1010 lines
No EOL
44 KiB
Python
1010 lines
No EOL
44 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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'''
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@author: tanu
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'''
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#%% Description
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# FIXME: include error checking to enure you only
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# concentrate on positions that have structural info?
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# FIXME: import dirs.py to get the basic dir paths available
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#=======================================================================
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# TASK: extract ALL <gene> matched mutations from GWAS data
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# Input data file has the following format: each row = unique sample id
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# id,country,lineage,sublineage,drtype,drug,dr_muts_col,other_muts_col...
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# 0,sampleID,USA,lineage2,lineage2.2.1,Drug-resistant,0.0,WT,gene_match<wt>POS<mut>; pncA_c.<wt>POS<mut>...
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# where multiple mutations and multiple mutation types are separated by ';'.
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# We are interested in the protein coding region i.e mutation with the<gene>_'p.' format.
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# This script splits the mutations on the ';' and extracts protein coding muts only
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# where each row is a separate mutation
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# sample ids AND mutations are NOT unique, but the COMBINATION (sample id + mutation) = unique
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# output files: all lower case
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# 1) <gene>_gwas.csv
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# 2) <gene>_common_ids.csv
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# 3) <gene>_ambiguous_muts.csv
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# 4) <gene>_mcsm_formatted_snps.csv
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# 5) <gene>_metadata_poscounts.csv
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# 6) <gene>_metadata.csv
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# 7) <gene>_all_muts_msa.csv
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# 8) <gene>_mutational_positons.csv
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#------------
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# NOTE
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#-----------
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# drtype is renamed to 'resistance' in the 35k dataset
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# all colnames in the ouput files lowercase
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#-------------
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# requires
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#-------------
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#reference_dict.py
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#tidy_split.py
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# bash counting for cross checks: example
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#grep -oP 'pncA_p.[A-Za-z]{3}[0-9]+[A-Za-z]{3}' mtb_gwas_meta_v6.csv | sort | uniq -c | wc -l
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#=======================================================================
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#%% load libraries
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import os, sys
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import re
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import pandas as pd
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import numpy as np
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import argparse
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from statistics import mean, median, mode
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from statistics import multimode
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# adding values for common keys
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import itertools
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import collections
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#=======================================================================
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#%% dir and local imports
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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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#=======================================================================
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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 (case sensitive)', default = None)
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arg_parser.add_argument('-g', '--gene', help='gene name (case sensitive)', 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('-m', '--make_dirs', help = 'Make dir for input and output', action='store_true')
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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 paths & filenames
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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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make_dirs = args.make_dirs
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###############################################################################
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#%% variable assignment: input and output dirs and files
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#=======
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# dirs
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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_v2'
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if not outdir:
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outdir = datadir + '/' + drug + '/output_v2'
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if make_dirs:
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print('make_dirs is turned on, creating data dir:', datadir)
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try:
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os.makedirs(datadir, exist_ok = True)
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print("Directory '%s' created successfully" %datadir)
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except OSError as error:
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print("Directory '%s' can not be created")
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print('make_dirs is turned on, creating indir:', indir)
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try:
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os.makedirs(indir, exist_ok = True)
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print("Directory '%s' created successfully" %indir)
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except OSError as error:
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print("Directory '%s' can not be created")
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print('make_dirs is turned on, creating outdir:', outdir)
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try:
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os.makedirs(outdir, exist_ok = True)
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print("Directory '%s' created successfully" %outdir)
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except OSError as error:
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print("Directory '%s' can not be created")
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# handle missing dirs here
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if not os.path.isdir(datadir):
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print('ERROR: Data directory does not exist:', datadir
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, '\nPlease create and ensure gwas data is present and then rerun\nelse specify cmd option --make_dirs')
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sys.exit()
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if not os.path.isdir(indir):
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print('ERROR: Input directory does not exist:', indir
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, '\nPlease either create or specify indir and rerun\nelse specify cmd option --make_dirs')
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sys.exit()
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if not os.path.isdir(outdir):
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print('ERROR: Output directory does not exist:', outdir
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, '\nPlease create or specify outdir and rerun\nelse specify cmd option --make_dirs')
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sys.exit()
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###############################################################################
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#%% required local imports
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from reference_dict import my_aa_dict # CHECK DIR STRUC THERE!
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from tidy_split import tidy_split
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###############################################################################
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#%% creating required variables: gene and drug dependent, and input filename
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gene_match = gene + '_p.'
