def test_maximal_independent_set_types(self):
ht = hl.utils.range_table(10)
ht = ht.annotate(i=hl.struct(a='1', b=hl.rand_norm(0, 1)),
j=hl.struct(a='2', b=hl.rand_norm(0, 1)))
ht = ht.annotate(ii=hl.struct(id=ht.i, rank=hl.rand_norm(0, 1)),
jj=hl.struct(id=ht.j, rank=hl.rand_norm(0, 1)))
hl.maximal_independent_set(ht.ii, ht.jj).count()
def test_maximal_independent_set_types(self):
ht = hl.utils.range_table(10)
ht = ht.annotate(i=hl.struct(a='1', b=hl.rand_norm(0, 1)),
j=hl.struct(a='2', b=hl.rand_norm(0, 1)))
ht = ht.annotate(ii=hl.struct(id=ht.i, rank=hl.rand_norm(0, 1)),
jj=hl.struct(id=ht.j, rank=hl.rand_norm(0, 1)))
hl.maximal_independent_set(ht.ii, ht.jj).count()
def test_maximal_independent_set(self):
# prefer to remove nodes with higher index
t = hl.utils.range_table(10)
graph = t.select(i=hl.int64(t.idx), j=hl.int64(t.idx + 10), bad_type=hl.float32(t.idx))
mis_table = hl.maximal_independent_set(graph.i, graph.j, True, lambda l, r: l - r)
mis = [row['node'] for row in mis_table.collect()]
self.assertEqual(sorted(mis), list(range(0, 10)))
self.assertEqual(mis_table.row.dtype, hl.tstruct(node=hl.tint64))
self.assertRaises(ValueError, lambda: hl.maximal_independent_set(graph.i, graph.bad_type, True))
self.assertRaises(ValueError, lambda: hl.maximal_independent_set(graph.i, hl.utils.range_table(10).idx, True))
self.assertRaises(ValueError, lambda: hl.maximal_independent_set(hl.literal(1), hl.literal(2), True))
def test_maximal_independent_set(self):
# prefer to remove nodes with higher index
t = hl.utils.range_table(10)
graph = t.select(i=hl.int64(t.idx), j=hl.int64(t.idx + 10), bad_type=hl.float32(t.idx))
mis_table = hl.maximal_independent_set(graph.i, graph.j, True, lambda l, r: l - r)
mis = [row['node'] for row in mis_table.collect()]
self.assertEqual(sorted(mis), list(range(0, 10)))
self.assertEqual(mis_table.row.dtype, hl.tstruct(node=hl.tint64))
self.assertEqual(mis_table.key.dtype, hl.tstruct(node=hl.tint64))
self.assertRaises(ValueError, lambda: hl.maximal_independent_set(graph.i, graph.bad_type, True))
self.assertRaises(ValueError, lambda: hl.maximal_independent_set(graph.i, hl.utils.range_table(10).idx, True))
self.assertRaises(ValueError, lambda: hl.maximal_independent_set(hl.literal(1), hl.literal(2), True))
def test_maximal_independent_set3(self):
is_case = {"A", "C", "E", "G", "H"}
edges = [("A", "B"), ("C", "D"), ("E", "F"), ("G", "H")]
edges = [{
"i": {
"id": l,
"is_case": l in is_case
},
"j": {
"id": r,
"is_case": r in is_case
}
} for l, r in edges]
t = hl.Table.parallelize(
edges,
hl.tstruct(i=hl.tstruct(id=hl.tstr, is_case=hl.tbool),
j=hl.tstruct(id=hl.tstr, is_case=hl.tbool)))
tiebreaker = lambda l, r: (hl.case().when(l.is_case & (
~r.is_case), -1).when(~(l.is_case) & r.is_case, 1).default(0))
mis = hl.maximal_independent_set(t.i, t.j, tie_breaker=tiebreaker)
expected_sets = [{"A", "C", "E", "G"}, {"A", "C", "E", "H"}]
self.assertTrue(mis.all(mis.node.is_case))
self.assertTrue(
set([row.id for row in mis.select(mis.node.id).collect()]) in
expected_sets)
def query():
"""Query script entry point."""
hl.init(default_reference='GRCh38')
mt = hl.read_matrix_table(HGDP1KG_TOBWGS)
# Perform kinship test with pc_relate
pc_rel_path = output_path('pc_relate_kinship_estimate.ht')
pc_rel = hl.pc_relate(mt.GT, 0.01, k=10, statistics='kin')
pc_rel.write(pc_rel_path, overwrite=True)
pairs = pc_rel.filter(pc_rel['kin'] >= 0.125)
related_samples_to_remove = hl.maximal_independent_set(
pairs.i, pairs.j, False)
n_related_samples = related_samples_to_remove.count()
print(f'related_samples_to_remove.count() = {n_related_samples}')
# save as html
html = pd.DataFrame({
'removed_individual':
related_samples_to_remove.node.s.collect()
}).to_html()
plot_filename_html = output_path(f'removed_samples.html', 'web')
with hl.hadoop_open(plot_filename_html, 'w') as f:
f.write(html)
def query():
"""Query script entry point."""
