149 lines
5.7 KiB
Python
Executable file
149 lines
5.7 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: format deep ddg df to allow easy merging
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# Input: 2 dfs
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#1) <gene>.lower()'_mcsm_formatted_snps.csv'
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#2) <gene>.lower()_complex_ddg_results.csv'
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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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#=======================================================================#%% 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 = 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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#%%=======================================================================
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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_snps = gene.lower() + '_mcsm_formatted_snps.csv'
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infile_mcsm_snps = outdir + in_filename_mcsm_snps
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in_filename_deepddg = gene.lower() + '_complex_ddg_results.csv'
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infile_deepddg = outdir + 'deep_ddg/' + in_filename_deepddg
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print('\nInput path:', indir
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, '\nOutput path:', outdir, '\n'
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, '\nInput filename mcsm snps', infile_mcsm_snps , '\n'
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, '\nInput filename deepddg', infile_deepddg , '\n'
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, '\n============================================================')
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#=======
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# output
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#=======
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#out_filename_deepddg = gene.lower() + '_ni_deepddg.txt'
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out_filename_deepddg = gene.lower() + '_ni_deepddg.csv'
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outfile_deepddg_f = outdir + out_filename_deepddg
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print('Output filename:', outfile_deepddg_f
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, '\n===================================================================')
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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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, '\nmcsm muts'
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, '\n===================================')
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mcsm_muts_df = pd.read_csv(infile_mcsm_snps , header = None, sep = ',', names = ['mutationinformation'])
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mcsm_muts_df.columns
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#%%============================================================================
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print('==================================='
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, '\nDeep ddg'
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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.columns
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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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deepddg_df.columns
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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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# should be identical in count ot Destabilising and stabilising respectively
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len(deepddg_df.loc[deepddg_df['deepddg'] < 0])
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len(deepddg_df.loc[deepddg_df['deepddg'] >= 0])
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#----------------------------------------------
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# drop extra columns to allow clean merging
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#----------------------------------------------
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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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#----------------------------------------------
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# embb (where gene-target has > 1 chain)
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# include chain else the numbering will be messed up!
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#----------------------------------------------
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deepddg_short_df = deepddg_df.drop(['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[["chain_id", "mutationinformation", "deepddg", "deepddg_outcome"]]
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#%% combine with mcsm snps
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deepddg_mcsm_muts_dfs = pd.merge(deepddg_short_df
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, mcsm_muts_df
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, on = 'mutationinformation'
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, how = 'right')
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deepddg_mcsm_muts_dfs ['deepddg_outcome'].value_counts()
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#%%============================================================================
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# write csv
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print('Writing file: formatted deepddg and only mcsm muts')
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deepddg_mcsm_muts_dfs.to_csv(outfile_deepddg_f, index = False)
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print('\nFinished writing file:'
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, '\nNo. of rows:', deepddg_mcsm_muts_dfs.shape[0]
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, '\nNo. of cols:', deepddg_mcsm_muts_dfs.shape[1])
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#%% end of script
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