preference_transform.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f121a4d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "AA  = ['A', 'R', 'N', 'D', 'C', 'E', 'Q', 'G', 'H', 'I','L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V']\n",
    "Amino_acid_dict = {'Ala': 'A',\n",
    "                   'Arg': 'R',\n",
    "                   'Asn': 'N',\n",
    "                   'Asp': 'D',\n",
    "                   'Cys': 'C',\n",
    "                   'Gln': 'Q',\n",
    "                   'Glu': 'E',\n",
    "                   'Gly': 'G',\n",
    "                   'His': 'H',\n",
    "                   'Ile': 'I',\n",
    "                   'Leu': 'L',\n",
    "                   'Lys': 'K',\n",
    "                   'Met': 'M',\n",
    "                   'Phe': 'F',\n",
    "                   'Pro': 'P',\n",
    "                   'Ser': 'S',\n",
    "                   'Thr': 'T',\n",
    "                   'Trp': 'W',\n",
    "                   'Tyr': 'Y',\n",
    "                   'Val': 'V',\n",
    "                   'Ter': '*'\n",
    "                  }\n",
    "def codon2aa(c, noq=False):              # Returns the amino acid character corresponding to the input codon.\n",
    "    if c[0]=='-' and c[1]=='-' and c[2]=='-': return '-'        # If all nucleotides are missing, return gap\n",
    "    elif c[0]=='-' or c[1]=='-' or c[2]=='-':                   # Else if some nucleotides are missing, return '?'\n",
    "        if noq: return '-'\n",
    "        else:   return '?'\n",
    "    # If the first or second nucleotide is ambiguous, AA cannot be determined, return 'X'\n",
    "    elif c[0] in ['W', 'S', 'M', 'K', 'R', 'Y'] or c[1] in ['W', 'S', 'M', 'K', 'R', 'Y']: return 'X'     \n",
    "                                                    \n",
    "    elif c[0]=='T':                                             # Else go to tree\n",
    "        if c[1]=='T':\n",
    "            if    c[2] in ['T', 'C', 'Y']: return 'F'\n",
    "            elif  c[2] in ['A', 'G', 'R']: return 'L'\n",
    "            else:                          return 'X'\n",
    "        elif c[1]=='C':                    return 'S'\n",
    "        elif c[1]=='A':\n",
    "            if    c[2] in ['T', 'C', 'Y']: return 'Y'\n",
    "            elif  c[2] in ['A', 'G', 'R']: return '*'\n",
    "            else:                          return 'X'\n",
    "        elif c[1]=='G':\n",
    "            if    c[2] in ['T', 'C', 'Y']: return 'C'\n",
    "            elif  c[2]=='A':               return '*'\n",
    "            elif  c[2]=='G':               return 'W'\n",
    "            else:                          return 'X'\n",
    "        else:                              return 'X'\n",
    "        \n",
    "    elif c[0]=='C':\n",
    "        if   c[1]=='T':                    return 'L'\n",
    "        elif c[1]=='C':                    return 'P'\n",
    "        elif c[1]=='A':\n",
    "            if    c[2] in ['T', 'C', 'Y']: return 'H'\n",
    "            elif  c[2] in ['A', 'G', 'R']: return 'Q'\n",
    "            else:                          return 'X'\n",
    "        elif c[1]=='G':                    return 'R'\n",
    "        else:                              return 'X'\n",
    "        \n",
    "    elif c[0]=='A':\n",
    "        if c[1]=='T':\n",
    "            if    c[2] in ['T', 'C', 'Y']: return 'I'\n",
    "            elif  c[2] in ['A', 'M', 'W']: return 'I'\n",
    "            elif  c[2]=='G':               return 'M'\n",
    "            else:                          return 'X'\n",
    "        elif c[1]=='C':                    return 'T'\n",
    "        elif c[1]=='A':\n",
    "            if    c[2] in ['T', 'C', 'Y']: return 'N'\n",
    "            elif  c[2] in ['A', 'G', 'R']: return 'K'\n",
    "            else:                          return 'X'\n",
    "        elif c[1]=='G':\n",
    "            if    c[2] in ['T', 'C', 'Y']: return 'S'\n",
    "            elif  c[2] in ['A', 'G', 'R']: return 'R'\n",
    "            else:                          return 'X'\n",
    "        else:                              return 'X'\n",
    "        \n",
    "    elif c[0]=='G':\n",
    "        if   c[1]=='T':                    return 'V'\n",
    "        elif c[1]=='C':                    return 'A'\n",
    "        elif c[1]=='A':\n",
    "            if    c[2] in ['T', 'C', 'Y']: return 'D'\n",
    "            elif  c[2] in ['A', 'G', 'R']: return 'E'\n",
    "            else:                          return 'X'\n",
    "        elif c[1]=='G':                    return 'G'\n",
    "        else:                              return 'X'\n",
    "\n",
    "    else:                                  return 'X'\n",
    "\n",
    "\n",
    "\n",
    "PREF_MERGED_DIR = './output/merged_preference/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffb55a3",
   "metadata": {},
   "source": [
    "# HIV Env BF520 human host, rhesus host"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25abafe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_host(index_file, pref_file, rep_num):\n",
    "    df_index = pd.read_csv(index_file, delimiter = ',')\n",
    "    df = pd.read_csv(pref_file)\n",
    "    df = df.dropna()\n",
    "    df['site'] = df['site'].astype('string')\n",
    "    df_index['original'] = df_index['original'].astype('string')\n",
    "    df_index['new'] = df_index['new'].astype('string')\n",
    "\n",
    "    df['site'] = df['site'].map(df_index.set_index('new')['original'])\n",
    "    df = df.dropna()\n",
    "    df['site'] = df['site'].astype('int')\n",
    "    \n",
    "    site_list = []\n",
    "    AA_list = []\n",
    "    df_pref_only = df[AA]\n",
    "    for i in df['site'].tolist():\n",
    "        site_list += [i]*20\n",
    "        AA_list += AA\n",
    "    df_pref = pd.DataFrame(columns=['site', 'amino_acid', rep_num]) \n",
    "    df_pref['site'] = site_list\n",
    "    df_pref['amino_acid'] = AA_list\n",
    "    df_pref[rep_num] = df_pref_only.values.flatten()\n",
    "    return df_pref\n",
    "\n",
    "# BG505\n",
    "index_file = './data/prefs/BG505_to_HXB2_numbering.txt'\n",
    "Target_name = 'HIV BG505'\n",
    "\n",
    "pref_file = './data/prefs/HIV Env BG505-1_prefs.csv'\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "pref_file = './data/prefs/HIV Env BG505-2_prefs.csv'\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "pref_file = './data/prefs/HIV Env BG505-3_prefs.csv'\n",
    "rep_num = 'rep_3'\n",
    "df3 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df3, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2', 'rep_3']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "\n",
    "# BF520\n",
    "index_file = './data/prefs/BF520c2_to_HXB2.csv'\n",
    "Target_name = 'HIV BF520'\n",
    "\n",
    "pref_file = './data/prefs/HIV Env BF520-1_prefs.csv'\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "pref_file = './data/prefs/HIV Env BF520-2_prefs.csv'\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "pref_file = './data/prefs/HIV Env BF520-3_prefs.csv'\n",
    "rep_num = 'rep_3'\n",
    "df3 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df3, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2', 'rep_3']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "# BF520 human host\n",
    "index_file = './data/prefs/BF520c2_to_HXB2.csv'\n",
    "Target_name = 'HIV BF520 human host'\n",
    "\n",
    "pref_file = './data/prefs/HIV BF520 human host-1_prefs.csv'\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "pref_file = './data/prefs/HIV BF520 human host-2_prefs.csv'\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "# BF520 rhesus host\n",
