def test_annotate_intervals(self):
ds = get_dataset()
bed1 = hl.import_bed(resource('example1.bed'), reference_genome='GRCh37')
bed2 = hl.import_bed(resource('example2.bed'), reference_genome='GRCh37')
bed3 = hl.import_bed(resource('example3.bed'), reference_genome='GRCh37')
self.assertTrue(list(bed2.key.dtype) == ['interval'])
self.assertTrue(list(bed2.row.dtype) == ['interval', 'target'])
interval_list1 = hl.import_locus_intervals(resource('exampleAnnotation1.interval_list'))
interval_list2 = hl.import_locus_intervals(resource('exampleAnnotation2.interval_list'))
self.assertTrue(list(interval_list2.key.dtype) == ['interval'])
self.assertTrue(list(interval_list2.row.dtype) == ['interval', 'target'])
ann = ds.annotate_rows(in_interval=bed1[ds.locus]).rows()
self.assertTrue(ann.all((ann.locus.position <= 14000000) |
(ann.locus.position >= 17000000) |
(hl.is_missing(ann.in_interval))))
for bed in [bed2, bed3]:
ann = ds.annotate_rows(target=bed[ds.locus].target).rows()
expr = (hl.case()
.when(ann.locus.position <= 14000000, ann.target == 'gene1')
.when(ann.locus.position >= 17000000, ann.target == 'gene2')
.default(ann.target == hl.null(hl.tstr)))
self.assertTrue(ann.all(expr))
self.assertTrue(ds.annotate_rows(in_interval=interval_list1[ds.locus]).rows()
._same(ds.annotate_rows(in_interval=bed1[ds.locus]).rows()))
self.assertTrue(ds.annotate_rows(target=interval_list2[ds.locus].target).rows()
._same(ds.annotate_rows(target=bed2[ds.locus].target).rows()))
def test_import_locus_intervals_badly_defined_intervals(self):
interval_file = resource('example3.interval_list')
t = hl.import_locus_intervals(interval_file, reference_genome='GRCh37', skip_invalid_intervals=True)
self.assertTrue(t.count() == 21)
t = hl.import_locus_intervals(interval_file, reference_genome=None, skip_invalid_intervals=True)
self.assertTrue(t.count() == 22)
def annotate_in_segdup(mt, genome_version="GRCh38"):
if genome_version == "GRCh37":
segdup_regions = hl.import_locus_intervals("gs://broad-dsp-spec-ops/scratch/weisburd/ref/GRCh37/GRCh37GenomicSuperDup.bed", reference_genome="GRCh37")
elif genome_version == "GRCh38":
segdup_regions = hl.import_locus_intervals("gs://broad-dsp-spec-ops/scratch/weisburd/ref/GRCh38/GRCh38GenomicSuperDup.without_decoys.bed", reference_genome="GRCh38")
else:
raise ValueError(f"Invalid genome version: {genome_version}")
return mt.annotate_rows(info = mt.info.annotate(
in_segdup=hl.is_defined(segdup_regions[mt.locus])))
def annotate_in_LCR(mt, genome_version="GRCh38"):
if genome_version == "GRCh37":
lcr_regions = hl.import_locus_intervals("gs://broad-dsp-spec-ops/scratch/weisburd/ref/GRCh37/grch37_LCRs_without_decoys.bed", reference_genome="GRCh37")
elif genome_version == "GRCh38":
lcr_regions = hl.import_locus_intervals("gs://broad-dsp-spec-ops/scratch/weisburd/ref/GRCh38/grch38_LCRs_without_decoys.bed", reference_genome="GRCh38")
else:
raise ValueError(f"Invalid genome version: {genome_version}")
return mt.annotate_rows(info = mt.info.annotate(
in_LCR=hl.is_defined(lcr_regions[mt.locus])))
def test_import_locus_intervals_badly_defined_intervals(self):
interval_file = resource('example3.interval_list')
t = hl.import_locus_intervals(interval_file,
reference_genome='GRCh37',
skip_invalid_intervals=True)
self.assertTrue(t.count() == 21)
t = hl.import_locus_intervals(interval_file,
reference_genome=None,
skip_invalid_intervals=True)
self.assertTrue(t.count() == 22)
def run_platform_imputation(
mt: hl.MatrixTable,
plat_min_cluster_size: int,
plat_min_sample_size: int,
plat_assignment_pcs: int,
) -> hl.Table:
"""
Run PCA using sample callrate across Broad's evaluation intervals and create Hail Table
with platform PCs and assigned platform.
:param MatrixTable mt: QC MatrixTable
:param plat_min_cluster_size: min cluster size for HBDscan clustering
:param plat_min_sample_size: min sample size for HBDscan clustering
:param plat_assignment_pcs: Number PCs used for HBDscan clustering
:return: Table with platform PCs and assigned platform
:rtype: Table
"""
intervals = hl.import_locus_intervals(
"gs://gcp-public-data--broad-references/hg38/v0/exome_evaluation_regions.v1.interval_list"
)
callrate_mt = compute_callrate_mt(mt, intervals)
eigenvalues, scores_ht, ignore = run_platform_pca(callrate_mt)
plat_ht = assign_platform_from_pcs(
scores_ht,
hdbscan_min_cluster_size=plat_min_cluster_size,
hdbscan_min_samples=plat_min_sample_size,
)
plat_pcs = {
f"plat_PC{i+1}": scores_ht.scores[i]
for i in list(range(0, plat_assignment_pcs))
}
scores_ht = scores_ht.annotate(**plat_pcs).drop("scores")
plat_ht = plat_ht.annotate(**scores_ht[plat_ht.key])
return plat_ht
def run_pipeline(args):
hl.init(log='./hail_annotation_pipeline.log')
'''
rg = hl.get_reference('GRCh37')
grch37_contigs = [x for x in rg.contigs if not x.startswith('GL') and not x.startswith('M')]
contig_dict = dict(zip(grch37_contigs, ['chr'+x for x in grch37_contigs]))
'''
exome_intervals = hl.import_locus_intervals(
'/gpfs/ycga/project/lek/shared/resources/hg38/exome_evaluation_regions.v1.interval_list',
reference_genome='GRCh38')
#mt = hl.import_vcf(args.vcf,reference_genome='GRCh38',contig_recoding=contig_dict,array_elements_required=False,force_bgz=True,filter='MONOALLELIC')
mt = hl.import_vcf(args.vcf,
reference_genome='GRCh38',
array_elements_required=False,
force_bgz=True,
filter='MONOALLELIC')
mt = mt.filter_rows(hl.is_defined(exome_intervals[mt.locus]))
pprint.pprint(mt.describe())
pprint.pprint(mt.show())
mt = mt.repartition(hl.eval(hl.int(mt.n_partitions() / 10)))
mt.write(args.out, overwrite=True)
def filter_out_segdups(mt, genome_version="GRCh38"):
if genome_version == "GRCh38":
segdup_regions = hl.import_locus_intervals("gs://broad-dsp-spec-ops/scratch/weisburd/ref/GRCh38/GRCh38GenomicSuperDup.without_decoys.bed", reference_genome="GRCh38")
else:
raise ValueError(f"Invalid genome version: {genome_version}")
return mt.filter_rows(hl.is_missing(segdup_regions[mt.locus]))
def test_annotate_intervals(self):
ds = get_dataset()
bed1 = hl.import_bed(resource('example1.bed'),
reference_genome='GRCh37')
bed2 = hl.import_bed(resource('example2.bed'),
reference_genome='GRCh37')
bed3 = hl.import_bed(resource('example3.bed'),
reference_genome='GRCh37')
self.assertTrue(list(bed2.key.dtype) == ['interval'])
self.assertTrue(list(bed2.row.dtype) == ['interval', 'target'])
interval_list1 = hl.import_locus_intervals(
resource('exampleAnnotation1.interval_list'))
interval_list2 = hl.import_locus_intervals(
resource('exampleAnnotation2.interval_list'))
self.assertTrue(list(interval_list2.key.dtype) == ['interval'])
self.assertTrue(
list(interval_list2.row.dtype) == ['interval', 'target'])
ann = ds.annotate_rows(in_interval=bed1[ds.locus]).rows()
self.assertTrue(
ann.all((ann.locus.position <= 14000000)
| (ann.locus.position >= 17000000)
| (hl.is_missing(ann.in_interval))))
for bed in [bed2, bed3]:
