template.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8b308f8b-5460-4ea6-9e91-151c5e92d354",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "# Refer to injected parameters for any run.ipynb (similar cell below)\n",
    "fname = 'BY4741_FKS1-HS2_single_ortho_anidulafungin_variant_data_merge.tsv'\n",
    "\n",
    "min_input_readcount_high = 10\n",
    "min_input_readcount_mid = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92739bd9-9f71-4f4b-800c-cde644e33f07",
   "metadata": {},
   "source": [
    "# Master notebook to analyze DMS data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa0cda4-2357-49a7-b5b3-3967fa25948c",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "13b73f43-866d-40d9-8944-b03d4a4c6d53",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "from upsetplot import from_indicators\n",
    "from upsetplot import UpSet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc459c82-ea46-43e1-a619-9e2f79d92fea",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a60ef3e-3bfa-4b4e-99e7-392dd959b240",
   "metadata": {},
   "source": [
    "## Specify paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "65cedf42-7b87-4b21-b755-85476c012c12",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "metadata_path = 'data/'\n",
    "variantdata_path = 'dimsum_output/variant_data/'\n",
    "dimsumfitness_path = 'dimsum_output/fitness_data/'\n",
    "expdesign_path = 'dimsum_expdesign/'\n",
    "codon_table_path = 'data/general_use/ScerevisiaeTAXID559292_Cocoputs_codon_table.csv'\n",
    "\n",
    "processed_data_outpath = 'processed_data/'\n",
    "graph_outpath = 'graphs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32c96f1-3510-4ba3-8fc7-f92c9f0a5bad",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6ba18aa1-258c-4543-b3b7-d99396d7674a",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = variantdata_path+fname\n",
    "pos_offset = {'FKS1-HS1': 639,\n",
    "              'FKS1-HS2': 1353,\n",
    "              'FKS2-HS1': 659,\n",
    "              'FKS2-HS2': 1372\n",
    "             }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520fa8c9-a895-4250-98f4-320eb24d1e62",
   "metadata": {},
   "source": [
    "### Get conditions from filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5836fcf6-ac33-4267-a898-f6bfa094f2bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strain: BY4741\n",
      "Locus: FKS1-HS2\n",
      "Pool type: single\n",
      "Compound: anidulafungin\n"
     ]
    }
   ],
   "source": [
    "cset = f.split('/')[-1].split('_')[:-3] # Extract conditions from name of file\n",
    "cset_name = '_'.join(cset) # Basename based on conditions\n",
    "strain, locus, pool_type, compound = [cset[i] for i in (0,1,2,-1)]\n",
    "print(f'Strain: {strain}')\n",
    "print(f'Locus: {locus}')\n",
    "print(f'Pool type: {pool_type}')\n",
    "print(f'Compound: {compound}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "177d1dcd-d1de-4d5a-9bb7-7d723c1adef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "if pool_type == 'single':\n",
    "    nhamaa = 1\n",
    "elif pool_type == 'double':\n",
    "    nhamaa = 2\n",
    "else:\n",
    "    print('Could not determine pool type')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c836522-0528-4007-8773-3d7a43ec311b",
   "metadata": {},
   "source": [
    "### Create output folders if they don't already exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6da3eed5-c3b5-4989-980c-8d80f9f5e64a",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(processed_data_outpath+cset_name):\n",
    "    os.makedirs(processed_data_outpath+cset_name)\n",
    "if not os.path.exists(graph_outpath+cset_name):\n",
    "    os.makedirs(graph_outpath+cset_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f43f13-aef3-4b06-bcbd-3afeef14a391",
   "metadata": {},
   "source": [
    "### Read dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b2b79ead-cf09-4e1d-bc70-f1f4ac8cc103",
   "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>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>output1anidulafungin1_e1_s1_b1_count</th>\n",
       "      <th>output1anidulafungin2_e1_s1_b2_count</th>\n",
       "      <th>...</th>\n",
       "      <th>output3anidulafungin3_e3_s1_b3_count</th>\n",
       "      <th>input1_e1_s0_bNA_count</th>\n",
       "      <th>input2_e2_s0_bNA_count</th>\n",
       "      <th>input3_e3_s0_bNA_count</th>\n",
       "      <th>barcode_valid</th>\n",
       "      <th>indel</th>\n",
       "      <th>constant_region</th>\n",
       "      <th>permitted</th>\n",
       "      <th>too_many_substitutions</th>\n",
       "      <th>mixed_substitutions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaccaacaatttattattcaattg</td>\n",
       "      <td>NQQFIIQL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aactgggtgagacgttatacattg</td>\n",
       "      <td>NWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aactgggttaatatatgtgttttg</td>\n",
       "      <td>NWVNICVL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aactgggtttggagatgtgttatt</td>\n",
       "      <td>NWVWRCVI</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aagcttgtgagacgttatacattg</td>\n",
       "      <td>KLVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>4579</th>\n",
       "      <td>ttttggttgagacgttatacattg</td>\n",
       "      <td>FWLRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4580</th>\n",
       "      <td>ttttgtgtgagacgttatacattg</td>\n",
       "      <td>FCVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4581</th>\n",
       "      <td>tttttgggtgagacgttatacattg</td>\n",
       "      <td>FLGETLYI</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4582</th>\n",
       "      <td>tttttggtgagacgttatacaaag</td>\n",
       "      <td>FLVRRYTK</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4583</th>\n",
       "      <td>tttttggtgagacgttatacattg</td>\n",
       "      <td>FLVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4584 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         nt_seq    aa_seq    WT   STOP  STOP_readthrough  \\\n",
       "0      aaccaacaatttattattcaattg  NQQFIIQL  <NA>  False             False   \n",
       "1      aactgggtgagacgttatacattg  NWVRRYTL  <NA>  False             False   \n",
       "2      aactgggttaatatatgtgttttg  NWVNICVL  <NA>  False             False   \n",
       "3      aactgggtttggagatgtgttatt  NWVWRCVI  <NA>  False             False   \n",
       "4      aagcttgtgagacgttatacattg  KLVRRYTL  <NA>  False             False   \n",
       "...                         ...       ...   ...    ...               ...   \n",
       "4579   ttttggttgagacgttatacattg  FWLRRYTL  <NA>  False             False   \n",
       "4580   ttttgtgtgagacgttatacattg  FCVRRYTL  <NA>  False             False   \n",
       "4581  tttttgggtgagacgttatacattg  FLGETLYI  <NA>  False             False   \n",
       "4582   tttttggtgagacgttatacaaag  FLVRRYTK  <NA>  False             False   \n",
       "4583   tttttggtgagacgttatacattg  FLVRRYTL  <NA>  False             False   \n",
       "\n",
       "      Nham_nt  Nham_aa  Nmut_codons  output1anidulafungin1_e1_s1_b1_count  \\\n",
       "0        17.0      7.0          7.0                                     0   \n",
       "1         2.0      1.0          1.0                                     0   \n",
       "2        12.0      5.0          6.0                                     0   \n",
       "3        13.0      5.0          7.0                                     0   \n",
       "4         5.0      2.0          2.0                                     1   \n",
       "...       ...      ...          ...                                   ...   \n",
       "4579      3.0      2.0          2.0                                     0   \n",
       "4580      3.0      2.0          2.0                                     0   \n",
       "4581      NaN      NaN          NaN                                     0   \n",
       "4582      5.0      3.0          3.0                                     1   \n",
       "4583      3.0      2.0          2.0                                     0   \n",
       "\n",
       "      output1anidulafungin2_e1_s1_b2_count  ...  \\\n",
       "0                                        0  ...   \n",
       "1                                        0  ...   \n",
       "2                                        1  ...   \n",
       "3                                        0  ...   \n",
       "4                                        0  ...   \n",
       "...                                    ...  ...   \n",
       "4579                                     0  ...   \n",
       "4580                                     0  ...   \n",
       "4581                                     0  ...   \n",
       "4582                                     0  ...   \n",
       "4583                                     0  ...   \n",
       "\n",
       "      output3anidulafungin3_e3_s1_b3_count  input1_e1_s0_bNA_count  \\\n",
       "0                                        0                       0   \n",
       "1                                        0                       2   \n",
       "2                                        0                       0   \n",
       "3                                        0                       1   \n",
       "4                                        0                       0   \n",
       "...                                    ...                     ...   \n",
       "4579                                     0                       1   \n",
       "4580                                     0                       4   \n",
       "4581                                     0                       3   \n",
       "4582                                     0                       0   \n",
       "4583                                     0                       5   \n",
       "\n",
       "      input2_e2_s0_bNA_count  input3_e3_s0_bNA_count  barcode_valid  indel  \\\n",
       "0                          1                       0           True  False   \n",
       "1                          1                       0           True  False   \n",
       "2                          0                       0           True  False   \n",
       "3                          0                       0           True  False   \n",
       "4                          0                       0           True  False   \n",
       "...                      ...                     ...            ...    ...   \n",
       "4579                       0                       0           True  False   \n",
       "4580                       2                       1           True  False   \n",
       "4581                       1                       0           True   True   \n",
       "4582                       0                       0           True  False   \n",
       "4583                       4                       7           True  False   \n",
       "\n",
       "      constant_region  permitted  too_many_substitutions  mixed_substitutions  \n",
       "0                True       True                   False                False  \n",
       "1                True       True                   False                False  \n",
       "2                True       True                   False                False  \n",
       "3                True       True                   False                False  \n",
       "4                True       True                   False                False  \n",
       "...               ...        ...                     ...                  ...  \n",
       "4579             True       True                   False                False  \n",
       "4580             True       True                   False                False  \n",
       "4581             True        NaN                     NaN                  NaN  \n",
       "4582             True       True                   False                False  \n",
       "4583             True       True                   False                False  \n",
       "\n",
       "[4584 rows x 26 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(f, sep='\\t', header=0, dtype={'WT': 'boolean'}) # Boolean type supports missing data, helps pandas interpret column content\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "383e72b8-63fa-4a13-b54f-575dfab3ad6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['input1', 'input2', 'input3', 'b1', 'b2', 'b3']\n"
     ]
    }
   ],
   "source": [
    "# Extract sample names, based on naming pattern in experimental design file (input/output)\n",
    "input_conditions = [x for x in df.columns if 'input' in x]\n",
    "output_conditions = [x for x in df.columns if ('output' in x) & ('_e1_' in x)]\n",
    "duplicated_output = [x for x in df.columns if ('output' in x) & ('_e1_' not in x)]\n",
    "input_samples = [x.split('_')[0] for x in input_conditions]\n",
    "output_samples = [x.split('_')[-2] for x in output_conditions]\n",
    "conditions = input_samples+output_samples\n",
    "print(conditions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bbc821-3bba-44c2-80b8-097148406d86",
   "metadata": {},
   "source": [
    "#### ⚠️ **Filtering step** ⚠️ - Formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2cfc3c7e-28e4-44d6-af07-b33bdec01bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop redundant columns due to duplicates in experimental design file\n",
    "df.drop(columns=duplicated_output, inplace=True)\n",
    "df.rename(columns=lambda x: x.split('_')[0] if 'input' in x else x, inplace=True)\n",
    "df.rename(columns=lambda x: x.split('_')[-2] if 'output' in x else x, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "aa750e51-2689-460b-a69d-e2b4192cb389",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT (nt): gattgggtgagacgttatacattg | (aa): DWVRRYTL\n"
     ]
    }
   ],
   "source": [
    "# Extract WT sequences (nucleotide and protein)\n",
    "wtseq = df.loc[df.WT==True, 'nt_seq'].values[0]\n",
    "wtaa = df.loc[df.WT==True, 'aa_seq'].values[0]\n",
    "print(f'WT (nt): {wtseq} | (aa): {wtaa}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8dcda7d-5ccd-4a7f-8d41-7a2426c2e765",
   "metadata": {},
   "source": [
    "### Mean readcount per variant (raw data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7964fc9f-62fe-44a2-9041-f857bb44fb38",
   "metadata": {},
   "source": [
    "This matrix shows the average readcount per unique nucleotide sequence per number of mutated codons across samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "14c72e56-bd5b-446c-899e-f22533703c9a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fe435c3b250>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Purge graph space\n",
    "sns.set(rc = {'figure.figsize':(6,3)})\n",
    "sns.set_theme(style='whitegrid')\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "count_per_mut_codon = df.groupby('Nmut_codons')[conditions].count()\n",
    "mean_rc_per_variant = df.groupby('Nmut_codons')[conditions].mean() / count_per_mut_codon\n",
    "mean_rc_per_variant.reset_index(inplace=True)\n",
    "mean_rc_long = mean_rc_per_variant.melt(id_vars=['Nmut_codons'], value_vars=conditions, var_name='sample')\n",
    "sns.barplot(mean_rc_long, x='Nmut_codons',y='value',hue='sample',palette='hls')\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Average readcount per unique sequence')\n",
    "plt.xlabel('Number of mutated codons')\n",
    "plt.axhline(y=1, linestyle='--', c='k', zorder=-10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31139d1f-11d3-4d9a-9f48-04c2bc55cee2",
   "metadata": {},
   "source": [
    "### Overrepresented variants (raw data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4076115-05bd-41c9-9043-193498219182",
   "metadata": {},
   "source": [
    "The table below shows the 5 most represented sequences (mean readcount across samples) in % read depth per sample."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a148532-5125-4ac1-a135-1994f5dd4ecb",
   "metadata": {},
   "source": [
    "#### Overrepresented variants in input samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0242b24c-0671-422b-a5d5-ab20a26fdf9e",
   "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>input1</th>\n",
       "      <th>input2</th>\n",
       "      <th>input3</th>\n",
       "      <th>b1</th>\n",
       "      <th>b2</th>\n",
       "      <th>b3</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nmut_codons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2566</th>\n",
       "      <td>4.79</td>\n",
       "      <td>4.90</td>\n",
       "      <td>4.79</td>\n",
       "      <td>14.20</td>\n",
       "      <td>14.55</td>\n",
       "      <td>14.96</td>\n",
       "      <td>gattgggtgagatggtatacattg</td>\n",
       "      <td>DWVRWYTL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2898</th>\n",
       "      <td>3.99</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.09</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.18</td>\n",
       "      <td>gattgggtgtagcgttatacattg</td>\n",
       "      <td>DWV*RYTL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2.47</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.23</td>\n",
       "      <td>aattgggtgagacgttatacattg</td>\n",
       "      <td>NWVRRYTL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2363</th>\n",
       "      <td>1.84</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.84</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>gattgggtgagacgttattagttg</td>\n",
       "      <td>DWVRRY*L</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>1.80</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.09</td>\n",
       "      <td>gattgggtgagatagtatacattg</td>\n",
       "      <td>DWVR*YTL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      input1  input2  input3     b1     b2     b3                    nt_seq  \\\n",
       "2566    4.79    4.90    4.79  14.20  14.55  14.96  gattgggtgagatggtatacattg   \n",
       "2898    3.99    4.01    4.09   0.17   0.21   0.18  gattgggtgtagcgttatacattg   \n",
       "123     2.47    2.51    2.49   0.26   0.36   0.23  aattgggtgagacgttatacattg   \n",
       "2363    1.84    1.86    1.84   0.08   0.10   0.09  gattgggtgagacgttattagttg   \n",
       "2497    1.80    1.82    1.81   0.06   0.09   0.09  gattgggtgagatagtatacattg   \n",
       "\n",
       "        aa_seq  Nmut_codons  \n",
       "2566  DWVRWYTL          1.0  \n",
       "2898  DWV*RYTL          1.0  \n",
       "123   NWVRRYTL          1.0  \n",
       "2363  DWVRRY*L          1.0  \n",
       "2497  DWVR*YTL          1.0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overrep_index = df[input_samples].mean(axis=1).sort_values(ascending=False).index[:5]\n",
    "overrep = 100*round(df.iloc[overrep_index][conditions] / df[conditions].sum(axis=0), 4)\n",
    "pd.concat([overrep, df.iloc[overrep_index][['nt_seq', 'aa_seq', 'Nmut_codons']]], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd64230-dc5f-4ebb-8714-387123f4aa98",
   "metadata": {},
   "source": [
    "#### Overrepresented variants in output samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4da33f18-3c5d-4883-930f-637d4e0f56e4",
   "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>input1</th>\n",
       "      <th>input2</th>\n",
       "      <th>input3</th>\n",
       "      <th>b1</th>\n",
       "      <th>b2</th>\n",
       "      <th>b3</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nmut_codons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2566</th>\n",
       "      <td>4.79</td>\n",
       "      <td>4.90</td>\n",
       "      <td>4.79</td>\n",
       "      <td>14.20</td>\n",
       "      <td>14.55</td>\n",
       "      <td>14.96</td>\n",
       "      <td>gattgggtgagatggtatacattg</td>\n",
       "      <td>DWVRWYTL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>1.72</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.71</td>\n",
       "      <td>14.37</td>\n",
       "      <td>13.65</td>\n",
       "      <td>14.19</td>\n",
       "      <td>gattgggtgagaacgtatacattg</td>\n",
       "      <td>DWVRTYTL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2644</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>11.29</td>\n",
       "      <td>10.71</td>\n",
       "      <td>10.97</td>\n",
       "      <td>gattgggtgagattttatacattg</td>\n",
       "      <td>DWVRFYTL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.65</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.43</td>\n",
       "      <td>4.35</td>\n",
       "      <td>gattgggtgagacgttatacattt</td>\n",
       "      <td>DWVRRYTF</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.74</td>\n",
       "      <td>3.60</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.59</td>\n",
       "      <td>gattgggtgagacattatacattg</td>\n",
       "      <td>DWVRHYTL</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      input1  input2  input3     b1     b2     b3                    nt_seq  \\\n",
       "2566    4.79    4.90    4.79  14.20  14.55  14.96  gattgggtgagatggtatacattg   \n",
       "1911    1.72    1.75    1.71  14.37  13.65  14.19  gattgggtgagaacgtatacattg   \n",
       "2644    1.06    1.07    1.07  11.29  10.71  10.97  gattgggtgagattttatacattg   \n",
       "2302    0.68    0.72    0.65   4.50   4.43   4.35  gattgggtgagacgttatacattt   \n",
       "2026    0.76    0.77    0.74   3.60   3.69   3.59  gattgggtgagacattatacattg   \n",
       "\n",
       "        aa_seq  Nmut_codons  \n",
       "2566  DWVRWYTL          1.0  \n",
       "1911  DWVRTYTL          1.0  \n",
       "2644  DWVRFYTL          1.0  \n",
       "2302  DWVRRYTF          1.0  \n",
       "2026  DWVRHYTL          1.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overrep_index = df[output_samples].mean(axis=1).sort_values(ascending=False).index[:5]\n",
    "overrep = 100*round(df.iloc[overrep_index][conditions] / df[conditions].sum(axis=0), 4)\n",
    "pd.concat([overrep, df.iloc[overrep_index][['nt_seq', 'aa_seq', 'Nmut_codons']]], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "890bdc78-9743-4290-baeb-2ee3c8873b1e",
   "metadata": {},
   "source": [
    "### Calculate % read depth per sample corresponding to variants with no indels + read count above threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fff39ee2-144e-41c8-bb3c-d2c83e9e589b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_reads</th>\n",
       "      <th>Reads_no_indels</th>\n",
       "      <th>Reads_no_indels_%</th>\n",
       "      <th>No_indels_acceptable_RC</th>\n",
       "      <th>Acceptable_RC_over_no_indels_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>input1</th>\n",
       "      <td>281639</td>\n",
       "      <td>279318</td>\n",
       "      <td>99.2</td>\n",
       "      <td>276649</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input2</th>\n",
       "      <td>226059</td>\n",
       "      <td>224369</td>\n",
       "      <td>99.3</td>\n",
       "      <td>220920</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input3</th>\n",
       "      <td>218453</td>\n",
       "      <td>216795</td>\n",
       "      <td>99.2</td>\n",
       "      <td>214418</td>\n",
       "      <td>98.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1</th>\n",
       "      <td>295598</td>\n",
       "      <td>294890</td>\n",
       "      <td>99.8</td>\n",
       "      <td>284322</td>\n",
       "      <td>96.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2</th>\n",
       "      <td>263753</td>\n",
       "      <td>263159</td>\n",
       "      <td>99.8</td>\n",
       "      <td>253750</td>\n",
       "      <td>96.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3</th>\n",
       "      <td>196813</td>\n",
       "      <td>196315</td>\n",
       "      <td>99.7</td>\n",
       "      <td>187113</td>\n",
       "      <td>95.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total_reads  Reads_no_indels  Reads_no_indels_%  \\\n",
       "input1       281639           279318               99.2   \n",
       "input2       226059           224369               99.3   \n",
       "input3       218453           216795               99.2   \n",
       "b1           295598           294890               99.8   \n",
       "b2           263753           263159               99.8   \n",
       "b3           196813           196315               99.7   \n",
       "\n",
       "        No_indels_acceptable_RC  Acceptable_RC_over_no_indels_%  \n",
       "input1                   276649                            99.0  \n",
       "input2                   220920                            98.5  \n",
       "input3                   214418                            98.9  \n",
       "b1                       284322                            96.4  \n",
       "b2                       253750                            96.4  \n",
       "b3                       187113                            95.3  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum all reads for each sample\n",
    "# Check proportion that corresponds to variants with no indels\n",
    "rcNoIndel = df[conditions].sum(axis=0).to_frame('Total_reads')\n",
    "rcNoIndel['Reads_no_indels'] = df[df.indel == False][conditions].sum(axis=0)\n",
    "rcNoIndel['Reads_no_indels_%'] = 100*round(rcNoIndel['Reads_no_indels']/rcNoIndel['Total_reads'], 3)\n",
    "\n",
    "# Check proportion that corresponds to variants with a read count above threshold in all input samples\n",
    "# Here we only show the variant surpassing the highest threshold = variants which will be labeled with a high confidence score\n",
    "rcNoIndel['No_indels_acceptable_RC'] = df[((df[input_samples] > min_input_readcount_high).all(1)) & (df.indel == False)][conditions].sum(axis=0)\n",
    "rcNoIndel['Acceptable_RC_over_no_indels_%'] = 100*round(rcNoIndel['No_indels_acceptable_RC']/rcNoIndel['Reads_no_indels'], 3)\n",
    "\n",
    "# Export summary to csv\n",
    "rcNoIndel.to_csv(processed_data_outpath+cset_name+'/read_count_no_indel.csv')\n",
    "rcNoIndel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e60bf3-92ce-470b-b3a9-84ede2e47eea",
   "metadata": {},
   "source": [