    "index_file = './data/prefs/BF520c2_to_HXB2.csv'\n",
    "Target_name = 'HIV BF520 rhesus host'\n",
    "\n",
    "pref_file = './data/prefs/HIV BF520 rhesus host-1_prefs.csv'\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "pref_file = './data/prefs/HIV BF520 rhesus host-2_prefs.csv'\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_host(index_file, pref_file, rep_num)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c34e67",
   "metadata": {},
   "source": [
    "# PR8, Aichi68C, MS, MxA, FP16, FP20, VRC34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "394f6a40",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def preference_merge(REP, Target_name, count_file):\n",
    "    pref_list = ['./data/prefs/'+Target_name+'-1_prefs.csv',\n",
    "                 './data/prefs/'+Target_name+'-2_prefs.csv',\n",
    "                 './data/prefs/'+Target_name+'-3_prefs.csv']\n",
    "\n",
    "    df_count = pd.read_csv(count_file)\n",
    "    site_list = df_count['site'].tolist()\n",
    "    site_list_merged = []\n",
    "    for site in site_list:\n",
    "        site_list_merged += [site]*20\n",
    "\n",
    "    aa_list_merged = []\n",
    "    for i in range(len(site_list)):\n",
    "        aa_list_merged += AA\n",
    "\n",
    "    rep=[i+1 for i in range(REP)]\n",
    "    rep_list=[]\n",
    "    for replicate in rep:\n",
    "        rep_list.append('rep_'+str(replicate))\n",
    "    df_pref_merged = pd.DataFrame(columns=['site', 'amino_acid'] + rep_list + ['average'])\n",
    "    df_pref_merged['site'] = site_list_merged\n",
    "    df_pref_merged['amino_acid'] = aa_list_merged\n",
    "\n",
    "    for i in rep:\n",
    "        df_pref = pd.read_csv(pref_list[i-1])\n",
    "        df_pref_aa = df_pref[AA]\n",
    "        df_pref_merged['rep_'+str(i)] = df_pref_aa.values.flatten()\n",
    "    df_pref_merged['average'] = df_pref_merged[rep_list].mean(axis = 1)\n",
    "    df_pref_merged.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "# PR8\n",
    "REP=3\n",
    "Target_name = 'PR8'\n",
    "count_file = './data/raw_data/PR8_DNA_codoncounts.csv'\n",
    "preference_merge(REP, Target_name, count_file)\n",
    "\n",
    "# Aichi68C\n",
    "REP=2\n",
    "Target_name = 'Aichi68C'\n",
    "count_file = './data/raw_data/Aichi68C_DNA_codoncounts.csv'\n",
    "preference_merge(REP, Target_name, count_file)\n",
    "\n",
    "# MS\n",
    "REP=2\n",
    "Target_name = 'MS'\n",
    "count_file = './data/raw_data/MS_DNA_codoncounts.csv'\n",
    "preference_merge(REP, Target_name, count_file)\n",
    "\n",
    "# MxA\n",
    "REP=2\n",
    "Target_name = 'MxA'\n",
    "count_file = './data/raw_data/MxA_DNA_codoncounts.csv'\n",
    "preference_merge(REP, Target_name, count_file)\n",
    "\n",
    "# MxAneg\n",
    "REP=2\n",
    "Target_name = 'MxAneg'\n",
    "count_file = './data/raw_data/MxAneg_DNA_codoncounts.csv'\n",
    "preference_merge(REP, Target_name, count_file)\n",
    "\n",
    "# FP16\n",
    "REP=2\n",
    "Target_name = 'HIV bnAbs FP16'\n",
    "count_file = './data/raw_data/FP16_DNA_codoncounts.csv'\n",
    "preference_merge(REP, Target_name, count_file)\n",
    "\n",
    "# FP20\n",
    "REP=2\n",
    "Target_name = 'HIV bnAbs FP20'\n",
    "count_file = './data/raw_data/FP20_DNA_codoncounts.csv'\n",
    "preference_merge(REP, Target_name, count_file)\n",
    "\n",
    "# VRC34\n",
    "REP=2\n",
    "Target_name = 'HIV bnAbs VRC34'\n",
    "count_file = './data/raw_data/VRC34_DNA_codoncounts.csv'\n",
    "preference_merge(REP, Target_name, count_file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ff6d34",
   "metadata": {},
   "source": [
    "# Flu WSN, A549, CCL141, Matrix_M1, ZIKV, Perth2009"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b7ad2823",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_WSN(df, rep_num, WSN):\n",
    "    if WSN == True:\n",
    "        df = df.drop(['WT_AA', 'SITE_ENTROPY'], axis = 1)\n",
    "        df.set_axis(['site']+[i[3] for i in df.columns[1:]], axis=1,inplace=True)\n",
    "    site_list = []\n",
    "    AA_list = []\n",
    "    df_pref_only = df[AA]\n",
    "    for i in df['site'].tolist():\n",
    "        site_list += [i]*20\n",
    "        AA_list += AA\n",
    "    df_pref = pd.DataFrame(columns=['site', 'amino_acid', rep_num]) \n",
    "    df_pref['site'] = site_list\n",
    "    df_pref['amino_acid'] = AA_list\n",
    "    df_pref[rep_num] = df_pref_only.values.flatten()\n",
    "    return df_pref\n",
    "\n",
    "# WSN\n",
    "Target_name = 'WSN'\n",
    "df = pd.read_csv('./data/prefs/WSN-1_prefs.txt', delimiter = '\\t')\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_WSN(df, rep_num, True)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/WSN-2_prefs.txt', delimiter = '\\t')\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_WSN(df, rep_num, True)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/WSN-3_prefs.txt', delimiter = '\\t')\n",
    "rep_num = 'rep_3'\n",
    "df3 = transform_WSN(df, rep_num, True)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df3, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2', 'rep_3']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "# A549\n",
    "Target_name = 'A549'\n",
    "df = pd.read_csv('./data/prefs/A549-1_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/A549-2_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "Target_name = 'A549'\n",
    "df = pd.read_csv('./data/prefs/A549-3_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_3'\n",
    "df3 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df3, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2', 'rep_3']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "#CCL141\n",
    "Target_name = 'CCL141'\n",
    "df = pd.read_csv('./data/prefs/CCL141-1_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/CCL141-2_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/CCL141-3_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_3'\n",
    "df3 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df3, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2', 'rep_3']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "# Matrix_M1\n",
    "Target_name = 'Matrix_M1'\n",
    "df = pd.read_csv('./data/prefs/Matrix_M1-1_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/Matrix_M1-2_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/Matrix_M1-3_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_3'\n",
    "df3 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df3, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2', 'rep_3']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "# ZIKV\n",
    "Target_name = 'ZIKV'\n",
    "df = pd.read_csv('./data/prefs/ZIKV-1_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/ZIKV-2_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/ZIKV-3_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_3'\n",
    "df3 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df3, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2', 'rep_3']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "\n",
    "# Perth2009\n",
    "Target_name = 'Perth2009'\n",
    "df = pd.read_csv('./data/prefs/Perth2009-1_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_1'\n",
    "df1 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/Perth2009-2_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_2'\n",
    "df2 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/Perth2009-3_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_3'\n",
