growth_curves/dose_response.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dynamic-classroom",
   "metadata": {},
   "source": [
    "# Notebook to generate dose-response curves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7885934-e195-4e96-a100-2be8f69e58df",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "stable-bumper",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['svg.fonttype'] = 'none'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a735d16e-a5b3-43fa-aa0c-8fc80f0fbf01",
   "metadata": {},
   "source": [
    "## Specify paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "58c4291c-7f15-4e92-b0bc-40224f38acff",
   "metadata": {},
   "outputs": [],
   "source": [
    "tecan = {'anidulafungin': ['20220215_growth_curves_anidulafungin_1_originalfile.xlsx', '20220216_growth_curves_anidulafungin_2_originalfile.xlsx'], # two excel files because of accidental interruption of tecan run\n",
    "         'caspofungin': '20220214_growth_curves_caspofungin.xlsx',\n",
    "         'micafungin': '20220214_growth_curves_micafungin.xlsx'}\n",
    "layout = 'dose-response_full-layout.xlsx'\n",
    "graph_outpath = 'graphs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de2d626-227b-495b-84fa-84ee95da4bc8",
   "metadata": {},
   "source": [
    "## Import layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "precise-district",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>compound</th>\n",
       "      <th>concentration</th>\n",
       "      <th>strain</th>\n",
       "      <th>genotype</th>\n",
       "      <th>replicate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>well</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B2</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B3</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B4</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>WT</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B5</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G6</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G7</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G8</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G9</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G10</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           compound  concentration  strain        genotype  replicate\n",
       "well                                                                 \n",
       "B2    anidulafungin            2.0  BY4741              WT          1\n",
       "B3    anidulafungin            2.0  BY4741              WT          2\n",
       "B4    anidulafungin            2.0  BY4741              WT          3\n",
       "B5    anidulafungin            2.0  BY4741  delta-FKS1-HS1          1\n",
       "B6    anidulafungin            2.0  BY4741  delta-FKS1-HS1          2\n",
       "...             ...            ...     ...             ...        ...\n",
       "G6       micafungin            0.0  BY4741  delta-FKS1-HS1          2\n",
       "G7       micafungin            0.0  BY4741  delta-FKS1-HS1          3\n",
       "G8       micafungin            0.0  BY4741  delta-FKS2-HS1          1\n",
       "G9       micafungin            0.0  BY4741  delta-FKS2-HS1          2\n",
       "G10      micafungin            0.0  BY4741  delta-FKS2-HS1          3\n",
       "\n",
       "[162 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plate_df = pd.read_excel(layout, header=0, index_col=0)\n",
    "plate_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loose-return",
   "metadata": {},
   "source": [
    "## Import data for accidentally interrupted run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "solved-japanese",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximate lag between runs (from start to start) = 25.26 h\n"
     ]
    },
    {
     "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>time (h)</th>\n",
       "      <th>OD</th>\n",
       "      <th>compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10523</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10733</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10565</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10668</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10783</td>\n",
       "      <td>anidulafungin</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>11935</th>\n",
       "      <td>G7</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.11410</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11936</th>\n",
       "      <td>G8</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.16420</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11937</th>\n",
       "      <td>G9</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.15560</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11938</th>\n",
       "      <td>G10</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.16380</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11939</th>\n",
