328 lines
12 KiB
Python
Executable file
328 lines
12 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
'''
|
|
Created on Tue Aug 6 12:56:03 2019
|
|
|
|
@author: tanu
|
|
'''
|
|
|
|
# FIXME: include error checking to enure you only
|
|
# concentrate on positions that have structural info?
|
|
|
|
# FIXME: import dirs.py to get the basic dir paths available
|
|
#=======================================================================
|
|
# TASK: extract ALL <gene> matched mutations from GWAS data
|
|
# Input data file has the following format: each row = unique sample id
|
|
# id,country,lineage,sublineage,drtype,drug,dr_muts_col,other_muts_col...
|
|
# 0,sampleID,USA,lineage2,lineage2.2.1,Drug-resistant,0.0,WT,gene_match<wt>POS<mut>; pncA_c.<wt>POS<mut>...
|
|
# where multiple mutations and multiple mutation types are separated by ';'.
|
|
# We are interested in the protein coding region i.e mutation with the<gene>_'p.' format.
|
|
# This script splits the mutations on the ';' and extracts protein coding muts only
|
|
# where each row is a separate mutation
|
|
# sample ids AND mutations are NOT unique, but the COMBINATION (sample id + mutation) = unique
|
|
|
|
|
|
# output files: all lower case
|
|
# 1) <gene>_gwas.csv
|
|
# 2) <gene>_common_ids.csv
|
|
# 3) <gene>_ambiguous_muts.csv
|
|
# 4) <gene>_mcsm_formatted_snps.csv
|
|
# 5) <gene>_metadata_poscounts.csv
|
|
# 6) <gene>_metadata.csv
|
|
# 7) <gene>_all_muts_msa.csv
|
|
# 8) <gene>_mutational_positons.csv
|
|
|
|
#------------
|
|
# NOTE
|
|
#-----------
|
|
# drtype is renamed to 'resistance' in the 35k dataset
|
|
# all colnames in the ouput files lowercase
|
|
|
|
#-------------
|
|
# requires
|
|
#-------------
|
|
#reference_dict.py
|
|
#tidy_split.py
|
|
#=======================================================================
|
|
#%% load libraries
|
|
import os, sys
|
|
import re
|
|
import pandas as pd
|
|
import numpy as np
|
|
import argparse
|
|
#=======================================================================
|
|
#%% dir and local imports
|
|
homedir = os.path.expanduser('~')
|
|
# set working dir
|
|
os.getcwd()
|
|
os.chdir(homedir + '/git/LSHTM_analysis/scripts')
|
|
os.getcwd()
|
|
|
|
#=======================================================================
|
|
#%% command line args
|
|
arg_parser = argparse.ArgumentParser()
|
|
arg_parser.add_argument('-d', '--drug', help='drug name (case sensitive)', default = None)
|
|
arg_parser.add_argument('-g', '--gene', help='gene name (case sensitive)', default = None)
|
|
arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
|
|
arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
|
|
arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
|
|
arg_parser.add_argument('-m', '--make_dirs', help = 'Make dir for input and output', action='store_true')
|
|
|
|
arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode')
|
|
|
|
|
|
args = arg_parser.parse_args()
|
|
#=======================================================================
|
|
#%% variable assignment: input and output paths & filenames
|
|
drug = args.drug
|
|
gene = args.gene
|
|
datadir = args.datadir
|
|
indir = args.input_dir
|
|
outdir = args.output_dir
|
|
make_dirs = args.make_dirs
|
|
|
|
#drug = 'streptomycin'
|
|
#gene = 'gid'
|
|
|
|
#%% input and output dirs and files
|
|
#=======
|
|
# dirs
|
|
#=======
|
|
if not datadir:
|
|
datadir = homedir + '/' + 'git/Data'
|
|
|
|
if not indir:
|
|
indir = datadir + '/' + drug + '/input'
|
|
|
|
if not outdir:
|
|
outdir = datadir + '/' + drug + '/output'
|
|
|
|
if make_dirs:
|
|
print('make_dirs is turned on, creating data dir:', datadir)
|
|
try:
|
|
os.makedirs(datadir, exist_ok = True)
|
|
print("Directory '%s' created successfully" %datadir)
|
|
except OSError as error:
|
|
print("Directory '%s' can not be created")
|
|
|
|
print('make_dirs is turned on, creating indir:', indir)
|
|
try:
|
|
os.makedirs(indir, exist_ok = True)
|
|
print("Directory '%s' created successfully" %indir)
|
|
except OSError as error:
|
|
print("Directory '%s' can not be created")
|
|
|
|
print('make_dirs is turned on, creating outdir:', outdir)
|
|
try:
|
|
os.makedirs(outdir, exist_ok = True)
|
|
print("Directory '%s' created successfully" %outdir)
|
|
except OSError as error:
|
|
print("Directory '%s' can not be created")
|
|
|
|
# handle missing dirs here
|
|
if not os.path.isdir(datadir):
|
|
print('ERROR: Data directory does not exist:', datadir
|
|
, '\nPlease create and ensure gwas data is present and then rerun\nelse specify cmd option --make_dirs')
|
|
sys.exit()
|
|
if not os.path.isdir(indir):
|
|
print('ERROR: Input directory does not exist:', indir
|
|
, '\nPlease either create or specify indir and rerun\nelse specify cmd option --make_dirs')
|
|
sys.exit()
|
|
if not os.path.isdir(outdir):
|
|
print('ERROR: Output directory does not exist:', outdir
|
|
, '\nPlease create or specify outdir and rerun\nelse specify cmd option --make_dirs')
|
|
sys.exit()
|
|
|
|
# Requires
|
|
from reference_dict import my_aa_dict # CHECK DIR STRUC THERE!