    "df3 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df = pd.read_csv('./data/prefs/Perth2009-4_prefs.csv', delimiter = ',')\n",
    "rep_num = 'rep_4'\n",
    "df4 = transform_WSN(df, rep_num, False)\n",
    "\n",
    "df_merge = pd.merge(df1, df2, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df3, on = ['site', 'amino_acid'])\n",
    "df_merge = pd.merge(df_merge, df4, on = ['site', 'amino_acid'])\n",
    "df_merge['average'] = df_merge[['rep_1', 'rep_2', 'rep_3', 'rep_4']].mean(axis=1)\n",
    "df_merge = df_merge.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec7eb24",
   "metadata": {},
   "source": [
    "# TpoR, TpoR_S505N, Ube4b, YAP1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87fdb7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Transfor_MaveDB(Target_name, pref_column, pref_file, raw_sequence):\n",
    "    with open(raw_sequence, 'r') as file:\n",
    "        sequence = file.read().replace('\\n', '')\n",
    "    AA_seq = [codon2aa(sequence[3*i:3*i+3]) for i in range(int(len(sequence)/3))]\n",
    "    \n",
    "    df = pd.read_csv(pref_file)\n",
    "    site_list = []\n",
    "    amino_acid = []\n",
    "    pref_list = []\n",
    "    WT_indicator = []\n",
    "    for _ in range(len(pref_column)):\n",
    "        pref_list.append([])\n",
    "    for i in range(df.shape[0]):\n",
    "        parser = re.split('(\\d+)', df['hgvs_pro'][i])\n",
    "        if len(parser) == 3 and '?' not in parser:\n",
    "            site_list.append(int(parser[1]))\n",
    "            amino_acid.append(Amino_acid_dict[parser[-1]])\n",
    "            WT_indicator.append(False)\n",
    "            for idx in range(len(pref_column)):\n",
    "                pref_list[idx].append(df[pref_column[idx]][i])\n",
    "                \n",
    "#     print(len(AA_seq), set(site_list),len(pref_list[1]))\n",
    "    unique_site = list(set(site_list))\n",
    "    for i in range(len(unique_site)):\n",
    "        site_list.append(unique_site[i])\n",
    "        amino_acid.append(AA_seq[i])\n",
    "        WT_indicator.append(True)\n",
    "        \n",
    "    df_merge = pd.DataFrame(columns=['site', 'amino_acid', 'WT_indicator']+['rep_'+str(i+1) for i in range(len(pref_column))]+['average'])\n",
    "    df_merge['site'] = site_list\n",
    "    df_merge['amino_acid']= amino_acid\n",
    "    df_merge['WT_indicator']= WT_indicator\n",
    "    for i in range(len(pref_column)):\n",
    "        pref_list[i] += [0]*len(AA_seq)\n",
    "        df_merge['rep_'+str(i+1)] = pref_list[i]\n",
    "    df_merge['average'] = df_merge[['rep_'+str(i+1) for i in range(len(pref_column))]].mean(axis=1)\n",
    "    df_merge = df_merge.sort_values('site')\n",
    "    df_merge.to_csv(PREF_MERGED_DIR + Target_name + '.csv.gz', index = False, compression = 'gzip')\n",
    "\n",
    "Target_name = 'YAP1'\n",
    "rep = 2\n",
    "pref_file = './data/prefs/YAP1_prefs.csv.gz'\n",
    "pref_column = ['score_101208', \n",
    "               'score_110307']\n",
    "raw_sequence = './data/raw_data/YAP1_Reference_sequence.txt'\n",
    "Transfor_MaveDB(Target_name, pref_column, pref_file, raw_sequence)\n",
    "\n",
    "\n",
    "Target_name = 'TpoR'\n",
    "rep = 6\n",
    "pref_file = './data/prefs/TpoR_prefs.csv.gz'\n",
    "pref_column = ['score_Replicate_A', \n",
    "               'score_Replicate_B', \n",
    "               'score_Replicate_C', \n",
    "               'score_Replicate_D', \n",
    "               'score_Replicate_E', \n",
    "               'score_Replicate_F.1']\n",
    "raw_sequence = './data/raw_data/TpoR_Reference_sequence.txt'\n",
    "Transfor_MaveDB(Target_name, pref_column, pref_file, raw_sequence)\n",
    "\n",
    "\n",
    "Target_name = 'TpoR_S505N'\n",
    "rep = 6\n",
    "pref_file = './data/prefs/TpoR_S505N_prefs.csv.gz'\n",
    "pref_column = ['score_Replicate_1', \n",
    "               'score_Replicate_2', \n",
    "               'score_Replicate_3', \n",
    "               'score_Replicate_4', \n",
    "               'score_Replicate_5', \n",
    "               'score_Replicate_6.1']\n",
    "raw_sequence = './data/raw_data/TpoR_Reference_sequence.txt'\n",
    "Transfor_MaveDB(Target_name, pref_column, pref_file, raw_sequence)\n",
    "\n",
    "\n",
    "Target_name = 'Ube4b'\n",
    "rep = 2\n",
    "pref_file = './data/prefs/Ube4b_prefs.csv.gz'\n",
    "pref_column = ['score_Rep_2', \n",
    "               'score_Rep_3']\n",
    "raw_sequence = './data/raw_data/Ube4b_Reference_sequence.txt'\n",
    "Transfor_MaveDB(Target_name, pref_column, pref_file, raw_sequence)\n",
    "\n",
    "\n",
    "Target_name = 'E3'\n",
    "rep = 6\n",
    "pref_file = './data/prefs/E3_prefs.csv'\n",
    "pref_column = ['score_PlusE2NewRep3', \n",
    "               'score_PlusE2NewRep4',\n",
    "               'score_PlusE2NewRep5',\n",
    "               'score_PlusE2Rep3', \n",
    "               'score_PlusE2Rep4',\n",
    "               'score_PlusE2Rep5']\n",
    "raw_sequence = './data/raw_data/BRCA1_Reference_sequence.txt'\n",
    "Transfor_MaveDB(Target_name, pref_column, pref_file, raw_sequence)\n",
    "\n",
    "\n",
    "Target_name = 'Y2H_1'\n",
    "rep = 3\n",
    "pref_file = './data/prefs/Y2H_prefs.csv'\n",
    "pref_column = ['score_Y2H_1_Rep1', \n",
    "               'score_Y2H_1_Rep2',\n",
    "               'score_Y2H_1_Rep3']\n",
    "raw_sequence = './data/raw_data/BRCA1_Reference_sequence.txt'\n",
    "Transfor_MaveDB(Target_name, pref_column, pref_file, raw_sequence)\n",
    "\n",
    "\n",
    "Target_name = 'Y2H_2'\n",
    "rep = 3\n",
    "pref_file = './data/prefs/Y2H_prefs.csv'\n",
    "pref_column = ['score_Y2H_2_Rep1', \n",
    "               'score_Y2H_2_Rep2',\n",
    "               'score_Y2H_2_Rep3']\n",
    "raw_sequence = './data/raw_data/BRCA1_Reference_sequence.txt'\n",
    "Transfor_MaveDB(Target_name, pref_column, pref_file, raw_sequence)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede35385",
   "metadata": {},
   "source": [
    "# DBR1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53fe5288",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/prefs/DBR1_prefs.csv.gz')\n",
    "df = df.dropna()\n",
    "df = df.drop(['Sequence', 'Reference AA', 'Variant Class'], axis = 1)\n",
    "df = df.rename(columns={'Affected codon': 'site', \n",
    "                        'Substituted AA': 'amino_acid',\n",
    "                        'Day 11 log2 enrichment score (replicate 1)': 'rep_1', \n",
    "                        'Day 11 log2 enrichment score (replicate 2)': 'rep_2'})\n",
    "df['average'] = df[['rep_1', 'rep_2']].mean(axis = 1)\n",
    "df['site'] = df['site'].astype('int')\n",
    "df_merge = df.sort_values('site')\n",
    "df_merge.to_csv(PREF_MERGED_DIR+'DBR1.csv.gz', index = False, compression = 'gzip')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7a707fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf3741fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# def wildtype(codon_file, selection_file, output_file, replicates):\n",
    "#     df = pd.read_csv(codon_file)\n",
    "#     WT_aa = [codon2aa(i) for i in df['wildtype'].tolist()]\n",
    "#     df['WT_aa'] = WT_aa\n",
    "#     df['WT_indicator'] = True\n",
    "#     df_WT = df[['site', 'WT_aa','WT_indicator']]\n",
    "#     df_WT = df_WT.rename(columns={'WT_aa': 'amino_acid'})\n",
    "#     df_selection = pd.read_csv(selection_file)\n",
    "    \n",
    "#     if 'gauged' not in ' '.join(df_selection.columns.tolist()):\n",
    "#         df_merge = pd.merge(df_WT, df_selection, on=['site','amino_acid'], how='right')\n",
