def merge_linked_depth_calls(vcf, ID_links):
"""
vcf : pysam.VariantFile
ID_links : list of (str, str)
"""
# Make list of linked IDs and build map to corresponding records
linked_IDs = sorted(set([ID for link in ID_links for ID in link]))
record_map = {}
# If a record wasn't linked with a bedtools merge, just return it
for record in vcf:
if record.id not in linked_IDs:
yield record
else:
record_map[record.id] = record
# Ignore links on other chromosomes
linked_IDs = sorted(record_map.keys())
ID_links = [
l for l in ID_links if l[0] in linked_IDs and l[1] in linked_IDs
]
# Convert links from pairs of IDs to pairs of records
record_links = np.empty([len(ID_links), 2], dtype=object)
for i, link in enumerate(ID_links):
record_links[i, 0] = record_map[link[0]]
record_links[i, 1] = record_map[link[1]]
clusters = slink(record_links, record_map)
# Merge clusters
for cluster in clusters:
if len(cluster) == 1:
yield cluster[0]
continue
# Take maximal region
start = np.min([record.pos for record in cluster])
end = np.max([record.stop for record in cluster])
merged_record = cluster[0].copy()
merged_record.pos = start
merged_record.stop = end
merged_record.info['SVLEN'] = end - start
members = list(record.info['MEMBERS']) + [r.id for r in cluster]
merged_record.info['MEMBERS'] = members
# Take union of called samples
svu.update_best_genotypes(merged_record,
cluster,
preserve_multiallelic=True)
yield merged_record
def _merge_pair(record_a, record_b):
is_depth_a = _record_is_depth(record_a)
is_depth_b = _record_is_depth(record_b)
if is_depth_a == is_depth_b:
raise ValueError(
"Attempted to write pesr/pesr or depth/depth pair")
if is_depth_a:
depth_record = record_a
pesr_record = record_b
else:
pesr_record = record_a
depth_record = record_b
pesr_record.info['ALGORITHMS'] = tuple(
sorted(set(pesr_record.info['ALGORITHMS'] + ('depth', ))))
pesr_record.info['MEMBERS'] = tuple(
sorted(
set(pesr_record.info['MEMBERS'] +
depth_record.info['MEMBERS'])))
svu.update_best_genotypes(pesr_record, [pesr_record, depth_record],
preserve_multiallelic=True)
if 'varGQ' in pesr_record.info.keys(
) and 'varGQ' in depth_record.info.keys():
pesr_record.info['varGQ'] = max(pesr_record.info['varGQ'],
depth_record.info['varGQ'])
for sample in pesr_record.samples:
if 'EV' in pesr_record.samples[sample].keys(
) and 'EV' in depth_record.info.keys():
pesr_ev = pesr_record.samples[sample]['EV']
depth_ev = depth_record.samples[sample]['EV']
pesr_record.samples[sample]['EV'] = tuple(
sorted(set(pesr_ev).union(depth_ev)))
_cache_sample_overlap(pesr_record, force=True)
def make_record(self):
self.vcf_record = self.records[0].copy()
svu.update_best_genotypes(self.vcf_record,
self.records,
preserve_multiallelic=False)
def merge_pesr_depth(pesr_vcf, depth_vcf, frac=0.5, sample_overlap=0.5):
# Memory inefficient but it's easier and shouldn't matter too much
# now that the variants have been filtered down
records = dict()
records['pesr'] = {record.id: record for record in pesr_vcf}
records['depth'] = {record.id: record for record in depth_vcf}
# Wipe MEMBERS from prior clustering
for source in 'pesr depth'.split():
for ID, record in records[source].items():
record.info['MEMBERS'] = [ID]
# Reset for bedtool creation
pesr_vcf.reset()
base_record = next(pesr_vcf)
# Reset for bedtool creation
pesr_vcf.reset()
depth_vcf.reset()
pesr_bed = svu.vcf2bedtool(pesr_vcf, split_bnd=False,
include_samples=True,
include_strands=False,
report_alt=False)
depth_bed = svu.vcf2bedtool(depth_vcf, split_bnd=False,
include_samples=True,
include_strands=False,
report_alt=False)