hl.init(default_reference='GRCh38')
mt = hl.read_matrix_table(HGDP1KG_TOBWGS)
mt = mt.filter_cols(
(mt.hgdp_1kg_metadata.population_inference.pop == 'nfe')
| (mt.s.contains('TOB'))
)
# Remove related samples (at the 2nd degree or closer)
king = hl.king(mt.GT)
king_path = output_path('king_kinship_estimate_NFE.ht')
king.write(king_path)
ht = king.entries()
related_samples = ht.filter((ht.s_1 != ht.s) & (ht.phi > 0.125), keep=True)
struct = hl.struct(i=related_samples.s_1, j=related_samples.s)
struct = struct.annotate(phi=related_samples.phi)
related_samples_to_remove = hl.maximal_independent_set(
struct.i, struct.j, False # pylint: disable=E1101
)
n_related_samples = related_samples_to_remove.count()
print(f'related_samples_to_remove.count() = {n_related_samples}')
# save as html
html = pd.DataFrame(
{'related_individual': related_samples_to_remove.node.collect()}
).to_html()
plot_filename_html = output_path(f'related_samples.html', 'web')
with hl.hadoop_open(plot_filename_html, 'w') as f:
f.write(html)
def run_pc_relate(mt: hl.MatrixTable,
pca_prefix: str,
overwrite: bool = False):
"""
Runs PC-relate to identify relatives in a matrix table
:param mt: Matrix table to run PC-relate on
:param pca_prefix: Prefix to path to output relatedness information
:param overwrite: if True, overwrites existing data
:return:
"""
relatedness_ht = hl.pc_relate(mt.GT,
min_individual_maf=0.05,
min_kinship=0.05,
statistics='kin',
k=20).key_by()
relatedness_ht.write(pca_prefix + 'relatedness.ht', args.overwrite)
relatedness_ht = hl.read_table(pca_prefix + 'relatedness.ht')
# identify individuals in pairs to remove
related_samples_to_remove = hl.maximal_independent_set(
relatedness_ht.i, relatedness_ht.j, False)
mt_unrel = mt.filter_cols(hl.is_defined(
related_samples_to_remove[mt.col_key]),
keep=False)
mt_rel = mt.filter_cols(hl.is_defined(
related_samples_to_remove[mt.col_key]),
keep=True)
mt_unrel.write(pca_prefix + 'unrel.mt', args.overwrite)
mt_rel.write(pca_prefix + 'rel.mt', args.overwrite)
def test_maximal_independent_set_on_floats(self):
t = hl.utils.range_table(1).annotate(l=hl.struct(s="a", x=3.0),
r=hl.struct(s="b", x=2.82))
expected = [hl.Struct(node=hl.Struct(s="a", x=3.0))]
actual = hl.maximal_independent_set(
t.l, t.r, keep=False,
tie_breaker=lambda l, r: l.x - r.x).collect()
assert actual == expected
def test_maximal_independent_set2(self):
edges = [(0, 4), (0, 1), (0, 2), (1, 5), (1, 3), (2, 3), (2, 6),
(3, 7), (4, 5), (4, 6), (5, 7), (6, 7)]
edges = [{"i": l, "j": r} for l, r in edges]
t = hl.Table.parallelize(edges, hl.tstruct(i=hl.tint64, j=hl.tint64))
mis_t = hl.maximal_independent_set(t.i, t.j)
self.assertTrue(mis_t.row.dtype == hl.tstruct(node=hl.tint64) and
mis_t.globals.dtype == hl.tstruct())
mis = set([row.node for row in mis_t.collect()])
maximal_indep_sets = [{0, 6, 5, 3}, {1, 4, 7, 2}]
non_maximal_indep_sets = [{0, 7}, {6, 1}]
self.assertTrue(mis in non_maximal_indep_sets or mis in maximal_indep_sets)
def test_maximal_independent_set2(self):
edges = [(0, 4), (0, 1), (0, 2), (1, 5), (1, 3), (2, 3), (2, 6),
(3, 7), (4, 5), (4, 6), (5, 7), (6, 7)]
edges = [{"i": l, "j": r} for l, r in edges]
t = hl.Table.parallelize(edges, hl.tstruct(i=hl.tint64, j=hl.tint64))
mis_t = hl.maximal_independent_set(t.i, t.j)
self.assertTrue(mis_t.row.dtype == hl.tstruct(node=hl.tint64)
and mis_t.globals.dtype == hl.tstruct())
mis = set([row.node for row in mis_t.collect()])
maximal_indep_sets = [{0, 6, 5, 3}, {1, 4, 7, 2}]
non_maximal_indep_sets = [{0, 7}, {6, 1}]
self.assertTrue(mis in non_maximal_indep_sets
or mis in maximal_indep_sets)
def get_related_samples_to_drop(rank_table: hl.Table,
relatedness_ht: hl.Table) -> hl.Table:
"""
Use the maximal independence function in Hail to intelligently prune clusters of related individuals, removing
less desirable samples while maximizing the number of unrelated individuals kept in the sample set
:param Table rank_table: Table with ranking annotations across exomes and genomes, computed via make_rank_file()
:param Table relatedness_ht: Table with kinship coefficient annotations computed via pc_relate()
:return: Table containing sample IDs ('s') to be pruned from the combined exome and genome sample set
:rtype: Table
"""
# Define maximal independent set, using rank list
related_pairs = relatedness_ht.filter(
relatedness_ht.kin > 0.08838835).select('i', 'j')
n_related_samples = hl.eval(
hl.len(
related_pairs.aggregate(hl.agg.explode(
lambda x: hl.agg.collect_as_set(x),
[related_pairs.i, related_pairs.j]),
_localize=False)))
logger.info(
'{} samples with at least 2nd-degree relatedness found in callset'.