    "#         df_merge['WT_indicator'] = df_merge['WT_indicator'].fillna(False)\n",
    "#         column_name = df_merge.columns[-(replicates+1):].tolist()\n",
    "#         gauged_name = [i+'_gauged' for i in column_name]\n",
    "#         gauged_list = []\n",
    "#         for i in gauged_name:\n",
    "#             df_merge[i] = 0\n",
    "#             gauged_list.append([])\n",
    "\n",
    "#         for site in df_merge['site'].unique():\n",
    "#             for i in range(len(column_name)):\n",
    "#                 gauge_value = df_merge[(df_merge['site']==site)&(df_merge['WT_indicator'])][column_name[i]].values[0]\n",
    "#                 original_list = df_merge[df_merge['site']==site][column_name[i]].tolist()\n",
    "#                 gauged_list[i] +=  [i - gauge_value for i in original_list]\n",
    "\n",
    "#         for i in range(len(gauged_name)):\n",
    "#             df_merge[gauged_name[i]] = gauged_list[i]\n",
    "\n",
    "#         df_merge.to_csv(output_file, compression = 'gzip', index = False)\n",
    "#         print('gauge done')\n",
    "#         return df_merge\n",
    "#     else:\n",
    "#         print('gauge existed')\n",
    "\n",
    "\n",
    "# codon_file = './data/raw_data/ZIKV_DNA_codoncounts.csv'\n",
    "# selection_file = './output/selection_coefficients/ZIKV.csv.gz'\n",
    "# output_file = './output/gauged_selection_coefficients/ZIKV.csv.gz'\n",
    "# replicates = 3\n",
    "# wildtype(codon_file, selection_file, output_file, replicates)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d85e81fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT indicator added\n"
     ]
    },
    {
     "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>site</th>\n",
       "      <th>amino_acid</th>\n",
       "      <th>WT_indicator</th>\n",
       "      <th>rep_1</th>\n",
       "      <th>rep_2</th>\n",
       "      <th>rep_3</th>\n",
       "      <th>joint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>-0.009139</td>\n",
       "      <td>-1.203026e-02</td>\n",
       "      <td>-8.203877e-03</td>\n",
       "      <td>-0.025103</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-0.037421</td>\n",
       "      <td>-3.088728e-02</td>\n",
       "      <td>-2.985570e-02</td>\n",
       "      <td>-0.081780</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-0.011831</td>\n",
       "      <td>-9.553025e-03</td>\n",
       "      <td>-1.172133e-02</td>\n",
       "      <td>-0.028050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.003844</td>\n",
       "      <td>-3.454097e-03</td>\n",
       "      <td>-3.031401e-03</td>\n",
       "      <td>-0.008886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.006201</td>\n",
       "      <td>-9.180511e-03</td>\n",
       "      <td>-3.895432e-03</td>\n",
       "      <td>-0.016547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>10579</th>\n",
       "      <td>504</td>\n",
       "      <td>T</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.017051</td>\n",
       "      <td>-1.609082e-02</td>\n",
       "      <td>-1.713299e-02</td>\n",
       "      <td>-0.041580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10580</th>\n",
       "      <td>504</td>\n",
       "      <td>W</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.001415</td>\n",
       "      <td>-1.735121e-07</td>\n",
       "      <td>-1.789024e-03</td>\n",
       "      <td>-0.002801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10581</th>\n",
       "      <td>504</td>\n",
       "      <td>Y</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.001415</td>\n",
       "      <td>-1.812894e-03</td>\n",
       "      <td>-2.320765e-07</td>\n",
       "      <td>-0.002808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10582</th>\n",
       "      <td>504</td>\n",
       "      <td>V</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.003488</td>\n",
       "      <td>1.104854e-03</td>\n",
       "      <td>3.992824e-03</td>\n",
       "      <td>0.006708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10583</th>\n",
       "      <td>504</td>\n",
       "      <td>*</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.036092</td>\n",
       "      <td>-2.152075e-02</td>\n",
       "      <td>-2.298831e-02</td>\n",
       "      <td>-0.068429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10584 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       site amino_acid  WT_indicator     rep_1         rep_2         rep_3  \\\n",
       "0         1          A         False -0.009139 -1.203026e-02 -8.203877e-03   \n",
       "1         1          R         False -0.037421 -3.088728e-02 -2.985570e-02   \n",
       "2         1          N         False -0.011831 -9.553025e-03 -1.172133e-02   \n",
       "3         1          D         False -0.003844 -3.454097e-03 -3.031401e-03   \n",
       "4         1          C         False -0.006201 -9.180511e-03 -3.895432e-03   \n",
       "...     ...        ...           ...       ...           ...           ...   \n",
       "10579   504          T         False -0.017051 -1.609082e-02 -1.713299e-02   \n",
       "10580   504          W         False -0.001415 -1.735121e-07 -1.789024e-03   \n",
       "10581   504          Y         False -0.001415 -1.812894e-03 -2.320765e-07   \n",
       "10582   504          V         False -0.003488  1.104854e-03  3.992824e-03   \n",
       "10583   504          *         False -0.036092 -2.152075e-02 -2.298831e-02   \n",
       "\n",
       "          joint  \n",
       "0     -0.025103  \n",
       "1     -0.081780  \n",
       "2     -0.028050  \n",
       "3     -0.008886  \n",
       "4     -0.016547  \n",
       "...         ...  \n",
       "10579 -0.041580  \n",
       "10580 -0.002801  \n",
       "10581 -0.002808  \n",
       "10582  0.006708  \n",
       "10583 -0.068429  \n",
       "\n",
       "[10584 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def wildtype_independent_site(codon_file, selection_file, output_file, replicates):\n",
    "    df = pd.read_csv(codon_file)\n",
    "    WT_aa = [codon2aa(i) for i in df['wildtype'].tolist()]\n",
    "    df['WT_aa'] = WT_aa\n",
    "    df['WT_indicator'] = True\n",
    "    df_WT = df[['site', 'WT_aa','WT_indicator']]\n",
    "    df_WT = df_WT.rename(columns={'WT_aa': 'amino_acid'})\n",
    "    df_selection = pd.read_csv(selection_file)\n",
    "    if 'WT_indicator' not in ' '.join(df_selection.columns.tolist()):\n",
    "        df_merge = pd.merge(df_WT, df_selection, on=['site','amino_acid'], how='right')\n",
    "        df_merge['WT_indicator'] = df_merge['WT_indicator'].fillna(False)\n",
    "\n",
    "\n",
    "        df_merge.to_csv(output_file, compression = 'gzip', index = False)\n",
    "        print('WT indicator added')\n",
    "        return df_merge\n",
    "    else:\n",
    "        print('WT indicator existed')\n",
    "        return df_selection\n",
    "\n",
    "\n",
    "codon_file = './data/raw_data/ZIKV_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/ZIKV.csv.gz'\n",
    "output_file = './output/selection_coefficients/ZIKV.csv.gz'\n",
    "replicates = 3\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa888013",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT indicator added\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>amino_acid</th>\n",
       "      <th>WT_indicator</th>\n",
       "      <th>rep_1</th>\n",
       "      <th>rep_2</th>\n",
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      ],
      "text/plain": [
