def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
parser = buildOptionParser(argv)
## add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv, add_output_options=True)
if len(args) != 2:
raise ValueError("please specify a bam and bed file")
infile, bedfile = args
control_file = None
if options.control_file:
E.info("using control file %s" % options.control_file)
if options.format == "bigwig":
fg_file = bx.bbi.bigwig_file.BigWigFile(open(infile))
if options.control_file:
control_file = bx.bbi.bigwig_file.BigWigFile(
open(options.control_file))
counter = _bam2peakshape.CounterBigwig()
elif options.format == "bam":
fg_file = pysam.Samfile(infile, "rb")
if options.control_file:
control_file = pysam.Samfile(options.control_file, "rb")
counter = _bam2peakshape.CounterBam(shift=options.shift)
result, bins = buildResults(bedfile, fg_file, control_file, counter,
options)
if len(result) == 0:
E.warn("no data - no output")
E.Stop()
return
outputResults(result, bins, options)
## write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
parser = buildOptionParser(argv)
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv, add_output_options=True)
if len(args) != 2:
raise ValueError(
"please specify one bam- or wig-file and one bed file")
if options.control_files:
E.info("using control files: %s" % ",".join(options.control_files))
infile, bedfile = args
control_files = []
if options.format == "bigwig":
fg_file = bx.bbi.bigwig_file.BigWigFile(open(infile))
for control_file in options.control_files:
control_files.append(
bx.bbi.bigwig_file.BigWigFile(open(control_file)))
counter = _bam2peakshape.CounterBigwig(
smooth_method=options.smooth_method)
elif options.format == "bam":
fg_file = pysam.Samfile(infile, "rb")
for control_file in options.control_files:
control_files.append(pysam.Samfile(control_file, "rb"))
counter = _bam2peakshape.CounterBam(
shift=options.shift, smooth_method=options.smooth_method)
features_per_interval, bins = buildDensityMatrices(
Bed.iterator(IOTools.openFile(bedfile)),
fg_file,
control_files,
counter,
window_size=options.window_size,
bin_size=options.bin_size,
strand_specific=options.strand_specific,
centring_method=options.centring_method,
use_interval=options.use_interval,
random_shift=options.random_shift,
smooth_method=options.smooth_method,
report_step=options.report_step)
if len(features_per_interval) == 0:
E.warn("no data - no output")
E.Stop()
return
outputFeatureTable(options.stdout, features_per_interval, bins)
# apply normalization
# Note: does not normalize control?
# Needs reworking, currently it does not normalize across
# all samples nor does the work "sum" reflect the per million
# normalization.
if options.normalization == "sum":
E.info("starting sum normalization")
# get total counts across all intervals
norm = 0.0
for foreground, bed, controls, shifted in features_per_interval:
norm += sum(foreground.counts)
# per million
norm /= float(1000000)
E.info("sum/million normalization with %f" % norm)
# normalise
new_data = []
for foreground, bed, controls, shifted in features_per_interval:
foreground = foreground._replace(
counts=numpy.array(foreground.counts, dtype=numpy.float) /
norm)
new_controls = []
for control in controls:
new_controls.append(
control._replace(
counts=numpy.array(control.counts, dtype=numpy.float) /
norm))
if shifted:
shifted = shifted._replace(
counts=numpy.array(shifted.counts, dtype=numpy.float) /
norm)
new_data.append(
IntervalData._make((foreground, bed, new_controls, shifted)))
features_per_interval = new_data
else:
E.info("no normalization performed")
# center bins
out_bins = bins[:-1] + options.bin_size
# build tracks
def _toTrack(filename):
return os.path.splitext(os.path.basename(filename))[0]
outputMatrices(features_per_interval,
out_bins,
foreground_track=_toTrack(infile),
control_tracks=[_toTrack(x) for x in options.control_files],
shifted=options.random_shift,
sort_orders=options.sort_orders)
# write footer and output benchmark information.
E.Stop()