diagnostics_scripts/MiSeqRun_orthos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 = 'MiSeqRun2-FKS2-HS2_variant_data_merge.tsv' # All MiSeqRun2\n",
    "strain = 'BY4741'\n",
    "locus = 'FKS2-HS2'\n",
    "pool_type = 'ortho'\n",
    "\n",
    "min_input_readcount_high = 10\n",
    "min_input_readcount_mid = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92739bd9-9f71-4f4b-800c-cde644e33f07",
   "metadata": {},
   "source": [
    "# Notebook to assess diversity from initial libraries sequenced in pre-runs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa0cda4-2357-49a7-b5b3-3967fa25948c",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13b73f43-866d-40d9-8944-b03d4a4c6d53",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import re\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",
    "from matplotlib import cm\n",
    "%matplotlib inline\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "from upsetplot import from_indicators\n",
    "from upsetplot import UpSet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a60ef3e-3bfa-4b4e-99e7-392dd959b240",
   "metadata": {},
   "source": [
    "## Specify paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65cedf42-7b87-4b21-b755-85476c012c12",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "metadata_path = '../data/'\n",
    "variantdata_path = '../preruns/'\n",
    "DMSdata_path = '../processed_data/'\n",
    "orthoTAXpath = '../orthologs/fks1_dataframe.csv'\n",
    "codon_table_path = '../data/general_use/ScerevisiaeTAXID559292_Cocoputs_codon_table.csv'\n",
    "graph_outpath = '../diagnostics_graphs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32c96f1-3510-4ba3-8fc7-f92c9f0a5bad",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ba18aa1-258c-4543-b3b7-d99396d7674a",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = variantdata_path+fname"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f43f13-aef3-4b06-bcbd-3afeef14a391",
   "metadata": {},
   "source": [
    "### Read dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2b79ead-cf09-4e1d-bc70-f1f4ac8cc103",
   "metadata": {
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   },
   "outputs": [
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      "text/plain": [
       "                           nt_seq    aa_seq    WT   STOP  STOP_readthrough  \\\n",
       "0        aaaaaggtgagacgttatacactc  KKVRRYTL  <NA>  False             False   \n",
       "1       aaaatggattaaaagatgtattatt  KMD*KMYY  <NA>   True             False   \n",
       "2        aaacggattaaaagatgtattatt  KRIKRCII  <NA>  False             False   \n",
       "3          aaaggtgagacgtctgacactc   KGETSDT  <NA>  False             False   \n",
       "4          aaaggtgagacgttatgggctc   KGETLWA  <NA>  False             False   \n",
       "...                           ...       ...   ...    ...               ...   \n",
       "137668   ttttttgtgagacgttatactctc  FFVRRYTL  <NA>  False             False   \n",
       "137669  ttttttgtgagacgttatgtggaag  FFVRRYVE  <NA>  False             False   \n",
       "137670   ttttttgtgagacgttattatctc  FFVRRYYL  <NA>  False             False   \n",
       "137671  tttttttgtgagacgttatacactc  FFCETLYT  <NA>  False             False   \n",
       "137672   ttttttttgagacgttatacactc  FFLRRYTL  <NA>  False             False   \n",
       "\n",
       "        Nham_nt  Nham_aa  Nmut_codons  inputD1_e1_s0_bNA_count  \\\n",
       "0           4.0      2.0          2.0                        0   \n",
       "1           NaN      NaN          NaN                        0   \n",
       "2          13.0      7.0          8.0                        0   \n",
       "3           NaN      NaN          NaN                        0   \n",
       "4           NaN      NaN          NaN                        0   \n",
       "...         ...      ...          ...                      ...   \n",
       "137668      5.0      2.0          3.0                        0   \n",
       "137669      NaN      NaN          NaN                        0   \n",
       "137670      7.0      3.0          3.0                        1   \n",
       "137671      NaN      NaN          NaN                        1   \n",
       "137672      5.0      3.0          3.0                        0   \n",
       "\n",
       "        inputD2_e2_s0_bNA_count  ...  outputD6_e6_s1_b1_count  \\\n",
       "0                             1  ...                        0   \n",
       "1                             0  ...                        0   \n",
       "2                             0  ...                        0   \n",
       "3                             0  ...                        1   \n",
       "4                             1  ...                        0   \n",
       "...                         ...  ...                      ...   \n",
       "137668                        0  ...                        1   \n",
       "137669                        0  ...                        0   \n",
       "137670                        0  ...                        0   \n",
       "137671                        0  ...                        0   \n",
       "137672                        0  ...                        0   \n",
       "\n",
       "        outputD7_e7_s1_b1_count  outputD8_e8_s1_b1_count  \\\n",
       "0                             0                        0   \n",
       "1                             0                        0   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "...                         ...                      ...   \n",
       "137668                        0                        0   \n",
       "137669                        0                        0   \n",
       "137670                        0                        0   \n",
       "137671                        0                        0   \n",
       "137672                        0                        1   \n",
       "\n",
       "        outputO1_e9_s1_b1_count  barcode_valid  indel  constant_region  \\\n",
       "0                             0           True  False             True   \n",
       "1                             1           True   True             True   \n",
       "2                             1           True  False             True   \n",
       "3                             0           True   True             True   \n",
       "4                             0           True   True             True   \n",
       "...                         ...            ...    ...              ...   \n",
       "137668                        0           True  False             True   \n",
       "137669                        0           True   True             True   \n",
       "137670                        0           True  False             True   \n",
       "137671                        0           True   True             True   \n",
       "137672                        0           True  False             True   \n",
       "\n",
       "        permitted  too_many_substitutions  mixed_substitutions  \n",
       "0            True                   False                False  \n",
       "1             NaN                     NaN                  NaN  \n",
       "2            True                   False                False  \n",
       "3             NaN                     NaN                  NaN  \n",
       "4             NaN                     NaN                  NaN  \n",
       "...           ...                     ...                  ...  \n",
       "137668       True                   False                False  \n",
       "137669        NaN                     NaN                  NaN  \n",
       "137670       True                   False                False  \n",
       "137671        NaN                     NaN                  NaN  \n",
       "137672       True                   False                False  \n",
       "\n",
       "[137673 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "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"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "a6fd9d57-24c5-4869-8277-0562e95cb64c",
   "metadata": {},
   "outputs": [
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       "      <th>outputD7_e7_s1_b1_count</th>\n",
       "      <th>outputD8_e8_s1_b1_count</th>\n",
       "      <th>outputO1_e9_s1_b1_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>aaaaaggtgagacgttatacactc</td>\n",
       "      <td>KKVRRYTL</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     nt_seq    aa_seq    WT   STOP  STOP_readthrough  Nham_nt  \\\n",
       "0  aaaaaggtgagacgttatacactc  KKVRRYTL  <NA>  False             False      4.0   \n",
       "\n",
       "   Nham_aa  Nmut_codons  inputD1_e1_s0_bNA_count  inputD2_e2_s0_bNA_count  \\\n",
       "0      2.0          2.0                        0                        1   \n",
       "\n",
       "   ...  outputD6_e6_s1_b1_count  outputD7_e7_s1_b1_count  \\\n",
       "0  ...                        0                        0   \n",
       "\n",
       "   outputD8_e8_s1_b1_count  outputO1_e9_s1_b1_count  barcode_valid  indel  \\\n",
       "0                        0                        0           True  False   \n",
       "\n",
       "   constant_region  permitted  too_many_substitutions  mixed_substitutions  \n",
       "0             True       True                   False                False  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_cols = [x for x in df.columns if 'inputO' in x]\n",
    "conditions = [x.split('_')[0] for x in raw_cols]\n",
    "df.rename(columns=dict(zip(raw_cols, conditions)), inplace=True)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa750e51-2689-460b-a69d-e2b4192cb389",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT (nt): gattgggtgagacgttatacactc | (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": 8,
   "id": "14c72e56-bd5b-446c-899e-f22533703c9a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2714389b250>"
      ]
     },
     "execution_count": 8,
     "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": "code",
   "execution_count": 9,
   "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>inputO1</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>61587</th>\n",
       "      <td>2.67</td>\n",
       "      <td>gattggattagaagatgtgttttg</td>\n",
       "      <td>DWIRRCVL</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62331</th>\n",
       "      <td>2.41</td>\n",
       "      <td>gattggatttatagatgtgttttg</td>\n",
       "      <td>DWIYRCVL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62166</th>\n",
       "      <td>2.35</td>\n",
       "      <td>gattggattgatagatgtgttttg</td>\n",
       "      <td>DWIDRCVL</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97569</th>\n",
       "      <td>2.27</td>\n",
       "      <td>gattgggttgctagatctattgct</td>\n",
       "      <td>DWVARSIA</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60182</th>\n",
       "      <td>2.27</td>\n",
       "      <td>gattggatgagacaatctttgttg</td>\n",
       "      <td>DWMRQSLL</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       inputO1                    nt_seq    aa_seq  Nmut_codons\n",
       "61587     2.67  gattggattagaagatgtgttttg  DWIRRCVL          5.0\n",
       "62331     2.41  gattggatttatagatgtgttttg  DWIYRCVL          6.0\n",
       "62166     2.35  gattggattgatagatgtgttttg  DWIDRCVL          6.0\n",
       "97569     2.27  gattgggttgctagatctattgct  DWVARSIA          6.0\n",
       "60182     2.27  gattggatgagacaatctttgttg  DWMRQSLL          5.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overrep_index = df[conditions].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": 10,
   "id": "fff39ee2-144e-41c8-bb3c-d2c83e9e589b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>inputO1</th>\n",
       "      <td>486006</td>\n",
       "      <td>480267</td>\n",
       "      <td>98.8</td>\n",
       "      <td>477821</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Total_reads  Reads_no_indels  Reads_no_indels_%  \\\n",
       "inputO1       486006           480267               98.8   \n",
       "\n",
       "         No_indels_acceptable_RC  Acceptable_RC_over_no_indels_%  \n",
       "inputO1                   477821                            99.5  "
      ]
     },
     "execution_count": 10,
     "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 >= 3 in any sample\n",
    "rcNoIndel['No_indels_acceptable_RC'] = df[((df[conditions] > 2).any(axis=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",
    "rcNoIndel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e60bf3-92ce-470b-b3a9-84ede2e47eea",
   "metadata": {},
   "source": [
    "#### ⚠️ **Filtering step** ⚠️ - Discard variants with indels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e768508-89da-42ec-b3f4-24e1b01919b3",
   "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",
       "      <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>inputD1_e1_s0_bNA_count</th>\n",
       "      <th>inputD2_e2_s0_bNA_count</th>\n",
       "      <th>...</th>\n",
       "      <th>outputD6_e6_s1_b1_count</th>\n",
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       "      <th>outputD8_e8_s1_b1_count</th>\n",
       "      <th>outputO1_e9_s1_b1_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",
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       "      <th>0</th>\n",
       "      <td>aaaaaggtgagacgttatacactc</td>\n",
       "      <td>KKVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>aaacggattaaaagatgtattatt</td>\n",
       "      <td>KRIKRCII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>&lt;NA&gt;</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>3</th>\n",
       "      <td>aaatgaattaaaagatgtattatt</td>\n",
       "      <td>K*IKRCII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aaatgcattaaaagatgtattatt</td>\n",
       "      <td>KCIKRCII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>False</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>94361</th>\n",
       "      <td>ttttttgtgagacgttatacactc</td>\n",
       "      <td>FFVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>13</td>\n",
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       "      <td>18</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>94362</th>\n",
       "      <td>ttttttgtgagacgttatacatag</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>94363</th>\n",
       "      <td>ttttttgtgagacgttatactctc</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>94364</th>\n",
       "      <td>ttttttgtgagacgttattatctc</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>False</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>94365</th>\n",
       "      <td>ttttttttgagacgttatacactc</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94366 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         nt_seq    aa_seq    WT   STOP  STOP_readthrough  \\\n",
       "0      aaaaaggtgagacgttatacactc  KKVRRYTL  <NA>  False             False   \n",
       "1      aaacggattaaaagatgtattatt  KRIKRCII  <NA>  False             False   \n",
       "2      aaatagattaaaagatgtattatt  K*IKRCII  <NA>   True             False   \n",
       "3      aaatgaattaaaagatgtattatt  K*IKRCII  <NA>   True             False   \n",
       "4      aaatgcattaaaagatgtattatt  KCIKRCII  <NA>  False             False   \n",
       "...                         ...       ...   ...    ...               ...   \n",
       "94361  ttttttgtgagacgttatacactc  FFVRRYTL  <NA>  False             False   \n",
       "94362  ttttttgtgagacgttatacatag  FFVRRYT*  <NA>   True             False   \n",
       "94363  ttttttgtgagacgttatactctc  FFVRRYTL  <NA>  False             False   \n",
       "94364  ttttttgtgagacgttattatctc  FFVRRYYL  <NA>  False             False   \n",
       "94365  ttttttttgagacgttatacactc  FFLRRYTL  <NA>  False             False   \n",
       "\n",
       "       Nham_nt  Nham_aa  Nmut_codons  inputD1_e1_s0_bNA_count  \\\n",
       "0          4.0      2.0          2.0                        0   \n",
       "1         13.0      7.0          8.0                        0   \n",
       "2         13.0      7.0          8.0                        0   \n",
       "3         13.0      7.0          8.0                        0   \n",
       "4         13.0      7.0          8.0                        0   \n",
       "...        ...      ...          ...                      ...   \n",
       "94361      4.0      2.0          2.0                       25   \n",
       "94362      7.0      3.0          3.0                        0   \n",
       "94363      5.0      2.0          3.0                        0   \n",
       "94364      7.0      3.0          3.0                        1   \n",
       "94365      5.0      3.0          3.0                        0   \n",
       "\n",
       "       inputD2_e2_s0_bNA_count  ...  outputD6_e6_s1_b1_count  \\\n",
       "0                            1  ...                        0   \n",
       "1                            0  ...                        0   \n",
       "2                            0  ...                        0   \n",
       "3                            0  ...                        0   \n",
       "4                            0  ...                        0   \n",
       "...                        ...  ...                      ...   \n",
       "94361                       13  ...                       18   \n",
       "94362                        1  ...                        0   \n",
       "94363                        0  ...                        1   \n",
       "94364                        0  ...                        0   \n",
       "94365                        0  ...                        0   \n",
       "\n",
       "       outputD7_e7_s1_b1_count  outputD8_e8_s1_b1_count  \\\n",
       "0                            0                        0   \n",
       "1                            0                        0   \n",
       "2                            0                        0   \n",
       "3                            0                        0   \n",
       "4                            0                        0   \n",
       "...                        ...                      ...   \n",
       "94361                       21                       22   \n",
       "94362                        0                        0   \n",
       "94363                        0                        0   \n",
       "94364                        0                        0   \n",
