added aa_prop.py and add_aa_prop.py to add aa properties for wt and mutant in a given file containing one letter code wt and mut cols as csv
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scripts/aa_prop.py
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113
scripts/aa_prop.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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'''
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Created on Mon June 14 2021
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@author: tanu
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'''
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# FIXME: import dirs.py to get the basic dir paths available
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#=======================================================================
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# TASK
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# Input:
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# Output:
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#=======================================================================
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#%% load libraries
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import os, sys
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import pandas as pd
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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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DEBUG = False
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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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from reference_dict import oneletter_aa_dict
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#=======================================================================
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#%%
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def get_aa_prop(df, col1 = 'aap1', col2 = 'aap2', col3 = 'aap_taylor', col4 = 'aap_kd', col5 = 'aap_polarity', col6 = 'aap_calcprop'):
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"""Add amino acid properties for wt and mutant residues
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@df: df containing one letter aa code for wt and mutant respectively
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@type: pandas df
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@col1: column adding 7 aa categories (no overlap; acidic, basic, amidic, hydrophobic, hydroxylic, aromatic, sulphur)
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@type: str
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@col2: column adding 9 aa categories (overlap; acidic, basic, polar, hydrophobic, hydrophilic, small, aromatic, aliphatic, special)
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@type: str
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@col3: column adding 8 aa categories (overlap; acidic, basic, polar, hydrophobic, small, aromatic, aliphatic, special)
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@type: str
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@col4: column adding 3 aa categories (no overlap, hydrophobic, neutral and hydrophilic according to KD scale)
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@type: str
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@col5: column adding 4 aa categories (no overlap, acidic, basic, neutral, non-polar)
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@type: str
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@col6: column adding 4 aa categories (neg, pos, polar, non-polar)
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@type: str
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returns df: with 6 added columns. If column names clash, the function column
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name will override original column
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@rtype: pandas df
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"""
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lookup_dict_p1 = dict()
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lookup_dict_p2 = dict()
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lookup_dict_taylor = dict()
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lookup_dict_kd = dict()
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lookup_dict_polarity = dict()
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lookup_dict_calcprop = dict()
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for k, v in oneletter_aa_dict.items():
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lookup_dict_p1[k] = v['aa_prop1']
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lookup_dict_p2[k] = v['aa_prop2']
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lookup_dict_taylor[k] = v['aa_taylor']
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lookup_dict_kd[k] = v['aa_prop_water']
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lookup_dict_polarity[k] = v['aa_prop_polarity']
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lookup_dict_calcprop[k] = v['aa_calcprop']
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#if DEBUG:
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# print('Key:', k, 'value:', v
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# , '\n============================================================'
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# , '\nlook up dict:\n')
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df['wt_aap1'] = df['wild_type'].map(lookup_dict_p1)
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df['mut_aap1'] = df['mutant_type'].map(lookup_dict_p1)
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df['wt_aap2'] = df['wild_type'].map(lookup_dict_p2)
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df['mut_aap2'] = df['mutant_type'].map(lookup_dict_p2)
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df['wt_aap_taylor'] = df['wild_type'].map(lookup_dict_taylor)
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df['mut_aap_taylor'] = df['mutant_type'].map(lookup_dict_taylor)
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df['wt_aap_kd'] = df['wild_type'].map(lookup_dict_kd)
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df['mut_aap_kd'] = df['mutant_type'].map(lookup_dict_kd)
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df['wt_aap_polarity'] = df['wild_type'].map(lookup_dict_polarity)
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df['mut_aap_polarity'] = df['mutant_type'].map(lookup_dict_polarity)
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df['wt_aa_calcprop'] = df['wild_type'].map(lookup_dict_calcprop)
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df['mut_aa_calcprop'] = df['mutant_type'].map(lookup_dict_calcprop)
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return df
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#========================================
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191
scripts/add_aa_prop.py
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scripts/add_aa_prop.py
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#!/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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# FIXME: import dirs.py to get the basic dir paths available
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#=======================================================================
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# TASK:
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#
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# Input:
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# Output:
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#=======================================================================
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#%% load libraries
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import os, sys
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import pandas as pd
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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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from aa_prop import get_aa_prop
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#=======================================================================
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#%% command line args
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help='drug name', default = None)
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arg_parser.add_argument('-g', '--gene', help='gene name', default = None)
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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') # not used atm
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args = arg_parser.parse_args()
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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 = 'merged_df3.csv'
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#in_filename = gene.lower() + '_complex_mcsm_norm.csv'
