Mercurial > repos > greg > vsnp_add_zero_coverage
view vsnp_add_zero_coverage.py @ 1:eaf4c304fd22 draft
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author | greg |
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date | Tue, 21 Apr 2020 09:51:00 -0400 |
parents | 3cb0bf7e1b2d |
children | 01312f8a6ca9 |
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#!/usr/bin/env python import argparse import multiprocessing import os import pandas import pysam import queue import re import shutil from numpy import mean from Bio import SeqIO INPUT_BAM_DIR = 'input_bam_dir' INPUT_VCF_DIR = 'input_vcf_dir' OUTPUT_VCF_DIR = 'output_vcf_dir' OUTPUT_METRICS_DIR = 'output_metrics_dir' def get_base_file_name(file_path): base_file_name = os.path.basename(file_path) if base_file_name.find(".") > 0: # Eliminate the extension. return os.path.splitext(base_file_name)[0] elif base_file_name.find("_") > 0: # The dot extension was likely changed to # the " character. items = base_file_name.split("_") return "_".join(items[0:-1]) else: return base_file_name def get_coverage_and_snp_count(task_queue, reference, output_metrics, output_vcf, timeout): while True: try: tup = task_queue.get(block=True, timeout=timeout) except queue.Empty: break bam_file, vcf_file = tup # Create a coverage dictionary. coverage_dict = {} coverage_list = pysam.depth(bam_file, split_lines=True) for line in coverage_list: chrom, position, depth = line.split('\t') coverage_dict["%s-%s" % (chrom, position)] = depth # Convert it to a data frame. coverage_df = pandas.DataFrame.from_dict(coverage_dict, orient='index', columns=["depth"]) # Create a zero coverage dictionary. zero_dict = {} for record in SeqIO.parse(reference, "fasta"): chrom = record.id total_len = len(record.seq) for pos in list(range(1, total_len + 1)): zero_dict["%s-%s" % (str(chrom), str(pos))] = 0 # Convert it to a data frame with depth_x # and depth_y columns - index is NaN. zero_df = pandas.DataFrame.from_dict(zero_dict, orient='index', columns=["depth"]) coverage_df = zero_df.merge(coverage_df, left_index=True, right_index=True, how='outer') # depth_x "0" column no longer needed. coverage_df = coverage_df.drop(columns=['depth_x']) coverage_df = coverage_df.rename(columns={'depth_y': 'depth'}) # Covert the NaN to 0 coverage and get some metrics. coverage_df = coverage_df.fillna(0) coverage_df['depth'] = coverage_df['depth'].apply(int) total_length = len(coverage_df) average_coverage = coverage_df['depth'].mean() zero_df = coverage_df[coverage_df['depth'] == 0] total_zero_coverage = len(zero_df) total_coverage = total_length - total_zero_coverage genome_coverage = "{:.2%}".format(total_coverage / total_length) # Process the associated VCF input. column_names = ["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT", "Sample"] vcf_df = pandas.read_csv(vcf_file, sep='\t', header=None, names=column_names, comment='#') good_snp_count = len(vcf_df[(vcf_df['ALT'].str.len() == 1) & (vcf_df['REF'].str.len() == 1) & (vcf_df['QUAL'] > 150)]) base_file_name = get_base_file_name(vcf_file) if total_zero_coverage > 0: header_file = "%s_header.csv" % base_file_name with open(header_file, 'w') as outfile: with open(vcf_file) as infile: for line in infile: if re.search('^#', line): outfile.write("%s" % line) vcf_df_snp = vcf_df[vcf_df['REF'].str.len() == 1] vcf_df_snp = vcf_df_snp[vcf_df_snp['ALT'].str.len() == 1] vcf_df_snp['ABS_VALUE'] = vcf_df_snp['CHROM'].map(str) + "-" + vcf_df_snp['POS'].map(str) vcf_df_snp = vcf_df_snp.set_index('ABS_VALUE') cat_df = pandas.concat([vcf_df_snp, zero_df], axis=1, sort=False) cat_df = cat_df.drop(columns=['CHROM', 'POS', 'depth']) cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']] = cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']].fillna('.') cat_df['REF'] = cat_df['REF'].fillna('N') cat_df['FORMAT'] = cat_df['FORMAT'].fillna('GT') cat_df['Sample'] = cat_df['Sample'].fillna('./.') cat_df['temp'] = cat_df.index.str.rsplit('-', n=1) cat_df[['CHROM', 