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print('mut pattern for gene', gene, ':', gene_match)
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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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nssnp_match_captureG = "(" + 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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# building cols to extract
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dr_muts_col = 'dr_mutations_' + drug
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other_muts_col = 'other_mutations_' + drug
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resistance_col = 'drtype'
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print('Extracting columns based on variables:\n'
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, drug
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, '\n'
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, dr_muts_col
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, '\n'
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, other_muts_col
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, '\n'
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, resistance_col
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, '\n===============================================================')
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#=======
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# input
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#=======
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#in_filename_master_master = 'original_tanushree_data_v2.csv' #19k
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in_filename_master = 'mtb_gwas_meta_v6.csv' #35k
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infile_master = datadir + '/' + in_filename_master
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print('Input file: ', infile_master
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, '\n============================================================')
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#=======
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# output
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#=======
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# several output files: in respective sections at the time of outputting files
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print('Output filename: in the respective sections'
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, '\nOutput path: ', outdir
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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 file
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master_data = pd.read_csv(infile_master, sep = ',')
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# column names
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#list(master_data.columns)
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# extract elevant columns to extract from meta data related to the drug
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if in_filename_master == 'original_tanushree_data_v2.csv':
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meta_data = master_data[['id'
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, 'country'
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, 'lineage'
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, 'sublineage'
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, 'drtype'
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, drug
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, dr_muts_col
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, other_muts_col]]
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else:
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core_cols = ['id'
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, 'sample'
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#, 'patient_id'
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#, 'strain'
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, 'lineage'
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, 'sublineage'
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, 'country_code'
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#, 'geographic_source'
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, resistance_col]
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variable_based_cols = [drug
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, dr_muts_col
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, other_muts_col]
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cols_to_extract = core_cols + variable_based_cols
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print('Extracting', len(cols_to_extract), 'columns from master data')
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meta_data = master_data[cols_to_extract]
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del(master_data, variable_based_cols, cols_to_extract)
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print('Extracted meta data from filename:', in_filename_master
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, '\nDim:', meta_data.shape)
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# checks and results
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total_samples = meta_data['id'].nunique()
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print('RESULT: Total samples:', total_samples
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, '\n===========================================================')
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# counts NA per column
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meta_data.isna().sum()
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print('No. of NAs/column:' + '\n', meta_data.isna().sum()
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, '\n===========================================================\n')
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###############################################################################
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#%% Quick checks:
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meta_data['lineage'].value_counts()
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meta_data['lineage'].value_counts().sum()
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meta_data['lineage'].nunique()
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# replace lineage with 'L' in lineage_labels
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#meta_data['lineage_labels'] = meta_data['lineage']
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#meta_data['lineage_labels'].equals(meta_data['lineage'])
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#all(meta_data['lineage_labels'].value_counts() == meta_data['lineage'].value_counts())
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#meta_data['lineage_labels'] = meta_data['lineage_labels'].str.replace('lineage', 'L')
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#meta_data['lineage'].value_counts()
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#meta_data['lineage_labels'].value_counts()
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meta_data['lineage'] = meta_data['lineage'].str.replace('lineage', 'L')
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meta_data['lineage'].value_counts()
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print("\n================================"
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, "\nLineage numbers"
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, "\nComplete lineage samples:", meta_data['lineage'].value_counts().sum()
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, "\nMissing lineage samples:", meta_data['id'].nunique() - meta_data['lineage'].value_counts().sum()
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, "\n================================")
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meta_data['id'].nunique()
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meta_data['sample'].nunique()
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meta_data['id'].equals(meta_data['sample'])
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foo = meta_data.copy()
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foo['Diff'] = np.where( foo['id'] == foo['sample'] , '1', '0')
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foo['Diff'].value_counts()
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meta_data['drtype'].value_counts()
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meta_data['drtype'].value_counts().sum()
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print("\n================================"
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, "\ndrtype numbers"
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, "\nComplete drtype samples:", meta_data['drtype'].value_counts().sum()
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, "\nMissing drtype samples:", meta_data['id'].nunique() - meta_data['drtype'].value_counts().sum()
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, "\n================================")
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meta_data['drug_name'] = meta_data[drug].map({1:'R'
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, 0:'S'})
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meta_data['drug_name'].value_counts()
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meta_data[drug].value_counts()
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meta_data[drug].value_counts().sum()
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print("\n================================"
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, "\ndrug", drug, "numbers"
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, "\nComplete drug samples:", meta_data[drug].value_counts().sum()
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, "\nMissing drug samples:", meta_data['id'].nunique() - meta_data[drug].value_counts().sum()
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, "\n================================")
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print("\n================================"
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, "\ndrug", drug, "numbers"
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, "\nComplete drug samples:", meta_data['drug_name'].value_counts().sum()
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, "\nMissing drug samples:", meta_data['id'].nunique() - meta_data['drug_name'].value_counts().sum()
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, "\n================================")
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#%% Quick counts: All samples, drug:
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print('===========================================================\n'
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, 'RESULT: Total no. of samples tested for', drug, ':', meta_data[drug].value_counts().sum()
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, '\n===========================================================\n'
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, 'RESULT: No. of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts()
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, '\n===========================================================\n'
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, 'RESULT: Percentage of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts(normalize = True)*100
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, '\n===========================================================')
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print('===========================================================\n'
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, 'RESULT: Total no. of samples tested for', drug, ':', meta_data['drug_name'].value_counts().sum()
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, '\n===========================================================\n'
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, 'RESULT: No. of Sus and Res', drug, 'samples:\n', meta_data['drug_name'].value_counts()
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, '\n===========================================================\n'
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, 'RESULT: Percentage of Sus and Res', drug, 'samples:\n', meta_data['drug_name'].value_counts(normalize = True)*100
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, '\n===========================================================')
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##############################################################################
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#%% Extracting nsSNP for gene (meta_gene_all): from dr_muts col and other_muts_col
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#meta_data_gene = meta_data.loc[ (meta_data[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ) | (meta_data[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ) ]
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# so downstream code doesn't change
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meta_gene_all = meta_data.loc[ (meta_data[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ) | (meta_data[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False) ) ]
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#%% DF: with dr_muts_col
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meta_gene_dr = meta_gene_all.loc[meta_gene_all[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)]
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meta_gene_dr1 = meta_data.loc[meta_data[dr_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)]
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if meta_gene_dr.equals(meta_gene_dr1):
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print('\nPASS: DF with column:', dr_muts_col, 'extracted successfully'
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, '\ngene_snp_match in column:',dr_muts_col, meta_gene_dr.shape)
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else:
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sys.exit('\nFAIL: DF with column:', dr_muts_col,'could not be extracted'
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, '\nshape of df1:', meta_gene_dr.shape
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, '\nshape of df2:', meta_gene_dr1.shape
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, '\nCheck again!')
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##############################################################################
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#%% DF: with other_muts_col
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meta_gene_other = meta_gene_all.loc[meta_gene_all[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)]
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meta_gene_other1 = meta_data.loc[meta_data[other_muts_col].str.contains(nssnp_match, na = False, regex = True, case = False)]
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print('gene_snp_match in dr:', len(meta_gene_other))
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meta_gene_other.equals(meta_gene_other1)
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if meta_gene_other.equals(meta_gene_other1):
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print('\nPASS: DF with column:', other_muts_col,'extracted successfully'
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, '\ngene_snp_match in column:',other_muts_col, meta_gene_other.shape)
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else:
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sys.exit('\nFAIL: DF with column:', other_muts_col,'could not be extracted'
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, '\nshape of df1:', meta_gene_other.shape
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, '\nshape of df2:', meta_gene_other1.shape
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, '\nCheck again!')