       "       site amino_acid  WT_indicator     rep_1     rep_2     rep_3     joint\n",
       "0        30          A          True -0.014099 -0.042150  0.012509  0.015768\n",
       "1        30          R         False -0.021543 -0.006815 -0.002915 -0.015291\n",
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       "...     ...        ...           ...       ...       ...       ...       ...\n",
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       "14068   699          V         False -0.028447  0.050812 -0.016001  0.011528\n",
       "14069   699          *         False -0.062201 -0.031564 -0.063090 -0.148297\n",
       "\n",
       "[14070 rows x 7 columns]"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "codon_file = './data/raw_data/BG505_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/HIV Env BG505.csv.gz'\n",
    "output_file = './output/selection_coefficients/HIV Env BG505.csv.gz'\n",
    "replicates = 3\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9da7b12b",
   "metadata": {},
   "outputs": [
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     "text": [
      "WT indicator added\n"
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       "       site amino_acid  WT_indicator         rep_1         rep_2  \\\n",
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   "source": [
    "codon_file = './data/raw_data/BF520_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/HIV Env BF520.csv.gz'\n",
    "output_file = './output/selection_coefficients/HIV Env BF520.csv.gz'\n",
    "replicates = 3\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)\n"
   ]
  },
  {
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   "execution_count": 12,
   "id": "aae87841",
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   "outputs": [
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      "WT indicator added\n"
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       "       site amino_acid  WT_indicator         rep_1         rep_2         joint\n",
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "codon_file = './data/raw_data/human_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/HIV BF520 human host.csv.gz'\n",
    "output_file = './output/selection_coefficients/HIV BF520 human host.csv.gz'\n",
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   "execution_count": 13,
   "id": "a8ca6537",
   "metadata": {},
   "outputs": [
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     "text": [
      "WT indicator added\n"
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       "       site amino_acid  WT_indicator         rep_1         rep_2         joint\n",
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    "codon_file = './data/raw_data/rhesus_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/HIV BF520 rhesus host.csv.gz'\n",
    "output_file = './output/selection_coefficients/HIV BF520 rhesus host.csv.gz'\n",
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       "       site amino_acid  WT_indicator     rep_1     rep_2     joint\n",
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    "codon_file = './data/raw_data/VRC34_DNA_codoncounts.csv'\n",
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    "output_file = './output/selection_coefficients/HIV bnAbs VRC34.csv.gz'\n",
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       "       site amino_acid  WT_indicator     rep_1     rep_2     joint\n",
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       "\n",
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     },
     "execution_count": 15,
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    "codon_file = './data/raw_data/FP16_DNA_codoncounts.csv'\n",
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    "output_file = './output/selection_coefficients/HIV bnAbs FP16.csv.gz'\n",
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    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)\n"
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      ],
      "text/plain": [
       "       site amino_acid  WT_indicator     rep_1     rep_2     joint\n",
       "0        30          A          True -0.038100  0.003191 -0.022484\n",
       "1        30          R         False -0.001999 -0.009691 -0.011616\n",
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       "...     ...        ...           ...       ...       ...       ...\n",
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       "14068   699          V         False -0.001602  0.005857  0.004215\n",
       "14069   699          *         False -0.006600 -0.006701 -0.013247\n",
       "\n",
       "[14070 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_file = './data/raw_data/FP20_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/HIV bnAbs FP20.csv.gz'\n",
    "output_file = './output/selection_coefficients/HIV bnAbs FP20.csv.gz'\n",
    "replicates = 2\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
   ]
  },
  {
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   "execution_count": 17,
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      "WT indicator added\n"
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       "       site amino_acid  WT_indicator     rep_1     rep_2     rep_3     rep_4  \\\n",
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    "codon_file = './data/raw_data/Perth2009_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/Perth2009.csv.gz'\n",
    "output_file = './output/selection_coefficients/Perth2009.csv.gz'\n",
    "replicates = 4\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b102789",
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   "outputs": [
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      "WT indicator added\n"
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       "       site amino_acid  WT_indicator     rep_1     rep_2     rep_3     joint\n",
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    "codon_file = './data/raw_data/CCL141_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/CCL141.csv.gz'\n",
    "output_file = './output/selection_coefficients/CCL141.csv.gz'\n",
    "replicates = 3\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
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  },
  {
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   "execution_count": 19,
   "id": "f5257d25",
   "metadata": {},
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     "text": [
      "WT indicator added\n"
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       "      <td>-0.048427</td>\n",
       "      <td>-0.058717</td>\n",
       "      <td>-0.103474</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>-0.136801</td>\n",
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       "      <td>-0.022438</td>\n",
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      ],
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       "       site amino_acid  WT_indicator     rep_1     rep_2     rep_3     joint\n",
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     },
     "execution_count": 19,
     "metadata": {},
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    "codon_file = './data/raw_data/A549_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/A549.csv.gz'\n",
    "output_file = './output/selection_coefficients/A549.csv.gz'\n",
    "replicates = 3\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
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       "       site amino_acid  WT_indicator         rep_1         rep_2         joint\n",
