def doCoverage(self):
if self._state != "DO_COVERAGE":
raise Exception("Invalid state transition")
bamFname = self.bamUrl.toLocalFile()
self.logMessage.emit("Invoking cnvlib.do_coverage(" +
self._target_path() + ", " + bamFname + ")")
self._target_coverage = cnvlib.do_coverage(self._target_path(),
bamFname)
self.logMessage.emit("Invoking cnvlib.do_coverage(" +
self._antitarget_path() + ", " + bamFname + ")")
self._antitarget_coverage = cnvlib.do_coverage(self._antitarget_path(),
bamFname)
self.logMessage.emit("Saving target coverage file to " +
self._target_coverage_path())
skgenome.tabio.write(self._target_covegae,
self._target_coverage_path())
self.logMessage.emit("Saving antitarget coverage file to " +
self._antitarget_coverage_path())
skgenome.tabio.write(self._antitarget_coverage,
self._antitarget_coverage_path())
self._set_state("DO_REFERENCE")
def filter_targets(target_bed, sample_bams, procs):
"""Check if each potential target has significant coverage."""
baits = tabio.read_auto(target_bed)
# Loop over BAMs to calculate weighted averages of bin coverage depths
total_depths = np.zeros(len(baits), dtype=np.float_)
for bam_fname in sample_bams:
logging.info("Evaluating targets in %s", bam_fname)
sample = cnvlib.do_coverage(target_bed, bam_fname, processes=procs)
total_depths += sample['depth'].values
baits['depth'] = total_depths / len(sample_bams)
return baits
def filter_targets(target_bed, sample_bams, procs):
"""Check if each potential target has significant coverage."""
try:
baits = tabio.read(target_bed, 'bed4')
except:
raise RuntimeError("Targets must be in BED format; try skg_convert.py")
logging.info("Loaded %d candidate regions from %s", len(baits), target_bed)
# Loop over BAMs to calculate weighted averages of bin coverage depths
total_depths = np.zeros(len(baits), dtype=np.float_)
for bam_fname in sample_bams:
logging.info("Evaluating targets in %s", bam_fname)
sample = cnvlib.do_coverage(target_bed, bam_fname, processes=procs)
assert len(sample) == len(baits), \
"%d != %d" % (len(sample), len(baits))
total_depths += sample['depth'].values
baits['depth'] = total_depths / len(sample_bams)
logging.info("Average candidate-target depth:\n%s",
baits['depth'].describe())
return baits