       "      <td>G11</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>0.07825</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11940 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      well   time (h)       OD       compound\n",
       "0       B2        0.0  0.10523  anidulafungin\n",
       "1       B3        0.0  0.10733  anidulafungin\n",
       "2       B4        0.0  0.10565  anidulafungin\n",
       "3       B5        0.0  0.10668  anidulafungin\n",
       "4       B6        0.0  0.10783  anidulafungin\n",
       "...    ...        ...      ...            ...\n",
       "11935   G7  49.500833  1.11410  anidulafungin\n",
       "11936   G8  49.500833  1.16420  anidulafungin\n",
       "11937   G9  49.500833  1.15560  anidulafungin\n",
       "11938  G10  49.500833  1.16380  anidulafungin\n",
       "11939  G11  49.500833  0.07825  anidulafungin\n",
       "\n",
       "[11940 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To be used only if run was accidentally interrupted\n",
    "# t0 timepoints for both parts of the interrupted run must be manually entered\n",
    "# Both values are indicated in the metadata lines at the bottom of the original excel sheet\n",
    "\n",
    "import datetime\n",
    "\n",
    "t0_part1 = datetime.datetime.strptime('2022-02-15_09-50-23', '%Y-%m-%d_%H-%M-%S')\n",
    "t0_part2 = datetime.datetime.strptime('2022-02-16_11-06-15', '%Y-%m-%d_%H-%M-%S')\n",
    "lag = (t0_part2-t0_part1).total_seconds()/3600\n",
    "print('Approximate lag between runs (from start to start) = {} h'.format(round(lag,2)))\n",
    "\n",
    "st1 = tecan['anidulafungin'][0]\n",
    "df1 = pd.read_excel(st1, header=1, index_col=0, skiprows=[2], skipfooter=34)\n",
    "df1.rename(columns=lambda x: float(x.strip('s'))/3600, inplace=True)\n",
    "\n",
    "st2 = tecan['anidulafungin'][1]\n",
    "df2 = pd.read_excel(st2, header=1, index_col=0,skiprows=[2], skipfooter=34)\n",
    "df2.rename(columns=lambda x: float(x.strip('s'))/3600, inplace=True)\n",
    "\n",
    "add_lag = [x+lag for x in df2.columns.tolist()]\n",
    "df2.columns = add_lag\n",
    "\n",
    "ANI_df = pd.concat([df1, df2], axis=1)\n",
    "ANI_long = ANI_df.reset_index().rename(columns = {'index' : 'well'})\n",
    "ANI_long = ANI_long.melt(id_vars = ['well'], var_name = 'time (h)', value_name = 'OD')\n",
    "ANI_long['compound'] = 'anidulafungin'\n",
    "ANI_long"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hourly-diary",
   "metadata": {},
   "source": [
    "## Import 'properly formatted' data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bacterial-soldier",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>time (h)</th>\n",
       "      <th>OD</th>\n",
       "      <th>compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10130</td>\n",
       "      <td>caspofungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.12595</td>\n",
       "      <td>caspofungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.13467</td>\n",
       "      <td>caspofungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14067</td>\n",
       "      <td>caspofungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.13990</td>\n",
       "      <td>caspofungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>45955</th>\n",
       "      <td>G7</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.11410</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45956</th>\n",
       "      <td>G8</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.16420</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45957</th>\n",
       "      <td>G9</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.15560</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45958</th>\n",
       "      <td>G10</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.16380</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45959</th>\n",
       "      <td>G11</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>0.07825</td>\n",
       "      <td>anidulafungin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45960 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      well   time (h)       OD       compound\n",
       "0       B2        0.0  0.10130    caspofungin\n",
       "1       B3        0.0  0.12595    caspofungin\n",
       "2       B4        0.0  0.13467    caspofungin\n",
       "3       B5        0.0  0.14067    caspofungin\n",
       "4       B6        0.0  0.13990    caspofungin\n",
       "...    ...        ...      ...            ...\n",
       "45955   G7  49.500833  1.11410  anidulafungin\n",
       "45956   G8  49.500833  1.16420  anidulafungin\n",
       "45957   G9  49.500833  1.15560  anidulafungin\n",
       "45958  G10  49.500833  1.16380  anidulafungin\n",
       "45959  G11  49.500833  0.07825  anidulafungin\n",
       "\n",
