#!/usr/bin/env python # -*- coding: utf-8 -*- import os import sys import argparse import numpy as np import matplotlib matplotlib.use('Agg') matplotlib.rcParams['pdf.fonttype'] = 42 matplotlib.rcParams['svg.fonttype'] = 'none' import matplotlib.pyplot as plt import deeptools.countReadsPerBin as countR from deeptools import parserCommon from deeptools._version import __version__ old_settings = np.seterr(all='ignore') def parse_arguments(args=None): parent_parser = parserCommon.getParentArgParse(binSize=False) read_options_parser = parserCommon.read_options() parser = \ argparse.ArgumentParser( parents=[required_args(), parent_parser, read_options_parser], formatter_class=argparse.RawDescriptionHelpFormatter, add_help=False, description=""" This tool is useful to assess the sequencing depth of a given sample. It samples 1 million bp, counts the number of overlapping reads and can report a histogram that tells you how many bases are covered how many times. Multiple BAM files are accepted, but they all should correspond to the same genome assembly. detailed usage help: $ plotCoverage -h """, epilog='example usages:\nplotCoverage ' '--bamfiles file1.bam file2.bam -o results.png\n\n' ' \n\n', conflict_handler='resolve') parser.add_argument('--version', action='version', version='plotCoverage {}'.format(__version__)) return parser def process_args(args=None): args = parse_arguments().parse_args(args) if args.labels and len(args.bamfiles) != len(args.labels): sys.exit("The number of labels does not match the number of BAM files.") if not args.labels: args.labels = [os.path.basename(x) for x in args.bamfiles] return args def required_args(): parser = argparse.ArgumentParser(add_help=False) required = parser.add_argument_group('Required arguments') required.add_argument('--bamfiles', '-b', metavar='FILE1 FILE2', help='List of indexed BAM files separated by spaces.', nargs='+', required=True) required.add_argument('--plotFile', '-o', help='File name to save the plot to.', required=True) optional = parser.add_argument_group('Optional arguments') optional.add_argument("--help", "-h", action="help", help="show this help message and exit") optional.add_argument('--labels', '-l', metavar='sample1 sample2', help='User defined labels instead of default labels from ' 'file names. ' 'Multiple labels have to be separated by spaces, e.g. ' '--labels sample1 sample2 sample3', nargs='+') optional.add_argument('--plotTitle', '-T', help='Title of the plot, to be printed on top of ' 'the generated image. Leave blank for no title.', default='') optional.add_argument('--skipZeros', help='By setting this option, genomic regions ' 'that have zero or nan values in _all_ samples ' 'are excluded.', action='store_true', required=False) optional.add_argument('--numberOfSamples', '-n', help='Number of 1 bp regions to sample. Default 1 million.', required=False, type=int, default=1000000) optional.add_argument('--outRawCounts', help='Save raw counts (coverages) to file.', metavar='FILE') optional.add_argument('--plotHeight', help='Plot height in cm.', type=float, default=5.0) optional.add_argument('--plotWidth', help='Plot width in cm. The minimum value is 1 cm.', type=float, default=15.0) optional.add_argument('--plotFileFormat', metavar='FILETYPE', help='Image format type. If given, this option ' 'overrides the image format based on the plotFile ' 'ending. The available options are: png, ' 'eps, pdf and svg.', default=None, choices=['png', 'pdf', 'svg', 'eps']) return parser def main(args=None): args = process_args(args) cr = countR.CountReadsPerBin(args.bamfiles, binLength=1, numberOfSamples=args.numberOfSamples, numberOfProcessors=args.numberOfProcessors, verbose=args.verbose, region=args.region, blackListFileName=args.blackListFileName, extendReads=args.extendReads, minMappingQuality=args.minMappingQuality, ignoreDuplicates=args.ignoreDuplicates, center_read=args.centerReads, samFlag_include=args.samFlagInclude, samFlag_exclude=args.samFlagExclude, minFragmentLength=args.minFragmentLength, maxFragmentLength=args.maxFragmentLength, out_file_for_raw_data=args.outRawCounts) num_reads_per_bin = cr.run() sys.stderr.write("Number of non zero bins " "used: {}\n".format(num_reads_per_bin.shape[0])) if args.outRawCounts: # append to the generated file the # labels header = "#'chr'\t'start'\t'end'\t" header += "'" + "'\t'".join(args.labels) + "'\n" f = open(args.outRawCounts, 'r+') content = f.read() f.seek(0, 0) f.write(header + content) f.close() if num_reads_per_bin.shape[0] < 2: exit("ERROR: too few non-zero bins found.\n" "If using --region please check that this " "region is covered by reads.\n") if args.skipZeros: num_reads_per_bin = countR.remove_row_of_zeros(num_reads_per_bin) fig, axs = plt.subplots(1, 2, figsize=(args.plotWidth, args.plotHeight)) plt.suptitle(args.plotTitle) # plot up to two std from mean num_reads_per_bin = num_reads_per_bin.astype(int) sample_mean = num_reads_per_bin.mean(axis=0) sample_std = num_reads_per_bin.std(axis=0) sample_max = num_reads_per_bin.max(axis=0) sample_min = num_reads_per_bin.min(axis=0) sample_25 = np.percentile(num_reads_per_bin, 25, axis=0) sample_50 = np.percentile(num_reads_per_bin, 50, axis=0) sample_75 = np.percentile(num_reads_per_bin, 75, axis=0) # use the largest 99th percentile from all samples to set the x_max value x_max = np.max(np.percentile(num_reads_per_bin, 99, axis=0)) # plot coverage # print headers for text output print("sample\tmean\tstd\tmin\t25%\t50%\t75%\tmax") # the determination of a sensible value for y_max of the first plot (fraction of bases sampled vs. # coverage) is important because, depending on the data, # it becomes very difficult to see the lines in the plot. For example, if the coverage of a sample # is a nice gaussian curve with a large mean of 50. Then a sensible range for the y axis (fraction of # reads having coverage=x) is (0, 0.02) which nicely shows the coverage curve. If instead the coverage is # very por and centers close to 1 then a good y axis range is (0,1). # the current implementation aims to find the y_value for which 50% of the reads >= x (coverage) and # sets that as the x_axis range. y_max = [] for idx, col in enumerate(num_reads_per_bin.T): frac_reads_per_coverage = np.bincount(col.astype(int)).astype(float) / num_reads_per_bin.shape[0] axs[0].plot(frac_reads_per_coverage, label="{}, mean={:.1f}".format(args.labels[idx], sample_mean[idx])) csum = np.bincount(col.astype(int))[::-1].cumsum() csum_frac = csum.astype(float)[::-1] / csum.max() axs[1].plot(csum_frac, label=args.labels[idx]) # find the indexes (i.e. the x values) for which the cumulative distribution 'fraction of bases # sampled >= coverage' where fraction of bases sampled = 50%: `np.flatnonzero(csum_frac>0.5)` # then find the fraction of bases sampled that that have the largest x y_max.append(frac_reads_per_coverage[max(np.flatnonzero(csum_frac > 0.5))]) print("{}\t{:0.2f}\t{:0.2f}\t{}\t{}\t{}\t{}\t{}\t".format(args.labels[idx], sample_mean[idx], sample_std[idx], sample_min[idx], sample_25[idx], sample_50[idx], sample_75[idx], sample_max[idx], )) # Don't clip plots y_max = max(y_max) axs[0].set_ylim(0, min(1, y_max + (y_max * 0.10))) axs[0].set_xlim(0, x_max) axs[0].set_xlabel('coverage (#reads per bp)') axs[0].legend(fancybox=True, framealpha=0.5) axs[0].set_ylabel('fraction of bases sampled') # plot cumulative coverage axs[1].set_xlim(0, x_max) axs[1].set_xlabel('coverage (#reads per bp)') axs[1].set_ylabel('fraction of bases sampled >= coverage') axs[1].legend(fancybox=True, framealpha=0.5) plt.savefig(args.plotFile, format=args.plotFileFormat) plt.close() if __name__ == "__main__": main()