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in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SRY.csv' # gid
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infile_mcsm = outdir + '/' + in_filename_mcsm
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print('Input file: ', infile_mcsm
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, '\n============================================================')
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#=======
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# output
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#=======
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out_filename_aaps = gene.lower() + '_mcsm_aaps.txt'
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outfile_aaps = outdir + '/' + out_filename_aaps
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print('Output file: ', outfile_aaps
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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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#%% Read input files
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print('Reading input file (merged file):', infile_mcsm)
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comb_df = pd.read_csv(infile_mcsm, sep = ',')
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print('Input filename: ', in_filename_mcsm
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, '\nPath :', outdir
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, '\nNo. of rows: ', len(comb_df)
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, '\nNo. of cols: ', len(comb_df.columns)
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, '\n============================================================')
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# column names
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list(comb_df.columns)
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#%% sanity check
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nrows_df = len(comb_df)
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ncols_add = 12
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expected_cols = len(comb_df.columns) + ncols_add
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print('\nAdding aa properties for wt and mutant: ', ncols_add, ' columns in total'
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, '\n===============================================================')
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#%% call get_aa_prop():
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get_aa_prop(df = comb_df)
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#%% check dim of df
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if len(comb_df) == nrows_df and len(comb_df.columns) == expected_cols:
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print('Checking dim of df: '
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, '\nPASS: df dim match'
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, '\nno.of rows: ', len(comb_df)
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, '\nno. of cols: ', len(comb_df.columns))
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else:
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print('\FAIL: dim mismatch'
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, 'Expected rows: ', nrows_df
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, '\Got: ', len(comb_df)
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, '\nExpected cols: ', expected_cols
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, '\nGot: ', len(comb_df.columns))
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sys.exit('Aborting')
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#%% summary output
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sys.stdout = open(outfile_aaps, 'w')
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print('################################################'
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, '\n aa properties: ', gene
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, '\n################################################')
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print('\n-------------------'
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, '\nWild-type: aap 1'
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, '\n-------------------\n'
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, comb_df['wt_aap1'].value_counts()
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, '\n-------------------'
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, '\nmutant-type: aap 1'
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, '\n-------------------\n'
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, comb_df['mut_aap1'].value_counts()
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, '\n===================================================')
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print('\n-------------------'
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, '\nWild-type: aap 2'
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, '\n-------------------\n'
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, comb_df['wt_aap2'].value_counts()
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, '\n-------------------'
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, '\nmutant-type: aap 2'
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, '\n-------------------\n'
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, comb_df['mut_aap2'].value_counts()
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, '\n===================================================')
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print('\n-------------------'
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, '\nWild-type: aap taylor'
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, '\n-------------------\n'
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, comb_df['wt_aap_taylor'].value_counts()
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, '\n-------------------'
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, '\nmutant-type: taylor'
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, '\n-------------------\n'
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, comb_df['mut_aap_taylor'].value_counts()
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, '\n===================================================')
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print('\n-------------------'
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, '\nWild-type: aap water/kd'
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, '\n-------------------\n'
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, comb_df['wt_aap_kd'].value_counts()
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, '\n-------------------'
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, '\nmutant-type: water/kd'
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, '\n-------------------\n'
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, comb_df['mut_aap_kd'].value_counts()
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, '\n===================================================')
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print('\n-------------------'
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, '\nWild-type: aap polarity'
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, '\n-------------------\n'
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, comb_df['wt_aap_polarity'].value_counts()
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, '\n-------------------'
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, '\nmutant-type: polarity'
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, '\n-------------------\n'
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, comb_df['mut_aap_polarity'].value_counts()
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, '\n===================================================')
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print('\n-------------------'
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, '\nWild-type: aa calcprop'
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, '\n-------------------\n'
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, comb_df['wt_aa_calcprop'].value_counts()
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, '\n-------------------'
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, '\nmutant-type: aa calcprop'
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, '\n-------------------\n'
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, comb_df['mut_aa_calcprop'].value_counts()
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, '\n===================================================')
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#%% end of script
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#=======================================================================
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