    "#### ⚠️ **Filtering step** ⚠️ - Discard variants with indels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2e768508-89da-42ec-b3f4-24e1b01919b3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>b1</th>\n",
       "      <th>b2</th>\n",
       "      <th>b3</th>\n",
       "      <th>input1</th>\n",
       "      <th>input2</th>\n",
       "      <th>input3</th>\n",
       "      <th>barcode_valid</th>\n",
       "      <th>indel</th>\n",
       "      <th>constant_region</th>\n",
       "      <th>permitted</th>\n",
       "      <th>too_many_substitutions</th>\n",
       "      <th>mixed_substitutions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaccaacaatttattattcaattg</td>\n",
       "      <td>NQQFIIQL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aactgggtgagacgttatacattg</td>\n",
       "      <td>NWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aactgggttaatatatgtgttttg</td>\n",
       "      <td>NWVNICVL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aactgggtttggagatgtgttatt</td>\n",
       "      <td>NWVWRCVI</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aagcttgtgagacgttatacattg</td>\n",
       "      <td>KLVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>4024</th>\n",
       "      <td>ttttggttgagacgttatacatct</td>\n",
       "      <td>FWLRRYTS</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4025</th>\n",
       "      <td>ttttggttgagacgttatacattg</td>\n",
       "      <td>FWLRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026</th>\n",
       "      <td>ttttgtgtgagacgttatacattg</td>\n",
       "      <td>FCVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4027</th>\n",
       "      <td>tttttggtgagacgttatacaaag</td>\n",
       "      <td>FLVRRYTK</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4028</th>\n",
       "      <td>tttttggtgagacgttatacattg</td>\n",
       "      <td>FLVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4029 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        nt_seq    aa_seq    WT   STOP  STOP_readthrough  \\\n",
       "0     aaccaacaatttattattcaattg  NQQFIIQL  <NA>  False             False   \n",
       "1     aactgggtgagacgttatacattg  NWVRRYTL  <NA>  False             False   \n",
       "2     aactgggttaatatatgtgttttg  NWVNICVL  <NA>  False             False   \n",
       "3     aactgggtttggagatgtgttatt  NWVWRCVI  <NA>  False             False   \n",
       "4     aagcttgtgagacgttatacattg  KLVRRYTL  <NA>  False             False   \n",
       "...                        ...       ...   ...    ...               ...   \n",
       "4024  ttttggttgagacgttatacatct  FWLRRYTS  <NA>  False             False   \n",
       "4025  ttttggttgagacgttatacattg  FWLRRYTL  <NA>  False             False   \n",
       "4026  ttttgtgtgagacgttatacattg  FCVRRYTL  <NA>  False             False   \n",
       "4027  tttttggtgagacgttatacaaag  FLVRRYTK  <NA>  False             False   \n",
       "4028  tttttggtgagacgttatacattg  FLVRRYTL  <NA>  False             False   \n",
       "\n",
       "      Nham_nt  Nham_aa  Nmut_codons  b1  b2  b3  input1  input2  input3  \\\n",
       "0        17.0      7.0          7.0   0   0   0       0       1       0   \n",
       "1         2.0      1.0          1.0   0   0   0       2       1       0   \n",
       "2        12.0      5.0          6.0   0   1   0       0       0       0   \n",
       "3        13.0      5.0          7.0   0   0   0       1       0       0   \n",
       "4         5.0      2.0          2.0   1   0   0       0       0       0   \n",
       "...       ...      ...          ...  ..  ..  ..     ...     ...     ...   \n",
       "4024      5.0      3.0          3.0   0   1   0       0       0       0   \n",
       "4025      3.0      2.0          2.0   0   0   0       1       0       0   \n",
       "4026      3.0      2.0          2.0   0   0   0       4       2       1   \n",
       "4027      5.0      3.0          3.0   1   0   0       0       0       0   \n",
       "4028      3.0      2.0          2.0   0   0   0       5       4       7   \n",
       "\n",
       "      barcode_valid  indel  constant_region permitted too_many_substitutions  \\\n",
       "0              True  False             True      True                  False   \n",
       "1              True  False             True      True                  False   \n",
       "2              True  False             True      True                  False   \n",
       "3              True  False             True      True                  False   \n",
       "4              True  False             True      True                  False   \n",
       "...             ...    ...              ...       ...                    ...   \n",
       "4024           True  False             True      True                  False   \n",
       "4025           True  False             True      True                  False   \n",
       "4026           True  False             True      True                  False   \n",
       "4027           True  False             True      True                  False   \n",
       "4028           True  False             True      True                  False   \n",
       "\n",
       "     mixed_substitutions  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  \n",
       "3                  False  \n",
       "4                  False  \n",
       "...                  ...  \n",
       "4024               False  \n",
       "4025               False  \n",
       "4026               False  \n",
       "4027               False  \n",
       "4028               False  \n",
       "\n",
       "[4029 rows x 20 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping only variants with no indels\n",
    "df.drop(df[df.indel].index, inplace=True)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3688f2ea-13a2-474b-b75a-caff98dffd12",
   "metadata": {},
   "source": [
    "#### ⚠️ **Filtering step** ⚠️ - Keep only variants with at least 3 reads in at least 1 input replicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1cb73316-1b4b-4093-a37b-e85d7aacda6e",
   "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>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>b1</th>\n",
       "      <th>b2</th>\n",
       "      <th>b3</th>\n",
       "      <th>input1</th>\n",
       "      <th>input2</th>\n",
       "      <th>input3</th>\n",
       "      <th>barcode_valid</th>\n",
       "      <th>indel</th>\n",
       "      <th>constant_region</th>\n",
       "      <th>permitted</th>\n",
       "      <th>too_many_substitutions</th>\n",
       "      <th>mixed_substitutions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>aagtgggtgagacgttataatttg</td>\n",
       "      <td>KWVRRYNL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>aagtgggtgagacgttatacattg</td>\n",
       "      <td>KWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>aagtgggtgagacgttattagttg</td>\n",
       "      <td>KWVRRY*L</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>aagtgggtgagacgttggacattg</td>\n",
       "      <td>KWVRRWTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>758</td>\n",
       "      <td>714</td>\n",
       "      <td>664</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>aagtgggtgatacgttggacattg</td>\n",
       "      <td>KWVIRWTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>4018</th>\n",
       "      <td>ttttgggtgatacgttatacattg</td>\n",
       "      <td>FWVIRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>ttttgggtgctgcgttatacattg</td>\n",
       "      <td>FWVLRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>ttttgggttagacgttatacattg</td>\n",
       "      <td>FWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4026</th>\n",
       "      <td>ttttgtgtgagacgttatacattg</td>\n",
       "      <td>FCVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4028</th>\n",
       "      <td>tttttggtgagacgttatacattg</td>\n",
       "      <td>FLVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>818 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        nt_seq    aa_seq    WT   STOP  STOP_readthrough  \\\n",
       "13    aagtgggtgagacgttataatttg  KWVRRYNL  <NA>  False             False   \n",
       "16    aagtgggtgagacgttatacattg  KWVRRYTL  <NA>  False             False   \n",
       "17    aagtgggtgagacgttattagttg  KWVRRY*L  <NA>   True             False   \n",
       "20    aagtgggtgagacgttggacattg  KWVRRWTL  <NA>  False             False   \n",
       "26    aagtgggtgatacgttggacattg  KWVIRWTL  <NA>  False             False   \n",
       "...                        ...       ...   ...    ...               ...   \n",
       "4018  ttttgggtgatacgttatacattg  FWVIRYTL  <NA>  False             False   \n",
       "4020  ttttgggtgctgcgttatacattg  FWVLRYTL  <NA>  False             False   \n",
       "4023  ttttgggttagacgttatacattg  FWVRRYTL  <NA>  False             False   \n",
       "4026  ttttgtgtgagacgttatacattg  FCVRRYTL  <NA>  False             False   \n",
       "4028  tttttggtgagacgttatacattg  FLVRRYTL  <NA>  False             False   \n",
       "\n",
       "      Nham_nt  Nham_aa  Nmut_codons  b1  b2  b3  input1  input2  input3  \\\n",
       "13        4.0      2.0          2.0   3   0   0       1       3       1   \n",
       "16        2.0      1.0          1.0   0   5   1      40      35      38   \n",
       "17        5.0      2.0          2.0   0   0   0       3       0       0   \n",
       "20        4.0      2.0          2.0  24  32  25     758     714     664   \n",
       "26        5.0      3.0          3.0   0   0   0       0       3       0   \n",
       "...       ...      ...          ...  ..  ..  ..     ...     ...     ...   \n",
       "4018      3.0      2.0          2.0   0   0   0       2       4       2   \n",
       "4020      5.0      2.0          2.0   6   5   8       1       4       2   \n",
       "4023      3.0      1.0          2.0   0   0   0       4       2       0   \n",
       "4026      3.0      2.0          2.0   0   0   0       4       2       1   \n",
       "4028      3.0      2.0          2.0   0   0   0       5       4       7   \n",
       "\n",
       "      barcode_valid  indel  constant_region permitted too_many_substitutions  \\\n",
       "13             True  False             True      True                  False   \n",
       "16             True  False             True      True                  False   \n",
       "17             True  False             True      True                  False   \n",
       "20             True  False             True      True                  False   \n",
       "26             True  False             True      True                  False   \n",
       "...             ...    ...              ...       ...                    ...   \n",
       "4018           True  False             True      True                  False   \n",
       "4020           True  False             True      True                  False   \n",
       "4023           True  False             True      True                  False   \n",
       "4026           True  False             True      True                  False   \n",
       "4028           True  False             True      True                  False   \n",
       "\n",
       "     mixed_substitutions  \n",
       "13                 False  \n",
       "16                 False  \n",
       "17                 False  \n",
       "20                 False  \n",
       "26                 False  \n",
       "...                  ...  \n",
       "4018               False  \n",
       "4020               False  \n",
       "4023               False  \n",
       "4026               False  \n",
       "4028               False  \n",
       "\n",
       "[818 rows x 20 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputRCdf = df[(df[input_samples] > 2).any(axis=1)]\n",
    "inputRCdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d2df4aa-1aaf-466e-9150-8f8937abf5fe",
   "metadata": {},
   "source": [
    "### Label variants with confidence score (related to readcount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b2cb9800-befa-483c-8417-52af90f56642",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_confidence_score(g, thresh_high, thresh_mid):\n",
    "    # The read count in all input replicates is parsed to label each variant with a confidence score\n",
    "    # Score is an integer for easy aggregation of synonymous codons\n",
    "    if (g >= thresh_high).all():\n",
    "        return 1 # high confidence score\n",
    "    elif (g > thresh_mid).any():\n",
    "        return 2 # medium confidence score\n",
    "    else:\n",
    "        return 3 # low confidence score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f93ed480-b24c-4ee8-b213-2076adc31902",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
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       "      <th>STOP_readthrough</th>\n",
       "      <th>Nham_nt</th>\n",
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       "      <th>b2</th>\n",
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       "      <th>input2</th>\n",
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       "      <th>confidence_score</th>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>aagtgggtgagacgttataatttg</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <th>16</th>\n",
       "      <td>aagtgggtgagacgttatacattg</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <th>17</th>\n",
       "      <td>aagtgggtgagacgttattagttg</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>aagtgggtgagacgttggacattg</td>\n",
       "      <td>KWVRRWTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>758</td>\n",
       "      <td>714</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>aagtgggtgatacgttggacattg</td>\n",
       "      <td>KWVIRWTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>ttttgggtgctgcgttatacattg</td>\n",
       "      <td>FWVLRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4023</th>\n",
       "      <td>ttttgggttagacgttatacattg</td>\n",
       "      <td>FWVRRYTL</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4026</th>\n",
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       "      <td>FCVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>4028</th>\n",
       "      <td>tttttggtgagacgttatacattg</td>\n",
       "      <td>FLVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
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       "<p>818 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        nt_seq    aa_seq    WT   STOP  STOP_readthrough  \\\n",
       "13    aagtgggtgagacgttataatttg  KWVRRYNL  <NA>  False             False   \n",
       "16    aagtgggtgagacgttatacattg  KWVRRYTL  <NA>  False             False   \n",
       "17    aagtgggtgagacgttattagttg  KWVRRY*L  <NA>   True             False   \n",
       "20    aagtgggtgagacgttggacattg  KWVRRWTL  <NA>  False             False   \n",
       "26    aagtgggtgatacgttggacattg  KWVIRWTL  <NA>  False             False   \n",
       "...                        ...       ...   ...    ...               ...   \n",
       "4018  ttttgggtgatacgttatacattg  FWVIRYTL  <NA>  False             False   \n",
       "4020  ttttgggtgctgcgttatacattg  FWVLRYTL  <NA>  False             False   \n",
       "4023  ttttgggttagacgttatacattg  FWVRRYTL  <NA>  False             False   \n",
       "4026  ttttgtgtgagacgttatacattg  FCVRRYTL  <NA>  False             False   \n",
       "4028  tttttggtgagacgttatacattg  FLVRRYTL  <NA>  False             False   \n",
       "\n",
       "      Nham_nt  Nham_aa  Nmut_codons  b1  b2  ...  input1  input2  input3  \\\n",
       "13        4.0      2.0          2.0   3   0  ...       1       3       1   \n",
       "16        2.0      1.0          1.0   0   5  ...      40      35      38   \n",
       "17        5.0      2.0          2.0   0   0  ...       3       0       0   \n",
       "20        4.0      2.0          2.0  24  32  ...     758     714     664   \n",
       "26        5.0      3.0          3.0   0   0  ...       0       3       0   \n",
       "...       ...      ...          ...  ..  ..  ...     ...     ...     ...   \n",
       "4018      3.0      2.0          2.0   0   0  ...       2       4       2   \n",
       "4020      5.0      2.0          2.0   6   5  ...       1       4       2   \n",
       "4023      3.0      1.0          2.0   0   0  ...       4       2       0   \n",
       "4026      3.0      2.0          2.0   0   0  ...       4       2       1   \n",
       "4028      3.0      2.0          2.0   0   0  ...       5       4       7   \n",
       "\n",
       "      barcode_valid  indel  constant_region  permitted too_many_substitutions  \\\n",
       "13             True  False             True       True                  False   \n",
       "16             True  False             True       True                  False   \n",
       "17             True  False             True       True                  False   \n",
       "20             True  False             True       True                  False   \n",
       "26             True  False             True       True                  False   \n",
       "...             ...    ...              ...        ...                    ...   \n",
       "4018           True  False             True       True                  False   \n",
       "4020           True  False             True       True                  False   \n",
       "4023           True  False             True       True                  False   \n",
       "4026           True  False             True       True                  False   \n",
       "4028           True  False             True       True                  False   \n",
       "\n",
       "     mixed_substitutions confidence_score  \n",
       "13                 False                3  \n",
       "16                 False                1  \n",
       "17                 False                3  \n",
       "20                 False                1  \n",
       "26                 False                3  \n",
       "...                  ...              ...  \n",
       "4018               False                2  \n",
       "4020               False                2  \n",
       "4023               False                2  \n",
       "4026               False                2  \n",
       "4028               False                2  \n",
       "\n",
       "[818 rows x 21 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('mode.chained_assignment', None) # Disable settingWithCopy warning\n",
    "inputRCdf['confidence_score'] = inputRCdf[input_samples].apply(lambda row: get_confidence_score(row, min_input_readcount_high, min_input_readcount_mid), axis=1)\n",
    "inputRCdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f616c6-69ce-4088-8f44-85d1b160f5cc",
   "metadata": {},
   "source": [
    "### Merge with dataframe of expected variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a02389a6-9342-47ed-b63c-1bd5a52208b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>55501</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattggatttatagatctattttg</td>\n",
       "      <td>5.0</td>\n",
       "      <td>tct</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55502</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattggatttatagatctattttg</td>\n",
       "      <td>6.0</td>\n",
       "      <td>att</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55503 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0           WT  gattgggtgagacgttatacattg     NaN        NaN            NaN   \n",
       "1       single  ttttgggtgagacgttatacattg     0.0        ttt            1.0   \n",
       "2       single  ttgtgggtgagacgttatacattg     0.0        ttg            1.0   \n",
       "3       single  ctttgggtgagacgttatacattg     0.0        ctt            1.0   \n",
       "4       single  ctgtgggtgagacgttatacattg     0.0        ctg            1.0   \n",
       "...        ...                       ...     ...        ...            ...   \n",
       "55498    ortho  gattggatttatagatctattttg     2.0        att            1.0   \n",
       "55499    ortho  gattggatttatagatctattttg     3.0        tat            2.0   \n",
       "55500    ortho  gattggatttatagatctattttg     4.0        aga            3.0   \n",
       "55501    ortho  gattggatttatagatctattttg     5.0        tct            4.0   \n",
       "55502    ortho  gattggatttatagatctattttg     6.0        att            5.0   \n",
       "\n",
       "      combination  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "...           ...  \n",
       "55498         NaN  \n",
       "55499         NaN  \n",
       "55500         NaN  \n",
       "55501         NaN  \n",
       "55502         NaN  \n",
       "\n",
       "[55503 rows x 6 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe containing all expected mutants for the different libraries (single, double, ortholog) is imported\n",
    "# /!\\ Important to note that I did not actually separate by type of library /!\\\n",
    "# \"Double\" mutants do not include single mutants\n",
    "# \"Ortho\" are standalone (double mutants in orthologous sequences are duplicated in \"double\" and \"ortho\" seq types)\n",
    "source = metadata_path+locus+f'/{locus}_var_df.csv'\n",
    "vardf = pd.read_csv(source, header=0, index_col=0)\n",
    "vardf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e0f228-6ef7-417a-af4a-edaa22108073",
   "metadata": {},
   "source": [
    "#### ⚠️ **Filtering step** ⚠️ - Keep only mutants corresponding to pool_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2c75a014-5f0e-4537-ad6e-437bb061924e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "817"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Depending on the type of pool (single or double), expected variants are filtered\n",
    "# The dictionary specifies which sequence types must be kept\n",
    "keep_seq_type = {'single' : ['WT', 'single', 'ortho'], 'double': ['WT', 'single', 'double']}\n",
    "var_filtered_df = vardf[vardf.seq_type.isin(keep_seq_type[pool_type])]\n",
    "len(var_filtered_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8cf5a749-4bdd-40ff-8654-515882e59d97",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "346"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(var_filtered_df.nt_seq.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "257e4367-c8e9-4145-8667-9d2c085b34d1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_type</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_codons</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>combination</th>\n",
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       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
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       "      <th>constant_region</th>\n",
       "      <th>permitted</th>\n",
       "      <th>too_many_substitutions</th>\n",
       "      <th>mixed_substitutions</th>\n",
       "      <th>confidence_score</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
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       "      <td>both</td>\n",
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       "    <tr>\n",
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       "      <td>single</td>\n",
       "      <td>ttttgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>3485.0</td>\n",
       "      <td>3422.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>ttg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>498.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>ctttgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>251.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>single</td>\n",
       "      <td>ctgtgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctg</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1400</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ttttgggtgatacgttatacattg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FWVIRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ttttgggtgctgcgttatacattg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FWVLRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ttttgggttagacgttatacattg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>ttttgtgtgagacgttatacattg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FCVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>NaN</td>\n",
       "      <td>tttttggtgagacgttatacattg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1405 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0          WT  gattgggtgagacgttatacattg     NaN        NaN            NaN   \n",
       "1      single  ttttgggtgagacgttatacattg     0.0        ttt            1.0   \n",
       "2      single  ttgtgggtgagacgttatacattg     0.0        ttg            1.0   \n",
       "3      single  ctttgggtgagacgttatacattg     0.0        ctt            1.0   \n",
       "4      single  ctgtgggtgagacgttatacattg     0.0        ctg            1.0   \n",
       "...       ...                       ...     ...        ...            ...   \n",
       "1400      NaN  ttttgggtgatacgttatacattg     NaN        NaN            NaN   \n",
       "1401      NaN  ttttgggtgctgcgttatacattg     NaN        NaN            NaN   \n",
       "1402      NaN  ttttgggttagacgttatacattg     NaN        NaN            NaN   \n",
       "1403      NaN  ttttgtgtgagacgttatacattg     NaN        NaN            NaN   \n",
       "1404      NaN  tttttggtgagacgttatacattg     NaN        NaN            NaN   \n",
       "\n",
       "     combination    aa_seq    WT   STOP STOP_readthrough  ...  input2  input3  \\\n",
       "0            NaN  DWVRRYTL  True  False            False  ...   760.0   707.0   \n",
       "1            NaN  FWVRRYTL  <NA>  False            False  ...  3485.0  3422.0   \n",
       "2            NaN  LWVRRYTL  <NA>  False            False  ...   498.0   531.0   \n",
       "3            NaN  LWVRRYTL  <NA>  False            False  ...   251.0   257.0   \n",
       "4            NaN  LWVRRYTL  <NA>  False            False  ...   683.0   637.0   \n",
       "...          ...       ...   ...    ...              ...  ...     ...     ...   \n",
       "1400         NaN  FWVIRYTL  <NA>  False            False  ...     4.0     2.0   \n",