ann = ds.annotate_rows(target=bed[ds.locus].target).rows()
expr = (hl.case().when(ann.locus.position <= 14000000,
ann.target == 'gene1').when(
ann.locus.position >= 17000000,
ann.target == 'gene2').default(
ann.target == hl.null(hl.tstr)))
self.assertTrue(ann.all(expr))
self.assertTrue(
ds.annotate_rows(
in_interval=interval_list1[ds.locus]).rows()._same(
ds.annotate_rows(in_interval=bed1[ds.locus]).rows()))
self.assertTrue(
ds.annotate_rows(
target=interval_list2[ds.locus].target).rows()._same(
ds.annotate_rows(target=bed2[ds.locus].target).rows()))
def get_lcr_ht(overwrite: bool = False) -> hl.Table:
lcr_interval = hl.import_locus_intervals(
f'{nfs_dir}/resources/grch38/LCRFromHengHg38.txt.bgz',
skip_invalid_intervals=True,
min_partitions=50,
reference_genome='GRCh38')
return lcr_interval.checkpoint(
f'{nfs_dir}/resources/grch38/LCRFromHengHg38.ht',
overwrite=overwrite,
_read_if_exists=not overwrite)
def filter_low_conf_regions(
mt: Union[hl.MatrixTable, hl.Table],
filter_lcr: bool = True,
filter_decoy: bool = True,
filter_segdup: bool = True,
filter_exome_low_coverage_regions: bool = False,
high_conf_regions: Optional[List[str]] = None,
) -> Union[hl.MatrixTable, hl.Table]:
"""
Filters low-confidence regions
:param mt: MatrixTable or Table to filter
:param filter_lcr: Whether to filter LCR regions
:param filter_decoy: Whether to filter decoy regions
:param filter_segdup: Whether to filter Segdup regions
:param filter_exome_low_coverage_regions: Whether to filter exome low confidence regions
:param high_conf_regions: Paths to set of high confidence regions to restrict to (union of regions)
:return: MatrixTable or Table with low confidence regions removed
"""
build = get_reference_genome(mt.locus).name
if build == "GRCh37":
import gnomad.resources.grch37.reference_data as resources
elif build == "GRCh38":
import gnomad.resources.grch38.reference_data as resources
criteria = []
if filter_lcr:
lcr = resources.lcr_intervals.ht()
criteria.append(hl.is_missing(lcr[mt.locus]))
if filter_decoy:
decoy = resources.decoy_intervals.ht()
criteria.append(hl.is_missing(decoy[mt.locus]))
if filter_segdup:
segdup = resources.seg_dup_intervals.ht()
criteria.append(hl.is_missing(segdup[mt.locus]))
if filter_exome_low_coverage_regions:
high_cov = resources.high_coverage_intervals.ht()
criteria.append(hl.is_missing(high_cov[mt.locus]))
if high_conf_regions is not None:
for region in high_conf_regions:
region = hl.import_locus_intervals(region)
criteria.append(hl.is_defined(region[mt.locus]))
if criteria:
filter_criteria = functools.reduce(operator.iand, criteria)
if isinstance(mt, hl.MatrixTable):
mt = mt.filter_rows(filter_criteria)
else:
mt = mt.filter(filter_criteria)
return mt
def test_import_locus_intervals(self):
interval_file = resource('annotinterall.interval_list')
intervals = hl.import_locus_intervals(interval_file, reference_genome='GRCh37')
nint = intervals.count()
i = 0
with open(interval_file) as f:
for line in f:
if len(line.strip()) != 0:
i += 1
self.assertEqual(nint, i)
self.assertEqual(intervals.interval.dtype.point_type, hl.tlocus('GRCh37'))
def _import_purcell_5k(path) -> hl.Table:
p5k = hl.import_locus_intervals(path, reference_genome='GRCh37')
rg37 = hl.get_reference('GRCh37')
rg38 = hl.get_reference('GRCh38')
if not rg37.has_liftover('GRCh38'):
rg37.add_liftover(
'gs://hail-common/references/grch37_to_grch38.over.chain.gz', rg38)
p5k = p5k.annotate(start=hl.liftover(p5k.interval.start, 'GRCh38'),
end=hl.liftover(p5k.interval.start, 'GRCh38'))
p5k = p5k.filter((p5k.start.contig == 'chr' + p5k.interval.start.contig)
& (p5k.end.contig == 'chr' + p5k.interval.end.contig))
p5k = p5k.key_by()
p5k = p5k.select(locus=p5k.start, locus_b37=p5k.interval.start)
return p5k.key_by('locus')
def filter_low_conf_regions(
mt: hl.MatrixTable,
filter_lcr: bool = True,
filter_decoy: bool = True,
filter_segdup: bool = True,
high_conf_regions: Optional[List[str]] = None) -> hl.MatrixTable:
"""
Filters low-confidence regions
:param MatrixTable mt: MT to filter
:param bool filter_lcr: Whether to filter LCR regions
:param bool filter_decoy: Whether to filter decoy regions
:param bool filter_segdup: Whether to filter Segdup regions
:param list of str high_conf_regions: Paths to set of high confidence regions to restrict to (union of regions)
:return: MT with low confidence regions removed
:rtype: MatrixTable
"""
from gnomad_hail.resources import lcr_intervals_path, decoy_intervals_path, segdup_intervals_path
if filter_lcr:
lcr = hl.import_locus_intervals(lcr_intervals_path)
mt = mt.filter_rows(hl.is_defined(lcr[mt.locus]), keep=False)
if filter_decoy:
decoy = hl.import_bed(decoy_intervals_path)
mt = mt.filter_rows(hl.is_defined(decoy[mt.locus]), keep=False)
if filter_segdup:
segdup = hl.import_bed(segdup_intervals_path)
mt = mt.filter_rows(hl.is_defined(segdup[mt.locus]), keep=False)
if high_conf_regions is not None:
for region in high_conf_regions:
region = hl.import_locus_intervals(region)
mt = mt.filter_rows(hl.is_defined(region[mt.locus]), keep=True)
return mt
def test_import_locus_intervals(self):
interval_file = resource('annotinterall.interval_list')
t = hl.import_locus_intervals(interval_file, reference_genome='GRCh37')
nint = t.count()
i = 0
with open(interval_file) as f:
for line in f:
if len(line.strip()) != 0:
i += 1
self.assertEqual(nint, i)
self.assertEqual(t.interval.dtype.point_type, hl.tlocus('GRCh37'))
tmp_file = new_temp_file(prefix="test", suffix="interval_list")
start = t.interval.start
end = t.interval.end
(t
.key_by(interval=hl.locus_interval(start.contig, start.position, end.position, True, True))
.select()
.export(tmp_file, header=False))
t2 = hl.import_locus_intervals(tmp_file)
self.assertTrue(t.select()._same(t2))
def _import_purcell_5k(path) -> hl.Table:
p5k = hl.import_locus_intervals(path, reference_genome="GRCh37")
rg37 = hl.get_reference("GRCh37")
rg38 = hl.get_reference("GRCh38")
if not rg37.has_liftover("GRCh38"):
rg37.add_liftover("references/grch37_to_grch38.over.chain.gz", rg38)
p5k = p5k.annotate(
start=hl.liftover(p5k.interval.start, "GRCh38"),
end=hl.liftover(p5k.interval.start, "GRCh38"),
)
p5k = p5k.filter((p5k.start.contig == "chr" + p5k.interval.start.contig)
& (p5k.end.contig == "chr" + p5k.interval.end.contig))
p5k = p5k.key_by()
p5k = p5k.select(locus=p5k.start, locus_b37=p5k.interval.start)
return p5k.key_by("locus")
def test_import_locus_intervals(self):
interval_file = resource('annotinterall.interval_list')
t = hl.import_locus_intervals(interval_file, reference_genome='GRCh37')
nint = t.count()
i = 0
with open(interval_file) as f:
for line in f:
if len(line.strip()) != 0:
i += 1
self.assertEqual(nint, i)
self.assertEqual(t.interval.dtype.point_type, hl.tlocus('GRCh37'))
tmp_file = new_temp_file(prefix="test", suffix="interval_list")
start = t.interval.start
end = t.interval.end
(t.key_by(
interval=hl.locus_interval(start.contig, start.position, end.