format(n_related_samples))
max_rank = rank_table.count()
related_pairs = related_pairs.annotate(
id1_rank=hl.struct(id=related_pairs.i,
rank=rank_table[related_pairs.i].rank),
id2_rank=hl.struct(id=related_pairs.j,
rank=rank_table[related_pairs.j].rank)).select(
'id1_rank', 'id2_rank')
def tie_breaker(l, r):
return hl.or_else(l.rank, max_rank + 1) - hl.or_else(
r.rank, max_rank + 1)
related_samples_to_drop_ranked = hl.maximal_independent_set(
related_pairs.id1_rank,
related_pairs.id2_rank,
keep=False,
tie_breaker=tie_breaker)
return related_samples_to_drop_ranked.select(
**related_samples_to_drop_ranked.node.id).key_by('data_type', 's')
def test_maximal_independent_set3(self):
is_case = {"A", "C", "E", "G", "H"}
edges = [("A", "B"), ("C", "D"), ("E", "F"), ("G", "H")]
edges = [{"i": {"id": l, "is_case": l in is_case},
"j": {"id": r, "is_case": r in is_case}} for l, r in edges]
t = hl.Table.parallelize(edges, hl.tstruct(i=hl.tstruct(id=hl.tstr, is_case=hl.tbool),
j=hl.tstruct(id=hl.tstr, is_case=hl.tbool)))
tiebreaker = lambda l, r: (hl.case()
.when(l.is_case & (~r.is_case), -1)
.when(~(l.is_case) & r.is_case, 1)
.default(0))
mis = hl.maximal_independent_set(t.i, t.j, tie_breaker=tiebreaker)
expected_sets = [{"A", "C", "E", "G"}, {"A", "C", "E", "H"}]
self.assertTrue(mis.all(mis.node.is_case))
self.assertTrue(set([row.id for row in mis.select(mis.node.id).collect()]) in expected_sets)
def main(args):
mt = hl.read_matrix_table(args.matrixtable)
# ld pruning
pruned_ht = hl.ld_prune(mt.GT, r2=0.1)
pruned_mt = mt.filter_rows(hl.is_defined(pruned_ht[mt.row_key]))
pruned_mt.write(f"{args.output_dir}/mt_ldpruned.mt", overwrite=True)
# PC relate
pruned_mt = pruned_mt.select_entries(
GT=hl.unphased_diploid_gt_index_call(pruned_mt.GT.n_alt_alleles()))
eig, scores, _ = hl.hwe_normalized_pca(pruned_mt.GT,
k=10,
compute_loadings=False)
scores.write(f"{args.output_dir}/mt_pruned.pca_scores.ht", overwrite=True)
relatedness_ht = hl.pc_relate(pruned_mt.GT,
min_individual_maf=0.05,
scores_expr=scores[pruned_mt.col_key].scores,
block_size=4096,
min_kinship=0.05,
statistics='kin2')
relatedness_ht.write(f"{args.output_dir}/mt_relatedness.ht",
overwrite=True)
pairs = relatedness_ht.filter(relatedness_ht['kin'] > 0.125)
related_samples_to_remove = hl.maximal_independent_set(pairs.i,
pairs.j,
keep=False)
related_samples_to_remove.write(
f"{args.output_dir}/mt_related_samples_to_remove.ht", overwrite=True)
pca_mt = pruned_mt.filter_cols(hl.is_defined(
related_samples_to_remove[pruned_mt.col_key]),
keep=False)
related_mt = pruned_mt.filter_cols(hl.is_defined(
related_samples_to_remove[pruned_mt.col_key]),
keep=True)
variants, samples = pca_mt.count()
print(f"{samples} samples after relatedness step.")
# Population pca
plink_mt = pca_mt.annotate_cols(uid=pca_mt.s).key_cols_by('uid')
hl.export_plink(plink_mt,
f"{args.output_dir}/mt_unrelated.plink",
fam_id=plink_mt.uid,
ind_id=plink_mt.uid)
pca_evals, pca_scores, pca_loadings = hl.hwe_normalized_pca(
pca_mt.GT, k=20, compute_loadings=True)
pca_af_ht = pca_mt.annotate_rows(
pca_af=hl.agg.mean(pca_mt.GT.n_alt_alleles()) / 2).rows()
pca_loadings = pca_loadings.annotate(
pca_af=pca_af_ht[pca_loadings.key].pca_af)
pca_scores.write(f"{args.output_dir}/mt_pca_scores.ht", overwrite=True)
pca_loadings.write(f"{args.output_dir}/mt_pca_loadings.ht", overwrite=True)
pca_mt = pca_mt.annotate_cols(scores=pca_scores[pca_mt.col_key].scores)
variants, samples = related_mt.count()
print(
'Projecting population PCs for {} related samples...'.format(samples))
#related_scores = pc_project(related_mt, pca_loadings)
#relateds = related_mt.cols()
#relateds = relateds.annotate(scores=related_scores[relateds.key].scores)
pca_mt.write(f"{args.output_dir}/mt_pca.mt", overwrite=True)
p = hl.plot.scatter(pca_mt.scores[0],
pca_mt.scores[1],
title='PCA',
xlabel='PC1',
ylabel='PC2')
output_file(f"{args.plot_dir}/pca.html")
save(p)
k=10,
compute_loadings=False)
# scores.write(
# f"{tmp_dir}/ddd-elgh-ukbb/chr1_chr20_XY_pruned.pca_scores.ht", overwrite=True)
relatedness_ht = hl.pc_relate(pruned_mt.GT,
min_individual_maf=0.05,
scores_expr=scores[pruned_mt.col_key].scores,
block_size=4096,
min_kinship=0.05,
statistics='kin2')
# relatedness_ht.write(
# f"{tmp_dir}/ddd-elgh-ukbb/chr1_chr20_XY_relatedness.ht", overwrite=True)
pairs = relatedness_ht.filter(relatedness_ht['kin'] > 0.125)
related_samples_to_remove = hl.maximal_independent_set(pairs.i,
pairs.j,
keep=False)
# related_samples_to_remove.write(
# f"{tmp_dir}/ddd-elgh-ukbb/chr1_chr20_XY_related_samples_to_remove.ht", overwrite=True)
pca_mt = pruned_mt.filter_cols(hl.is_defined(
related_samples_to_remove[pruned_mt.col_key]),
keep=False)
related_mt = pruned_mt.filter_cols(hl.is_defined(
related_samples_to_remove[pruned_mt.col_key]),
keep=True)
variants, samples = pca_mt.count()
print(f"{samples} samples after relatedness step.")