       "94365                        0                        1   \n",
       "\n",
       "       outputO1_e9_s1_b1_count  barcode_valid  indel  constant_region  \\\n",
       "0                            0           True  False             True   \n",
       "1                            1           True  False             True   \n",
       "2                            3           True  False             True   \n",
       "3                            2           True  False             True   \n",
       "4                            1           True  False             True   \n",
       "...                        ...            ...    ...              ...   \n",
       "94361                        0           True  False             True   \n",
       "94362                        0           True  False             True   \n",
       "94363                        0           True  False             True   \n",
       "94364                        0           True  False             True   \n",
       "94365                        0           True  False             True   \n",
       "\n",
       "       permitted  too_many_substitutions  mixed_substitutions  \n",
       "0           True                   False                False  \n",
       "1           True                   False                False  \n",
       "2           True                   False                False  \n",
       "3           True                   False                False  \n",
       "4           True                   False                False  \n",
       "...          ...                     ...                  ...  \n",
       "94361       True                   False                False  \n",
       "94362       True                   False                False  \n",
       "94363       True                   False                False  \n",
       "94364       True                   False                False  \n",
       "94365       True                   False                False  \n",
       "\n",
       "[94366 rows x 32 columns]"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "id": "1cb73316-1b4b-4093-a37b-e85d7aacda6e",
   "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",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>Nham_nt</th>\n",
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       "      <th>Nmut_codons</th>\n",
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       "      <th>inputD2_e2_s0_bNA_count</th>\n",
       "      <th>...</th>\n",
       "      <th>outputD6_e6_s1_b1_count</th>\n",
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       "      <th>outputD8_e8_s1_b1_count</th>\n",
       "      <th>outputO1_e9_s1_b1_count</th>\n",
       "      <th>barcode_valid</th>\n",
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       "      <th>permitted</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aaatagattaaaagatgtattatt</td>\n",
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       "      <td>True</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>23</th>\n",
       "      <td>aaatggattaaaagatgtattatg</td>\n",
       "      <td>KWIKRCIM</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>aaatggattaaaagatgtattatt</td>\n",
       "      <td>KWIKRCII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "    <tr>\n",
       "      <th>91996</th>\n",
       "      <td>ttgtggattaaaagatctattatt</td>\n",
       "      <td>LWIKRSII</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>91999</th>\n",
       "      <td>ttgtggattaaaagatgtattatt</td>\n",
       "      <td>LWIKRCII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</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>92000</th>\n",
       "      <td>ttgtggattaaaagatgtattgtt</td>\n",
       "      <td>LWIKRCIV</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>92005</th>\n",
       "      <td>ttgtggattaaaataactattatt</td>\n",
       "      <td>LWIKITII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>93139</th>\n",
       "      <td>ttgttgattaaaagaactattatt</td>\n",
       "      <td>LLIKRTII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1402 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         nt_seq    aa_seq    WT   STOP  STOP_readthrough  \\\n",
       "2      aaatagattaaaagatgtattatt  K*IKRCII  <NA>   True             False   \n",
       "11     aaatggattaaaagaactattatt  KWIKRTII  <NA>  False             False   \n",
       "14     aaatggattaaaagatctattatt  KWIKRSII  <NA>  False             False   \n",
       "23     aaatggattaaaagatgtattatg  KWIKRCIM  <NA>  False             False   \n",
       "24     aaatggattaaaagatgtattatt  KWIKRCII  <NA>  False             False   \n",
       "...                         ...       ...   ...    ...               ...   \n",
       "91996  ttgtggattaaaagatctattatt  LWIKRSII  <NA>  False             False   \n",
       "91999  ttgtggattaaaagatgtattatt  LWIKRCII  <NA>  False             False   \n",
       "92000  ttgtggattaaaagatgtattgtt  LWIKRCIV  <NA>  False             False   \n",
       "92005  ttgtggattaaaataactattatt  LWIKITII  <NA>  False             False   \n",
       "93139  ttgttgattaaaagaactattatt  LLIKRTII  <NA>  False             False   \n",
       "\n",
       "       Nham_nt  Nham_aa  Nmut_codons  inputD1_e1_s0_bNA_count  \\\n",
       "2         13.0      7.0          8.0                        0   \n",
       "11        13.0      6.0          7.0                        0   \n",
       "14        12.0      6.0          7.0                        0   \n",
       "23        12.0      6.0          7.0                        0   \n",
       "24        12.0      6.0          7.0                        0   \n",
       "...        ...      ...          ...                      ...   \n",
       "91996     13.0      6.0          7.0                        0   \n",
       "91999     13.0      6.0          7.0                        0   \n",
       "92000     13.0      6.0          7.0                        0   \n",
       "92005     15.0      7.0          7.0                        0   \n",
       "93139     15.0      7.0          8.0                        0   \n",
       "\n",
       "       inputD2_e2_s0_bNA_count  ...  outputD6_e6_s1_b1_count  \\\n",
       "2                            0  ...                        0   \n",
       "11                           0  ...                        0   \n",
       "14                           0  ...                        0   \n",
       "23                           0  ...                        0   \n",
       "24                           0  ...                        0   \n",
       "...                        ...  ...                      ...   \n",
       "91996                        0  ...                        0   \n",
       "91999                        0  ...                        0   \n",
       "92000                        0  ...                        0   \n",
       "92005                        0  ...                        0   \n",
       "93139                        0  ...                        0   \n",
       "\n",
       "       outputD7_e7_s1_b1_count  outputD8_e8_s1_b1_count  \\\n",
       "2                            0                        0   \n",
       "11                           0                        0   \n",
       "14                           0                        0   \n",
       "23                           0                        0   \n",
       "24                           0                        0   \n",
       "...                        ...                      ...   \n",
       "91996                        0                        0   \n",
       "91999                        0                        0   \n",
       "92000                        0                        0   \n",
       "92005                        0                        0   \n",
       "93139                        0                        0   \n",
       "\n",
       "       outputO1_e9_s1_b1_count  barcode_valid  indel  constant_region  \\\n",
       "2                            3           True  False             True   \n",
       "11                           4           True  False             True   \n",
       "14                          10           True  False             True   \n",
       "23                           6           True  False             True   \n",
       "24                        5333           True  False             True   \n",
       "...                        ...            ...    ...              ...   \n",
       "91996                       11           True  False             True   \n",
       "91999                        9           True  False             True   \n",
       "92000                        4           True  False             True   \n",
       "92005                        4           True  False             True   \n",
       "93139                        3           True  False             True   \n",
       "\n",
       "       permitted  too_many_substitutions  mixed_substitutions  \n",
       "2           True                   False                False  \n",
       "11          True                   False                False  \n",
       "14          True                   False                False  \n",
       "23          True                   False                False  \n",
       "24          True                   False                False  \n",
       "...          ...                     ...                  ...  \n",
       "91996       True                   False                False  \n",
       "91999       True                   False                False  \n",
       "92000       True                   False                False  \n",
       "92005       True                   False                False  \n",
       "93139       True                   False                False  \n",
       "\n",
       "[1402 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputRCdf = df[(df[conditions] > 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": 13,
   "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": 14,
   "id": "f93ed480-b24c-4ee8-b213-2076adc31902",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>aaatggattaaaagaactattatt</td>\n",
       "      <td>KWIKRTII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>aaatggattaaaagatctattatt</td>\n",
       "      <td>KWIKRSII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>aaatggattaaaagatgtattatg</td>\n",
       "      <td>KWIKRCIM</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>aaatggattaaaagatgtattatt</td>\n",
       "      <td>KWIKRCII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5333</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</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>91996</th>\n",
       "      <td>ttgtggattaaaagatctattatt</td>\n",
       "      <td>LWIKRSII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91999</th>\n",
       "      <td>ttgtggattaaaagatgtattatt</td>\n",
       "      <td>LWIKRCII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92000</th>\n",
       "      <td>ttgtggattaaaagatgtattgtt</td>\n",
       "      <td>LWIKRCIV</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92005</th>\n",
       "      <td>ttgtggattaaaataactattatt</td>\n",
       "      <td>LWIKITII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93139</th>\n",
       "      <td>ttgttgattaaaagaactattatt</td>\n",
       "      <td>LLIKRTII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1402 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         nt_seq    aa_seq    WT   STOP  STOP_readthrough  \\\n",
       "2      aaatagattaaaagatgtattatt  K*IKRCII  <NA>   True             False   \n",
       "11     aaatggattaaaagaactattatt  KWIKRTII  <NA>  False             False   \n",
       "14     aaatggattaaaagatctattatt  KWIKRSII  <NA>  False             False   \n",
       "23     aaatggattaaaagatgtattatg  KWIKRCIM  <NA>  False             False   \n",
       "24     aaatggattaaaagatgtattatt  KWIKRCII  <NA>  False             False   \n",
       "...                         ...       ...   ...    ...               ...   \n",
       "91996  ttgtggattaaaagatctattatt  LWIKRSII  <NA>  False             False   \n",
       "91999  ttgtggattaaaagatgtattatt  LWIKRCII  <NA>  False             False   \n",
       "92000  ttgtggattaaaagatgtattgtt  LWIKRCIV  <NA>  False             False   \n",
       "92005  ttgtggattaaaataactattatt  LWIKITII  <NA>  False             False   \n",
       "93139  ttgttgattaaaagaactattatt  LLIKRTII  <NA>  False             False   \n",
       "\n",
       "       Nham_nt  Nham_aa  Nmut_codons  inputD1_e1_s0_bNA_count  \\\n",
       "2         13.0      7.0          8.0                        0   \n",
       "11        13.0      6.0          7.0                        0   \n",
       "14        12.0      6.0          7.0                        0   \n",
       "23        12.0      6.0          7.0                        0   \n",
       "24        12.0      6.0          7.0                        0   \n",
       "...        ...      ...          ...                      ...   \n",
       "91996     13.0      6.0          7.0                        0   \n",
       "91999     13.0      6.0          7.0                        0   \n",
       "92000     13.0      6.0          7.0                        0   \n",
       "92005     15.0      7.0          7.0                        0   \n",
       "93139     15.0      7.0          8.0                        0   \n",
       "\n",
       "       inputD2_e2_s0_bNA_count  ...  outputD7_e7_s1_b1_count  \\\n",
       "2                            0  ...                        0   \n",
       "11                           0  ...                        0   \n",
       "14                           0  ...                        0   \n",
       "23                           0  ...                        0   \n",
       "24                           0  ...                        0   \n",
       "...                        ...  ...                      ...   \n",
       "91996                        0  ...                        0   \n",
       "91999                        0  ...                        0   \n",
       "92000                        0  ...                        0   \n",
       "92005                        0  ...                        0   \n",
       "93139                        0  ...                        0   \n",
       "\n",
       "       outputD8_e8_s1_b1_count  outputO1_e9_s1_b1_count  barcode_valid  indel  \\\n",
       "2                            0                        3           True  False   \n",
       "11                           0                        4           True  False   \n",
       "14                           0                       10           True  False   \n",
       "23                           0                        6           True  False   \n",
       "24                           0                     5333           True  False   \n",
       "...                        ...                      ...            ...    ...   \n",
       "91996                        0                       11           True  False   \n",
       "91999                        0                        9           True  False   \n",
       "92000                        0                        4           True  False   \n",
       "92005                        0                        4           True  False   \n",
       "93139                        0                        3           True  False   \n",
       "\n",
       "       constant_region  permitted  too_many_substitutions  \\\n",
       "2                 True       True                   False   \n",
       "11                True       True                   False   \n",
       "14                True       True                   False   \n",
       "23                True       True                   False   \n",
       "24                True       True                   False   \n",
       "...                ...        ...                     ...   \n",
       "91996             True       True                   False   \n",
       "91999             True       True                   False   \n",
       "92000             True       True                   False   \n",
       "92005             True       True                   False   \n",
       "93139             True       True                   False   \n",
       "\n",
       "       mixed_substitutions  confidence_score  \n",
       "2                    False                 3  \n",
       "11                   False                 2  \n",
       "14                   False                 1  \n",
       "23                   False                 2  \n",
       "24                   False                 1  \n",
       "...                    ...               ...  \n",
       "91996                False                 1  \n",
       "91999                False                 2  \n",
       "92000                False                 2  \n",
       "92005                False                 2  \n",
       "93139                False                 3  \n",
       "\n",
       "[1402 rows x 33 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('mode.chained_assignment', None) # Disable settingWithCopy warning\n",
    "inputRCdf['confidence_score'] = inputRCdf[conditions].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": 15,
   "id": "a02389a6-9342-47ed-b63c-1bd5a52208b8",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacactc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>single</td>\n",
       "      <td>ttttgggtgagacgttatacactc</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>single</td>\n",
       "      <td>ttgtgggtgagacgttatacactc</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ttg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>ctttgggtgagacgttatacactc</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctt</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>single</td>\n",
       "      <td>ctgtgggtgagacgttatacactc</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ctg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</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>55760</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattgggttaatagatgtattatt</td>\n",
       "      <td>3.0</td>\n",
       "      <td>aat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55761</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattgggttaatagatgtattatt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>aga</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55762</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattgggttaatagatgtattatt</td>\n",