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##############################################################################
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#%% Quick counts: <gene>nsSNP samples, drug
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meta_gene_all[drug].value_counts()
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print('===========================================================\n'
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, 'RESULT: Total no. of samples for', drug, 'with nsSNP mutations:', meta_gene_all['id'].nunique()
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, '\n===========================================================\n'
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, '===========================================================\n'
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, 'RESULT: Total no. of samples tested for', drug, 'with nsSNP mutations:', meta_gene_all[drug].value_counts().sum()
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, '\n===========================================================\n'
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, '===========================================================\n'
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, 'RESULT: Total no. of samples tested for', drug, 'with nsSNP mutations:', meta_gene_all['drug_name'].value_counts().sum()
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, '\n===========================================================\n'
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, 'RESULT: No. of Sus and Res', drug, 'samples with nsSNP:\n', meta_gene_all['drug_name'].value_counts()
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, '\n===========================================================\n'
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, 'RESULT: Percentage of Sus and Res', drug, 'samples with nsSNP mutations:\n', meta_gene_all['drug_name'].value_counts(normalize = True)*100
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, '\n===========================================================')
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###############################################################################
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#%% Create a copy of indices for downstream mergeing
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meta_gene_all['index_orig'] = meta_gene_all.index
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meta_gene_all['index_orig_copy'] = meta_gene_all.index
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all(meta_gene_all.index.values == meta_gene_all['index_orig'].values)
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all(meta_gene_all.index.values == meta_gene_all['index_orig_copy'].values)
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##############################################################################
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#%% Important sanity checks: Dr muts column for tidy split(), nsSNPs, etc.
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# Split based on semi colon dr_muts_col
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search = ";"
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# count of occurrence of ";" in dr_muts_col: No.of semicolons + 1 is no. of rows created * occurence
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count_df_dr = meta_gene_dr[['id', dr_muts_col]]
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count_df_dr['dr_semicolon_count'] = meta_gene_dr.loc[:, dr_muts_col].str.count(search, re.I)
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dr_sc_C = count_df_dr['dr_semicolon_count'].value_counts().reset_index()
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dr_sc_C
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dr_sc_C['index_semicolon'] = (dr_sc_C['index'] + 1) *dr_sc_C['dr_semicolon_count']
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dr_sc_C
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expected_drC = dr_sc_C['index_semicolon'].sum()
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expected_drC
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# count no. of nsSNPs and extract those nsSNPs
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count_df_dr['dr_geneSNP_count'] = meta_gene_dr.loc[:, dr_muts_col].str.count(nssnp_match, re.I)
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dr_gene_count1 = count_df_dr['dr_geneSNP_count'].sum()
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###############################################################################
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# This is to find out how many samples have 1 and more than 1 mutation,so you
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# can use it to check if your data extraction process for dr_muts
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# and other_muts has worked correctly AND also to check the dim of the
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# final formatted data.
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# This will have: unique COMBINATION of sample id and <gene_match> mutations
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#========
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# First: counting <gene_match> mutations in dr_muts_col column
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#========
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print('Now counting WT &', nssnp_match, 'muts within the column:', dr_muts_col)
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# drop na and extract a clean df
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clean_df = meta_data.dropna(subset=[dr_muts_col])
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# sanity check: count na
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na_count = meta_data[dr_muts_col].isna().sum()
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if len(clean_df) == (total_samples - na_count):
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print('PASS: clean_df extracted: length is', len(clean_df)
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, '\nNo.of NAs in', dr_muts_col, '=', na_count, '/', total_samples
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, '\n==========================================================')
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else:
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sys.exit('FAIL: Could not drop NAs')
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dr_gene_count = 0