def main(args):
if args.load_ref:
load_ref(args.dirname, args.basename)
if args.load_ukbb:
samples = hl.read_table(
'gs://ukb-diverse-pops/pigmentation_phenos_covs_pops.ht')
ukbb = hl.read_matrix_table('gs://ukb31063/ukb31063.genotype.mt')
ukbb = ukbb.annotate_cols(**samples[ukbb.s])
if args.intersect_ref:
intersect_ref(args.dirname, args.basename, ukbb)
if args.pca_project:
"""
Compute PCA in global reference panel, project UKBB individuals into PCA space
"""
ref_in_ukbb = hl.read_matrix_table(args.dirname + 'intersect_' +
args.basename + 'ukbb.mt')
print('Computing reference PCs')
run_pca(ref_in_ukbb, args.out_prefix + args.basename + '_ukbb_')
# project ukbb
pca_loadings = hl.read_table(
f'{args.out_prefix}{args.basename}_ukbb_loadings.ht')
project_mt = hl.read_matrix_table(args.dirname + 'intersect_ukbb_' +
args.basename + '.mt')
ht = project_individuals(pca_loadings, project_mt)
ht.export(args.out_prefix + 'ukbb_' + args.basename +
'_scores.txt.bgz')
# if args.continental_pca:
# """
# Compute PCA within reference panel super pops, project UKBB individuals into PCA space
# 1. Filter UKBB to individuals in continental population
# 2. Run PCA on continental ref
# 3. Project UKBB inds
# """
# pass
if args.ukbb_pop_pca:
"""
Compute PCA in each UKBB population (unrelateds), project reference individuals and relateds into PCA space
1. Filter UKBB to individuals in continental population
2. Run PC-relate on these individuals
# New
2.5 Filter to pruned set of individuals
#
3. Filter UKBB population to unrelated individuals
4. Run PCA on UKBB unrelateds within population
5. Project relateds
"""
for pop in POPS:
mt = hl.read_matrix_table(get_ukb_grm_mt_path(pop))
pruned_ht = hl.read_table(get_ukb_grm_pruned_ht_path(pop))
mt = mt.filter_rows(hl.is_defined(pruned_ht[mt.row_key]))
# run PC-relate
if args.overwrite or not hl.hadoop_exists(
get_relatedness_path(pop,
extension='all_scores.ht/_SUCCESS')):
_, scores, _ = hl.hwe_normalized_pca(mt.GT,
k=10,
compute_loadings=False)
scores.write(
get_relatedness_path(pop, extension='all_scores.ht'),
args.overwrite)
scores = hl.read_table(
get_relatedness_path(pop, extension='all_scores.ht'))
mt = mt.annotate_cols(scores=scores[mt.col_key].scores)
# For EUR, required highmem machines with SSDs (Needed ~6T of hdfs space, so 20 workers + 100 pre-emptibles ran in ~7 hours)
relatedness_ht = hl.pc_relate(
mt.GT,
min_individual_maf=0.05,
scores_expr=mt.scores,
min_kinship=0.05,
statistics='kin',
block_size=4096 if pop == 'EUR' else 512).key_by()
relatedness_ht.write(get_relatedness_path(pop, extension='ht'),
args.overwrite)
relatedness_ht = hl.read_table(
get_relatedness_path(pop, extension='ht'))
# identify individuals in pairs to remove
related_samples_to_remove = hl.maximal_independent_set(
relatedness_ht.i, relatedness_ht.j, False)
mt_unrel = mt.filter_cols(hl.is_defined(
related_samples_to_remove[mt.col_key]),
keep=False)
mt_rel = mt.filter_cols(hl.is_defined(
related_samples_to_remove[mt.col_key]),
keep=True)
mt_unrel.write(get_relatedness_path(pop, True, 'mt'),
args.overwrite)
mt_rel.write(get_relatedness_path(pop, extension='mt'),
args.overwrite)
if args.ukb_prune_pca_project:
for pop in POPS:
mt_unrel = hl.read_matrix_table(
get_relatedness_path(pop, True, 'mt'))
mt_rel = hl.read_matrix_table(
get_relatedness_path(pop, extension='mt'))
# Removing individuals
pruned_inds = hl.import_table(get_pruned_tsv_path(), key='s')
mt_rel = mt_rel.filter_cols(
hl.is_defined(pruned_inds[mt_rel.col_key]))
mt_unrel = mt_unrel.filter_cols(
hl.is_defined(pruned_inds[mt_unrel.col_key]))
# Removing sites
window = '1e6' if pop != 'EUR' else '1e7'
pruned_ht = hl.read_table(get_ukb_grm_pruned_ht_path(pop, window))
mt_unrel = mt_unrel.filter_rows(
hl.is_defined(pruned_ht[mt_unrel.row_key]))
mt_unrel = mt_unrel.repartition(500).checkpoint(
hl.utils.new_temp_file())
pop = pop if window == '1e6' else f'{pop}_{window}'
run_pca(
mt_unrel,
get_relatedness_path(pop, unrelated=True, extension='') + '.',
args.overwrite)
pca_loadings = hl.read_table(
get_relatedness_path(pop,
unrelated=True,
extension='loadings.ht'))
ht = project_individuals(pca_loadings, mt_rel)
ht.write(
get_relatedness_path(pop, extension='scores_projected.ht'),
args.overwrite)
hl.read_table(
get_relatedness_path(
pop, extension='scores_projected.ht')).export(
get_relatedness_path(
pop, extension='scores_projected.txt.bgz'))
if args.generate_covariates:
hts = []
for pop in POPS:
pop_path = pop if pop != 'EUR' else f'EUR_1e7'
ht = hl.read_table(
get_relatedness_path(pop_path,
extension='scores_projected.ht'))
hts.append(ht.annotate(pop=pop, related=True))
ht = hl.read_table(
get_relatedness_path(pop_path, True, extension='scores.ht'))
ht = ht.transmute(
**{f'PC{i}': ht.scores[i - 1]
for i in range(1, 21)})
hts.append(ht.annotate(pop=pop, related=False))
ht = hts[0].union(*hts[1:])
cov_ht = hl.import_table(get_age_sex_tsv_path(),
impute=True,
force=True,
quote='"',
key='userId').select('age', 'sex')
cov_ht = cov_ht.annotate(age_sex=cov_ht.age * cov_ht.sex,
age2=hl.int32(cov_ht.age**2),
age2_sex=hl.int32(cov_ht.age**2) * cov_ht.sex)
ht = ht.annotate(**cov_ht.key_by(userId=hl.str(cov_ht.userId))[ht.key])
ht.write(get_covariates_ht_path(), args.overwrite)
get_filtered_mt(imputed=False).cols().export(get_final_sample_set())
def main(args):
global output_prefix
output_prefix = args.output_dir.rstrip("/") + "/" + splitext(
basename(args.input_mt))[0]
if args.compute_qc_mt:
qc_mt = get_qc_mt(hl.read_matrix_table(args.input_mt))
qc_mt = qc_mt.repartition(n_partitions=200)
qc_mt.write(path('qc.mt'), overwrite=args.overwrite)