position, True, True)).select().export(
tmp_file, header=False))
t2 = hl.import_locus_intervals(tmp_file)
self.assertTrue(t.select()._same(t2))
def test_skat(self):
ds2 = hl.import_vcf(resource('sample2.vcf'))
covariatesSkat = (hl.import_table(resource("skat.cov"), impute=True)
.key_by("Sample"))
phenotypesSkat = (hl.import_table(resource("skat.pheno"),
types={"Pheno": hl.tfloat64},
missing="0")
.key_by("Sample"))
intervalsSkat = (hl.import_locus_intervals(resource("skat.interval_list")))
weightsSkat = (hl.import_table(resource("skat.weights"),
types={"locus": hl.tlocus(),
"weight": hl.tfloat64})
.key_by("locus"))
ds = hl.split_multi_hts(ds2)
ds = ds.annotate_rows(gene=intervalsSkat[ds.locus],
weight=weightsSkat[ds.locus].weight)
ds = ds.annotate_cols(pheno=phenotypesSkat[ds.s].Pheno,
cov=covariatesSkat[ds.s])
ds = ds.annotate_cols(pheno=hl.cond(ds.pheno == 1.0,
False,
hl.cond(ds.pheno == 2.0,
True,
hl.null(hl.tbool))))
hl.skat(ds,
key_expr=ds.gene,
weight_expr=ds.weight,
y=ds.pheno,
x=ds.GT.n_alt_alleles(),
covariates=[ds.cov.Cov1, ds.cov.Cov2],
logistic=False).count()
hl.skat(ds,
key_expr=ds.gene,
weight_expr=ds.weight,
y=ds.pheno,
x=hl.pl_dosage(ds.PL),
covariates=[ds.cov.Cov1, ds.cov.Cov2],
logistic=True).count()
def main(args):
hl.init(log='/platform_pca.log')
if not args.skip_prepare_data_for_platform_pca:
# ~1 hour on 800 cores (3/8/18)
logger.info('Preparing data for platform PCA...')
mt = get_gnomad_data('exomes', adj=True, raw=False, meta_root=None, fam_root=None, split=False)
mt = filter_to_autosomes(mt)
intervals = hl.import_locus_intervals(evaluation_intervals_path)
mt = mt.annotate_rows(interval=intervals[mt.locus].target)
mt = mt.filter_rows(hl.is_defined(mt.interval) & (hl.len(mt.alleles) == 2))
mt = mt.select_entries(GT=hl.or_missing(hl.is_defined(mt.GT), hl.struct()))
callrate_mt = mt.group_rows_by(mt.interval).aggregate(callrate=hl.agg.fraction(hl.is_defined(mt.GT)))
callrate_mt.write(exome_callrate_mt_path, args.overwrite)
if not args.skip_run_platform_pca:
logger.info('Running platform PCA...')
qc_ht = hl.read_table(qc_ht_path('exomes', 'hard_filters')).key_by('s')
callrate_mt = hl.read_matrix_table(exome_callrate_mt_path)
callrate_mt = callrate_mt.filter_cols(hl.len(qc_ht[callrate_mt.col_key].hard_filters) == 0)
callrate_mt = callrate_mt.annotate_entries(callrate=hl.int(callrate_mt.callrate > 0.25))
# Center until Hail's PCA does it for you
callrate_mt = callrate_mt.annotate_rows(mean_callrate=hl.agg.mean(callrate_mt.callrate))
callrate_mt = callrate_mt.annotate_entries(callrate=callrate_mt.callrate - callrate_mt.mean_callrate)
eigenvalues, scores, _ = hl.pca(callrate_mt.callrate, compute_loadings=False)
logger.info('Eigenvalues: {}'.format(eigenvalues))
# [731282566.2824697, 78687228.90071851, 43837650.51729764, 33969298.61827205, 26308703.539534636, 21102437.512725923, 16949828.555817757, 12994894.187041137, 8372332.274295175, 8128326.814388647]
scores.write(exome_callrate_scores_ht_path)
logger.info('Annotating with platform PCs and known platform annotations...')
scores = hl.read_table(exome_callrate_scores_ht_path).annotate(data_type='exomes')
if args.pc_scores_in_separate_fields:
scores = scores.transmute(scores=[
scores[ann] for ann in sorted(
[ann for ann in scores.row if ann.startswith("PC")],
key=lambda x: int(x[2:])
)
])
platform_pcs = assign_platform_pcs(scores)
platform_pcs.write(qc_ht_path('exomes', 'platforms'), overwrite=args.overwrite)
def write_truth_concordance(data_type: str, truth_sample: str,
overwrite: bool) -> None:
sample_mapping = {
'NA12878': {
'exomes': 'C1975::NA12878',
'genomes': 'G94982_NA12878'
},
'syndip': {
'exomes': 'CHMI_CHMI3_Nex1',
'genomes': 'CHMI_CHMI3_WGS1'
}
}
mt = get_qc_samples_filtered_gnomad_data(data_type, autosomes_only=False)
mt = mt.filter_cols(mt.s == sample_mapping[truth_sample][data_type])
mt = mt.annotate_entries(GT=unphase_call_expr(mt.GT))
mt = mt.key_cols_by(s=hl.str(truth_sample))
mt = mt.repartition(1000 if data_type == 'genomes' else 100, shuffle=False)
truth_mt = hl.read_matrix_table(NA12878_mt_path() if truth_sample ==
'NA12878' else syndip_mt_path())
truth_mt = truth_mt.key_cols_by(s=hl.str(truth_sample))
if data_type == 'exomes':
exome_calling_intervals = hl.import_locus_intervals(
exome_calling_intervals_path, skip_invalid_intervals=True)
truth_mt = truth_mt.filter_rows(
hl.is_defined(exome_calling_intervals[truth_mt.locus]))
truth_mt = hl.split_multi_hts(truth_mt, left_aligned=False)
truth_mt = truth_mt.annotate_entries(GT=unphase_call_expr(truth_mt.GT))
sample_concordance_ht, sites_concordance_ht = compute_concordance(
mt, truth_mt, name=truth_sample)
sites_concordance_ht.write(annotations_ht_path(
data_type, f'{truth_sample}_concordance'),
overwrite=overwrite)
sample_concordance_ht.write(sample_annotations_table_path(
data_type, f'{truth_sample}_concordance'),
overwrite=overwrite)
def hailthread(cond1, q, cond2, qcm, inputDir, outputDir, qaws_size):
#Load id_conversion file
#table_idconv=hl.import_table('id_conversion')
#Load markers files
#table_makers_pos=hl.import_table('800k_to_extract_indexed2.txt',delimiter=':',no_header=True,impute=True)
#table_markers_all=hl.import_table('800k_to_extract_indexed_alleles_gt2.txt',delimiter=':',no_header=True,impute=True)
#cut -f 1 -d',' 800k_to_extract_indexed2.txt > interval_table
#awk -F':' '{print $1"\t"$2"\t"$2}' interval_table > interval_table2
hl.init()
cond1.acquire()
while not an_item_is_available(q):
#print("Thread hail to sleep")
#time.sleep(300)
print("Thread hail to wait")
cond1.wait()
file = get_an_available_item(q)
print("Thread hail get item " + file)
qaws_size = qaws_size - 1
cond1.release()
interval_table = hl.import_locus_intervals('interval_table2',
reference_genome='GRCh38')
while file != "END":
fileParts = file.split("/")[-1]
fileName = fileParts.replace(".vcf.gz", "").replace(".gvcf.gz", "")
chrName = fileName.split("_")[-3]
#myFNAL=fileName.split("\\.")
