20230324_validations_FUR1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "863eab24-56e1-4b58-b120-201103038c9d",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c9c1946-d025-4b1b-b0ba-40a9bb970315",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2cc6578-6837-4350-8e9e-b7f5b7e68892",
   "metadata": {},
   "source": [
    "## Specify filepaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02ddd5fc-1a6b-44fb-9a67-5169c8ad420c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "source_plate = 'data/20221115_plates.xlsx'\n",
    "layout_path = 'data/20220517_mutant_plates_rearray.xlsx'\n",
    "variantinfo_path = 'data/20230324_bysample.xlsx'\n",
    "plate1_data = 'data/20221117_growth-curves_FUR1_5FC.xlsx'\n",
    "plate2_data = 'data/20221117_growth-curves_FUR1_SD.xlsx'\n",
    "graph_outpath = 'graphs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82062f5c-8fba-45c0-9168-aecd43c5cf76",
   "metadata": {},
   "source": [
    "## Import layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "effa1d0f-2278-4d4d-81e7-813b713e750d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tecan_plate</th>\n",
       "      <th>well</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>strain</th>\n",
       "      <th>genotype</th>\n",
       "      <th>s_index</th>\n",
       "      <th>replicate</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>H3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>4.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2</td>\n",
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       "      <th>4</th>\n",
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       "      <td>5-FC</td>\n",
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       "      <td>F10</td>\n",
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       "      <td>3</td>\n",
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       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2</td>\n",
       "      <td>C12</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>169</th>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>FCY2</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2</td>\n",
       "      <td>E12</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2</td>\n",
       "      <td>F12</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tecan_plate well condition  mutant_plate 96_well    strain genotype  \\\n",
       "0              1   A2      5-FC           1.0      H3       NaN      NaN   \n",
       "1              1   B2      5-FC           1.0      H3       NaN      NaN   \n",
       "2              1   C2      5-FC           4.0     G10       NaN      NaN   \n",
       "3              1   D2      5-FC           4.0     G10       NaN      NaN   \n",
       "4              1   E2      5-FC           4.0     F10       NaN      NaN   \n",
       "..           ...  ...       ...           ...     ...       ...      ...   \n",
       "167            2  B12   control           NaN     NaN     NC-02     FCY1   \n",
       "168            2  C12   control           NaN     NaN  LL13-040     FCY2   \n",
       "169            2  D12   control           NaN     NaN  LL13-040     FCY2   \n",
       "170            2  E12   control           NaN     NaN     NC-02     FCY2   \n",
       "171            2  F12   control           NaN     NaN     NC-02     FCY2   \n",
       "\n",
       "     s_index  replicate  \n",
       "0          1          1  \n",
       "1          1          2  \n",
       "2          2          1  \n",
       "3          2          2  \n",
       "4          3          1  \n",
       "..       ...        ...  \n",
       "167       41          2  \n",
       "168       42          1  \n",
       "169       42          2  \n",
       "170       43          1  \n",
       "171       43          2  \n",
       "\n",
       "[172 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plate_df = pd.read_excel(source_plate, header=0)\n",
    "plate_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1434ffa3-6167-4e59-b264-1ea3f08b9c6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "layout_df = pd.read_excel(layout_path, header=0)[['mutant_plate','96_well','strain','fluc_assay','pre_culture','fluc_mutant','genotype','rearray_plate_nb','RA_well']]\n",
    "layout_df = layout_df[layout_df.rearray_plate_nb==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f68f4e01-c576-412f-8883-a565aa648dfe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tecan_plate</th>\n",
       "      <th>well</th>\n",
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       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>C2</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>G10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
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       "      <td>2</td>\n",
       "      <td>D10</td>\n",
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       "      <td>G5</td>\n",
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       "      <td>2</td>\n",
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       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>LL13-040</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
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       "      <td>F11</td>\n",
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       "      <td>2</td>\n",
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       "      <td>control</td>\n",
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       "      <td>A6</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tecan_plate well condition  mutant_plate 96_well  s_index  replicate  \\\n",
       "0              1   A2      5-FC           1.0      H3        1          1   \n",
       "1              1   B2      5-FC           1.0      H3        1          2   \n",
       "2              2   A2   control           1.0      H3        1          1   \n",
       "3              2   B2   control           1.0      H3        1          2   \n",
       "4              1   C2      5-FC           4.0     G10        2          1   \n",
       "..           ...  ...       ...           ...     ...      ...        ...   \n",
       "135            2  D10   control           5.0      G5       34          2   \n",
       "136            1  E10      5-FC           5.0      A6       35          1   \n",
       "137            1  F10      5-FC           5.0      A6       35          2   \n",
       "138            2  E10   control           5.0      A6       35          1   \n",
       "139            2  F10   control           5.0      A6       35          2   \n",
       "\n",
       "       strain  fluc_assay  pre_culture  fluc_mutant genotype  \\\n",
       "0    LL13-040         3.0          2.0          2.0   mutant   \n",
       "1    LL13-040         3.0          2.0          2.0   mutant   \n",
       "2    LL13-040         3.0          2.0          2.0   mutant   \n",
       "3    LL13-040         3.0          2.0          2.0   mutant   \n",
       "4       NC-02         5.0          4.0          3.0   mutant   \n",
       "..        ...         ...          ...          ...      ...   \n",
       "135  LL13-040         6.0          3.0          1.0   mutant   \n",
       "136  LL13-040         6.0          3.0          3.0   mutant   \n",
       "137  LL13-040         6.0          3.0          3.0   mutant   \n",
       "138  LL13-040         6.0          3.0          3.0   mutant   \n",
       "139  LL13-040         6.0          3.0          3.0   mutant   \n",
       "\n",
       "     rearray_plate_nb RA_well  \n",
       "0                 1.0      E3  \n",
       "1                 1.0      E3  \n",
       "2                 1.0      E3  \n",
       "3                 1.0      E3  \n",
       "4                 1.0     N22  \n",
       "..                ...     ...  \n",
       "135               1.0     D11  \n",
       "136               1.0     F11  \n",
       "137               1.0     F11  \n",
       "138               1.0     F11  \n",
       "139               1.0     F11  \n",
       "\n",
       "[140 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annot_df1 = plate_df[plate_df.strain.isnull()][['tecan_plate','well','condition','mutant_plate','96_well','s_index','replicate']].merge(right=layout_df, on=['mutant_plate','96_well'])\n",