if args.compute_qc_metrics:
logger.info("Computing sample QC")
mt = filter_to_autosomes(hl.read_matrix_table(args.input_mt))
strats = {
'bi_allelic': bi_allelic_expr(mt),
'multi_allelic': ~bi_allelic_expr(mt)
}
for strat, filter_expr in strats.items():
strat_sample_qc_ht = hl.sample_qc(
mt.filter_rows(filter_expr)).cols()
strat_sample_qc_ht.write(path(f'{strat}_sample_qc.ht'),
overwrite=args.overwrite)
strat_hts = [
hl.read_table(path(f'{strat}_sample_qc.ht')) for strat in strats
]
sample_qc_ht = strat_hts.pop()
sample_qc_ht = sample_qc_ht.select(
sample_qc=merge_sample_qc_expr([sample_qc_ht.sample_qc] + [
strat_hts[i][sample_qc_ht.key].sample_qc
for i in range(0, len(strat_hts))
]))
sample_qc_ht.write(path('sample_qc.ht'), overwrite=args.overwrite)
if args.compute_callrate_mt:
callrate_mt = compute_callrate_mt(
hl.read_matrix_table(args.input_mt),
hl.import_locus_intervals(exome_calling_intervals_path))
callrate_mt.write(path('callrate.mt'), args.overwrite)
if args.run_platform_pca:
eigenvalues, scores_ht, loadings_ht = run_platform_pca(
hl.read_matrix_table(path('callrate.mt')))
scores_ht.write(path('platform_pca_scores.ht'),
overwrite=args.overwrite)
loadings_ht.write(path('platform_pca_loadings.ht'),
overwrite=args.overwrite)
if args.assign_platforms:
platform_ht = assign_platform_from_pcs(
hl.read_table(path('platform_pca_scores.ht')),
hdbscan_min_cluster_size=args.hdbscan_min_cluster_size,
hdbscan_min_samples=args.hdbscan_min_samples)
platform_ht.write(f'{output_prefix}.platform_pca_results.ht',
overwrite=args.overwrite)
if args.impute_sex:
sex_ht = infer_sex(hl.read_matrix_table(path('qc.mt')),
hl.read_matrix_table(args.input_mt),
hl.read_table(path('platform_pca_results.ht')),
args.male_threshold, args.female_threshold,
args.min_male_y_sites_called,
args.max_y_female_call_rate,
args.min_y_male_call_rate)
sex_ht.write(path('sex.ht'), overwrite=args.overwrite)
if args.run_pc_relate:
logger.info('Running PCA for PC-Relate')
qc_mt = hl.read_matrix_table(path('qc.mt')).unfilter_entries()
eig, scores, _ = hl.hwe_normalized_pca(qc_mt.GT,
k=10,
compute_loadings=False)
scores.write(path('pruned.pca_scores.ht'), args.overwrite)
logger.info('Running PC-Relate')
logger.warn(
"PC-relate requires SSDs and doesn't work with preemptible workers!"
)
scores = hl.read_table(path('pruned.pca_scores.ht'))
relatedness_ht = hl.pc_relate(qc_mt.GT,
min_individual_maf=0.05,
scores_expr=scores[qc_mt.col_key].scores,
block_size=4096,
min_kinship=args.min_emission_kinship,
statistics='all')
relatedness_ht.write(path('relatedness.ht'), args.overwrite)
if args.filter_dups:
logger.info("Filtering duplicate samples")
sample_qc_ht = hl.read_table(path('sample_qc.ht'))
samples_rankings_ht = sample_qc_ht.select(
rank=-1 * sample_qc_ht.sample_qc.dp_stats.mean)
dups_ht = filter_duplicate_samples(
hl.read_table(path('relatedness.ht')), samples_rankings_ht)
dups_ht.write(path('duplicates.ht'), overwrite=args.overwrite)
if args.infer_families:
logger.info("Inferring families")
duplicates_ht = hl.read_table(path('duplicates.ht'))
dups_to_remove = duplicates_ht.aggregate(
hl.agg.explode(lambda x: hl.agg.collect_as_set(x.s),
duplicates_ht.filtered))
ped = infer_families(hl.read_table(path('relatedness.ht')),
hl.read_table(path('sex.ht')), dups_to_remove)
ped.write(path('pedigree.ped'))
if args.filter_related_samples:
logger.info("Filtering related samples")
related_pairs_ht, related_pairs_tie_breaker = rank_related_samples(
hl.read_table(path('relatedness.ht')), hl.read_table(args.meta),
hl.read_table(path('sample_qc.ht')),
hl.import_fam(path('pedigree.ped'), delimiter="\t"))
related_samples_to_drop_ht = hl.maximal_independent_set(
related_pairs_ht.i,
related_pairs_ht.j,
keep=False,
tie_breaker=related_pairs_tie_breaker)
related_samples_to_drop_ht = related_samples_to_drop_ht.key_by()
related_samples_to_drop_ht = related_samples_to_drop_ht.select(
**related_samples_to_drop_ht.node)
related_samples_to_drop_ht = related_samples_to_drop_ht.key_by('s')
related_samples_to_drop_ht.write(path('related_samples_to_drop.ht'),
overwrite=args.overwrite)
if args.run_pca:
logger.info("Running population PCA")
pca_evals, pop_pca_scores_ht, pop_pca_loadings_ht = run_pca_with_relateds(
hl.read_matrix_table(path('qc.mt')),
hl.read_table(path('related_samples_to_drop.ht')), args.n_pcs)
pop_pca_loadings_ht.write(path('pop_pca_loadings.ht'), args.overwrite)
pop_pca_scores_ht.write(path('pop_pca_scores.ht'), args.overwrite)
if args.assign_pops:
logger.info("Assigning global population labels")
pop_pca_scores_ht = hl.read_table(path("pop_pca_scores.ht"))
gnomad_meta_ht = get_gnomad_meta('exomes').select("pop")[
pop_pca_scores_ht.key]
pop_pca_scores_ht = pop_pca_scores_ht.annotate(known_pop=hl.or_missing(
gnomad_meta_ht.pop != "oth", gnomad_meta_ht.pop))
pop_ht, pops_rf_model = assign_population_pcs(
pop_pca_scores_ht,
pc_cols=pop_pca_scores_ht.scores[:args.n_pcs],
known_col='known_pop',
min_prob=args.min_pop_prob)
pop_ht.write(path('pop.ht'), args.overwrite)
with hl.hadoop_open(path('pop_rf_model.pkl'), 'wb') as out:
pickle.dump(pops_rf_model, out)
if args.assign_subpops:
qc_mt = hl.read_matrix_table(path('qc.mt'))
pop_ht = hl.read_table(path('pop.ht'))
meta_ht = hl.read_table(args.meta)[qc_mt.col_key]
qc_mt = qc_mt.annotate_cols(pop=pop_ht[qc_mt.col_key].pop,
is_case=meta_ht.is_case,
country=meta_ht.country)
platform_specific_intervals = get_platform_specific_intervals(
hl.read_table(path('platform_pca_loadings.ht')), threshold=0.01)
logger.info(
f'Excluding {len(platform_specific_intervals)} platform-specific intervals for subpop PCA.'
