326 lines
12 KiB
Python
Executable file
326 lines
12 KiB
Python
Executable file
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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'''
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Created on Tue Aug 6 12:56:03 2019
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@author: tanu
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'''
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#=======================================================================
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# Task: combining all dfs to a single one
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# Input: 8 dfs
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#1) <gene>.lower()'_complex_mcsm_norm.csv'
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#2) <gene>.lower()_foldx.csv'
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#3) <gene>.lower()_dssp.csv'
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#4) <gene>.lower()_kd.csv'
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#5) <gene>.lower()_rd.csv'
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#6) 'ns' + <gene>.lower()_snp_info.csv'
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#7) <gene>.lower()_af_or.csv'
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#8) <gene>.lower() _af_or_kinship.csv
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# combining order
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#Merge1 = 1 + 2
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#Merge2 = 3 + 4
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#Merge3 = Merge2 + 5
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#Merge4 = Merge1 + Merge3
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#Merge5 = 6 + 7
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#Merge6 = Merge5 + 8
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#Merge7 = Merge4 + Merge6
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# Output: single csv of all 8 dfs combined
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# useful link
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# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
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#=======================================================================
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#%% load packages
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import sys, os
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import pandas as pd
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from pandas import DataFrame
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import numpy as np
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#from varname import nameof
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import argparse
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#=======================================================================
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#%% specify input and curr dir
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homedir = os.path.expanduser('~')
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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os.getcwd()
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# FIXME: local imports
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#from combining import combine_dfs_with_checks
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from combining_FIXME import detect_common_cols
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from reference_dict import oneletter_aa_dict
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from reference_dict import low_3letter_dict
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from aa_code import get_aa_3lower
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from aa_code import get_aa_1upper
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# REGEX: as required
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# mcsm_regex = r'^([A-Za-z]{1})([0-9]+)([A-Za-z]{1})$'
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# mcsm_wt = mcsm_df['mutationinformation'].str.extract(mcsm_regex)[0]
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# mcsm_mut = mcsm_df['mutationinformation'].str.extract(mcsm_regex)[2]
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# gwas_regex = r'^([A-Za-z]{3})([0-9]+)([A-Za-z]{3})$'
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# gwas_wt = mcsm_df['mutation'].str.extract(gwas_regex)[0]
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# gwas_pos = mcsm_df['mutation'].str.extract(gwas_regex)[1]
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# gwas_mut = mcsm_df['mutation'].str.extract(gwas_regex)[2]
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#=======================================================================
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#%% command line args: case sensitive
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
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arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
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arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
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arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
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arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
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arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode')
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args = arg_parser.parse_args()
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#=======================================================================
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#%% variable assignment: input and output
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#drug = 'pyrazinamide'
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#gene = 'pncA'
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drug = args.drug
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gene = args.gene
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datadir = args.datadir
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indir = args.input_dir
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outdir = args.output_dir
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gene_match = gene + '_p.'
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print('mut pattern for gene', gene, ':', gene_match)
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# !"Redundant, now that improvements have been made!