       "      <td>5.0</td>\n",
       "      <td>tgt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55763</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattgggttaatagatgtattatt</td>\n",
       "      <td>6.0</td>\n",
       "      <td>att</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55764</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattgggttaatagatgtattatt</td>\n",
       "      <td>7.0</td>\n",
       "      <td>att</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55765 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0           WT  gattgggtgagacgttatacactc     NaN        NaN            NaN   \n",
       "1       single  ttttgggtgagacgttatacactc     0.0        ttt            1.0   \n",
       "2       single  ttgtgggtgagacgttatacactc     0.0        ttg            1.0   \n",
       "3       single  ctttgggtgagacgttatacactc     0.0        ctt            1.0   \n",
       "4       single  ctgtgggtgagacgttatacactc     0.0        ctg            1.0   \n",
       "...        ...                       ...     ...        ...            ...   \n",
       "55760    ortho  gattgggttaatagatgtattatt     3.0        aat            2.0   \n",
       "55761    ortho  gattgggttaatagatgtattatt     4.0        aga            3.0   \n",
       "55762    ortho  gattgggttaatagatgtattatt     5.0        tgt            4.0   \n",
       "55763    ortho  gattgggttaatagatgtattatt     6.0        att            5.0   \n",
       "55764    ortho  gattgggttaatagatgtattatt     7.0        att            6.0   \n",
       "\n",
       "      combination  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "...           ...  \n",
       "55760         NaN  \n",
       "55761         NaN  \n",
       "55762         NaN  \n",
       "55763         NaN  \n",
       "55764         NaN  \n",
       "\n",
       "[55765 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "id": "2c75a014-5f0e-4537-ad6e-437bb061924e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "390"
      ]
     },
     "execution_count": 16,
     "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 = {'ortho':['WT','ortho']}\n",
    "var_filtered_df = vardf[vardf.seq_type.isin(keep_seq_type[pool_type])]\n",
    "len(var_filtered_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdc86a19-6984-4f63-86a1-ffb0b2ba3f99",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Here we need to translate expected variants to aa,.. which will also us to count the expected number of unique aa variants\n",
    "# I did not do this in the first place because I didn't need it to merge with the dataframe of variant counts\n",
    "codon_table = pd.read_csv(codon_table_path, header=0)\n",
    "codon_table['codon'] = codon_table['codon'].str.lower()\n",
    "codon_dic = dict(zip(codon_table['codon'], codon_table['aminoacid']))\n",
    "def get_aa_seq(seq, cdic):\n",
    "    clist = [seq[i:i+3] for i in range(0, len(seq), 3)] # Converting nucleotide sequence to list of codons\n",
    "    return ''.join([cdic[x] for x in clist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "032436f2-2e42-4529-b48d-373bd95fb92a",
   "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",
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       "      <td>NaN</td>\n",
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       "      <td>ortho</td>\n",
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       "      <td>ortho</td>\n",
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       "      <td>aga</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>55762</th>\n",
       "      <td>ortho</td>\n",
       "      <td>gattgggttaatagatgtattatt</td>\n",
       "      <td>5.0</td>\n",
       "      <td>tgt</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>ortho</td>\n",
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       "      <td>att</td>\n",
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      "text/plain": [
       "      seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0           WT  gattgggtgagacgttatacactc     NaN        NaN            NaN   \n",
       "55376    ortho  ggtgttattttgaatcaacaattt     0.0        ggt            1.0   \n",
       "55377    ortho  ggtgttattttgaatcaacaattt     1.0        gtt            2.0   \n",
       "55378    ortho  ggtgttattttgaatcaacaattt     2.0        att            3.0   \n",
       "55379    ortho  ggtgttattttgaatcaacaattt     3.0        ttg            4.0   \n",
       "...        ...                       ...     ...        ...            ...   \n",
       "55760    ortho  gattgggttaatagatgtattatt     3.0        aat            2.0   \n",
       "55761    ortho  gattgggttaatagatgtattatt     4.0        aga            3.0   \n",
       "55762    ortho  gattgggttaatagatgtattatt     5.0        tgt            4.0   \n",
       "55763    ortho  gattgggttaatagatgtattatt     6.0        att            5.0   \n",
       "55764    ortho  gattgggttaatagatgtattatt     7.0        att            6.0   \n",
       "\n",
       "      combination    aa_seq  \n",
       "0             NaN  DWVRRYTL  \n",
       "55376         NaN  GVILNQQF  \n",
       "55377         NaN  GVILNQQF  \n",
       "55378         NaN  GVILNQQF  \n",
       "55379         NaN  GVILNQQF  \n",
       "...           ...       ...  \n",
       "55760         NaN  DWVNRCII  \n",
       "55761         NaN  DWVNRCII  \n",
       "55762         NaN  DWVNRCII  \n",
       "55763         NaN  DWVNRCII  \n",
       "55764         NaN  DWVNRCII  \n",
       "\n",
       "[390 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_filtered_df['aa_seq'] = var_filtered_df.nt_seq.apply(lambda x: get_aa_seq(x, codon_dic))\n",
    "var_filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8cf5a749-4bdd-40ff-8654-515882e59d97",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(var_filtered_df.nt_seq.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9b73882-cca9-405d-b5aa-3eb99e082f2b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_aa = len(var_filtered_df.aa_seq.unique())\n",
    "unique_aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "257e4367-c8e9-4145-8667-9d2c085b34d1",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ttgttgattaaaagaactattatt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LLIKRTII</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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>3.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1729 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0          WT  gattgggtgagacgttatacactc     NaN        NaN            NaN   \n",
       "1       ortho  ggtgttattttgaatcaacaattt     0.0        ggt            1.0   \n",
       "2       ortho  ggtgttattttgaatcaacaattt     1.0        gtt            2.0   \n",
       "3       ortho  ggtgttattttgaatcaacaattt     2.0        att            3.0   \n",
       "4       ortho  ggtgttattttgaatcaacaattt     3.0        ttg            4.0   \n",
       "...       ...                       ...     ...        ...            ...   \n",
       "1724      NaN  ttgtggattaaaagatctattatt     NaN        NaN            NaN   \n",
       "1725      NaN  ttgtggattaaaagatgtattatt     NaN        NaN            NaN   \n",
       "1726      NaN  ttgtggattaaaagatgtattgtt     NaN        NaN            NaN   \n",
       "1727      NaN  ttgtggattaaaataactattatt     NaN        NaN            NaN   \n",
       "1728      NaN  ttgttgattaaaagaactattatt     NaN        NaN            NaN   \n",
       "\n",
       "     combination    aa_seq    WT   STOP STOP_readthrough  ...  \\\n",
       "0            NaN       NaN  <NA>    NaN              NaN  ...   \n",
       "1            NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "2            NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "3            NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "4            NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "...          ...       ...   ...    ...              ...  ...   \n",
       "1724         NaN  LWIKRSII  <NA>  False            False  ...   \n",
       "1725         NaN  LWIKRCII  <NA>  False            False  ...   \n",
       "1726         NaN  LWIKRCIV  <NA>  False            False  ...   \n",
       "1727         NaN  LWIKITII  <NA>  False            False  ...   \n",
       "1728         NaN  LLIKRTII  <NA>  False            False  ...   \n",
       "\n",
       "      outputD8_e8_s1_b1_count  outputO1_e9_s1_b1_count  barcode_valid  indel  \\\n",
       "0                         NaN                      NaN            NaN    NaN   \n",
       "1                         0.0                   6372.0           True  False   \n",
       "2                         0.0                   6372.0           True  False   \n",
       "3                         0.0                   6372.0           True  False   \n",
       "4                         0.0                   6372.0           True  False   \n",
       "...                       ...                      ...            ...    ...   \n",
       "1724                      0.0                     11.0           True  False   \n",
       "1725                      0.0                      9.0           True  False   \n",
       "1726                      0.0                      4.0           True  False   \n",
       "1727                      0.0                      4.0           True  False   \n",
       "1728                      0.0                      3.0           True  False   \n",
       "\n",
       "      constant_region  permitted  too_many_substitutions  mixed_substitutions  \\\n",
       "0                 NaN        NaN                     NaN                  NaN   \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",
       "1724             True       True                   False                False   \n",
       "1725             True       True                   False                False   \n",
       "1726             True       True                   False                False   \n",
       "1727             True       True                   False                False   \n",
       "1728             True       True                   False                False   \n",
       "\n",
       "      confidence_score    Location  \n",
       "0                  NaN   left_only  \n",
       "1                  1.0        both  \n",
       "2                  1.0        both  \n",
       "3                  1.0        both  \n",
       "4                  1.0        both  \n",
       "...                ...         ...  \n",
       "1724               1.0  right_only  \n",
       "1725               2.0  right_only  \n",
       "1726               2.0  right_only  \n",
       "1727               2.0  right_only  \n",
       "1728               3.0  right_only  \n",
       "\n",
       "[1729 rows x 39 columns]"
      ]
     },
     "execution_count": 21,
     "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.drop(columns='aa_seq'), right=inputRCdf, how='outer', on='nt_seq', indicator='Location')\n",
    "comparedf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dacdba6b-b384-4ff0-89d6-7e35f9f45aff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <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>AA</th>\n",
       "      <th>Covered_variants_%</th>\n",
       "      <th>AA_%</th>\n",
       "      <th>Usable_variants_rc</th>\n",
       "      <th>Usable_variants_%rc</th>\n",
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       "      <th>Unexpected_variants</th>\n",
       "      <th>Unexpected_variants_rc</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>inputO1</th>\n",
       "      <td>1402</td>\n",
       "      <td>477821</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>98.4</td>\n",
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      ],
      "text/plain": [
       "         Filtered_sequenced_variants  Filtered_sequenced_variants_rc  \\\n",
       "inputO1                         1402                          477821   \n",
       "\n",
       "         Usable_variants  AA  Covered_variants_%   AA_%  Usable_variants_rc  \\\n",
       "inputO1               63  63                98.4  100.0            463992.0   \n",
       "\n",
       "         Usable_variants_%rc  Unsequenced_variants  Unexpected_variants  \\\n",
       "inputO1                 97.1                     1                 1339   \n",
       "\n",
       "         Unexpected_variants_rc  \n",
       "inputO1                 13829.0  "
      ]
     },
     "execution_count": 22,
     "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(['aa_seq','nt_seq'])[conditions].first().astype(bool).sum(axis=0)\n",
    "seqdf['AA'] = comparedf[comparedf.Location.str.contains('both')].groupby(['aa_seq','nt_seq'])[conditions].first().groupby('aa_seq')[conditions].sum().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['AA_%'] = 100*round(seqdf['AA']/unique_aa,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",
    "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": 23,
   "id": "58f60ed3-5aeb-4da0-8204-13e6d1fdc54b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <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",
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       "      <th>Location</th>\n",
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       "      <th>0</th>\n",
       "      <td>ortho</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
       "      <td>False</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>ttg</td>\n",
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       "      <td>GVILNQQF</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>ortho</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>aat</td>\n",
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       "      <td>GVILNQQF</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>both</td>\n",
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       "<p>5 rows × 39 columns</p>\n",
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      ],
      "text/plain": [
       "  seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0    ortho  ggtgttattttgaatcaacaattt     0.0        ggt            1.0   \n",
       "1    ortho  ggtgttattttgaatcaacaattt     1.0        gtt            2.0   \n",
       "2    ortho  ggtgttattttgaatcaacaattt     2.0        att            3.0   \n",
       "3    ortho  ggtgttattttgaatcaacaattt     3.0        ttg            4.0   \n",
       "4    ortho  ggtgttattttgaatcaacaattt     4.0        aat            5.0   \n",
       "\n",
       "  combination    aa_seq    WT   STOP STOP_readthrough  ...  \\\n",
       "0         NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "1         NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "2         NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "3         NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "4         NaN  GVILNQQF  <NA>  False            False  ...   \n",
       "\n",
       "   outputD8_e8_s1_b1_count  outputO1_e9_s1_b1_count  barcode_valid  indel  \\\n",
       "0                      0.0                   6372.0           True  False   \n",
       "1                      0.0                   6372.0           True  False   \n",
       "2                      0.0                   6372.0           True  False   \n",
       "3                      0.0                   6372.0           True  False   \n",
       "4                      0.0                   6372.0           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",
       "   confidence_score  Location  \n",
       "0               1.0      both  \n",
       "1               1.0      both  \n",
       "2               1.0      both  \n",
       "3               1.0      both  \n",
       "4               1.0      both  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "id": "7e4b4ba4-8ec5-4c61-aa6b-7793dce306ea",
   "metadata": {},
   "outputs": [
    {
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       "      <td>14592.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>419847.0</td>\n",
       "      <td>90.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_readcount      WT  WT_%  NhamAA1_rc  NhamAA1_rc_%  NhamAA2_rc  \\\n",
       "inputO1         463992.0  6175.0   1.3     23378.0           5.0     14592.0   \n",
       "\n",
       "         NhamAA2_rc_%  NhamAA3+_rc  NhamAA3+_rc_%  \n",
       "inputO1           3.1     419847.0           90.5  "
      ]
     },
     "execution_count": 24,
     "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.Nham_aa == 0].groupby('nt_seq')[conditions].first().sum(axis=0)\n",
    "totrc['WT_%'] = 100*round(totrc['WT'] / totrc['total_readcount'], 3)\n",
    "totrc['NhamAA1_rc'] = covereddf[covereddf.Nham_aa == 1].groupby('nt_seq')[conditions].first().sum(axis=0)\n",
    "totrc['NhamAA1_rc_%'] = 100*round(totrc['NhamAA1_rc'] / totrc['total_readcount'], 3)\n",
    "totrc['NhamAA2_rc'] = covereddf[covereddf.Nham_aa == 2].groupby('nt_seq')[conditions].first().sum(axis=0)\n",
    "totrc['NhamAA2_rc_%'] = 100*round(totrc['NhamAA2_rc'] / totrc['total_readcount'], 3)\n",
    "totrc['NhamAA3+_rc'] = covereddf[covereddf.Nham_aa > 2].groupby('nt_seq')[conditions].first().sum(axis=0)\n",
    "totrc['NhamAA3+_rc_%'] = 100*round(totrc['NhamAA3+_rc'] / totrc['total_readcount'], 3)\n",
    "        \n",
    "totrc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a90f45-1dd5-4560-959b-7b416f6d6c8f",
   "metadata": {},
   "source": [
    "### Average read frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "53856cb7-c0fc-4a92-b55d-5c5fbc3516f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cov_upset = covereddf.groupby(['aa_seq','nt_seq']).agg(dict(zip(conditions+['Nham_aa', 'confidence_score'], ['first']*(len(conditions)+2))))\n",
    "aggdic = dict(zip(conditions, ['median']*len(conditions)))\n",
    "aggdic['Nham_aa'] = 'first'\n",
    "aggdic['confidence_score'] = 'min'\n",
    "aa_upset = cov_upset.groupby('aa_seq').agg(aggdic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "043273a6-dd0c-4d33-b7a4-0a46a35eb0d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>aa_seq</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>confidence_score</th>\n",
       "      <th>inputO1</th>\n",
       "      <th>average_freq_pool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AWVNRCIA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016938</td>\n",
