287 lines
10 KiB
Python
Executable file
287 lines
10 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
'''
|
|
Created on Tue Aug 6 12:56:03 2019
|
|
|
|
@author: tanu
|
|
'''
|
|
# FIXME: change filename 2(mcsm normalised data)
|
|
# to be consistent like (pnca_complex_mcsm_norm.csv) : changed manually, but ensure this is done in the mcsm pipeline
|
|
#=======================================================================
|
|
# Task: combine 2 dfs with aa position as linking column
|
|
|
|
# Input: 2 dfs
|
|
# <gene.lower()>_complex_mcsm_norm.csv
|
|
# <gene.lower()>_foldx.csv
|
|
|
|
# Output: .csv of all 2 dfs combined
|
|
|
|
# useful link
|
|
# https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
|
|
#=======================================================================
|
|
#%% load packages
|
|
import sys, os
|
|
import pandas as pd
|
|
import numpy as np
|
|
#from varname import nameof
|
|
import argparse
|
|
|
|
#=======================================================================
|
|
#%% specify input and curr dir
|
|
homedir = os.path.expanduser('~')
|
|
|
|
# set working dir
|
|
os.getcwd()
|
|
os.chdir(homedir + '/git/LSHTM_analysis/scripts')
|
|
os.getcwd()
|
|
|
|
# FIXME: local imports
|
|
#from combining import combine_dfs_with_checks
|
|
from combining_FIXME import detect_common_cols
|
|
#=======================================================================
|
|
#%% command line args
|
|
#arg_parser = argparse.ArgumentParser()
|
|
#arg_parser.add_argument('-d', '--drug', help='drug name', default = 'pyrazinamide')
|
|
#arg_parser.add_argument('-g', '--gene', help='gene name', default = 'pncA') # case sensitive
|
|
#args = arg_parser.parse_args()
|
|
#=======================================================================
|
|
#%% variable assignment: input and output
|
|
drug = 'pyrazinamide'
|
|
gene = 'pncA'
|
|
gene_match = gene + '_p.'
|
|
|
|
#drug = args.drug
|
|
#gene = args.gene
|
|
#======
|
|
# dirs
|
|
#======
|
|
datadir = homedir + '/' + 'git/Data'
|
|
indir = datadir + '/' + drug + '/' + 'input'
|
|
outdir = datadir + '/' + drug + '/' + 'output'
|
|
|
|
#=======
|
|
# input
|
|
#=======
|
|
in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv'
|
|
in_filename_foldx = gene.lower() + '_foldx.csv'
|
|
in_filename_dssp = gene.lower() + '_dssp.csv'
|
|
in_filename_kd = gene.lower() + '_kd.csv'
|
|
in_filename_rd = gene.lower() + '_rd.csv'
|
|
in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info.csv'
|
|
in_filename_afor = gene.lower() + '_af_or.csv'
|
|
in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
|
|
|
|
|
|
infile_mcsm = outdir + '/' + in_filename_mcsm
|
|
infile_foldx = outdir + '/' + in_filename_foldx
|
|
infile_dssp = outdir + '/' + in_filename_dssp
|
|
infile_kd = outdir + '/' + in_filename_kd
|
|
infile_rd = outdir + '/' + in_filename_rd
|
|
infile_snpinfo = indir + '/' + in_filename_snpinfo
|
|
infile_afor = outdir + '/' + in_filename_afor
|
|
infile_afor_kin = outdir + '/' + in_filename_afor_kin
|
|
|
|
|
|
print('\nInput path:', outdir
|
|
, '\nInput filename mcsm:', infile_mcsm
|
|
, '\nInput filename foldx:', infile_foldx
|
|
, '\nInput filename dssp:', infile_dssp
|
|
, '\nInput filename kd:', infile_kd
|
|
, '\nInput filename rd', infile_rd
|
|
, '\nInput filename snp info:', infile_snpinfo
|
|
, '\nInput filename af or:', infile_afor
|
|
, '\nInput filename afor kinship:', infile_afor_kin
|
|
, '\n============================================================')
|
|
|
|
#=======
|
|
# output
|
|
#=======
|
|
out_filename_comb = gene.lower() + '_all_params.csv'
|
|
outfile_comb = outdir + '/' + out_filename_comb
|
|
