwiYj4F+XxF4BvLb//9Yg4BTSAnwauR8SngL9D8bySyYj4BMV8/+N3s2jpZryDVqpIRDyfmZtN70g9Z9hLUg04Zy9JNWDYS1INGPaSVAOGvSTVgGEvSTXwfwEBRyJb8QHW/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.scatterplot(data=df_sg3, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bffe1f3-3923-42eb-a02b-bac1ab9501dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}