qPCR-analysis-220525-plate1n2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 25,
   "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": 29,
   "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": 30,
   "id": "efcf0422-23ad-4b71-b55b-26a6719ed5a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in both plate 1 and 2 data\n",
    "df_plate1 = get_plate_data('20220525-plate1')\n",
    "df_plate2 = get_plate_data('20220525-plate2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4a33278c-da84-40cb-aec5-68f7e58deaa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate the data (stacking up and down)\n",
    "df = pd.concat([df_plate1, df_plate2])\n",
    "df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f9906e3e-cd0e-4837-aba8-23a2c5d37064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>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>B-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>22.365004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A02</td>\n",
       "      <td>B-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>22.255025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A03</td>\n",
       "      <td>B-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>22.269795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A04</td>\n",
       "      <td>B-188-1</td>\n",
       "      <td>Itga9 sg1 v1</td>\n",
       "      <td>30.422193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A05</td>\n",
       "      <td>B-188-1</td>\n",
       "      <td>Itga9 sg1 v1</td>\n",
       "      <td>30.570743</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>145</th>\n",
       "      <td>H07</td>\n",
       "      <td>B-319-1</td>\n",
       "      <td>Itga9 sg3 v2</td>\n",
       "      <td>33.991596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>H08</td>\n",
       "      <td>B-319-2</td>\n",
       "      <td>Itga9 sg3 v2</td>\n",
       "      <td>34.287464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>H09</td>\n",
       "      <td>B-319-3</td>\n",
       "      <td>Itga9 sg3 v2</td>\n",
       "      <td>34.444428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>H10</td>\n",
       "      <td>B-319-4</td>\n",
       "      <td>Itga9 sg3 v2</td>\n",
       "      <td>36.009095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>H11</td>\n",
       "      <td>B-319-5</td>\n",
       "      <td>Itga9 sg3 v2</td>\n",
       "      <td>37.797811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Well   Sample        Primer         Cq\n",
       "0    A01  B-188-1      Rps29 v1  22.365004\n",
       "1    A02  B-188-1      Rps29 v1  22.255025\n",
       "2    A03  B-188-1      Rps29 v1  22.269795\n",
       "3    A04  B-188-1  Itga9 sg1 v1  30.422193\n",
       "4    A05  B-188-1  Itga9 sg1 v1  30.570743\n",
       "..   ...      ...           ...        ...\n",
       "145  H07  B-319-1  Itga9 sg3 v2  33.991596\n",
       "146  H08  B-319-2  Itga9 sg3 v2  34.287464\n",
       "147  H09  B-319-3  Itga9 sg3 v2  34.444428\n",
       "148  H10  B-319-4  Itga9 sg3 v2  36.009095\n",
       "149  H11  B-319-5  Itga9 sg3 v2  37.797811\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c4c82282-37d7-48e3-870c-7a0f420f172f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sg1 = get_expression_data(df, 'Itga9 sg1 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "697fd19e-be65-4465-847c-fe2eed124674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>B-188-1</td>\n",
       "      <td>22.296608</td>\n",
       "      <td>30.482439</td>\n",
       "      <td>8.185831</td>\n",
       "      <td>0.003434</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B-188-2</td>\n",
       "      <td>19.685096</td>\n",
       "      <td>28.442445</td>\n",
       "      <td>8.757349</td>\n",
       "      <td>0.002311</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B-188-3</td>\n",
       "      <td>19.329868</td>\n",