)
qc_mt = hl.filter_intervals(qc_mt,
platform_specific_intervals,
keep=False)
assign_and_write_subpops(
qc_mt,
hl.read_table(path('related_samples_to_drop.ht')),
min_samples_for_subpop=args.min_samples_for_subpop,
n_pcs=args.n_pcs,
min_pop_prob=args.min_pop_prob,
overwrite=args.overwrite,
pop_ann='pop',
subpop_ann='country',
include_in_pop_count=qc_mt.is_case)
if args.run_kgp_pca:
logger.info("Joining data with 1000 Genomes")
qc_mt = hl.read_matrix_table(
path('qc.mt')).select_rows().select_entries("GT")
qc_mt = qc_mt.select_cols(known_pop=hl.null(hl.tstr),
known_subpop=hl.null(hl.tstr))
qc_mt = qc_mt.key_cols_by(_kgp=False, *qc_mt.col_key)
kgp_mt = hl.read_matrix_table(
kgp_phase3_genotypes_mt_path()).select_rows()
kgp_mt = kgp_mt.select_cols(known_pop=kgp_mt.super_pops.get(
kgp_mt.population, "oth").lower(),
known_subpop=kgp_mt.population.lower())
kgp_mt = kgp_mt.filter_rows(hl.is_defined(
qc_mt.rows()[kgp_mt.row_key]))
kgp_mt = filter_rows_for_qc(kgp_mt)
kgp_mt = kgp_mt.key_cols_by(_kgp=True, *kgp_mt.col_key)
union_kgp_qc_mt = qc_mt.union_cols(kgp_mt)
union_kgp_qc_mt.write(path('union_kgp_qc.mt'),
overwrite=args.overwrite)
logger.info("Computing PCA on data with 1000 Genomes")
union_kgp_qc_mt = hl.read_matrix_table(path('union_kgp_qc.mt'))
related_samples_to_drop_ht = hl.read_table(
path('related_samples_to_drop.ht'))
related_samples_to_drop_ht = related_samples_to_drop_ht.key_by(
_kgp=False, *related_samples_to_drop_ht.key)
pca_evals, union_kgp_pca_scores_ht, union_kgp_pca_loadings_ht = run_pca_with_relateds(
union_kgp_qc_mt, related_samples_to_drop_ht, args.n_kgp_pcs)
union_kgp_pca_loadings_ht.write(path('union_kgp_pca_loadings.ht'),
args.overwrite)
union_kgp_pca_scores_ht.write(path('union_kgp_pca_scores.ht'),
args.overwrite)
if args.assign_pops_kgp:
logger.info("Assigning populations based on 1000 Genomes labels")
union_kgp_qc_mt = hl.read_matrix_table(path('union_kgp_qc.mt'))
union_kgp_pca_scores_ht = hl.read_table(
path('union_kgp_pca_scores.ht'))
union_kgp_pca_scores_ht = union_kgp_pca_scores_ht.annotate(
known_pop=union_kgp_qc_mt[union_kgp_pca_scores_ht.key].known_pop)
union_kgp_pop_ht, union_kgp_pop_rf_model = assign_population_pcs(
union_kgp_pca_scores_ht,
pc_cols=union_kgp_pca_scores_ht.scores[:args.n_kgp_pcs],
known_col='known_pop',
min_prob=args.min_kgp_pop_prob)
union_kgp_pop_ht.write(path('union_kgp_pop.ht'), args.overwrite)
with hl.hadoop_open(path('union_kgp_pop_rf_model.pkl'), 'wb') as out:
pickle.dump(union_kgp_pop_rf_model, out)
if args.assign_subpops_kgp:
union_kgp_qc_mt = hl.read_matrix_table(path('union_kgp_qc.mt'))
meta_ht = hl.read_table(args.meta)
union_kgp_pop_ht = hl.read_table(path('union_kgp_pop.ht'))
union_kgp_qc_mt = union_kgp_qc_mt.annotate_cols(
is_case=meta_ht[union_kgp_qc_mt.col_key].is_case,
pop=union_kgp_pop_ht[union_kgp_qc_mt.col_key].pop)
platform_specific_intervals = get_platform_specific_intervals(
hl.read_table(path('platform_pca_loadings.ht')))
logger.info(
f'Excluding {len(platform_specific_intervals)} platform-specific intervals for subpop PCA.'