#myTempId=myFNAL[0]
#Load gVCF file
#data=hl.import_vcf("/mnt/vol1/java/gel_test.vcf",force_bgz=True,reference_genome='GRCh38')
#data=hl.import_vcf("/mnt/vol1/java/gel_mainProgramme_aggV2_chr10_129040437_131178399.vcf.gz",force_bgz=True,reference_genome='GRCh38')
try:
#Extract INFO fields
data = hl.import_vcf(inputDir + "/" + fileParts,
force_bgz=True,
reference_genome='GRCh38',
drop_samples=True)
#Filters PASS
if chrName != "chrY":
data = data.filter_rows(data.filters.size() > 0, keep=False)
#Multiallelic
data = hl.split_multi_hts(data)
#Join with markers
data_filtered = data.filter_rows(
hl.is_defined(interval_table[data.locus]))
data_sr = data_filtered.select_rows(
data_filtered.info.medianDepthAll,
data_filtered.info.medianDepthNonMiss,
data_filtered.info.medianGQ, data_filtered.info.missingness,
data_filtered.info.completeGTRatio, data_filtered.info.ABratio,
data_filtered.info.MendelSite, data_filtered.info.AN,
data_filtered.info.AC, data_filtered.info.AC_Hom,
data_filtered.info.AC_Het)
ht = data_sr.make_table()
ht.export(outputDir + "/" + fileName + "_INFO.tsv")
os.system("sed -i 's/\[//g' " + outputDir + "/" + fileName +
"_INFO.tsv")
os.system("sed -i 's/]//g' " + outputDir + "/" + fileName +
"_INFO.tsv")
os.system("cat " + outputDir + "/" + fileName +
"_INFO.tsv | grep -v locus " + " >> " + outputDir +
"/INFO_" + chrName)
os.system("rm " + inputDir + "/" + fileParts)
cond2.acquire()
print("Thread hail make item available " + fileName)
make_an_item_available(qcm, file)
cond2.notify_all()
cond2.release()
except FatalError as e:
print("Exception2 in file:" + file)
os.system("rm " + inputDir + "/" + fileParts)
except AssertionError as e:
print("Exception3 in file:" + file)
os.system("rm " + inputDir + "/" + fileParts)
except Exception as e:
print("Exception in file:" + file)
os.system("rm " + inputDir + "/" + fileParts)
#raise Exception
cond1.acquire()
while not an_item_is_available(q):
#print("Thread hail to sleep")
#time.sleep(300)
print("Thread hail to wait")
cond1.wait()
file = get_an_available_item(q)
print("Thread hail get item " + file)
qaws_size = qaws_size - 1
cond1.release()
time.sleep(300)
cond2.acquire()
print("Thread hail make END available")
make_an_item_available(qcm, "END")
cond2.notify_all()
cond2.release()
def write_omes_concordance(data_type: str, dup_version: str, by_platform: bool,
overwrite: bool) -> None:
other_data_type = 'exomes' if data_type == 'genomes' else 'genomes'
mt = get_qc_samples_filtered_gnomad_data(data_type)
other_mt = get_qc_samples_filtered_gnomad_data(other_data_type)
dup_ht = hl.import_table(
genomes_exomes_duplicate_ids_tsv_path(dup_version), impute=True)
dup_ht = dup_ht.filter(dup_ht.dup_pair_rank == 0)
# Unify sample names based on inferred duplicates (from pc_relate)
mt = mt.filter_cols(hl.is_defined(dup_ht.key_by(f'{data_type}_s')[mt.s]))
mt = mt.annotate_entries(GT=unphase_call_expr(mt.GT))
other_mt = other_mt.key_cols_by(
s=dup_ht.key_by(f'{other_data_type}_s')[other_mt.s][f'{data_type}_s'])
other_mt = other_mt.filter_cols(hl.is_defined(other_mt.s))
other_mt = other_mt.annotate_entries(GT=unphase_call_expr(other_mt.GT))
exome_calling_intervals = hl.import_locus_intervals(
exome_calling_intervals_path, skip_invalid_intervals=True)
mt = mt.filter_rows(hl.is_defined(exome_calling_intervals[mt.locus]))
other_mt = other_mt.filter_rows(
hl.is_defined(exome_calling_intervals[other_mt.locus]))
if by_platform:
omes_conc = hl.read_table(
annotations_ht_path(data_type, "omes_concordance"))
omes_conc = omes_conc.transmute(concordance=[
hl.struct(concordance_matrix=omes_conc.concordance,
n_discordant=omes_conc.n_discordant,
meta={
'data_type': 'omes',
'platform': 'all'
})
])
platforms = mt.aggregate_cols(
hl.agg.collect_as_set(mt.meta.qc_platform))
logger.info(
"Computing concordance by platform for platforms: {}".format(
",".join([str(x) for x in platforms])))
for platform in platforms:
plat_mt = mt.filter_cols(mt.meta.qc_platform == platform)
_, sites_concordance_ht = compute_concordance(
plat_mt, other_mt, name=f'omes (platform: {platform})')
omes_conc = omes_conc.annotate(
concordance=omes_conc.concordance.append(
hl.struct(concordance_matrix=sites_concordance_ht[
omes_conc.key].concordance,
n_discordant=sites_concordance_ht[
omes_conc.key].n_discordant,
meta={
'data_type': 'omes',
'platform': hl.str(platform)
})))
omes_conc.write(annotations_ht_path(data_type,
"omes_by_platform_concordance"),
overwrite=overwrite)
else:
sample_concordance_ht, sites_concordance_ht = compute_concordance(
mt, other_mt, name='omes')
sites_concordance_ht.write(annotations_ht_path(data_type,
"omes_concordance"),
overwrite=overwrite)
sample_concordance_ht.write(sample_annotations_table_path(
data_type, "omes_concordance"),
overwrite=overwrite)
def create_binned_concordance(data_type: str, truth_sample: str, metric: str,
nbins: int, overwrite: bool) -> None:
"""
Creates and writes a concordance table binned by rank (both absolute and relative) for a given data type, truth sample and metric.