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wt = 0
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id_dr = []
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id2_dr = []
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dr_gene_mutsL = []
|
|
#nssnp_match_regex = re.compile(nssnp_match)
|
|
|
|
for i, id in enumerate(clean_df.id):
|
|
#print (i, id)
|
|
#id_dr.append(id)
|
|
#count_gene_dr = clean_df[dr_muts_col].iloc[i].count(gene_match) # can include stop muts
|
|
count_gene_dr = len(re.findall(nssnp_match, clean_df[dr_muts_col].iloc[i], re.IGNORECASE))
|
|
gene_drL = re.findall(nssnp_match, clean_df[dr_muts_col].iloc[i], re.IGNORECASE)
|
|
#print(count_gene_dr)
|
|
if count_gene_dr > 0:
|
|
id_dr.append(id)
|
|
if count_gene_dr > 1:
|
|
id2_dr.append(id)
|
|
#print(id, count_gene_dr)
|
|
dr_gene_count = dr_gene_count + count_gene_dr
|
|
dr_gene_mutsL = dr_gene_mutsL + gene_drL
|
|
count_wt = clean_df[dr_muts_col].iloc[i].count('WT')
|
|
wt = wt + count_wt
|
|
|
|
print('RESULTS:')
|
|
print('Total WT in dr_muts_col:', wt)
|
|
print('Total matches of', gene, 'SNP matches in', dr_muts_col, ':', dr_gene_count)
|
|
print('Total samples with > 1', gene, 'nsSNPs in dr_muts_col:', len(id2_dr) )
|
|
print('Total matches of UNIQUE', gene, 'SNP matches in', dr_muts_col, ':', len(set(dr_gene_mutsL)))
|
|
print('=================================================================')
|
|
|
|
if dr_gene_count == dr_gene_count1:
|
|
print('\nPass: dr gene SNP count match')
|
|
else:
|
|
sys.exit('\nFAIL: dr gene SNP count MISmatch')
|
|
|
|
del(clean_df, na_count, i, id, wt, id2_dr, count_gene_dr, count_wt)
|
|
|
|
#%% tidy_split(): dr_muts_col
|
|
#=========
|
|
# DF1: dr_muts_col
|
|
# and remove leading white spaces
|
|
#=========
|
|
col_to_split1 = dr_muts_col
|
|
print ('Firstly, applying tidy split on dr muts df', meta_gene_dr.shape
|
|
, '\ncolumn name to apply tidy_split():' , col_to_split1
|
|
, '\n============================================================')
|
|
|
|
# apply tidy_split()
|
|
dr_WF0 = tidy_split(meta_gene_dr, col_to_split1, sep = ';')
|
|
# remove leading white space else these are counted as distinct mutations as well
|
|
dr_WF0[dr_muts_col] = dr_WF0[dr_muts_col].str.strip()
|
|
|
|
if len(dr_WF0) == expected_drC:
|
|
print('\nPass: tidy split on', dr_muts_col, 'worked'
|
|
, '\nExpected nrows:', expected_drC
|
|
, '\nGot nrows:', len(dr_WF0) )
|
|
else:
|
|
print('\nFAIL: tidy split on', dr_muts_col, 'did not work'
|
|
, '\nExpected nrows:', expected_drC
|
|
, '\nGot nrows:', len(dr_WF0) )
|
|
|
|
# Extract only the samples/rows with nssnp_match
|
|
#dr_gene_WF0 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(gene_match)]
|
|
dr_gene_WF0 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(nssnp_match, regex = True, case = False)]
|
|
#dr_gene_WF0_v2 = dr_WF0.loc[dr_WF0[dr_muts_col].str.contains(nssnp_match_captureG, regex = True, case = False)]
|
|
|
|
print('Lengths after tidy split and extracting', nssnp_match, 'muts:'
|
|
, '\nOld length:' , len(meta_gene_dr)
|
|
, '\nLength after split:', len(dr_WF0)
|
|
, '\nLength of nssnp df:', len(dr_gene_WF0)
|
|
, '\nExpected len:', dr_gene_count
|
|
, '\n=============================================================')
|
|
|
|
# Important: Assign 'column name' on which split was performed as an extra column
|
|
# This is so you can identify if mutations are dr_type or other in the final df
|
|
dr_df = dr_gene_WF0.assign(mutation_info = dr_muts_col)
|
|
print('Dim of dr_df:', dr_df.shape
|
|
, '\n=============================================================='
|
|
, '\nEnd of tidy split() on dr_muts, and added an extra column relecting mut_category'
|
|
, '\n===============================================================')
|
|
|
|
if other_muts_col in dr_df.columns:
|
|
print('Dropping:', other_muts_col, 'from WF gene dr_df')
|
|
dr_df = dr_df.drop([other_muts_col], axis = 1)
|
|
########################################################################
|
|
#%% Important sanity checks: other muts column for tidy split(), nsSNPs, etc.
|
|
# Split based on semi colon on other_muts_col
|
|
# count of occurrence of ";" in other_muts_col: No.of semicolons + 1 is no. of rows created * occurence
|
|
count_df_other = meta_gene_other[['id', other_muts_col]]
|
|
count_df_other['other_semicolon_count'] = meta_gene_other.loc[:, other_muts_col].str.count(search, re.I)
|
|
other_sc_C = count_df_other['other_semicolon_count'].value_counts().reset_index()
|
|
other_sc_C
|
|
other_sc_C['index_semicolon'] = (other_sc_C['index']+1)*other_sc_C['other_semicolon_count']
|
|
other_sc_C
|
|
expected_otherC = other_sc_C['index_semicolon'].sum()
|
|
expected_otherC
|
|
|
|
# count no. of nsSNPs and extract those nsSNPs
|
|
count_df_other['other_geneSNP_count'] = meta_gene_other.loc[:, other_muts_col].str.count(nssnp_match, re.I)
|
|
other_gene_count1 = count_df_other['other_geneSNP_count'].sum()
|
|
|
|
# This is to find out how many samples have 1 and more than 1 mutation,so you
|
|
# can use it to check if your data extraction process for dr_muts
|
|
# and other_muts has worked correctly AND also to check the dim of the
|
|
# final formatted data.
|
|
# This will have: unique COMBINATION of sample id and <gene_match> mutations
|
|
|
|
#========
|
|
# Second: counting <gene_match> mutations in other_muts_col column
|
|
#========
|
|
print('Now counting WT &', nssnp_match, 'muts within the column:', other_muts_col)
|
|
# drop na and extract a clean df
|
|
clean_df = meta_data.dropna(subset=[other_muts_col])
|
|
|
|
# sanity check: count na
|
|
na_count = meta_data[other_muts_col].isna().sum()
|
|
|
|
if len(clean_df) == (total_samples - na_count):
|
|
print('PASS: clean_df extracted: length is', len(clean_df)
|
|
, '\nNo.of NAs =', na_count, '/', total_samples
|
|
, '\n=========================================================')
|
|
else:
|
|
sys.exit('FAIL: Could not drop NAs')
|
|
|
|
other_gene_count = 0
|
|
wt_other = 0
|
|
id_other = []
|
|
id2_other = []
|
|
other_gene_mutsL = []
|
|
|
|
for i, id in enumerate(clean_df.id):
|
|
#print (i, id)
|
|
#id_other.append(id)
|
|
#count_gene_other = clean_df[other_muts_col].iloc[i].count(gene_match) # can include stop muts
|
|
count_gene_other = len(re.findall(nssnp_match, clean_df[other_muts_col].iloc[i], re.IGNORECASE))
|
|
gene_otherL = re.findall(nssnp_match, clean_df[other_muts_col].iloc[i], re.IGNORECASE)
|
|
#print(count_gene_other)
|
|
if count_gene_other > 0:
|
|
id_other.append(id)
|
|
if count_gene_other > 1:
|
|
id2_other.append(id)
|
|
#print(id, count_gene_other)
|
|
other_gene_count = other_gene_count + count_gene_other
|
|