    "annot_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae2323a5-b2b3-42bf-8dfc-fb95ef9dc09d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "annot_df1.to_excel('df/validations_annot_df1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b54f878-e0ec-420d-956e-72b089a58a42",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>background</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>Ngenes</th>\n",
       "      <th>Lgenes</th>\n",
       "      <th>gatk</th>\n",
       "      <th>samtools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>B10</td>\n",
       "      <td>114</td>\n",
       "      <td>['PAU8' 'CDC15' 'SWH1' 'SAS3' 'PRP6' 'TEC1' 'P...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>B11</td>\n",
       "      <td>133</td>\n",
       "      <td>['SEO1' 'CLN3' 'PHO11' 'FIG1' 'GRS1' 'TAF5' 'M...</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LL13-040</td>\n",
       "      <td>B12</td>\n",
       "      <td>116</td>\n",
       "      <td>['FUN12' 'TFC3' 'SWH1' 'FLO1' 'CMC2' 'ERD2' 'Y...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>LL13-040</td>\n",
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       "      <td>116</td>\n",
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       "      <th>4</th>\n",
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       "      <td>B2</td>\n",
       "      <td>125</td>\n",
       "      <td>['CLN3' 'PHO11' 'ERD2' 'PRP6' 'RPL19A' 'GRS1' ...</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>O17</td>\n",
       "      <td>121</td>\n",
       "      <td>['PAU8' 'SEO1' 'DRS2' 'CCR4' 'FLO1' 'SCT1' 'KA...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>O18</td>\n",
       "      <td>93</td>\n",
       "      <td>['PAU8' 'SEO1' 'DRS2' 'CCR4' 'FLO1' 'COR1' 'KA...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>O19</td>\n",
       "      <td>120</td>\n",
       "      <td>['MDM10' 'FLO1' 'ILS1' 'CMC2' 'PRP6' 'TIP1' 'T...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>O21</td>\n",
       "      <td>105</td>\n",
       "      <td>['DRS2' 'FLO1' 'COR1' 'KAP104' 'YPK3' 'PRP6' '...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>NC-02</td>\n",
       "      <td>O22</td>\n",
       "      <td>84</td>\n",
       "      <td>['DRS2' 'CCR4' 'DFP1' 'FLO1' 'ILS1' 'COR1' 'SC...</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    background RA_well  Ngenes  \\\n",
       "0     LL13-040     B10     114   \n",
       "1     LL13-040     B11     133   \n",
       "2     LL13-040     B12     116   \n",
       "3     LL13-040     B13     116   \n",
       "4     LL13-040      B2     125   \n",
       "..         ...     ...     ...   \n",
       "275      NC-02     O17     121   \n",
       "276      NC-02     O18      93   \n",
       "277      NC-02     O19     120   \n",
       "278      NC-02     O21     105   \n",
       "279      NC-02     O22      84   \n",
       "\n",
       "                                                Lgenes  gatk  samtools  \n",
       "0    ['PAU8' 'CDC15' 'SWH1' 'SAS3' 'PRP6' 'TEC1' 'P...     1         1  \n",
       "1    ['SEO1' 'CLN3' 'PHO11' 'FIG1' 'GRS1' 'TAF5' 'M...     1         1  \n",
       "2    ['FUN12' 'TFC3' 'SWH1' 'FLO1' 'CMC2' 'ERD2' 'Y...     1         1  \n",
       "3    ['PAU8' 'LTE1' 'PHO11' 'ERD2' 'PRP6' 'CDC28' '...     1         1  \n",
       "4    ['CLN3' 'PHO11' 'ERD2' 'PRP6' 'RPL19A' 'GRS1' ...     1         1  \n",
       "..                                                 ...   ...       ...  \n",
       "275  ['PAU8' 'SEO1' 'DRS2' 'CCR4' 'FLO1' 'SCT1' 'KA...     1         1  \n",
       "276  ['PAU8' 'SEO1' 'DRS2' 'CCR4' 'FLO1' 'COR1' 'KA...     1         1  \n",
       "277  ['MDM10' 'FLO1' 'ILS1' 'CMC2' 'PRP6' 'TIP1' 'T...     1         1  \n",
       "278  ['DRS2' 'FLO1' 'COR1' 'KAP104' 'YPK3' 'PRP6' '...     1         1  \n",
       "279  ['DRS2' 'CCR4' 'DFP1' 'FLO1' 'ILS1' 'COR1' 'SC...     1         1  \n",
       "\n",
       "[280 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variantinfo = pd.read_excel(variantinfo_path, index_col=0)\n",
    "variantinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c830d9bf-688f-44ea-bd81-a7995ce0edf3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tecan_plate</th>\n",
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       "      <th>3</th>\n",
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       "      <td>LL13-040</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2</td>\n",
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       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N22</td>\n",
       "      <td>118</td>\n",
       "      <td>['FLO9' 'DRS2' 'FLO1' 'COR1' 'SCT1' 'PRP6' 'TI...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
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       "      <td>119</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>127</td>\n",
       "      <td>['SAS3' 'ERD2' 'PET9' 'SCT1' 'KAP104' 'YPK3' '...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1</td>\n",
       "      <td>F10</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>127</td>\n",
       "      <td>['SAS3' 'ERD2' 'PET9' 'SCT1' 'KAP104' 'YPK3' '...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F11</td>\n",
       "      <td>127</td>\n",
       "      <td>['SAS3' 'ERD2' 'PET9' 'SCT1' 'KAP104' 'YPK3' '...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2</td>\n",
       "      <td>F10</td>\n",
       "      <td>control</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F11</td>\n",
       "      <td>127</td>\n",
       "      <td>['SAS3' 'ERD2' 'PET9' 'SCT1' 'KAP104' 'YPK3' '...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tecan_plate well condition  mutant_plate 96_well  s_index  replicate  \\\n",
       "0              1   A2      5-FC           1.0      H3        1          1   \n",
       "1              1   B2      5-FC           1.0      H3        1          2   \n",
       "2              2   A2   control           1.0      H3        1          1   \n",
       "3              2   B2   control           1.0      H3        1          2   \n",
       "4              1   C2      5-FC           4.0     G10        2          1   \n",
       "..           ...  ...       ...           ...     ...      ...        ...   \n",
       "135            2  D10   control           5.0      G5       34          2   \n",
       "136            1  E10      5-FC           5.0      A6       35          1   \n",
       "137            1  F10      5-FC           5.0      A6       35          2   \n",
       "138            2  E10   control           5.0      A6       35          1   \n",
       "139            2  F10   control           5.0      A6       35          2   \n",
       "\n",
       "       strain  fluc_assay  pre_culture  fluc_mutant genotype  \\\n",
       "0    LL13-040         3.0          2.0          2.0   mutant   \n",
       "1    LL13-040         3.0          2.0          2.0   mutant   \n",
       "2    LL13-040         3.0          2.0          2.0   mutant   \n",
       "3    LL13-040         3.0          2.0          2.0   mutant   \n",
       "4       NC-02         5.0          4.0          3.0   mutant   \n",
       "..        ...         ...          ...          ...      ...   \n",
       "135  LL13-040         6.0          3.0          1.0   mutant   \n",
       "136  LL13-040         6.0          3.0          3.0   mutant   \n",
       "137  LL13-040         6.0          3.0          3.0   mutant   \n",
       "138  LL13-040         6.0          3.0          3.0   mutant   \n",
       "139  LL13-040         6.0          3.0          3.0   mutant   \n",
       "\n",
       "     rearray_plate_nb RA_well  Ngenes  \\\n",
       "0                 1.0      E3     145   \n",
       "1                 1.0      E3     145   \n",
       "2                 1.0      E3     145   \n",
       "3                 1.0      E3     145   \n",
       "4                 1.0     N22     118   \n",
       "..                ...     ...     ...   \n",
       "135               1.0     D11     119   \n",
       "136               1.0     F11     127   \n",
