def test_summarize_variants_ti_tv(self):
mt = hl.import_vcf(resource('sample.vcf'))
# check that summarize can run with the print control flow
hl.summarize_variants(mt, handler=lambda s: ())
r = hl.summarize_variants(mt, show=False)
assert r['allele_types'] == {'Deletion': 27, 'Insertion': 18, 'SNP': 301}
assert r['contigs'] == {'20': 346}
assert r['n_variants'] == 346
assert r['r_ti_tv'] == 2.5
assert r['allele_counts'] == {2: 346}
def test_summarize_variants(self):
mt = hl.utils.range_matrix_table(3, 3)
variants = hl.literal({0: hl.Struct(locus=hl.Locus('1', 1), alleles=['A', 'T', 'C']),
1: hl.Struct(locus=hl.Locus('2', 1), alleles=['A', 'AT', '@']),
2: hl.Struct(locus=hl.Locus('2', 1), alleles=['AC', 'GT'])})
mt = mt.annotate_rows(**variants[mt.row_idx]).key_rows_by('locus', 'alleles')
r = hl.summarize_variants(mt, show=False)
self.assertEqual(r.n_variants, 3)
self.assertEqual(r.contigs, {'1': 1, '2': 2})
self.assertEqual(r.allele_types, {'SNP': 2, 'MNP': 1, 'Unknown': 1, 'Insertion': 1})
self.assertEqual(r.allele_counts, {2: 1, 3: 2})
def test_summarize_variants(self):
mt = hl.utils.range_matrix_table(3, 3)
variants = hl.literal({
0:
hl.Struct(locus=hl.Locus('1', 1), alleles=['A', 'T', 'C']),
1:
hl.Struct(locus=hl.Locus('2', 1), alleles=['A', 'AT', '@']),
2:
hl.Struct(locus=hl.Locus('2', 1), alleles=['AC', 'GT'])
})
mt = mt.annotate_rows(**variants[mt.row_idx]).key_rows_by(
'locus', 'alleles')
r = hl.summarize_variants(mt, show=False)
self.assertEqual(r.n_variants, 3)
self.assertEqual(r.contigs, {'1': 1, '2': 2})
self.assertEqual(r.allele_types, {
'SNP': 2,
'MNP': 1,
'Unknown': 1,
'Insertion': 1
})
self.assertEqual(r.allele_counts, {2: 1, 3: 2})
def summarize_variants(t: Union[hl.MatrixTable, hl.Table], ) -> hl.Struct:
"""
Get summary of variants in a MatrixTable or Table.
Print the number of variants to stdout and check that each chromosome has variant calls.
:param t: Input MatrixTable or Table to be checked.
:return: Struct of variant summary
"""
if isinstance(t, hl.MatrixTable):
logger.info("Dataset has %d samples.", t.count_cols())
var_summary = hl.summarize_variants(t, show=False)
logger.info(
"Dataset has %d variants distributed across the following contigs: %s",
var_summary.n_variants,
var_summary.contigs,
)
for contig in var_summary.contigs:
if var_summary.contigs[contig] == 0:
logger.warning("%s has no variants called", var_summary.contigs)
return var_summary
(6 * stats_het_hom_var.stdev)))
mt = mt.filter_cols(
mt.sample_qc.r_het_hom_var > (stats_het_hom_var.mean -
(6 * stats_het_hom_var.stdev)))
#Number of heterozygous calls
mt = mt.filter_cols(mt.sample_qc.n_het < (stats_het.mean +
(6 * stats_het.stdev)))
mt = mt.filter_cols(mt.sample_qc.n_het > (stats_het.mean -
(6 * stats_het.stdev)))
######## 3.4 Remove non-autosomes(X, Y and MT DNA)
mt = mt.filter_rows(mt.locus.in_autosome())
######## 4. BASELINE CHARACTERISTICS QC-FILTERED DATA
# Summary on number of SNPs, indels and variants per chromosomes
hl.summarize_variants(mt)
#Partition data into cases (mt_case) and controls (mt_ctrl)
mt_case = mt.filter_cols(mt.Affection == 'Case')
mt_ctrl = mt.filter_cols(mt.Affection == 'Control')
#Calculate subject statistics
print('Age of cases =', mt_case.aggregate_cols(hl.agg.stats(mt_case.Age)))
print('Age of controls =', mt_ctrl.aggregate_cols(hl.agg.stats(mt_ctrl.Age)))
print('#Individuals of Cases:',
mt_case.aggregate_cols(hl.agg.counter(mt_case.Race)))
print('#Individuals of Controls:',
mt_ctrl.aggregate_cols(hl.agg.counter(mt_ctrl.Race)))
print('Gender Cases:',
def populate_clinvar():
clinvar_release_date = _parse_clinvar_release_date('clinvar.vcf.gz')
mt = import_vcf('clinvar.vcf.gz',
"38",
drop_samples=True,
min_partitions=2000,
skip_invalid_loci=True)
mt = mt.annotate_globals(version=clinvar_release_date)
print("\n=== Running VEP ===")
mt = hl.vep(mt, 'vep85-loftee-ruddle-b38.json', name="vep")
print("\n=== Processing ===")
mt = mt.annotate_rows(
sortedTranscriptConsequences=
get_expr_for_vep_sorted_transcript_consequences_array(vep_root=mt.vep))
mt = mt.annotate_rows(
main_transcript=
get_expr_for_worst_transcript_consequence_annotations_struct(
vep_sorted_transcript_consequences_root=mt.