       "      <td>0.016938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AWVNRCVV</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017339</td>\n",
       "      <td>0.017339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DWIDRCVL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024576</td>\n",
       "      <td>0.024576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DWIKRCIM</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010996</td>\n",
       "      <td>0.010996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DWIKRCIV</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014140</td>\n",
       "      <td>0.014140</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>58</th>\n",
       "      <td>NWVWRCVI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010739</td>\n",
       "      <td>0.010739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>NWVYRCII</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008487</td>\n",
       "      <td>0.008487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>QWIKRCII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012856</td>\n",
       "      <td>0.012856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>RWIKRCII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012388</td>\n",
       "      <td>0.012388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>SWLKRCVI</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>0.014233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      aa_seq  Nham_aa  confidence_score   inputO1  average_freq_pool\n",
       "0   AWVNRCIA      5.0               1.0  0.016938           0.016938\n",
       "1   AWVNRCVV      5.0               1.0  0.017339           0.017339\n",
       "2   DWIDRCVL      4.0               1.0  0.024576           0.024576\n",
       "3   DWIKRCIM      5.0               1.0  0.010996           0.010996\n",
       "4   DWIKRCIV      5.0               1.0  0.014140           0.014140\n",
       "..       ...      ...               ...       ...                ...\n",
       "58  NWVWRCVI      5.0               1.0  0.010739           0.010739\n",
       "59  NWVYRCII      5.0               1.0  0.008487           0.008487\n",
       "60  QWIKRCII      6.0               1.0  0.012856           0.012856\n",
       "61  RWIKRCII      6.0               1.0  0.012388           0.012388\n",
       "62  SWLKRCVI      6.0               1.0  0.014233           0.014233\n",
       "\n",
       "[63 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = conditions\n",
    "pool_df = aa_upset.reset_index()[['aa_seq','Nham_aa','confidence_score']+samples]\n",
    "pool_df[samples] = pool_df[samples].div(pool_df[samples].sum(axis=0), axis=1)\n",
    "pool_df['average_freq_pool'] = pool_df[samples].mean(axis=1)\n",
    "pool_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec3d3129-c5a6-44c9-814c-b0251766d65a",
   "metadata": {},
   "source": [
    "### Compare with input in DMS experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3f159786-08dd-44ad-9a58-14e7402d579f",
   "metadata": {},
   "outputs": [],
   "source": [
    "DMSf = f'{DMSdata_path}{strain}_{locus}_single_ortho_none/selcoeff_all_libraries.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "932638a3-4266-461a-b4f4-fe03e856c7ac",
   "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",
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       "      <th>Log2FC_2</th>\n",
       "      <th>Log2FC_3</th>\n",
       "      <th>selcoeff_1</th>\n",
       "      <th>selcoeff_2</th>\n",
       "      <th>selcoeff_3</th>\n",
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       "      <td>FWVRRYTL</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.008401</td>\n",
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       "      <td>-0.008363</td>\n",
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       "      <td>LWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <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>-2.700099</td>\n",
       "      <td>L</td>\n",
       "      <td>non-synonymous</td>\n",
       "      <td>0.086955</td>\n",
       "      <td>0.314590</td>\n",
       "      <td>0.030692</td>\n",
       "      <td>0.081048</td>\n",
       "      <td>0.020197</td>\n",
       "      <td>0.015821</td>\n",
       "      <td>0.018009</td>\n",
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       "      <td>ctg</td>\n",
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       "      <td>LWVRRYTL</td>\n",
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       "      <td>non-synonymous</td>\n",
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       "      <td>0.319952</td>\n",
       "      <td>-0.066403</td>\n",
       "      <td>0.014655</td>\n",
       "      <td>0.025560</td>\n",
       "      <td>-0.081274</td>\n",
       "      <td>-0.027857</td>\n",
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       "      <th>4</th>\n",
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       "      <td>att</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IWVRRYTL</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>False</td>\n",
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       "      <td>-2.321518</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.022774</td>\n",
       "      <td>-1.169304</td>\n",
       "      <td>0.016097</td>\n",
       "      <td>0.016867</td>\n",
       "      <td>-1.463696</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>-0.731235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacactc</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",
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       "      <td>R</td>\n",
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       "      <td>-1.463696</td>\n",
       "      <td>0.001226</td>\n",
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       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacactc</td>\n",
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       "      <td>tat</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>DWVRRYTL</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>synonymous</td>\n",
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       "      <td>-1.169304</td>\n",
       "      <td>0.016097</td>\n",
       "      <td>0.016867</td>\n",
       "      <td>-1.463696</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>-0.731235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacactc</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>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.022774</td>\n",
       "      <td>-1.169304</td>\n",
       "      <td>0.016097</td>\n",
       "      <td>0.016867</td>\n",
       "      <td>-1.463696</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>-0.731235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>WT</td>\n",
       "      <td>gattgggtgagacgttatacactc</td>\n",
       "      <td>7.0</td>\n",
       "      <td>ctc</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>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>synonymous</td>\n",
       "      <td>0.022774</td>\n",
       "      <td>-1.169304</td>\n",
       "      <td>0.016097</td>\n",
       "      <td>0.016867</td>\n",
       "      <td>-1.463696</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>-0.731235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    seq_type                    nt_seq  aa_pos alt_codons  mutated_codon  \\\n",
       "0     single  ttttgggtgagacgttatacactc     0.0        ttt            1.0   \n",
       "1     single  ttgtgggtgagacgttatacactc     0.0        ttg            1.0   \n",
       "2     single  ctttgggtgagacgttatacactc     0.0        ctt            1.0   \n",
       "3     single  ctgtgggtgagacgttatacactc     0.0        ctg            1.0   \n",
       "4     single  atttgggtgagacgttatacactc     0.0        att            1.0   \n",
       "..       ...                       ...     ...        ...            ...   \n",
       "510       WT  gattgggtgagacgttatacactc     3.0        aga            NaN   \n",
       "511       WT  gattgggtgagacgttatacactc     4.0        cgt            NaN   \n",
       "512       WT  gattgggtgagacgttatacactc     5.0        tat            NaN   \n",
       "513       WT  gattgggtgagacgttatacactc     6.0        aca            NaN   \n",
       "514       WT  gattgggtgagacgttatacactc     7.0        ctc            NaN   \n",
       "\n",
       "     combination    aa_seq    WT   STOP STOP_readthrough  ...  b3_log10  \\\n",
       "0            NaN  FWVRRYTL  <NA>  False            False  ... -2.227509   \n",
       "1            NaN  LWVRRYTL  <NA>  False            False  ... -4.859966   \n",
       "2            NaN  LWVRRYTL  <NA>  False            False  ... -2.700099   \n",
       "3            NaN  LWVRRYTL  <NA>  False            False  ... -2.598704   \n",
       "4            NaN  IWVRRYTL  <NA>  False            False  ... -2.321518   \n",
       "..           ...       ...   ...    ...              ...  ...       ...   \n",
       "510          NaN  DWVRRYTL  True    NaN              NaN  ...       NaN   \n",
       "511          NaN  DWVRRYTL  True    NaN              NaN  ...       NaN   \n",
       "512          NaN  DWVRRYTL  True    NaN              NaN  ...       NaN   \n",
       "513          NaN  DWVRRYTL  True    NaN              NaN  ...       NaN   \n",
       "514          NaN  DWVRRYTL  True    NaN              NaN  ...       NaN   \n",
       "\n",
       "     alt_aa   mutation_type  Log2FC_1  Log2FC_2  Log2FC_3  selcoeff_1  \\\n",
       "0         F  non-synonymous  0.014308  0.293089  0.006508    0.008401   \n",
       "1         L  non-synonymous  0.300812  0.195227 -0.132171    0.294905   \n",
       "2         L  non-synonymous  0.086955  0.314590  0.030692    0.081048   \n",
       "3         L  non-synonymous  0.020562  0.319952 -0.066403    0.014655   \n",
       "4         I  non-synonymous  0.027371  0.252786 -0.000701    0.021464   \n",
       "..      ...             ...       ...       ...       ...         ...   \n",
       "510       R      synonymous  0.022774 -1.169304  0.016097    0.016867   \n",
       "511       R      synonymous  0.022774 -1.169304  0.016097    0.016867   \n",
       "512       Y      synonymous  0.022774 -1.169304  0.016097    0.016867   \n",
       "513       T      synonymous  0.022774 -1.169304  0.016097    0.016867   \n",
       "514       L      synonymous  0.022774 -1.169304  0.016097    0.016867   \n",
       "\n",
       "     selcoeff_2  selcoeff_3  median_s  \n",
       "0     -0.001303   -0.008363 -0.004833  \n",
       "1     -0.099165   -0.147042 -0.123104  \n",
       "2      0.020197    0.015821  0.018009  \n",
       "3      0.025560   -0.081274 -0.027857  \n",
       "4     -0.041606   -0.015572 -0.028589  \n",
       "..          ...         ...       ...  \n",
       "510   -1.463696    0.001226 -0.731235  \n",
       "511   -1.463696    0.001226 -0.731235  \n",
       "512   -1.463696    0.001226 -0.731235  \n",
       "513   -1.463696    0.001226 -0.731235  \n",
       "514   -1.463696    0.001226 -0.731235  \n",
       "\n",
       "[515 rows x 48 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DMSdf = pd.read_csv(DMSf, header=0, index_col=0,\n",
    "                    dtype={'WT': 'boolean'}) # Boolean type supports missing data, helps pandas interpret column content\n",
    "DMSdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7aff26ec-929a-4131-a05a-01c34fc21363",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['input1_abundance', 'input2_abundance', 'input3_abundance']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = re.compile(r'input[1-3]_abundance')\n",
    "input_samples = [x for x in DMSdf.columns if bool(p.fullmatch(x))]\n",
    "input_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "744a4b8c-61dc-4914-a890-17afa3abf1fb",
   "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>aa_seq</th>\n",
       "      <th>input1_abundance</th>\n",
       "      <th>input2_abundance</th>\n",
       "      <th>input3_abundance</th>\n",
       "      <th>average_freq_input</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AWVNRCIA</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>0.001739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AWVNRCVV</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DWIDRCVL</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DWIKRCIM</td>\n",
       "      <td>0.006020</td>\n",
       "      <td>0.002409</td>\n",
       "      <td>0.005151</td>\n",
       "      <td>0.004527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DWIKRCIV</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DWIKRCVI</td>\n",
       "      <td>0.004254</td>\n",
       "      <td>0.001629</td>\n",
       "      <td>0.003428</td>\n",
       "      <td>0.003103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DWIKRSII</td>\n",
       "      <td>0.005566</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.004903</td>\n",
       "      <td>0.004206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DWIRRCII</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>0.002548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DWIRRCIV</td>\n",
       "      <td>0.004995</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>0.004545</td>\n",
       "      <td>0.003804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DWIRRCVL</td>\n",
       "      <td>0.005843</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>0.004317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DWIRRYTL</td>\n",
       "      <td>0.003123</td>\n",
       "      <td>0.001417</td>\n",
       "      <td>0.002499</td>\n",
       "      <td>0.002346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DWIYRCIV</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DWIYRCVI</td>\n",
       "      <td>0.001859</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.001454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DWIYRCVL</td>\n",
       "      <td>0.017005</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>0.014838</td>\n",
       "      <td>0.012773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DWLQRCML</td>\n",
       "      <td>0.002091</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.002007</td>\n",
       "      <td>0.001698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DWLRRCII</td>\n",
       "      <td>0.002788</td>\n",
       "      <td>0.001122</td>\n",
       "      <td>0.002787</td>\n",
       "      <td>0.002232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DWMRQSLL</td>\n",
       "      <td>0.009474</td>\n",
       "      <td>0.004055</td>\n",
       "      <td>0.008966</td>\n",
       "      <td>0.007499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DWVARCIV</td>\n",
       "      <td>0.003622</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>0.003249</td>\n",
       "      <td>0.002790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DWVFRSVL</td>\n",
       "      <td>0.002740</td>\n",
       "      <td>0.001113</td>\n",
       "      <td>0.002444</td>\n",
       "      <td>0.002099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DWVKRCIL</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.001341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DWVNRCIA</td>\n",
       "      <td>0.009194</td>\n",
       "      <td>0.003647</td>\n",
       "      <td>0.008028</td>\n",
       "      <td>0.006956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DWVNRCII</td>\n",
       "      <td>0.007732</td>\n",
       "      <td>0.002850</td>\n",
       "      <td>0.007178</td>\n",
       "      <td>0.005920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DWVNRCVI</td>\n",
       "      <td>0.004117</td>\n",
       "      <td>0.001789</td>\n",
       "      <td>0.003711</td>\n",
       "      <td>0.003206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DWVQRCIV</td>\n",
       "      <td>0.002371</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.001864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DWVQRCVF</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>DWVRRCIL</td>\n",
       "      <td>0.004937</td>\n",
       "      <td>0.002105</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.003886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>DWVRRFTL</td>\n",
       "      <td>0.004264</td>\n",
       "      <td>0.001555</td>\n",
       "      <td>0.003408</td>\n",
       "      <td>0.003076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0.012266</td>\n",
       "      <td>0.277230</td>\n",
       "      <td>0.012541</td>\n",
       "      <td>0.100679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>DWVTRSIV</td>\n",
       "      <td>0.010370</td>\n",
       "      <td>0.003981</td>\n",
       "      <td>0.009374</td>\n",
       "      <td>0.007908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>DWVYRCIL</td>\n",
       "      <td>0.006365</td>\n",
       "      <td>0.002387</td>\n",
       "      <td>0.005663</td>\n",
       "      <td>0.004805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>DWVYRCVL</td>\n",
       "      <td>0.002395</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>DWVYRSIL</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>DWVYRSVL</td>\n",
       "      <td>0.013144</td>\n",
       "      <td>0.004964</td>\n",
       "      <td>0.011997</td>\n",
       "      <td>0.010035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>EWIRRCII</td>\n",
       "      <td>0.006130</td>\n",
       "      <td>0.002335</td>\n",
       "      <td>0.005484</td>\n",
       "      <td>0.004650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>EWIRRYTL</td>\n",
       "      <td>0.005077</td>\n",
       "      <td>0.002058</td>\n",
       "      <td>0.004257</td>\n",
       "      <td>0.003797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>EWLKRCIL</td>\n",
       "      <td>0.004691</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.004143</td>\n",
       "      <td>0.003514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>EWVQRCTA</td>\n",
       "      <td>0.010585</td>\n",
       "      <td>0.004176</td>\n",
       "      <td>0.009359</td>\n",
       "      <td>0.008040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>GAILNQQF</td>\n",
       "      <td>0.011043</td>\n",
       "      <td>0.004665</td>\n",
       "      <td>0.009836</td>\n",
       "      <td>0.008515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>GVILNQQF</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>0.001997</td>\n",