other_gene_mutsL = other_gene_mutsL + gene_otherL
|
|
count_wt = clean_df[other_muts_col].iloc[i].count('WT')
|
|
wt_other = wt_other + count_wt
|
|
|
|
print('RESULTS:')
|
|
print('Total WT in other_muts_col:', wt_other)
|
|
print('Total matches of', gene, 'SNP matches in', other_muts_col, ':', other_gene_count)
|
|
print('Total samples with > 1', gene, 'nsSNPs in other_muts_col:', len(id2_other) )
|
|
print('Total matches of UNIQUE', gene, 'SNP matches in', other_muts_col, ':', len(set(other_gene_mutsL)))
|
|
print('=================================================================')
|
|
|
|
if other_gene_count == other_gene_count1:
|
|
print('\nPass: other gene SNP count match')
|
|
else:
|
|
sys.exit('\nFAIL: other gene SNP count MISmatch')
|
|
|
|
del(clean_df, na_count, i, id, wt_other, id2_other, count_gene_other, count_wt )
|
|
#%% tidy_split(): other_muts_col
|
|
#=========
|
|
# DF2: other_muts_col
|
|
# and remove leading white spaces
|
|
#=========
|
|
col_to_split2 = other_muts_col
|
|
print ('applying second tidy split() separately on other muts df', meta_gene_other.shape
|
|
, '\ncolumn name to apply tidy_split():', col_to_split2
|
|
, '\n============================================================')
|
|
|
|
# apply tidy_split()
|
|
other_WF1 = tidy_split(meta_gene_other, col_to_split2, sep = ';')
|
|
# remove the leading white spaces in the column
|
|
other_WF1[other_muts_col] = other_WF1[other_muts_col].str.strip()
|
|
|
|
# extract only the samples/rows with nssnp_match
|
|
#other_gene_WF1 = other_WF1.loc[other_WF1[other_muts_col].str.contains(gene_match)]
|
|
other_gene_WF1 = other_WF1.loc[other_WF1[other_muts_col].str.contains(nssnp_match, regex = True, case = False)]
|
|
|
|
print('Lengths after tidy split and extracting', gene_match, 'muts:',
|
|
'\nOld length:' , len(meta_gene_other),
|
|
'\nLength after split:', len(other_WF1),
|
|
'\nLength of nssnp df:', len(other_gene_WF1),
|
|
'\nExpected len:', other_gene_count
|
|
, '\n=============================================================')
|
|
|
|
# Important: Assign 'column name' on which split was performed as an extra column
|
|
# This is so you can identify if mutations are dr_type or other in the final df
|
|
other_df = other_gene_WF1.assign(mutation_info = other_muts_col)
|
|
print('dim of other_df:', other_df.shape
|
|
, '\n==============================================================='
|
|
, '\nEnd of tidy split() on other_muts, and added an extra column relecting mut_category'
|
|
, '\n===============================================================')
|
|
|
|
if dr_muts_col in other_df.columns:
|
|
print('Dropping:', dr_muts_col, 'from WF gene other_df')
|
|
other_df = other_df.drop([dr_muts_col], axis = 1)
|
|
###############################################################################
|
|
#%% Finding ambiguous muts
|
|
common_snps_dr_other = set(dr_gene_mutsL).intersection(set(other_gene_mutsL))
|
|
|
|
#%% More sanity checks: expected unique snps and nrows in LF data
|
|
expected_unique_snps = len(set(dr_gene_mutsL)) + len(set(other_gene_mutsL)) - len(common_snps_dr_other)
|
|
expected_rows = dr_gene_count + other_gene_count
|
|
|
|
#%% Useful results to note==> counting dr, other and common muts
|
|
print('\n==================================================================='
|
|
, '\nCount unique nsSNPs for', gene, ':'
|
|
, expected_unique_snps
|
|
, '\n===================================================================')
|
|
|
|
Vcounts_dr = pd.Series(dr_gene_mutsL).value_counts()
|
|
Vcounts_common_dr = Vcounts_dr.get(list(common_snps_dr_other))
|
|
print('\n==================================================================='
|
|
, "\nCount of samples for common muts in dr muts\n"
|
|
, Vcounts_common_dr
|
|
, '\n===================================================================')
|
|
|
|
Vcounts_other = pd.Series(other_gene_mutsL).value_counts()
|
|
Vcounts_common_other = Vcounts_other.get(list(common_snps_dr_other))
|
|
|
|
print('\n==================================================================='
|
|
, '\nCount of samples for common muts in other muts\n'
|
|
, Vcounts_common_other
|
|
, '\n===================================================================')
|
|
|
|
print('\n==================================================================='
|
|
, '\nPredicting total no. of rows in the curated df:', expected_rows
|
|
, '\n===================================================================')
|
|
|
|
#%%another way: Add value checks for dict so you can know if its correct for LF data count below
|
|
dr_snps_vc_dict = pd.Series(dr_gene_mutsL).value_counts().to_dict()
|
|
other_snps_vc_dict = pd.Series(other_gene_mutsL).value_counts().to_dict()
|
|
|
|
for k, v in dr_snps_vc_dict.items():
|
|
if k in common_snps_dr_other:
|
|
print(k,v)
|
|
|
|
for k, v in other_snps_vc_dict.items():
|
|
if k in common_snps_dr_other:
|
|
print(k,v)
|
|
|
|
# using defaultdict
|
|
Cdict = collections.defaultdict(int)
|
|
|
|
# iterating key, val with chain()
|
|
for key, val in itertools.chain(dr_snps_vc_dict.items(), other_snps_vc_dict.items()):
|
|
if key in common_snps_dr_other:
|
|
Cdict[key] += val
|
|
else:
|
|
Cdict[key] = val
|
|
|
|
print(dict(Cdict))
|
|
|
|
for k, v in Cdict.items():
|
|
if k in common_snps_dr_other:
|
|
print(k,v)
|
|
###############################################################################
|
|
# USE Vcounts to get expected curated df
|
|
# resolve dm om muts funda
|
|
#%%#%% Concatenating two dfs: gene_LF0
|
|
# equivalent of rbind in R
|
|
#==========
|
|
# Concatentating the two dfs: equivalent of rbind in R
|
|
#==========
|
|
# Important: Change column names to allow concat:
|
|
# dr_muts.. & other_muts : 'mutation'
|
|
print('Now concatenating the two dfs by row'
|
|
, '\nFirst assigning a common colname: "mutation" to the col containing muts'
|
|
, '\nThis is done for both dfs'
|
|
, '\n===================================================================')
|
|
|
|
dr_df.columns
|
|
dr_df.rename(columns = {dr_muts_col: 'mutation'}, inplace = True)
|
|
dr_df.columns
|
|
|
|
other_df.columns
|
|
other_df.rename(columns = {other_muts_col: 'mutation'}, inplace = True)
|
|
other_df.columns
|
|
|
|
if len(dr_df.columns) == len(other_df.columns):
|
|
print('Checking dfs for concatening by rows:'
|
|
, '\nDim of dr_df:', dr_df.shape
|
|
, '\nDim of other_df:', other_df.shape
|
|
, '\nExpected nrows:', len(dr_df) + len(other_df)
|
|
, '\n=============================================================')
|
|
else:
|
|
sys.exit('FAIL: No. of cols mismatch for concatenating')
|
|
|
|
# checking colnames before concat
|
|
print('Checking colnames BEFORE concatenating the two dfs...')