       "1401         NaN  FWVLRYTL  <NA>  False            False  ...     4.0     2.0   \n",
       "1402         NaN  FWVRRYTL  <NA>  False            False  ...     2.0     0.0   \n",
       "1403         NaN  FCVRRYTL  <NA>  False            False  ...     2.0     1.0   \n",
       "1404         NaN  FLVRRYTL  <NA>  False            False  ...     4.0     7.0   \n",
       "\n",
       "      barcode_valid  indel  constant_region  permitted  \\\n",
       "0              True  False             True       True   \n",
       "1              True  False             True       True   \n",
       "2              True  False             True       True   \n",
       "3              True  False             True       True   \n",
       "4              True  False             True       True   \n",
       "...             ...    ...              ...        ...   \n",
       "1400           True  False             True       True   \n",
       "1401           True  False             True       True   \n",
       "1402           True  False             True       True   \n",
       "1403           True  False             True       True   \n",
       "1404           True  False             True       True   \n",
       "\n",
       "      too_many_substitutions  mixed_substitutions  confidence_score  \\\n",
       "0                      False                False               1.0   \n",
       "1                      False                False               1.0   \n",
       "2                      False                False               1.0   \n",
       "3                      False                False               1.0   \n",
       "4                      False                False               1.0   \n",
       "...                      ...                  ...               ...   \n",
       "1400                   False                False               2.0   \n",
       "1401                   False                False               2.0   \n",
       "1402                   False                False               2.0   \n",
       "1403                   False                False               2.0   \n",
       "1404                   False                False               2.0   \n",
       "\n",
       "        Location  \n",
       "0           both  \n",
       "1           both  \n",
       "2           both  \n",
       "3           both  \n",
       "4           both  \n",
       "...          ...  \n",
       "1400  right_only  \n",
       "1401  right_only  \n",
       "1402  right_only  \n",
       "1403  right_only  \n",
       "1404  right_only  \n",
       "\n",
       "[1405 rows x 27 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The dataframe of sequenced variants ( with no indels and readcount > threshold in all input replicates) and the dataframe of expected mutants are compared\n",
    "# The column 'Location' indicates if a variant is found only in the set of expected variants (left), only in the sequenced dataset (right), or in both (covered variants)\n",
    "comparedf = pd.merge(left=var_filtered_df, right=inputRCdf, how='outer', on='nt_seq', indicator='Location')\n",
    "comparedf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "dacdba6b-b384-4ff0-89d6-7e35f9f45aff",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filtered_sequenced_variants</th>\n",
       "      <th>Filtered_sequenced_variants_rc</th>\n",
       "      <th>Usable_variants</th>\n",
       "      <th>Covered_variants_%</th>\n",
       "      <th>Usable_variants_rc</th>\n",
       "      <th>Usable_variants_%rc</th>\n",
       "      <th>Unsequenced_variants</th>\n",
       "      <th>Unexpected_variants</th>\n",
       "      <th>Unexpected_variants_rc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>input1</th>\n",
       "      <td>699</td>\n",
       "      <td>278176</td>\n",
       "      <td>225</td>\n",
       "      <td>65.0</td>\n",
       "      <td>265618.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>116</td>\n",
       "      <td>474</td>\n",
       "      <td>12558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input2</th>\n",
       "      <td>732</td>\n",
       "      <td>223275</td>\n",
       "      <td>228</td>\n",
       "      <td>65.9</td>\n",
       "      <td>212178.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>116</td>\n",
       "      <td>504</td>\n",
       "      <td>11097.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input3</th>\n",
       "      <td>679</td>\n",
       "      <td>215798</td>\n",
       "      <td>224</td>\n",
       "      <td>64.7</td>\n",
       "      <td>205873.0</td>\n",
       "      <td>95.4</td>\n",
       "      <td>116</td>\n",
       "      <td>455</td>\n",
       "      <td>9925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1</th>\n",
       "      <td>578</td>\n",
       "      <td>292386</td>\n",
       "      <td>205</td>\n",
       "      <td>59.2</td>\n",
       "      <td>274699.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>116</td>\n",
       "      <td>373</td>\n",
       "      <td>17687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2</th>\n",
       "      <td>563</td>\n",
       "      <td>260894</td>\n",
       "      <td>205</td>\n",
       "      <td>59.2</td>\n",
       "      <td>245720.0</td>\n",
       "      <td>94.2</td>\n",
       "      <td>116</td>\n",
       "      <td>358</td>\n",
       "      <td>15174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3</th>\n",
       "      <td>574</td>\n",
       "      <td>194189</td>\n",
       "      <td>205</td>\n",
       "      <td>59.2</td>\n",
       "      <td>181212.0</td>\n",
       "      <td>93.3</td>\n",
       "      <td>116</td>\n",
       "      <td>369</td>\n",
       "      <td>12977.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Filtered_sequenced_variants  Filtered_sequenced_variants_rc  \\\n",
       "input1                          699                          278176   \n",
       "input2                          732                          223275   \n",
       "input3                          679                          215798   \n",
       "b1                              578                          292386   \n",
       "b2                              563                          260894   \n",
       "b3                              574                          194189   \n",
       "\n",
       "        Usable_variants  Covered_variants_%  Usable_variants_rc  \\\n",
       "input1              225                65.0            265618.0   \n",
       "input2              228                65.9            212178.0   \n",
       "input3              224                64.7            205873.0   \n",
       "b1                  205                59.2            274699.0   \n",
       "b2                  205                59.2            245720.0   \n",
       "b3                  205                59.2            181212.0   \n",
       "\n",
       "        Usable_variants_%rc  Unsequenced_variants  Unexpected_variants  \\\n",
       "input1                 95.5                   116                  474   \n",
       "input2                 95.0                   116                  504   \n",
       "input3                 95.4                   116                  455   \n",
       "b1                     94.0                   116                  373   \n",
       "b2                     94.2                   116                  358   \n",
       "b3                     93.3                   116                  369   \n",
       "\n",
       "        Unexpected_variants_rc  \n",
       "input1                 12558.0  \n",
       "input2                 11097.0  \n",
       "input3                  9925.0  \n",
       "b1                     17687.0  \n",
       "b2                     15174.0  \n",
       "b3                     12977.0  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From that we build a summary with number of variants and read counts across samples\n",
    "# Careful, some variants and their associated reads are shared among different samples\n",
    "seqdf = inputRCdf[conditions].astype(bool).sum(axis=0).to_frame('Filtered_sequenced_variants') # No duplicates in this dataframe - corresponds to sequenced variants (expected AND unexpected)\n",
    "seqdf['Filtered_sequenced_variants_rc'] = inputRCdf[conditions].sum(axis=0)\n",
    "seqdf['Usable_variants'] = comparedf[comparedf.Location.str.contains('both')].groupby('nt_seq')[conditions].first().astype(bool).sum(axis=0)\n",
    "# Below we calculate how many variants (of the proper seq type) are found in our sequencing dataset after filtering steps\n",
    "# Only the highest percentage value is relevant, since variants are shared between input and output samples (some of them being depleted after selection)\n",
    "# Expected but unsequenced variants might have been absent from our initial libraries of mutants\n",
    "seqdf['Covered_variants_%'] = 100*round(seqdf['Usable_variants']/len(var_filtered_df.nt_seq.unique()),3)\n",
    "seqdf['Usable_variants_rc'] = comparedf[comparedf.Location.str.contains('both')].groupby('nt_seq')[conditions].first().sum(axis=0)\n",
    "seqdf['Usable_variants_%rc'] = 100*round(seqdf['Usable_variants_rc']/seqdf['Filtered_sequenced_variants_rc'],3)\n",
    "seqdf['Unsequenced_variants'] = comparedf[comparedf.Location.str.contains('left')].groupby('nt_seq')[conditions].first().astype(bool).sum(axis=0)\n",
    "# Readcount for unsequenced variants is by definition = 0\n",
    "seqdf['Unexpected_variants'] = comparedf[comparedf.Location.str.contains('right')][conditions].astype(bool).sum(axis=0)\n",
    "seqdf['Unexpected_variants_rc'] = comparedf[comparedf.Location.str.contains('right')][conditions].sum(axis=0)\n",
    "# %Readcount for unexpected variants is always 100 - %rc for covered variants\n",
    "\n",
    "# Export summary\n",
    "seqdf.to_csv(processed_data_outpath+cset_name+'/detected_variants.csv')\n",
    "seqdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e58468a4-c211-46ad-90dd-6ce083b1a4ac",
   "metadata": {},
   "source": [
    "### Read counts on **sequenced** variants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1139b3-712e-4cdb-bf0e-df7c30248ee1",
   "metadata": {},
   "source": [
    "#### ⚠️ **Filtering step** ⚠️ - Keep only expected variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "58f60ed3-5aeb-4da0-8204-13e6d1fdc54b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th>seq_type</th>\n",
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       "    seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
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       "167    ortho  aatcaacaatttattattcaattg     1.0        caa            2.0   \n",
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       "169    ortho  aatcaacaatttattattcaattg     3.0        ttt            4.0   \n",
       "170    ortho  aatcaacaatttattattcaattg     4.0        att            5.0   \n",
       "\n",
       "    combination    aa_seq    WT   STOP STOP_readthrough  ...  input2  input3  \\\n",
       "166         NaN  NQQFIIQL  <NA>  False            False  ...  1090.0  1183.0   \n",
       "167         NaN  NQQFIIQL  <NA>  False            False  ...  1090.0  1183.0   \n",
       "168         NaN  NQQFIIQL  <NA>  False            False  ...  1090.0  1183.0   \n",
       "169         NaN  NQQFIIQL  <NA>  False            False  ...  1090.0  1183.0   \n",
       "170         NaN  NQQFIIQL  <NA>  False            False  ...  1090.0  1183.0   \n",
       "\n",
       "     barcode_valid  indel  constant_region  permitted  too_many_substitutions  \\\n",
       "166           True  False             True       True                   False   \n",
       "167           True  False             True       True                   False   \n",
       "168           True  False             True       True                   False   \n",
       "169           True  False             True       True                   False   \n",
       "170           True  False             True       True                   False   \n",
       "\n",
       "     mixed_substitutions  confidence_score Location  \n",
       "166                False               1.0     both  \n",
       "167                False               1.0     both  \n",
       "168                False               1.0     both  \n",
       "169                False               1.0     both  \n",
       "170                False               1.0     both  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('mode.chained_assignment', None) # Disable settingWithCopy warning\n",
    "covereddf = comparedf[comparedf.Location.str.contains('both')].reset_index(drop=True)\n",
    "covereddf[covereddf.duplicated(subset='nt_seq', keep=False)].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "655c6baf-829e-49cc-9fd8-351ca1a0bdc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>1675.0</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>ortho</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.267954</td>\n",
       "      <td>1.851258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tgttgggtgagacgttatacattg</th>\n",
       "      <td>240.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.290776</td>\n",
       "      <td>1.091080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttgtggattaaaagaactattatt</th>\n",
       "      <td>444.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>ortho</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.532330</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttgtgggtgagacgttatacattg</th>\n",
       "      <td>643.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.746894</td>\n",
       "      <td>1.361728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttttgggtgagacgttatacattg</th>\n",
       "      <td>4305.0</td>\n",
       "      <td>3485.0</td>\n",
       "      <td>3422.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.572678</td>\n",
       "      <td>2.096910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          input1  input2  input3     b1     b2     b3  \\\n",
       "nt_seq                                                                  \n",
       "aagtgggtgagacgttatacattg    40.0    35.0    38.0    0.0    5.0    1.0   \n",
       "aatcaacaatttattattcaattg  1333.0  1090.0  1183.0   39.0   62.0   27.0   \n",
       "aattgggtgagacgttatacattg  6967.0  5680.0  5435.0  756.0  955.0  444.0   \n",
       "aattgggttaatagatgtgttatt  1753.0  1255.0  1275.0   54.0   80.0   38.0   \n",
       "aattgggttcaaagatgtgttgtt   365.0   262.0   272.0  161.0  194.0  117.0   \n",
       "...                          ...     ...     ...    ...    ...    ...   \n",
       "tcttggttgaaaagatgtgttatt  2175.0  1675.0  1707.0   65.0   87.0   58.0   \n",
       "tgttgggtgagacgttatacattg   240.0   179.0   164.0   16.0   12.0    6.0   \n",
       "ttgtggattaaaagaactattatt   444.0   285.0   290.0   11.0    9.0    7.0   \n",
       "ttgtgggtgagacgttatacattg   643.0   498.0   531.0   22.0   24.0   20.0   \n",
       "ttttgggtgagacgttatacattg  4305.0  3485.0  3422.0  129.0  147.0   96.0   \n",
       "\n",
       "                         seq_type  confidence_score  mean_input  mean_output  \n",
       "nt_seq                                                                        \n",
       "aagtgggtgagacgttatacattg   single               1.0    1.587337     0.477121  \n",
       "aatcaacaatttattattcaattg    ortho               1.0    3.080266     1.640150  \n",
       "aattgggtgagacgttatacattg   single               1.0    3.780197     2.856930  \n",
       "aattgggttaatagatgtgttatt    ortho               1.0    3.154931     1.765917  \n",
       "aattgggttcaaagatgtgttgtt    ortho               1.0    2.478085     2.199572  \n",
       "...                           ...               ...         ...          ...  \n",
       "tcttggttgaaaagatgtgttatt    ortho               1.0    3.267954     1.851258  \n",
       "tgttgggtgagacgttatacattg   single               1.0    2.290776     1.091080  \n",
       "ttgtggattaaaagaactattatt    ortho               1.0    2.532330     1.000000  \n",
       "ttgtgggtgagacgttatacattg   single               1.0    2.746894     1.361728  \n",
       "ttttgggtgagacgttatacattg   single               1.0    3.572678     2.096910  \n",
       "\n",
       "[230 rows x 10 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_upset = covereddf.groupby('nt_seq').agg(dict(zip(conditions+['seq_type', 'confidence_score'], ['first']*(len(conditions)+2))))\n",
    "cov_upset['mean_input'] = np.log10(cov_upset[input_samples].mean(axis=1).add(1))\n",
    "cov_upset['mean_output'] = np.log10(cov_upset[output_samples].mean(axis=1).add(1))\n",
    "cov_upset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f456f60c-565e-4f47-942a-dad79f7f7fc4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "confidence_score\n",
       "1.0    193\n",
       "2.0     30\n",
       "3.0      7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_upset.groupby('confidence_score').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b6c042c5-76fa-426e-bfb8-5a197939c117",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cov_upset[conditions] = cov_upset[conditions].astype(bool)\n",
    "seq_types = ['WT', 'single', 'ortho', 'double']\n",
    "seq_type_colors = ['black', 'skyblue', 'mediumpurple', 'pink']\n",
    "cscores = [1,2,3]\n",
    "cscore_colors = ['green','orange','red']\n",
    "\n",
    "if pool_type == 'single':\n",
    "    min_sub = 3\n",
    "else:\n",
    "    min_sub = 100\n",
    "\n",
    "fig = plt.figure(figsize=(6, 8))\n",
    "upset_obj = UpSet(from_indicators(conditions, data=cov_upset),\n",
    "                   #show_percentages=True,\n",
    "                   show_counts=True,\n",
    "                   min_subset_size=min_sub,\n",
    "                   sort_by='cardinality',\n",
    "                   element_size=None,\n",
    "                   intersection_plot_elements=0, # height of intersection barplot in matrix elements\n",
    "                   totals_plot_elements = 2 # width of totals barplot in matrix elements\n",
    "                  )\n",
    "\n",
    "upset_obj.add_stacked_bars(by='seq_type',\n",
    "                           colors=dict(zip(seq_types, seq_type_colors)),\n",
    "                           elements=3\n",
    "                          )\n",
    "\n",
    "upset_obj.add_stacked_bars(by='confidence_score',\n",
    "                           colors=dict(zip(cscores, cscore_colors)),\n",
    "                           elements=3\n",
    "                          )\n",
    "\n",
    "upset_obj.add_catplot(value='mean_input', kind='violin', cut=0, density_norm='count',\n",
    "                      hue='seq_type', hue_order=seq_types, palette=seq_type_colors,\n",
    "                      linewidth=0.5,\n",
    "                      elements=3 # height in number of matrix elements\n",
    "                      )\n",
    "\n",
    "upset_obj.add_catplot(value='mean_output', kind='violin', cut=0, density_norm='count',\n",
    "                      hue='seq_type', hue_order=seq_types, palette=seq_type_colors,\n",
    "                      linewidth=0.5,\n",
    "                      elements=3 # height in number of matrix elements\n",
    "                      )\n",
    "\n",
    "d = upset_obj.plot(fig=fig) # Assigns all plots to a dictionary containing axes subplots - same keys as gridspec returned by upset_obj.make_grid()\n",
    "ax0 = d['extra0'] # Key corresponding to 1st stacked barplot - type of sequence ('intersections' = intersection barplot)\n",
    "ax1 = d['extra1'] # Key corresponding to 2nd stacked barplot (confidence score)\n",
    "ax2 = d['extra2'] # Key corresponding to 1st catplot - read count for input samples\n",
    "ax3 = d['extra3'] # Key corresponding to 2nd catplot - read count for output samples\n",
    "ax0.legend().remove()\n",
    "ax0.set_ylabel('# variants') # (unique nucleotide sequences)\n",
    "\n",
    "ax1.legend().remove()\n",
    "ax1.set_ylabel('confidence score')\n",
    "\n",
    "ax2.legend().remove()\n",
    "ax2.set(ylim=(0,5.5))\n",
    "ax2.set_ylabel('log10\\ninput RC')\n",
    "ax2.axhline(y=np.log10(min_input_readcount_mid), linestyle='--', color=cscore_colors[1])\n",
    "ax2.axhline(y=np.log10(min_input_readcount_high), linestyle='--', color=cscore_colors[0])\n",
    "\n",
    "ax3.set(ylim=(0,5.5))\n",
    "ax3.set_ylabel('log10\\noutput RC')\n",
    "ax3.legend(title='Sequence type')\n",
    "\n",
    "plt.savefig(graph_outpath+cset_name+'/upset_variants_rc.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0072ab87-6c1e-4851-b422-9cc842a987f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_readcount</th>\n",
       "      <th>WT</th>\n",
       "      <th>WT_%</th>\n",
       "      <th>single_mut_readcount</th>\n",
       "      <th>single_mut_readcount_%</th>\n",
       "      <th>ortho_mut_readcount</th>\n",
       "      <th>ortho_mut_readcount_%</th>\n",
       "      <th>double_mut_readcount</th>\n",
       "      <th>double_mut_readcount_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>input1</th>\n",
       "      <td>265618.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>199093.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>65605.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input2</th>\n",
       "      <td>212178.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>163050.0</td>\n",
       "      <td>76.8</td>\n",
       "      <td>48368.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input3</th>\n",
       "      <td>205873.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>156241.0</td>\n",
       "      <td>75.9</td>\n",
       "      <td>48925.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1</th>\n",
       "      <td>274699.0</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>264382.0</td>\n",
       "      <td>96.2</td>\n",
       "      <td>9284.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b2</th>\n",
       "      <td>245720.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>235417.0</td>\n",
       "      <td>95.8</td>\n",
       "      <td>9762.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b3</th>\n",
       "      <td>181212.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>173900.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6835.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_readcount      WT  WT_%  single_mut_readcount  \\\n",
       "input1         265618.0   920.0   0.3              199093.0   \n",
       "input2         212178.0   760.0   0.4              163050.0   \n",
       "input3         205873.0   707.0   0.3              156241.0   \n",
       "b1             274699.0  1033.0   0.4              264382.0   \n",
       "b2             245720.0   541.0   0.2              235417.0   \n",
       "b3             181212.0   477.0   0.3              173900.0   \n",
       "\n",
       "        single_mut_readcount_%  ortho_mut_readcount  ortho_mut_readcount_%  \\\n",
       "input1                    75.0              65605.0                   24.7   \n",
       "input2                    76.8              48368.0                   22.8   \n",
       "input3                    75.9              48925.0                   23.8   \n",
       "b1                        96.2               9284.0                    3.4   \n",
       "b2                        95.8               9762.0                    4.0   \n",
       "b3                        96.0               6835.0                    3.8   \n",
       "\n",
       "        double_mut_readcount  double_mut_readcount_%  \n",
       "input1                   0.0                     0.0  \n",
       "input2                   0.0                     0.0  \n",
       "input3                   0.0                     0.0  \n",
       "b1                       0.0                     0.0  \n",
       "b2                       0.0                     0.0  \n",
       "b3                       0.0                     0.0  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totrc = covereddf.groupby('nt_seq')[conditions].first().sum(axis=0).to_frame('total_readcount')\n",
    "totrc['WT'] = covereddf[covereddf.WT==True][conditions].squeeze()\n",
    "totrc['WT_%'] = 100*round(totrc['WT'] / totrc['total_readcount'], 3)\n",
    "totrc['single_mut_readcount'] = covereddf[covereddf.seq_type=='single'].groupby('nt_seq')[conditions].first().sum(axis=0)\n",
    "totrc['single_mut_readcount_%'] = 100*round(totrc['single_mut_readcount'] / totrc['total_readcount'], 3)\n",
    "totrc['ortho_mut_readcount'] = covereddf[covereddf.seq_type=='ortho'].groupby('nt_seq')[conditions].first().sum(axis=0)\n",
    "totrc['ortho_mut_readcount_%'] = 100*round(totrc['ortho_mut_readcount'] / totrc['total_readcount'], 3)\n",
    "totrc['double_mut_readcount'] = covereddf[covereddf.seq_type=='double'].groupby('nt_seq')[conditions].first().sum(axis=0)\n",
    "totrc['double_mut_readcount_%'] = 100*round(totrc['double_mut_readcount'] / totrc['total_readcount'], 3)\n",
    "        \n",
    "# Export summary - unexpected variants can have arisen through sequencing errors\n",
    "totrc.to_csv(processed_data_outpath+cset_name+'/readcount_per_library.csv')\n",
    "totrc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af45cd5c-b7f5-4f24-8c97-e9404ff2bd5c",
   "metadata": {},
   "source": [
    "The following is particularly important.\n",
    "\n",
    "We add 1 read to all read counts to not have any null values (because of the dataframe's wide format).\n",
    "\n",
    "Later on, when the selection coefficient is calculated, a zero could either mean that:\n",
    "\n",
    "1. the sequence wasn't in the pool at t0\n",
    "2. the sequence was in the pool at t0 but was not sequenced because of a low coverage\n",
    "3. the sequence was so deleterious upon selective pressure that it is no longer sequenced at later timepoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ccfdb2d4-aa02-4b21-974d-e19ce828ab63",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\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>input1</th>\n",
       "      <th>input2</th>\n",