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    "codon_file = './data/raw_data/MxA_DNA_codoncounts.csv'\n",
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   "id": "1a14ed80",
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       "       site amino_acid  WT_indicator         rep_1         rep_2         joint\n",
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    "selection_file = './output/selection_coefficients/MS.csv.gz'\n",
    "output_file = './output/selection_coefficients/MS.csv.gz'\n",
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   "id": "a78f50da",
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       "       site amino_acid  WT_indicator         rep_1         rep_2         joint\n",
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       "\n",
       "[10458 rows x 6 columns]"
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "codon_file = './data/raw_data/MxAneg_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/MxAneg.csv.gz'\n",
    "output_file = './output/selection_coefficients/MxAneg.csv.gz'\n",
    "replicates = 2\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
   ]
  },
  {
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   "execution_count": 23,
   "id": "32074687",
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      "WT indicator added\n"
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       "      site amino_acid  WT_indicator     rep_1     rep_2     rep_3     joint\n",
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     "execution_count": 23,
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   "source": [
    "codon_file = './data/raw_data/HomM1_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/Matrix_M1.csv.gz'\n",
    "output_file = './output/selection_coefficients/Matrix_M1.csv.gz'\n",
    "replicates = 3\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
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   "execution_count": 24,
   "id": "47447b40",
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      "WT indicator added\n"
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       "       site amino_acid  WT_indicator         rep_1         rep_2  \\\n",
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       "\n",
       "[10458 rows x 7 columns]"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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    "codon_file = './data/raw_data/PR8_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/PR8.csv.gz'\n",
    "output_file = './output/selection_coefficients/PR8.csv.gz'\n",
    "replicates = 3\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
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   "execution_count": 25,
   "id": "79732e8a",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>amino_acid</th>\n",
       "      <th>WT_indicator</th>\n",
       "      <th>rep_1</th>\n",
       "      <th>rep_2</th>\n",
       "      <th>joint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>1.260099e-04</td>\n",
       "      <td>-1.022034e-07</td>\n",
       "      <td>1.277259e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>R</td>\n",
       "      <td>False</td>\n",
       "      <td>4.552973e-04</td>\n",
       "      <td>7.052332e-04</td>\n",
       "      <td>1.197320e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>False</td>\n",
       "      <td>-6.355282e-08</td>\n",
       "      <td>-4.109946e-08</td>\n",
       "      <td>-1.046523e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-5.827963e-10</td>\n",
       "      <td>-5.827963e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>False</td>\n",
       "      <td>-3.727336e-10</td>\n",
       "      <td>-3.885309e-10</td>\n",
       "      <td>-7.612645e-10</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10453</th>\n",
       "      <td>498</td>\n",
       "      <td>T</td>\n",
       "      <td>False</td>\n",
       "      <td>4.667052e-02</td>\n",
       "      <td>4.290091e-02</td>\n",
       "      <td>9.936530e-02</td>\n",
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       "      <td>-6.087270e-02</td>\n",
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       "      <th>10455</th>\n",
       "      <td>498</td>\n",
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       "      <td>-4.273999e-02</td>\n",
       "      <td>-4.471882e-02</td>\n",
       "      <td>-7.991174e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10456</th>\n",
       "      <td>498</td>\n",
       "      <td>V</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.273709e-01</td>\n",
       "      <td>-1.342239e-01</td>\n",
       "      <td>-2.139638e-01</td>\n",
       "    </tr>\n",
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       "      <th>10457</th>\n",
       "      <td>498</td>\n",
       "      <td>*</td>\n",
       "      <td>False</td>\n",
       "      <td>3.831856e-02</td>\n",
       "      <td>-7.300603e-02</td>\n",
       "      <td>-2.379879e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10458 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       site amino_acid  WT_indicator         rep_1         rep_2         joint\n",
       "0         1          A         False  1.260099e-04 -1.022034e-07  1.277259e-04\n",
       "1         1          R         False  4.552973e-04  7.052332e-04  1.197320e-03\n",
       "2         1          N         False -6.355282e-08 -4.109946e-08 -1.046523e-07\n",
       "3         1          D         False  0.000000e+00 -5.827963e-10 -5.827963e-10\n",
       "4         1          C         False -3.727336e-10 -3.885309e-10 -7.612645e-10\n",
       "...     ...        ...           ...           ...           ...           ...\n",
       "10453   498          T         False  4.667052e-02  4.290091e-02  9.936530e-02\n",
       "10454   498          W         False -5.076745e-02 -6.087270e-02 -1.009676e-01\n",
       "10455   498          Y         False -4.273999e-02 -4.471882e-02 -7.991174e-02\n",
       "10456   498          V         False -1.273709e-01 -1.342239e-01 -2.139638e-01\n",
       "10457   498          *         False  3.831856e-02 -7.300603e-02 -2.379879e-02\n",
       "\n",
       "[10458 rows x 6 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_file = './data/raw_data/Aichi68C_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/Aichi68C.csv.gz'\n",
    "output_file = './output/selection_coefficients/Aichi68C.csv.gz'\n",
    "replicates = 2\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ae8c5c65",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT indicator added\n"
     ]
    },
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      "text/plain": [
       "       site amino_acid  WT_indicator         rep_1         rep_2  \\\n",
       "0         1          A         False -5.774015e-10  0.000000e+00   \n",
       "1         1          R         False -2.999165e-04 -2.999176e-04   \n",
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       "4         1          C         False  0.000000e+00  0.000000e+00   \n",
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       "11864 -1.241787e-02 -3.912170e-02  \n",
       "\n",
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     },
     "execution_count": 26,
     "metadata": {},
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    }
   ],
   "source": [
    "codon_file = './data/raw_data/WSN_DNA_codoncounts.csv'\n",
    "selection_file = './output/selection_coefficients/WSN.csv.gz'\n",
    "output_file = './output/selection_coefficients/WSN.csv.gz'\n",
    "replicates = 3\n",