|
|
if (set(dr_df.columns) == set(other_df.columns)):
|
|
print('PASS: column names match necessary for merging two dfs')
|
|
else:
|
|
sys.exit('FAIL: Colnames mismatch for concatenating!')
|
|
|
|
# concatenate (axis = 0): Rbind, adn keeo original index
|
|
print('Now appending the two dfs: Rbind')
|
|
gene_LF_comb = pd.concat([dr_df, other_df]
|
|
#, ignore_index = True
|
|
, axis = 0)
|
|
|
|
if gene_LF_comb.index.nunique() == len(meta_gene_all.index):
|
|
print('\nPASS:length of index in LF data:', len(gene_LF_comb.index)
|
|
, '\nLength of unique index in LF data:', gene_LF_comb.index.nunique()
|
|
)
|
|
else:
|
|
sys.exit('\nFAIL: expected length for combined LF data mismatch:'
|
|
, '\nExpected length:', len(meta_gene_all.index)
|
|
, '\nGot:', gene_LF_comb.index.nunique() )
|
|
|
|
print('Finding stop mutations in concatenated df')
|
|
stop_muts = gene_LF_comb['mutation'].str.contains('\*').sum()
|
|
if stop_muts == 0:
|
|
print('PASS: No stop mutations detected')
|
|
else:
|
|
print('stop mutations detected'
|
|
, '\nNo. of stop muts:', stop_muts, '\n'
|
|
, gene_LF_comb.groupby(['mutation_info'])['mutation'].apply(lambda x: x[x.str.contains('\*')].count())
|
|
, '\nNow removing them')
|
|
|
|
gene_LF0_nssnp = gene_LF_comb[~gene_LF_comb['mutation'].str.contains('\*')]
|
|
print('snps only: by subtracting stop muts:', len(gene_LF0_nssnp))
|
|
|
|
gene_LF0 = gene_LF_comb[gene_LF_comb['mutation'].str.contains(nssnp_match, case = False)]
|
|
print('snps only by direct regex:', len(gene_LF0))
|
|
|
|
if gene_LF0_nssnp.equals(gene_LF0):
|
|
print('PASS: regex for extracting nssnp worked correctly & stop mutations successfully removed'
|
|
, '\nUsing the regex extracted df')
|
|
else:
|
|
sys.exit('FAIL: posssibly regex or no of stop mutations'
|
|
, 'Regex being used:', nssnp_match)
|
|
#sys.exit()
|
|
|
|
# checking colnames and length after concat
|
|
print('Checking colnames AFTER concatenating the two dfs...')
|
|
if (set(dr_df.columns) == set(gene_LF0.columns)):
|
|
print('PASS: column names match'
|
|
, '\n=============================================================')
|
|
else:
|
|
sys.exit('FAIL: Colnames mismatch AFTER concatenating')
|
|
|
|
print('Checking concatenated df')
|
|
if len(gene_LF0) == (len(dr_df) + len(other_df))- stop_muts:
|
|
print('PASS:length of df after concat match'
|
|
, '\n===============================================================')
|
|
else:
|
|
sys.exit('FAIL: length mismatch')
|
|
|
|
#%% NEW check2:id, sample, etc.
|
|
gene_LF0['id'].count()
|
|
gene_LF0['id'].nunique()
|
|
gene_LF0['sample'].nunique()
|
|
gene_LF0['mutation_info'].value_counts()
|
|
gene_LF0[drug].isna().sum()
|
|
#%% Concatenating two dfs sanity checks: gene_LF1
|
|
#=========================================================
|
|
# This is hopefully clean data, but just double check
|
|
# Filter LF data so that you only have
|
|
# mutations corresponding to nssnp_match (case insensitive)
|
|
# this will be your list you run OR calcs
|
|
#=========================================================
|
|
print('Length of gene_LF0:', len(gene_LF0)
|
|
, '\nThis should be what we need. But just double checking and extracting nsSNP for', gene
|
|
, '\nfrom LF0 (concatenated data) using case insensitive regex match:', nssnp_match)
|
|
|
|
gene_LF1 = gene_LF0[gene_LF0['mutation'].str.contains(nssnp_match, regex = True, case = False)]
|
|
|
|
if len(gene_LF0) == len(gene_LF1):
|
|
print('PASS: length of gene_LF0 and gene_LF1 match',
|
|
'\nConfirming extraction and concatenating worked correctly'
|
|
, '\n==========================================================')
|
|
else:
|
|
print('FAIL: BUT NOT FATAL!'