       "      <td>27.929631</td>\n",
       "      <td>8.599764</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B-188-4</td>\n",
       "      <td>20.838192</td>\n",
       "      <td>28.543398</td>\n",
       "      <td>7.705205</td>\n",
       "      <td>0.004792</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B-188-5</td>\n",
       "      <td>20.293072</td>\n",
       "      <td>28.970089</td>\n",
       "      <td>8.677016</td>\n",
       "      <td>0.002443</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B-317-1</td>\n",
       "      <td>22.139458</td>\n",
       "      <td>31.110888</td>\n",
       "      <td>8.971430</td>\n",
       "      <td>0.001992</td>\n",
       "      <td>B-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B-317-2</td>\n",
       "      <td>19.743959</td>\n",
       "      <td>28.762788</td>\n",
       "      <td>9.018830</td>\n",
       "      <td>0.001928</td>\n",
       "      <td>B-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B-317-3</td>\n",
       "      <td>20.788395</td>\n",
       "      <td>28.016689</td>\n",
       "      <td>7.228294</td>\n",
       "      <td>0.006669</td>\n",
       "      <td>B-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B-317-4</td>\n",
       "      <td>20.320073</td>\n",
       "      <td>27.917217</td>\n",
       "      <td>7.597144</td>\n",
       "      <td>0.005165</td>\n",
       "      <td>B-317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B-317-5</td>\n",
       "      <td>19.854365</td>\n",
       "      <td>28.056246</td>\n",
       "      <td>8.201881</td>\n",
       "      <td>0.003396</td>\n",
       "      <td>B-317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sample     Cq_ref    Cq_test   deltaCq    relExp  Group\n",
       "0  B-188-1  22.296608  30.482439  8.185831  0.003434  B-188\n",
       "1  B-188-2  19.685096  28.442445  8.757349  0.002311  B-188\n",
       "2  B-188-3  19.329868  27.929631  8.599764  0.002578  B-188\n",
       "3  B-188-4  20.838192  28.543398  7.705205  0.004792  B-188\n",
       "4  B-188-5  20.293072  28.970089  8.677016  0.002443  B-188\n",
       "5  B-317-1  22.139458  31.110888  8.971430  0.001992  B-317\n",
       "6  B-317-2  19.743959  28.762788  9.018830  0.001928  B-317\n",
       "7  B-317-3  20.788395  28.016689  7.228294  0.006669  B-317\n",
       "8  B-317-4  20.320073  27.917217  7.597144  0.005165  B-317\n",
       "9  B-317-5  19.854365  28.056246  8.201881  0.003396  B-317"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sg1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4b470502-bb83-4815-97e9-a9dd204d42ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "id": "b96876ad-46e2-446c-8b58-5947c1b37e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AL2fmOSSpx9bKsIh4A/BeIIC7MvPEikz7GO38ug74jcz84gvZ7sCHf8cHM/PJiPjriBjNzIWIeB3tf5SXdD7z48DrgBrtGyZ+kvY/6qjBL6nPnpdhtLPrfbSz6k0R8XWWZVpm/lZEfA/wkRca/DA8bZ+PR8QXgHkgATLzycz85LLP/C3w08AXgB8D7snM3cCTEfGqXhcsScuslmFfAq4FPgh8dZVMA7gd+MyVbHBYwv/XMvNW4DTwvxHx4VU+c2dmtoBZYAp4f2d8cVmS+uV5GRYRbwSOAW8D7lzje6/OzMevZIPD0vb5REQ8C2wBZjLzO6t85p8i4k+BFvAe4LqI+Awwn5lf6WGtkrTS8zIsIm4GPgc0WeVa/4iYon2e4Ip4nb8kFWhY2j6SpBfA8JekAhn+klQgw1+SCjQsV/tIXRERm4BPApO0/798MTMf6G9V0otn+EuXtpd24D8CEBFviYhvAA8DXwbeDozTvm/kjzPznRHxLuBbtG/A+RqwjfZ9Jud7X760OsNfurQfBh6IiKuBTwDXAN/MzA9FxF9m5i9GxPXAO1f57nOZ+dGI+BngrcDf9a5s6dLs+UuX9u/AWzPz25n5LuAE/39jTSz73Miy5atWvP8S2rftSxuGR/7SpX0W+HRE/Fxn+SvA6zs/fz4iDgGjwK8DFyLic8DLga8CkxHxadrnCw4ibSDe4St1SUT8RWau1g6S+s7wl6QC2fOXpAIZ/pJUIMNfkgpk+EtSgQx/SSrQ/wF+lQLVB1Xw8gAAAABJRU5ErkJggg==\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": 37,
   "id": "b6c69dab-e451-4279-9b6f-bf2f39fc6943",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sg2 = get_expression_data(df, 'Itga9 sg2 v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>B-188-1</td>\n",
       "      <td>22.296608</td>\n",
       "      <td>35.959532</td>\n",