       "[45960 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf = []\n",
    "for c in ['caspofungin','micafungin']:\n",
    "    df = pd.read_excel(tecan[c], header=1, index_col=0,skiprows=[2])\n",
    "    df.rename(columns=lambda x: float(x.strip('s'))/3600, inplace=True)\n",
    "    longdf = df.reset_index().rename(columns = {'index' : 'well'})\n",
    "    longdf = longdf.melt(id_vars = ['well'], var_name = 'time (h)', value_name = 'OD')\n",
    "    longdf['compound'] = c\n",
    "    ldf.append(longdf)\n",
    "\n",
    "master = pd.concat(ldf+[ANI_long], ignore_index=True)\n",
    "master"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neural-rates",
   "metadata": {},
   "source": [
    "## Annotate dataframe with layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "floating-scheduling",
   "metadata": {},
   "outputs": [
    {
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       "    }\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>time (h)</th>\n",
       "      <th>OD</th>\n",
       "      <th>compound</th>\n",
       "      <th>concentration</th>\n",
       "      <th>strain</th>\n",
       "      <th>genotype</th>\n",
       "      <th>replicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10130</td>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.25</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.10452</td>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.25</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.10538</td>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.25</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.10608</td>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.25</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.10905</td>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.25</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41359</th>\n",
       "      <td>G10</td>\n",
       "      <td>48.500833</td>\n",
       "      <td>1.16090</td>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.00</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41360</th>\n",
       "      <td>G10</td>\n",
       "      <td>48.750833</td>\n",
       "      <td>1.16160</td>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.00</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41361</th>\n",
       "      <td>G10</td>\n",
       "      <td>49.000833</td>\n",
       "      <td>1.16280</td>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.00</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41362</th>\n",
       "      <td>G10</td>\n",
       "      <td>49.250833</td>\n",
       "      <td>1.16320</td>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.00</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41363</th>\n",
       "      <td>G10</td>\n",
       "      <td>49.500833</td>\n",
       "      <td>1.16380</td>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.00</td>\n",
       "      <td>BY4741</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41364 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      well   time (h)       OD       compound  concentration  strain  \\\n",
       "0       B2        0.0  0.10130    caspofungin           0.25  BY4741   \n",
       "1       B2       0.25  0.10452    caspofungin           0.25  BY4741   \n",
       "2       B2        0.5  0.10538    caspofungin           0.25  BY4741   \n",
       "3       B2       0.75  0.10608    caspofungin           0.25  BY4741   \n",
       "4       B2        1.0  0.10905    caspofungin           0.25  BY4741   \n",
       "...    ...        ...      ...            ...            ...     ...   \n",
       "41359  G10  48.500833  1.16090  anidulafungin           0.00  BY4741   \n",
       "41360  G10  48.750833  1.16160  anidulafungin           0.00  BY4741   \n",
       "41361  G10  49.000833  1.16280  anidulafungin           0.00  BY4741   \n",
       "41362  G10  49.250833  1.16320  anidulafungin           0.00  BY4741   \n",
       "41363  G10  49.500833  1.16380  anidulafungin           0.00  BY4741   \n",
       "\n",
       "             genotype  replicate  \n",
       "0                  WT          1  \n",
       "1                  WT          1  \n",
       "2                  WT          1  \n",
       "3                  WT          1  \n",
       "4                  WT          1  \n",
       "...               ...        ...  \n",
       "41359  delta-FKS2-HS1          3  \n",
       "41360  delta-FKS2-HS1          3  \n",
       "41361  delta-FKS2-HS1          3  \n",
       "41362  delta-FKS2-HS1          3  \n",
       "41363  delta-FKS2-HS1          3  \n",
       "\n",
       "[41364 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = master.merge(right=plate_df, on=['compound','well'])\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "transsexual-buying",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07d059eb-b33f-44ef-8752-2170e1598ad6",
