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#!/usr/bin/python3
# -*- 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'
from deeptools import cm # noqa: F401
import matplotlib.pyplot as plt
import plotly.offline as py
import plotly.graph_objs as go
from importlib.metadata import version
import deeptools.countReadsPerBin as countR
from deeptools import parserCommon
from deeptools.utilities import smartLabels
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',
usage='plotCoverage -b sample1.bam sample2.bam -o coverage.png \n'
'help: plotCoverage -h / plotCoverage --help\n')
parser.add_argument('--version', action='version',
version='%(prog)s {}'.format(version('deeptools')))
return parser
def process_args(args=None):
args = parse_arguments().parse_args(args)
if not args.labels:
if args.smartLabels:
args.labels = smartLabels(args.bamfiles)
else:
args.labels = [os.path.basename(x) for x in args.bamfiles]
if args.labels and len(args.bamfiles) != len(args.labels):
sys.exit("The number of labels does not match the number of BAM files.")
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)
optional = parser.add_argument_group('Optional arguments')
optional.add_argument("--help", "-h", action="help",
help="show this help message and exit")
optional.add_argument('--plotFile', '-o',
type=parserCommon.writableFile,
help='File name to save the plot to.')
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('--smartLabels',
action='store_true',
help='Instead of manually specifying labels for the input '
'BAM files, this causes deepTools to use the file name '
'after removing the path and extension.')
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: %(default)s)',
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: %(default)s)',
required=False,
type=int,
default=1000000)
optional.add_argument('--BED',
help='Limits the coverage analysis to '
'the regions specified in these files. This overrides --numberOfSamples. '
'Due to memory requirements, it is inadvised to combine this with '
'--outRawCounts or many tens of thousands of regions, as per-base '
'coverage is used!',
metavar='FILE1.bed FILE2.bed',
nargs='+')
optional.add_argument('--outRawCounts',
help='Save raw counts (coverages) to file.',
type=parserCommon.writableFile,
metavar='FILE')
optional.add_argument('--outCoverageMetrics',
help='Save percentage of bins/regions above the specified thresholds to '
'the specified file. The coverage thresholds are specified by '
'--coverageThresholds. If no coverage thresholds are specified, the file '
'will be empty.',
type=parserCommon.writableFile,
metavar='FILE')
optional.add_argument('--coverageThresholds', '-ct',
type=int,
action="append",
help='The percentage of reported bins/regions with signal at least as '
'high as the given threshold. This can be specified multiple times.')
optional.add_argument('--plotHeight',
help='Plot height in cm. (Default: %(default)s)',
type=float,
default=5.0)
optional.add_argument('--plotWidth',
help='Plot width in cm. The minimum value is 1 cm. (Default: %(default)s)',
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, svg and plotly.',
default=None,
choices=['png', 'pdf', 'svg', 'eps', 'plotly'])
return parser
def main(args=None):
args = process_args(args)
if not args.outRawCounts and not args.plotFile and not args.outCoverageMetrics:
sys.exit("At least one of --plotFile, --outRawCounts and --outCoverageMetrics are required.\n")
if 'BED' in args:
bed_regions = args.BED
else:
bed_regions = None
cr = countR.CountReadsPerBin(args.bamfiles,
binLength=1,
bedFile=bed_regions,
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,
bed_and_bin=True,
out_file_for_raw_data=args.outRawCounts)
num_reads_per_bin = cr.run()
if args.outCoverageMetrics and args.coverageThresholds:
args.coverageThresholds.sort() # Galaxy in particular tends to give things in a weird order
of = open(args.outCoverageMetrics, "w")
of.write("Sample\tThreshold\tPercent\n")
nbins = float(num_reads_per_bin.shape[0])
for thresh in args.coverageThresholds:
vals = np.sum(num_reads_per_bin >= thresh, axis=0)
for lab, val in zip(args.labels, vals):
of.write("{}\t{}\t{:6.3f}\n".format(lab, thresh, 100. * val / nbins))
of.close()
if args.outRawCounts:
# append to the generated file the
# labels
header = "#plotCoverage --outRawCounts\n#'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)
if args.plotFile:
if args.plotFileFormat == 'plotly':
fig = go.Figure()
fig['layout']['xaxis1'] = {'domain': [0.0, 0.48], 'anchor': 'x1', 'title': 'coverage (#reads per base)'}
fig['layout']['xaxis2'] = {'domain': [0.52, 1.0], 'anchor': 'x2', 'title': 'coverage (#reads per base)'}
fig['layout']['yaxis1'] = {'domain': [0.0, 1.0], 'anchor': 'x1', 'title': 'fraction of bases sampled'}
fig['layout']['yaxis2'] = {'domain': [0.0, 1.0], 'anchor': 'x2', 'title': 'fraction of bases sampled >= coverage'}
fig['layout'].update(title=args.plotTitle)
else:
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 = []
data = []
# We need to manually set the line colors so they're shared between the two plots.
plotly_colors = ["#d73027", "#fc8d59", "#f33090", "#e0f3f8", "#91bfdb", "#4575b4"]
plotly_styles = sum([6 * ["solid"], 6 * ["dot"], 6 * ["dash"], 6 * ["longdash"], 6 * ["dashdot"], 6 * ["longdashdot"]], [])
for idx, col in enumerate(num_reads_per_bin.T):
if args.plotFile:
frac_reads_per_coverage = np.bincount(col.astype(int)).astype(float) / num_reads_per_bin.shape[0]
csum = np.bincount(col.astype(int))[::-1].cumsum()
csum_frac = csum.astype(float)[::-1] / csum.max()
if args.plotFileFormat == 'plotly':
color = plotly_colors[idx % len(plotly_colors)]
dash = plotly_styles[idx % len(plotly_styles)]
trace = go.Scatter(x=np.arange(0, int(x_max) - 1),
y=frac_reads_per_coverage[:int(x_max)],
mode='lines',
xaxis='x1',
yaxis='y1',
line=dict(color=color, dash=dash),
name="{}, mean={:.1f}".format(args.labels[idx], sample_mean[idx]),
legendgroup="{}".format(idx))
data.append(trace)
trace = go.Scatter(x=np.arange(0, int(x_max) - 1),
y=csum_frac[:int(x_max)],
mode='lines',
xaxis='x2',
yaxis='y2',
line=dict(color=color, dash=dash),
name=args.labels[idx],
showlegend=False,
legendgroup="{}".format(idx))
data.append(trace)
else:
axs[0].plot(frac_reads_per_coverage, label="{}, mean={:.1f}".format(args.labels[idx], sample_mean[idx]))
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],
))
if args.plotFile:
# Don't clip plots
y_max = max(y_max)
if args.plotFileFormat == "plotly":
fig.add_traces(data)
fig['layout']['yaxis1'].update(range=[0.0, min(1, y_max + (y_max * 0.10))])
fig['layout']['yaxis2'].update(range=[0.0, 1.0])
py.plot(fig, filename=args.plotFile, auto_open=False)
else:
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()
|