|
|
from tidy_split import tidy_split
|
|
|
|
#=======================================================================
|
|
gene_match = gene + '_p.'
|
|
print('mut pattern for gene', gene, ':', gene_match)
|
|
|
|
nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}'
|
|
print('nsSNP for gene', gene, ':', nssnp_match)
|
|
|
|
wt_regex = gene_match.lower()+'([A-Za-z]{3})'
|
|
print('wt regex:', wt_regex)
|
|
|
|
mut_regex = r'[0-9]+(\w{3})$'
|
|
print('mt regex:', mut_regex)
|
|
|
|
pos_regex = r'([0-9]+)'
|
|
print('position regex:', pos_regex)
|
|
|
|
# building cols to extract
|
|
dr_muts_col = 'dr_mutations_' + drug
|
|
other_muts_col = 'other_mutations_' + drug
|
|
resistance_col = 'drtype'
|
|
|
|
print('Extracting columns based on variables:\n'
|
|
, drug
|
|
, '\n'
|
|
, dr_muts_col
|
|
, '\n'
|
|
, other_muts_col
|
|
, '\n'
|
|
, resistance_col
|
|
, '\n===============================================================')
|
|
#=======================================================================
|
|
|
|
#=======
|
|
# input
|
|
#=======
|
|
#in_filename_master_master = 'original_tanushree_data_v2.csv' #19k
|
|
in_filename_master = 'mtb_gwas_meta_v6.csv' #35k
|
|
infile_master = datadir + '/' + in_filename_master
|
|
print('Input file: ', infile_master
|
|
, '\n============================================================')
|
|
|
|
#=======
|
|
# output
|
|
#=======
|
|
# several output files: in respective sections at the time of outputting files
|
|
print('Output filename: in the respective sections'
|
|
, '\nOutput path: ', outdir
|
|
, '\n=============================================================')
|
|
|
|
#%%end of variable assignment for input and output files
|
|
#=======================================================================
|
|
#%% Read input file
|
|
master_data = pd.read_csv(infile_master, sep = ',')
|
|
|
|
# column names
|
|
#list(master_data.columns)
|
|
|
|
# extract elevant columns to extract from meta data related to the drug
|
|
if in_filename_master == 'original_tanushree_data_v2.csv':
|
|
meta_data = master_data[['id'
|
|
, 'country'
|
|
, 'lineage'
|
|
, 'sublineage'
|
|
, 'drtype'
|
|
, drug
|
|
, dr_muts_col
|
|
, other_muts_col]]
|
|
else:
|
|
core_cols = ['id'
|
|
, 'sample'
|
|
#, 'patient_id'
|
|
#, 'strain'
|
|
, 'lineage'
|
|
, 'sublineage'
|
|
#, 'country'
|
|
, 'country_code'
|
|
, 'geographic_source'
|
|
#, 'region'
|
|
#, 'location'
|
|
#, 'host_body_site'
|
|
#, 'environment_material'
|
|
#, 'host_status'
|
|
#, 'host_sex'
|
|
#, 'submitted_host_sex'
|
|
#, 'hiv_status'
|
|
#, 'HIV_status'
|
|
#, 'tissue_type'
|
|
#, 'isolation_source'
|
|
, resistance_col]
|
|
|
|
variable_based_cols = [drug
|
|
, dr_muts_col
|
|
, other_muts_col]
|
|
|
|
cols_to_extract = core_cols + variable_based_cols
|
|
print('Extracting', len(cols_to_extract), 'columns from master data')
|
|
|
|
meta_data = master_data[cols_to_extract]
|
|
del(master_data, variable_based_cols, cols_to_extract)
|
|
|
|
print('Extracted meta data from filename:', in_filename_master
|
|
, '\nDim:', meta_data.shape)
|
|
|
|
# checks and results
|
|
total_samples = meta_data['id'].nunique()
|
|
print('RESULT: Total samples:', total_samples
|
|
, '\n===========================================================')
|
|
|
|
# counts NA per column
|
|
meta_data.isna().sum()
|
|
print('No. of NAs/column:' + '\n', meta_data.isna().sum()
|
|
, '\n===========================================================\n')
|
|
|
|
#%% Write check file
|
|
check_file = outdir + '/' + gene.lower() + '_gwas.csv'
|
|
meta_data.to_csv(check_file, index = False)
|
|
print('\n----------------------------------'
|
|
, '\nWriting subsetted gwas data:', gene
|
|
, '\n----------------------------------'
|
|
, '\nFile', check_file
|
|
, '\nDim:', meta_data.shape)
|
|
|
|
# equivalent of table in R
|
|
# drug counts: complete samples for OR calcs
|
|
meta_data[drug].value_counts()
|
|
print('===========================================================\n'
|
|
, 'RESULT: No. of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts()
|
|
, '\n===========================================================\n'
|
|
, 'RESULT: Percentage of Sus and Res', drug, 'samples:\n', meta_data[drug].value_counts(normalize = True)*100
|
|
, '\n===========================================================')
|
|
#%%
|
|
#!!!!!!!!!!