   "metadata": {},
   "source": [
    "```\n",
    "grid = sns.FacetGrid(data=merged_df[merged_df['time (h)'] <=40],\n",
    "                     row = 'compound', col = 'genotype',\n",
    "                     hue='concentration', palette='rocket_r')\n",
    "grid.map(sns.lineplot, 'time (h)', 'OD')\n",
    "grid.set_titles(row_template='{row_name}', col_template='{col_name}')\n",
    "grid.set_axis_labels('time (h)', 'OD')\n",
    "grid.add_legend(title = 'Concentration (ug/mL)')\n",
    "grid.tight_layout()\n",
    "plt.savefig(f'{graph_outpath}/dose-response_40h.svg', format='svg', dpi=300)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proud-laundry",
   "metadata": {},
   "source": [
    "## Calculate derivative growth rate (DGR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "linear-hughes",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_derivative_growth_rate(g):\n",
    "    # rate is multiplied by 4 to convert from /15min (measurements every 15 min) to /h\n",
    "    data_diff = g.OD.diff()*4\n",
    "    get_top_5 = data_diff.nlargest(5)    \n",
    "    growth_rate = np.median(get_top_5)\n",
    "    return growth_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "measured-spring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>compound</th>\n",
       "      <th>well</th>\n",
       "      <th>concentration</th>\n",
       "      <th>genotype</th>\n",
       "      <th>replicate</th>\n",
       "      <th>dgr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.09868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.09920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.11240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          compound well  concentration        genotype  replicate      dgr\n",
       "0    anidulafungin  B10            2.0  delta-FKS2-HS1          3  0.00820\n",
       "1    anidulafungin   B2            2.0              WT          1  0.00808\n",
       "2    anidulafungin   B3            2.0              WT          2  0.00840\n",
       "3    anidulafungin   B4            2.0              WT          3  0.00828\n",
       "4    anidulafungin   B5            2.0  delta-FKS1-HS1          1  0.00800\n",
       "..             ...  ...            ...             ...        ...      ...\n",
       "157     micafungin   G5            0.0  delta-FKS1-HS1          1  0.09868\n",
       "158     micafungin   G6            0.0  delta-FKS1-HS1          2  0.10008\n",
       "159     micafungin   G7            0.0  delta-FKS1-HS1          3  0.09920\n",
       "160     micafungin   G8            0.0  delta-FKS2-HS1          1  0.11000\n",
       "161     micafungin   G9            0.0  delta-FKS2-HS1          2  0.11240\n",
       "\n",
       "[162 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dgr = merged_df[(merged_df['time (h)'] > .25)\n",
    "                & (merged_df['time (h)'] <= 40)\n",
    "               ].groupby(['compound','well','concentration','genotype','replicate']\n",
    "                        )[['OD']].apply(func=get_derivative_growth_rate).reset_index(name='dgr')\n",
    "dgr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subjective-angle",
   "metadata": {},
   "source": [
    "## Convert concentrations (ug/mL -> uM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "specific-volunteer",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_uM(ug, mass_mol):\n",
    "    # a simple function to convert ug/ml to uM with the molecular weigth of the compound\n",
    "    vol = 0.001\n",
    "    uM = (((ug*10**-6)/(vol*mass_mol))*10**6)\n",
    "    return uM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d09e26e-c75b-4ae0-9bfb-c5aae831b5b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "877.0083491194836\n"
     ]
    }
   ],
   "source": [
    "print(convert_to_uM(1000,1140.24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aging-colors",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound</th>\n",
       "      <th>well</th>\n",
       "      <th>concentration</th>\n",
       "      <th>genotype</th>\n",
       "      <th>replicate</th>\n",
       "      <th>dgr</th>\n",
       "      <th>concentration_uM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00820</td>\n",
       "      <td>1.754017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00808</td>\n",
       "      <td>1.754017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00840</td>\n",
       "      <td>1.754017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00828</td>\n",
       "      <td>1.754017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>1.754017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.09868</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10008</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.09920</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.11240</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          compound well  concentration        genotype  replicate      dgr  \\\n",