       "      <th>input3</th>\n",
       "      <th>b1</th>\n",
       "      <th>b2</th>\n",
       "      <th>b3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>921.0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>478.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4306.0</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>3423.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>644.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>726.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>258.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>258.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>258.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>258.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>258.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>548 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     input1  input2  input3      b1     b2     b3\n",
       "0     921.0   761.0   708.0  1034.0  542.0  478.0\n",
       "1    4306.0  3486.0  3423.0   130.0  148.0   97.0\n",
       "2     644.0   499.0   532.0    23.0   25.0   21.0\n",
       "3     324.0   252.0   258.0    12.0   13.0    8.0\n",
       "4     726.0   684.0   638.0    17.0   30.0   28.0\n",
       "..      ...     ...     ...     ...    ...    ...\n",
       "543   258.0   220.0   171.0     7.0   11.0    4.0\n",
       "544   258.0   220.0   171.0     7.0   11.0    4.0\n",
       "545   258.0   220.0   171.0     7.0   11.0    4.0\n",
       "546   258.0   220.0   171.0     7.0   11.0    4.0\n",
       "547   258.0   220.0   171.0     7.0   11.0    4.0\n",
       "\n",
       "[548 rows x 6 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pd.set_option('mode.chained_assignment', None) # Disable settingWithCopy warning\n",
    "covereddf[conditions] += 1\n",
    "covereddf[conditions]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae603d9-3ac4-4fc9-aa8c-b1f6f9edf661",
   "metadata": {},
   "source": [
    "### Calculate frequencies (relative abundance of each variant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "21893a68-2c10-4ac3-b30f-fcde57d5e1e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>input1_abundance</th>\n",
       "      <th>input2_abundance</th>\n",
       "      <th>input3_abundance</th>\n",
       "      <th>b1_abundance</th>\n",
       "      <th>b2_abundance</th>\n",
       "      <th>b3_abundance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.016197</td>\n",
       "      <td>0.016412</td>\n",
       "      <td>0.016608</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.000535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002422</td>\n",
       "      <td>0.002349</td>\n",
       "      <td>0.002581</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001219</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002731</td>\n",
       "      <td>0.003220</td>\n",
       "      <td>0.003096</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000154</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>543</th>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>548 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     input1_abundance  input2_abundance  input3_abundance  b1_abundance  \\\n",
       "0            0.003464          0.003583          0.003435      0.003761   \n",
       "1            0.016197          0.016412          0.016608      0.000473   \n",
       "2            0.002422          0.002349          0.002581      0.000084   \n",
       "3            0.001219          0.001186          0.001252      0.000044   \n",
       "4            0.002731          0.003220          0.003096      0.000062   \n",
       "..                ...               ...               ...           ...   \n",
       "543          0.000970          0.001036          0.000830      0.000025   \n",
       "544          0.000970          0.001036          0.000830      0.000025   \n",
       "545          0.000970          0.001036          0.000830      0.000025   \n",
       "546          0.000970          0.001036          0.000830      0.000025   \n",
       "547          0.000970          0.001036          0.000830      0.000025   \n",
       "\n",
       "     b2_abundance  b3_abundance  \n",
       "0        0.002204      0.002634  \n",
       "1        0.000602      0.000535  \n",
       "2        0.000102      0.000116  \n",
       "3        0.000053      0.000044  \n",
       "4        0.000122      0.000154  \n",
       "..            ...           ...  \n",
       "543      0.000045      0.000022  \n",
       "544      0.000045      0.000022  \n",
       "545      0.000045      0.000022  \n",
       "546      0.000045      0.000022  \n",
       "547      0.000045      0.000022  \n",
       "\n",
       "[548 rows x 6 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abund = covereddf[conditions].div(covereddf.groupby('nt_seq')[conditions].first().sum(axis=0), axis=1)\n",
    "abund.rename(columns=lambda x: x+'_abundance', inplace=True)\n",
    "abund_conditions = [x+'_abundance' for x in conditions]\n",
    "\n",
    "# Abundance values are added as columns on the existing dataframe\n",
    "abundf = pd.concat([covereddf, abund], axis=1)\n",
    "        \n",
    "# Export dataframe\n",
    "abundf.to_csv(processed_data_outpath+cset_name+'/abundance_all_libraries.csv')\n",
    "#>>>>> DATAFRAME TO VISUALIZE DISTRIBUTION OF FREQUENCIES\n",
    "abundf[abund_conditions]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf43f47a-9c7e-4dd3-87bd-9ad4fe9bb672",
   "metadata": {},
   "source": [
    "### Visualize distribution of frequencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41fb38c2-825c-482d-ac85-933fa7943d26",
   "metadata": {},
   "source": [
    "#### Convert to log10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "49a7aaf8-78a6-4786-8832-dfffabcb9dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "log10_conditions = [x.replace('abundance', 'log10') for x in abund_conditions]\n",
    "abundf[log10_conditions] = abundf[abund_conditions].transform(func='log10')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f4c7c0-e2d7-4857-859a-2f385e10e1a0",
   "metadata": {},
   "source": [
    "#### Conditions --> long format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f1c7b966-ed0c-4627-afb1-41db4da9135b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>seq_type</th>\n",
       "      <th>sample_id</th>\n",
       "      <th>abundance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>aagtgggtgagacgttatacattg</td>\n",
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       "      <td>input1_log10</td>\n",
       "      <td>-3.811850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aatcaacaatttattattcaattg</td>\n",
       "      <td>ortho</td>\n",
       "      <td>input1_log10</td>\n",
       "      <td>-2.299478</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>aattgggtgagacgttatacattg</td>\n",
       "      <td>single</td>\n",
       "      <td>input1_log10</td>\n",
       "      <td>-1.581525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aattgggttaatagatgtgttatt</td>\n",
       "      <td>ortho</td>\n",
       "      <td>input1_log10</td>\n",
       "      <td>-2.180604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aattgggttcaaagatgtgttgtt</td>\n",
       "      <td>ortho</td>\n",
       "      <td>input1_log10</td>\n",
       "      <td>-2.861152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1375</th>\n",
       "      <td>tcttggttgaaaagatgtgttatt</td>\n",
       "      <td>ortho</td>\n",
       "      <td>b3_log10</td>\n",
       "      <td>-3.487886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>tgttgggtgagacgttatacattg</td>\n",
       "      <td>single</td>\n",
       "      <td>b3_log10</td>\n",
       "      <td>-4.413640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>ttgtggattaaaagaactattatt</td>\n",
       "      <td>ortho</td>\n",
       "      <td>b3_log10</td>\n",
       "      <td>-4.355648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>ttgtgggtgagacgttatacattg</td>\n",
       "      <td>single</td>\n",
       "      <td>b3_log10</td>\n",
       "      <td>-3.936519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>ttttgggtgagacgttatacattg</td>\n",
       "      <td>single</td>\n",
       "      <td>b3_log10</td>\n",
       "      <td>-3.271966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1380 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        nt_seq seq_type     sample_id  abundance\n",
       "0     aagtgggtgagacgttatacattg   single  input1_log10  -3.811850\n",
       "1     aatcaacaatttattattcaattg    ortho  input1_log10  -2.299478\n",
       "2     aattgggtgagacgttatacattg   single  input1_log10  -1.581525\n",
       "3     aattgggttaatagatgtgttatt    ortho  input1_log10  -2.180604\n",
       "4     aattgggttcaaagatgtgttgtt    ortho  input1_log10  -2.861152\n",
       "...                        ...      ...           ...        ...\n",
       "1375  tcttggttgaaaagatgtgttatt    ortho      b3_log10  -3.487886\n",
       "1376  tgttgggtgagacgttatacattg   single      b3_log10  -4.413640\n",
       "1377  ttgtggattaaaagaactattatt    ortho      b3_log10  -4.355648\n",
       "1378  ttgtgggtgagacgttatacattg   single      b3_log10  -3.936519\n",
       "1379  ttttgggtgagacgttatacattg   single      b3_log10  -3.271966\n",
       "\n",
       "[1380 rows x 4 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abundlong = pd.melt(abundf.groupby(['nt_seq', 'seq_type'])[log10_conditions].first().reset_index(),\n",
    "                    id_vars=['nt_seq','seq_type'], value_vars=log10_conditions, var_name='sample_id', value_name='abundance')\n",
    "abundlong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c163280c-95cd-4e98-ba5e-f63ba99c5d38",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rodur28/anaconda3/envs/dms-env/lib/python3.11/site-packages/seaborn/utils.py:493: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  new_legend = legend_func(handles, labels, loc=loc, **props)\n",
      "/tmp/ipykernel_1060/3281301397.py:17: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  fig.set_xticklabels([x.split('_')[0] for x in abundlong.sample_id.unique()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Purge graph space\n",
    "sns.set(rc = {'figure.figsize':(10,5)})\n",
    "sns.set_theme(style='whitegrid')\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "# Generate graph\n",
    "fig = sns.violinplot(data=abundlong, x='sample_id', y='abundance',\n",
    "                     hue='seq_type', hue_order=['WT', 'single', 'ortho', 'double'], palette=['black', 'skyblue', 'mediumpurple', 'pink'],\n",
    "                     cut=0, # To make sure violins do not extend past the value range (because of log scale)\n",
    "                     density_norm='count') # Width of violin proportional to number of observations\n",
    "\n",
    "# Move legend\n",
    "handles, labels = fig.get_legend_handles_labels()\n",
    "sns.move_legend(fig, bbox_to_anchor=(1, 1), loc='upper left', title=None, frameon=True, handles=handles, labels=labels)\n",
    "\n",
    "fig.axes.set(xlabel='', ylabel='Log10 normalized read abundance')\n",
    "fig.set_xticklabels([x.split('_')[0] for x in abundlong.sample_id.unique()])\n",
    "plt.title('Distribution of normalized read abundance per sequence type'+\n",
    "         '\\n'+' '.join(cset))\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save graph to outpath\n",
    "plt.savefig(graph_outpath+cset_name+'/abundance_distri.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0018b782-36ca-4417-a89b-792363c52376",
   "metadata": {},
   "source": [
    "### Add alt aminoacid and mutation type (synonymous / non-synonymous)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "70cbab1f-4ff2-48ac-a83f-b3b22eef8cab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_mutation_type(alt, wt_aa, pos):\n",
    "    if alt == wt_aa[int(pos)]:\n",
    "        return 'synonymous'\n",
    "    elif alt == '*':\n",
    "        return 'stop'\n",
    "    else:\n",
    "        return 'non-synonymous'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9258d713-7c5c-42cc-a533-02a721617e8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_type</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_codons</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>combination</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>...</th>\n",
       "      <th>b2_abundance</th>\n",
       "      <th>b3_abundance</th>\n",
       "      <th>input1_log10</th>\n",
       "      <th>input2_log10</th>\n",
       "      <th>input3_log10</th>\n",
       "      <th>b1_log10</th>\n",
       "      <th>b2_log10</th>\n",
       "      <th>b3_log10</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>mutation_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>-2.460374</td>\n",
       "      <td>-2.445786</td>\n",
       "      <td>-2.464051</td>\n",
       "      <td>-2.424700</td>\n",
       "      <td>-2.656848</td>\n",
       "      <td>-2.579310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>single</td>\n",
       "      <td>ttttgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>-1.790559</td>\n",
       "      <td>-1.784843</td>\n",
       "      <td>-1.779677</td>\n",
       "      <td>-3.325277</td>\n",
       "      <td>-3.220585</td>\n",
       "      <td>-3.271966</td>\n",
       "      <td>F</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>single</td>\n",
       "      <td>ttgtgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>-2.615748</td>\n",
       "      <td>-2.629070</td>\n",
       "      <td>-2.588173</td>\n",
       "      <td>-4.077493</td>\n",
       "      <td>-3.992907</td>\n",
       "      <td>-3.936519</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>ctttgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>-2.914088</td>\n",
       "      <td>-2.925770</td>\n",
       "      <td>-2.902465</td>\n",
       "      <td>-4.360039</td>\n",
       "      <td>-4.276903</td>\n",
       "      <td>-4.355648</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>single</td>\n",
       "      <td>ctgtgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>-2.563697</td>\n",
       "      <td>-2.492115</td>\n",
       "      <td>-2.509264</td>\n",
       "      <td>-4.208772</td>\n",
       "      <td>-3.913726</td>\n",
       "      <td>-3.811580</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattggatttatagatctattttg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>att</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWIYRSIL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>-3.013014</td>\n",
       "      <td>-2.984748</td>\n",
       "      <td>-3.081088</td>\n",
       "      <td>-4.594123</td>\n",
       "      <td>-4.349454</td>\n",
       "      <td>-4.656678</td>\n",
       "      <td>I</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattggatttatagatctattttg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>tat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWIYRSIL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>-3.013014</td>\n",
       "      <td>-2.984748</td>\n",
       "      <td>-3.081088</td>\n",
       "      <td>-4.594123</td>\n",
       "      <td>-4.349454</td>\n",
       "      <td>-4.656678</td>\n",
       "      <td>Y</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattggatttatagatctattttg</td>\n",
       "      <td>4.0</td>\n",
       "      <td>aga</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWIYRSIL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>-3.013014</td>\n",
       "      <td>-2.984748</td>\n",
       "      <td>-3.081088</td>\n",
       "      <td>-4.594123</td>\n",
       "      <td>-4.349454</td>\n",
       "      <td>-4.656678</td>\n",
       "      <td>R</td>\n",
       "      <td>synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattggatttatagatctattttg</td>\n",
       "      <td>5.0</td>\n",
       "      <td>tct</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWIYRSIL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>-3.013014</td>\n",
       "      <td>-2.984748</td>\n",
       "      <td>-3.081088</td>\n",
       "      <td>-4.594123</td>\n",
       "      <td>-4.349454</td>\n",
       "      <td>-4.656678</td>\n",
       "      <td>S</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattggatttatagatctattttg</td>\n",
       "      <td>6.0</td>\n",
       "      <td>att</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWIYRSIL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>-3.013014</td>\n",
       "      <td>-2.984748</td>\n",
       "      <td>-3.081088</td>\n",
       "      <td>-4.594123</td>\n",
       "      <td>-4.349454</td>\n",
       "      <td>-4.656678</td>\n",
       "      <td>I</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>548 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0         WT  gattgggtgagacgttatacattg     NaN        NaN            NaN   \n",
       "1     single  ttttgggtgagacgttatacattg     0.0        ttt            1.0   \n",
       "2     single  ttgtgggtgagacgttatacattg     0.0        ttg            1.0   \n",
       "3     single  ctttgggtgagacgttatacattg     0.0        ctt            1.0   \n",
       "4     single  ctgtgggtgagacgttatacattg     0.0        ctg            1.0   \n",
       "..       ...                       ...     ...        ...            ...   \n",
       "543    ortho  gattggatttatagatctattttg     2.0        att            1.0   \n",
       "544    ortho  gattggatttatagatctattttg     3.0        tat            2.0   \n",
       "545    ortho  gattggatttatagatctattttg     4.0        aga            3.0   \n",
       "546    ortho  gattggatttatagatctattttg     5.0        tct            4.0   \n",
       "547    ortho  gattggatttatagatctattttg     6.0        att            5.0   \n",
       "\n",
       "    combination    aa_seq    WT   STOP STOP_readthrough  ...  b2_abundance  \\\n",
       "0           NaN  DWVRRYTL  True  False            False  ...      0.002204   \n",
       "1           NaN  FWVRRYTL  <NA>  False            False  ...      0.000602   \n",
       "2           NaN  LWVRRYTL  <NA>  False            False  ...      0.000102   \n",
       "3           NaN  LWVRRYTL  <NA>  False            False  ...      0.000053   \n",
       "4           NaN  LWVRRYTL  <NA>  False            False  ...      0.000122   \n",
       "..          ...       ...   ...    ...              ...  ...           ...   \n",
       "543         NaN  DWIYRSIL  <NA>  False            False  ...      0.000045   \n",
       "544         NaN  DWIYRSIL  <NA>  False            False  ...      0.000045   \n",
       "545         NaN  DWIYRSIL  <NA>  False            False  ...      0.000045   \n",
       "546         NaN  DWIYRSIL  <NA>  False            False  ...      0.000045   \n",
       "547         NaN  DWIYRSIL  <NA>  False            False  ...      0.000045   \n",
       "\n",
       "     b3_abundance  input1_log10  input2_log10  input3_log10  b1_log10  \\\n",
       "0        0.002634     -2.460374     -2.445786     -2.464051 -2.424700   \n",
       "1        0.000535     -1.790559     -1.784843     -1.779677 -3.325277   \n",
       "2        0.000116     -2.615748     -2.629070     -2.588173 -4.077493   \n",
       "3        0.000044     -2.914088     -2.925770     -2.902465 -4.360039   \n",
       "4        0.000154     -2.563697     -2.492115     -2.509264 -4.208772   \n",
       "..            ...           ...           ...           ...       ...   \n",
       "543      0.000022     -3.013014     -2.984748     -3.081088 -4.594123   \n",
       "544      0.000022     -3.013014     -2.984748     -3.081088 -4.594123   \n",
       "545      0.000022     -3.013014     -2.984748     -3.081088 -4.594123   \n",
       "546      0.000022     -3.013014     -2.984748     -3.081088 -4.594123   \n",
       "547      0.000022     -3.013014     -2.984748     -3.081088 -4.594123   \n",
       "\n",
       "     b2_log10  b3_log10  alt_aa   mutation_type  \n",
       "0   -2.656848 -2.579310     NaN             NaN  \n",
       "1   -3.220585 -3.271966       F  non-synonymous  \n",
       "2   -3.992907 -3.936519       L  non-synonymous  \n",
       "3   -4.276903 -4.355648       L  non-synonymous  \n",
       "4   -3.913726 -3.811580       L  non-synonymous  \n",
       "..        ...       ...     ...             ...  \n",
       "543 -4.349454 -4.656678       I  non-synonymous  \n",
       "544 -4.349454 -4.656678       Y  non-synonymous  \n",
       "545 -4.349454 -4.656678       R      synonymous  \n",
       "546 -4.349454 -4.656678       S  non-synonymous  \n",
       "547 -4.349454 -4.656678       I  non-synonymous  \n",
       "\n",
       "[548 rows x 41 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abundf['alt_aa'] = abundf[abundf.seq_type != 'WT'].apply(lambda row: row['aa_seq'][int(row['aa_pos'])], axis=1)\n",
    "abundf['mutation_type'] = abundf[abundf.seq_type != 'WT'].apply(lambda row: get_mutation_type(row.alt_aa, wtaa, row.aa_pos), axis=1)\n",
    "abundf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f838a17-ccd8-4072-ba3d-4f1e12cf4d0b",
   "metadata": {},
   "source": [
    "### Duplicate WT abundance for each position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "48a6c5db-1270-4252-bf38-d1172b2124e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'gat'), (1, 'tgg'), (2, 'gtg'), (3, 'aga'), (4, 'cgt'), (5, 'tat'), (6, 'aca'), (7, 'ttg')]\n"
     ]
    }
   ],
   "source": [
    "# Turn WT sequence into list of codons with 0-based position\n",
    "codcoord = [(i//3,wtseq[i:i+3]) for i in range(0,len(wtseq),3)]\n",
    "print(codcoord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "1d61ac9d-ba2f-4628-85c6-f5a774e2e337",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# We create a custom list of columns to create a dataframe with WT codons\n",
    "wtdf_cols = ['aa_pos', 'alt_codons', 'alt_aa', 'seq_type', 'mutation_type', 'WT', 'nt_seq', 'aa_seq', 'Nham_aa', 'confidence_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e6ac9b2f-6fb7-4b7f-bccd-45fdf0211c6f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_codons</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>seq_type</th>\n",
       "      <th>mutation_type</th>\n",
       "      <th>WT</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>confidence_score</th>\n",
       "      <th>input1_abundance</th>\n",
       "      <th>input2_abundance</th>\n",
       "      <th>input3_abundance</th>\n",
       "      <th>b1_abundance</th>\n",
       "      <th>b2_abundance</th>\n",
       "      <th>b3_abundance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>gat</td>\n",
       "      <td>D</td>\n",
       "      <td>WT</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>True</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>tgg</td>\n",
       "      <td>W</td>\n",
       "      <td>WT</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>True</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>gtg</td>\n",
       "      <td>V</td>\n",
       "      <td>WT</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>True</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>aga</td>\n",
       "      <td>R</td>\n",
       "      <td>WT</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>True</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>cgt</td>\n",
       "      <td>R</td>\n",
       "      <td>WT</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>True</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>tat</td>\n",
       "      <td>Y</td>\n",
       "      <td>WT</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>True</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>aca</td>\n",
       "      <td>T</td>\n",
       "      <td>WT</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>True</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>ttg</td>\n",
       "      <td>L</td>\n",
       "      <td>WT</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>True</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aa_pos alt_codons alt_aa seq_type mutation_type    WT  \\\n",
       "0       0        gat      D       WT    synonymous  True   \n",
       "1       1        tgg      W       WT    synonymous  True   \n",
       "2       2        gtg      V       WT    synonymous  True   \n",
       "3       3        aga      R       WT    synonymous  True   \n",
       "4       4        cgt      R       WT    synonymous  True   \n",
       "5       5        tat      Y       WT    synonymous  True   \n",
       "6       6        aca      T       WT    synonymous  True   \n",
       "7       7        ttg      L       WT    synonymous  True   \n",
       "\n",
       "                     nt_seq    aa_seq  Nham_aa  confidence_score  \\\n",
       "0  gattgggtgagacgttatacattg  DWVRRYTL        0               1.0   \n",
       "1  gattgggtgagacgttatacattg  DWVRRYTL        0               1.0   \n",
       "2  gattgggtgagacgttatacattg  DWVRRYTL        0               1.0   \n",
       "3  gattgggtgagacgttatacattg  DWVRRYTL        0               1.0   \n",
       "4  gattgggtgagacgttatacattg  DWVRRYTL        0               1.0   \n",
       "5  gattgggtgagacgttatacattg  DWVRRYTL        0               1.0   \n",
       "6  gattgggtgagacgttatacattg  DWVRRYTL        0               1.0   \n",
       "7  gattgggtgagacgttatacattg  DWVRRYTL        0               1.0   \n",
       "\n",
       "   input1_abundance  input2_abundance  input3_abundance  b1_abundance  \\\n",
       "0          0.003464          0.003583          0.003435      0.003761   \n",
       "1          0.003464          0.003583          0.003435      0.003761   \n",
       "2          0.003464          0.003583          0.003435      0.003761   \n",
       "3          0.003464          0.003583          0.003435      0.003761   \n",
       "4          0.003464          0.003583          0.003435      0.003761   \n",
       "5          0.003464          0.003583          0.003435      0.003761   \n",
       "6          0.003464          0.003583          0.003435      0.003761   \n",
       "7          0.003464          0.003583          0.003435      0.003761   \n",
