FugenevXfect_barplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e5388d1-3e0b-48b8-aa9f-9ca530eae0c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "kozak_data = pd.read_csv ('Combined_data_IDT_Kozak.csv')\n",
    "mCherry_data = pd.read_csv ('Combined_data_mCherry.csv')\n",
    "merge = [kozak_data, mCherry_data]\n",
    "combine_df = pd.concat(merge).reset_index(drop=True)\n",
    "combine_df = combine_df[[\"Recombined_construct\", \"Reagent\", \"pct_mCherry\"]]\n",
    "\n",
    "def average(lst):\n",
    "    return sum(lst) / len(lst)\n",
    "\n",
    "kozak_Xfect = combine_df.loc[(combine_df['Recombined_construct'] == 'IDT_Std_ACE2_Kozak') & \n",
    "                             (combine_df['Reagent'] == 'Xfect')]\n",
    "kozak_Xfect = average(kozak_Xfect['pct_mCherry'].tolist())\n",
    "\n",
    "kozak_Fugene = combine_df.loc[(combine_df['Recombined_construct'] == 'IDT_Std_ACE2_Kozak') & \n",
    "                             (combine_df['Reagent'] == 'Fugene')]\n",
    "kozak_Fugene = average(kozak_Fugene['pct_mCherry'].tolist())\n",
    "\n",
    "mCherry_Xfect = combine_df.loc[(combine_df['Recombined_construct'] == 'mCherry') & \n",
    "                             (combine_df['Reagent'] == 'Xfect')]\n",
    "mCherry_Xfect = average(mCherry_Xfect['pct_mCherry'].tolist())\n",
    "\n",
    "mCherry_Fugene = combine_df.loc[(combine_df['Recombined_construct'] == 'mCherry') & \n",
    "                             (combine_df['Reagent'] == 'Fugene')]\n",
    "mCherry_Fugene = average(mCherry_Fugene['pct_mCherry'].tolist())\n",
    "\n",
    "final_data = [[\"Xfect\", kozak_Xfect, mCherry_Xfect],\n",
    "              [\"Fugene\", kozak_Fugene, mCherry_Fugene]]\n",
    "\n",
    "df = pd.DataFrame(final_data, columns = [\"Reagent\", \"Kozak\", \"mCherry\"])\n",
    "df.plot(x = \"Reagent\", y = [\"Kozak\", \"mCherry\"], kind=\"bar\",figsize=(9,8))\n",
    "plt.xlabel('Reagent', fontweight ='bold', fontsize = 15)\n",
    "plt.ylabel('% Expression of mCherry', fontweight ='bold', fontsize = 15)\n",
    "plt.savefig('ReagentVmCherryExpression_Bar.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3b3f3162-e785-4d8a-bee5-b7c4c9327324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Xfect', [3.114512, 3.26, 5.01], [19.959993, 26.0, 28.6]],\n",
       " ['Fugene', [2.472758, 1.72, 2.1], [5.042017, 6.54, 8.17]]]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bc20dc84-168b-4f2e-8cce-e8aa4f55d1a3",
   "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>Recombined_construct</th>\n",
       "      <th>Reagent</th>\n",
       "      <th>pct_mCherry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IDT_Std_ACE2_Kozak</td>\n",
       "      <td>Xfect</td>\n",
       "      <td>3.114512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IDT_Std_ACE2_Kozak</td>\n",
       "      <td>Xfect</td>\n",
       "      <td>3.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IDT_Std_ACE2_Kozak</td>\n",
       "      <td>Xfect</td>\n",
       "      <td>5.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDT_Std_ACE2_Kozak</td>\n",
       "      <td>Fugene</td>\n",
       "      <td>2.472758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IDT_Std_ACE2_Kozak</td>\n",
       "      <td>Fugene</td>\n",
       "      <td>1.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IDT_Std_ACE2_Kozak</td>\n",
       "      <td>Fugene</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mCherry</td>\n",
       "      <td>Xfect</td>\n",
       "      <td>19.959993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mCherry</td>\n",
       "      <td>Xfect</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mCherry</td>\n",
       "      <td>Xfect</td>\n",
       "      <td>28.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mCherry</td>\n",
       "      <td>Fugene</td>\n",
       "      <td>5.042017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mCherry</td>\n",
       "      <td>Fugene</td>\n",
       "      <td>6.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mCherry</td>\n",
       "      <td>Fugene</td>\n",
       "      <td>8.170000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recombined_construct Reagent  pct_mCherry\n",
       "0    IDT_Std_ACE2_Kozak   Xfect     3.114512\n",
       "1    IDT_Std_ACE2_Kozak   Xfect     3.260000\n",
       "2    IDT_Std_ACE2_Kozak   Xfect     5.010000\n",
       "3    IDT_Std_ACE2_Kozak  Fugene     2.472758\n",
       "4    IDT_Std_ACE2_Kozak  Fugene     1.720000\n",
       "5    IDT_Std_ACE2_Kozak  Fugene     2.100000\n",
       "6               mCherry   Xfect    19.959993\n",
       "7               mCherry   Xfect    26.000000\n",
       "8               mCherry   Xfect    28.600000\n",
       "9               mCherry  Fugene     5.042017\n",
       "10              mCherry  Fugene     6.540000\n",
       "11              mCherry  Fugene     8.170000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4a47772-aaa1-40fd-95b6-21ab902c8c1a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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