    "wildtype_independent_site(codon_file, selection_file, output_file, replicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "499b483e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df77d956",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def wildtype_full_length(sequence_file, selection_file, output_file, replicates):\n",
    "#     with open(sequence_file, 'r') as file:\n",
    "#         nucleotide = file.read().rstrip()\n",
    "#         codon_list = [nucleotide[i:i+3] for i in range(0, len(nucleotide), 3)]\n",
    "#         WT_aa = [codon2aa(i) for i in codon_list]\n",
    "\n",
    "#     df_selection = pd.read_csv(selection_file)\n",
    "#     site_list = df_selection['site'].unique()\n",
    "#     df_WT = pd.DataFrame(columns = ['site', 'WT_aa', 'WT_indicator'])\n",
    "#     df_WT['site'] = site_list\n",
    "#     df_WT['WT_aa'] = WT_aa\n",
    "#     df_WT['WT_indicator'] = True\n",
    "#     # df_WT = df[['site', 'WT_aa','WT_indicator']]\n",
    "#     df_WT = df_WT.rename(columns={'WT_aa': 'amino_acid'})\n",
    "\n",
    "#     if 'gauged' not in ' '.join(df_selection.columns.tolist()):\n",
    "#         df_merge = pd.merge(df_WT, df_selection, on=['site','amino_acid'], how='right')\n",
    "#         df_merge['WT_indicator'] = df_merge['WT_indicator'].fillna(False)\n",
    "#         column_name = df_merge.columns[-(replicates+1):].tolist()\n",
    "#         gauged_name = [i+'_gauged' for i in column_name]\n",
    "#         gauged_list = []\n",
    "#         for i in gauged_name:\n",
    "#             df_merge[i] = 0\n",
    "#             gauged_list.append([])\n",
    "\n",
    "#         for site in df_merge['site'].unique():\n",
    "#             for i in range(len(column_name)):\n",
    "#                 gauge_value = df_merge[(df_merge['site']==site)&(df_merge['WT_indicator'])][column_name[i]].values[0]\n",
    "#                 original_list = df_merge[df_merge['site']==site][column_name[i]].tolist()\n",
    "#     #                 print(gauge_value)\n",
    "#                 gauged_list[i] +=  [i - gauge_value for i in original_list]\n",
    "\n",
    "#         for i in range(len(gauged_name)):\n",
    "#             df_merge[gauged_name[i]] = gauged_list[i]\n",
    "\n",
    "#         df_merge.to_csv(output_file, compression = 'gzip', index = False)\n",
    "#         print('gauge done')\n",
    "#         return df_merge\n",
    "#     else:\n",
    "#         print('gauge existed')\n",
    "#         return df_selection\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b659113b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wildtype_full_length(sequence_file, selection_file, output_file, replicates):\n",
    "    with open(sequence_file, 'r') as file:\n",
    "        nucleotide = file.read().rstrip()\n",
    "        codon_list = [nucleotide[i:i+3] for i in range(0, len(nucleotide), 3)]\n",
    "        WT_aa = [codon2aa(i) for i in codon_list]\n",
    "\n",
    "    df_selection = pd.read_csv(selection_file)\n",
    "    site_list = df_selection['site'].unique()\n",
    "    df_WT = pd.DataFrame(columns = ['site', 'WT_aa', 'WT_indicator'])\n",
    "    df_WT['site'] = site_list\n",
    "    df_WT['WT_aa'] = WT_aa\n",
    "    df_WT['WT_indicator'] = True\n",
    "    df_WT = df_WT.rename(columns={'WT_aa': 'amino_acid'})\n",
    "\n",
    "    if 'WT_indicator' not in ' '.join(df_selection.columns.tolist()):\n",
    "        df_merge = pd.merge(df_WT, df_selection, on=['site','amino_acid'], how='right')\n",
    "        df_merge['WT_indicator'] = df_merge['WT_indicator'].fillna(False)\n",
    "        df_merge.to_csv(output_file, compression = 'gzip', index = False)\n",
    "        print('gauge done')\n",
    "        return df_merge\n",
    "    else:\n",
    "        print('gauge existed')\n",
    "        return df_selection\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "10b4391e",
   "metadata": {},
   "outputs": [
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     "text": [
      "gauge done\n"
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       "</table>\n",
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      ],
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       "     site amino_acid  WT_indicator    rep_1    rep_2    joint\n",
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       "..    ...        ...           ...      ...      ...      ...\n",
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       "\n",
       "[714 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sequence_file = './data/raw_data/YAP1_Reference_sequence.txt'\n",
    "selection_file = './output/selection_coefficients/YAP1.csv.gz'\n",
    "output_file = './output/selection_coefficients/YAP1.csv.gz'\n",
    "replicates = 2\n",
    "\n",
    "wildtype_full_length(sequence_file, selection_file, output_file, replicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a25a3788",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "gauge done\n"
     ]
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       "      site amino_acid  WT_indicator     rep_1    rep_2   joint\n",
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    "sequence_file = './data/raw_data/Ube4b_Reference_sequence.txt'\n",
    "selection_file = './output/selection_coefficients/Ube4b.csv.gz'\n",
    "output_file = './output/selection_coefficients/Ube4b.csv.gz'\n",
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    "\n",
    "wildtype_full_length(sequence_file, selection_file, output_file, replicates)"
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55b9db6c",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "gauge done\n"
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       "      site amino_acid  WT_indicator     rep_1     rep_2     rep_3     joint\n",
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   "execution_count": 32,
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      "gauge done\n"
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       "      site amino_acid  WT_indicator     rep_1     rep_2         rep_3  \\\n",
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       "1        1          R         False  0.000000  0.000000  0.000000e+00   \n",
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       "\n",
       "         joint  \n",
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       "\n",
       "[6363 rows x 7 columns]"
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     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_file = './data/raw_data/BRCA1_Reference_sequence.txt'\n",
    "selection_file = './output/selection_coefficients/Y2H_2.csv.gz'\n",
    "output_file = './output/selection_coefficients/Y2H_2.csv.gz'\n",
    "replicates = 3\n",
    "\n",
    "wildtype_full_length(sequence_file, selection_file, output_file, replicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "737afec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gauge done\n"
     ]
    },
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     "execution_count": 33,
     "metadata": {},
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   "source": [
    "sequence_file = './data/raw_data/BRCA1_Reference_sequence.txt'\n",
    "selection_file = './output/selection_coefficients/E3.csv.gz'\n",
    "output_file = './output/selection_coefficients/E3.csv.gz'\n",
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    "\n",
    "wildtype_full_length(sequence_file, selection_file, output_file, replicates)"
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  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f92c7248",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "gauge done\n"