       "\n",
       "   b2_abundance  b3_abundance  \n",
       "0      0.002204      0.002634  \n",
       "1      0.002204      0.002634  \n",
       "2      0.002204      0.002634  \n",
       "3      0.002204      0.002634  \n",
       "4      0.002204      0.002634  \n",
       "5      0.002204      0.002634  \n",
       "6      0.002204      0.002634  \n",
       "7      0.002204      0.002634  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A dataframe is created with 1 row per WT codon\n",
    "pos_df = pd.DataFrame([[a, codcoord[a][1], wtaa[a], 'WT', 'synonymous', True, wtseq, wtaa, 0, abundf.loc[abundf.WT, 'confidence_score'][0]]\n",
    "                       for a in range(0,len(wtaa))], columns=wtdf_cols)\n",
    "for a in abund_conditions:\n",
    "    pos_df[a] = abundf.loc[abundf.WT, a].values[0]\n",
    "pos_df  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "66b76f6f-d6ff-4600-b3d5-c96df5686a00",
   "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>seq_type</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_codons</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>combination</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>...</th>\n",
       "      <th>b2_abundance</th>\n",
       "      <th>b3_abundance</th>\n",
       "      <th>input1_log10</th>\n",
       "      <th>input2_log10</th>\n",
       "      <th>input3_log10</th>\n",
       "      <th>b1_log10</th>\n",
       "      <th>b2_log10</th>\n",
       "      <th>b3_log10</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>mutation_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>ttttgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>-1.790559</td>\n",
       "      <td>-1.784843</td>\n",
       "      <td>-1.779677</td>\n",
       "      <td>-3.325277</td>\n",
       "      <td>-3.220585</td>\n",
       "      <td>-3.271966</td>\n",
       "      <td>F</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>single</td>\n",
       "      <td>ttgtgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>-2.615748</td>\n",
       "      <td>-2.629070</td>\n",
       "      <td>-2.588173</td>\n",
       "      <td>-4.077493</td>\n",
       "      <td>-3.992907</td>\n",
       "      <td>-3.936519</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>single</td>\n",
       "      <td>ctttgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>-2.914088</td>\n",
       "      <td>-2.925770</td>\n",
       "      <td>-2.902465</td>\n",
       "      <td>-4.360039</td>\n",
       "      <td>-4.276903</td>\n",
       "      <td>-4.355648</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>ctgtgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>-2.563697</td>\n",
       "      <td>-2.492115</td>\n",
       "      <td>-2.509264</td>\n",
       "      <td>-4.208772</td>\n",
       "      <td>-3.913726</td>\n",
       "      <td>-3.811580</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>single</td>\n",
       "      <td>atttgggtgagacgttatacattg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>att</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>-2.553645</td>\n",
       "      <td>-2.544698</td>\n",
       "      <td>-2.562805</td>\n",
       "      <td>-4.096798</td>\n",
       "      <td>-4.276903</td>\n",
       "      <td>-4.082647</td>\n",
       "      <td>I</td>\n",
       "      <td>non-synonymous</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>aga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>4.0</td>\n",
       "      <td>cgt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>5.0</td>\n",
       "      <td>tat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>6.0</td>\n",
       "      <td>aca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>synonymous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>7.0</td>\n",
       "      <td>ttg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>555 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0     single  ttttgggtgagacgttatacattg     0.0        ttt            1.0   \n",
       "1     single  ttgtgggtgagacgttatacattg     0.0        ttg            1.0   \n",
       "2     single  ctttgggtgagacgttatacattg     0.0        ctt            1.0   \n",
       "3     single  ctgtgggtgagacgttatacattg     0.0        ctg            1.0   \n",
       "4     single  atttgggtgagacgttatacattg     0.0        att            1.0   \n",
       "..       ...                       ...     ...        ...            ...   \n",
       "550       WT  gattgggtgagacgttatacattg     3.0        aga            NaN   \n",
       "551       WT  gattgggtgagacgttatacattg     4.0        cgt            NaN   \n",
       "552       WT  gattgggtgagacgttatacattg     5.0        tat            NaN   \n",
       "553       WT  gattgggtgagacgttatacattg     6.0        aca            NaN   \n",
       "554       WT  gattgggtgagacgttatacattg     7.0        ttg            NaN   \n",
       "\n",
       "    combination    aa_seq    WT   STOP STOP_readthrough  ...  b2_abundance  \\\n",
       "0           NaN  FWVRRYTL  <NA>  False            False  ...      0.000602   \n",
       "1           NaN  LWVRRYTL  <NA>  False            False  ...      0.000102   \n",
       "2           NaN  LWVRRYTL  <NA>  False            False  ...      0.000053   \n",
       "3           NaN  LWVRRYTL  <NA>  False            False  ...      0.000122   \n",
       "4           NaN  IWVRRYTL  <NA>  False            False  ...      0.000053   \n",
       "..          ...       ...   ...    ...              ...  ...           ...   \n",
       "550         NaN  DWVRRYTL  True    NaN              NaN  ...      0.002204   \n",
       "551         NaN  DWVRRYTL  True    NaN              NaN  ...      0.002204   \n",
       "552         NaN  DWVRRYTL  True    NaN              NaN  ...      0.002204   \n",
       "553         NaN  DWVRRYTL  True    NaN              NaN  ...      0.002204   \n",
       "554         NaN  DWVRRYTL  True    NaN              NaN  ...      0.002204   \n",
       "\n",
       "     b3_abundance  input1_log10  input2_log10  input3_log10  b1_log10  \\\n",
       "0        0.000535     -1.790559     -1.784843     -1.779677 -3.325277   \n",
       "1        0.000116     -2.615748     -2.629070     -2.588173 -4.077493   \n",
       "2        0.000044     -2.914088     -2.925770     -2.902465 -4.360039   \n",
       "3        0.000154     -2.563697     -2.492115     -2.509264 -4.208772   \n",
       "4        0.000083     -2.553645     -2.544698     -2.562805 -4.096798   \n",
       "..            ...           ...           ...           ...       ...   \n",
       "550      0.002634           NaN           NaN           NaN       NaN   \n",
       "551      0.002634           NaN           NaN           NaN       NaN   \n",
       "552      0.002634           NaN           NaN           NaN       NaN   \n",
       "553      0.002634           NaN           NaN           NaN       NaN   \n",
       "554      0.002634           NaN           NaN           NaN       NaN   \n",
       "\n",
       "     b2_log10  b3_log10  alt_aa   mutation_type  \n",
       "0   -3.220585 -3.271966       F  non-synonymous  \n",
       "1   -3.992907 -3.936519       L  non-synonymous  \n",
       "2   -4.276903 -4.355648       L  non-synonymous  \n",
       "3   -3.913726 -3.811580       L  non-synonymous  \n",
       "4   -4.276903 -4.082647       I  non-synonymous  \n",
       "..        ...       ...     ...             ...  \n",
       "550       NaN       NaN       R      synonymous  \n",
       "551       NaN       NaN       R      synonymous  \n",
       "552       NaN       NaN       Y      synonymous  \n",
       "553       NaN       NaN       T      synonymous  \n",
       "554       NaN       NaN       L      synonymous  \n",
       "\n",
       "[555 rows x 41 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "withSyndf = pd.concat([abundf[abundf.WT.isnull()], pos_df], ignore_index=True)\n",
    "withSyndf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bced9a5-a7e7-4432-b0ec-6f7ecd0e54e2",
   "metadata": {},
   "source": [
    "### Pair input/output samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "33180568-57a8-478c-b044-c0afb3ac1aa0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('input1_abundance', 'b1_abundance'), ('input2_abundance', 'b2_abundance'), ('input3_abundance', 'b3_abundance')]\n"
     ]
    }
   ],
   "source": [
    "input_conditions = [x for x in abund_conditions if 'input' in x]\n",
    "output_conditions = [x for x in abund_conditions if x not in input_conditions]\n",
    "inpoutp_tuples = [(x, y) for x in input_conditions for y in output_conditions if x.split('_')[0][-1] == y.split('_')[0][-1]]\n",
    "print(inpoutp_tuples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529fb459-eb7a-49d9-b9e4-20759800cc11",
   "metadata": {},
   "source": [
    "### Calculate log2 fold-change (WT=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "86626a2f-4aca-4316-beef-65631c9bede1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Log2FC_1</th>\n",
       "      <th>Log2FC_2</th>\n",
       "      <th>Log2FC_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-5.098222</td>\n",
       "      <td>-4.769430</td>\n",
       "      <td>-4.957276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.855812</td>\n",
       "      <td>-4.530567</td>\n",
       "      <td>-4.479108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.803345</td>\n",
       "      <td>-4.488367</td>\n",
       "      <td>-4.827370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-5.464820</td>\n",
       "      <td>-4.722489</td>\n",
       "      <td>-4.326201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.126244</td>\n",
       "      <td>-5.754261</td>\n",
       "      <td>-5.048804</td>\n",
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       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>0.118506</td>\n",
       "      <td>-0.701131</td>\n",
       "      <td>-0.382882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>0.118506</td>\n",
       "      <td>-0.701131</td>\n",
       "      <td>-0.382882</td>\n",
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       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0.118506</td>\n",
       "      <td>-0.701131</td>\n",
       "      <td>-0.382882</td>\n",
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       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0.118506</td>\n",
       "      <td>-0.701131</td>\n",
       "      <td>-0.382882</td>\n",
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       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>0.118506</td>\n",
       "      <td>-0.701131</td>\n",
       "      <td>-0.382882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>555 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log2FC_1  Log2FC_2  Log2FC_3\n",
       "0   -5.098222 -4.769430 -4.957276\n",
       "1   -4.855812 -4.530567 -4.479108\n",
       "2   -4.803345 -4.488367 -4.827370\n",
       "3   -5.464820 -4.722489 -4.326201\n",
       "4   -5.126244 -5.754261 -5.048804\n",
       "..        ...       ...       ...\n",
       "550  0.118506 -0.701131 -0.382882\n",
       "551  0.118506 -0.701131 -0.382882\n",
       "552  0.118506 -0.701131 -0.382882\n",
       "553  0.118506 -0.701131 -0.382882\n",
       "554  0.118506 -0.701131 -0.382882\n",
       "\n",
       "[555 rows x 3 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log2FC_conditions = ['Log2FC_'+ x[0].split('_')[0][-1] for x in inpoutp_tuples]\n",
    "for i, v in enumerate(log2FC_conditions):\n",
    "    withSyndf[v] = withSyndf[abund_conditions].apply(lambda row: math.log2(row[inpoutp_tuples[i][1]]/row[inpoutp_tuples[i][0]]), axis=1)\n",
    "withSyndf[log2FC_conditions]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4553c4-e835-4b3c-9246-9568ba7976dc",
   "metadata": {},
   "source": [
    "### Get number of mitotic generations from experimental design file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "96890e6c-80f0-41c8-98eb-a029e33017ba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 2.260904578452373), (2, 1.931994138743502), (3, 1.873366444910416)]\n"
     ]
    }
   ],
   "source": [
    "designdf = pd.read_csv(expdesign_path+cset_name+'_expdesign.txt', sep='\\t', header=0)\n",
    "nbgen = designdf.loc[(designdf.experiment_replicate == 1) & (designdf.selection_id == 1), 'generations'].tolist()\n",
    "list_replicates = designdf.loc[(designdf.experiment_replicate == 1) & (designdf.selection_id == 1), 'selection_replicate'].tolist()\n",
    "repgen = [(int(list_replicates[i]), nbgen[i]) for i in range(0,len(nbgen))]\n",
    "print(repgen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687d500c-db92-48e4-80f2-0f969962c085",
   "metadata": {},
   "source": [
    "### Normalize log2FC with number of mitotic generations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "7ce7fb65-2692-4c17-9d50-9e938f6f91a1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Log2FC_1</th>\n",
       "      <th>Log2FC_2</th>\n",
       "      <th>Log2FC_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.254948</td>\n",
       "      <td>-2.468657</td>\n",
       "      <td>-2.646186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.147730</td>\n",
       "      <td>-2.345021</td>\n",
       "      <td>-2.390941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.124524</td>\n",
       "      <td>-2.323178</td>\n",
       "      <td>-2.576842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.417095</td>\n",
       "      <td>-2.444360</td>\n",
       "      <td>-2.309319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.267342</td>\n",
       "      <td>-2.978405</td>\n",
       "      <td>-2.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>0.052415</td>\n",
       "      <td>-0.362905</td>\n",
       "      <td>-0.204382</td>\n",
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       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>0.052415</td>\n",
       "      <td>-0.362905</td>\n",
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       "      <th>553</th>\n",
       "      <td>0.052415</td>\n",
       "      <td>-0.362905</td>\n",
       "      <td>-0.204382</td>\n",
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       "      <th>554</th>\n",
       "      <td>0.052415</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>555 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Log2FC_1  Log2FC_2  Log2FC_3\n",
       "0   -2.254948 -2.468657 -2.646186\n",
       "1   -2.147730 -2.345021 -2.390941\n",
       "2   -2.124524 -2.323178 -2.576842\n",
       "3   -2.417095 -2.444360 -2.309319\n",
       "4   -2.267342 -2.978405 -2.695043\n",
       "..        ...       ...       ...\n",
       "550  0.052415 -0.362905 -0.204382\n",
       "551  0.052415 -0.362905 -0.204382\n",
       "552  0.052415 -0.362905 -0.204382\n",
       "553  0.052415 -0.362905 -0.204382\n",
       "554  0.052415 -0.362905 -0.204382\n",
       "\n",
       "[555 rows x 3 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, v in enumerate(log2FC_conditions):\n",
    "    withSyndf[v] = withSyndf[v].apply(lambda x: x/repgen[i][1] if int(v[-1])==repgen[i][0] else 'error')\n",
    "\n",
    "withSyndf[log2FC_conditions]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc8176cb-b113-43a6-9fe6-4831e05791af",
   "metadata": {},
   "source": [
    "### Calculate median of synonymous codons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe5c5354-7378-4679-8ae6-cdb14ce337cf",
   "metadata": {},
   "source": [
    "Below we show that some synonymous variants appear to have an impact.\n",
    "\n",
    "If their proportion in the population is small enough, taking the median from what appears to be almost always bimodal should be robust enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3fb51d1b-c3cf-48fb-88a4-f77fa3513f4c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synOnly = withSyndf[(withSyndf.aa_seq == wtaa) & (withSyndf.nt_seq != wtseq)\n",
    "                   ].groupby('nt_seq')[['aa_pos','alt_aa','alt_codons'] + log2FC_conditions\n",
    "                                        ].first().sort_values(by=['aa_pos','alt_aa','alt_codons']).reset_index()\n",
    "len(synOnly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4676cc47-7b96-45a7-898c-763e49afc9d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>replicate</th>\n",
       "      <th>norm_log2FC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>gattgggttagaagatatactttg</td>\n",
       "      <td>Log2FC_1</td>\n",
       "      <td>-1.875536</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>-0.463734</td>\n",
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       "      <th>3</th>\n",
       "      <td>gattgggtgcggcgttatacattg</td>\n",
       "      <td>Log2FC_1</td>\n",
       "      <td>0.679597</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gattgggtgagaaggtatacattg</td>\n",
       "      <td>Log2FC_1</td>\n",
       "      <td>-2.015234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gattgggtgagacggtatacattg</td>\n",
       "      <td>Log2FC_1</td>\n",
       "      <td>-2.274431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gattgggtgagacgttatacgttg</td>\n",
       "      <td>Log2FC_1</td>\n",
       "      <td>-2.114387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gattgggtgagacgttatactttg</td>\n",
       "      <td>Log2FC_1</td>\n",
       "      <td>-2.075414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gattgggttagaagatatactttg</td>\n",
       "      <td>Log2FC_2</td>\n",
       "      <td>-2.174132</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gattgggttagacgttatacattg</td>\n",
       "      <td>Log2FC_2</td>\n",
       "      <td>-0.412263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gattgggtgaggcgttatacattg</td>\n",
       "      <td>Log2FC_2</td>\n",
       "      <td>-0.627086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gattgggtgcggcgttatacattg</td>\n",
       "      <td>Log2FC_2</td>\n",
       "      <td>0.105337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gattgggtgagaaggtatacattg</td>\n",
       "      <td>Log2FC_2</td>\n",
       "      <td>-2.335629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gattgggtgagacggtatacattg</td>\n",
       "      <td>Log2FC_2</td>\n",
       "      <td>-2.146073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gattgggtgagacgttatacgttg</td>\n",
       "      <td>Log2FC_2</td>\n",
       "      <td>-2.507900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gattgggtgagacgttatactttg</td>\n",
       "      <td>Log2FC_2</td>\n",
       "      <td>-1.643380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gattgggttagaagatatactttg</td>\n",
       "      <td>Log2FC_3</td>\n",
       "      <td>-2.250128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gattgggttagacgttatacattg</td>\n",
       "      <td>Log2FC_3</td>\n",
       "      <td>-0.295248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>gattgggtgaggcgttatacattg</td>\n",
       "      <td>Log2FC_3</td>\n",
       "      <td>-0.607500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>gattgggtgcggcgttatacattg</td>\n",
       "      <td>Log2FC_3</td>\n",
       "      <td>-0.295248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>gattgggtgagaaggtatacattg</td>\n",
       "      <td>Log2FC_3</td>\n",
       "      <td>-2.392965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gattgggtgagacggtatacattg</td>\n",
       "      <td>Log2FC_3</td>\n",
       "      <td>-2.117577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>gattgggtgagacgttatacgttg</td>\n",
       "      <td>Log2FC_3</td>\n",
       "      <td>-2.647160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>gattgggtgagacgttatactttg</td>\n",
       "      <td>Log2FC_3</td>\n",
       "      <td>-1.730792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      nt_seq replicate  norm_log2FC\n",
       "0   gattgggttagaagatatactttg  Log2FC_1    -1.875536\n",
       "1   gattgggttagacgttatacattg  Log2FC_1     0.420868\n",
       "2   gattgggtgaggcgttatacattg  Log2FC_1    -0.463734\n",
       "3   gattgggtgcggcgttatacattg  Log2FC_1     0.679597\n",
       "4   gattgggtgagaaggtatacattg  Log2FC_1    -2.015234\n",
       "5   gattgggtgagacggtatacattg  Log2FC_1    -2.274431\n",
       "6   gattgggtgagacgttatacgttg  Log2FC_1    -2.114387\n",
       "7   gattgggtgagacgttatactttg  Log2FC_1    -2.075414\n",
       "8   gattgggttagaagatatactttg  Log2FC_2    -2.174132\n",
       "9   gattgggttagacgttatacattg  Log2FC_2    -0.412263\n",
       "10  gattgggtgaggcgttatacattg  Log2FC_2    -0.627086\n",
       "11  gattgggtgcggcgttatacattg  Log2FC_2     0.105337\n",
       "12  gattgggtgagaaggtatacattg  Log2FC_2    -2.335629\n",
       "13  gattgggtgagacggtatacattg  Log2FC_2    -2.146073\n",
       "14  gattgggtgagacgttatacgttg  Log2FC_2    -2.507900\n",
       "15  gattgggtgagacgttatactttg  Log2FC_2    -1.643380\n",
       "16  gattgggttagaagatatactttg  Log2FC_3    -2.250128\n",
       "17  gattgggttagacgttatacattg  Log2FC_3    -0.295248\n",
       "18  gattgggtgaggcgttatacattg  Log2FC_3    -0.607500\n",
       "19  gattgggtgcggcgttatacattg  Log2FC_3    -0.295248\n",
       "20  gattgggtgagaaggtatacattg  Log2FC_3    -2.392965\n",
       "21  gattgggtgagacggtatacattg  Log2FC_3    -2.117577\n",
       "22  gattgggtgagacgttatacgttg  Log2FC_3    -2.647160\n",
       "23  gattgggtgagacgttatactttg  Log2FC_3    -1.730792"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synLong = pd.melt(synOnly, id_vars = 'nt_seq', value_vars = log2FC_conditions, var_name='replicate', value_name='norm_log2FC')\n",
    "synLong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "27980288-a4fe-450a-8458-c54ad4449670",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'log2FC / nb_gen')]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Purge graph space\n",
    "sns.set(rc = {'figure.figsize':(5,3)})\n",
    "sns.set_theme(style='whitegrid')\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "g = sns.histplot(synLong, x='norm_log2FC',\n",
    "                 hue='replicate', palette='husl',\n",
    "                 kde=True)\n",
    "\n",
    "g.set(xlabel = 'log2FC / nb_gen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ebbaaa1f-1476-4360-a68a-5580351e715b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Log2FC_1   -1.945385\n",
       "Log2FC_2   -1.894727\n",
       "Log2FC_3   -1.924185\n",
       "dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medianSyn = synOnly[log2FC_conditions].median().squeeze()\n",
    "medianSyn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214d1bd7-46c4-4d3b-aadd-2d265319fbc7",
   "metadata": {},
   "source": [
    "### Calculate selection coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24075ab1-01fe-4dc4-9166-3d35acf7b131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>selcoeff_1</th>\n",
       "      <th>selcoeff_2</th>\n",
       "      <th>selcoeff_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.309563</td>\n",
       "      <td>-0.573930</td>\n",
       "      <td>-0.722001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.202345</td>\n",
       "      <td>-0.450294</td>\n",
       "      <td>-0.466756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.179138</td>\n",
       "      <td>-0.428452</td>\n",
       "      <td>-0.652658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.471709</td>\n",
       "      <td>-0.549633</td>\n",
       "      <td>-0.385134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.321957</td>\n",
       "      <td>-1.083678</td>\n",
       "      <td>-0.770859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>1.997800</td>\n",
       "      <td>1.531822</td>\n",
       "      <td>1.719803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>1.997800</td>\n",
       "      <td>1.531822</td>\n",
       "      <td>1.719803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>1.997800</td>\n",
       "      <td>1.531822</td>\n",
       "      <td>1.719803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>1.997800</td>\n",
       "      <td>1.531822</td>\n",
       "      <td>1.719803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>1.997800</td>\n",
       "      <td>1.531822</td>\n",
       "      <td>1.719803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>555 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     selcoeff_1  selcoeff_2  selcoeff_3\n",
       "0     -0.309563   -0.573930   -0.722001\n",
       "1     -0.202345   -0.450294   -0.466756\n",
       "2     -0.179138   -0.428452   -0.652658\n",
       "3     -0.471709   -0.549633   -0.385134\n",
       "4     -0.321957   -1.083678   -0.770859\n",
       "..          ...         ...         ...\n",
       "550    1.997800    1.531822    1.719803\n",
       "551    1.997800    1.531822    1.719803\n",
       "552    1.997800    1.531822    1.719803\n",
       "553    1.997800    1.531822    1.719803\n",
       "554    1.997800    1.531822    1.719803\n",
       "\n",
       "[555 rows x 3 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selcoeff_conditions = [x.replace('Log2FC','selcoeff') for x in log2FC_conditions]\n",
    "withSyndf[selcoeff_conditions] = withSyndf[log2FC_conditions].subtract(medianSyn, axis=1)\n",
    "withSyndf[selcoeff_conditions]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f94675b-bb27-4988-8067-665abbe0971c",
   "metadata": {},
   "source": [
    "### Compare replicates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179acd0d-1a94-4486-ab17-880e5941eddb",
   "metadata": {},
   "source": [
    "Note: here the dataframe is reshaped to have 1 value per nucleotide sequence, with replicates in wide format. To minimize the number of steps, the same dataframe is used to calculate the correlation coefficient (using only the selection coefficients) and to look at the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08c489d4-be3e-4772-9806-13cee52ffe9e",
   "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>Nham_aa</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>gattgggtgagaaggtatacattg</td>\n",
       "      <td>-0.069849</td>\n",
       "      <td>-0.440902</td>\n",