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# See section "REGEX"
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# nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}'
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# print('nsSNP for gene', gene, ':', nssnp_match)
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# wt_regex = gene_match.lower()+'([A-Za-z]{3})'
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# print('wt regex:', wt_regex)
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# mut_regex = r'[0-9]+(\w{3})$'
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# print('mt regex:', mut_regex)
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# pos_regex = r'([0-9]+)'
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# print('position regex:', pos_regex)
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#%%=======================================================================
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#==============
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# directories
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#==============
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if not datadir:
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datadir = homedir + '/git/Data/'
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if not indir:
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indir = datadir + drug + '/input/'
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if not outdir:
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outdir = datadir + drug + '/output/'
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#=======
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# input
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#=======
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#in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
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in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SAM.csv' # gidb
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in_filename_foldx = gene.lower() + '_foldx.csv'
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in_filename_dssp = gene.lower() + '_dssp.csv'
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in_filename_kd = gene.lower() + '_kd.csv'
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in_filename_rd = gene.lower() + '_rd.csv'
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in_filename_deepddg = gene.lower() + '_complex_ddg_results.txt' # change to decent filename and put it in the correct dir
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in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info_f.csv' # gwas f info
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in_filename_afor = gene.lower() + '_af_or.csv'
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in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
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infile_mcsm = outdir + in_filename_mcsm
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infile_foldx = outdir + in_filename_foldx
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infile_dssp = outdir + in_filename_dssp
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infile_kd = outdir + in_filename_kd
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infile_rd = outdir + in_filename_rd
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infile_deepddg = outdir + 'deep_ddg/' + in_filename_deepddg
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infile_snpinfo = outdir + '/' + in_filename_snpinfo
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infile_afor = outdir + '/' + in_filename_afor
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infile_afor_kin = outdir + '/' + in_filename_afor_kin
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print('\nInput path:', indir
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, '\nOutput path:', outdir, '\n'
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, '\nInput filename mcsm:', infile_mcsm
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, '\nInput filename foldx:', infile_foldx, '\n'
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, '\nInput filename dssp:', infile_dssp
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, '\nInput filename kd:', infile_kd
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, '\nInput filename rd', infile_rd
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# , '\nInput filename rd', infile_deepddg , '\n'
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, '\nInput filename snp info:', infile_snpinfo, '\n'
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, '\nInput filename af or:', infile_afor
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, '\nInput filename afor kinship:', infile_afor_kin
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, '\n============================================================')
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#=======
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# output
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#=======
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out_filename_comb = gene.lower() + '_all_params.csv'
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outfile_comb = outdir + '/' + out_filename_comb
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print('Output filename:', outfile_comb
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, '\n===================================================================')
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o_join = 'outer'
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l_join = 'left'
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r_join = 'right'
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i_join = 'inner'
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# end of variable assignment for input and output files
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#%%============================================================================
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print('==================================='
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, '\nFirst merge: mcsm + foldx'
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, '\n===================================')
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mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
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# add 3 lowercase aa code for wt and mutant
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get_aa_3lower(df = mcsm_df
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, wt_colname = 'wild_type'
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, mut_colname = 'mutant_type'
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, col_wt = 'wt_aa_3lower'
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, col_mut = 'mut_aa_3lower')
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#mcsm_df.columns = mcsm_df.columns.str.lower()
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foldx_df = pd.read_csv(infile_foldx , sep = ',')
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#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join)
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merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df)
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mcsm_foldx_dfs = pd.merge(mcsm_df, foldx_df, on = merging_cols_m1, how = o_join)
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ncols_m1 = len(mcsm_foldx_dfs.columns)
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print('\n\nResult of first merge:', mcsm_foldx_dfs.shape
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, '\n===================================================================')
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mcsm_foldx_dfs[merging_cols_m1].apply(len)
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mcsm_foldx_dfs[merging_cols_m1].apply(len) == len(mcsm_foldx_dfs)
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#%%
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print('==================================='
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, '\nSecond merge: mcsm_foldx_dfs + deepddg'
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, '\n===================================')
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deepddg_df = pd.read_csv(infile_deepddg, sep = ' ')
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deepddg_df.columns
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deepddg_df.rename(columns = {'#chain' : 'chain_id'
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, 'WT' : 'wild_type_deepddg'
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, 'ResID' : 'position'
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, 'Mut' : 'mutant_type_deepddg'}
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, inplace = True)
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deepddg_df['mutationinformation'] = deepddg_df['wild_type_deepddg'] + deepddg_df['position'].map(str) + deepddg_df['mutant_type_deepddg']