|
|
foo = meta_data[dr_muts_col].value_counts()
|
|
foo = foo.reset_index(name = 'values')
|
|
foo.columns = [dr_muts_col, 'dr_muts_count'] #171
|
|
foo_count = foo.loc[foo[dr_muts_col].str.contains('del', na = False, regex = True, case = False) ]
|
|
|
|
bar = meta_data[other_muts_col].value_counts()
|
|
bar = bar.reset_index(name = 'values')
|
|
bar.columns = [other_muts_col, 'other_muts_count'] #64
|
|
bar_count = bar.loc[bar[other_muts_col].str.contains('del', na = False, regex = True, case = False)]
|
|
|
|
|
|
tot = len(foo_count) + len(bar_count)
|
|
n_del = tot/len(meta_data)
|
|
n_del*100 #0.6
|
|
|
|
#============================
|
|
foo2 = meta_data[dr_muts_col].value_counts()
|
|
foo2 = foo2.reset_index(name = 'values')
|
|
foo2.columns = [dr_muts_col, 'dr_muts_count'] #171
|
|
foo2_count = foo2.loc[foo2[dr_muts_col].str.contains('ins', na = False, regex = True, case = False) ]
|
|
|
|
bar2 = meta_data[other_muts_col].value_counts()
|
|
bar2 = bar2.reset_index(name = 'values')
|
|
bar2.columns = [other_muts_col, 'pther_muts_count'] #64
|
|
bar2_count = bar2.loc[bar2[other_muts_col].str.contains('ins', na = False, regex = True, case = False)]
|
|
|
|
tot2 = len(foo2_count) + len(bar2_count)
|
|
n_ins = tot2/len(meta_data)
|
|
n_ins*100 #0.5
|
|
|
|
#=========
|
|
del_ins_tot = n_del*100 + n_ins*100
|
|
#==========
|
|
# stop codons
|
|
baz_count = foo.loc[foo[dr_muts_col].str.contains('\*', na = False, regex = True, case = False) ]
|
|
baz_count2 = bar.loc[bar[other_muts_col].str.contains('\*', na = False, regex = True, case = False)]
|
|
tot3 = len(baz_count) + len(baz_count2)
|
|
n_stop = tot3/len(meta_data)
|
|
n_stop*100 #0.11
|
|
|
|
all_tot = n_del*100 + n_ins*100 + n_stop*100 #1.2
|
|
#%% count pncA WT
|
|
wt_gene = meta_data.loc[meta_data[dr_muts_col].str.contains('WT', na = False, regex = True, case = False) | meta_data[other_muts_col].str.contains('WT', na = False, regex = True, case = False) ]
|
|
|
|
meta_data['muts_and_lineage'] = meta_data[dr_muts_col] + meta_data[other_muts_col] + meta_data['lineage']
|
|
|
|
wt_gene_v2 = meta_data.loc[meta_data['muts_and_lineage'].str.contains('*WT*lineage1', na = False, regex = True, case = False)]
|
|
|
|
|
|
lin1_wt_gene = wt_gene.loc[wt_gene['lineage'].str.contains('lineage1', na = False, regex = True, case = False)]
|
|
|
|
dr_lin1 = wt_gene.groupby(['lineage'])[dr_muts_col].apply(lambda x: x[x.str.contains('WT')].count())
|
|
other_lin1 = wt_gene.groupby(['lineage'])[other_muts_col].apply(lambda x: x[x.str.contains('WT')].count())
|
|
|
|
dr_lin1_v2 = wt_gene.groupby(['lineage'])[dr_muts_col].value_counts()
|
|
|
|
other_lin1_v2 = wt_gene.groupby(['lineage'])[other_muts_col].value_counts()
|