|
|
, '\ngene_LF0 and gene_LF1 lengths differ'
|
|
, '\nsuggesting error in extraction process'
|
|
, ' use gene_LF1 for downstreama analysis'
|
|
, '\n=========================================================')
|
|
|
|
print('Length of dfs pre and post processing...'
|
|
, '\ngene WF data (unique samples in each row):',len(meta_gene_all)
|
|
, '\ngene LF data (unique mutation in each row):', len(gene_LF1)
|
|
, '\n=============================================================')
|
|
|
|
# sanity check for extraction
|
|
# This ought to pass if nsnsp_match happens right at the beginning of creating 'expected_rows'
|
|
print('Verifying whether extraction process worked correctly...')
|
|
if len(gene_LF1) == expected_rows:
|
|
print('PASS: extraction process performed correctly'
|
|
, '\nExpected length:', expected_rows
|
|
, '\nGot:', len(gene_LF1)
|
|
, '\nRESULT: Total no. of mutant sequences for logo plot:', len(gene_LF1)
|
|
, '\n=========================================================')
|
|
else:
|
|
print('FAIL: extraction process has bugs'
|
|
, '\nExpected length:', expected_rows
|
|
, '\nGot:', len(gene_LF1)
|
|
, '\nDebug please'
|
|
, '\n=========================================================')
|
|
|
|
# more sanity checks
|
|
print('Performing some more sanity checks...')
|
|
|
|
# From LF1: useful for OR counts
|
|
# no. of unique muts
|
|
distinct_muts = gene_LF1.mutation.value_counts()
|
|
print('Distinct genomic mutations:', len(distinct_muts))
|
|
|
|
# no. of samples contributing the unique muts
|
|
gene_LF1.id.nunique()
|
|
print('No.of samples contributing to distinct genomic muts:', gene_LF1.id.nunique())
|
|
|
|
# no. of dr and other muts
|
|
mut_grouped = gene_LF1.groupby('mutation_info').mutation.nunique()
|
|
print('No.of unique dr and other muts:\n', gene_LF1.groupby('mutation_info').mutation.nunique())
|
|
|
|
# sanity check
|
|
if len(distinct_muts) == mut_grouped.sum() :
|
|
print('PASS:length of LF1 is as expected'
|
|
, '\n===============================================================')
|
|
else:
|
|
print('FAIL: Mistmatch in count of muts'
|
|
, '\nExpected count:', len(distinct_muts)
|
|
, '\nActual count:', mut_grouped.sum()
|
|
, '\nMuts should be distinct within dr* and other* type'
|
|
, '\nInspecting...possibly ambiguous muts'
|
|
, '\nNo. of possible ambiguous muts:', mut_grouped.sum() - len(distinct_muts)
|
|
, '\n=========================================================')
|
|
|
|
muts_split = list(gene_LF1.groupby('mutation_info'))
|
|
dr_muts = muts_split[0][1].mutation
|
|
other_muts = muts_split[1][1].mutation
|
|
print('splitting muts by mut_info:', muts_split)
|
|
print('no.of dr_muts samples:', len(dr_muts))
|
|
print('no. of other_muts samples', len(other_muts))
|
|
#%% Ambiguous muts
|
|
# IMPORTANT: The same mutation cannot be classed as a drug AND 'others'
|
|
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
|
|
print('WARNING: Ambiguous muts detected in dr_ and other_ mutation category'
|
|
, '\n===============================================================')
|
|
else:
|
|
print('PASS: NO ambiguous muts detected'
|
|
, '\nMuts are unique to dr_ and other_ mutation class'
|
|
, '\n=========================================================')
|
|
|
|
# inspect dr_muts and other muts: Fixed in case no ambiguous muts detected!
|
|
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
|
|
print('Finding ambiguous muts...'
|
|
, '\n========================================================='
|
|
, '\nTotal no. of samples in dr_muts present in other_muts:', dr_muts.isin(other_muts).sum()
|
|
, '\nThese are:', dr_muts[dr_muts.isin(other_muts)]
|
|
, '\n========================================================='
|
|
, '\nTotal no. of samples in other_muts present in dr_muts:', other_muts.isin(dr_muts).sum()
|
|
, '\nThese are:\n', other_muts[other_muts.isin(dr_muts)]
|
|
, '\n=========================================================')
|
|
|
|
print('Counting no. of ambiguous muts...'
|
|
, '\nNo. of ambiguous muts in dr:'
|
|
, len(dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist())
|
|
, '\nNo. of ambiguous muts in other:'
|
|
, len(other_muts[other_muts.isin(dr_muts)].value_counts().keys().tolist())
|
|
, '\n=========================================================')
|
|
|
|
if dr_muts[dr_muts.isin(other_muts)].nunique() == other_muts[other_muts.isin(dr_muts)].nunique():
|
|
common_muts = dr_muts[dr_muts.isin(other_muts)].value_counts().keys().tolist()
|
|
print('Distinct no. of ambigiuous muts detected:'+ str(len(common_muts))
|
|
, '\nlist of ambiguous mutations (see below):', *common_muts, sep = '\n')
|
|
print('\n===========================================================')
|
|
else:
|
|
#sys.exit('Error: ambiguous muts present, but extraction failed. Debug!')