       "      <td>-0.468780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>gattgggtgagacggtatacattg</td>\n",
       "      <td>-0.329046</td>\n",
       "      <td>-0.251346</td>\n",
       "      <td>-0.193393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>gattgggtgagacgttatacattg</td>\n",
       "      <td>1.997800</td>\n",
       "      <td>1.531822</td>\n",
       "      <td>1.719803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>gattgggtgagacgttatacgttg</td>\n",
       "      <td>-0.169001</td>\n",
       "      <td>-0.613174</td>\n",
       "      <td>-0.722976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>gattgggtgagacgttatactttg</td>\n",
       "      <td>-0.130029</td>\n",
       "      <td>0.251346</td>\n",
       "      <td>0.193393</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>225</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NWLKRCII</td>\n",
       "      <td>aattggttgaaaagatgtattatt</td>\n",
       "      <td>-0.429350</td>\n",
       "      <td>-0.433350</td>\n",
       "      <td>-0.569792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NWLKRVVI</td>\n",
       "      <td>aattggttgaaaagagttgttatt</td>\n",
       "      <td>-0.786205</td>\n",
       "      <td>-0.561780</td>\n",
       "      <td>-1.198957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>6.0</td>\n",
       "      <td>RWIKRCII</td>\n",
       "      <td>agatggattaaaagatgtattatt</td>\n",
       "      <td>-0.375653</td>\n",
       "      <td>-0.685962</td>\n",
       "      <td>-0.623134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>6.0</td>\n",
       "      <td>SWLKRCVI</td>\n",
       "      <td>tcttggttgaaaagatgtgttatt</td>\n",
       "      <td>-0.306601</td>\n",
       "      <td>-0.415271</td>\n",
       "      <td>-0.569504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NQQFIIQL</td>\n",
       "      <td>aatcaacaatttattattcaattg</td>\n",
       "      <td>-0.313919</td>\n",
       "      <td>-0.344246</td>\n",
       "      <td>-0.861304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Nham_aa    aa_seq                    nt_seq         1         2         3\n",
       "0        0.0  DWVRRYTL  gattgggtgagaaggtatacattg -0.069849 -0.440902 -0.468780\n",
       "1        0.0  DWVRRYTL  gattgggtgagacggtatacattg -0.329046 -0.251346 -0.193393\n",
       "2        0.0  DWVRRYTL  gattgggtgagacgttatacattg  1.997800  1.531822  1.719803\n",
       "3        0.0  DWVRRYTL  gattgggtgagacgttatacgttg -0.169001 -0.613174 -0.722976\n",
       "4        0.0  DWVRRYTL  gattgggtgagacgttatactttg -0.130029  0.251346  0.193393\n",
       "..       ...       ...                       ...       ...       ...       ...\n",
       "225      6.0  NWLKRCII  aattggttgaaaagatgtattatt -0.429350 -0.433350 -0.569792\n",
       "226      6.0  NWLKRVVI  aattggttgaaaagagttgttatt -0.786205 -0.561780 -1.198957\n",
       "227      6.0  RWIKRCII  agatggattaaaagatgtattatt -0.375653 -0.685962 -0.623134\n",
       "228      6.0  SWLKRCVI  tcttggttgaaaagatgtgttatt -0.306601 -0.415271 -0.569504\n",
       "229      7.0  NQQFIIQL  aatcaacaatttattattcaattg -0.313919 -0.344246 -0.861304\n",
       "\n",
       "[230 rows x 6 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gby_cols = ['Nham_aa','aa_seq','nt_seq']\n",
    "permut = withSyndf.groupby(gby_cols)[selcoeff_conditions].first().reset_index()\n",
    "permut.columns = [x[-1] if 'selcoeff' in x else x for x in permut.columns]\n",
    "permut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2625c5af-e1b5-4f33-8321-64fc8a359d78",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('1', '2'), ('1', '3'), ('2', '3')]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "rep_combinations = list(itertools.combinations(permut.columns[len(gby_cols):], 2))\n",
    "rep_combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25e4873a-dfff-4d20-8627-de64a8abdf5b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('1', '2'): 0.9433295068288146,\n",
       " ('1', '3'): 0.9545272714520651,\n",
       " ('2', '3'): 0.93807989529264}"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "pearson_dict = {}\n",
    "for c in rep_combinations:\n",
    "    pearson_dict[c] = stats.pearsonr(permut[c[0]], permut[c[1]])[0]\n",
    "    # One possibility is to calculate this for stop, non-synonymous and synonymous separately\n",
    "    # Pearson r for non-synonymous is the most important (the most affected by distribution of effects)\n",
    "    # Depending on the level of noise, we could select the two lowest values, see if it's above a fixed threshold (both should be above .8)\n",
    "    # then see which replicate was common to the 2 corresponding combinations and calculate the median from the two remaining\n",
    "    \n",
    "pearson_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "cf39a7a5-3b6c-4595-bd09-de7f9bcffacf",
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(pearson_dict)>=1:\n",
    "    pearson_df = pd.DataFrame.from_dict(pearson_dict, orient='index', columns=['Pearson']).reset_index(names='rep_compared')\n",
    "    pearson_df[['replicate1','replicate2']] = pd.DataFrame(pearson_df.rep_compared.to_list())\n",
    "    pearson_df.to_csv(processed_data_outpath+cset_name+'/Pearson_replicates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0ddf4af7-1f1b-40cc-a9f2-fcd04db69734",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 590.222x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Purge graph space\n",
    "sns.set(rc = {'figure.figsize':(5,3)})\n",
    "sns.set_theme(style='whitegrid')\n",
    "\n",
    "fig = sns.pairplot(permut.sort_values(by='Nham_aa', ascending=False),\n",
    "                   hue='Nham_aa', palette='flare',\n",
    "                   corner=True)\n",
    "\n",
    "# Code below does not work for all possible scenarios (replicate# does not necessarily match with facet#)\n",
    "#for i in range(len(permut.columns[2:])):\n",
    "#    for j in range(len(permut.columns[2:])):\n",
    "#        if i < j:\n",
    "#            fig.axes[j][i].annotate(round(pearson_dict[(str(i+1),str(j+1))],2), (-3,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "edcc0cc2-1c23-4a42-a58f-579df97aed29",
   "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>Nham_aa</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>replicate</th>\n",
       "      <th>selcoeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>tttctggttttatctttgagagatcca</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>tttcttgttttatctttgagagatcca</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.196415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>tttttagtgttatctttgagagatcca</td>\n",
       "      <td>1</td>\n",
       "      <td>0.956919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>tttttagttctgtctttgagagatcca</td>\n",
       "      <td>1</td>\n",
       "      <td>0.270414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>tttttagttctttctttgagagatcca</td>\n",
       "      <td>1</td>\n",
       "      <td>0.126739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>6.0</td>\n",
       "      <td>YLTRAFRDS</td>\n",
       "      <td>tatttgactagagcttttagagattct</td>\n",
       "      <td>2</td>\n",
       "      <td>1.887793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>7.0</td>\n",
       "      <td>AICSDLRED</td>\n",
       "      <td>gctatttgttctgatttgagagaagat</td>\n",
       "      <td>2</td>\n",
       "      <td>1.885560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>7.0</td>\n",
       "      <td>LQIRPLIAP</td>\n",
       "      <td>ttgcaaattagaccattgattgctcca</td>\n",
       "      <td>2</td>\n",
       "      <td>2.105314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>7.0</td>\n",
       "      <td>LQVKPMIKP</td>\n",
       "      <td>ttgcaagttaaaccaatgattaaacca</td>\n",
       "      <td>2</td>\n",
       "      <td>2.108609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>7.0</td>\n",
       "      <td>VLTLLVTQH</td>\n",
       "      <td>gttttgactttgttggttactcaacat</td>\n",
       "      <td>2</td>\n",
       "      <td>1.818790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>632 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Nham_aa     aa_seq                       nt_seq replicate  selcoeff\n",
       "0        0.0  FLVLSLRDP  tttctggttttatctttgagagatcca         1  0.000000\n",
       "1        0.0  FLVLSLRDP  tttcttgttttatctttgagagatcca         1 -0.196415\n",
       "2        0.0  FLVLSLRDP  tttttagtgttatctttgagagatcca         1  0.956919\n",
       "3        0.0  FLVLSLRDP  tttttagttctgtctttgagagatcca         1  0.270414\n",
       "4        0.0  FLVLSLRDP  tttttagttctttctttgagagatcca         1  0.126739\n",
       "..       ...        ...                          ...       ...       ...\n",
       "627      6.0  YLTRAFRDS  tatttgactagagcttttagagattct         2  1.887793\n",
       "628      7.0  AICSDLRED  gctatttgttctgatttgagagaagat         2  1.885560\n",
       "629      7.0  LQIRPLIAP  ttgcaaattagaccattgattgctcca         2  2.105314\n",
       "630      7.0  LQVKPMIKP  ttgcaagttaaaccaatgattaaacca         2  2.108609\n",
       "631      7.0  VLTLLVTQH  gttttgactttgttggttactcaacat         2  1.818790\n",
       "\n",
       "[632 rows x 5 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permutlong = pd.melt(permut, id_vars = gby_cols, value_vars = [x[-1] for x in selcoeff_conditions], var_name='replicate', value_name='selcoeff')\n",
    "permutlong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b8e46408-b763-439b-bf4f-ba2205178741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>replicate</th>\n",
       "      <th>selcoeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>1</td>\n",
       "      <td>0.015232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.014021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>*LVLSLRDP</td>\n",
       "      <td>1</td>\n",
       "      <td>0.488397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>*LVLSLRDP</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.027127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>ALVLSLRDP</td>\n",
       "      <td>1</td>\n",
       "      <td>1.875195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>7.0</td>\n",
       "      <td>LQIRPLIAP</td>\n",
       "      <td>2</td>\n",
       "      <td>2.105314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>7.0</td>\n",
       "      <td>LQVKPMIKP</td>\n",
       "      <td>1</td>\n",
       "      <td>2.206596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>7.0</td>\n",
       "      <td>LQVKPMIKP</td>\n",
       "      <td>2</td>\n",
       "      <td>2.108609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>7.0</td>\n",
       "      <td>VLTLLVTQH</td>\n",
       "      <td>1</td>\n",
       "      <td>1.854448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>7.0</td>\n",
       "      <td>VLTLLVTQH</td>\n",
       "      <td>2</td>\n",
       "      <td>1.818790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>444 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Nham_aa     aa_seq replicate  selcoeff\n",
       "0        0.0  FLVLSLRDP         1  0.015232\n",
       "1        0.0  FLVLSLRDP         2 -0.014021\n",
       "2        1.0  *LVLSLRDP         1  0.488397\n",
       "3        1.0  *LVLSLRDP         2 -0.027127\n",
       "4        1.0  ALVLSLRDP         1  1.875195\n",
       "..       ...        ...       ...       ...\n",
       "439      7.0  LQIRPLIAP         2  2.105314\n",
       "440      7.0  LQVKPMIKP         1  2.206596\n",
       "441      7.0  LQVKPMIKP         2  2.108609\n",
       "442      7.0  VLTLLVTQH         1  1.854448\n",
       "443      7.0  VLTLLVTQH         2  1.818790\n",
       "\n",
       "[444 rows x 4 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permutlong_aa = permutlong.groupby(['Nham_aa','aa_seq','replicate'])[['selcoeff']].median().reset_index()\n",
    "permutlong_aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "105ccb05-8d89-45a6-807b-e27fca1d1f63",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2484.72x300 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Purge graph space\n",
    "sns.set(rc = {'figure.figsize':(5,3)})\n",
    "sns.set_theme(style='whitegrid')\n",
    "\n",
    "fig = sns.displot(permutlong_aa, x='selcoeff', col = 'Nham_aa',\n",
    "                  hue='replicate', palette='husl', rug=True,\n",
    "                  element='step', common_norm=False,\n",
    "                  common_bins=False, facet_kws={'sharex': True, 'sharey': False}, # different y scales for each facet\n",
    "                  height=3\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4acd574d-79c0-4739-a5d7-7ef62003e920",
   "metadata": {},
   "source": [
    "### Drop replicate if uncorrelated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf4a7d0-54ae-4923-b851-153e4eb75094",
   "metadata": {},
   "source": [
    "#### ⚠️ **Filtering step** ⚠️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c935490b-8c29-4a90-b37d-28846a562829",
   "metadata": {},
   "outputs": [],
   "source": [
    "pearson_lowest = sorted(pearson_dict.items(), key=lambda x:x[1])\n",
    "if (len(pearson_lowest) == 3) & (compound in ['caspofungin','micafungin','anidulafungin']):\n",
    "    if (pearson_lowest[0][1] < .8) & (pearson_lowest[1][1] < .8):\n",
    "        selcoeff_conditions.remove(f'selcoeff_{\"\".join(set(pearson_lowest[0][0]) & set(pearson_lowest[1][0]))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274071e9-426b-4763-8282-d705b37968a0",
   "metadata": {},
   "source": [
    "### Calculate median selection coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1b23355f-5541-4c3a-8620-dff04d73ef13",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>selcoeff_1</th>\n",
       "      <th>selcoeff_2</th>\n",
       "      <th>median_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.160526</td>\n",
       "      <td>0.182273</td>\n",
       "      <td>0.171399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.227162</td>\n",
       "      <td>0.245442</td>\n",
       "      <td>0.009140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.016984</td>\n",
       "      <td>0.325731</td>\n",
       "      <td>0.171357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.097376</td>\n",
       "      <td>2.047951</td>\n",
       "      <td>2.072663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.318549</td>\n",
       "      <td>-0.052749</td>\n",
       "      <td>-0.185649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     selcoeff_1  selcoeff_2  median_s\n",
       "0      0.160526    0.182273  0.171399\n",
       "1     -0.227162    0.245442  0.009140\n",
       "2      0.016984    0.325731  0.171357\n",
       "3      2.097376    2.047951  2.072663\n",
       "4     -0.318549   -0.052749 -0.185649\n",
       "..          ...         ...       ...\n",
       "521    0.152545   -0.032773  0.059886\n",
       "522    0.152545   -0.032773  0.059886\n",
       "523    0.152545   -0.032773  0.059886\n",
       "524    0.152545   -0.032773  0.059886\n",
       "525    0.152545   -0.032773  0.059886\n",
       "\n",
       "[526 rows x 3 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "withSyndf['median_s'] = withSyndf[selcoeff_conditions].median(axis=1)\n",
    "withSyndf[selcoeff_conditions+['median_s']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa68c1fa-169e-4fd1-b67a-53b258e7fd02",
   "metadata": {},
   "source": [
    "### Export dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3e806202-b3f5-48e1-a7fa-8b90d67bbbbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "withSyndf.to_csv(processed_data_outpath+cset_name+'/selcoeff_all_libraries.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d4792a9-d146-489e-9ea4-7e67175c888f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Visualize single mutants as heatmap (codon level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1db94412-29e3-4609-b59a-5896e6d7d416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_type</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_codons</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>combination</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>WT</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>...</th>\n",
       "      <th>b1_log10</th>\n",
       "      <th>b2_log10</th>\n",
       "      <th>b3_log10</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>mutation_type</th>\n",
       "      <th>Log2FC_1</th>\n",
       "      <th>Log2FC_2</th>\n",
       "      <th>selcoeff_1</th>\n",
       "      <th>selcoeff_2</th>\n",
       "      <th>median_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>ttgttagttttatctttgagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LLVLSLRDP</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.975666</td>\n",
       "      <td>-3.908412</td>\n",
       "      <td>-4.154620</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-1.549318</td>\n",
       "      <td>-1.406645</td>\n",
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       "      <td>0.182273</td>\n",
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       "      <th>1</th>\n",
       "      <td>single</td>\n",
       "      <td>cttttagttttatctttgagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LLVLSLRDP</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.017058</td>\n",
       "      <td>-4.452480</td>\n",
       "      <td>-4.894983</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-1.937006</td>\n",
       "      <td>-1.343475</td>\n",
       "      <td>-0.227162</td>\n",
       "      <td>0.245442</td>\n",
       "      <td>0.009140</td>\n",
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       "      <th>2</th>\n",
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       "      <td>ctgttagttttatctttgagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LLVLSLRDP</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.539937</td>\n",
       "      <td>-4.151450</td>\n",
       "      <td>-3.940741</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>-1.692860</td>\n",
       "      <td>-1.263187</td>\n",
       "      <td>0.016984</td>\n",
       "      <td>0.325731</td>\n",
       "      <td>0.171357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>attttagttttatctttgagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>att</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ILVLSLRDP</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.087129</td>\n",
       "      <td>-2.048074</td>\n",
       "      <td>-2.085760</td>\n",
       "      <td>I</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>0.387532</td>\n",
       "      <td>0.459033</td>\n",
       "      <td>2.097376</td>\n",
       "      <td>2.047951</td>\n",
       "      <td>2.072663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>single</td>\n",
       "      <td>atgttagttttatctttgagagatcca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>atg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MLVLSLRDP</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.539937</td>\n",
       "      <td>-4.209442</td>\n",
       "      <td>-4.292923</td>\n",
       "      <td>M</td>\n",
       "      <td>non-synonymous</td>\n",
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       "      <td>-0.052749</td>\n",
       "      <td>-0.185649</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>WT</td>\n",
       "      <td>tttttagttttatctttgagagatcca</td>\n",
       "      <td>4.0</td>\n",
       "      <td>tct</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>-1.557299</td>\n",
       "      <td>-1.621690</td>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>WT</td>\n",
       "      <td>tttttagttttatctttgagagatcca</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ttg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>-1.557299</td>\n",
       "      <td>-1.621690</td>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>WT</td>\n",
       "      <td>tttttagttttatctttgagagatcca</td>\n",
       "      <td>6.0</td>\n",
       "      <td>aga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>-1.557299</td>\n",
       "      <td>-1.621690</td>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>WT</td>\n",
       "      <td>tttttagttttatctttgagagatcca</td>\n",
       "      <td>7.0</td>\n",
       "      <td>gat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>-1.557299</td>\n",
       "      <td>-1.621690</td>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>WT</td>\n",
       "      <td>tttttagttttatctttgagagatcca</td>\n",
       "      <td>8.0</td>\n",
       "      <td>cca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLVLSLRDP</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>-1.557299</td>\n",
       "      <td>-1.621690</td>\n",
       "      <td>0.152545</td>\n",
       "      <td>-0.032773</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>275 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    seq_type                       nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0     single  ttgttagttttatctttgagagatcca     0.0        ttg            1.0   \n",
       "1     single  cttttagttttatctttgagagatcca     0.0        ctt            1.0   \n",
       "2     single  ctgttagttttatctttgagagatcca     0.0        ctg            1.0   \n",
       "3     single  attttagttttatctttgagagatcca     0.0        att            1.0   \n",
       "4     single  atgttagttttatctttgagagatcca     0.0        atg            1.0   \n",
       "..       ...                          ...     ...        ...            ...   \n",
       "521       WT  tttttagttttatctttgagagatcca     4.0        tct            NaN   \n",
       "522       WT  tttttagttttatctttgagagatcca     5.0        ttg            NaN   \n",
       "523       WT  tttttagttttatctttgagagatcca     6.0        aga            NaN   \n",
       "524       WT  tttttagttttatctttgagagatcca     7.0        gat            NaN   \n",
       "525       WT  tttttagttttatctttgagagatcca     8.0        cca            NaN   \n",
       "\n",
       "    combination     aa_seq    WT   STOP STOP_readthrough  ...  b1_log10  \\\n",
       "0           NaN  LLVLSLRDP  <NA>  False            False  ... -3.975666   \n",
       "1           NaN  LLVLSLRDP  <NA>  False            False  ... -5.017058   \n",
       "2           NaN  LLVLSLRDP  <NA>  False            False  ... -4.539937   \n",
       "3           NaN  ILVLSLRDP  <NA>  False            False  ... -2.087129   \n",
       "4           NaN  MLVLSLRDP  <NA>  False            False  ... -4.539937   \n",
       "..          ...        ...   ...    ...              ...  ...       ...   \n",
       "521         NaN  FLVLSLRDP  True    NaN              NaN  ...       NaN   \n",
       "522         NaN  FLVLSLRDP  True    NaN              NaN  ...       NaN   \n",
       "523         NaN  FLVLSLRDP  True    NaN              NaN  ...       NaN   \n",
       "524         NaN  FLVLSLRDP  True    NaN              NaN  ...       NaN   \n",
       "525         NaN  FLVLSLRDP  True    NaN              NaN  ...       NaN   \n",
       "\n",
       "     b2_log10  b3_log10  alt_aa   mutation_type  Log2FC_1  Log2FC_2  \\\n",
       "0   -3.908412 -4.154620       L  non-synonymous -1.549318 -1.406645   \n",
       "1   -4.452480 -4.894983       L  non-synonymous -1.937006 -1.343475   \n",
       "2   -4.151450 -3.940741       L  non-synonymous -1.692860 -1.263187   \n",
       "3   -2.048074 -2.085760       I  non-synonymous  0.387532  0.459033   \n",
       "4   -4.209442 -4.292923       M  non-synonymous -2.028393 -1.641667   \n",
       "..        ...       ...     ...             ...       ...       ...   \n",
       "521       NaN       NaN       S      synonymous -1.557299 -1.621690   \n",
       "522       NaN       NaN       L      synonymous -1.557299 -1.621690   \n",
       "523       NaN       NaN       R      synonymous -1.557299 -1.621690   \n",
       "524       NaN       NaN       D      synonymous -1.557299 -1.621690   \n",
       "525       NaN       NaN       P      synonymous -1.557299 -1.621690   \n",
       "\n",
       "     selcoeff_1 selcoeff_2  median_s  \n",
       "0      0.160526   0.182273  0.171399  \n",
       "1     -0.227162   0.245442  0.009140  \n",
       "2      0.016984   0.325731  0.171357  \n",
       "3      2.097376   2.047951  2.072663  \n",
       "4     -0.318549  -0.052749 -0.185649  \n",
       "..          ...        ...       ...  \n",
       "521    0.152545  -0.032773  0.059886  \n",
       "522    0.152545  -0.032773  0.059886  \n",
       "523    0.152545  -0.032773  0.059886  \n",
       "524    0.152545  -0.032773  0.059886  \n",
       "525    0.152545  -0.032773  0.059886  \n",
       "\n",
       "[275 rows x 43 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if pool_type == 'single':\n",
    "    withWTdf = withSyndf[withSyndf.seq_type.isin(['WT','single'])]\n",
    "else:\n",
    "    withWTdf = withSyndf[withSyndf.Nham_aa <=1]\n",
    "withWTdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4131dac-6977-4082-ace4-1607fb301cbc",
   "metadata": {},
   "source": [
    "#### Pivot and sort by aminoacid (properties) and codon (alphabetical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0467eaf9-24da-42ad-af46-2d5f65952179",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "codon_table = pd.read_csv(codon_table_path, header=0)\n",
    "codon_table['codon'] = codon_table['codon'].str.lower()\n",
    "codon_table.sort_values(by='codon', inplace=True)\n",
    "list_codons = codon_table.codon.unique()\n",
    "cod_sort_dic = dict(zip(list_codons, list(range(0, len(list_codons)))))\n",
    "\n",
    "aa_sort_order = '*PGCQNTSEDKHRWYFMLIVA'\n",
    "aa_sort_dic = dict(zip(list(aa_sort_order), list(range(0,len(aa_sort_order)))))\n",
    "\n",
    "aa_sort_dic.update(cod_sort_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "66c3d343-aee2-499d-90cb-f7aefe3148c9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if pool_type == 'single':\n",
    "    wide_single = withWTdf.pivot(index=['alt_aa','alt_codons'], columns='aa_pos', values='median_s')\n",
    "    wide_single.sort_index(key=lambda x: x.map(aa_sort_dic), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94595fe4-f3a5-4b8c-bc05-3d3f4bf37b7d",