# Remove records with no samples
def _filter_allref(feature):
"Returns False if feature has no called samples"
exclude = False
if len(feature.fields) == 6:
samples = feature.fields[5]
if samples not in ['.', '']:
exclude = True
return exclude
pesr_bed = pesr_bed.filter(_filter_allref).saveas('filtered_pesr.bed')
depth_bed = depth_bed.filter(_filter_allref).saveas('filtered_depth.bed')
# Merge depth records with PE/SR records if they share 50% recip overlap
sect = pesr_bed.intersect(depth_bed, wa=True, wb=True, r=True, f=frac)
filtered_depth_IDs = deque()
for pair in sect.intervals:
# Check SV types match
if pair.fields[4] != pair.fields[10]:
continue
# Get vcf records
pesr_id, depth_id = pair.fields[3], pair.fields[9]
pesr_record = records['pesr'][pesr_id]
depth_record = records['depth'][depth_id]
# Check for >=50% sample overlap
samp_ovr = svu.samples_overlap(samplesA=pair.fields[5].split(','),
samplesB=pair.fields[11].split(','))
if not samp_ovr:
continue
# Note removal of depth ID
filtered_depth_IDs.append(depth_id)
# Update metadata and samples
pesr_record.info['MEMBERS'] = (pesr_record.info.get('MEMBERS', ()) +
(depth_record.id, ))
pesr_record.info['ALGORITHMS'] = pesr_record.info['ALGORITHMS'] + ('depth', )
svu.update_best_genotypes(pesr_record,
[pesr_record, depth_record],
preserve_multiallelic=True)
if 'varGQ' in pesr_record.info.keys() and 'varGQ' in depth_record.info.keys():
pesr_record.info['varGQ'] = max(pesr_record.info['varGQ'],
depth_record.info['varGQ'])
for sample in pesr_record.samples:
if 'EV' in pesr_record.samples[sample].keys() and 'EV' in depth_record.info.keys():
pesr_ev = pesr_record.samples[sample]['EV']
depth_ev = depth_record.samples[sample]['EV']
pesr_record.samples[sample]['EV'] = tuple(sorted(set(pesr_ev).union(depth_ev)))
# Remove overlapping depth records (not performed in for loop to account
# for double overlaps
# TODO: handle double overlap of depth calls
for ID in set(filtered_depth_IDs):
records['depth'].pop(ID)
# In remaining depth-only calls, add samples to PE/SR record if the
# record covers 90% of the depth-only call.
# SFARI ONLY - REMOVED FOR OTHER ANALYSES
# sect = pesr_bed.intersect(depth_bed, wa=True, wb=True, F=0.9)
#
# for pair in sect.intervals:
# # Check SV types match
# if pair.fields[4] != pair.fields[10]:
# continue
#
# pesr_id, depth_id = pair.fields[3], pair.fields[9]
#
# # Skip depth records we already added with 50% reciprocal
# if depth_id in filtered_depth_IDs:
# continue
#
# # If sample is in both depth record and pe/sr record, remove it from
# # depth record
# depth_record = records['depth'][depth_id]
# pesr_record = records['pesr'][pesr_id]
#
# merge_nested_depth_record(pesr_record, depth_record)
# Merge records together
def _sort_key(record):
return (record.chrom, record.pos, record.info['CHR2'], record.stop)
pesr_records = sorted(records['pesr'].values(), key=_sort_key)
depth_records = sorted(records['depth'].values(), key=_sort_key)
depth_records = [clean_depth_record(base_record, r) for r in depth_records]
for record in heapq.merge(pesr_records, depth_records, key=_sort_key):
# Clean out unwanted format keys
# EDIT - this should be handled upstream by add_genotypes
# FORMATS = 'GT GQ RD_CN RD_GQ PE_GT PE_GQ SR_GT SR_GQ EV'.split()
# for key in record.format.keys():
# if key not in FORMATS:
# del record.format[key]
record.info['ALGORITHMS'] = sorted(set(record.info['ALGORITHMS']))
record.info['MEMBERS'] = sorted(set(record.info.get('MEMBERS', ())))
# Skip emptied depth records
if len(svu.get_called_samples(record)) == 0:
continue
yield record