       "      <td>0.004486</td>\n",
       "      <td>0.003910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>GWCQRYIL</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.000871</td>\n",
       "      <td>0.002057</td>\n",
       "      <td>0.001719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>GWLYRCIL</td>\n",
       "      <td>0.007196</td>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.006637</td>\n",
       "      <td>0.005531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>KWIKRCII</td>\n",
       "      <td>0.008750</td>\n",
       "      <td>0.003318</td>\n",
       "      <td>0.007928</td>\n",
       "      <td>0.006666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>LWIKRTII</td>\n",
       "      <td>0.000540</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.000322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NWLKRVVI</td>\n",
       "      <td>0.007930</td>\n",
       "      <td>0.002855</td>\n",
       "      <td>0.007114</td>\n",
       "      <td>0.005966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>NWVNRCIV</td>\n",
       "      <td>0.007544</td>\n",
       "      <td>0.003145</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.005916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>QWIKRCII</td>\n",
       "      <td>0.007893</td>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.007109</td>\n",
       "      <td>0.005995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RWIKRCII</td>\n",
       "      <td>0.005426</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>0.004804</td>\n",
       "      <td>0.004070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>SWLKRCVI</td>\n",
       "      <td>0.005033</td>\n",
       "      <td>0.002001</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>0.003928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      aa_seq  input1_abundance  input2_abundance  input3_abundance  \\\n",
       "0   AWVNRCIA          0.002310          0.000875          0.002032   \n",
       "1   AWVNRCVV          0.000461          0.000130          0.000368   \n",
       "2   DWIDRCVL          0.000103          0.000039          0.000124   \n",
       "3   DWIKRCIM          0.006020          0.002409          0.005151   \n",
       "4   DWIKRCIV          0.000403          0.000139          0.000343   \n",
       "5   DWIKRCVI          0.004254          0.001629          0.003428   \n",
       "6   DWIKRSII          0.005566          0.002149          0.004903   \n",
       "7   DWIRRCII          0.003348          0.001321          0.002976   \n",
       "8   DWIRRCIV          0.004995          0.001871          0.004545   \n",
       "9   DWIRRCVL          0.005843          0.002261          0.004848   \n",
       "10  DWIRRYTL          0.003123          0.001417          0.002499   \n",
       "11  DWIYRCIV          0.000178          0.000082          0.000169   \n",
       "12  DWIYRCVI          0.001859          0.000780          0.001724   \n",
       "13  DWIYRCVL          0.017005          0.006476          0.014838   \n",
       "14  DWLQRCML          0.002091          0.000996          0.002007   \n",
       "15  DWLRRCII          0.002788          0.001122          0.002787   \n",
       "16  DWMRQSLL          0.009474          0.004055          0.008966   \n",
       "17  DWVARCIV          0.003622          0.001499          0.003249   \n",
       "18  DWVFRSVL          0.002740          0.001113          0.002444   \n",
       "19  DWVKRCIL          0.001712          0.000663          0.001649   \n",
       "20  DWVNRCIA          0.009194          0.003647          0.008028   \n",
       "21  DWVNRCII          0.007732          0.002850          0.007178   \n",
       "22  DWVNRCVI          0.004117          0.001789          0.003711   \n",
       "23  DWVQRCIV          0.002371          0.001009          0.002211   \n",
       "24  DWVQRCVF          0.000246          0.000078          0.000333   \n",
       "25  DWVRRCIL          0.004937          0.002105          0.004615   \n",
       "26  DWVRRFTL          0.004264          0.001555          0.003408   \n",
       "27  DWVRRYTL          0.012266          0.277230          0.012541   \n",
       "28  DWVTRSIV          0.010370          0.003981          0.009374   \n",
       "29  DWVYRCIL          0.006365          0.002387          0.005663   \n",
       "30  DWVYRCVL          0.002395          0.000910          0.002116   \n",
       "31  DWVYRSIL          0.000352          0.000052          0.000328   \n",
       "32  DWVYRSVL          0.013144          0.004964          0.011997   \n",
       "33  EWIRRCII          0.006130          0.002335          0.005484   \n",
       "34  EWIRRYTL          0.005077          0.002058          0.004257   \n",
       "35  EWLKRCIL          0.004691          0.001707          0.004143   \n",
       "36  EWVQRCTA          0.010585          0.004176          0.009359   \n",
       "37  GAILNQQF          0.011043          0.004665          0.009836   \n",
       "38  GVILNQQF          0.005248          0.001997          0.004486   \n",
       "39  GWCQRYIL          0.002231          0.000871          0.002057   \n",
       "40  GWLYRCIL          0.007196          0.002759          0.006637   \n",
       "41  KWIKRCII          0.008750          0.003318          0.007928   \n",
       "42  LWIKRTII          0.000540          0.000100          0.000328   \n",
       "43  NWLKRVVI          0.007930          0.002855          0.007114   \n",
       "44  NWVNRCIV          0.007544          0.003145          0.007059   \n",
       "45  QWIKRCII          0.007893          0.002985          0.007109   \n",
       "46  RWIKRCII          0.005426          0.001980          0.004804   \n",
       "47  SWLKRCVI          0.005033          0.002001          0.004749   \n",
       "\n",
       "    average_freq_input  \n",
       "0             0.001739  \n",
       "1             0.000320  \n",
       "2             0.000089  \n",
       "3             0.004527  \n",
       "4             0.000295  \n",
       "5             0.003103  \n",
       "6             0.004206  \n",
       "7             0.002548  \n",
       "8             0.003804  \n",
       "9             0.004317  \n",
       "10            0.002346  \n",
       "11            0.000143  \n",
       "12            0.001454  \n",
       "13            0.012773  \n",
       "14            0.001698  \n",
       "15            0.002232  \n",
       "16            0.007499  \n",
       "17            0.002790  \n",
       "18            0.002099  \n",
       "19            0.001341  \n",
       "20            0.006956  \n",
       "21            0.005920  \n",
       "22            0.003206  \n",
       "23            0.001864  \n",
       "24            0.000219  \n",
       "25            0.003886  \n",
       "26            0.003076  \n",
       "27            0.100679  \n",
       "28            0.007908  \n",
       "29            0.004805  \n",
       "30            0.001807  \n",
       "31            0.000244  \n",
       "32            0.010035  \n",
       "33            0.004650  \n",
       "34            0.003797  \n",
       "35            0.003514  \n",
       "36            0.008040  \n",
       "37            0.008515  \n",
       "38            0.003910  \n",
       "39            0.001719  \n",
       "40            0.005531  \n",
       "41            0.006666  \n",
       "42            0.000322  \n",
       "43            0.005966  \n",
       "44            0.005916  \n",
       "45            0.005995  \n",
       "46            0.004070  \n",
       "47            0.003928  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DMSfreq = DMSdf[DMSdf.seq_type.isin(keep_seq_type[pool_type])\n",
    "               ].groupby(['aa_seq','nt_seq'])[input_samples].first().groupby('aa_seq').median().reset_index()\n",
    "DMSfreq['average_freq_input'] = DMSfreq[input_samples].mean(axis=1)\n",
    "DMSfreq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "01599ac3-bc16-487a-84cd-170e219a58a1",
   "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>Nham_aa</th>\n",
       "      <th>confidence_score</th>\n",
       "      <th>inputO1</th>\n",
       "      <th>average_freq_pool</th>\n",
       "      <th>input1_abundance</th>\n",
       "      <th>input2_abundance</th>\n",
       "      <th>input3_abundance</th>\n",
       "      <th>average_freq_input</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AWVNRCIA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016938</td>\n",
       "      <td>0.016938</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AWVNRCVV</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017339</td>\n",
       "      <td>0.017339</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DWIDRCVL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024576</td>\n",
       "      <td>0.024576</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DWIKRCIM</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010996</td>\n",
       "      <td>0.010996</td>\n",
       "      <td>0.006020</td>\n",
       "      <td>0.002409</td>\n",
       "      <td>0.005151</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DWIKRCIV</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014140</td>\n",
       "      <td>0.014140</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000295</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>NWVWRCVI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010739</td>\n",
       "      <td>0.010739</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>59</th>\n",
       "      <td>NWVYRCII</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008487</td>\n",
       "      <td>0.008487</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>60</th>\n",
       "      <td>QWIKRCII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012856</td>\n",
       "      <td>0.012856</td>\n",
       "      <td>0.007893</td>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.007109</td>\n",
       "      <td>0.005995</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>RWIKRCII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012388</td>\n",
       "      <td>0.012388</td>\n",
       "      <td>0.005426</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>0.004804</td>\n",
       "      <td>0.004070</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>SWLKRCVI</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>0.005033</td>\n",
       "      <td>0.002001</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>0.003928</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      aa_seq  Nham_aa  confidence_score   inputO1  average_freq_pool  \\\n",
       "0   AWVNRCIA      5.0               1.0  0.016938           0.016938   \n",
       "1   AWVNRCVV      5.0               1.0  0.017339           0.017339   \n",
       "2   DWIDRCVL      4.0               1.0  0.024576           0.024576   \n",
       "3   DWIKRCIM      5.0               1.0  0.010996           0.010996   \n",
       "4   DWIKRCIV      5.0               1.0  0.014140           0.014140   \n",
       "..       ...      ...               ...       ...                ...   \n",
       "58  NWVWRCVI      5.0               1.0  0.010739           0.010739   \n",
       "59  NWVYRCII      5.0               1.0  0.008487           0.008487   \n",
       "60  QWIKRCII      6.0               1.0  0.012856           0.012856   \n",
       "61  RWIKRCII      6.0               1.0  0.012388           0.012388   \n",
       "62  SWLKRCVI      6.0               1.0  0.014233           0.014233   \n",
       "\n",
       "    input1_abundance  input2_abundance  input3_abundance  average_freq_input  \\\n",
       "0           0.002310          0.000875          0.002032            0.001739   \n",
       "1           0.000461          0.000130          0.000368            0.000320   \n",
       "2           0.000103          0.000039          0.000124            0.000089   \n",
       "3           0.006020          0.002409          0.005151            0.004527   \n",
       "4           0.000403          0.000139          0.000343            0.000295   \n",
       "..               ...               ...               ...                 ...   \n",
       "58               NaN               NaN               NaN                 NaN   \n",
       "59               NaN               NaN               NaN                 NaN   \n",
       "60          0.007893          0.002985          0.007109            0.005995   \n",
       "61          0.005426          0.001980          0.004804            0.004070   \n",
       "62          0.005033          0.002001          0.004749            0.003928   \n",
       "\n",
       "     Location  \n",
       "0        both  \n",
       "1        both  \n",
       "2        both  \n",
       "3        both  \n",
       "4        both  \n",
       "..        ...  \n",
       "58  left_only  \n",
       "59  left_only  \n",
       "60       both  \n",
       "61       both  \n",
       "62       both  \n",
       "\n",
       "[63 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrdf = pool_df.merge(right=DMSfreq, on='aa_seq', how='outer', indicator='Location')\n",
    "corrdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1aa913d1-dad5-4472-8000-99ac360b5df3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Local\\Temp\\ipykernel_4820\\2413502137.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  ratios = corrdf.groupby('Location')[['aa_seq']].nunique().reset_index()\n"
     ]
    },
    {
     "data": {
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       "      <th>Location</th>\n",
       "      <th>aa_seq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>48</td>\n",
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      ],
      "text/plain": [
       "     Location  aa_seq\n",
       "0   pool_only      15\n",
       "1  input_only       0\n",
       "2        both      48"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratios = corrdf.groupby('Location')[['aa_seq']].nunique().reset_index()\n",
    "ratios.Location = ratios.Location.replace({'left_only':'pool_only',\n",
    "                                           'right_only':'input_only',\n",
    "                                          })\n",
    "ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f1080715-bad9-4be8-a773-9e08b63055db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>confidence_score</th>\n",
       "      <th>inputO1</th>\n",
       "      <th>average_freq_pool</th>\n",
       "      <th>input1_abundance</th>\n",
       "      <th>input2_abundance</th>\n",
       "      <th>input3_abundance</th>\n",
       "      <th>average_freq_input</th>\n",
       "      <th>Location</th>\n",
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       "  <tbody>\n",
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       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [aa_seq, Nham_aa, confidence_score, inputO1, average_freq_pool, input1_abundance, input2_abundance, input3_abundance, average_freq_input, Location]\n",
       "Index: []"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mutants sequenced in the input but not in the initial pool (not enough coverage)\n",
    "corrdf[corrdf.Location == 'right_only']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ce6d226b-02b3-41ae-ac44-212438923c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "corrdf['Nham_aa_1p'] = corrdf.Nham_aa.apply(lambda x: 'non-WT' if x > 0 else 'WT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "94e71cdc-2fd5-4325-a993-1a698767c164",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Local\\Temp\\ipykernel_4820\\3903731632.py:19: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.\n",
      "  sns.kdeplot(corrdf,x='average_freq_pool', y='average_freq_input',\n"
     ]
    },
    {
     "data": {