sortedTranscriptConsequences))
mt = mt.annotate_rows(gene_ids=get_expr_for_vep_gene_ids_set(
vep_transcript_consequences_root=mt.sortedTranscriptConsequences), )
review_status_str = hl.delimit(
hl.sorted(hl.array(hl.set(mt.info.CLNREVSTAT)),
key=lambda s: s.replace("^_", "z")))
mt = mt.select_rows(
allele_id=mt.info.ALLELEID,
alt=get_expr_for_alt_allele(mt),
chrom=get_expr_for_contig(mt.locus),
clinical_significance=hl.delimit(
hl.sorted(hl.array(hl.set(mt.info.CLNSIG)),
key=lambda s: s.replace("^_", "z"))),
domains=get_expr_for_vep_protein_domains_set(
vep_transcript_consequences_root=mt.vep.transcript_consequences),
gene_ids=mt.gene_ids,
gene_id_to_consequence_json=get_expr_for_vep_gene_id_to_consequence_map(
vep_sorted_transcript_consequences_root=mt.
sortedTranscriptConsequences,
gene_ids=mt.gene_ids),
gold_stars=CLINVAR_GOLD_STARS_LOOKUP[review_status_str],
**{
f"main_transcript_{field}": mt.main_transcript[field]
for field in mt.main_transcript.dtype.fields
},
pos=get_expr_for_start_pos(mt),
ref=get_expr_for_ref_allele(mt),
review_status=review_status_str,
transcript_consequence_terms=get_expr_for_vep_consequence_terms_set(
vep_transcript_consequences_root=mt.sortedTranscriptConsequences),
transcript_ids=get_expr_for_vep_transcript_ids_set(
vep_transcript_consequences_root=mt.sortedTranscriptConsequences),
transcript_id_to_consequence_json=
get_expr_for_vep_transcript_id_to_consequence_map(
vep_transcript_consequences_root=mt.sortedTranscriptConsequences),
variant_id=get_expr_for_variant_id(mt),
xpos=get_expr_for_xpos(mt.locus),
)
print("\n=== Summary ===")
hl.summarize_variants(mt)
# Drop key columns for export
rows = mt.rows()
rows = rows.order_by(rows.variant_id).drop("locus", "alleles")
rows.write('clinvar.ht', overwrite=True)
'''
maf
hwe
relatedness
'''
# Getting total counts for samples/variants
printCount(mt_auto)
# Getting variant counts per site
siteVarCount(mt_auto)
# Getting sample counts per site
siteSampleCount(mt_auto)
# Getting the indel counts
hl.summarize_variants(mt_auto)
# SNP call rate 1st pass filtering
mt_qc = mt_auto.filter_rows((mt_auto.var_cr_flag.contains(True) == True),
keep=False)
# Printing out counts post filter
printFilterCounts('SNP call rate', mt_qc, mt_auto.count_rows(), 'variants')
# Getting the indel counts
hl.summarize_variants(mt_qc)
# Getting counts per site for variants and samples post filter
siteVarCount(mt_qc)
siteSampleCount(mt_qc)