print('Output filename:', outfile_comb
|
|
, '\n============================================================')
|
|
|
|
o_join = 'outer'
|
|
l_join = 'left'
|
|
r_join = 'right'
|
|
i_join = 'inner'
|
|
|
|
# end of variable assignment for input and output files
|
|
#&%%====================================================================
|
|
mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
|
|
mcsm_df.columns = mcsm_df.columns.str.lower()
|
|
foldx_df = pd.read_csv(infile_foldx , sep = ',')
|
|
|
|
print('==================================='
|
|
, '\nFirst merge: mcsm + foldx'
|
|
, '\n===================================')
|
|
#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join)
|
|
merging_cols_m1 = detect_common_cols(mcsm_df, foldx_df)
|
|
|
|
mcsm_foldx_dfs = pd.merge(mcsm_df, foldx_df, on = merging_cols_m1, how = 'outer')
|
|
ncols_m1 = len(mcsm_foldx_dfs.columns)
|
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
print('==================================='
|
|
, '\nSecond merge: dssp + kd'
|
|
, '\n===================================')
|
|
|
|
dssp_df = pd.read_csv(infile_dssp, sep = ',')
|
|
kd_df = pd.read_csv(infile_kd, sep = ',')
|
|
rd_df = pd.read_csv(infile_rd, sep = ',')
|
|
|
|
#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
|
|
merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
|
|
|
|
dssp_kd_dfs = pd.merge(dssp_df, kd_df, on = merging_cols_m2, how = 'outer')
|
|
|
|
print('==================================='
|
|
, '\nThird merge: dssp_kd_dfs + rd_df'
|
|
, '\n===================================')
|
|
#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = o_join)
|
|
merging_cols_m3 = detect_common_cols(dssp_df, kd_df)
|
|
dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs, rd_df, on = merging_cols_m3, how = 'outer')
|
|
|
|
ncols_m3 = len(dssp_kd_rd_dfs.columns)
|
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
print('==================================='
|
|
, '\nFourth merge: First merge + Third merge'
|
|
, '\n===================================')
|
|
#combined_dfs = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = i_join)# gives wrong!
|
|
merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
|
|
combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
|
|
|
|
combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = 'inner')
|
|
|
|
|
|
if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
|
|
print('PASS: successfully combined 5 dfs'
|
|
, '\nnrows combined_df:', len(combined_df)
|
|
, '\ncols combined_df:', len(combined_df.columns))
|
|
else:
|
|
sys.exit('FAIL: check individual df merges')
|
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
#%% OR combining
|
|
afor_df = pd.read_csv(infile_afor, sep = ',')
|
|
afor_df.columns = afor_df.columns.str.lower()
|
|
|
|
if afor_df['mutation'].shape[0] == afor_df['mutation'].nunique():
|
|
print('No duplicate muts detected in afor_df')
|
|
else:
|
|
print('Dropping duplicate muts detected in afor_df')
|
|
afor_df = afor_df.drop_duplicates(subset = 'mutation', keep = 'first')
|
|
|
|
|
|
snpinfo_df_all = pd.read_csv(infile_snpinfo, sep = ',')
|
|
snpinfo_df = snpinfo_df_all[['mutation', 'mutationinformation']]
|
|
|
|
|
|
if snpinfo_df['mutation'].shape[0] == snpinfo_df['mutation'].nunique():
|
|
print('No duplicate muts detected in snpinfo_df')
|
|
else:
|
|
dups = snpinfo_df['mutation'].duplicated().sum()
|
|
print( dups, 'Duplicate muts detected in snpinfo_df'
|
|
, '\nDim:', snpinfo_df.shape)
|
|
print('Dropping duplicate muts')
|
|
snpinfo_df = snpinfo_df.drop_duplicates(subset = 'mutation', keep = 'first')
|
|
print('Dim:', snpinfo_df.shape)
|
|
|
|