       "      <td>13.662924</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B-188-2</td>\n",
       "      <td>19.685096</td>\n",
       "      <td>34.736312</td>\n",
       "      <td>15.051216</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B-188-3</td>\n",
       "      <td>19.329868</td>\n",
       "      <td>34.833278</td>\n",
       "      <td>15.503410</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B-188-4</td>\n",
       "      <td>20.838192</td>\n",
       "      <td>34.498038</td>\n",
       "      <td>13.659846</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B-188-5</td>\n",
       "      <td>20.293072</td>\n",
       "      <td>35.893704</td>\n",
       "      <td>15.600631</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>B-188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B-318-1</td>\n",
       "      <td>22.519284</td>\n",
       "      <td>34.588696</td>\n",
       "      <td>12.069412</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>B-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B-318-2</td>\n",
       "      <td>20.849718</td>\n",
       "      <td>34.355151</td>\n",
       "      <td>13.505434</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>B-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B-318-3</td>\n",
       "      <td>20.775141</td>\n",
       "      <td>35.867548</td>\n",
       "      <td>15.092407</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>B-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B-318-4</td>\n",
       "      <td>19.641979</td>\n",
       "      <td>33.940019</td>\n",
       "      <td>14.298040</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>B-318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B-318-5</td>\n",
       "      <td>20.237146</td>\n",
       "      <td>35.166236</td>\n",
       "      <td>14.929090</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>B-318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sample     Cq_ref    Cq_test    deltaCq    relExp  Group\n",
       "0  B-188-1  22.296608  35.959532  13.662924  0.000077  B-188\n",
       "1  B-188-2  19.685096  34.736312  15.051216  0.000029  B-188\n",
       "2  B-188-3  19.329868  34.833278  15.503410  0.000022  B-188\n",
       "3  B-188-4  20.838192  34.498038  13.659846  0.000077  B-188\n",
       "4  B-188-5  20.293072  35.893704  15.600631  0.000020  B-188\n",
       "5  B-318-1  22.519284  34.588696  12.069412  0.000233  B-318\n",
       "6  B-318-2  20.849718  34.355151  13.505434  0.000086  B-318\n",
       "7  B-318-3  20.775141  35.867548  15.092407  0.000029  B-318\n",
       "8  B-318-4  19.641979  33.940019  14.298040  0.000050  B-318\n",
       "9  B-318-5  20.237146  35.166236  14.929090  0.000032  B-318"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "67053bf4-dc70-423b-beab-9025898c0df9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 39,
     "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_sg2, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9c68a898-c7f6-47ab-9684-32ff9fc44529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "id": "e33bb47a-22d2-4c9b-b4a7-82f96edaf06e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Itgb1 = get_expression_data(df, 'Itga9 sg3 v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fa52bedc-2de0-4bfd-84c6-311510d8ea91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 42,
     "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_Itgb1, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "30609e94-6208-4e91-8aad-f173f72cf360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KPwc+GxEfXv37c8C7V//8txHxOWAA+C3g9Yh4DHgr8DwwEhGfZWV9/RhSzfhkp7SFiHhy9RvtpFoy5JJUONfIJalwhlySCmfIJalwhlySCmfIJalw/w/lKR5UcApENAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.scatterplot(data=df_Itgb1, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "290aa8ee-5268-4e3b-9673-2c2f8d0ee91e",
   "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
}