       "0    anidulafungin  B10            2.0  delta-FKS2-HS1          3  0.00820   \n",
       "1    anidulafungin   B2            2.0              WT          1  0.00808   \n",
       "2    anidulafungin   B3            2.0              WT          2  0.00840   \n",
       "3    anidulafungin   B4            2.0              WT          3  0.00828   \n",
       "4    anidulafungin   B5            2.0  delta-FKS1-HS1          1  0.00800   \n",
       "..             ...  ...            ...             ...        ...      ...   \n",
       "157     micafungin   G5            0.0  delta-FKS1-HS1          1  0.09868   \n",
       "158     micafungin   G6            0.0  delta-FKS1-HS1          2  0.10008   \n",
       "159     micafungin   G7            0.0  delta-FKS1-HS1          3  0.09920   \n",
       "160     micafungin   G8            0.0  delta-FKS2-HS1          1  0.11000   \n",
       "161     micafungin   G9            0.0  delta-FKS2-HS1          2  0.11240   \n",
       "\n",
       "     concentration_uM  \n",
       "0            1.754017  \n",
       "1            1.754017  \n",
       "2            1.754017  \n",
       "3            1.754017  \n",
       "4            1.754017  \n",
       "..                ...  \n",
       "157          0.000000  \n",
       "158          0.000000  \n",
       "159          0.000000  \n",
       "160          0.000000  \n",
       "161          0.000000  \n",
       "\n",
       "[162 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw = {'caspofungin': 1093.31,\n",
    "      'micafungin': 1270.28,\n",
    "      'anidulafungin': 1140.24}\n",
    "\n",
    "dgr['concentration_uM'] = dgr.apply(lambda row: convert_to_uM(row.concentration, mw[row.compound]), axis=1)\n",
    "dgr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74df7ee0-e9b5-4c6e-a0c7-ad27464f90ef",
   "metadata": {},
   "source": [
    "## Convert concentrations (uM -> ug/mL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dce4f52a-1d7b-4d55-b41f-5b1bb6845627",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_ug(uM, mass_mol):\n",
    "    # a simple function to convert uM to ug/ml with the molecular weigth of the compound\n",
    "    ug = (uM*mass_mol)/1000\n",
    "    return ug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7bb739ef-7b04-494a-96a2-440d3d6dd70d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5815224000000001\n"
     ]
    }
   ],
   "source": [
    "print(convert_to_ug(0.51, 1140.24))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "standard-expert",
   "metadata": {},
   "source": [
    "## Calculate inhibition coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "hired-walnut",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_inhib_coeff(df, comp, fitness):\n",
    "    ref = df.loc[(df.genotype=='WT')\n",
    "                 & (df.concentration_uM==0)\n",
    "                 & (df.compound == comp)\n",
    "                 , 'dgr'].mean()\n",
    "    return (ref - fitness)/ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "accepted-drink",
   "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>compound</th>\n",
       "      <th>well</th>\n",
       "      <th>concentration</th>\n",
       "      <th>genotype</th>\n",
       "      <th>replicate</th>\n",
       "      <th>dgr</th>\n",
       "      <th>concentration_uM</th>\n",
       "      <th>IC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00820</td>\n",
       "      <td>1.754017</td>\n",
       "      <td>0.924252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00808</td>\n",
       "      <td>1.754017</td>\n",
       "      <td>0.925360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00840</td>\n",
       "      <td>1.754017</td>\n",
       "      <td>0.922404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WT</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00828</td>\n",
       "      <td>1.754017</td>\n",
       "      <td>0.923513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>B5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00800</td>\n",
       "      <td>1.754017</td>\n",
       "      <td>0.926099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.09868</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS1-HS1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.09920</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>G9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>delta-FKS2-HS1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.11240</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.023431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          compound well  concentration        genotype  replicate      dgr  \\\n",