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       "\n",
       "[651 rows x 10 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
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    }
   ],
   "source": [
    "sequence_file = './data/raw_data/TpoR_Reference_sequence.txt'\n",
    "selection_file = './output/selection_coefficients/TpoR.csv.gz'\n",
    "output_file = './output/selection_coefficients/TpoR.csv.gz'\n",
    "replicates = 6\n",
    "\n",
    "wildtype_full_length(sequence_file, selection_file, output_file, replicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9762da5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gauge done\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>amino_acid</th>\n",
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      ],
      "text/plain": [
       "     site amino_acid  WT_indicator     rep_1     rep_2     rep_3     rep_4  \\\n",
       "0       1          A         False -0.000112 -0.000128  0.001430  0.000504   \n",
       "1       1          R         False  0.000000  0.000000  0.000000  0.000000   \n",
       "2       1          N         False  0.000251 -0.000101  0.000591 -0.000506   \n",
       "3       1          D         False  0.000000  0.000000  0.000000  0.000000   \n",
       "4       1          C         False  0.000000  0.000000  0.000000  0.000000   \n",
       "..    ...        ...           ...       ...       ...       ...       ...   \n",
       "646    31          T         False  0.000000  0.000000  0.000000  0.000000   \n",
       "647    31          W         False  0.000000  0.000000  0.000000  0.000000   \n",
       "648    31          Y         False -0.000375  0.000089  0.000246  0.000107   \n",
       "649    31          V         False  0.000094 -0.000020 -0.000006 -0.000177   \n",
       "650    31          *         False  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "        rep_5     rep_6     joint  \n",
       "0    0.000246  0.000175  0.002090  \n",
       "1    0.000000  0.000000  0.000000  \n",
       "2    0.000484  0.000531  0.001210  \n",
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       "4    0.000000  0.000000  0.000000  \n",
       "..        ...       ...       ...  \n",
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       "648  0.000136  0.000078  0.000279  \n",
       "649  0.000198  0.000160  0.000250  \n",
       "650  0.000000  0.000000  0.000000  \n",
       "\n",
       "[651 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_file = './data/raw_data/TpoR_Reference_sequence.txt'\n",
    "selection_file = './output/selection_coefficients/TpoR_S505N.csv.gz'\n",
    "output_file = './output/selection_coefficients/TpoR_S505N.csv.gz'\n",
    "replicates = 6\n",
    "\n",
    "wildtype_full_length(sequence_file, selection_file, output_file, replicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "05c5d795",
   "metadata": {},
   "outputs": [],
   "source": [
    "# excel_file = './data/raw_data/DBR1.xlsx'\n",
    "# selection_file = './output/selection_coefficients/DBR1.csv.gz'\n",
    "# output_file = './output/gauged_selection_coefficients/DBR1.csv.gz'\n",
    "# replicates = 2\n",
    "\n",
    "# df_selection = pd.read_csv(selection_file)\n",
    "# site_list = df_selection['site'].unique()\n",
    "# df_WT = pd.read_excel(excel_file)[['Affected codon', 'Reference AA']]\n",
    "# df_WT = df_WT.drop_duplicates( keep='last').dropna()\n",
    "# df_WT['WT_indicator'] = True\n",
    "# # df_WT = df[['site', 'WT_aa','WT_indicator']]\n",
    "# df_WT = df_WT.rename(columns={'Affected codon': 'site', 'Reference AA': 'amino_acid'})\n",
    "\n",
    "# if 'gauged' not in ' '.join(df_selection.columns.tolist()):\n",
    "#     df_merge = pd.merge(df_WT, df_selection, on=['site','amino_acid'], how='right')\n",
    "#     df_merge['WT_indicator'] = df_merge['WT_indicator'].fillna(False)\n",
    "#     column_name = df_merge.columns[-(replicates+1):].tolist()\n",
    "#     gauged_name = [i+'_gauged' for i in column_name]\n",
    "#     gauged_list = []\n",
    "#     for i in gauged_name:\n",
    "#         df_merge[i] = 0\n",
    "#         gauged_list.append([])\n",
    "\n",
    "#     for site in df_merge['site'].unique():\n",
    "#         for i in range(len(column_name)):\n",
    "#             gauge_value = df_merge[(df_merge['site']==site)&(df_merge['WT_indicator'])][column_name[i]].values[0]\n",
    "#             original_list = df_merge[df_merge['site']==site][column_name[i]].tolist()\n",
    "#             gauged_list[i] +=  [i - gauge_value for i in original_list]\n",
    "\n",
    "#     for i in range(len(gauged_name)):\n",
    "#         df_merge[gauged_name[i]] = gauged_list[i]\n",
    "\n",
    "#     df_merge.to_csv(output_file, compression = 'gzip', index = False)\n",
    "#     print('gauge done')\n",
    "# else:\n",
    "#     print('gauge existed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cfa21690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gauge done\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "     site amino_acid  WT_indicator         rep_1         rep_2         joint\n",
       "0      84          A         False -5.910000e-04 -7.470000e-04 -1.260000e-03\n",
       "1      84          R         False -5.560000e-04 -1.150000e-03 -1.570000e-03\n",
       "2      84          N          True  1.010000e-02  1.520000e-02  2.280000e-02\n",
       "3      84          D         False -5.160000e-04 -4.840000e-04 -9.240000e-04\n",
       "4      84          C         False -5.300000e-04 -3.520000e-04 -8.200000e-04\n",
       "..    ...        ...           ...           ...           ...           ...\n",
       "520   108          T         False -9.630000e-08 -9.920000e-08 -1.900000e-07\n",
       "521   108          W         False -2.300000e-07 -5.160000e-07 -7.320000e-07\n",
       "522   108          Y         False -4.010000e-07 -4.480000e-07 -8.360000e-07\n",
       "523   108          V         False -3.060000e-04 -2.230000e-04 -5.220000e-04\n",
       "524   108          *         False -2.470000e-07 -1.900000e-07 -4.360000e-07\n",
       "\n",
       "[525 rows x 6 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excel_file = './data/raw_data/DBR1.xlsx'\n",
    "selection_file = './output/selection_coefficients/DBR1.csv.gz'\n",
    "output_file = './output/selection_coefficients/DBR1.csv.gz'\n",
    "replicates = 2\n",
    "\n",
    "df_selection = pd.read_csv(selection_file)\n",
    "site_list = df_selection['site'].unique()\n",
    "df_WT = pd.read_excel(excel_file)[['Affected codon', 'Reference AA']]\n",
    "df_WT = df_WT.drop_duplicates( keep='last').dropna()\n",
    "df_WT['WT_indicator'] = True\n",
    "# df_WT = df[['site', 'WT_aa','WT_indicator']]\n",
    "df_WT = df_WT.rename(columns={'Affected codon': 'site', 'Reference AA': 'amino_acid'})\n",
    "\n",
    "if 'WT_indicator' not in ' '.join(df_selection.columns.tolist()):\n",
    "    df_merge = pd.merge(df_WT, df_selection, on=['site','amino_acid'], how='right')\n",
    "    df_merge['WT_indicator'] = df_merge['WT_indicator'].fillna(False)\n",
    "    df_merge['site'] = df_merge['site'].astype('int')\n",
    "    df_merge.to_csv(output_file, compression = 'gzip', index = False)\n",
    "    print('gauge done')\n",
    "    df = df_merge\n",
    "else:\n",
    "    print('gauge existed')\n",
    "    df = df_selection\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "966d97b4",
   "metadata": {},
   "outputs": [],
   "source": []
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