print('==================================='
|
|
, '\nFifth merge: afor_df + snpinfo_df'
|
|
, '\n===================================')
|
|
|
|
merging_cols_m5 = detect_common_cols(afor_df, snpinfo_df)
|
|
|
|
afor_snpinfo_dfs = pd.merge(afor_df, snpinfo_df, on = merging_cols_m5, how = 'left')
|
|
if len(afor_snpinfo_dfs) == afor_df.shape[0]:
|
|
print('PASS: succesfully combined with left join'
|
|
, '\nDim of df1:', afor_df.shape
|
|
, '\nDim of df2:', snpinfo_df.shape)
|
|
else:
|
|
sys.exit('FAIL: unsuccessful merge')
|
|
|
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
afor_kin_df = pd.read_csv(infile_afor_kin, sep = ',')
|
|
afor_kin_df.columns = afor_kin_df.columns.str.lower()
|
|
|
|
print('==================================='
|
|
, '\nSixth merge: afor_snpinfo_dfs + afor_kin_df'
|
|
, '\n===================================')
|
|
|
|
merging_cols_m6 = detect_common_cols(afor_snpinfo_dfs, afor_kin_df)
|
|
|
|
print('Dim of df1:', afor_snpinfo_dfs.shape
|
|
, '\nDim of df2:', afor_kin_df.shape
|
|
, '\nno. of merging_cols:', len(merging_cols_m6))
|
|
|
|
ors_df = pd.merge(afor_snpinfo_dfs, afor_kin_df, on = merging_cols_m6, how = 'outer')
|
|
|
|
print('Dim of ors_df:', ors_df.shape)
|
|
|
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
print('==================================='
|
|
, '\nSeventh merge: combined_df + ors_df'
|
|
, '\n===================================')
|
|
|
|
merging_cols_m7 = detect_common_cols(combined_df, ors_df)
|
|
|
|
print('Dim of df1:', combined_df.shape
|
|
, '\nDim of df2:', ors_df.shape
|
|
, '\nno. of merging_cols:', len(merging_cols_m7))
|
|
|
|
print('checking mutations in the two dfs:'
|
|
, '\nmuts in df1 but NOT in df2:'
|
|
, combined_df['mutationinformation'].isin(ors_df['mutationinformation']).sum()
|
|
, '\nmuts in df2 but NOT in df1:'
|
|
, ors_df['mutationinformation'].isin(combined_df['mutationinformation']).sum())
|
|
|
|
#print('\nNo. of common muts:', np.intersect1d(combined_df['mutationinformation'], ors_df['mutationinformation']) )
|
|
|
|
#combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = 'outer') # FIXME
|
|
combined_df_all = pd.merge(combined_df, ors_df, on = merging_cols_m7, how = 'left')
|
|
|
|
outdf_expected_rows = len(combined_df)
|
|
outdf_expected_cols = len(combined_df.columns) + len(ors_df.columns) - len(merging_cols_m7)
|
|
|
|
print('\nDim of combined_df_all:', combined_df_all.shape
|
|
, '\nwith join type: ????')
|
|
|
|
if combined_df_all.shape[1] == outdf_expected_cols:
|
|
print('combined_df has expected no. of cols')
|
|
if combined_df_all.shape[0] == outdf_expected_rows:
|
|
print('combined_df has expected no. of rows')
|
|
else:
|
|
print('WARNING: nrows discrepancy noted'
|
|
, '\nFIX IT')
|
|
print ('thing finished')
|
|
#%%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
# write csv
|
|
|
|
combined_df_all.to_csv(outfile_comb, index = False)
|
|
|
|
#=======================================================================
|
|
#%% incase you FIX the the function: combine_dfs_with_checks
|
|
#def main():
|
|
|
|
# print('Reading input files:')
|
|
#mcsm_df = pd.read_csv(infile_mcsm, sep = ',')
|
|
#mcsm_df.columns = mcsm_df.columns.str.lower()
|
|
|
|
#foldx_df = pd.read_csv(infile_foldx , sep = ',')
|
|
|
|
#dssp_df = pd.read_csv(infile_dssp, sep = ',')
|
|
#dssp_df.columns = dssp_df.columns.str.lower()
|
|
|
|
#kd_df = pd.read_csv(infile_kd, sep = ',')
|
|
#kd_df.columns = kd_df.columns.str.lower()
|
|
|
|
#rd_df = pd.read_csv(infile_kd, sep = ',')
|
|
|
|
|
|
|
|
#if __name__ == '__main__':
|
|
# main()
|
|
#=======================================================================
|
|
#%% end of script
|