:param str data_type: One 'exomes' or 'genomes'
:param str truth_sample: Which truth sample concordance to load
:param str metric: One of the evaluation metrics (or a RF hash)
:param int nbins: Number of bins for the rank
:param bool overwrite: Whether to overwrite existing table
:return: Nothing -- just writes the table
:rtype: None
"""
if hl.hadoop_exists(
binned_concordance_path(data_type, truth_sample, metric) +
'/_SUCCESS') and not overwrite:
logger.warn(
f"Skipping binned concordance creation as {binned_concordance_path(data_type, truth_sample, metric)} exists and overwrite=False"
)
else:
ht = hl.read_table(
annotations_ht_path(data_type, f'{truth_sample}_concordance'))
# Remove 1bp indels for syndip as cannot be trusted
if truth_sample == 'syndip':
ht = ht.filter(
hl.is_indel(ht.alleles[0], ht.alleles[1]) &
(hl.abs(hl.len(ht.alleles[0]) - hl.len(ht.alleles[1])) == 1),
keep=False)
high_conf_intervals = hl.import_locus_intervals(
syndip_high_conf_regions_bed_path)
else:
high_conf_intervals = hl.import_locus_intervals(
NA12878_high_conf_regions_bed_path)
lcr = hl.import_locus_intervals(lcr_intervals_path)
segdup = hl.import_locus_intervals(segdup_intervals_path)
ht = ht.filter(
hl.is_defined(high_conf_intervals[ht.locus])
& hl.is_missing(lcr[ht.locus]) & hl.is_missing(segdup[ht.locus]))
if metric in ['vqsr', 'rf_2.0.2', 'rf_2.0.2_beta', 'cnn']:
metric_ht = hl.read_table(score_ranking_path(data_type, metric))
else:
metric_ht = hl.read_table(
rf_path(data_type, 'rf_result', run_hash=metric))
metric_snvs, metrics_indels = metric_ht.aggregate([
hl.agg.count_where(
hl.is_snp(metric_ht.alleles[0], metric_ht.alleles[1])),
hl.agg.count_where(
~hl.is_snp(metric_ht.alleles[0], metric_ht.alleles[1]))
])
snvs, indels = ht.aggregate([
hl.agg.count_where(hl.is_snp(ht.alleles[0], ht.alleles[1])),
hl.agg.count_where(~hl.is_snp(ht.alleles[0], ht.alleles[1]))
])
ht = ht.annotate_globals(global_counts=hl.struct(
snvs=metric_snvs, indels=metrics_indels),
counts=hl.struct(snvs=snvs, indels=indels))
ht = ht.annotate(
snv=hl.is_snp(ht.alleles[0], ht.alleles[1]),
score=metric_ht[ht.key].score,
global_rank=metric_ht[ht.key].rank,
# TP => allele is found in both data sets
n_tp=ht.concordance[3][3] + ht.concordance[3][4] +
ht.concordance[4][3] + ht.concordance[4][4],
# FP => allele is found only in test data set
n_fp=hl.sum(ht.concordance[3][:2]) + hl.sum(ht.concordance[4][:2]),
# FN => allele is found only in truth data set
n_fn=hl.sum(ht.concordance[:2].map(lambda x: x[3] + x[4])))
ht = add_rank(ht, -1.0 * ht.score)
ht = ht.annotate(rank=[
hl.tuple([
'global_rank', (ht.global_rank + 1) /
hl.cond(ht.snv, ht.globals.global_counts.snvs,
ht.globals.global_counts.indels)
]),
hl.tuple([
'truth_sample_rank', (ht.rank + 1) / hl.cond(
ht.snv, ht.globals.counts.snvs, ht.globals.counts.indels)
])
])
ht = ht.explode(ht.rank)
ht = ht.annotate(rank_name=ht.rank[0], bin=hl.int(ht.rank[1] * nbins))
ht = ht.group_by('rank_name', 'snv', 'bin').aggregate(
# Look at site-level metrics -> tp > fp > fn -- only important for multi-sample comparisons
tp=hl.agg.count_where(ht.n_tp > 0),
fp=hl.agg.count_where((ht.n_tp == 0) & (ht.n_fp > 0)),
fn=hl.agg.count_where((ht.n_tp == 0) & (ht.n_fp == 0)
& (ht.n_fn > 0)),
min_score=hl.agg.min(ht.score),
max_score=hl.agg.max(ht.score),
n_alleles=hl.agg.count()).repartition(5)
ht.write(binned_concordance_path(data_type, truth_sample, metric),
overwrite=overwrite)
age=hl.rand_norm(65, 10),
height=hl.rand_norm(70, 10),
blood_pressure=hl.rand_norm(120, 20),
cohort_name="cohort1"),
cov=hl.struct(PC1=hl.rand_norm(0, 1)),
cov1=hl.rand_norm(0, 1),
cov2=hl.rand_norm(0, 1),
cohort="SIGMA")
ds = ds.annotate_globals(global_field_1=5,
global_field_2=10,
pli={'SCN1A': 0.999, 'SONIC': 0.014},
populations=['AFR', 'EAS', 'EUR', 'SAS', 'AMR', 'HIS'])
ds = ds.annotate_rows(gene=['TTN'])
ds = ds.annotate_cols(cohorts=['1kg'], pop='EAS')
ds.write('data/example.vds', overwrite=True)
lmmreg_ds = hl.variant_qc(hl.split_multi_hts(hl.import_vcf('data/sample.vcf.bgz')))
lmmreg_tsv = hl.import_table('data/example_lmmreg.tsv', 'Sample', impute=True)
lmmreg_ds = lmmreg_ds.annotate_cols(**lmmreg_tsv[lmmreg_ds['s']])
lmmreg_ds = lmmreg_ds.annotate_rows(use_in_kinship = lmmreg_ds.variant_qc.AF[1] > 0.05)
lmmreg_ds.write('data/example_lmmreg.vds', overwrite=True)
burden_ds = hl.import_vcf('data/example_burden.vcf')
burden_kt = hl.import_table('data/example_burden.tsv', key='Sample', impute=True)
burden_ds = burden_ds.annotate_cols(burden = burden_kt[burden_ds.s])
burden_ds = burden_ds.annotate_rows(weight = hl.float64(burden_ds.locus.position))
burden_ds = hl.variant_qc(burden_ds)
genekt = hl.import_locus_intervals('data/gene.interval_list')
burden_ds = burden_ds.annotate_rows(gene=genekt[burden_ds.locus])
burden_ds.write('data/example_burden.vds', overwrite=True)
global_field_1=5,
global_field_2=10,
pli={
'SCN1A': 0.999,
'SONIC': 0.014
},
populations=['AFR', 'EAS', 'EUR', 'SAS', 'AMR', 'HIS'])
ds = ds.annotate_rows(gene=['TTN'])
ds = ds.annotate_cols(cohorts=['1kg'], pop='EAS')
ds.write('data/example.vds', overwrite=True)
lmmreg_ds = hl.variant_qc(
hl.split_multi_hts(hl.import_vcf('data/sample.vcf.bgz')))
lmmreg_tsv = hl.import_table('data/example_lmmreg.tsv', 'Sample', impute=True)
lmmreg_ds = lmmreg_ds.annotate_cols(**lmmreg_tsv[lmmreg_ds['s']])
lmmreg_ds = lmmreg_ds.annotate_rows(
use_in_kinship=lmmreg_ds.variant_qc.AF[1] > 0.05)
lmmreg_ds.write('data/example_lmmreg.vds', overwrite=True)
burden_ds = hl.import_vcf('data/example_burden.vcf')
burden_kt = hl.import_table('data/example_burden.tsv',
key='Sample',
impute=True)
burden_ds = burden_ds.annotate_cols(burden=burden_kt[burden_ds.s])
burden_ds = burden_ds.annotate_rows(
weight=hl.float64(burden_ds.locus.position))
burden_ds = hl.variant_qc(burden_ds)
genekt = hl.import_locus_intervals('data/gene.interval_list')
burden_ds = burden_ds.annotate_rows(gene=genekt[burden_ds.locus])
burden_ds.write('data/example_burden.vds', overwrite=True)
def get_lcr_intervals() -> hl.Table:
return hl.import_locus_intervals('gs://gnomad-public/resources/grch38/LCRFromHengHg38.txt', reference_genome='GRCh38', skip_invalid_intervals=True)
pprint(n_6_multi) # 02_prefilter_variants.py INITIAL_VARIANT_LIST = 'gs://dalio_bipolar_w1_w2_hail_02/data/variants/02_prefilter.keep.variant_list' INITIAL_VARIANT_QC_FILE = 'gs://dalio_bipolar_w1_w2_hail_02/data/variants/02_prefilter_metrics.tsv' # Read in the target intervals TARGET_INTERVALS = 'gs://raw_data_bipolar_dalio_w1_w2_hail_02/inputs/ice_coding_v1_targets.interval_list' # Read in the padded target intervals (50bp padding) PADDED_TARGET_INTERVALS = 'gs://raw_data_bipolar_dalio_w1_w2_hail_02/inputs/ice_coding_v1_padded_targets.interval_list' # Low complexity regions in the data. LCRs = 'gs://raw_data_bipolar_dalio_w1_w2_hail_02/inputs/LCR-hs38.bed' # Import the interval lists for the target intervals. target_intervals = hl.import_locus_intervals(TARGET_INTERVALS, reference_genome='GRCh38') # Import the interval lists for the padded target intervals. padded_target_intervals = hl.import_locus_intervals(PADDED_TARGET_INTERVALS, reference_genome='GRCh38') # Import the interval lists for the LCRs. LCR_intervals = hl.import_locus_intervals(LCRs, reference_genome='GRCh38') # Annotate variants with flag indicating if they are in LCR or failed VQSR. mt = mt.annotate_rows(fail_VQSR = hl.len(mt.filters) != 0) mt = mt.annotate_rows(in_LCR = hl.is_defined(LCR_intervals[mt.locus])) mt = mt.annotate_rows(not_in_target_intervals = ~hl.is_defined(target_intervals[mt.locus])) mt = mt.annotate_rows(not_in_padded_target_intervals = ~hl.is_defined(padded_target_intervals[mt.locus])) # Get information about the number of variants that were excluded. fail_VQSR = mt.filter_rows(mt.fail_VQSR).count_rows() in_LCR = mt.filter_rows(mt.in_LCR).count_rows() not_in_target_intervals = mt.filter_rows(mt.not_in_target_intervals).count_rows()
def test_import_locus_intervals_no_reference_specified(self):
interval_file = resource('annotinterall.interval_list')
t = hl.import_locus_intervals(interval_file, reference_genome=None)
self.assertTrue(t.count() == 2)
self.assertEqual(t.interval.dtype.point_type, hl.tstruct(contig=hl.tstr, position=hl.tint32))
def create_binned_data(ht: hl.Table, data: str, data_type: str,
n_bins: int) -> hl.Table:
"""
Creates binned data from a rank Table grouped by rank_id (rank, biallelic, etc.), contig, snv, bi_allelic and singleton
containing the information needed for evaluation plots.