       "137               1.0     F11     127   \n",
       "138               1.0     F11     127   \n",
       "139               1.0     F11     127   \n",
       "\n",
       "                                                Lgenes  gatk  samtools  \n",
       "0    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...     1         1  \n",
       "1    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...     1         1  \n",
       "2    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...     1         1  \n",
       "3    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...     1         1  \n",
       "4    ['FLO9' 'DRS2' 'FLO1' 'COR1' 'SCT1' 'PRP6' 'TI...     1         1  \n",
       "..                                                 ...   ...       ...  \n",
       "135  ['FLO1' 'SAS3' 'ERD2' 'KAP104' 'PRP6' 'RPG1' '...     1         1  \n",
       "136  ['SAS3' 'ERD2' 'PET9' 'SCT1' 'KAP104' 'YPK3' '...     1         1  \n",
       "137  ['SAS3' 'ERD2' 'PET9' 'SCT1' 'KAP104' 'YPK3' '...     1         1  \n",
       "138  ['SAS3' 'ERD2' 'PET9' 'SCT1' 'KAP104' 'YPK3' '...     1         1  \n",
       "139  ['SAS3' 'ERD2' 'PET9' 'SCT1' 'KAP104' 'YPK3' '...     1         1  \n",
       "\n",
       "[140 rows x 18 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variantinfo.rename(columns={'background':'strain'}, inplace=True)\n",
    "annot_df1b = annot_df1.merge(right=variantinfo, on=['strain','RA_well'])\n",
    "annot_df1b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff728bd3-0bce-4062-8224-0d71493ce95f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<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",
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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>tecan_plate</th>\n",
       "      <th>well</th>\n",
       "      <th>condition</th>\n",
       "      <th>strain</th>\n",
       "      <th>genotype</th>\n",
       "      <th>s_index</th>\n",
       "      <th>replicate</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>RA_well</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>G10</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>wt</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>D12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H13</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>H10</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>wt</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>D12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>G10</td>\n",
       "      <td>control</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>wt</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>D12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>H10</td>\n",
       "      <td>control</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>wt</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>D12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>A11</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>wt</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>B11</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>wt</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>A11</td>\n",
       "      <td>control</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>wt</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>B11</td>\n",
       "      <td>control</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>wt</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>E12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>G11</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>H11</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>G11</td>\n",
       "      <td>control</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>H11</td>\n",
       "      <td>control</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>A12</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>H12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>B12</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>A12</td>\n",
       "      <td>control</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>H12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>B12</td>\n",
       "      <td>control</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>H12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>C12</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>J13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>D12</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>J13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>C12</td>\n",
       "      <td>control</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>J13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>D12</td>\n",
       "      <td>control</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>J13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>E12</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>F12</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>E12</td>\n",
       "      <td>control</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>F12</td>\n",
       "      <td>control</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>G12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tecan_plate well condition    strain genotype  s_index  replicate  \\\n",
       "0             1  G10      5-FC  LL13-040       wt       36          1   \n",
       "1             1  H10      5-FC  LL13-040       wt       36          2   \n",
       "2             2  G10   control  LL13-040       wt       36          1   \n",
       "3             2  H10   control  LL13-040       wt       36          2   \n",
       "4             1  A11      5-FC     NC-02       wt       37          1   \n",
       "5             1  B11      5-FC     NC-02       wt       37          2   \n",
       "6             2  A11   control     NC-02       wt       37          1   \n",
       "7             2  B11   control     NC-02       wt       37          2   \n",
       "8             1  G11      5-FC  LL13-040     FCY1       40          1   \n",
       "9             1  H11      5-FC  LL13-040     FCY1       40          2   \n",
       "10            2  G11   control  LL13-040     FCY1       40          1   \n",
       "11            2  H11   control  LL13-040     FCY1       40          2   \n",
       "12            1  A12      5-FC     NC-02     FCY1       41          1   \n",
       "13            1  B12      5-FC     NC-02     FCY1       41          2   \n",
       "14            2  A12   control     NC-02     FCY1       41          1   \n",
       "15            2  B12   control     NC-02     FCY1       41          2   \n",
       "16            1  C12      5-FC  LL13-040     FCY2       42          1   \n",
       "17            1  D12      5-FC  LL13-040     FCY2       42          2   \n",
       "18            2  C12   control  LL13-040     FCY2       42          1   \n",
       "19            2  D12   control  LL13-040     FCY2       42          2   \n",
       "20            1  E12      5-FC     NC-02     FCY2       43          1   \n",
       "21            1  F12      5-FC     NC-02     FCY2       43          2   \n",
       "22            2  E12   control     NC-02     FCY2       43          1   \n",
       "23            2  F12   control     NC-02     FCY2       43          2   \n",
       "\n",
       "    mutant_plate 96_well  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0              3     D12         NaN          NaN          NaN   \n",
       "1              3     D12         NaN          NaN          NaN   \n",
       "2              3     D12         NaN          NaN          NaN   \n",
       "3              3     D12         NaN          NaN          NaN   \n",
       "4              3     E12         NaN          NaN          NaN   \n",
       "5              3     E12         NaN          NaN          NaN   \n",
       "6              3     E12         NaN          NaN          NaN   \n",
       "7              3     E12         NaN          NaN          NaN   \n",
       "8              1     G12         NaN          NaN          NaN   \n",
       "9              1     G12         NaN          NaN          NaN   \n",
       "10             1     G12         NaN          NaN          NaN   \n",