   "metadata": {},
   "source": [
    "#### Generate heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "216969e2-6799-410d-9075-cc1b069f9ec5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if pool_type == 'single':\n",
    "    # Purge graph space\n",
    "    sns.set(rc = {'figure.figsize':(5,8)})\n",
    "    sns.set_theme(style='white')\n",
    "    f, ax = plt.subplots()\n",
    "    \n",
    "    # Custom color palette\n",
    "    ccmap = sns.color_palette(\"blend:#009B9E,#42B7B9,#A7D3D4,#F1F1F1,#E4C1D9,#D691C1,#C75DAB\", # CARTOColors Tropic\n",
    "                              as_cmap=True)\n",
    "    ccmap.set_bad('.5') # Color for missing values\n",
    "    \n",
    "    # Mask for missing values\n",
    "    mask = pd.isnull(wide_single)\n",
    "    \n",
    "    # Draw heatmap\n",
    "    ax = sns.heatmap(wide_single, mask=mask,\n",
    "                     cmap=ccmap, center = 0,\n",
    "                     vmin=-2, vmax=2)\n",
    "    ax.set_xlabel('Position in hotspot')\n",
    "    ax.set_ylabel(None)\n",
    "    ax.set_title('Strain: '+strain+\n",
    "                 '\\n'+locus[:4]+' - Hotspot '+locus[-1]+\n",
    "                 '\\nSelection coefficients'+\n",
    "                 '\\nCondition: '+compound)\n",
    "\n",
    "    # Coordinates of WT codons on the heatmap are extracted from the dataframe\n",
    "    # Coordinates should be OK even if some codons are missing from the dataframe\n",
    "    codon_list = [x[1] for x in wide_single.index]\n",
    "    wtcoord = [((i//3)+0.5,codon_list.index(wtseq[i:i+3])+0.5) for i in range(0,len(wtseq),3)]\n",
    "\n",
    "    for o in wtcoord:\n",
    "        ax.plot(o[0],o[1],'or') # displays WT codons by marking them with red (r) circles (o)\n",
    "\n",
    "    # Graphs are output in the specified folder\n",
    "    plt.savefig(graph_outpath+cset_name+'/nt_heatmap_median_s.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dcab827-636c-4872-b147-b5ded515fcf3",
   "metadata": {},
   "source": [
    "### Aggregate at protein level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf6cd26-0717-4b4e-a8f2-1ebf87cf1d95",
   "metadata": {},
   "source": [
    "### Visualize single mutants as heatmap (protein level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e877b35d-c75f-4425-a8bf-170265212813",
   "metadata": {},
   "outputs": [],
   "source": [
    "withWTdf_gby = withWTdf.groupby(['alt_aa','aa_pos','nt_seq'])[['median_s']].first().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5a0c2ae9-8c47-4bec-a17f-94a55dafc6dd",
   "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>aa_pos</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alt_aa</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>*</th>\n",
       "      <td>0.230635</td>\n",
       "      <td>0.165205</td>\n",
       "      <td>0.109633</td>\n",
       "      <td>0.033522</td>\n",
       "      <td>0.681268</td>\n",
       "      <td>-0.119258</td>\n",
       "      <td>-0.243796</td>\n",
       "      <td>0.463103</td>\n",
       "      <td>0.104349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>2.033359</td>\n",
       "      <td>0.105654</td>\n",
       "      <td>0.221247</td>\n",
       "      <td>-0.190345</td>\n",
       "      <td>2.097655</td>\n",
       "      <td>-0.016790</td>\n",
       "      <td>0.812163</td>\n",
       "      <td>2.019377</td>\n",
       "      <td>0.059886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>2.164996</td>\n",
       "      <td>1.509557</td>\n",
       "      <td>0.610365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.170877</td>\n",
       "      <td>-0.076009</td>\n",
       "      <td>2.071182</td>\n",
       "      <td>1.829797</td>\n",
       "      <td>1.978833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209295</td>\n",
       "      <td>0.061929</td>\n",
       "      <td>-0.142012</td>\n",
       "      <td>1.627046</td>\n",
       "      <td>0.070437</td>\n",
       "      <td>0.063626</td>\n",
       "      <td>2.059917</td>\n",
       "      <td>1.434822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>-0.732074</td>\n",
       "      <td>0.181034</td>\n",
       "      <td>-0.183346</td>\n",
       "      <td>0.011048</td>\n",
       "      <td>2.057751</td>\n",
       "      <td>2.062573</td>\n",
       "      <td>0.173719</td>\n",
       "      <td>1.424850</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>1.341761</td>\n",
       "      <td>-0.001005</td>\n",
       "      <td>1.507173</td>\n",
       "      <td>0.089103</td>\n",
       "      <td>2.103087</td>\n",
       "      <td>-0.158037</td>\n",
       "      <td>0.686277</td>\n",
       "      <td>1.585690</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1.807587</td>\n",
       "      <td>0.979786</td>\n",
       "      <td>-0.045587</td>\n",
       "      <td>-0.167156</td>\n",
       "      <td>1.873882</td>\n",
       "      <td>-0.114306</td>\n",
       "      <td>0.315458</td>\n",
       "      <td>2.058348</td>\n",
       "      <td>2.045670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>2.093610</td>\n",
       "      <td>1.714943</td>\n",
       "      <td>1.434822</td>\n",
       "      <td>-0.238664</td>\n",
       "      <td>0.004823</td>\n",
       "      <td>-0.145032</td>\n",
       "      <td>1.128074</td>\n",
       "      <td>2.082255</td>\n",
       "      <td>1.955437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.266907</td>\n",
       "      <td>0.723382</td>\n",
       "      <td>-0.297118</td>\n",
       "      <td>-0.232651</td>\n",
       "      <td>2.062594</td>\n",
       "      <td>0.125017</td>\n",
       "      <td>-0.071361</td>\n",
       "      <td>1.685392</td>\n",
       "      <td>2.042599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.047285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.357288</td>\n",
       "      <td>0.077660</td>\n",
       "      <td>1.765331</td>\n",
       "      <td>-0.170386</td>\n",
       "      <td>0.291009</td>\n",
       "      <td>0.059886</td>\n",
       "      <td>2.056910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>2.053510</td>\n",
       "      <td>-0.030765</td>\n",
       "      <td>1.776634</td>\n",
       "      <td>0.752623</td>\n",
       "      <td>2.018324</td>\n",
       "      <td>0.331239</td>\n",
       "      <td>0.018945</td>\n",
       "      <td>1.174687</td>\n",
       "      <td>1.889744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>1.934950</td>\n",
       "      <td>0.450009</td>\n",
       "      <td>0.088811</td>\n",
       "      <td>2.132488</td>\n",
       "      <td>1.912106</td>\n",
       "      <td>2.078043</td>\n",
       "      <td>0.956745</td>\n",
       "      <td>1.999754</td>\n",
       "      <td>1.973266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>2.098229</td>\n",
       "      <td>1.770655</td>\n",
       "      <td>1.744491</td>\n",
       "      <td>-0.109934</td>\n",
       "      <td>2.076408</td>\n",
       "      <td>2.060063</td>\n",
       "      <td>0.078244</td>\n",
       "      <td>2.036869</td>\n",
       "      <td>1.540867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>1.771801</td>\n",
       "      <td>0.072562</td>\n",
       "      <td>1.492155</td>\n",
       "      <td>2.102931</td>\n",
       "      <td>1.002374</td>\n",
       "      <td>1.950981</td>\n",
       "      <td>0.520335</td>\n",
       "      <td>1.808480</td>\n",
       "      <td>0.648365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>2.092629</td>\n",
       "      <td>0.323997</td>\n",
       "      <td>0.135243</td>\n",
       "      <td>2.050965</td>\n",
       "      <td>1.986246</td>\n",
       "      <td>-0.286967</td>\n",
       "      <td>-0.010091</td>\n",
       "      <td>2.026663</td>\n",
       "      <td>1.844483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.059886</td>\n",
       "      <td>-0.258876</td>\n",
       "      <td>0.674065</td>\n",
       "      <td>2.094693</td>\n",
       "      <td>2.017853</td>\n",
       "      <td>0.117987</td>\n",
       "      <td>-0.073230</td>\n",
       "      <td>1.985803</td>\n",
       "      <td>1.942983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>-0.185649</td>\n",
       "      <td>0.378951</td>\n",
       "      <td>-0.203405</td>\n",
       "      <td>-0.573491</td>\n",
       "      <td>1.823486</td>\n",
       "      <td>0.072490</td>\n",
       "      <td>-0.112487</td>\n",
       "      <td>1.653074</td>\n",
       "      <td>2.061750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>0.171357</td>\n",
       "      <td>-0.017120</td>\n",
       "      <td>0.029842</td>\n",
       "      <td>-0.076870</td>\n",
       "      <td>1.978596</td>\n",
       "      <td>-0.099020</td>\n",
       "      <td>0.041392</td>\n",
       "      <td>1.077575</td>\n",
       "      <td>1.975275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>2.072663</td>\n",
       "      <td>0.003387</td>\n",
       "      <td>-0.350893</td>\n",
       "      <td>-0.136325</td>\n",
       "      <td>1.982241</td>\n",
       "      <td>-0.137349</td>\n",
       "      <td>-0.263173</td>\n",
       "      <td>2.032306</td>\n",
       "      <td>2.006296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>1.960922</td>\n",
       "      <td>-0.065232</td>\n",
       "      <td>0.524271</td>\n",
       "      <td>0.569145</td>\n",
       "      <td>1.495835</td>\n",
       "      <td>-0.221810</td>\n",
       "      <td>-0.033855</td>\n",
       "      <td>2.053951</td>\n",
       "      <td>1.956531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.756300</td>\n",
       "      <td>0.991259</td>\n",
       "      <td>0.870346</td>\n",
       "      <td>0.537230</td>\n",
       "      <td>0.109297</td>\n",
       "      <td>-0.250781</td>\n",
       "      <td>1.240801</td>\n",
       "      <td>2.073048</td>\n",
       "      <td>2.037222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "aa_pos       0.0       1.0       2.0       3.0       4.0       5.0       6.0  \\\n",
       "alt_aa                                                                         \n",
       "*       0.230635  0.165205  0.109633  0.033522  0.681268 -0.119258 -0.243796   \n",
       "P       2.033359  0.105654  0.221247 -0.190345  2.097655 -0.016790  0.812163   \n",
       "G       2.164996  1.509557  0.610365       NaN  1.170877 -0.076009  2.071182   \n",
       "C            NaN  0.209295  0.061929 -0.142012  1.627046  0.070437  0.063626   \n",
       "Q      -0.732074  0.181034 -0.183346  0.011048  2.057751  2.062573  0.173719   \n",
       "N       1.341761 -0.001005  1.507173  0.089103  2.103087 -0.158037  0.686277   \n",
       "T       1.807587  0.979786 -0.045587 -0.167156  1.873882 -0.114306  0.315458   \n",
       "S       2.093610  1.714943  1.434822 -0.238664  0.004823 -0.145032  1.128074   \n",
       "E       0.266907  0.723382 -0.297118 -0.232651  2.062594  0.125017 -0.071361   \n",
       "D       0.047285       NaN  1.357288  0.077660  1.765331 -0.170386  0.291009   \n",
       "K       2.053510 -0.030765  1.776634  0.752623  2.018324  0.331239  0.018945   \n",
       "H       1.934950  0.450009  0.088811  2.132488  1.912106  2.078043  0.956745   \n",
       "R       2.098229  1.770655  1.744491 -0.109934  2.076408  2.060063  0.078244   \n",
       "W       1.771801  0.072562  1.492155  2.102931  1.002374  1.950981  0.520335   \n",
       "Y       2.092629  0.323997  0.135243  2.050965  1.986246 -0.286967 -0.010091   \n",
       "F       0.059886 -0.258876  0.674065  2.094693  2.017853  0.117987 -0.073230   \n",
       "M      -0.185649  0.378951 -0.203405 -0.573491  1.823486  0.072490 -0.112487   \n",
       "L       0.171357 -0.017120  0.029842 -0.076870  1.978596 -0.099020  0.041392   \n",
       "I       2.072663  0.003387 -0.350893 -0.136325  1.982241 -0.137349 -0.263173   \n",
       "V       1.960922 -0.065232  0.524271  0.569145  1.495835 -0.221810 -0.033855   \n",
       "A       1.756300  0.991259  0.870346  0.537230  0.109297 -0.250781  1.240801   \n",
       "\n",
       "aa_pos       7.0       8.0  \n",
       "alt_aa                      \n",
       "*       0.463103  0.104349  \n",
       "P       2.019377  0.059886  \n",
       "G       1.829797  1.978833  \n",
       "C       2.059917  1.434822  \n",
       "Q       1.424850       NaN  \n",
       "N       1.585690       NaN  \n",
       "T       2.058348  2.045670  \n",
       "S       2.082255  1.955437  \n",
       "E       1.685392  2.042599  \n",
       "D       0.059886  2.056910  \n",
       "K       1.174687  1.889744  \n",
       "H       1.999754  1.973266  \n",
       "R       2.036869  1.540867  \n",
       "W       1.808480  0.648365  \n",
       "Y       2.026663  1.844483  \n",
       "F       1.985803  1.942983  \n",
       "M       1.653074  2.061750  \n",
       "L       1.077575  1.975275  \n",
       "I       2.032306  2.006296  \n",
       "V       2.053951  1.956531  \n",
       "A       2.073048  2.037222  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa_single = withWTdf_gby.groupby(['alt_aa','aa_pos'])[['median_s']].median().reset_index()\n",
    "wide_aa = aa_single.pivot(index='alt_aa', columns='aa_pos', values='median_s')\n",
    "wide_aa.sort_index(key=lambda x: x.map(aa_sort_dic), inplace=True)\n",
    "wide_aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "71c176e6-1a71-403a-8a00-2c5d09c62ba8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Purge graph space\n",
    "sns.set(rc = {'figure.figsize':(4,6),\n",
    "             })\n",
    "sns.set_theme(style='white')\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "# Custom color palette\n",
    "ccmap = sns.color_palette(\"blend:#009B9E,#42B7B9,#A7D3D4,#F1F1F1,#E4C1D9,#D691C1,#C75DAB\", # CARTOColors Tropic\n",
    "                          as_cmap=True)\n",
    "ccmap.set_bad('.5') # Color for missing values\n",
    "\n",
    "# Mask for missing values\n",
    "mask = pd.isnull(wide_aa)\n",
    "\n",
    "# Draw heatmap\n",
    "ax = sns.heatmap(wide_aa, mask=mask, \n",
    "                 cmap=ccmap, center = 0,\n",
    "                 vmin=-2,\n",
    "                 vmax=2\n",
    "                )\n",
    "ax.set_xlabel('Position in hotspot')\n",
    "ax.set_ylabel(None)\n",
    "plt.yticks(rotation=0)\n",
    "ax.set_title('Strain: '+strain+\n",
    "             '\\n'+locus[:4]+' - Hotspot '+locus[-1]+\n",
    "             '\\nSelection coefficients'+\n",
    "             '\\nCondition: '+compound)\n",
    "\n",
    "# Coordinates of WT protein sequence on the heatmap are extracted from the dataframe\n",
    "# Coordinates should be OK even if the dataframe contains missing values\n",
    "wtcoord_aa = [(i+0.5, list(aa_sort_order).index(v)+0.5) for i,v in enumerate(wtaa)]\n",
    "\n",
    "for o in wtcoord_aa:\n",
    "    ax.plot(o[0],o[1],'or') # displays WT codons by marking them with red (r) circles (o)\n",
    "    \n",
    "# Graphs are output in the specified folder\n",
    "plt.savefig(graph_outpath+cset_name+'/aa_heatmap_median_s.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5d928f3-6859-4397-864a-26d440954248",
   "metadata": {},
   "source": [
    "### Compare with DiMSum-corrected fitness values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e800bfa2-ab33-4e14-b8f3-4c644e57c7ec",
   "metadata": {},
   "source": [
    "For some samples, there was no fitness values calculated by DiMSum. There's no easy way to solve this until [this PR](https://github.com/nteract/papermill/pull/738) moves forward. Which means I have to put an if upstream of every following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a5c5e1d4-93e7-4433-baa4-f13d2cd75f2c",
   "metadata": {
    "papermill": {
     "duration": 0.24915,
     "end_time": "2024-01-12T17:06:35.977738",
     "exception": true,
     "start_time": "2024-01-12T17:06:35.728588",
     "status": "failed"
    },
    "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>Pos</th>\n",
       "      <th>WT_AA</th>\n",
       "      <th>Mut</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>mean_count</th>\n",
       "      <th>fitness</th>\n",
       "      <th>sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>363.0</td>\n",
       "      <td>2.048347</td>\n",
       "      <td>0.018700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.753144</td>\n",
       "      <td>0.059661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>658.5</td>\n",
       "      <td>2.023226</td>\n",
       "      <td>0.014309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>627.5</td>\n",
       "      <td>2.017544</td>\n",
       "      <td>0.014660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>594.5</td>\n",
       "      <td>-0.193175</td>\n",
       "      <td>0.077258</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>3</td>\n",
       "      <td>V</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLYLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>860.0</td>\n",
       "      <td>0.179259</td>\n",
       "      <td>0.042135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>3</td>\n",
       "      <td>V</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1240.5</td>\n",
       "      <td>1.466987</td>\n",
       "      <td>0.012451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>3</td>\n",
       "      <td>V</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLCLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>692.0</td>\n",
       "      <td>0.182517</td>\n",
       "      <td>0.046746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>3</td>\n",
       "      <td>V</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLFLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1415.5</td>\n",
       "      <td>0.612577</td>\n",
       "      <td>0.021106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2</td>\n",
       "      <td>L</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FFVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2087.5</td>\n",
       "      <td>-0.187872</td>\n",
       "      <td>0.041267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pos WT_AA Mut  nt_seq     aa_seq  Nham_nt  Nham_aa  Nmut_codons   STOP  \\\n",
       "0      1     F   K     NaN  KLVLSLRDP      NaN        1          NaN  False   \n",
       "1      1     F   N     NaN  NLVLSLRDP      NaN        1          NaN  False   \n",
       "2      1     F   T     NaN  TLVLSLRDP      NaN        1          NaN  False   \n",
       "3      1     F   R     NaN  RLVLSLRDP      NaN        1          NaN  False   \n",
       "4      1     F   M     NaN  MLVLSLRDP      NaN        1          NaN  False   \n",
       "..   ...   ...  ..     ...        ...      ...      ...          ...    ...   \n",
       "166    3     V   Y     NaN  FLYLSLRDP      NaN        1          NaN  False   \n",
       "167    3     V   W     NaN  FLWLSLRDP      NaN        1          NaN  False   \n",
       "168    3     V   C     NaN  FLCLSLRDP      NaN        1          NaN  False   \n",
       "169    3     V   F     NaN  FLFLSLRDP      NaN        1          NaN  False   \n",
       "170    2     L   F     NaN  FFVLSLRDP      NaN        1          NaN  False   \n",
       "\n",
       "     STOP_readthrough  mean_count   fitness     sigma  \n",
       "0               False       363.0  2.048347  0.018700  \n",
       "1               False        38.0  1.753144  0.059661  \n",
       "2               False       658.5  2.023226  0.014309  \n",
       "3               False       627.5  2.017544  0.014660  \n",
       "4               False       594.5 -0.193175  0.077258  \n",
       "..                ...         ...       ...       ...  \n",
       "166             False       860.0  0.179259  0.042135  \n",
       "167             False      1240.5  1.466987  0.012451  \n",
       "168             False       692.0  0.182517  0.046746  \n",
       "169             False      1415.5  0.612577  0.021106  \n",
       "170             False      2087.5 -0.187872  0.041267  \n",
       "\n",
       "[171 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dimsum_fpath = f'{dimsumfitness_path}/fitness_{pool_type}s_{cset_name}.txt'\n",
    "if os.path.exists(dimsum_fpath):\n",
    "    dimsum = pd.read_csv(dimsum_fpath, sep=' ')\n",
    "    display(dimsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "44e645d6-1126-4d19-a958-ae24f9d45685",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pos</th>\n",
       "      <th>WT_AA</th>\n",
       "      <th>Mut</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>mean_count</th>\n",
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       "      <th>sigma</th>\n",
       "      <th>mutated_codon</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>658.5</td>\n",
       "      <td>2.023226</td>\n",
       "      <td>0.014309</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>F</td>\n",
       "      <td>R</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <td>MLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>594.5</td>\n",
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       "      <td>False</td>\n",
       "      <td>860.0</td>\n",
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       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>3</td>\n",
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       "      <td>FLWLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
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       "      <th>168</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1415.5</td>\n",
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       "      <td>2087.5</td>\n",
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       "      <td>0.041267</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pos WT_AA Mut  nt_seq     aa_seq  Nham_nt  Nham_aa  Nmut_codons   STOP  \\\n",
       "0      1     F   K     NaN  KLVLSLRDP      NaN        1          NaN  False   \n",
       "1      1     F   N     NaN  NLVLSLRDP      NaN        1          NaN  False   \n",
       "2      1     F   T     NaN  TLVLSLRDP      NaN        1          NaN  False   \n",
       "3      1     F   R     NaN  RLVLSLRDP      NaN        1          NaN  False   \n",
       "4      1     F   M     NaN  MLVLSLRDP      NaN        1          NaN  False   \n",
       "..   ...   ...  ..     ...        ...      ...      ...          ...    ...   \n",
       "166    3     V   Y     NaN  FLYLSLRDP      NaN        1          NaN  False   \n",
       "167    3     V   W     NaN  FLWLSLRDP      NaN        1          NaN  False   \n",
       "168    3     V   C     NaN  FLCLSLRDP      NaN        1          NaN  False   \n",
       "169    3     V   F     NaN  FLFLSLRDP      NaN        1          NaN  False   \n",
       "170    2     L   F     NaN  FFVLSLRDP      NaN        1          NaN  False   \n",
       "\n",
       "     STOP_readthrough  mean_count   fitness     sigma  mutated_codon  \n",
       "0               False       363.0  2.048347  0.018700              1  \n",
       "1               False        38.0  1.753144  0.059661              1  \n",
       "2               False       658.5  2.023226  0.014309              1  \n",
       "3               False       627.5  2.017544  0.014660              1  \n",
       "4               False       594.5 -0.193175  0.077258              1  \n",
       "..                ...         ...       ...       ...            ...  \n",
       "166             False       860.0  0.179259  0.042135              1  \n",
       "167             False      1240.5  1.466987  0.012451              1  \n",
       "168             False       692.0  0.182517  0.046746              1  \n",
       "169             False      1415.5  0.612577  0.021106              1  \n",
       "170             False      2087.5 -0.187872  0.041267              1  \n",
       "\n",
       "[171 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.exists(dimsum_fpath):\n",
    "    if pool_type == 'single':\n",
    "        dimsum_long = dimsum\n",
    "        dimsum_long['mutated_codon'] = 1\n",
    "    else:\n",
    "        dimsum_long = pd.wide_to_long(dimsum,\n",
    "                                      stubnames = ['Pos', 'WT_AA', 'Mut', 'fitness', 'sigma'],\n",
    "                                      i = ['aa_seq','Nham_aa','mean_count','fitness_uncorr','sigma_uncorr'],\n",
    "                                      j = 'mutated_codon').reset_index()\n",
    "    display(dimsum_long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "295afe63-2e8f-41c1-ae8a-02d1e3aad56d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>WT_AA</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>mean_count</th>\n",
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       "      <th>sigma</th>\n",
       "      <th>mutated_codon</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>TLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>658.5</td>\n",
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       "      <td>0.014309</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>627.5</td>\n",
       "      <td>2.017544</td>\n",
       "      <td>0.014660</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MLVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>594.5</td>\n",
       "      <td>-0.193175</td>\n",
       "      <td>0.077258</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLYLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>860.0</td>\n",
       "      <td>0.179259</td>\n",
       "      <td>0.042135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLWLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1240.5</td>\n",
       "      <td>1.466987</td>\n",
       "      <td>0.012451</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLCLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>692.0</td>\n",
       "      <td>0.182517</td>\n",
       "      <td>0.046746</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>2</td>\n",
       "      <td>V</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLFLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1415.5</td>\n",