:param Table ht: Input rank table
:param str data: Which data/run hash is being created
:param str data_type: one of 'exomes' or 'genomes'
:param int n_bins: Number of bins.
:return: Binned Table
:rtype: Table
"""
# Count variants for ranking
count_expr = {
x: hl.agg.filter(
hl.is_defined(ht[x]),
hl.agg.counter(
hl.cond(hl.is_snp(ht.alleles[0], ht.alleles[1]), 'snv',
'indel')))
for x in ht.row if x.endswith('rank')
}
rank_variant_counts = ht.aggregate(hl.Struct(**count_expr))
logger.info(
f"Found the following variant counts:\n {pformat(rank_variant_counts)}"
)
ht = ht.annotate_globals(rank_variant_counts=rank_variant_counts)
# Load external evaluation data
clinvar_ht = hl.read_table(clinvar_ht_path)
denovo_ht = get_validated_denovos_ht()
if data_type == 'exomes':
denovo_ht = denovo_ht.filter(denovo_ht.gnomad_exomes.high_quality)
else:
denovo_ht = denovo_ht.filter(denovo_ht.gnomad_genomes.high_quality)
denovo_ht = denovo_ht.select(
validated_denovo=denovo_ht.validated,
high_confidence_denovo=denovo_ht.Confidence == 'HIGH')
ht_truth_data = hl.read_table(annotations_ht_path(data_type, 'truth_data'))
fam_ht = hl.read_table(annotations_ht_path(data_type, 'family_stats'))
fam_ht = fam_ht.select(family_stats=fam_ht.family_stats[0])
gnomad_ht = get_gnomad_data(data_type).rows()
gnomad_ht = gnomad_ht.select(
vqsr_negative_train_site=gnomad_ht.info.NEGATIVE_TRAIN_SITE,
vqsr_positive_train_site=gnomad_ht.info.POSITIVE_TRAIN_SITE,
fail_hard_filters=(gnomad_ht.info.QD < 2) | (gnomad_ht.info.FS > 60) |
(gnomad_ht.info.MQ < 30))
lcr_intervals = hl.import_locus_intervals(lcr_intervals_path)
ht = ht.annotate(
**ht_truth_data[ht.key],
**fam_ht[ht.key],
**gnomad_ht[ht.key],
**denovo_ht[ht.key],
clinvar=hl.is_defined(clinvar_ht[ht.key]),
indel_length=hl.abs(ht.alleles[0].length() - ht.alleles[1].length()),
rank_bins=hl.array([
hl.Struct(
rank_id=rank_name,
bin=hl.int(
hl.ceil(
hl.float(ht[rank_name] + 1) / hl.floor(
ht.globals.rank_variant_counts[rank_name][hl.cond(
hl.is_snp(ht.alleles[0], ht.alleles[1]), 'snv',
'indel')] / n_bins))))
for rank_name in rank_variant_counts
]),
lcr=hl.is_defined(lcr_intervals[ht.locus]))
ht = ht.explode(ht.rank_bins)
ht = ht.transmute(rank_id=ht.rank_bins.rank_id, bin=ht.rank_bins.bin)
ht = ht.filter(hl.is_defined(ht.bin))
ht = ht.checkpoint(
f'gs://gnomad-tmp/gnomad_score_binning_{data_type}_tmp_{data}.ht',
overwrite=True)
# Create binned data
return (ht.group_by(
rank_id=ht.rank_id,
contig=ht.locus.contig,
snv=hl.is_snp(ht.alleles[0], ht.alleles[1]),
bi_allelic=hl.is_defined(ht.biallelic_rank),
singleton=ht.singleton,
release_adj=ht.ac > 0,
bin=ht.bin)._set_buffer_size(20000).aggregate(
min_score=hl.agg.min(ht.score),
max_score=hl.agg.max(ht.score),
n=hl.agg.count(),
n_ins=hl.agg.count_where(
hl.is_insertion(ht.alleles[0], ht.alleles[1])),
n_del=hl.agg.count_where(
hl.is_deletion(ht.alleles[0], ht.alleles[1])),
n_ti=hl.agg.count_where(
hl.is_transition(ht.alleles[0], ht.alleles[1])),
n_tv=hl.agg.count_where(
hl.is_transversion(ht.alleles[0], ht.alleles[1])),
n_1bp_indel=hl.agg.count_where(ht.indel_length == 1),
n_mod3bp_indel=hl.agg.count_where((ht.indel_length % 3) == 0),
n_clinvar=hl.agg.count_where(ht.clinvar),
n_singleton=hl.agg.count_where(ht.singleton),
n_validated_de_novos=hl.agg.count_where(ht.validated_denovo),
n_high_confidence_de_novos=hl.agg.count_where(
ht.high_confidence_denovo),
n_de_novo=hl.agg.filter(
ht.family_stats.unrelated_qc_callstats.AC[1] == 0,
hl.agg.sum(ht.family_stats.mendel.errors)),
n_de_novo_no_lcr=hl.agg.filter(
~ht.lcr & (ht.family_stats.unrelated_qc_callstats.AC[1] == 0),
hl.agg.sum(ht.family_stats.mendel.errors)),
n_de_novo_sites=hl.agg.filter(
ht.family_stats.unrelated_qc_callstats.AC[1] == 0,
hl.agg.count_where(ht.family_stats.mendel.errors > 0)),
n_de_novo_sites_no_lcr=hl.agg.filter(
~ht.lcr & (ht.family_stats.unrelated_qc_callstats.AC[1] == 0),
hl.agg.count_where(ht.family_stats.mendel.errors > 0)),
n_trans_singletons=hl.agg.filter(
(ht.info_ac < 3) &
(ht.family_stats.unrelated_qc_callstats.AC[1] == 1),
hl.agg.sum(ht.family_stats.tdt.t)),
n_untrans_singletons=hl.agg.filter(
(ht.info_ac < 3) &
(ht.family_stats.unrelated_qc_callstats.AC[1] == 1),
hl.agg.sum(ht.family_stats.tdt.u)),
n_train_trans_singletons=hl.agg.count_where(
(ht.family_stats.unrelated_qc_callstats.AC[1] == 1)
& (ht.family_stats.tdt.t == 1)),
n_omni=hl.agg.count_where(ht.truth_data.omni),
n_mills=hl.agg.count_where(ht.truth_data.mills),
n_hapmap=hl.agg.count_where(ht.truth_data.hapmap),
n_kgp_high_conf_snvs=hl.agg.count_where(
ht.truth_data.kgp_high_conf_snvs),
fail_hard_filters=hl.agg.count_where(ht.fail_hard_filters),
n_vqsr_pos_train=hl.agg.count_where(ht.vqsr_positive_train_site),
n_vqsr_neg_train=hl.agg.count_where(ht.vqsr_negative_train_site)))
def test_import_locus_intervals_no_reference_specified(self):
interval_file = resource('annotinterall.interval_list')
t = hl.import_locus_intervals(interval_file, reference_genome=None)
self.assertEqual(t.interval.dtype.point_type, hl.tstruct(contig=hl.tstr, position=hl.tint32))
def generate_datasets(doctest_namespace):
doctest_namespace['hl'] = hl
doctest_namespace['np'] = np
ds = hl.import_vcf('data/sample.vcf.bgz')
ds = ds.sample_rows(0.03)
ds = ds.annotate_rows(use_as_marker=hl.rand_bool(0.5),
panel_maf=0.1,
anno1=5,
anno2=0,
consequence="LOF",
gene="A",
score=5.0)
ds = ds.annotate_rows(a_index=1)
ds = hl.sample_qc(hl.variant_qc(ds))
ds = ds.annotate_cols(is_case=True,
pheno=hl.struct(is_case=hl.rand_bool(0.5),
is_female=hl.rand_bool(0.5),