       "11             1     G12         NaN          NaN          NaN   \n",
       "12             1     H12         NaN          NaN          NaN   \n",
       "13             1     H12         NaN          NaN          NaN   \n",
       "14             1     H12         NaN          NaN          NaN   \n",
       "15             1     H12         NaN          NaN          NaN   \n",
       "16             5     G12         NaN          NaN          NaN   \n",
       "17             5     G12         NaN          NaN          NaN   \n",
       "18             5     G12         NaN          NaN          NaN   \n",
       "19             5     G12         NaN          NaN          NaN   \n",
       "20             4     G12         NaN          NaN          NaN   \n",
       "21             4     G12         NaN          NaN          NaN   \n",
       "22             4     G12         NaN          NaN          NaN   \n",
       "23             4     G12         NaN          NaN          NaN   \n",
       "\n",
       "    rearray_plate_nb RA_well  \n",
       "0                1.0     H13  \n",
       "1                1.0     H13  \n",
       "2                1.0     H13  \n",
       "3                1.0     H13  \n",
       "4                1.0     E23  \n",
       "5                1.0     E23  \n",
       "6                1.0     E23  \n",
       "7                1.0     E23  \n",
       "8                1.0     I13  \n",
       "9                1.0     I13  \n",
       "10               1.0     I13  \n",
       "11               1.0     I13  \n",
       "12               1.0     F23  \n",
       "13               1.0     F23  \n",
       "14               1.0     F23  \n",
       "15               1.0     F23  \n",
       "16               1.0     J13  \n",
       "17               1.0     J13  \n",
       "18               1.0     J13  \n",
       "19               1.0     J13  \n",
       "20               1.0     G23  \n",
       "21               1.0     G23  \n",
       "22               1.0     G23  \n",
       "23               1.0     G23  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annot_df2 = plate_df[plate_df.strain.notnull()][['tecan_plate','well','condition','strain','genotype','s_index','replicate']].merge(right=layout_df, on=['strain','genotype'])\n",
    "annot_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9ad8e04-8fe5-46b3-9445-15cf3fd42289",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>tecan_plate</th>\n",
       "      <th>well</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
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       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tecan_plate well condition  mutant_plate 96_well  s_index  replicate  \\\n",
       "0              1   A2      5-FC           1.0      H3        1          1   \n",
       "1              1   B2      5-FC           1.0      H3        1          2   \n",
       "2              2   A2   control           1.0      H3        1          1   \n",
       "3              2   B2   control           1.0      H3        1          2   \n",
       "4              1   C2      5-FC           4.0     G10        2          1   \n",
       "..           ...  ...       ...           ...     ...      ...        ...   \n",
       "77             1  F11      5-FC           NaN     NaN       39          2   \n",
       "160            2  C11   control           NaN     NaN       38          1   \n",
       "161            2  D11   control           NaN     NaN       38          2   \n",
       "162            2  E11   control           NaN     NaN       39          1   \n",
       "163            2  F11   control           NaN     NaN       39          2   \n",
       "\n",
       "       strain  fluc_assay  pre_culture  fluc_mutant genotype  \\\n",
       "0    LL13-040         3.0          2.0          2.0   mutant   \n",
       "1    LL13-040         3.0          2.0          2.0   mutant   \n",
       "2    LL13-040         3.0          2.0          2.0   mutant   \n",
       "3    LL13-040         3.0          2.0          2.0   mutant   \n",
       "4       NC-02         5.0          4.0          3.0   mutant   \n",
       "..        ...         ...          ...          ...      ...   \n",
       "77      NC-02         NaN          NaN          NaN     FUR1   \n",
       "160  LL13-040         NaN          NaN          NaN     FUR1   \n",
       "161  LL13-040         NaN          NaN          NaN     FUR1   \n",
       "162     NC-02         NaN          NaN          NaN     FUR1   \n",
       "163     NC-02         NaN          NaN          NaN     FUR1   \n",
       "\n",
       "     rearray_plate_nb RA_well  Ngenes  \\\n",
       "0                 1.0      E3   145.0   \n",
       "1                 1.0      E3   145.0   \n",
       "2                 1.0      E3   145.0   \n",
       "3                 1.0      E3   145.0   \n",
       "4                 1.0     N22   118.0   \n",
       "..                ...     ...     ...   \n",
       "77                NaN     NaN     0.0   \n",
       "160               NaN     NaN     0.0   \n",
       "161               NaN     NaN     0.0   \n",
       "162               NaN     NaN     0.0   \n",
       "163               NaN     NaN     0.0   \n",
       "\n",
       "                                                Lgenes  gatk  samtools  \n",
       "0    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "1    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "2    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "3    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "4    ['FLO9' 'DRS2' 'FLO1' 'COR1' 'SCT1' 'PRP6' 'TI...   1.0       1.0  \n",
       "..                                                 ...   ...       ...  \n",
       "77                                                 NaN   NaN       NaN  \n",
       "160                                                NaN   NaN       NaN  \n",
       "161                                                NaN   NaN       NaN  \n",
       "162                                                NaN   NaN       NaN  \n",
       "163                                                NaN   NaN       NaN  \n",
       "\n",
       "[172 rows x 18 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annot_df = pd.concat([annot_df1b, annot_df2, plate_df[plate_df.genotype=='FUR1']])\n",
    "annot_df.Ngenes.fillna(0, inplace=True)\n",
    "annot_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18fccdc4-4dbf-4195-b771-b130535979f0",
   "metadata": {},
   "source": [
    "## Get data from each plate reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bff55a8d-69cb-45e1-b961-7c1f97a60848",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_data(fpath):\n",
    "    source_df = pd.read_excel(fpath, header=1, index_col=0,skiprows=[2],skipfooter=34)\n",
    "    source_df.rename(columns=lambda x: float(x.strip('s'))/3600, inplace=True)\n",
    "    longdf = source_df.reset_index().rename(columns = {'index' : 'well'})\n",
    "    longdf = longdf.melt(id_vars = ['well'], var_name = 'time (h)', value_name = 'OD') # wide to long dataframe\n",
    "    return longdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abdaf2ae-c9af-49b2-8f48-f09d8c726dcc",
   "metadata": {
    "tags": []
   },
   "outputs": [
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      "text/plain": [
       "      well   time (h)        OD\n",
       "0       A1        0.0  0.081400\n",
       "1       A2        0.0  0.100430\n",
       "2       A3        0.0  0.096625\n",
       "3       A4        0.0  0.097500\n",
       "4       A5        0.0  0.098750\n",
       "...    ...        ...       ...\n",
       "17755   H8  46.006111  1.004800\n",
       "17756   H9  46.006111  0.996950\n",
       "17757  H10  46.006111  0.244070\n",
       "17758  H11  46.006111  0.963650\n",
       "17759  H12  46.006111  0.080950\n",
       "\n",
       "[17760 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plate1 = get_data(plate1_data)\n",
    "plate2 = get_data(plate2_data)\n",
    "plate1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038b3afa-46b7-45bf-ac79-3fe57196632a",
   "metadata": {},
   "source": [