       "0    anidulafungin  B10            2.0  delta-FKS2-HS1          3  0.00820   \n",
       "1    anidulafungin   B2            2.0              WT          1  0.00808   \n",
       "2    anidulafungin   B3            2.0              WT          2  0.00840   \n",
       "3    anidulafungin   B4            2.0              WT          3  0.00828   \n",
       "4    anidulafungin   B5            2.0  delta-FKS1-HS1          1  0.00800   \n",
       "..             ...  ...            ...             ...        ...      ...   \n",
       "157     micafungin   G5            0.0  delta-FKS1-HS1          1  0.09868   \n",
       "158     micafungin   G6            0.0  delta-FKS1-HS1          2  0.10008   \n",
       "159     micafungin   G7            0.0  delta-FKS1-HS1          3  0.09920   \n",
       "160     micafungin   G8            0.0  delta-FKS2-HS1          1  0.11000   \n",
       "161     micafungin   G9            0.0  delta-FKS2-HS1          2  0.11240   \n",
       "\n",
       "     concentration_uM        IC  \n",
       "0            1.754017  0.924252  \n",
       "1            1.754017  0.925360  \n",
       "2            1.754017  0.922404  \n",
       "3            1.754017  0.923513  \n",
       "4            1.754017  0.926099  \n",
       "..                ...       ...  \n",
       "157          0.000000  0.101493  \n",
       "158          0.000000  0.088746  \n",
       "159          0.000000  0.096759  \n",
       "160          0.000000 -0.001578  \n",
       "161          0.000000 -0.023431  \n",
       "\n",
       "[162 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dgr['IC'] = dgr.apply(lambda row: get_inhib_coeff(dgr, row.compound, row.dgr), axis=1)\n",
    "dgr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "minor-terrain",
   "metadata": {},
   "source": [
    "## Hill equation + fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "advanced-fitting",
   "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>compound</th>\n",
       "      <th>concentration_uM</th>\n",
       "      <th>IC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.109626</td>\n",
       "      <td>0.088435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.219252</td>\n",
       "      <td>0.119966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.438504</td>\n",
       "      <td>0.667939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>0.877008</td>\n",
       "      <td>0.922158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anidulafungin</td>\n",
       "      <td>1.754017</td>\n",
       "      <td>0.923759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.014291</td>\n",
       "      <td>0.051761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.028583</td>\n",
       "      <td>0.283244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.057166</td>\n",
       "      <td>0.862890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.114332</td>\n",
       "      <td>0.925680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>caspofungin</td>\n",
       "      <td>0.228663</td>\n",
       "      <td>0.911142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.049202</td>\n",
       "      <td>0.072235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.098404</td>\n",
       "      <td>0.088139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.196807</td>\n",
       "      <td>0.341751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.393614</td>\n",
       "      <td>0.913804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>micafungin</td>\n",
       "      <td>0.787228</td>\n",
       "      <td>0.925337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         compound  concentration_uM        IC\n",
       "0   anidulafungin          0.109626  0.088435\n",
       "1   anidulafungin          0.219252  0.119966\n",
       "2   anidulafungin          0.438504  0.667939\n",
       "3   anidulafungin          0.877008  0.922158\n",
       "4   anidulafungin          1.754017  0.923759\n",
       "5     caspofungin          0.014291  0.051761\n",
       "6     caspofungin          0.028583  0.283244\n",
       "7     caspofungin          0.057166  0.862890\n",
       "8     caspofungin          0.114332  0.925680\n",
       "9     caspofungin          0.228663  0.911142\n",
       "10     micafungin          0.049202  0.072235\n",
       "11     micafungin          0.098404  0.088139\n",
       "12     micafungin          0.196807  0.341751\n",
       "13     micafungin          0.393614  0.913804\n",
       "14     micafungin          0.787228  0.925337"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_data = dgr[(dgr.genotype=='WT') & (dgr.concentration_uM!=0)\n",
    "              ].groupby(['compound','concentration_uM'])[['IC']].mean().reset_index()\n",
    "fit_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "organizational-throat",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hill_equation(x, E, n):\n",