      "image/png": 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rrruSXm/fvj1l3Z226K5Cx3Dsix1vkopActBLPcjO7cn1WSPIbJF6oQO5BCnxNlDpPfS+HwsVchZNHPpNrEIjmxAZhHEKFSs6CjqS1U6zJhHSJYKSgh8rPslKRMiU+3TKfTrvE8GJSiZhPHoIDxHsaFgdTjRdQ4TDAMiKgq5Y0IWEYrVglSDs8yLZbPhVw8nltkqooRBZshXIp9yr8elXX2OntSg0JGSpD82qzKZt3+Ah0ml/3y4pdlxUVERRUVGHz48tasFYa1xyySV8+9vf7vDnu6vQMRzbYsc+VfDWN4Yl6fyBxWRak4v9vlnlJ+SDTAuM75WFRc6Ov7etSWVrg4rA6IBDsxSGZVkodshIUkbSdTQB9aEUUx4haIwY/1X6Nfb6dQJYCGChWjamx4oELl3CJoMnQ8KpGH6LHJthNfJGdFw2hV4FBcjR0SSiG3O0fLuMXZbYuztIdVDHUjyAodmpu97OyiC7/TqWon4Mze2caVOXFTu++eabqa+vZ9euXfGqa7GOmYrLL7+cmpoaPv30U2RZ5swzz0xrI8PuKnQMx7bY8Y46w3tf4rJQnJW86KwPamz3GdOn7xTa8LiMEHtV1/m4JsD2RsNGWuRUOCffwYBMG5ou8GqCg0GV/UGNhpBOU1gnqOmAhEORcFskcm0KxS6FDJtCsVNmkEXiAptCfUBlZ3OE/X6VmoBKfUhDE9AcnS4dCB+SlAT0cimckmHFZlMIIZFtTw4lcVgkPDaFgdmC6v0BqsMSo9r42w7Iht1+P9Xhzvv7pzP1SksQ7777Lg899BCaprFs2TKmTp3KQw89lNIXAbBixQp++9vfcu6556JpGr/+9a9ZsGAB48ePT+e2SZyIhY4rvMbU4JTM1g+LTbUBBJAv/PR2OYlgPNG/rA+xvdH43JBsGwMzrTgV2XDOBVR2NIY52O5CIoIiQbFT4ewCBy63Ma2wyBJ9PVb6eoz26ELgjeh4IzrNEY2DIQ2vKjgY0mmO6Oz1azSFdcYUObEpENZ17MqhhXPM8tTLZXS32mAbjg6IL6b3+ds+pytJSxBPPPEEL774InPmzKGgoIAXX3yRefPmtSmIp556ihUrVsSnWXv27GHu3LlHJYgTsdDxHp/RsUtbWFY0IfiywfA1lIpmbHI+KtAQ1vg86oM4NctqpGUi8EY0PqkLURs05ucSkO9Q4jkNbgUqfYYX2xvRaQjphHTBHr/Gvgofowo0LujtbmUpkiWJTJtCpk0BrKDrhIQhlLqgxtr9fryq4IuGEAMzbWhCUOCQsMhy9PPGdXKi1qXGwwi1yGl0yeaIYRk71rkSad8tsWhu//79D/t0zsjIoKCgIP66pKTkqPd+PtEKHRvBcUYHKXIlP5+qvBGCmsCpQB5BFMnwFm+tD6EKyLbJDM6yIWGMkutrgtQGNSRgQKaVi/q4GF3kZFCWjQKHjMcq47bJ5DsUBmXZOKfAzuk5NjKtMjqwoTbI8rJmFNlIA01ECMNhF1SN69tkCYci47HInJnrAIyQDV0IEBLNYd2w7kA8FMMZ9WiHtLa3ybUpEplW47yGUGprVFeSttn1P//5D2Ds//z888/Tu3fvNs8/9dRT42XJLRYLr7/+OoWFhXHv9ZE46E60Qsf10R/daTEC7RKpjMZQ9HUpSI3GMQGUNxsjyrBsGxZJwmmRWLs/YPgCJBiRZ6c4YbSJaIKwJrBIElZJwq/phDWBwyKRY1fIs8nUh3W+jIZc/Kuimal9PTRHjGBAVddpDuvogMcqE0GiOaThsEhoQKFTwSKBKiCsCqx243hEF8Z70SEiUQRNIQ23TUkZ8Jdhk2mKGNOxY01agnjwwQdZtGgR+/fv56KLLmLMmDHxWv9tUVxczPr16wFwu9243W42btwIdFwQa9asSXo9efJkJk+enE7TeyxNYUMQ2bbWzqp9fqPjFztliArii/oQmjAShHIcChrwTVOEKp+KBAzPsZPrsBhPaiAU0WmOCDJtErIiQAg8FgkkUHVwWySsskKmA/p5rLxR5WNXU4TPDoQ4t9CJP6xRFxZGBp5yKHzDZZVpDGnIkuEvsMoSqiZwWWUciuGg81gPBfMFVT1pKufXBMGgljLgzx4NOQkfQYDi0ZKWIHJzc/nDH/7Q4fNTWYlMkvFHLTcuS+snZWz0yLXLeDFMmd80GT6A/hlWLJLxJN7RaBwrdSs4rRJ+VUfVBapujAKSLDgY0bHrEo0hDVVI2GXIslsAQxwKUOy2cH4vF2v2+vmw2s/IfAeyLOFKIVa7IpNhEwRVgUWKmliBPKclHqtkS4h9ihVAAHAohogSPdyJI4VMbEQ5qj/tEZGWIMaPH59yzfD222+nPH/dunU8+uijNDY2Jg2XbZ1/MhLSYp0k+SkphKA5urbItEh4MbzGddGnbLHLQrZdoT6kcTCsIwHDsu3IskRTRCeoCXQhiIQ1ZAFhIVEX0LHL0RIzGkSESonbilUYT/yQkCj1WMm2yRwM63zZEGJQpg0hjGhYXQjkaACfJBlrCKHrhIUxXVIkyIjO/xPXDkHV8H00Rb9P7BwwpoAtA/5i4uqOKPC0BBHLlgPDk7t69WoUpfXTI8aCBQu4/fbbGTJkyHFvGu0qYtNka4u5tBrtZGCsL4xzDesQELf1x0aRHLuMx6YYiTiSik0mGhJuLHB9qpGr4NPAowhcFhlVCBySwCFLKFGLkCRJlLitHAyH2ONT6edSOBjSE/zKAgVjnm+RZbLsMp8eMCxehU6LMYXCWPzHnvoxs2tMzLmO5D7TstRT7Du6u0ERaQmi5QL6xhtvZMaMGfz0pz9NeX52dvYxTQg6HhFRv3HLtWUkoZfEZlOqTtzL7IjOs5vjT10jM80iG3N9q274AzQ9+nQHdF2gCvAKUBFoujFl82o6GULDYzVyG2LdMKjq1ASNKZaU0De16H1z7BKZdiW+yB+UacVjkVplx8X+WR31LRQ4k7td4ndXdUFDdF2V42j7YdtVpCWIvXv3xv8thGDbtm34fL42zz/vvPN49tlnueCCC7DbD1Vqa7kP8clMzMDZ8imZOH+O9ZfYIGuUk4kuPKMftClgUw7Z/fW40CTsikxQ04lEo13DusAhJFwWCQ1BSAOnbjyZNV3EHWdWBfy6wCqBpoElIb9CxzCR7vGp1AY1LBKcXeBsZSkDY+pUG4xwIDqa9UowLydOrcBwyOnCWFNlWnv4CHHttdfG/y1JErm5udx3331tnh8zrybmS0iSZK4hEoj1sZbJ+Ym5PTHjYyx6VIhDEZyxnAmLdGhhalNkFHTA8APIQmABNBkkYdRfssgQ0gVWSSaEIKLrKJJMtd/IlwbItSvISMiyhCzAIQMYFiRbdCRZs9d4IJ6V70gpBjBGrZqAIbJ8hxJ3trWcWsEho0G/o8zvOFLSEkRL82dnn38yEstqa1kz1ZqgiOgMIm6OFBiFyRCC2KzCF9GJaDrWqGnUZZE4GNIN/4ZVpi4UIaAZUy2jlCVkWIyRwy5LiGgw3o4mI8Q82yaTa5MPDUvRUcllPTSN+bg2SE1Aw6FInNdO2ufXUUvYaVk2XFGzbMuplUjwzA/O7p7ar2kJomU4d0tiZtYnnniCefPmtXm+aY49RGwt0DLPQJEk7IpESBP4o/Z4q2wE5gU1Y54d1kR8mrQ/oOENq+Q4LNgUw7Z/SpaV+qDhMc51KIQ0Yz3isMhEdB27IqFLEh6LgkCw1x9hr99Q3xk5drLtCs0REV9QJ2r2y/ogn0SDEs/v5ULVBd6wlrK6xoGgUShZAkYWOPG0MRX6pilCU9ho16D2Uve6iLQE4Xa7KSsri3ueV61ahaqqrTzFsWp+ieHfJqlxRzuHL0VNokyrTK2m0Rw51BPzHQpVPpWdjWF6e6zk2I0CAmFdsDegg6TTyyFFhSJT7DJinOxWiXBEB0kQ1iQcihJ/+usYnuyt9cZTfGi2jYFZRu2nDMnwUmsYa5OwJvioxs/OJmMhfVaenRK3NS7axHTSGJ9GhTMw09amGIQQbKj2R6/paGV1O1akJYjPPvuMZcuWxU2tF198MVdeeSWXX3550nmx4L2Wx01aE7PJx2N/EubNudFKFPVhnVhd9d5uK1XRhWxvjxVZMiJTv2mKsO1giFyHTG0IbBY57i+IWZ2wGE47j80YedTo3GmvT+OLhjACw+F3bkKSv0WWybZLhDQj13lTbTDuTDynwMGwbBshTU/yUSQ620KaHg9EHJnvaPPvUNFseNsVCc4taPu8riatZXxzczMHDx6Mv66urkZVuydM90QhM+qQCuuiVS2lWORnTfDQ6DEgGiJeHVDjC/FBmVZsMjRHBP+tDeFVNZojOgdDGqquY5MlFIx1iceq4FCMAD+HLPF1Y4StUTEMzbYxoZeTUNTTDcaTu9Ib4c0qP+/vC+BXBbl2hRn9PQzJtnEgZOwhURfQjP8HNSK6Hq/C92ldkJBmfGZAivD22D3eiS7OR+Q7yEjhGT9WpDVCzJ07l8suu4xvfetb6LrOF198wcKFC7uqbScFVtkwLzZFdOqDGi7PoWdUidv4efb4dfpGj5V6rLgtEj5VsMer0s9jJaTp9PNY2dEUoTaosb46yJm5DrIdCs1hnWy7QoZNNnwWksCnws6mMBXNEQTGuuKcAienZdvQJAmHRRDRBTV+lU/qglRHrU4ORWJskZNvFTjxhjQqfBG8LfaRCGlGxKtHkQlpOhtrjEzA0UVtl5bZUh+iOqBhl9tfnHc1aQli+vTpjB07ls2bN+NwOFi4cGFSOPjhaGhoIDs72/RYpyDfqdAU0akJqPTxHHqK9nFbscrg00Q8X1qWJM7Od/DB/gBfN4bJtEoIyViLnJJhocKr0hQRrK0O4LEY2WuehGlZXXRHnxiDMq2clZf8VG4I6fy3LkCVL+qPkA3BjC48VFUvqOutxACGBcwb0QnqOpv3h/Crghy7zBm5qa1GQU3n3ejo8O1iJ65u8D0kktbd/X4/zz77LCtXrmTkyJE8/fTTKavK1dfXM2/ePDZu3Iiu68ydO5cLL7yQCRMm8PXXX3da408UiqNTo5ZZYhZZYmCmIYR90qGap6OKXGRYZYKa4OvGCHq0g+fYFMYW2unjtiABXlVQ5TOq5207GGaPXyWkC2yyxKlZNi7r5+HCEldcDM0RjQ/2+Xi1ojkpevZnw3K5oMSdtFAWumiz88hAjV/lo+joMKG3J2WNKYAP9vnj07BzCrp/B6q0RogHHniAgQMHUlVVhd1ux+fzceedd/LYY48lnbdgwQKGDx/OGWecwerVq/n6669Zu3YtFRUVLF68mL/97W+d+iXaYtu2bfzlL3/B6XQyefJkzjvvvGNy33TpFZsa+Vqvx87Mc7DtYJh9eAhpAhfGNGtqPw/LdjZRE9RwWST6ui04rUp0nWCh1G0hpAl8mkBCoOtG/FOsBGXM090Y0tCArfUhdjSG407A/hlWzs53UOy0pLQMybKMyyrjj+hJ9TMUDGvUu/uN1Ndh0Y0WU7Hfr/LfWsMCNbGPG6WbLEuJpDVC7Ny5k7lz56IoSnzKtGvXrlbnffPNN/z0pz/F7XbzwQcfcMkll+DxeDj99NOpqanptMa3h9/vZ/78+dx66609uuJ4qduKhBHu3RzWkt47JcNKrk1ClWQ+qT9UlqVfhi1eSr7cq7K9KYImYvFJOoosUey2MCTLxpm5dgbn2OnlssS3yBVCUO1XWV8T4JWyZrZHxdDLZWFqXw/jStxk2pRWMVYxnNEdijJsCm6LjNMi4bbIKLLER1FLVJ5dYVJp6mreuhC8XumNL+ZPyewev0NL0hohJEkiHA7H1wEHDhxo87wYGzZs4De/+U38dVuFe4+WVLVdzz77bMrKyrjrrruYNWtWl9y3M3BYZIpdFvb5VXY1Rzgr79B8XpIkxuZb+ffeMP9tUDmrUI0Hx51b6EQTgg+rA1QHNGr3+ilxWci1y2TbZGwSeGwyQcO+SjBaJbwmoLHbG0nKSOvlsnBWnoPiw8QZJeKyKWSqRjUPFMPTvcen8kVDiIhu+FCuGpTZ5lZZm2qD7Per2GWJCW2Ut+wO0hLE7NmzmT17NrW1tSxatIj//Oc/KYuUlZSUsGrVKnw+H4FAIO6ge+211zj11FM7p+UtSFXbdcuWLQwYMIBly5bxox/9iClTpnTJvTuDQVk29vlVvj4Y4qy8ZDv8AI9CvvBTh4uV5c1cN9jYk80iG0WC8+wWPqz2UxvUqPKpVPmMoT+2Ta4aNem2rBBukeCUTBvDc21k2SxJC+RUcUZJn5Ulcu0KAVVQ1RxhZ1P4UG64U+GKAZltmk8PBFXejy6kL+ztbtNZ1x2kJYjRo0dz+umns2HDBjRN46mnnkra/yHGAw88wP33309dXR2///3vsdlsPPzww7z99tv85S9/6bTGt0cwGOSee+4hNzeXCy644Jjd90g4LdvGB/v8lDVF8EX0uAcbjFFimDjAJxY3dUGNV3Y1c8XATCOUwyJzaraNPm6FSq8aL5gc0gVNEZ2Wxe/cFolil4U+bqNSuE2WyIsGRMUqeifGGYU1I/q1IWSUmvGpxiYs3ohOfUiLO+nAsEaNLnQytsjV5npA0wX/qvCiCmOdclZez9qvOq1NFy+55BJef/31I7pRY2MjGRkZyHJ6T4Ojqe16pGzZsgUhxDGr3BfjxQqjst15+Va+lXfI/BoIBCgvL8fdqx//roGIgGKHzKW97bhbpJ5qAg4ENbyawKca+1RLkpGWqUiCgoQcAwnItMnYW/wkB0I6O5s1yn0aNcHU+1UnXqPYITMoQ2FoliUem9UW79eE+bRBxS7DD/o7krLnuoqdO3ciSVKHNl1Ma4QYMmQIy5cvZ8SIEUmbtLfMb1iyZMlhr9PRDnwy1XYFyMNNtZzPptoA9pqdtJxw+PZVMAI7m6UC9gfh7zu9DBH1FOFPKhsj2x00R0BLKOqmyDJZdgsHdA1dSMiSQNEi1EUjDVQk9uOmSvLQLCU/tW1CxUMEBxo2NGxCw46GE+O44hfgh7J2Cijuw8VW2ShLNEStoWpn16wnU9EltV0///xzPv/886RjqfIbnnrqKXJycpg4cSJ5eXnp3CKJk6W2a4xTdcHusiBe1UKocBDfitY2jY0Q/fv3x+l0MjSss2pviLqQwhapgDqnzLm5Vvq5D5XFb1m52yZLtHx4CyGoDup82aixvUklFkMoA33dMgMzLPR1yZ3yFN8X0FhTGQIB38q1cF5B/6O+Zkfp9Nquixcv5q677uLee+/tUNW9999/n1WrVvHGG29QWVnJpZdeysSJE/F40rMmnCy1XRM5v0Rh1W4vHx2IcHq+Jyn/2Ol04nK5cLlgdqab9dV+NlYH2BfQeXVPiCybzJBsO6dkRveuTmHhCWuCff4IZc0Rvj4Yjudkg5EQNCLfwRm59k6tmHcgqPLankY0YWyfNaFvRrt7UXQmnV7b9a233uL888/nt7/9LZmZma2qrrXcUisvL4/rrruO6667jr1797Jq1SpuuOEGCgsLmTZt2lEVFjsRa7smMjzXzhf1ISq8EV4pa+LaU7NSnmeRJb7by82IPAcf1QT4vD4U3aUnEI8fyrDKuK0yFskYMXwRY2fSpOtIcFq2nTPz7Ee9C1EqmiMaL+5sIqAJerksXNb/2IohXTokiBtvvJFnnnmGmpqaVl7pw22pBcb6Yvbs2QwaNIinn36ae+6556gEcSLWdk1EkiSm9c/g2e0HqQtqLNvZxNSStst/ZtgUJvTxcH6Jm52NRuW9mI+huY3qdxlWmb4eKwMyjbqwbfkKjpaILvi/b5poiujk2hWuHJCZ1r533UGHBHHllVdy5ZVX8uSTT/Lzn/+8QxfWNI21a9eyevVq1q9fzznnnMMNN9xw1HP9sWPH8vjjj1NXV0dWVhavvvoq11xzzVFds6sJqjrl3gh1AY2wblTBK3Ao9MuwpuyMHqvM1QMzeWFnI/sDKi/t1jidw9fEtcoSQ3PsDM0xFsR+1Qj/9qk6mg6KDC6LTI5NOWYBdP+p8lId0HBaJK4amNntgXsdIa1FdUfFcPfdd7N27VpGjBjBlClTePDBB5OqbhwNx1NtV29E54N9PrZGy0+2xCrD2flOzit2tXpyFjgt/PDULF76psmYCknFZDWpjOjgssZlkY955exE9viMcpgA0/tnpNwzryeSlh+iowwZMoScnJy4abblvLSnV93YsmULQIfs1m1R1hRmZXkzwagS8uwKvaMbJAZUnSpfhIaQMZ0pcChcPSgrpcfWr+r885uDVPqNc8cWOTm/l6vHr5teLW/my4YQw3PtXNovo/0PdCHp/J5p71PdEXp6h+9qyprCvLyrCV0YYQwX9fEYIdkJnVgIwTdNEVbvbqY2qPHKriauHZzVasHpssh8r4+df23fT4WUxfrqAL6IzuS+nh4tiopmIz/7zLzuSwc9EtIaU3Vd5/nnn2fBggX4fD6ee+65lFaf3r1707t3b3w+H1u2bGHHjh3ouh4/fiLTHNZYUd6MLoxwjOsGZ1OawnojSUZliWsHZ2NXJPb6VT47EEx5TVmSGCwOMrHY2Avi8/oQH1YfO6fWkRDbRzvnOJkqxUhrhHjooYcIh8Ns2rQJIQTvv/8+u3bt4oEHHkg6r76+nl/84hds27aN/v37A7Br1y7OPfdcfv/736ftjzieeHevn5AmKHZZmNYvIx4cp+oiZaxQjl3hO8Uu3t7j45PaICPz206SGZplwWKzsXq3l3X7/AzLsffYDmeTJcK6IKjpPSp4rz3SaummTZtYsGABNpsNj8fDn//85/heD4ksWrSI4cOHs27dOl5++WVefvll1q9fzymnnHJC12TyRvR4oa1JfdxxMQRV3YgvUo0aS15VcCCoxRPxh+fakTCKATe1yIdoyVl5Dk7JsKIDW9oYUY6UWG2lppCGN6zFCw0cCTGHYk3g2O8CdDSkJQhJktB1PT78h0KhlMF6X375JXfccUdS/IjNZuP222+PL3BORHY2GtUrSlwWekV38IntjZAq97gxrEf3cDCqYADxMpKHY3C28XetDnSs4klHOnp7ok2XPtEswNha4nghLUFMmzaNW265hYMHD/Lss89y7bXXMm3atFbntWVilWU57WjX44nYjj/9Mw75DGJ7I6QitjcCEDeRBrX2O2CsiFdHNtjpSEfviGjTZUA0A25nYzi+m9HxQFpriB/96EesW7eO3r17s2/fPubNm8eFF17Y6rzDWT96smXkaPFFo+MyExJj2utLsffVaKfpSF7xwai5tr25eXsdPVZMrC3RqtH9KOwyZDssba6DUtHPY8WuGOVyKr0R+mX0jBTR9uiQID7++OP4v202W1KA38cff9wqlqm8vDxlyqYQgoqKiiNta48npvXEJ2J7/VuWDm1vC6n3mmtJVXQb316uw/98HRmdPDYlpWj3+iKs2eNDFYYD8YJiF3kOJV5MGVKXrYyhyBJDs+18eiDIlvrQiSWIxx9/vM33UsUyPf3000fXquOULJvRMWKdG4ynqE/VUnbMWM7ybm+EkGaEdBQ6Dy+IiG48cQH6ZRw+nKOjo1Mq0W6qDcZ3MIro8M4+PxP7uLFqIr6xSlt7xMUYnmcIYltDiAm93W2Wy+9JdEgQiVtpgZE8c7j9pk/WIselHiubaoPsaAwzoY9Aie7ZkGWTW01dYjnLigTr9xs+hWE59nYjQcubw2jC+GxeOybXjoxOkFq0sfecikRAM/IqQprAIkt4I3r8u6XaIy5GictCkVOhOqDx37og5xV3b1W+jpCWZMvKypg5cyYTJ06kurqaGTNmpCxDc7IyMNOGyyLRHNHj9YbA6HB5DgWPRcKlSHgsRh6zwyKzqTZIhTeCIhn5yO0R275qQKat3fWYwyLT1hmJFTViok08d0SeHYtEvN5snl2J75QqgHDC4r+tkUiSJEZFv9PHNYH4BpM9mbQE8eCDD3LPPfeQnZ1NUVERs2fP5u677+6qth13GDkKxlPw3b0+vmkMJ73nsSlk2hU8NgVFgo9qAry9x6g+Ma7E3aEAuKrodKmv5/DTpdg9W3Z0SF1Ro6VoS90WpvfP4DvFLs4rdjK6yJEkwEQRHG4kGppjJ9euENQEG3u4dx3SFERTUxNnn312/PVll13WZXWWjldG5DkYkm1DE/B/u5p4q8pLQ0JWmqoLdjaGeX5HI2uiYjinwME5HSgBrwsR3/w81YI6lb/hcKNTSxJF67Yawh2UZWNQpg1ri9EoJoLD1W4yzpMYV2I8JDbWBDgY6tmOurTMrk6nk71798afFJs2bTpsWPeKFStYvHgxTU1NwKF90Y518v6xRJIkpvXLwKZ4+fxAiE9qg3xSG8RpkbBKEt6o2RKMbLULe7s5O9/RIXO0TxVxk2dsAR8jqOqt1imJVqBUc/zDkbiukCQpXj1cwxCBTZHbrd0U49QsG/08Viq8Ef5T5eOKgZlpteVYkvaWWjfeeCO7d+/me9/7Ho2NjfzpT39q8/wlS5bw7LPPMnTo0KNt53GFIktM6ZvBsGw7G2sCVDRHCKiCQLS7ui1GMs+oQmeSz6I9Yg9XhyIlCaij/oZ0aGkMiG2cEtEFLouxafvh/BCJSJLExFI3/++rg+xsCrOrKRx33PU00hLEGWecwfLlyykrK0PTNAYMGHDY8h4FBQXdLgZVVZk9ezbz588/qvyGI6F/po3+mTbCmuBgdArjtspkWuVOdVB21N+QLrEOf8gZ13oP6o6S77BwTqGTj2oCvLPHxykZ3bPLaHt0SBAd3WyxJeeccw6/+c1vGDduXNLUqqUjrytZsmRJt+dc2xSJQufRp57E1txBVcS3sIKO+xuOhNi6ojP4dpGTz+qC1AY1djaFOTWrZ1Xtgw4K4kj9Cp9++ikA27dvjx9rryjBkZKq2PG6desYMWJEfE+84x23RcIqG46y+qBGflRkHfU3dDcOi8yIfAcbawJ8Whc8fgUxevToI7p4S4deV5Kq2PEbb7xBQUEBW7dupby8nD/84Q/HrD1dgSxJlLiMxWlZcyQuiI54w48lh4t5Gp5rrKvKmyPRbYV7iFqjdEgQ1157LVJ0T4EYsflfTU1Nq5Du++67j4ULF3LdddelnCd2xQiRiqeeegow9s0eN27cMblnV3Nqto0Kb4TPDwQ5p8CwTrXnDT+SOf+R0p61K8+hkGGVaY7oVAdUSjvgTzmWdEgQa9asSXrt8/lYuXIly5Ytw+FobT+PPannzZt31A3sjGLHR9IOIQR+v/+I2tzZxHw9gUCAgQ4H78lQG9T47/5mhmYd+gldKcpX6mHwH6OUBE1AfSj1Aj8YhFy7EaqSbYXmCNQ0B8iTIynO7lxabnd8ONJa6W3bto0XXniB1157jXA4zO23387MmTNbnXfGGWcARx/TdLIVO26P8vJyAPqRyU45hzX7gnj37sfTsuZ9BxBAEIVwtKSyBR0HaqsCy2lhd9J0GMdbnV2BUICQlA+Sm8q9+2Cv92ju2GE6tdjxihUrWLZsGXv37mXy5Mn87W9/45ZbbunyXXlOtmLHbdGy2PFpQvBKZYi9AdhsKWFyLzul7va78sGwzjdejd0+jf0BPV7cOIYEZNskejlk+rgV+roOxS91BK8qkvaLaInLYnjKv6oMgl+ntKRX0gjXVXR6seO77rqLSZMmsWjRIgYOHAgcm0Sfk7HY8eGIFTsGuHKQk5e+aWKfX+WfVSFOzbJxVp6DPtHaT0IYnbMmoFIR3eEnMSwdjGmVyyIjSRBQdSI6NIQFDWGNL5s0JKCPx8LpOQ6G5rRf8lIPa+jtCMJplTkQNqaAvTKduFxdv4bo9GLHr7/+OsuXL2fOnDnk5ORw6aWXomndE5Nyohc77ihOi8wPTs1izR4fm+uMkPMd0WBCi2RMiVqmmMpA3wyjnms/jzVpN1IhjA1W9vtVKr0RyprD1AQ0Kr0qlV4vb+8xaiyNPox3vSPWrkqfSkA1tgYu6gTfTGfToRb169ePW2+9lVtuuYX333+fV155hQMHDjB79myuvfZaLrrooq5uZ5wTvdhxOlhliUmlHr6V7+C/dUF2NYU5GNZJfEhn22T6eKyckmFlYKatTROsJEl4rEatqEFZNi7ETWNY46uGEFvqQxwIanxSG+TTuqCxbVaxK57bHaMjuR8b9huGiqE5th6xDW9L0pKoLMuMGzeOcePG0dDQwMqVK3niiSeOqSCOx2LHXU2+08LFpUatq7Am8Ks6kgTuIwyziJFlUxhT5GJ0oZOK5ghr9/up8ql8WB3gy4YQk/t6WqWGtg73OHTsswNBdjVHkCUYXdizpqMxjnjMysnJie9Keiw5nooddwc2RcLWyZ55SZLon2mjX4aVrxvD/KfKx8Gwzgs7mzgrz864kuT00FThHl81hHhjt2FR+k6xK2kjmJ5Ez5vEpaClH2Ty5MlMnjy5m1pz8iJJEqdl2+mfYeXdvX421wX57ECIbQfDnFvg5Kw8e6uteA8EVT7cH+CLaAG3YTl2xha1nxnYXRwXgjDpWdgVmUmlHobl2Hmz0kttUGPtfj9r9/vJdyhG5p+A+pCWtGXXmEIn55f07MrlpiBMjphSj5U5Q7LZdjDMf2sDVPlU6oJaknlXAgZkWjmv2EWJu2eFaaTCFITJUSFLEsNy7AzLseOL6OzzqzRHNCQkMqwyJW7LcVF+JoYpCJNOw22VGZTVMzPhOsrxI10Tk2OAKQgTkwRMQZiYJGAKwsQkAVMQJiYJmIIwMUnAFISJSQKmIExMEjAFYWKSgCkIE5METEGYmCRgxjKlIBKJIIToMXtqxwrE7dy5s0eHTvdUwuFw19RlOlnoaZ1OkqQO1xUyaY0kSR3+TSUhjqNdtU1MuhhzDWFikoApCBOTBExBmJgkYArCxCQBUxAmJgmYgjAxScAUhIlJAqYgTEwSMAVhYpKAKQgTkwTMWKbjnB07dvDMM8+QkZFBfn4+N910U3c36bjGFMRxTkNDA/Pnzyc/P5+f/OQn3d2c4x4zuO844sUXX+S1116Lv3700UcpKCgA4JlnnqGwsJDp06d3U+tODExBHOeEQiEWL17MhAkTOn0n1pMRUxDHOX/4wx/YsGED/fr1w263s2jRou5u0nGNKYgegNfr5ZprruGpp56iT58+AKxevZolS5YQiUS47LLLuPnmm7u5lScHptm1m9m8eTPXXHMNZWVl8WO1tbU88sgjPPfcc/z73/9m06ZNfPDBB93YypMHUxDdzEsvvcQDDzyQtBn9unXrGDNmDLm5uVitVqZPn86qVau6sZUnD6bZtZtZvHhxq2M1NTVJAiksLKS6uvpYNuukxRwheiC6rrc61tMKH5yomILogRQXF1NbWxt/XVNTQ3FxcTe26OTBFEQPZOzYsWzYsIG6ujoikQivvvoq48aN6+5mnRSYa4geSFFREbfffjtz5swhHA4zfvx4Jk6c2N3NOikw/RAmJgmYUyYTkwRMQZiYJGAKwsQkAVMQJiYJmIIwMUnAFISJSQKmIExMEjAF0UHKy8sZMmQIr7zySvzY+PHjqaqq4pVXXuHOO+887Oevu+46Nm7c2OH7vfzyy1x44YUnTMLP9773vW6575133pn0m7WHKYgOsnz5ci6++GKWLVt2TO73r3/9iwULFnDPPfcck/t1NStXruzuJnQIUxAdQNM0VqxYwbx589i3bx9fffVVm+du3bqVH/zgB1x++eXMmjWLb775ptU5//u//8v06dO57LLLWLhwIaqqJr2/ZMkStmzZwoIFC3j77bcZP348v/jFL5g0aRL79+9n5cqVzJgxg+9973v86le/wuv1ArBixQomTZrEzJkzWbhwYXzUio1kAFVVVYwfPx6A+vp6br75ZmbMmMGMGTNYs2YNAE888QT33nsvs2bNYsKECSxYsCDetkcffZRJkyYxZcoUnn76aaqqqvjud7+LpmkAbNmyhauvvrrVdz7ttNPavXaMqqoqpkyZws0338yll17Kj3/8YxoaGgD49NNPufLKK5k2bRrXX389FRUVAJSVlXHdddcxbdo0rr76aj7//PM2f6PDIkza5Z133hFTp04VQgixaNEicd999wkhhLjwwgtFZWWlWL58uZg/f74Ih8Ni2rRporKyUgghxKZNm8Tll18uhBDi2muvFRs2bBAffPCBuPnmm0UkEhG6rot7771XPPfcc63uGTs/dp+XXnpJCCHEzp07xfe//30RCASEEEIsXbpUPPzww2Lfvn1i7Nixorq6WoTDYTFnzhwxf/78pHYKIURlZaW48MILhRBC3HrrreKNN94QQghx4MABcdFFF4m6ujrx+OOPixkzZohQKCS8Xq/4zne+I7Zt2ybeeOMNcfXVV4tgMCiCwaC44oorRHl5ubj++uvFe++9J4QQ4sEHHxQvvvhiq+8zePBgIYRo89qJVFZWisGDB4sPP/xQCCHE4sWLxa9//WsRCoXEuHHjxObNm4UQQqxatUrMmDFDCCHEzJkzxapVq4QQQmzevFmMGzdOhEIhMX/+fLF8+fIO/c5CCGEG93WA5cuXM3XqVACmTp3K9ddfzx133NHqvLKyMnbv3s3Pf/7z+LH6+nrC4XD89bp16/j888+ZOXMmYFTNUBSl3TaMHDkSgPXr11NRURF/CquqSmlpKZs3b+bss8+OJxZdfvnlrFu37rDXXLt2LTt27ODJJ5+MXys2oo0dOxabzYbNZqNfv340NjayceNGJk+ejN1uB4x1DsAVV1zBypUrGTNmDGvWrOHWW2897H1TXbslpaWljB07FoDp06dz2223UV5eTmZmJiNGjABg8uTJ3H///TQ3N1NRUcHkyZMBGDFiBFlZWezatevwf9QUmIJoh/r6et555x0+++wzXnzxRcDYtjfVnFjXdUpLS+PvCSGorq5O2kFU0zRmz57NnDlzAGhubu5Q8o/D4Yh/fsqUKdx7770A+P1+wuEwmzdvjm/fC2CxJP+0sfcikUhSe//+97+TnZ0NGHkXubm5bNy4Md7pwUhOEkKgKEpSW/fu3UtmZiYTJ07kt7/9LW+88QajRo3C4/Ec9rukunZLEh8SQghkWU6ZOCWEoKmpKeXxllPRjmCuIdrh1Vdf5eyzz+b9999nzZo1rFmzhttuuy3l4nrAgAE0Njby8ccfA8bCeO7cuUnnjBkzhpUrV+Lz+dA0jV/+8pcsX768w+0ZPXo0b731FnV1dYCRgrp06VLOOusstm7dyp49e9B1PSkHOycnh+3btwPw+uuvJ7XlH//4B2BY0aZOnZryaR1j1KhRvPXWW4TDYcLhMHPnzqWsrAy73c7EiRP53e9+Fx/5jpbdu3ezdetWwBihzzvvvPjf99NPPwVg1apVFBcX07t3b0pLS1m9ejVgrDNqamoYPHhw2vc1R4h2WL58OfPmzUs6dsUVV7BkyRICgUDScZvNxmOPPcZDDz1EMBjE5XLx+9//Pumc8ePHs337dq666io0TWPUqFH88Ic/7HB7hgwZws0338ycOXPQdZ2BAwdy55134na7WbhwITfeeCMWiyUpJ/uWW25h4cKFPPnkk0yaNCl+/N577+WBBx5g2rRpCCFYtGgReXl5bd77oosu4ssvv2TmzJnous7MmTMZPnw4AFOmTOG9995j1KhRHf4uhyM7O5ulS5dSUVHBoEGDWLRoETabjT/+8Y8sWrSIQCBARkYGf/rTnwD43e9+x69//WuWLl2K1WrliSeeOKK9vc18iBOUV155hY8++oiHH364y++laRpPPPEEDoej1Yh4JFRVVTFr1qy41etYYo4QJkfNzJkzycjI4M9//nN3N+WoMUcIE5MEzEW1iUkCpiBMTBIwBWFikoApCBOTBExBmJgkYArCxCQBUxAmJgmYgjAxScAUhIlJAv8fjO8+T/08wwcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 150x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The following is sub-optimal for several reasons, including because it needs to be adapted to any hotspot\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(1.5,1.5),\n",
    "              'font.size':8,\n",
    "              'legend.title_fontsize':8, 'legend.fontsize':8,\n",
    "              'axes.labelsize':8,'axes.titlesize':8,\n",
    "              'xtick.labelsize':8, 'ytick.labelsize':8,\n",
    "              'xtick.major.pad':1, 'ytick.major.pad':1,\n",
    "             },\n",
    "       style='whitegrid')\n",
    "\n",
    "fig = sns.scatterplot(corrdf[corrdf.Nham_aa_1p == 'non-WT'],\n",
    "                      x='average_freq_pool', y='average_freq_input',\n",
    "                      color='skyblue', alpha=.2,\n",
    "                      legend=False)\n",
    "sns.scatterplot(corrdf[corrdf.Nham_aa_1p == 'WT'],\n",
    "                x='average_freq_pool', y='average_freq_input',\n",
    "                color='black', legend=False)\n",
    "sns.kdeplot(corrdf,x='average_freq_pool', y='average_freq_input',\n",
    "            hue='Nham_aa_1p', hue_order=['WT','non-WT'], palette=['black','skyblue'], common_norm=False,\n",
    "            levels=5, log_scale=True,legend=False)\n",
    "\n",
    "fig.set(xscale='log', xlabel='Allele frequency in pool',\n",
    "        yscale='log', ylabel='Allele frequency\\nin DMS input',\n",
    "        xlim=(None,5e-2) #7e-2for fks2-hs1, 5e-2 for fks2-hs2\n",
    "       )\n",
    "both_perc = ratios.loc[ratios.Location == 'both', 'aa_seq'].values[0] / unique_aa\n",
    "fig.annotate(f'{both_perc:.1%}',(.72,.9), xycoords='axes fraction')\n",
    "\n",
    "plt.savefig(f'{graph_outpath}/{\"_\".join([strain,locus,pool_type])}_bottleneck.svg', format='svg', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4bd006-9cd7-4aca-8f1a-edc04aebcea8",
   "metadata": {},
   "source": [
    "### Represented species in ortholog diversity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d51c55-6fd8-4969-9586-a9a2ca1af4da",
   "metadata": {},
   "source": [
    "The following section does not currently support FKS2 hotspots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "11a25451-1bef-4366-8452-f7a1971cea8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Identifier</th>\n",
       "      <th>Species</th>\n",
       "      <th>Kingdom</th>\n",
       "      <th>Phylum</th>\n",
       "      <th>Hotspot1</th>\n",
       "      <th>Hotspot2</th>\n",
       "      <th>GapsHotspot1</th>\n",
       "      <th>GapsHotspot2</th>\n",
       "      <th>HS1_in_oPools</th>\n",
       "      <th>HS2_in_oPools</th>\n",
       "      <th>Is_Human_Pathogen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M!149570508_CHLRE</td>\n",
       "      <td>Chlamydomonas reinhardtii</td>\n",
       "      <td>Viridiplantae</td>\n",
       "      <td>Chlorophyta</td>\n",
       "      <td>AICSDLRED</td>\n",
       "      <td>QLGT----</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>M!114407260_ARATH</td>\n",
       "      <td>Arabidopsis thaliana</td>\n",
       "      <td>Viridiplantae</td>\n",
       "      <td>Streptophyta</td>\n",
       "      <td>LQVKPMIKP</td>\n",
       "      <td>GVILNQQF</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M!114427513_ARATH</td>\n",
       "      <td>Arabidopsis thaliana</td>\n",
       "      <td>Viridiplantae</td>\n",
       "      <td>Streptophyta</td>\n",
       "      <td>LQIRPLIAP</td>\n",
       "      <td>NQQFIIQL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M!150549500_PHYBL</td>\n",
       "      <td>Phycomyces blakesleeanus</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Mucoromycota</td>\n",
       "      <td>FLALSFRDA</td>\n",
       "      <td>EWIQRCVL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M!85749882_RHIOR</td>\n",
       "      <td>Rhizopus oryzae</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Mucoromycota</td>\n",
       "      <td>FLALSFRDA</td>\n",
       "      <td>DWLQRCML</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>M!104818385_545650</td>\n",
       "      <td>Sporothrix brasiliensis</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>YLTRAFRDS</td>\n",
       "      <td>DWVYRCIL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>M!97086748_209285</td>\n",
       "      <td>Chaetomium thermophilum</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>YLTLSIRDP</td>\n",
       "      <td>DWIYRSVL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>M!76899967_CHAGS</td>\n",
       "      <td>Chaetomium globosum</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>FLALSIRDP</td>\n",
       "      <td>DWVYRCIL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>M!113676877_2587410</td>\n",
       "      <td>Thermothielavioides terrestris</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>YLTLSLRDP</td>\n",
       "      <td>DWVYRSVL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>M!90305978_100816</td>\n",
       "      <td>Madurella mycetomatis</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>YLTLSIRDP</td>\n",
       "      <td>DWIYRSIL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>231 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Identifier                         Species        Kingdom  \\\n",