    "## Annotate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee01392b-a07f-4178-aa13-92f1c2b07faf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>time (h)</th>\n",
       "      <th>OD</th>\n",
       "      <th>tecan_plate</th>\n",
       "      <th>condition</th>\n",
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       "      <th>rearray_plate_nb</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>Ngenes</th>\n",
       "      <th>Lgenes</th>\n",
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      ],
      "text/plain": [
       "      well   time (h)       OD  tecan_plate condition  mutant_plate 96_well  \\\n",
       "0       A2        0.0  0.10043            1      5-FC           1.0      H3   \n",
       "1       A2       0.25  0.10395            1      5-FC           1.0      H3   \n",
       "2       A2        0.5  0.10498            1      5-FC           1.0      H3   \n",
       "3       A2       0.75  0.10558            1      5-FC           1.0      H3   \n",
       "4       A2        1.0  0.10588            1      5-FC           1.0      H3   \n",
       "...    ...        ...      ...          ...       ...           ...     ...   \n",
       "15905  H11  45.005833  0.96273            1      5-FC           1.0     G12   \n",
       "15906  H11  45.255833  0.96337            1      5-FC           1.0     G12   \n",
       "15907  H11  45.505833  0.96365            1      5-FC           1.0     G12   \n",
       "15908  H11  45.756111  0.96378            1      5-FC           1.0     G12   \n",
       "15909  H11  46.006111  0.96365            1      5-FC           1.0     G12   \n",
       "\n",
       "       s_index  replicate    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0            1          1  LL13-040         3.0          2.0          2.0   \n",
       "1            1          1  LL13-040         3.0          2.0          2.0   \n",
       "2            1          1  LL13-040         3.0          2.0          2.0   \n",
       "3            1          1  LL13-040         3.0          2.0          2.0   \n",
       "4            1          1  LL13-040         3.0          2.0          2.0   \n",
       "...        ...        ...       ...         ...          ...          ...   \n",
       "15905       40          2  LL13-040         NaN          NaN          NaN   \n",
       "15906       40          2  LL13-040         NaN          NaN          NaN   \n",
       "15907       40          2  LL13-040         NaN          NaN          NaN   \n",
       "15908       40          2  LL13-040         NaN          NaN          NaN   \n",
       "15909       40          2  LL13-040         NaN          NaN          NaN   \n",
       "\n",
       "      genotype  rearray_plate_nb RA_well  Ngenes  \\\n",
       "0       mutant               1.0      E3   145.0   \n",
       "1       mutant               1.0      E3   145.0   \n",
       "2       mutant               1.0      E3   145.0   \n",
       "3       mutant               1.0      E3   145.0   \n",
       "4       mutant               1.0      E3   145.0   \n",
       "...        ...               ...     ...     ...   \n",
       "15905     FCY1               1.0     I13     0.0   \n",
       "15906     FCY1               1.0     I13     0.0   \n",
       "15907     FCY1               1.0     I13     0.0   \n",
       "15908     FCY1               1.0     I13     0.0   \n",
       "15909     FCY1               1.0     I13     0.0   \n",
       "\n",
       "                                                  Lgenes  gatk  samtools  \n",
       "0      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "1      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "2      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "3      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "4      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "...                                                  ...   ...       ...  \n",
       "15905                                                NaN   NaN       NaN  \n",
       "15906                                                NaN   NaN       NaN  \n",
       "15907                                                NaN   NaN       NaN  \n",
       "15908                                                NaN   NaN       NaN  \n",
       "15909                                                NaN   NaN       NaN  \n",
       "\n",
       "[15910 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged1 = plate1.merge(right=annot_df[annot_df.tecan_plate==1], on=['well'])\n",
    "merged2 = plate2.merge(right=annot_df[annot_df.tecan_plate==2], on=['well'])\n",
    "merged1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4d4553f-7fa5-406d-9c23-d4e9af9768d3",
   "metadata": {
    "tags": []
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   "outputs": [
    {
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       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.10558</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.10588</td>\n",
       "      <td>1</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15905</th>\n",
       "      <td>H11</td>\n",
       "      <td>45.005833</td>\n",
       "      <td>1.03260</td>\n",
       "      <td>2</td>\n",
       "      <td>control</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G12</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15906</th>\n",
       "      <td>H11</td>\n",
       "      <td>45.255833</td>\n",
       "      <td>1.03350</td>\n",
       "      <td>2</td>\n",
       "      <td>control</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G12</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15907</th>\n",
       "      <td>H11</td>\n",
       "      <td>45.506111</td>\n",
       "      <td>1.03370</td>\n",
       "      <td>2</td>\n",
       "      <td>control</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G12</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15908</th>\n",
       "      <td>H11</td>\n",
       "      <td>45.756111</td>\n",
       "      <td>1.03270</td>\n",
       "      <td>2</td>\n",
       "      <td>control</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G12</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15909</th>\n",
       "      <td>H11</td>\n",
       "      <td>46.006111</td>\n",
       "      <td>1.03300</td>\n",
       "      <td>2</td>\n",
       "      <td>control</td>\n",
       "      <td>1.0</td>\n",
       "      <td>G12</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31820 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      well   time (h)       OD  tecan_plate condition  mutant_plate 96_well  \\\n",
       "0       A2        0.0  0.10043            1      5-FC           1.0      H3   \n",
       "1       A2       0.25  0.10395            1      5-FC           1.0      H3   \n",
       "2       A2        0.5  0.10498            1      5-FC           1.0      H3   \n",
       "3       A2       0.75  0.10558            1      5-FC           1.0      H3   \n",
       "4       A2        1.0  0.10588            1      5-FC           1.0      H3   \n",
       "...    ...        ...      ...          ...       ...           ...     ...   \n",
       "15905  H11  45.005833  1.03260            2   control           1.0     G12   \n",
       "15906  H11  45.255833  1.03350            2   control           1.0     G12   \n",
       "15907  H11  45.506111  1.03370            2   control           1.0     G12   \n",
       "15908  H11  45.756111  1.03270            2   control           1.0     G12   \n",
       "15909  H11  46.006111  1.03300            2   control           1.0     G12   \n",
       "\n",
       "       s_index  replicate    strain  fluc_assay  pre_culture  fluc_mutant  \\\n",
       "0            1          1  LL13-040         3.0          2.0          2.0   \n",
       "1            1          1  LL13-040         3.0          2.0          2.0   \n",
       "2            1          1  LL13-040         3.0          2.0          2.0   \n",
       "3            1          1  LL13-040         3.0          2.0          2.0   \n",
       "4            1          1  LL13-040         3.0          2.0          2.0   \n",
       "...        ...        ...       ...         ...          ...          ...   \n",