       "      <td>0.612577</td>\n",
       "      <td>0.021106</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1</td>\n",
       "      <td>L</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FFVLSLRDP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2087.5</td>\n",
       "      <td>-0.187872</td>\n",
       "      <td>0.041267</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     aa_pos WT_AA alt_aa  nt_seq     aa_seq  Nham_nt  Nham_aa  Nmut_codons  \\\n",
       "0         0     F      K     NaN  KLVLSLRDP      NaN        1          NaN   \n",
       "1         0     F      N     NaN  NLVLSLRDP      NaN        1          NaN   \n",
       "2         0     F      T     NaN  TLVLSLRDP      NaN        1          NaN   \n",
       "3         0     F      R     NaN  RLVLSLRDP      NaN        1          NaN   \n",
       "4         0     F      M     NaN  MLVLSLRDP      NaN        1          NaN   \n",
       "..      ...   ...    ...     ...        ...      ...      ...          ...   \n",
       "166       2     V      Y     NaN  FLYLSLRDP      NaN        1          NaN   \n",
       "167       2     V      W     NaN  FLWLSLRDP      NaN        1          NaN   \n",
       "168       2     V      C     NaN  FLCLSLRDP      NaN        1          NaN   \n",
       "169       2     V      F     NaN  FLFLSLRDP      NaN        1          NaN   \n",
       "170       1     L      F     NaN  FFVLSLRDP      NaN        1          NaN   \n",
       "\n",
       "      STOP  STOP_readthrough  mean_count   fitness     sigma  mutated_codon  \n",
       "0    False             False       363.0  2.048347  0.018700              1  \n",
       "1    False             False        38.0  1.753144  0.059661              1  \n",
       "2    False             False       658.5  2.023226  0.014309              1  \n",
       "3    False             False       627.5  2.017544  0.014660              1  \n",
       "4    False             False       594.5 -0.193175  0.077258              1  \n",
       "..     ...               ...         ...       ...       ...            ...  \n",
       "166  False             False       860.0  0.179259  0.042135              1  \n",
       "167  False             False      1240.5  1.466987  0.012451              1  \n",
       "168  False             False       692.0  0.182517  0.046746              1  \n",
       "169  False             False      1415.5  0.612577  0.021106              1  \n",
       "170  False             False      2087.5 -0.187872  0.041267              1  \n",
       "\n",
       "[171 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.exists(dimsum_fpath):\n",
    "    dimsum_long.rename(columns={'Pos':'aa_pos', 'Mut':'alt_aa'}, inplace=True)\n",
    "    dimsum_long['aa_pos']-=1\n",
    "    display(dimsum_long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f854daaf-2574-4434-b70a-6968b9d93a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>aa_seq</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>median_s</th>\n",
       "      <th>cscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>*LVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>*</td>\n",
       "      <td>tagttagttttatctttgagagatcca</td>\n",
       "      <td>0.230635</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>gcgttagttttatctttgagagatcca</td>\n",
       "      <td>2.027668</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>gctttagttttatctttgagagatcca</td>\n",
       "      <td>1.484932</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>gatttagttttatctttgagagatcca</td>\n",
       "      <td>0.047285</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ELVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>gagttagttttatctttgagagatcca</td>\n",
       "      <td>0.266907</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>TLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>acgttagttttatctttgagagatcca</td>\n",
       "      <td>1.548584</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>TLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>actttagttttatctttgagagatcca</td>\n",
       "      <td>2.066590</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>VLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>V</td>\n",
       "      <td>gtgttagttttatctttgagagatcca</td>\n",
       "      <td>1.960922</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>WLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W</td>\n",
       "      <td>tggttagttttatctttgagagatcca</td>\n",
       "      <td>1.771801</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>YLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>tatttagttttatctttgagagatcca</td>\n",
       "      <td>2.092629</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>258 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        aa_seq  mutated_codon  Nham_aa  aa_pos alt_aa  \\\n",
       "0    *LVLSLRDP            1.0      1.0     0.0      *   \n",
       "1    ALVLSLRDP            1.0      1.0     0.0      A   \n",
       "2    ALVLSLRDP            1.0      1.0     0.0      A   \n",
       "3    DLVLSLRDP            1.0      1.0     0.0      D   \n",
       "4    ELVLSLRDP            1.0      1.0     0.0      E   \n",
       "..         ...            ...      ...     ...    ...   \n",
       "253  TLVLSLRDP            1.0      1.0     0.0      T   \n",
       "254  TLVLSLRDP            1.0      1.0     0.0      T   \n",
       "255  VLVLSLRDP            1.0      1.0     0.0      V   \n",
       "256  WLVLSLRDP            1.0      1.0     0.0      W   \n",
       "257  YLVLSLRDP            1.0      1.0     0.0      Y   \n",
       "\n",
       "                          nt_seq  median_s  cscore  \n",
       "0    tagttagttttatctttgagagatcca  0.230635     1.0  \n",
       "1    gcgttagttttatctttgagagatcca  2.027668     1.0  \n",
       "2    gctttagttttatctttgagagatcca  1.484932     1.0  \n",
       "3    gatttagttttatctttgagagatcca  0.047285     1.0  \n",
       "4    gagttagttttatctttgagagatcca  0.266907     1.0  \n",
       "..                           ...       ...     ...  \n",
       "253  acgttagttttatctttgagagatcca  1.548584     1.0  \n",
       "254  actttagttttatctttgagagatcca  2.066590     1.0  \n",
       "255  gtgttagttttatctttgagagatcca  1.960922     1.0  \n",
       "256  tggttagttttatctttgagagatcca  1.771801     1.0  \n",
       "257  tatttagttttatctttgagagatcca  2.092629     1.0  \n",
       "\n",
       "[258 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.exists(dimsum_fpath):\n",
    "    ntdf = withSyndf[withSyndf.Nham_aa == nhamaa \n",
    "    ].groupby(['aa_seq','mutated_codon','Nham_aa','aa_pos','alt_aa','nt_seq'],\n",
    "             ).agg(median_s = ('median_s', 'first'),\n",
    "                   cscore = ('confidence_score', 'first')\n",
    "                  ).reset_index()\n",
    "    display(ntdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "50914c3f-a8da-483e-b2ee-1bc0cb6fe3eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>median_s</th>\n",
       "      <th>cscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>*LVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>*</td>\n",
       "      <td>0.230635</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1.756300</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>0.047285</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ELVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>0.266907</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F*VLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>*</td>\n",
       "      <td>0.165205</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>SLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "      <td>2.093610</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>TLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1.807587</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>VLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.960922</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>WLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W</td>\n",
       "      <td>1.771801</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>YLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.092629</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        aa_seq  mutated_codon  Nham_aa  aa_pos alt_aa  median_s  cscore\n",
       "0    *LVLSLRDP            1.0      1.0     0.0      *  0.230635     1.0\n",
       "1    ALVLSLRDP            1.0      1.0     0.0      A  1.756300     1.0\n",
       "2    DLVLSLRDP            1.0      1.0     0.0      D  0.047285     1.0\n",
       "3    ELVLSLRDP            1.0      1.0     0.0      E  0.266907     1.0\n",
       "4    F*VLSLRDP            1.0      1.0     1.0      *  0.165205     1.0\n",
       "..         ...            ...      ...     ...    ...       ...     ...\n",
       "176  SLVLSLRDP            1.0      1.0     0.0      S  2.093610     1.0\n",
       "177  TLVLSLRDP            1.0      1.0     0.0      T  1.807587     1.0\n",
       "178  VLVLSLRDP            1.0      1.0     0.0      V  1.960922     1.0\n",
       "179  WLVLSLRDP            1.0      1.0     0.0      W  1.771801     1.0\n",
       "180  YLVLSLRDP            1.0      1.0     0.0      Y  2.092629     1.0\n",
       "\n",
       "[181 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.exists(dimsum_fpath):\n",
    "    aadf = ntdf.groupby(['aa_seq','mutated_codon','Nham_aa','aa_pos','alt_aa']\n",
    "                       ).agg(median_s = ('median_s', 'median'),\n",
    "                             cscore = ('cscore', 'min')\n",
    "                            ).reset_index()\n",
    "    display(aadf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8ee40361-3071-4920-9d8a-97d891d87148",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>aa_seq</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>median_s</th>\n",
       "      <th>cscore</th>\n",
       "      <th>WT_AA</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>mean_count</th>\n",
       "      <th>fitness</th>\n",
       "      <th>sigma</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>*LVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>*</td>\n",
       "      <td>0.230635</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>209.5</td>\n",
       "      <td>0.211434</td>\n",
       "      <td>0.081977</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>1.756300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>545.5</td>\n",
       "      <td>1.930781</td>\n",
       "      <td>0.015784</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>0.047285</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>336.5</td>\n",
       "      <td>-0.124982</td>\n",
       "      <td>0.094822</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ELVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>0.266907</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.615280</td>\n",
       "      <td>0.062596</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F*VLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>*</td>\n",
       "      <td>0.165205</td>\n",
       "      <td>1.0</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>401.5</td>\n",
       "      <td>0.147548</td>\n",
       "      <td>0.063666</td>\n",
       "      <td>both</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>SLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "      <td>2.093610</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1377.5</td>\n",
       "      <td>2.083655</td>\n",
       "      <td>0.010242</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>TLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1.807587</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>658.5</td>\n",
       "      <td>2.023226</td>\n",
       "      <td>0.014309</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>VLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>V</td>\n",
       "      <td>1.960922</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.960587</td>\n",
       "      <td>0.023208</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>WLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W</td>\n",
       "      <td>1.771801</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1482.5</td>\n",
       "      <td>1.756002</td>\n",
       "      <td>0.010519</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>YLVLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.092629</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>283.0</td>\n",
       "      <td>2.042375</td>\n",
       "      <td>0.021282</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        aa_seq  mutated_codon  Nham_aa  aa_pos alt_aa  median_s  cscore WT_AA  \\\n",
       "0    *LVLSLRDP            1.0      1.0     0.0      *  0.230635     1.0     F   \n",
       "1    ALVLSLRDP            1.0      1.0     0.0      A  1.756300     1.0     F   \n",
       "2    DLVLSLRDP            1.0      1.0     0.0      D  0.047285     1.0     F   \n",
       "3    ELVLSLRDP            1.0      1.0     0.0      E  0.266907     1.0     F   \n",
       "4    F*VLSLRDP            1.0      1.0     1.0      *  0.165205     1.0     L   \n",
       "..         ...            ...      ...     ...    ...       ...     ...   ...   \n",
       "176  SLVLSLRDP            1.0      1.0     0.0      S  2.093610     1.0     F   \n",
       "177  TLVLSLRDP            1.0      1.0     0.0      T  1.807587     1.0     F   \n",
       "178  VLVLSLRDP            1.0      1.0     0.0      V  1.960922     1.0     F   \n",
       "179  WLVLSLRDP            1.0      1.0     0.0      W  1.771801     1.0     F   \n",
       "180  YLVLSLRDP            1.0      1.0     0.0      Y  2.092629     1.0     F   \n",
       "\n",
       "     nt_seq  Nham_nt  Nmut_codons   STOP STOP_readthrough  mean_count  \\\n",
       "0       NaN      NaN          NaN   True            False       209.5   \n",
       "1       NaN      NaN          NaN  False            False       545.5   \n",
       "2       NaN      NaN          NaN  False            False       336.5   \n",
       "3       NaN      NaN          NaN  False            False       153.0   \n",
       "4       NaN      NaN          NaN   True            False       401.5   \n",
       "..      ...      ...          ...    ...              ...         ...   \n",
       "176     NaN      NaN          NaN  False            False      1377.5   \n",
       "177     NaN      NaN          NaN  False            False       658.5   \n",
       "178     NaN      NaN          NaN  False            False       240.0   \n",
       "179     NaN      NaN          NaN  False            False      1482.5   \n",
       "180     NaN      NaN          NaN  False            False       283.0   \n",
       "\n",
       "      fitness     sigma Location  \n",
       "0    0.211434  0.081977     both  \n",
       "1    1.930781  0.015784     both  \n",
       "2   -0.124982  0.094822     both  \n",
       "3    0.615280  0.062596     both  \n",
       "4    0.147548  0.063666     both  \n",
       "..        ...       ...      ...  \n",
       "176  2.083655  0.010242     both  \n",
       "177  2.023226  0.014309     both  \n",
       "178  1.960587  0.023208     both  \n",
       "179  1.756002  0.010519     both  \n",
       "180  2.042375  0.021282     both  \n",
       "\n",
       "[181 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.exists(dimsum_fpath):\n",
    "    dimsum_compare = pd.merge(left=aadf, right=dimsum_long, how='outer', on=['aa_seq','mutated_codon','Nham_aa','aa_pos','alt_aa'], indicator='Location')\n",
    "    display(dimsum_compare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0114cbb8-ca49-4de9-935f-a6585dd17a39",
   "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>aa_seq</th>\n",
       "      <th>mutated_codon</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>aa_pos</th>\n",
       "      <th>alt_aa</th>\n",
       "      <th>median_s</th>\n",
       "      <th>cscore</th>\n",
       "      <th>WT_AA</th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>mean_count</th>\n",
       "      <th>fitness</th>\n",
       "      <th>sigma</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FLDLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>D</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FLILSLRDP</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FLILSLRDP</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>I</td>\n",
       "      <td>-0.223979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>FLILSLRDP</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>L</td>\n",
       "      <td>-0.223979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>FLNLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>N</td>\n",
       "      <td>1.507173</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>FLTLSLRDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0.127139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>FLTLSLRDP</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>T</td>\n",
       "      <td>0.127139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>FLTLSLRDP</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0.127139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>FLVLSLHDP</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>H</td>\n",
       "      <td>0.956745</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>FLVLSLRDC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1.434822</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       aa_seq  mutated_codon  Nham_aa  aa_pos alt_aa  median_s  cscore WT_AA  \\\n",
       "16  FLDLSLRDP            1.0      1.0     2.0      D  1.357288     2.0   NaN   \n",
       "21  FLILSLRDP            1.0      1.0     1.0      L -0.223979     1.0   NaN   \n",
       "23  FLILSLRDP            2.0      1.0     2.0      I -0.223979     1.0   NaN   \n",
       "24  FLILSLRDP            3.0      1.0     3.0      L -0.223979     1.0   NaN   \n",
       "28  FLNLSLRDP            1.0      1.0     2.0      N  1.507173     2.0   NaN   \n",
       "33  FLTLSLRDP            1.0      1.0     1.0      L  0.127139     1.0   NaN   \n",
       "35  FLTLSLRDP            2.0      1.0     2.0      T  0.127139     1.0   NaN   \n",
       "36  FLTLSLRDP            3.0      1.0     3.0      L  0.127139     1.0   NaN   \n",
       "79  FLVLSLHDP            1.0      1.0     6.0      H  0.956745     2.0   NaN   \n",
       "92  FLVLSLRDC            1.0      1.0     8.0      C  1.434822     3.0   NaN   \n",
       "\n",
       "    nt_seq  Nham_nt  Nmut_codons STOP STOP_readthrough  mean_count  fitness  \\\n",
       "16     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "21     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "23     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "24     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "28     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "33     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "35     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "36     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "79     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "92     NaN      NaN          NaN  NaN              NaN         NaN      NaN   \n",
       "\n",
       "    sigma   Location  \n",
       "16    NaN  left_only  \n",
       "21    NaN  left_only  \n",
       "23    NaN  left_only  \n",
       "24    NaN  left_only  \n",
       "28    NaN  left_only  \n",
       "33    NaN  left_only  \n",
       "35    NaN  left_only  \n",
       "36    NaN  left_only  \n",
       "79    NaN  left_only  \n",
       "92    NaN  left_only  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check variants that did not merge\n",
    "if os.path.exists(dimsum_fpath):\n",
    "    display(dimsum_compare[dimsum_compare.Location != 'both'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba97c5de-804a-4716-a83f-3b9e676b77cd",
   "metadata": {},
   "source": [
    "The following plot shows the correlation between the median selection coefficient (as calculated in this notebook) and the fitness value calculated by DiMSum.\n",
    "\n",
    "The color corresponds to a homemade confidence score based on the input read count in ALL replicates:\n",
    "- 1 = highest confidence = at worst 10 reads in all replicates\n",
    "- 2 = good confidence = at worst 4 reads in any replicate\n",
    "- 3 = lowest confidence = at most 3 reads in any replicate\n",
    "\n",
    "The size of the dots corresponds to the error on the fitness value calculated by DiMSum:\n",
    "- small = little error (std), therefore higher confidence\n",
    "- big = larger error, therefore lower confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dff4c465-add7-490b-8b7d-c393ecc11f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>h</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>*LVLSLRDP</td>\n",
       "      <td>0.230635</td>\n",
       "      <td>0.211434</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.081977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALVLSLRDP</td>\n",
       "      <td>1.756300</td>\n",
       "      <td>1.930781</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DLVLSLRDP</td>\n",
       "      <td>0.047285</td>\n",
       "      <td>-0.124982</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.094822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ELVLSLRDP</td>\n",
       "      <td>0.266907</td>\n",
       "      <td>0.615280</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.062596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F*VLSLRDP</td>\n",
       "      <td>0.165205</td>\n",
       "      <td>0.147548</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.063666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>SLVLSLRDP</td>\n",
       "      <td>2.093610</td>\n",
       "      <td>2.083655</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>TLVLSLRDP</td>\n",
       "      <td>1.807587</td>\n",
       "      <td>2.023226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>VLVLSLRDP</td>\n",
       "      <td>1.960922</td>\n",
       "      <td>1.960587</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>WLVLSLRDP</td>\n",
       "      <td>1.771801</td>\n",
       "      <td>1.756002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>YLVLSLRDP</td>\n",
       "      <td>2.092629</td>\n",
       "      <td>2.042375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.021282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        aa_seq         x         y    h         s\n",
       "0    *LVLSLRDP  0.230635  0.211434  1.0  0.081977\n",
       "1    ALVLSLRDP  1.756300  1.930781  1.0  0.015784\n",
       "2    DLVLSLRDP  0.047285 -0.124982  1.0  0.094822\n",
       "3    ELVLSLRDP  0.266907  0.615280  1.0  0.062596\n",
       "4    F*VLSLRDP  0.165205  0.147548  1.0  0.063666\n",
       "..         ...       ...       ...  ...       ...\n",
       "166  SLVLSLRDP  2.093610  2.083655  1.0  0.010242\n",
       "167  TLVLSLRDP  1.807587  2.023226  1.0  0.014309\n",
       "168  VLVLSLRDP  1.960922  1.960587  1.0  0.023208\n",
       "169  WLVLSLRDP  1.771801  1.756002  1.0  0.010519\n",
       "170  YLVLSLRDP  2.092629  2.042375  1.0  0.021282\n",
       "\n",
       "[171 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.exists(dimsum_fpath):\n",
    "    if 'fitness_uncorr' in dimsum.columns:\n",
    "        fitcol = 'fitness_uncorr'\n",
    "        sigcol = 'sigma_uncorr'\n",
    "    else:\n",
    "        fitcol = 'fitness'\n",
    "        sigcol = 'sigma'\n",
    "\n",
    "    graphdf = dimsum_compare[dimsum_compare.Location == 'both'\n",
    "                            ].groupby('aa_seq')[['median_s', fitcol, sigcol, 'cscore']\n",
    "                                               ].agg(x = ('median_s', 'first'),\n",
    "                                                     y = (fitcol, 'first'),\n",
    "                                                     h = ('cscore', 'first'),\n",
    "                                                     s = (sigcol, 'first')\n",
    "                                                    ).reset_index()\n",
    "    display(graphdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "27ee1637-42b9-424e-8aa5-d7564cd5993b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 588.75x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Purge graph space\n",
    "sns.set(rc = {'figure.figsize':(4,4)})\n",
    "sns.set_theme(style='whitegrid')\n",
    "\n",
    "if os.path.exists(dimsum_fpath):\n",
    "    fig = sns.relplot(graphdf, x='x', y='y',\n",
    "                      hue='h', hue_order=[1,2,3], palette=['green','orange','red'],\n",
    "                      size='s', sizes=(10,100))\n",
    "    fig.set(xlabel='median selection coefficient', ylabel='DiMSum-corrected fitness',)\n",
    "\n",
    "    medianWToffset = withSyndf.median_s.iat[-1]\n",
    "    fig.axes[0][0].axline((1,1), slope=1, linestyle='--', c='.8', zorder=-10)\n",
    "    fig.axes[0][0].axline((1 - medianWToffset,1), slope=1, linestyle='--', c='purple', zorder=-20)\n",
    "    fig._legend.texts[0].set_text('Confidence score (input read count)')\n",
    "    fig._legend.texts[4].set_text('Error on DiMSum-corrected fitness')\n",
    "    sns.move_legend(fig, bbox_to_anchor=(.85, 0.85), loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749bd8a8-c800-4fc2-bea0-e74841d641d3",
   "metadata": {},
   "source": [
    "For NNK x NNK libraries, because there is more noise, the dots will almost surely be shifted from the (1,1) diagonal\n",
    "\n",
    "This is because DiMSum normalizes values with the WT, instead of taking the median of synonymous variants (excluding the WT nucleotide sequence)\n",
    "\n",
    "To confirm this, the diagonal in purple gives a rough estimate of where the values would fall if we had decided to normalize with the WT nucleotide sequence (median across replicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2786b50-5b4d-471a-8ad6-38094b0ed4c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dms-env",
   "language": "python",
   "name": "dms-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}