|
|
print('No: ambiguous muts present')
|
|
|
|
#%% Ambiguous muts: revised annotation for mutation_info
|
|
ambiguous_muts_df = gene_LF1[gene_LF1['mutation'].isin(common_muts)]
|
|
ambiguous_muts_value_counts = ambiguous_muts_df.groupby('mutation')['mutation_info'].value_counts()
|
|
ambiguous_muts_value_counts
|
|
|
|
gene_LF1_orig = gene_LF1.copy()
|
|
gene_LF1_orig.equals(gene_LF1)
|
|
|
|
# copy the old columns for checking
|
|
gene_LF1['mutation_info_orig'] = gene_LF1['mutation_info']
|
|
gene_LF1['mutation_info_v1'] = gene_LF1['mutation_info']
|
|
gene_LF1['mutation_info'].value_counts()
|
|
#%% Inspect ambiguous muts
|
|
#=====================================
|
|
# Now create a new df that will have:
|
|
# ambiguous muts
|
|
# mutation_info
|
|
# revised mutation_info
|
|
# The revised label is based on value_counts
|
|
# for mutaiton_info. The corresponding mutation_info:
|
|
# label is chosen that corresponds to the max of value counts
|
|
#=====================================
|
|
ambig_muts_rev_df = pd.DataFrame()
|
|
changes_val = []
|
|
changes_dict = {}
|
|
|
|
for i in common_muts:
|
|
#print(i)
|
|
temp_df = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info_orig']]
|
|
|
|
# DANGER: ASSUMES TWO STATES ONLY and that value_counts sorts by descending
|
|
max_info_table = gene_LF1[gene_LF1['mutation'] == i][['mutation', 'mutation_info_orig']].value_counts()
|
|
|
|
revised_label = max_info_table[[0]].index[0][1] # max value_count
|
|
old_label = max_info_table[[1]].index[0][1] # min value_count
|
|
print('\nAmbiguous mutation labels...'
|
|
, '\nSetting mutation_info for', i, 'to', revised_label)
|
|
temp_df['mutation_info_REV'] = np.where( (temp_df['mutation_info_orig'] == old_label)
|
|
, revised_label
|
|
, temp_df['mutation_info_orig'])
|
|
ambig_muts_rev_df = pd.concat([ambig_muts_rev_df,temp_df])
|
|
f = max_info_table[[1]]
|
|
old_label_count = f[[0]][0]
|
|
changes_val.append(old_label_count)
|
|
cc_dict = f.to_dict()
|
|
changes_dict.update(cc_dict)
|
|
|
|
ambig_muts_rev_df['mutation_info_REV'].value_counts()
|
|
ambig_muts_rev_df['mutation_info_orig'].value_counts()
|
|
changes_val
|
|
changes_total = sum(changes_val)
|
|
changes_dict
|
|
#%% OUTFILE 3, write file: ambiguous muts and ambiguous mut counts
|
|
#===================
|
|
# ambiguous muts
|
|
#=======================
|
|
#dr_muts.to_csv('dr_muts.csv', header = True)
|
|
#other_muts.to_csv('other_muts.csv', header = True)
|
|
if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
|
|
out_filename_ambig_muts = gene.lower() + '_ambiguous_muts.csv'
|
|
outfile_ambig_muts = outdir + '/' + out_filename_ambig_muts
|
|
print('\n----------------------------------'
|
|
, '\nWriting file: ambiguous muts'
|
|
, '\n----------------------------------'
|
|
, '\nFilename:', outfile_ambig_muts)
|
|
inspect = gene_LF1[gene_LF1['mutation'].isin(common_muts)]
|
|
inspect.to_csv(outfile_ambig_muts, index = True)
|
|
|
|
print('Finished writing:', out_filename_ambig_muts
|
|
, '\nNo. of rows:', len(inspect)
|
|
, '\nNo. of cols:', len(inspect.columns)
|
|
, '\nNo. of rows = no. of samples with the ambiguous muts present:'
|
|
, dr_muts.isin(other_muts).sum() + other_muts.isin(dr_muts).sum()
|
|
, '\n=============================================================')
|
|
del(out_filename_ambig_muts)
|
|
|
|
#%% FIXME: ambig mut counts
|
|
#=======================
|
|
# ambiguous mut counts
|
|
#=======================
|
|
out_filename_ambig_mut_counts = gene.lower() + '_ambiguous_mut_counts.csv'
|
|
outfile_ambig_mut_counts = outdir + '/' + out_filename_ambig_mut_counts
|
|
print('\n----------------------------------'
|
|
, '\nWriting file: ambiguous muts'
|
|
, '\n----------------------------------'
|
|
, '\nFilename:', outfile_ambig_mut_counts)
|
|
|
|
ambiguous_muts_value_counts.to_csv(outfile_ambig_mut_counts, index = True)
|
|
#%%FIXME: TODO: Add sanity check to make sure you can add value_count checks
|
|
#%% Resolving ambiguous muts
|
|
# Merging ambiguous muts
|
|
#=================
|
|
# Merge ambig muts
|
|
# with gene_LF1
|
|
#===================
|
|
ambig_muts_rev_df.index
|
|
gene_LF1.index
|
|
all(ambig_muts_rev_df.index.isin(gene_LF1.index))
|
|
|
|
gene_LF1.loc[ambig_muts_rev_df.index, 'mutation_info_v1'] = ambig_muts_rev_df['mutation_info_REV']
|
|
gene_LF1['mutation_info_orig'].value_counts()
|
|
gene_LF1['mutation_info_v1'].value_counts()
|
|
foo = gene_LF1.iloc[ambig_muts_rev_df.index]
|
|
|
|
# Sanity check1: if there are still any ambiguous muts
|
|
muts_split_rev = list(gene_LF1.groupby('mutation_info_v1'))
|
|
dr_muts_rev = muts_split_rev[0][1].mutation
|
|
other_muts_rev = muts_split_rev[1][1].mutation
|
|
print('splitting muts by mut_info:', muts_split_rev)
|
|
print('no.of dr_muts samples:', len(dr_muts_rev))
|
|
print('no. of other_muts samples', len(other_muts_rev))
|
|
|
|
if not dr_muts_rev.isin(other_muts_rev).sum() & other_muts_rev.isin(dr_muts_rev).sum() > 0:
|
|
print('\nAmbiguous muts corrected. Proceeding with downstream analysis')
|
|
else:
|
|
print('\nAmbiguous muts NOT corrected. Quitting!')
|
|
sys.exit()
|
|
|
|
gene_LF1['mutation_info_v1'].value_counts()
|
|
#%% PHEW! Good to go for downstream stuff |