       "15905       40          2  LL13-040         NaN          NaN          NaN   \n",
       "15906       40          2  LL13-040         NaN          NaN          NaN   \n",
       "15907       40          2  LL13-040         NaN          NaN          NaN   \n",
       "15908       40          2  LL13-040         NaN          NaN          NaN   \n",
       "15909       40          2  LL13-040         NaN          NaN          NaN   \n",
       "\n",
       "      genotype  rearray_plate_nb RA_well  Ngenes  \\\n",
       "0       mutant               1.0      E3   145.0   \n",
       "1       mutant               1.0      E3   145.0   \n",
       "2       mutant               1.0      E3   145.0   \n",
       "3       mutant               1.0      E3   145.0   \n",
       "4       mutant               1.0      E3   145.0   \n",
       "...        ...               ...     ...     ...   \n",
       "15905     FCY1               1.0     I13     0.0   \n",
       "15906     FCY1               1.0     I13     0.0   \n",
       "15907     FCY1               1.0     I13     0.0   \n",
       "15908     FCY1               1.0     I13     0.0   \n",
       "15909     FCY1               1.0     I13     0.0   \n",
       "\n",
       "                                                  Lgenes  gatk  samtools  \n",
       "0      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "1      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "2      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "3      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "4      ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0  \n",
       "...                                                  ...   ...       ...  \n",
       "15905                                                NaN   NaN       NaN  \n",
       "15906                                                NaN   NaN       NaN  \n",
       "15907                                                NaN   NaN       NaN  \n",
       "15908                                                NaN   NaN       NaN  \n",
       "15909                                                NaN   NaN       NaN  \n",
       "\n",
       "[31820 rows x 20 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.concat([merged1, merged2])\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49a16a5-70f7-4497-82b2-40ebacf3b0ad",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visualize growth curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b269874a-bdc1-4084-8856-8d36c112e4c9",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/rodur28/anaconda3/lib/python3.8/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 710.5x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "custom_params = {\"axes.spines.right\": False, \"axes.spines.top\": False}\n",
    "sns.set_theme(style=\"ticks\", rc=custom_params)\n",
    "grid = sns.FacetGrid(data=merged_df, row='condition', col = 'strain', hue='genotype', palette='hls')\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 = 'Genotype')\n",
    "grid.fig.subplots_adjust(top=0.9)\n",
    "plt.suptitle('Fitness of FUR1 mutants')\n",
    "grid.tight_layout()\n",
    "plt.savefig('/home/rodur28/growth_curve_files/graphs/20221124_FUR1_growth-curves.svg', format='svg', dpi=300)\n",
    "plt.savefig('/home/rodur28/growth_curve_files/graphs/20221124_FUR1_growth-curves.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7762ad2a-74a7-466a-95dc-0fde2b312054",
   "metadata": {},
   "source": [
    "## Calculate normalized area under the curve (AUC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5709751a-10b5-409a-876c-78a95f04a1b8",
   "metadata": {
    "tags": []
   },
   "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>tecan_plate</th>\n",
       "      <th>well</th>\n",
       "      <th>condition</th>\n",
       "      <th>mutant_plate</th>\n",
       "      <th>96_well</th>\n",
       "      <th>s_index</th>\n",
       "      <th>replicate</th>\n",
       "      <th>strain</th>\n",
       "      <th>fluc_assay</th>\n",
       "      <th>pre_culture</th>\n",
       "      <th>fluc_mutant</th>\n",
       "      <th>genotype</th>\n",
       "      <th>rearray_plate_nb</th>\n",
       "      <th>RA_well</th>\n",
       "      <th>Ngenes</th>\n",
       "      <th>Lgenes</th>\n",
       "      <th>gatk</th>\n",
       "      <th>samtools</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A2</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>148.763855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>B2</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>146.125270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A2</td>\n",
       "      <td>control</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149.066947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>B2</td>\n",
       "      <td>control</td>\n",
       "      <td>1.0</td>\n",
       "      <td>H3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>E3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.864062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>C2</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>G10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>mutant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N22</td>\n",
       "      <td>118.0</td>\n",
       "      <td>['FLO9' 'DRS2' 'FLO1' 'COR1' 'SCT1' 'PRP6' 'TI...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>153.304785</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1</td>\n",
       "      <td>F11</td>\n",
       "      <td>5-FC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.838045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2</td>\n",
       "      <td>C11</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134.568550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>2</td>\n",
       "      <td>D11</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149.854935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2</td>\n",
       "      <td>E11</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.480310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>2</td>\n",
       "      <td>F11</td>\n",
       "      <td>control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.057780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tecan_plate well condition  mutant_plate 96_well  s_index  replicate  \\\n",
       "0              1   A2      5-FC           1.0      H3        1          1   \n",
       "1              1   B2      5-FC           1.0      H3        1          2   \n",
       "2              2   A2   control           1.0      H3        1          1   \n",
       "3              2   B2   control           1.0      H3        1          2   \n",
       "4              1   C2      5-FC           4.0     G10        2          1   \n",
       "..           ...  ...       ...           ...     ...      ...        ...   \n",
       "167            1  F11      5-FC           NaN     NaN       39          2   \n",
       "168            2  C11   control           NaN     NaN       38          1   \n",
       "169            2  D11   control           NaN     NaN       38          2   \n",
       "170            2  E11   control           NaN     NaN       39          1   \n",
       "171            2  F11   control           NaN     NaN       39          2   \n",
       "\n",
       "       strain  fluc_assay  pre_culture  fluc_mutant genotype  \\\n",
       "0    LL13-040         3.0          2.0          2.0   mutant   \n",
       "1    LL13-040         3.0          2.0          2.0   mutant   \n",
       "2    LL13-040         3.0          2.0          2.0   mutant   \n",
       "3    LL13-040         3.0          2.0          2.0   mutant   \n",
       "4       NC-02         5.0          4.0          3.0   mutant   \n",
       "..        ...         ...          ...          ...      ...   \n",
       "167     NC-02         NaN          NaN          NaN     FUR1   \n",
       "168  LL13-040         NaN          NaN          NaN     FUR1   \n",
       "169  LL13-040         NaN          NaN          NaN     FUR1   \n",
       "170     NC-02         NaN          NaN          NaN     FUR1   \n",
       "171     NC-02         NaN          NaN          NaN     FUR1   \n",
       "\n",