)
union_kgp_qc_mt = hl.filter_intervals(union_kgp_qc_mt,
platform_specific_intervals,
keep=False)
related_samples_to_drop_ht = hl.read_table(
path('related_samples_to_drop.ht'))
related_samples_to_drop_ht = related_samples_to_drop_ht.key_by(
_kgp=False, *related_samples_to_drop_ht.key)
assign_and_write_subpops(
union_kgp_qc_mt,
related_samples_to_drop_ht,
min_samples_for_subpop=args.min_samples_for_subpop,
n_pcs=args.n_kgp_pcs,
min_pop_prob=args.min_kgp_pop_prob,
overwrite=args.overwrite,
pop_ann='pop',
subpop_ann='known_subpop',
include_in_pop_count=union_kgp_qc_mt.is_case,
files_prefix='union_kgp_')
if args.apply_stratified_filters:
logger.info("Computing stratified QC")
for variant_class_prefix in ['', 'bi_allelic_', 'multi_allelic_']:
sample_qc_ht = hl.read_table(
path(f'{variant_class_prefix}sample_qc.ht'))
pop_ht = hl.read_table(path('pops.ht'))
platform_ht = hl.read_table(path('platform_pca_results.ht'))
sample_qc_ht = sample_qc_ht.annotate(
qc_pop=pop_ht[sample_qc_ht.key].pop,
qc_platform=platform_ht[sample_qc_ht.key].qc_platform)
stratified_metrics_ht = compute_stratified_metrics_filter(
sample_qc_ht, args.filtering_qc_metrics.split(","),
['qc_pop', 'qc_platform'])
stratified_metrics_ht.write(
path(f'{variant_class_prefix}stratified_metrics_filters.ht'),
overwrite=args.overwrite)
if args.write_full_meta:
logger.info("Writing metadata table")
# List all tables to join with the base meta
meta_annotation_hts = [
hl.read_table(path('platform_pca_results.ht')).rename(
{'scores': 'platform_pc_scores'}),
hl.read_table(path('sex.ht')),
flatten_duplicate_samples_ht(hl.read_table(path('duplicates.ht'))),
hl.read_table(path('related_samples_to_drop.ht')).select(
related_filtered=True),
hl.read_table(path('pca_scores.ht')).rename(
{'scores': 'pop_pc_scores'}),
hl.read_table(path('pops.ht')).select('pop'),
hl.read_table(path('nfe.pca_scores.ht')).rename(
{'scores': 'nfe_pc_scores'}),
hl.read_table(path('subpops.nfe.ht')).select('subpop')
]
# union_kgp_pops_ht = hl.read_table(path('union_kgp_pops.ht'))
# union_kgp_pops_ht = union_kgp_pops_ht.filter(~union_kgp_pops_ht._kgp).key_by('s')
# union_kgp_pops_ht = union_kgp_pops_ht.select(kgp_pop=union_kgp_pops_ht.pop)
# meta_annotation_hts.append(union_kgp_pops_ht)
#
# union_kgp_pca_scores_ht = hl.read_table(path('union_kgp_pca_scores.ht')).rename({'scores': 'kgp_pop_pc_scores'})
# union_kgp_pca_scores_ht = union_kgp_pca_scores_ht.filter(~union_kgp_pca_scores_ht._kgp).key_by('s')
# meta_annotation_hts.append(union_kgp_pca_scores_ht)
gnomad_meta_ht = get_gnomad_meta('exomes')
gnomad_meta_ht = gnomad_meta_ht.select(
gnomad_pop=gnomad_meta_ht.pop, gnomad_subpop=gnomad_meta_ht.subpop)
meta_annotation_hts.append(gnomad_meta_ht)
for variant_class_prefix in ['', 'bi_allelic_', 'multi_allelic_']:
sample_qc_ht = hl.read_table(
path(f'{variant_class_prefix}sample_qc.ht'))
stratified_metrics_filters_ht = hl.read_table(
path(f'{variant_class_prefix}stratified_metrics_filters.ht'))
if variant_class_prefix:
sample_qc_ht = sample_qc_ht.rename(
{'sample_qc': f'{variant_class_prefix}sample_qc'})
stratified_metrics_filters_ht = stratified_metrics_filters_ht.rename(
{
f: f'{variant_class_prefix}{f}'
for f in list(stratified_metrics_filters_ht.globals) +
list(stratified_metrics_filters_ht.row_value)
})
meta_annotation_hts.extend(
[sample_qc_ht, stratified_metrics_filters_ht])
meta_ht = hl.read_table(args.meta)
meta_ht = meta_ht.annotate_globals(
**{
name: expr
for ann_ht in meta_annotation_hts
for name, expr in ann_ht.index_globals().items()
})
meta_ht = meta_ht.annotate(
**{
name: expr
for ann_ht in meta_annotation_hts
for name, expr in ann_ht[meta_ht.key].items()
})
filtering_col_prefix = '' if args.filtering_variant_class == 'all' else args.filtering_variant_class + "_"
meta_ht = meta_ht.annotate_globals(
filtering_variant_class=args.filtering_variant_class)
meta_ht = meta_ht.annotate(sample_filters=add_filters_expr(
filters={
"ambiguous sex": hl.is_missing(meta_ht.is_female),
'call_rate': meta_ht.sample_qc.call_rate < args.min_call_rate,
'duplicate': hl.is_defined(meta_ht.dup_filtered)
& meta_ht.dup_filtered,
'related': meta_ht.related_filtered
},
current_filters=meta_ht[
f'{filtering_col_prefix}pop_platform_filters']))
meta_ht.write(path('full_meta.ht'), overwrite=args.overwrite)
def compute_related_samples_to_drop(
relatedness_ht: hl.Table,
rank_ht: hl.Table,
kin_threshold: float,
filtered_samples: Optional[hl.expr.SetExpression] = None,
min_related_hard_filter: Optional[int] = None,
) -> hl.Table:
"""
Computes a Table with the list of samples to drop (and their global rank) to get the maximal independent set of unrelated samples.