    "    import numpy as np\n",
    "    # this function defines the hill equation, where:\n",
    "    # x is the drug concentration\n",
    "    # E is the concentration with half effect (IC50)\n",
    "    # n is the hill coefficient,\n",
    "    \n",
    "    # equation is this but we have to change the syntax because numpy does not deal well with fractional power of negative numbers\n",
    "    #1/(1+((E/x)**n))\n",
    "    a = E/x\n",
    "    eq = 1/(1+((np.sign(a) * (np.abs(a)))**n))\n",
    "    return eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dominant-secretariat",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_IC(v, E, n):\n",
    "    # This function calculates ICv based on IC50 (E) and hill coefficient (n) for any v value between 0 and 1\n",
    "    return E*((v/(1-v))**(1/n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5324078e-2492-4669-88c2-f9e8f884552b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fit(data):\n",
    "    import scipy as sci\n",
    "    # Get values for specified compound\n",
    "    wt_conc = data.concentration_uM.values\n",
    "    wt_inhib = data.IC.values\n",
    "    \n",
    "    # Get best fit parameters (popt) and covariance matrix (pcov)\n",
    "    popt, pcov = sci.optimize.curve_fit(hill_equation, wt_conc, wt_inhib,\n",
    "                                        p0=[0.1,3] # random guesses, otherwise function does not find correct values\n",
    "                                       )\n",
    "    # Get IC80\n",
    "    ic80 = round(get_IC(0.8,popt[0],popt[1]),2)\n",
    "    \n",
    "    # now we'll want to plot our curve on top of the data. However, since we'll be plotting on both linear and log\n",
    "    # axis, we need to choose x coordinates across our range of concentrations that are not log-distributed and not \n",
    "    # linearly distributed\n",
    "    X = np.logspace(np.log2(wt_conc.min()),np.log2(wt_conc.max()), num=50, base=2)\n",
    "    Y = [hill_equation(x, popt[0], popt[1]) for x in X]\n",
    "    \n",
    "    return (X, Y), ic80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "substantial-command",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(8,8),\n",
    "              'font.family':'Arial',\n",
    "              'font.size':8,\n",
    "              'legend.title_fontsize':8, 'legend.fontsize':8,\n",
    "              'axes.labelsize':8,'axes.titlesize':8,\n",
    "              'xtick.labelsize':8, 'ytick.labelsize':8,\n",
    "              'xtick.major.pad':2, 'ytick.major.pad':2,\n",
    "              'xtick.bottom': True, 'ytick.left': True,\n",
    "              'xtick.major.size':2, 'ytick.major.size':2,\n",
    "              \"axes.spines.right\": False, \"axes.spines.top\": False\n",
    "             },\n",
    "       style='ticks')\n",
    "\n",
    "fig, ax =plt.subplots(3,2, gridspec_kw = {'hspace':.4, 'wspace':.2})\n",
    "\n",
    "comp_order = {0: ('anidulafungin', 'Anidulafungin'),\n",
    "              1: ('caspofungin','Caspofungin'),\n",
    "              2: ('micafungin','Micafungin')\n",
    "             }\n",
    "genot_dict = {'WT':'WT',\n",
    "              'delta-FKS1-HS1':u'Δ$\\it{FKS1}$',\n",
    "              'delta-FKS2-HS1':u'Δ$\\it{FKS2}$'\n",
    "             }\n",
    "\n",
    "for i in [0,1,2]:\n",
    "    for j in [0,1]:\n",
    "        ax[i][j].set_title(comp_order[i][1], weight='bold')\n",
    "        fitdf = fit_data[fit_data.compound == comp_order[i][0]]\n",
    "        ic80 = get_fit(fitdf)[1]\n",
    "        \n",
    "        g1 = sns.scatterplot(data=dgr[dgr.compound == comp_order[i][0]], x='concentration_uM', y='IC',\n",
    "                             hue='genotype', hue_order=genot_dict.keys(), palette=['k','royalblue','pink'],\n",
    "                             ax=ax[i][j])\n",
    "        ax[i][j].plot(*get_fit(fitdf)[0], 'k-')\n",
    "        \n",
    "        if j==0:\n",
    "            g1.set(ylim=(None,1))\n",
    "            sns.move_legend(g1, \"lower right\", title=None)\n",
    "            for ii, v in enumerate(genot_dict.values()):\n",
    "                g1.legend_.texts[ii].set_text(v)\n",
    "        else:\n",
    "            g1.set(xlim=(2**-7,2), ylim=(None,1))\n",
    "            g1.legend_.remove()\n",
    "        ax[i][1].set_xscale('log', base=2)\n",
    "        g1.set_xlabel('Concentration (uM)')\n",
    "        g1.set_ylabel('Inhibition coefficient')\n",
    "        ax[i][1].text(2**-6.8, 0.8, f'IC80 = {ic80} uM',\n",
    "                      ha='left'\n",
    "                     )\n",
    "\n",
    "plt.savefig(f'{graph_outpath}/dose-response_IC.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08de6b13-df09-4c22-a19a-0a16b769dbdb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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