       "     rearray_plate_nb RA_well  Ngenes  \\\n",
       "0                 1.0      E3   145.0   \n",
       "1                 1.0      E3   145.0   \n",
       "2                 1.0      E3   145.0   \n",
       "3                 1.0      E3   145.0   \n",
       "4                 1.0     N22   118.0   \n",
       "..                ...     ...     ...   \n",
       "167               NaN     NaN     0.0   \n",
       "168               NaN     NaN     0.0   \n",
       "169               NaN     NaN     0.0   \n",
       "170               NaN     NaN     0.0   \n",
       "171               NaN     NaN     0.0   \n",
       "\n",
       "                                                Lgenes  gatk  samtools  \\\n",
       "0    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0   \n",
       "1    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0   \n",
       "2    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0   \n",
       "3    ['PAU8' 'SWH1' 'FLO1' 'HAP3' 'KAP104' 'PRP6' '...   1.0       1.0   \n",
       "4    ['FLO9' 'DRS2' 'FLO1' 'COR1' 'SCT1' 'PRP6' 'TI...   1.0       1.0   \n",
       "..                                                 ...   ...       ...   \n",
       "167                                                NaN   NaN       NaN   \n",
       "168                                                NaN   NaN       NaN   \n",
       "169                                                NaN   NaN       NaN   \n",
       "170                                                NaN   NaN       NaN   \n",
       "171                                                NaN   NaN       NaN   \n",
       "\n",
       "            auc  \n",
       "0    148.763855  \n",
       "1    146.125270  \n",
       "2    149.066947  \n",
       "3    150.864062  \n",
       "4    153.304785  \n",
       "..          ...  \n",
       "167  147.838045  \n",
       "168  134.568550  \n",
       "169  149.854935  \n",
       "170  156.480310  \n",
       "171  155.057780  \n",
       "\n",
       "[172 rows x 19 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from flucytosine_functions import get_auc\n",
    "aucdf = merged_df.groupby(['tecan_plate','well'])[['OD']].apply(func=get_auc).reset_index(name='auc')\n",
    "condensed_df = annot_df.merge(right=aucdf, on=['tecan_plate','well'])\n",
    "condensed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "200aff88-f5c0-46ce-b57b-3481e96cd2de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>s_index</th>\n",
       "      <th>strain</th>\n",
       "      <th>genotype</th>\n",
       "      <th>Ngenes</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>1</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>mutant</td>\n",
       "      <td>145.0</td>\n",
       "      <td>147.444562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>2</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>mutant</td>\n",
       "      <td>118.0</td>\n",
       "      <td>153.898458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>mutant</td>\n",
       "      <td>102.0</td>\n",
       "      <td>156.220668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>4</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>mutant</td>\n",
       "      <td>123.0</td>\n",
       "      <td>148.041157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>5</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>mutant</td>\n",
       "      <td>98.0</td>\n",
       "      <td>149.934000</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>81</th>\n",
       "      <td>control</td>\n",
       "      <td>39</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155.769045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>control</td>\n",
       "      <td>40</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.771678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>control</td>\n",
       "      <td>41</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.668201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>control</td>\n",
       "      <td>42</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.115567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>control</td>\n",
       "      <td>43</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.459460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition  s_index    strain genotype  Ngenes         auc\n",
       "0       5-FC        1  LL13-040   mutant   145.0  147.444562\n",
       "1       5-FC        2     NC-02   mutant   118.0  153.898458\n",
       "2       5-FC        3     NC-02   mutant   102.0  156.220668\n",
       "3       5-FC        4  LL13-040   mutant   123.0  148.041157\n",
       "4       5-FC        5     NC-02   mutant    98.0  149.934000\n",
       "..       ...      ...       ...      ...     ...         ...\n",
       "81   control       39     NC-02     FUR1     0.0  155.769045\n",
       "82   control       40  LL13-040     FCY1     0.0  152.771678\n",
       "83   control       41     NC-02     FCY1     0.0  146.668201\n",
       "84   control       42  LL13-040     FCY2     0.0  134.115567\n",
       "85   control       43     NC-02     FCY2     0.0  150.459460\n",
       "\n",
       "[86 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df = condensed_df.groupby(['condition','s_index','strain','genotype','Ngenes'])[['auc']].mean().reset_index()\n",
    "mean_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3aa8f985-82a2-4a68-8c2c-c2ed0dba3ed9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_norm_auc(df, c, s):\n",
    "    conditions = ['condition','strain','genotype']\n",
    "    condition_set = df.loc[(df.condition==c) & (df.s_index==s), conditions]\n",
    "    val = df.loc[(df.condition==c) & (df.s_index==s), 'auc'].values[0]\n",
    "    wtauc = df.loc[(df[conditions[0]]==condition_set.iat[0,0]) & (df[conditions[1]]==condition_set.iat[0,1]) & (df[conditions[2]]=='wt'), 'auc'].values[0]\n",
    "    return val/wtauc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "27f7fcff-c4b8-4868-982b-27a0767700f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>condition</th>\n",
       "      <th>s_index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>36</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>wt</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.316560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>37</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>wt</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.515242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
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       "      <th>79</th>\n",
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       "      <td>NC-02</td>\n",
       "      <td>wt</td>\n",
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       "      <td>153.353814</td>\n",
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      ],
      "text/plain": [
       "   condition  s_index    strain genotype  Ngenes         auc\n",
       "35      5-FC       36  LL13-040       wt     0.0   35.316560\n",
       "36      5-FC       37     NC-02       wt     0.0   34.515242\n",
       "78   control       36  LL13-040       wt     0.0  151.746742\n",
       "79   control       37     NC-02       wt     0.0  153.353814"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df[mean_df.genotype=='wt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "69c3a112-0198-443b-9dc7-55d6ade0f6d8",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>118.0</td>\n",
       "      <td>153.898458</td>\n",
       "      <td>4.458855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>3</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>mutant</td>\n",
       "      <td>102.0</td>\n",
       "      <td>156.220668</td>\n",
       "      <td>4.526136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>4</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>mutant</td>\n",
       "      <td>123.0</td>\n",
       "      <td>148.041157</td>\n",
       "      <td>4.191834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>5</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>mutant</td>\n",
       "      <td>98.0</td>\n",
       "      <td>149.934000</td>\n",
