def _combine_qc_samples(samples):
"""Combine split QC analyses into single samples based on BAM files.
"""
by_bam = collections.defaultdict(list)
for data in [utils.to_single_data(x) for x in samples]:
batch = dd.get_batch(data) or dd.get_sample_name(data)
if not isinstance(batch, (list, tuple)):
batch = [batch]
batch = tuple(batch)
by_bam[(dd.get_align_bam(data)
or dd.get_work_bam(data), batch)].append(data)
out = []
for data_group in by_bam.values():
data = data_group[0]
alg_qc = []
qc = {}
metrics = {}
for d in data_group:
qc.update(dd.get_summary_qc(d))
metrics.update(dd.get_summary_metrics(d))
alg_qc.extend(dd.get_algorithm_qc(d))
data["config"]["algorithm"]["qc"] = alg_qc
data["summary"]["qc"] = qc
data["summary"]["metrics"] = metrics
out.append([data])
return out
def _combine_qc_samples(samples):
"""Combine split QC analyses into single samples based on BAM files.
"""
by_bam = collections.defaultdict(list)
for data in [utils.to_single_data(x) for x in samples]:
batch = dd.get_batch(data) or dd.get_sample_name(data)
if not isinstance(batch, (list, tuple)):
batch = [batch]
batch = tuple(batch)
by_bam[(dd.get_align_bam(data), batch)].append(data)
out = []
for data_group in by_bam.values():
data = data_group[0]
alg_qc = []
qc = {}
metrics = {}
for d in data_group:
qc.update(dd.get_summary_qc(d))
metrics.update(dd.get_summary_metrics(d))
alg_qc.extend(dd.get_algorithm_qc(d))
data["config"]["algorithm"]["qc"] = alg_qc
data["summary"]["qc"] = qc
data["summary"]["metrics"] = metrics
out.append([data])
return out