age=hl.rand_norm(65, 10),
height=hl.rand_norm(70, 10),
blood_pressure=hl.rand_norm(120, 20),
cohort_name="cohort1"),
cov=hl.struct(PC1=hl.rand_norm(0, 1)),
cov1=hl.rand_norm(0, 1),
cov2=hl.rand_norm(0, 1),
cohort="SIGMA")
ds = ds.annotate_globals(
global_field_1=5,
global_field_2=10,
pli={
'SCN1A': 0.999,
'SONIC': 0.014
},
populations=['AFR', 'EAS', 'EUR', 'SAS', 'AMR', 'HIS'])
ds = ds.annotate_rows(gene=['TTN'])
ds = ds.annotate_cols(cohorts=['1kg'], pop='EAS')
ds = ds.checkpoint(f'output/example.mt', overwrite=True)
doctest_namespace['ds'] = ds
doctest_namespace['dataset'] = ds
doctest_namespace['dataset2'] = ds.annotate_globals(global_field=5)
doctest_namespace['dataset_to_union_1'] = ds
doctest_namespace['dataset_to_union_2'] = ds
v_metadata = ds.rows().annotate_globals(global_field=5).annotate(
consequence='SYN')
doctest_namespace['v_metadata'] = v_metadata
s_metadata = ds.cols().annotate(pop='AMR', is_case=False, sex='F')
doctest_namespace['s_metadata'] = s_metadata
doctest_namespace['cols_to_keep'] = s_metadata
doctest_namespace['cols_to_remove'] = s_metadata
doctest_namespace['rows_to_keep'] = v_metadata
doctest_namespace['rows_to_remove'] = v_metadata
small_mt = hl.balding_nichols_model(3, 4, 4)
doctest_namespace['small_mt'] = small_mt.checkpoint('output/small.mt',
overwrite=True)
# Table
table1 = hl.import_table('data/kt_example1.tsv', impute=True, key='ID')
table1 = table1.annotate_globals(global_field_1=5, global_field_2=10)
doctest_namespace['table1'] = table1
doctest_namespace['other_table'] = table1
table2 = hl.import_table('data/kt_example2.tsv', impute=True, key='ID')
doctest_namespace['table2'] = table2
table4 = hl.import_table('data/kt_example4.tsv',
impute=True,
types={
'B': hl.tstruct(B0=hl.tbool, B1=hl.tstr),
'D': hl.tstruct(cat=hl.tint32, dog=hl.tint32),
'E': hl.tstruct(A=hl.tint32, B=hl.tint32)
})
doctest_namespace['table4'] = table4
people_table = hl.import_table('data/explode_example.tsv',
delimiter='\\s+',
types={
'Age': hl.tint32,
'Children': hl.tarray(hl.tstr)
},
key='Name')
doctest_namespace['people_table'] = people_table
# TDT
doctest_namespace['tdt_dataset'] = hl.import_vcf('data/tdt_tiny.vcf')
ds2 = hl.variant_qc(ds)
doctest_namespace['ds2'] = ds2.select_rows(AF=ds2.variant_qc.AF)
# Expressions
doctest_namespace['names'] = hl.literal(['Alice', 'Bob', 'Charlie'])
doctest_namespace['a1'] = hl.literal([0, 1, 2, 3, 4, 5])
doctest_namespace['a2'] = hl.literal([1, -1, 1, -1, 1, -1])
doctest_namespace['t'] = hl.literal(True)
doctest_namespace['f'] = hl.literal(False)
doctest_namespace['na'] = hl.null(hl.tbool)
doctest_namespace['call'] = hl.call(0, 1, phased=False)
doctest_namespace['a'] = hl.literal([1, 2, 3, 4, 5])
doctest_namespace['d'] = hl.literal({
'Alice': 43,
'Bob': 33,
'Charles': 44
})
doctest_namespace['interval'] = hl.interval(3, 11)
doctest_namespace['locus_interval'] = hl.parse_locus_interval(
"1:53242-90543")
doctest_namespace['locus'] = hl.locus('1', 1034245)
doctest_namespace['x'] = hl.literal(3)
doctest_namespace['y'] = hl.literal(4.5)
doctest_namespace['s1'] = hl.literal({1, 2, 3})
doctest_namespace['s2'] = hl.literal({1, 3, 5})
doctest_namespace['s3'] = hl.literal({'Alice', 'Bob', 'Charlie'})
doctest_namespace['struct'] = hl.struct(a=5, b='Foo')
doctest_namespace['tup'] = hl.literal(("a", 1, [1, 2, 3]))
doctest_namespace['s'] = hl.literal('The quick brown fox')
doctest_namespace['interval2'] = hl.Interval(3, 6)
doctest_namespace['nd'] = hl._nd.array([[1, 2], [3, 4]])
# Overview
doctest_namespace['ht'] = hl.import_table("data/kt_example1.tsv",
impute=True)
doctest_namespace['mt'] = ds
gnomad_data = ds.rows()
doctest_namespace['gnomad_data'] = gnomad_data.select(gnomad_data.info.AF)
# BGEN
bgen = hl.import_bgen('data/example.8bits.bgen',
entry_fields=['GT', 'GP', 'dosage'])
doctest_namespace['variants_table'] = bgen.rows()
burden_ds = hl.import_vcf('data/example_burden.vcf')
burden_kt = hl.import_table('data/example_burden.tsv',
key='Sample',
impute=True)
burden_ds = burden_ds.annotate_cols(burden=burden_kt[burden_ds.s])
burden_ds = burden_ds.annotate_rows(
weight=hl.float64(burden_ds.locus.position))
burden_ds = hl.variant_qc(burden_ds)
genekt = hl.import_locus_intervals('data/gene.interval_list')
burden_ds = burden_ds.annotate_rows(gene=genekt[burden_ds.locus])
burden_ds = burden_ds.checkpoint(f'output/example_burden.vds',
overwrite=True)
doctest_namespace['burden_ds'] = burden_ds
ld_score_one_pheno_sumstats = hl.import_table(
'data/ld_score_regression.one_pheno.sumstats.tsv',
types={
'locus': hl.tlocus('GRCh37'),
'alleles': hl.tarray(hl.tstr),
'chi_squared': hl.tfloat64,
'n': hl.tint32,
'ld_score': hl.tfloat64,
'phenotype': hl.tstr,
'chi_squared_50_irnt': hl.tfloat64,
'n_50_irnt': hl.tint32,
'chi_squared_20160': hl.tfloat64,
'n_20160': hl.tint32
},
key=['locus', 'alleles'])
doctest_namespace[
'ld_score_one_pheno_sumstats'] = ld_score_one_pheno_sumstats
mt = hl.import_matrix_table(
'data/ld_score_regression.all_phenos.sumstats.tsv',
row_fields={
'locus': hl.tstr,
'alleles': hl.tstr,
'ld_score': hl.tfloat64
},
entry_type=hl.tstr)
mt = mt.key_cols_by(phenotype=mt.col_id)
mt = mt.key_rows_by(locus=hl.parse_locus(mt.locus),
alleles=mt.alleles.split(','))
mt = mt.drop('row_id', 'col_id')
mt = mt.annotate_entries(x=mt.x.split(","))
mt = mt.transmute_entries(chi_squared=hl.float64(mt.x[0]),
n=hl.int32(mt.x[1]))
mt = mt.annotate_rows(ld_score=hl.float64(mt.ld_score))
doctest_namespace['ld_score_all_phenos_sumstats'] = mt
print("finished setting up doctest...")