       "      <td>4.343994</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>control</td>\n",
       "      <td>39</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155.769045</td>\n",
       "      <td>1.015749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>control</td>\n",
       "      <td>40</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.771678</td>\n",
       "      <td>1.006754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>control</td>\n",
       "      <td>41</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.668201</td>\n",
       "      <td>0.956404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>control</td>\n",
       "      <td>42</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.115567</td>\n",
       "      <td>0.883812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>control</td>\n",
       "      <td>43</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.459460</td>\n",
       "      <td>0.981126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition  s_index    strain genotype  Ngenes         auc  norm_auc\n",
       "0       5-FC        1  LL13-040   mutant   145.0  147.444562  4.174941\n",
       "1       5-FC        2     NC-02   mutant   118.0  153.898458  4.458855\n",
       "2       5-FC        3     NC-02   mutant   102.0  156.220668  4.526136\n",
       "3       5-FC        4  LL13-040   mutant   123.0  148.041157  4.191834\n",
       "4       5-FC        5     NC-02   mutant    98.0  149.934000  4.343994\n",
       "..       ...      ...       ...      ...     ...         ...       ...\n",
       "81   control       39     NC-02     FUR1     0.0  155.769045  1.015749\n",
       "82   control       40  LL13-040     FCY1     0.0  152.771678  1.006754\n",
       "83   control       41     NC-02     FCY1     0.0  146.668201  0.956404\n",
       "84   control       42  LL13-040     FCY2     0.0  134.115567  0.883812\n",
       "85   control       43     NC-02     FCY2     0.0  150.459460  0.981126\n",
       "\n",
       "[86 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df['norm_auc'] = mean_df.apply(lambda row: get_norm_auc(mean_df, row.condition, row.s_index), axis=1)\n",
    "mean_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b834143b-f7e3-43c6-b355-31e64ab81bc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     86.000000\n",
       "mean      97.674419\n",
       "std       48.477877\n",
       "min        0.000000\n",
       "25%       98.500000\n",
       "50%      118.000000\n",
       "75%      128.500000\n",
       "max      145.000000\n",
       "Name: Ngenes, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df.Ngenes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a44aa482-431e-4264-b30c-22adc4e6dd72",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>s_index</th>\n",
       "      <th>strain</th>\n",
       "      <th>genotype</th>\n",
       "      <th>Ngenes</th>\n",
       "      <th>auc</th>\n",
       "      <th>norm_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>38</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>138.920775</td>\n",
       "      <td>3.933587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>39</td>\n",
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       "      <td>FUR1</td>\n",
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       "      <th>39</th>\n",
       "      <td>5-FC</td>\n",
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       "      <td>FCY1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145.327965</td>\n",
       "      <td>4.115009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>41</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.808527</td>\n",
       "      <td>4.369331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>42</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.854957</td>\n",
       "      <td>1.100191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5-FC</td>\n",
       "      <td>43</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.240678</td>\n",
       "      <td>1.426636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>control</td>\n",
       "      <td>38</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.211743</td>\n",
       "      <td>0.937165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>control</td>\n",
       "      <td>39</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FUR1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155.769045</td>\n",
       "      <td>1.015749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>control</td>\n",
       "      <td>40</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.771678</td>\n",
       "      <td>1.006754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>control</td>\n",
       "      <td>41</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.668201</td>\n",
       "      <td>0.956404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>control</td>\n",
       "      <td>42</td>\n",
       "      <td>LL13-040</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.115567</td>\n",
       "      <td>0.883812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>control</td>\n",
       "      <td>43</td>\n",
       "      <td>NC-02</td>\n",
       "      <td>FCY2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.459460</td>\n",
       "      <td>0.981126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   condition  s_index    strain genotype  Ngenes         auc  norm_auc\n",
       "37      5-FC       38  LL13-040     FUR1     0.0  138.920775  3.933587\n",
       "38      5-FC       39     NC-02     FUR1     0.0  150.961400  4.373760\n",
       "39      5-FC       40  LL13-040     FCY1     0.0  145.327965  4.115009\n",
       "40      5-FC       41     NC-02     FCY1     0.0  150.808527  4.369331\n",
       "41      5-FC       42  LL13-040     FCY2     0.0   38.854957  1.100191\n",
       "42      5-FC       43     NC-02     FCY2     0.0   49.240678  1.426636\n",
       "80   control       38  LL13-040     FUR1     0.0  142.211743  0.937165\n",
       "81   control       39     NC-02     FUR1     0.0  155.769045  1.015749\n",
       "82   control       40  LL13-040     FCY1     0.0  152.771678  1.006754\n",
       "83   control       41     NC-02     FCY1     0.0  146.668201  0.956404\n",
       "84   control       42  LL13-040     FCY2     0.0  134.115567  0.883812\n",
       "85   control       43     NC-02     FCY2     0.0  150.459460  0.981126"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df[mean_df.genotype.isin(['FUR1','FCY1','FCY2'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6af13198-ecb1-4559-a98e-f2e476a0c042",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 367.025x370 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genotype_order = ['wt','FUR1','FCY1','FCY2','mutant']\n",
    "genotype_palette = ['black','orange','green','blue','lightgrey']\n",
    "fig = sns.catplot(data=mean_df, x='condition',\n",
    "                  order=['control', '5-FC'],\n",
    "                  y='norm_auc',\n",
    "                  col = 'strain', col_order=['LL13-040','NC-02'],\n",
    "                  hue='genotype', hue_order=genotype_order, palette=genotype_palette, alpha=.5,\n",
    "                  legend_out=False,\n",
    "                  height=3.7, aspect=0.36 # height*aspect = width of EACH facet\n",
    "                 )\n",
    "fig.map(plt.axhline, y=1, ls='--', c='grey')\n",
    "plt.yscale('log',base=2)\n",
    "fig.set_xticklabels(['SD','5-FC'])\n",
    "fig.set_axis_labels('', 'Relative growth')\n",
    "fig.set_titles(col_template='{col_name}')\n",
    "fig._legend.set_title('Genotype')\n",
    "for i, v in enumerate(['WT',  u'$\\it{fur1}$Δ', u'$\\it{fcy1}$Δ', u'$\\it{fcy2}$Δ', u'$\\it{FUR1}$ mutant']):\n",
    "    fig._legend.texts[i].set_text(v)\n",
    "\n",
    "#fig.fig.subplots_adjust(top=0.92)\n",
    "#fig.fig.suptitle('Relative fitness of $\\it{FUR1}$ mutants')\n",
    "fig.tight_layout()\n",
    "plt.savefig(graph_outpath+'20230413_FUR1_validation_relfit.png', format='png', dpi=300)\n",
    "plt.savefig(graph_outpath+'20230413_FUR1_validation_relfit.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ee0b6b6-8ed7-4fed-b762-0c32b75bc215",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}