qPCR-analysis-220613-plate1to5.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": 2,
   "id": "f561ed82-2192-4426-8adf-df14b0d35732",
   "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",
       "      <th>plate_id</th>\n",
       "      <th>sgRNA_id</th>\n",
       "      <th>group</th>\n",
       "      <th>well_id</th>\n",
       "      <th>relExp_25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A01</td>\n",
       "      <td>i-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>20.478409</td>\n",
       "      <td>220613-plate1</td>\n",
       "      <td>188</td>\n",
       "      <td>i-188-1___Rps29 v1</td>\n",
       "      <td>220613-plate1-A01</td>\n",
       "      <td>22.968604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A02</td>\n",
       "      <td>i-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>20.392192</td>\n",
       "      <td>220613-plate1</td>\n",
       "      <td>188</td>\n",
       "      <td>i-188-1___Rps29 v1</td>\n",
       "      <td>220613-plate1-A02</td>\n",
       "      <td>24.383081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A03</td>\n",
       "      <td>i-188-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>20.442585</td>\n",
       "      <td>220613-plate1</td>\n",
       "      <td>188</td>\n",
       "      <td>i-188-1___Rps29 v1</td>\n",
       "      <td>220613-plate1-A03</td>\n",
       "      <td>23.546076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A04</td>\n",
       "      <td>i-401-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>19.597035</td>\n",
       "      <td>220613-plate1</td>\n",
       "      <td>401</td>\n",
       "      <td>i-401-1___Rps29 v1</td>\n",
       "      <td>220613-plate1-A04</td>\n",
       "      <td>42.311129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A05</td>\n",
       "      <td>i-401-1</td>\n",
       "      <td>Rps29 v1</td>\n",
       "      <td>19.547194</td>\n",
       "      <td>220613-plate1</td>\n",
       "      <td>401</td>\n",
       "      <td>i-401-1___Rps29 v1</td>\n",
       "      <td>220613-plate1-A05</td>\n",
       "      <td>43.798397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Well   Sample    Primer         Cq       plate_id sgRNA_id  \\\n",
       "0  A01  i-188-1  Rps29 v1  20.478409  220613-plate1      188   \n",
       "1  A02  i-188-1  Rps29 v1  20.392192  220613-plate1      188   \n",
       "2  A03  i-188-1  Rps29 v1  20.442585  220613-plate1      188   \n",
       "3  A04  i-401-1  Rps29 v1  19.597035  220613-plate1      401   \n",
       "4  A05  i-401-1  Rps29 v1  19.547194  220613-plate1      401   \n",
       "\n",
       "                group            well_id  relExp_25  \n",
       "0  i-188-1___Rps29 v1  220613-plate1-A01  22.968604  \n",
       "1  i-188-1___Rps29 v1  220613-plate1-A02  24.383081  \n",
       "2  i-188-1___Rps29 v1  220613-plate1-A03  23.546076  \n",
       "3  i-401-1___Rps29 v1  220613-plate1-A04  42.311129  \n",
       "4  i-401-1___Rps29 v1  220613-plate1-A05  43.798397  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in data from different plates and combine them\n",
    "plate_ids = ['220613-plate1',\n",
    "             '220613-plate2',\n",
    "             '220613-plate3',\n",
    "             '220613-plate4',\n",
    "             '220613-plate5']\n",
    "dfs = []\n",
    "for plate_id in plate_ids:\n",
    "    dfs.append(get_plate_data(plate_id))\n",
    "\n",
    "df = pd.concat(dfs)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# Add a few helpful columns\n",
    "df['group'] = [df.Sample[i] + '___' + df.Primer[i] for i in range(len(df))]\n",
    "df['well_id'] = [df.plate_id[i] + '-' + df.Well[i] for i in range(len(df))]\n",
    "df['relExp_25'] = [2**(25-df.Cq[i]) for i in range(len(df))]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4c82282-37d7-48e3-870c-7a0f420f172f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = get_expression_data(df, 'Serpinh1 sg1 v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "697fd19e-be65-4465-847c-fe2eed124674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 4,
     "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, x='Group', y='relExp')\n",
    "sns.scatterplot(data=df1, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "447cdb0a-ccb5-4a0f-8580-f695c4c47450",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = get_expression_data(df, 'Serpinh1 sg2 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc89fb9e-ebf5-45c0-be10-3bd1489dfd26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 6,
     "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')\n",
    "sns.scatterplot(data=df2, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c0e24ca-e97f-4d81-a48a-aaae4563e6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = get_expression_data(df, 'Serpinh1 sg3 v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3e9c6fe-be4f-44c1-9c62-5d8db957971f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Group', ylabel='relExp'>"
      ]
     },
     "execution_count": 8,
     "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=df3, x='Group', y='relExp')\n",
    "sns.scatterplot(data=df3, x='Group', y='relExp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ac3d259-5160-4644-9b05-fd27042cc42e",
   "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
}