logreg_pop_file = 'gs://ccdg-qc-multi/data/IBD/logreg_results_'
samples_file = 'gs://ccdg-qc-multi/data/IBD/samples_file.tsv'
plink_file = 'gs://ccdg-qc-multi/data/IBD/ibdvars'
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# read data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
vds = hl.read_matrix_table(vds_splitmulti_file)
vds_immune = hl.read_matrix_table(vds_immune_file)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# filter variants
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
ibd_vars = hl.import_locus_intervals(ibdvars_file)
intervallist = [x.interval for x in ibd_vars.collect()]
manuelvars = hl.import_locus_intervals(manuelvars_file)
intervallist = intervallist + [x.interval for x in manuelvars.collect()]
vds = hl.filter_intervals(vds, intervallist, keep=True)
##vds = vds.filter_rows(vds.qcpass)
vds_immune = hl.filter_intervals(vds_immune, intervallist, keep=True)
vds_immune = hl.split_multi_hts(
vds_immune.select_entries(vds_immune.GT, vds_immune.AD, vds_immune.DP,
vds_immune.GQ, vds_immune.PL))
vds_combined = vds_immune.union_cols(vds)
vds_combined = vds_combined.naive_coalesce(1000)
vds_combined.write(filtered_vds_combined_file, overwrite=True)
MT_HARDCALLS = 'gs://raw_data_bipolar_dalio_w1_w2_hail_02/bipolar_wes_dalio_W1_W2/filterGT.hardcalls.mt'
# Read in the hard calls matrix table.
mt = hl.read_matrix_table(MT_HARDCALLS)
# Read in the target intervals
TARGET_INTERVALS = 'gs://dalio_bipolar_w1_w2_hail_02/whole_exome_illumina_coding_v1.Homo_sapiens_assembly19.targets.fixed.interval_list'
# Low complexity regions in the data.
LCRs = 'gs://raw_data_bipolar_dalio_w1_w2_hail_02/inputs/inputs_low_complexity_regions_b37.interval_list'
INITIAL_VARIANT_QC_FILE = 'gs://dalio_bipolar_w1_w2_hail_02/data/variants/02_prefilter_metrics_b37_callset.tsv'
INITIAL_VARIANT_LIST = 'gs://dalio_bipolar_w1_w2_hail_02/data/variants/02_prefilter_b37_callset.keep.variant_list'
INITIAL_VARIANT_AUTO_LIST = 'gs://dalio_bipolar_w1_w2_hail_02/data/variants/02_prefilter_b37_callset.keep.autosome.variant_list'
# Import the interval lists for the LCRs.
target_intervals = hl.import_locus_intervals(TARGET_INTERVALS)
LCR_intervals = hl.import_locus_intervals(LCRs)
# Annotate variants with flag indicating if they are in LCR or failed VQSR.
mt = mt.annotate_rows(fail_VQSR=hl.len(mt.filters) != 0)
mt = mt.annotate_rows(in_LCR=hl.is_defined(LCR_intervals[mt.locus]))
mt = mt.annotate_rows(
not_in_target_intervals=~hl.is_defined(target_intervals[mt.locus]))
# Get information about the number of variants that were excluded.
fail_VQSR = mt.filter_rows(mt.fail_VQSR).count_rows()
in_LCR = mt.filter_rows(mt.in_LCR).count_rows()
not_in_target_intervals = mt.filter_rows(
mt.not_in_target_intervals).count_rows()
print('n variants failing VQSR:')
vds_1kg_file = 'gs://ccdg-qc-multi/data/1000genomes/vds/hail2_ALL.GRCh38.genotypes.20170504.vds'
mhc_chr8inv_file = 'gs://ccdg-qc-multi/data/MHC_invchr8_longLDreg_liftover_to_GRCh38.txt'
rel_exclusion_file = 'gs://ccdg-qc-multi/out/king/' + chrom + '/ibd_greater_0884_' + chrom + '.txt'
samples_to_keep_file = 'gs://ccdg-qc-multi/qc_measures/' + chrom + '/01_sample_qc_keep.txt'
# output
pca_value_file = 'gs://ccdg-qc-multi/qc_measures/pca/' + chrom + '/pca_values.tsv'
pca_score_file = 'gs://ccdg-qc-multi/qc_measures/pca/' + chrom + '/pca_scores.tsv'
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# read data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## interval list
#mhc_chr8inv = hl.import_table(mhc_chr8inv_file, no_header=True).key_by('f0')
mhc_chr8inv = hl.import_locus_intervals(mhc_chr8inv_file)
##
rel_exclusion = hl.import_table(rel_exclusion_file,
no_header=True).key_by('f0')
vds = hl.read_matrix_table(vds_ldpruned_common_file)
onekg = hl.read_matrix_table(vds_1kg_file)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# rename samples
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
print('annotate 1KG...')
onekg = onekg.annotate_cols(s='1KG_' + onekg.s)
HIGH_LD_INTERVALS = 'gs://raw_data_bipolar_dalio_w1_w2/inputs/b38_high_ld.bed'
INITIAL_SAMPLES = 'gs://dalio_bipolar_w1_w2_hail_02/data/samples/03_initial_qc.keep.sample_list'
INITIAL_VARIANT_LIST = 'gs://dalio_bipolar_w1_w2_hail_02/data/variants/02_prefilter.keep.variant_list'
ht_initial_samples = hl.import_table(INITIAL_SAMPLES, no_header=True, key='f0')
ht_initial_variants = hl.import_table(INITIAL_VARIANT_LIST,
types={
'locus':
hl.tlocus(reference_genome='GRCh38'),
'alleles':
hl.tarray(hl.tstr)
})
ht_initial_variants = ht_initial_variants.key_by(ht_initial_variants.locus,
ht_initial_variants.alleles)
high_LD_intervals = hl.import_locus_intervals(HIGH_LD_INTERVALS,
reference_genome='GRCh38')
mt = hl.read_matrix_table(MT_HARDCALLS)
mt = mt.filter_cols(hl.is_defined(ht_initial_samples[mt.col_key]))
mt = mt.annotate_rows(in_high_LD=hl.is_defined(high_LD_intervals[mt.locus]))
mt = mt.filter_rows(
hl.is_defined(ht_initial_variants[mt.row_key]) & (~mt.in_high_LD))
mt = mt.filter_rows(mt.locus.in_x_nonpar() | mt.locus.in_autosome_or_par())
mt = hl.variant_qc(mt, name='qc')
mt = mt.filter_rows((mt.qc.AF[0] > 0.01) & (mt.qc.AF[0] < 0.99)
& ((mt.qc.call_rate > 0.98) | mt.locus.in_x_nonpar()
| mt.locus.in_x_par())).persist()
mt.count()