'POS']] = pandas.DataFrame(cat_df.temp.values.tolist(), index=cat_df.index) cat_df = cat_df[['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'Sample']] cat_df['POS'] = cat_df['POS'].astype(int) cat_df = cat_df.sort_values(['CHROM', 'POS']) body_file = "%s_body.csv" % base_file_name cat_df.to_csv(body_file, sep='\t', header=False, index=False) if output_vcf is None: output_vcf_file = os.path.join(OUTPUT_VCF_DIR, "%s.vcf" % base_file_name) else: output_vcf_file = output_vcf with open(output_vcf_file, "w") as outfile: for cf in [header_file, body_file]: with open(cf, "r") as infile: for line in infile: outfile.write("%s" % line) else: if output_vcf is None: output_vcf_file = os.path.join(OUTPUT_VCF_DIR, "%s.vcf" % base_file_name) else: output_vcf_file = output_vcf shutil.copyfile(vcf_file, output_vcf_file) bam_metrics = [base_file_name, "", "%4f" % average_coverage, genome_coverage] vcf_metrics = [base_file_name, str(good_snp_count), "", ""] if output_metrics is None: output_metrics_file = os.path.join(OUTPUT_METRICS_DIR, "%s.tabular" % base_file_name) else: output_metrics_file = output_metrics metrics_columns = ["File", "Number of Good SNPs", "Average Coverage", "Genome Coverage"] with open(output_metrics_file, "w") as fh: fh.write("# %s\n" % "\t".join(metrics_columns)) fh.write("%s\n" % "\t".join(bam_metrics)) fh.write("%s\n" % "\t".join(vcf_metrics)) task_queue.task_done() def set_num_cpus(num_files, processes): num_cpus = int(multiprocessing.cpu_count()) if num_files < num_cpus and num_files < processes: return num_files if num_cpus < processes: half_cpus = int(num_cpus / 2) if num_files < half_cpus: return num_files return half_cpus return processes if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--output_metrics', action='store', dest='output_metrics', required=False, default=None, help='Output metrics text file') parser.add_argument('--output_vcf', action='store', dest='output_vcf', required=False, default=None, help='Output VCF file') parser.add_argument('--reference', action='store', dest='reference', help='Reference dataset') parser.add_argument('--processes', action='store', dest='processes', type=int, help='User-selected number of processes to use for job splitting') args = parser.parse_args() # The assumption here is that the list of files # in both INPUT_BAM_DIR and INPUT_VCF_DIR are # equal in number and named such that they are # properly matched if the directories contain # more than 1 file (i.e., hopefully the bam file # names and vcf file names will be something like # Mbovis-01D6_* so they can be # sorted and properly # associated with each other). bam_files = [] for file_name in sorted(os.listdir(INPUT_BAM_DIR)): file_path = os.path.abspath(os.path.join(INPUT_BAM_DIR, file_name)) bam_files.append(file_path) vcf_files = [] for file_name in sorted(os.listdir(INPUT_VCF_DIR)): file_path = os.path.abspath(os.path.join(INPUT_VCF_DIR, file_name)) vcf_files.append(file_path) multiprocessing.set_start_method('spawn') queue1 = multiprocessing.JoinableQueue() num_files = len(bam_files) cpus = set_num_cpus(num_files, args.processes) # Set a timeout for get()s in the queue. timeout = 0.05 # Add each associated bam and vcf file pair to the queue. for i, bam_file in enumerate(bam_files): vcf_file = vcf_files[i] queue1.put((bam_file, vcf_file)) # Complete the get_coverage_and_snp_count task. processes = [multiprocessing.Process(target=get_coverage_and_snp_count, args=(queue1, args.reference, args.output_metrics, args.output_vcf, timeout, )) for _ in range(cpus)] for p in processes: p.start() for p in processes: p.join() queue1.join() if queue1.empty(): queue1.close() queue1.join_thread()