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# add deepddg outcome column: <0--> Destabilising, >0 --> Stabilising
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deepddg_df['deepddg_outcome'] = np.where(deepddg_df['deepddg'] < 0, 'Destabilising', 'Stabilising')
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deepddg_df['deepddg_outcome'].value_counts()
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# drop extra columns to allow clean merging
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deepddg_short_df = deepddg_df.drop(['chain_id', 'wild_type_deepddg', 'position', 'mutant_type_deepddg'], axis = 1)
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# rearrange columns
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deepddg_short_df.columns
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deepddg_short_df = deepddg_short_df[["mutationinformation", "deepddg", "deepddg_outcome"]]
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mcsm_foldx_deepddg_dfs = pd.merge(mcsm_foldx_dfs, deepddg_short_df, on = 'mutationinformation', how = l_join)
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mcsm_foldx_deepddg_dfs['deepddg_outcome'].value_counts()
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ncols_deepddg_merge = len(mcsm_foldx_deepddg_dfs.columns)
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#%%============================================================================
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print('==================================='
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, '\nSecond merge: dssp + kd'
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, '\n===================================')
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dssp_df = pd.read_csv(infile_dssp, sep = ',')
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kd_df = pd.read_csv(infile_kd, sep = ',')
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rd_df = pd.read_csv(infile_rd, sep = ',')
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#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
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merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
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dssp_kd_dfs = pd.merge(dssp_df, kd_df, on = merging_cols_m2, how = o_join)
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print('\n\nResult of second merge:', dssp_kd_dfs.shape
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, '\n===================================================================')
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#%%============================================================================
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print('==================================='
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, '\nThird merge: second merge + rd_df'
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, '\ndssp_kd_dfs + rd_df'
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, '\n===================================')
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#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = o_join)
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merging_cols_m3 = detect_common_cols(dssp_kd_dfs, rd_df)
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dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs, rd_df, on = merging_cols_m3
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, how = o_join)
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ncols_m3 = len(dssp_kd_rd_dfs.columns)
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print('\n\nResult of Third merge:', dssp_kd_rd_dfs.shape
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, '\n===================================================================')
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dssp_kd_rd_dfs[merging_cols_m3].apply(len)
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dssp_kd_rd_dfs[merging_cols_m3].apply(len) == len(dssp_kd_rd_dfs)
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#%%============================================================================
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print('======================================='
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, '\nFourth merge: First merge + Third merge'
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, '\nmcsm_foldx_dfs + dssp_kd_rd_dfs'
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, '\n=======================================')
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#combined_df = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = i_join)
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#merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
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#combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = i_join)
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#combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
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# with deepddg values
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merging_cols_m4 = detect_common_cols(mcsm_foldx_deepddg_dfs, dssp_kd_rd_dfs)
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combined_df = pd.merge(mcsm_foldx_deepddg_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = i_join)
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combined_df_expected_cols = ncols_deepddg_merge + ncols_m3 - len(merging_cols_m4)
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if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
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print('PASS: successfully combined 5 dfs'
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, '\nNo. of rows combined_df:', len(combined_df)
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, '\nNo. of cols combined_df:', len(combined_df.columns))
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else:
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sys.exit('FAIL: check individual df merges')
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print('\nResult of Fourth merge:', combined_df.shape
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, '\n===================================================================')
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combined_df[merging_cols_m4].apply(len)
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combined_df[merging_cols_m4].apply(len) == len(combined_df)
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#%%============================================================================
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# Format the combined df columns
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combined_df_colnames = combined_df.columns
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# check redundant columns
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combined_df['chain'].equals(combined_df['chain_id'])
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combined_df['wild_type'].equals(combined_df['wild_type_kd']) # has nan
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combined_df['wild_type'].equals(combined_df['wild_type_dssp'])
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#sanity check
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foo = combined_df[['wild_type', 'wild_type_kd', 'wt_3letter_caps', 'wt_aa_3lower', 'mut_aa_3lower']]
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# Drop cols
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cols_to_drop = ['chain_id', 'wild_type_kd', 'wild_type_dssp', 'wt_3letter_caps' ]
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combined_df_clean = combined_df.drop(cols_to_drop, axis = 1)
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del(foo)
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#%%============================================================================
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# Output columns
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out_filename_stab_struc = gene.lower() + '_comb_stab_struc_params.csv'
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outfile_stab_struc = outdir + '/' + out_filename_stab_struc
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print('Output filename:', outfile_stab_struc
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, '\n===================================================================')
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# write csv
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print('Writing file: combined stability and structural parameters')
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combined_df.to_csv(outfile_stab_struc, index = False)
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print('\nFinished writing file:'
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, '\nNo. of rows:', combined_df.shape[0]
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, '\nNo. of cols:', combined_df.shape[1])
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#%% end of script
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