added count.py to count samples for quick checks
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scripts/count.py
Executable file
288
scripts/count.py
Executable file
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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: 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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#=======================================================================
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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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#=======================================================================
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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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#drug = 'streptomycin'
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#gene = 'gid'
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#%% 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'
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if not outdir:
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outdir = datadir + '/' + drug + '/output'
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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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# Requires
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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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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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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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#=======
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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'
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, 'country_code'
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, 'geographic_source'
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#, 'region'
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#, 'location'
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#, 'host_body_site'
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#, 'environment_material'
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#, 'host_status'
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#, 'host_sex'
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#, 'submitted_host_sex'
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#, 'hiv_status'
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#, 'HIV_status'
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#, 'tissue_type'
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#, 'isolation_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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#%% Write check file
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check_file = outdir + '/' + gene.lower() + '_gwas.csv'
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meta_data.to_csv(check_file, index = False)
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print('\n----------------------------------'
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, '\nWriting subsetted gwas data:', gene
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, '\n----------------------------------'
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, '\nFile', check_file
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, '\nDim:', meta_data.shape)
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# equivalent of table in R
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# drug counts: complete samples for OR calcs
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meta_data[drug].value_counts()
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print('===========================================================\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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#%%
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#!!!!!!!!!!
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foo = meta_data[dr_muts_col].value_counts()
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foo = foo.reset_index(name = 'values')
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foo.columns = [dr_muts_col, 'dr_muts_count'] #171
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foo_count = foo.loc[foo[dr_muts_col].str.contains('del', na = False, regex = True, case = False) ]
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bar = meta_data[other_muts_col].value_counts()
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bar = bar.reset_index(name = 'values')
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bar.columns = [other_muts_col, 'dr_muts_count'] #64
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bar_count = bar.loc[bar[other_muts_col].str.contains('del', na = False, regex = True, case = False)]
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tot = len(foo_count) + len(bar_count)
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n_del = tot/len(meta_data)
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n_del*100
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baz = 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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