       "0      M!149570508_CHLRE       Chlamydomonas reinhardtii  Viridiplantae   \n",
       "1      M!114407260_ARATH            Arabidopsis thaliana  Viridiplantae   \n",
       "2      M!114427513_ARATH            Arabidopsis thaliana  Viridiplantae   \n",
       "3      M!150549500_PHYBL        Phycomyces blakesleeanus          Fungi   \n",
       "4       M!85749882_RHIOR                 Rhizopus oryzae          Fungi   \n",
       "..                   ...                             ...            ...   \n",
       "226   M!104818385_545650         Sporothrix brasiliensis          Fungi   \n",
       "227    M!97086748_209285         Chaetomium thermophilum          Fungi   \n",
       "228     M!76899967_CHAGS             Chaetomium globosum          Fungi   \n",
       "229  M!113676877_2587410  Thermothielavioides terrestris          Fungi   \n",
       "230    M!90305978_100816           Madurella mycetomatis          Fungi   \n",
       "\n",
       "           Phylum   Hotspot1  Hotspot2  GapsHotspot1  GapsHotspot2  \\\n",
       "0     Chlorophyta  AICSDLRED  QLGT----         False          True   \n",
       "1    Streptophyta  LQVKPMIKP  GVILNQQF         False         False   \n",
       "2    Streptophyta  LQIRPLIAP  NQQFIIQL         False         False   \n",
       "3    Mucoromycota  FLALSFRDA  EWIQRCVL         False         False   \n",
       "4    Mucoromycota  FLALSFRDA  DWLQRCML         False         False   \n",
       "..            ...        ...       ...           ...           ...   \n",
       "226    Ascomycota  YLTRAFRDS  DWVYRCIL         False         False   \n",
       "227    Ascomycota  YLTLSIRDP  DWIYRSVL         False         False   \n",
       "228    Ascomycota  FLALSIRDP  DWVYRCIL         False         False   \n",
       "229    Ascomycota  YLTLSLRDP  DWVYRSVL         False         False   \n",
       "230    Ascomycota  YLTLSIRDP  DWIYRSIL         False         False   \n",
       "\n",
       "     HS1_in_oPools  HS2_in_oPools  Is_Human_Pathogen  \n",
       "0             True          False              False  \n",
       "1             True           True              False  \n",
       "2             True           True              False  \n",
       "3             True           True              False  \n",
       "4             True           True              False  \n",
       "..             ...            ...                ...  \n",
       "226           True           True              False  \n",
       "227           True           True              False  \n",
       "228           True           True              False  \n",
       "229           True           True              False  \n",
       "230           True           True              False  \n",
       "\n",
       "[231 rows x 11 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxdf = pd.read_csv(orthoTAXpath, header=0)\n",
    "taxdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "38e422fe-e352-4015-b2a1-2d2c4b021d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Hotspot2</th>\n",
       "      <th>Kingdom</th>\n",
       "      <th>Phylum</th>\n",
       "      <th>Nb_species</th>\n",
       "      <th>at_least_one_pathogen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>--------</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota, Ascomycota]</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AWVNRCIA</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DWIDRCIA</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DWIERCCV</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DWIKRCIM</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>QWVERCIV</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>QWVQRSIF</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>17</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>QWVQRSVL</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>RWIKRCII</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>SWLKRCVI</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hotspot2  Kingdom                       Phylum  Nb_species  \\\n",
       "0   --------  [Fungi]  [Basidiomycota, Ascomycota]           2   \n",
       "1   AWVNRCIA  [Fungi]                 [Ascomycota]           1   \n",
       "2   DWIDRCIA  [Fungi]                 [Ascomycota]           1   \n",
       "3   DWIERCCV  [Fungi]                 [Ascomycota]           1   \n",
       "4   DWIKRCIM  [Fungi]              [Basidiomycota]           1   \n",
       "..       ...      ...                          ...         ...   \n",
       "92  QWVERCIV  [Fungi]                 [Ascomycota]           1   \n",
       "93  QWVQRSIF  [Fungi]                 [Ascomycota]          17   \n",
       "94  QWVQRSVL  [Fungi]                 [Ascomycota]           1   \n",
       "95  RWIKRCII  [Fungi]              [Basidiomycota]           1   \n",
       "96  SWLKRCVI  [Fungi]                 [Ascomycota]           2   \n",
       "\n",
       "    at_least_one_pathogen  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "..                    ...  \n",
       "92                  False  \n",
       "93                  False  \n",
       "94                  False  \n",
       "95                   True  \n",
       "96                  False  \n",
       "\n",
       "[97 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxgby = taxdf.groupby('Hotspot'+locus[-1])[['Phylum','Kingdom','Species','Is_Human_Pathogen']\n",
    "                                  ].agg(Kingdom = ('Kingdom', 'unique'),\n",
    "                                        Phylum = ('Phylum', 'unique'),\n",
    "                                        Nb_species = ('Species', 'nunique'),\n",
    "                                        at_least_one_pathogen = ('Is_Human_Pathogen', 'any')\n",
    "                                       ).reset_index()\n",
    "taxgby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "228d25eb-4c23-4b3a-b6c2-a3e568ceefdd",
   "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>aa_seq</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>confidence_score</th>\n",
       "      <th>inputO1</th>\n",
       "      <th>average_freq_pool</th>\n",
       "      <th>input1_abundance</th>\n",
       "      <th>input2_abundance</th>\n",
       "      <th>input3_abundance</th>\n",
       "      <th>average_freq_input</th>\n",
       "      <th>Location</th>\n",
       "      <th>Nham_aa_1p</th>\n",
       "      <th>Kingdom</th>\n",
       "      <th>Phylum</th>\n",
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       "      <th>at_least_one_pathogen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AWVNRCIA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016938</td>\n",
       "      <td>0.016938</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>0.002032</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DWIKRCIM</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010996</td>\n",
       "      <td>0.010996</td>\n",
       "      <td>0.006020</td>\n",
       "      <td>0.002409</td>\n",
       "      <td>0.005151</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DWIKRCIV</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014140</td>\n",
       "      <td>0.014140</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DWIKRCVI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015028</td>\n",
       "      <td>0.015028</td>\n",
       "      <td>0.004254</td>\n",
       "      <td>0.001629</td>\n",
       "      <td>0.003428</td>\n",
       "      <td>0.003103</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DWIRRCII</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013576</td>\n",
       "      <td>0.013576</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>0.002548</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota, Basidiomycota]</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DWIRRCIV</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014780</td>\n",
       "      <td>0.014780</td>\n",
       "      <td>0.004995</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>0.004545</td>\n",
       "      <td>0.003804</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DWIRRCVL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.027985</td>\n",
       "      <td>0.027985</td>\n",
       "      <td>0.005843</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DWIRRYTL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011392</td>\n",
       "      <td>0.011392</td>\n",
       "      <td>0.003123</td>\n",
       "      <td>0.001417</td>\n",
       "      <td>0.002499</td>\n",
       "      <td>0.002346</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DWIYRCIV</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012584</td>\n",
       "      <td>0.012584</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DWIYRCVL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025216</td>\n",
       "      <td>0.025216</td>\n",
       "      <td>0.017005</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>0.014838</td>\n",
       "      <td>0.012773</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DWLQRCML</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022132</td>\n",
       "      <td>0.022132</td>\n",
       "      <td>0.002091</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.002007</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Mucoromycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DWLRRCII</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011405</td>\n",
       "      <td>0.011405</td>\n",
       "      <td>0.002788</td>\n",
       "      <td>0.001122</td>\n",
       "      <td>0.002787</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DWTRRYTL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.021171</td>\n",
       "      <td>0.021171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DWVARCIV</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014623</td>\n",
       "      <td>0.014623</td>\n",
       "      <td>0.003622</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>0.003249</td>\n",
       "      <td>0.002790</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DWVARSIA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DWVFRSIL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017233</td>\n",
       "      <td>0.017233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DWVFRSVL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022505</td>\n",
       "      <td>0.022505</td>\n",
       "      <td>0.002740</td>\n",
       "      <td>0.001113</td>\n",
       "      <td>0.002444</td>\n",
       "      <td>0.002099</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DWVKRCIL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013127</td>\n",
       "      <td>0.013127</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.001341</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Mucoromycota, Ascomycota]</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DWVNRCIA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022069</td>\n",
       "      <td>0.022069</td>\n",
       "      <td>0.009194</td>\n",
       "      <td>0.003647</td>\n",
       "      <td>0.008028</td>\n",
       "      <td>0.006956</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DWVNRCII</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015199</td>\n",
       "      <td>0.015199</td>\n",
       "      <td>0.007732</td>\n",
       "      <td>0.002850</td>\n",
       "      <td>0.007178</td>\n",
       "      <td>0.005920</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DWVNRCIL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016196</td>\n",
       "      <td>0.016196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DWVNRCVI</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015649</td>\n",
       "      <td>0.015649</td>\n",
       "      <td>0.004117</td>\n",
       "      <td>0.001789</td>\n",
       "      <td>0.003711</td>\n",
       "      <td>0.003206</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DWVQRCIV</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022412</td>\n",
       "      <td>0.022412</td>\n",
       "      <td>0.002371</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.001864</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DWVQRCTA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011599</td>\n",
       "      <td>0.011599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DWVQRCVF</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019173</td>\n",
       "      <td>0.019173</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>DWVRRYTL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013308</td>\n",
       "      <td>0.013308</td>\n",
       "      <td>0.012266</td>\n",
       "      <td>0.277230</td>\n",
       "      <td>0.012541</td>\n",
       "      <td>0.100679</td>\n",
       "      <td>both</td>\n",
       "      <td>WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>DWVTRSIV</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>0.018539</td>\n",
       "      <td>0.010370</td>\n",
       "      <td>0.003981</td>\n",
       "      <td>0.009374</td>\n",
       "      <td>0.007908</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>DWVYRCIL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019673</td>\n",
       "      <td>0.019673</td>\n",
       "      <td>0.006365</td>\n",
       "      <td>0.002387</td>\n",
       "      <td>0.005663</td>\n",
       "      <td>0.004805</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>DWVYRCVL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012395</td>\n",
       "      <td>0.012395</td>\n",
       "      <td>0.002395</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>DWVYRSIL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020046</td>\n",
       "      <td>0.020046</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>DWVYRSVL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.021203</td>\n",
       "      <td>0.021203</td>\n",
       "      <td>0.013144</td>\n",
       "      <td>0.004964</td>\n",
       "      <td>0.011997</td>\n",
       "      <td>0.010035</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>EWIERCII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013043</td>\n",
       "      <td>0.013043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>EWIQRCVL</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018752</td>\n",
       "      <td>0.018752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Mucoromycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>EWIRRCII</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014278</td>\n",
       "      <td>0.014278</td>\n",
       "      <td>0.006130</td>\n",
       "      <td>0.002335</td>\n",
       "      <td>0.005484</td>\n",
       "      <td>0.004650</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>EWIRRYTL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016977</td>\n",
       "      <td>0.016977</td>\n",
       "      <td>0.005077</td>\n",
       "      <td>0.002058</td>\n",
       "      <td>0.004257</td>\n",
       "      <td>0.003797</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>EWLKRCIL</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019502</td>\n",
       "      <td>0.019502</td>\n",
       "      <td>0.004691</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.004143</td>\n",
       "      <td>0.003514</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>EWVKRCIL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012852</td>\n",
       "      <td>0.012852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Mucoromycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>EWVQRCTA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015235</td>\n",
       "      <td>0.015235</td>\n",
       "      <td>0.010585</td>\n",
       "      <td>0.004176</td>\n",
       "      <td>0.009359</td>\n",
       "      <td>0.008040</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>GVILNQQF</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013733</td>\n",
       "      <td>0.013733</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>0.001997</td>\n",
       "      <td>0.004486</td>\n",
       "      <td>0.003910</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Viridiplantae]</td>\n",
       "      <td>[Streptophyta]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>KWIKRCII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.008750</td>\n",
       "      <td>0.003318</td>\n",
       "      <td>0.007928</td>\n",
       "      <td>0.006666</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>LWIKRTII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007498</td>\n",
       "      <td>0.007498</td>\n",
       "      <td>0.000540</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>NWLKRCII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006196</td>\n",
       "      <td>0.006196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>NWLKRVVI</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015276</td>\n",
       "      <td>0.015276</td>\n",
       "      <td>0.007930</td>\n",
       "      <td>0.002855</td>\n",
       "      <td>0.007114</td>\n",
       "      <td>0.005966</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Mucoromycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NWVDRCVI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012377</td>\n",
       "      <td>0.012377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>NWVNRCIV</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012670</td>\n",
       "      <td>0.012670</td>\n",
       "      <td>0.007544</td>\n",
       "      <td>0.003145</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.005916</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>NWVNRCVI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015563</td>\n",
       "      <td>0.015563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>NWVWRCVI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010739</td>\n",
       "      <td>0.010739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NWVYRCII</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008487</td>\n",
       "      <td>0.008487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>RWIKRCII</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012388</td>\n",
       "      <td>0.012388</td>\n",
       "      <td>0.005426</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>0.004804</td>\n",
       "      <td>0.004070</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Basidiomycota]</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>SWLKRCVI</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>0.014233</td>\n",
       "      <td>0.005033</td>\n",
       "      <td>0.002001</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>0.003928</td>\n",
       "      <td>both</td>\n",
       "      <td>non-WT</td>\n",
       "      <td>[Fungi]</td>\n",
       "      <td>[Ascomycota]</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      aa_seq  Nham_aa  confidence_score   inputO1  average_freq_pool  \\\n",
       "0   AWVNRCIA      5.0               1.0  0.016938           0.016938   \n",
       "1   DWIKRCIM      5.0               1.0  0.010996           0.010996   \n",
       "2   DWIKRCIV      5.0               1.0  0.014140           0.014140   \n",
       "3   DWIKRCVI      5.0               1.0  0.015028           0.015028   \n",
       "4   DWIRRCII      4.0               1.0  0.013576           0.013576   \n",
       "5   DWIRRCIV      4.0               1.0  0.014780           0.014780   \n",
       "6   DWIRRCVL      3.0               1.0  0.027985           0.027985   \n",
       "7   DWIRRYTL      1.0               1.0  0.011392           0.011392   \n",
       "8   DWIYRCIV      5.0               1.0  0.012584           0.012584   \n",
       "9   DWIYRCVL      4.0               1.0  0.025216           0.025216   \n",
       "10  DWLQRCML      4.0               1.0  0.022132           0.022132   \n",
       "11  DWLRRCII      4.0               1.0  0.011405           0.011405   \n",
       "12  DWTRRYTL      1.0               1.0  0.021171           0.021171   \n",
       "13  DWVARCIV      4.0               1.0  0.014623           0.014623   \n",
       "14  DWVARSIA      4.0               1.0  0.023772           0.023772   \n",
       "15  DWVFRSIL      3.0               1.0  0.017233           0.017233   \n",
       "16  DWVFRSVL      3.0               1.0  0.022505           0.022505   \n",
       "17  DWVKRCIL      3.0               1.0  0.013127           0.013127   \n",
       "18  DWVNRCIA      4.0               1.0  0.022069           0.022069   \n",
       "19  DWVNRCII      4.0               1.0  0.015199           0.015199   \n",
       "20  DWVNRCIL      3.0               1.0  0.016196           0.016196   \n",
       "21  DWVNRCVI      4.0               1.0  0.015649           0.015649   \n",
       "22  DWVQRCIV      4.0               1.0  0.022412           0.022412   \n",
       "23  DWVQRCTA      3.0               1.0  0.011599           0.011599   \n",
       "24  DWVQRCVF      4.0               1.0  0.019173           0.019173   \n",
       "25  DWVRRYTL      0.0               1.0  0.013308           0.013308   \n",
       "26  DWVTRSIV      4.0               1.0  0.018539           0.018539   \n",
       "27  DWVYRCIL      3.0               1.0  0.019673           0.019673   \n",
       "28  DWVYRCVL      3.0               1.0  0.012395           0.012395   \n",
       "29  DWVYRSIL      3.0               1.0  0.020046           0.020046   \n",
       "30  DWVYRSVL      3.0               1.0  0.021203           0.021203   \n",
       "31  EWIERCII      6.0               1.0  0.013043           0.013043   \n",
       "32  EWIQRCVL      5.0               1.0  0.018752           0.018752   \n",
       "33  EWIRRCII      5.0               1.0  0.014278           0.014278   \n",
       "34  EWIRRYTL      2.0               1.0  0.016977           0.016977   \n",
       "35  EWLKRCIL      5.0               1.0  0.019502           0.019502   \n",
       "36  EWVKRCIL      4.0               1.0  0.012852           0.012852   \n",
       "37  EWVQRCTA      4.0               1.0  0.015235           0.015235   \n",
       "38  GVILNQQF      8.0               1.0  0.013733           0.013733   \n",
       "39  KWIKRCII      6.0               1.0  0.011494           0.011494   \n",
       "40  LWIKRTII      6.0               1.0  0.007498           0.007498   \n",
       "41  NWLKRCII      6.0               1.0  0.006196           0.006196   \n",
       "42  NWLKRVVI      6.0               1.0  0.015276           0.015276   \n",
       "43  NWVDRCVI      5.0               1.0  0.012377           0.012377   \n",
       "44  NWVNRCIV      5.0               1.0  0.012670           0.012670   \n",
       "45  NWVNRCVI      5.0               1.0  0.015563           0.015563   \n",
       "46  NWVWRCVI      5.0               1.0  0.010739           0.010739   \n",
       "47  NWVYRCII      5.0               1.0  0.008487           0.008487   \n",
       "48  RWIKRCII      6.0               1.0  0.012388           0.012388   \n",
       "49  SWLKRCVI      6.0               1.0  0.014233           0.014233   \n",
       "\n",
       "    input1_abundance  input2_abundance  input3_abundance  average_freq_input  \\\n",
       "0           0.002310          0.000875          0.002032            0.001739   \n",
       "1           0.006020          0.002409          0.005151            0.004527   \n",
       "2           0.000403          0.000139          0.000343            0.000295   \n",
       "3           0.004254          0.001629          0.003428            0.003103   \n",
       "4           0.003348          0.001321          0.002976            0.002548   \n",
       "5           0.004995          0.001871          0.004545            0.003804   \n",
       "6           0.005843          0.002261          0.004848            0.004317   \n",
       "7           0.003123          0.001417          0.002499            0.002346   \n",
       "8           0.000178          0.000082          0.000169            0.000143   \n",
       "9           0.017005          0.006476          0.014838            0.012773   \n",
       "10          0.002091          0.000996          0.002007            0.001698   \n",
       "11          0.002788          0.001122          0.002787            0.002232   \n",
       "12               NaN               NaN               NaN                 NaN   \n",
       "13          0.003622          0.001499          0.003249            0.002790   \n",
       "14               NaN               NaN               NaN                 NaN   \n",
       "15               NaN               NaN               NaN                 NaN   \n",
       "16          0.002740          0.001113          0.002444            0.002099   \n",
       "17          0.001712          0.000663          0.001649            0.001341   \n",
       "18          0.009194          0.003647          0.008028            0.006956   \n",
       "19          0.007732          0.002850          0.007178            0.005920   \n",
       "20               NaN               NaN               NaN                 NaN   \n",
       "21          0.004117          0.001789          0.003711            0.003206   \n",
       "22          0.002371          0.001009          0.002211            0.001864   \n",
       "23               NaN               NaN               NaN                 NaN   \n",
       "24          0.000246          0.000078          0.000333            0.000219   \n",
       "25          0.012266          0.277230          0.012541            0.100679   \n",
       "26          0.010370          0.003981          0.009374            0.007908   \n",
       "27          0.006365          0.002387          0.005663            0.004805   \n",
       "28          0.002395          0.000910          0.002116            0.001807   \n",
       "29          0.000352          0.000052          0.000328            0.000244   \n",
       "30          0.013144          0.004964          0.011997            0.010035   \n",
       "31               NaN               NaN               NaN                 NaN   \n",
       "32               NaN               NaN               NaN                 NaN   \n",
       "33          0.006130          0.002335          0.005484            0.004650   \n",
       "34          0.005077          0.002058          0.004257            0.003797   \n",
       "35          0.004691          0.001707          0.004143            0.003514   \n",
       "36               NaN               NaN               NaN                 NaN   \n",
       "37          0.010585          0.004176          0.009359            0.008040   \n",
       "38          0.005248          0.001997          0.004486            0.003910   \n",
       "39          0.008750          0.003318          0.007928            0.006666   \n",
       "40          0.000540          0.000100          0.000328            0.000322   \n",
       "41               NaN               NaN               NaN                 NaN   \n",
       "42          0.007930          0.002855          0.007114            0.005966   \n",
       "43               NaN               NaN               NaN                 NaN   \n",
       "44          0.007544          0.003145          0.007059            0.005916   \n",
       "45               NaN               NaN               NaN                 NaN   \n",
       "46               NaN               NaN               NaN                 NaN   \n",
       "47               NaN               NaN               NaN                 NaN   \n",
       "48          0.005426          0.001980          0.004804            0.004070   \n",
       "49          0.005033          0.002001          0.004749            0.003928   \n",
       "\n",
       "     Location Nham_aa_1p          Kingdom                       Phylum  \\\n",
       "0        both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "1        both     non-WT          [Fungi]              [Basidiomycota]   \n",
       "2        both     non-WT          [Fungi]              [Basidiomycota]   \n",
       "3        both     non-WT          [Fungi]              [Basidiomycota]   \n",
       "4        both     non-WT          [Fungi]  [Ascomycota, Basidiomycota]   \n",
       "5        both     non-WT          [Fungi]              [Basidiomycota]   \n",
       "6        both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "7        both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "8        both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "9        both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "10       both     non-WT          [Fungi]               [Mucoromycota]   \n",
       "11       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "12  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "13       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "14  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "15  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "16       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "17       both     non-WT          [Fungi]   [Mucoromycota, Ascomycota]   \n",
       "18       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "19       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "20  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "21       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "22       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "23  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "24       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "25       both         WT          [Fungi]                 [Ascomycota]   \n",
       "26       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "27       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "28       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "29       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "30       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "31  left_only     non-WT          [Fungi]              [Basidiomycota]   \n",
       "32  left_only     non-WT          [Fungi]               [Mucoromycota]   \n",
       "33       both     non-WT          [Fungi]              [Basidiomycota]   \n",
       "34       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "35       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "36  left_only     non-WT          [Fungi]               [Mucoromycota]   \n",
       "37       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "38       both     non-WT  [Viridiplantae]               [Streptophyta]   \n",
       "39       both     non-WT          [Fungi]              [Basidiomycota]   \n",
       "40       both     non-WT          [Fungi]              [Basidiomycota]   \n",
       "41  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "42       both     non-WT          [Fungi]               [Mucoromycota]   \n",
       "43  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "44       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "45  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "46  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "47  left_only     non-WT          [Fungi]                 [Ascomycota]   \n",
       "48       both     non-WT          [Fungi]              [Basidiomycota]   \n",
       "49       both     non-WT          [Fungi]                 [Ascomycota]   \n",
       "\n",
       "    Nb_species  at_least_one_pathogen  \n",
       "0            1                  False  \n",
       "1            1                  False  \n",
       "2            2                  False  \n",
       "3            1                  False  \n",
       "4            3                  False  \n",
       "5            1                  False  \n",
       "6            1                  False  \n",
       "7            8                   True  \n",
       "8            5                  False  \n",
       "9            1                  False  \n",
       "10           1                  False  \n",
       "11           1                  False  \n",
       "12           1                  False  \n",
       "13           4                  False  \n",
       "14           1                  False  \n",
       "15           2                  False  \n",
       "16           1                  False  \n",
       "17           2                  False  \n",
       "18           1                  False  \n",
       "19           3                   True  \n",
       "20           7                  False  \n",
       "21           4                  False  \n",
       "22           7                  False  \n",
       "23           1                  False  \n",
       "24           6                  False  \n",
       "25          15                   True  \n",
       "26           1                  False  \n",
       "27           8                  False  \n",
       "28           3                  False  \n",
       "29           1                  False  \n",
       "30           1                  False  \n",
       "31           1                  False  \n",
       "32           1                  False  \n",
       "33           1                  False  \n",
       "34           1                  False  \n",
       "35           3                  False  \n",
       "36           1                  False  \n",
       "37           1                  False  \n",
       "38           1                  False  \n",
       "39           1                  False  \n",
       "40           1                  False  \n",
       "41           2                  False  \n",
       "42           1                  False  \n",
       "43           2                  False  \n",
       "44           5                   True  \n",
       "45           9                  False  \n",
       "46           1                  False  \n",
       "47           3                  False  \n",
       "48           1                   True  \n",
       "49           2                  False  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotdf = pd.merge(left=corrdf, right=taxgby.rename(columns={'Hotspot'+locus[-1]:'aa_seq'}), on='aa_seq')\n",
    "annotdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f3658cfe-4b0c-4587-b9e6-950d490c873c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\duran\\AppData\\Local\\Temp\\ipykernel_4820\\1366991320.py:23: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.\n",
      "  sns.kdeplot(annotdf,x='average_freq_pool', y='average_freq_input',\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 656.375x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The following is sub-optimal for several reasons, including because it needs to be adapted to any hotspot\n",
    "\n",
    "# Setting up palette for number of mutated residues\n",
    "Nham_norm = plt.Normalize(0, annotdf.Nham_aa.max())\n",
    "sm = cm.ScalarMappable(cmap='crest_r', norm=Nham_norm)\n",
    "sm.set_array([])\n",
    "\n",
    "sns.set(rc = {#'figure.figsize':(1.9,1.9),\n",
    "              'font.size':8,\n",
    "              'legend.title_fontsize':8, 'legend.fontsize':8,\n",
    "              'axes.labelsize':8,'axes.titlesize':8,\n",
    "              'xtick.labelsize':8, 'ytick.labelsize':8,\n",
    "              'xtick.major.pad':1, 'ytick.major.pad':1,\n",
    "             },\n",
    "       style='whitegrid')\n",
    "\n",
    "fig = sns.relplot(annotdf, x='average_freq_pool', y='average_freq_input',\n",
    "                  hue='Nham_aa', hue_order=sorted(annotdf.Nham_aa.unique()), palette='crest_r',\n",
    "                  size='Nb_species', sizes=(20,200),\n",
    "                  style='at_least_one_pathogen',\n",
    "                  #height=1.9\n",
    "                 )\n",
    "sns.kdeplot(annotdf,x='average_freq_pool', y='average_freq_input',\n",
    "            hue='Nham_aa_1p', hue_order=['WT','non-WT'], palette=['black','skyblue'], common_norm=False,\n",
    "            levels=5, log_scale=True,legend=False)\n",
    "\n",
    "fig.set(xscale='log', xlabel='Allele frequency in pool',\n",
    "        #xlim=(5e-3,1e-1), #for hotspot1\n",
    "        #xlim=(3e-3,3e-2),\n",
    "        yscale='log', ylabel='Allele frequency\\nin DMS input')\n",
    "\n",
    "# Custom color bar for number of mutated residues\n",
    "cax = plt.axes([0.67, 0.7, 0.2, 0.02]) # left bottom width height -- easy fix to prevent from stealing space to adjacent plot\n",
    "cbar = plt.colorbar(sm, orientation='horizontal', cax=cax, label='# Mutated residues')\n",
    "#cbar.ax.set_ylabel('# Mutated residues', rotation=0, loc='top')\n",
    "\n",
    "#print(fig._legend.texts)\n",
    "#for i, v in enumerate(['']*6+['Found in human pathogen', 'No', 'Yes', '# Species','6','12','18','24','30','36']):\n",
    "#    fig._legend.texts[i].set_text(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5175f70b-f0a7-43be-9782-61e3b6980010",
   "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
}