.. note::
- `relatedness_ht` should be keyed by exactly two fields of the same type, identifying the pair of samples for each row.
- `rank_ht` should be keyed by a single key of the same type as a single sample identifier in `relatedness_ht`.
:param relatedness_ht: relatedness HT, as produced by e.g. pc-relate
:param kin_threshold: Kinship threshold to consider two samples as related
:param rank_ht: Table with a global rank for each sample (smaller is preferred)
:param filtered_samples: An optional set of samples to exclude (e.g. these samples were hard-filtered) These samples will then appear in the resulting samples to drop.
:param min_related_hard_filter: If provided, any sample that is related to more samples than this parameter will be filtered prior to computing the maximal independent set and appear in the results.
:return: A Table with the list of the samples to drop along with their rank.
"""
# Make sure that the key types are valid
assert len(list(relatedness_ht.key)) == 2
assert relatedness_ht.key[0].dtype == relatedness_ht.key[1].dtype
assert len(list(rank_ht.key)) == 1
assert relatedness_ht.key[0].dtype == rank_ht.key[0].dtype
logger.info(
f"Filtering related samples using a kin threshold of {kin_threshold}")
relatedness_ht = relatedness_ht.filter(relatedness_ht.kin > kin_threshold)
filtered_samples_rel = set()
if min_related_hard_filter is not None:
logger.info(
f"Computing samples related to too many individuals (>{min_related_hard_filter}) for exclusion"
)
gbi = relatedness_ht.annotate(s=list(relatedness_ht.key))
gbi = gbi.explode(gbi.s)
gbi = gbi.group_by(gbi.s).aggregate(n=hl.agg.count())
filtered_samples_rel = gbi.aggregate(
hl.agg.filter(gbi.n > min_related_hard_filter,
hl.agg.collect_as_set(gbi.s)))
logger.info(
f"Found {len(filtered_samples_rel)} samples with too many 1st/2nd degree relatives. These samples will be excluded."
)
if filtered_samples is not None:
filtered_samples_rel = filtered_samples_rel.union(
relatedness_ht.aggregate(
hl.agg.explode(
lambda s: hl.agg.collect_as_set(s),
hl.array(list(relatedness_ht.key)).filter(
lambda s: filtered_samples.contains(s)),
)))
if len(filtered_samples_rel) > 0:
filtered_samples_lit = hl.literal(filtered_samples_rel)
relatedness_ht = relatedness_ht.filter(
filtered_samples_lit.contains(relatedness_ht.key[0])
| filtered_samples_lit.contains(relatedness_ht.key[1]),
keep=False,
)
logger.info("Annotating related sample pairs with rank.")
i, j = list(relatedness_ht.key)
relatedness_ht = relatedness_ht.key_by(s=relatedness_ht[i])
relatedness_ht = relatedness_ht.annotate(**{
i:
hl.struct(s=relatedness_ht.s, rank=rank_ht[relatedness_ht.key].rank)
})
relatedness_ht = relatedness_ht.key_by(s=relatedness_ht[j])
relatedness_ht = relatedness_ht.annotate(**{
j:
hl.struct(s=relatedness_ht.s, rank=rank_ht[relatedness_ht.key].rank)
})
relatedness_ht = relatedness_ht.key_by(i, j)
relatedness_ht = relatedness_ht.drop("s")
relatedness_ht = relatedness_ht.persist()
related_samples_to_drop_ht = hl.maximal_independent_set(
relatedness_ht[i],
relatedness_ht[j],
keep=False,
tie_breaker=lambda l, r: l.rank - r.rank,
)
related_samples_to_drop_ht = related_samples_to_drop_ht.key_by()
related_samples_to_drop_ht = related_samples_to_drop_ht.select(
**related_samples_to_drop_ht.node)
related_samples_to_drop_ht = related_samples_to_drop_ht.key_by("s")
if len(filtered_samples_rel) > 0:
related_samples_to_drop_ht = related_samples_to_drop_ht.union(
hl.Table.parallelize(
[
hl.struct(s=s, rank=hl.null(hl.tint64))
for s in filtered_samples_rel
],
key="s",
))
return related_samples_to_drop_ht
def main(args):
data_type = "exomes" if args.exomes else "genomes"
hl.init(log=f"/ccdg_sample_qc_{data_type}.log")
# gcloud compute scp wlu-m:/hard_filter_genomes.log .
if args.sample_qc:
compute_sample_qc(data_type).write(
get_ccdg_results_path(data_type=data_type, result="sample_qc_all"),
overwrite=args.overwrite,
)
if args.impute_sex:
compute_sex(data_type).write(
get_ccdg_results_path(data_type=data_type, result="sex"),
overwrite=args.overwrite,
)
# elif args.reannotate_sex:
# reannotate_sex(
# args.min_cov,
# (args.upper_x, (args.lower_xx, args.upper_xx), args.lower_xxx),
# ((args.lower_y, args.upper_y), args.lower_yy),
# ).write(
# get_ccdg_results_path(data_type=data_type, result="sex"),
# overwrite=args.overwrite,
# )
##### Wait for more information
# if args.compute_hard_filters:
# compute_hard_filters(args.min_cov).write(
# hard_filtered_samples.path, overwrite=args.overwrite
# )
if args.run_pc_relate or args.reannotate_relatedness:
if args.run_pc_relate:
logger.warning(
"PC-relate requires SSDs and doesn't work with preemptible workers!"
)
relatedness_ht = compute_relatedness(
data_type,
overwrite=args.overwrite,
)
else:
relatedness_ht = hl.read_table(
get_ccdg_results_path(data_type=data_type, result="relatedness")
).checkpoint(
"gs://ccdg/tmp/relatedness_ht_checkpoint.ht", overwrite=True
) # Copy HT to temp location to overwrite annotation
relatedness_ht = annotate_relatedness(
relatedness_ht,
first_degree_kin_thresholds=tuple(args.first_degree_kin_thresholds),
second_degree_min_kin=args.second_degree_kin_cutoff,
ibd0_0_max=args.ibd0_0_max,
)
relatedness_ht.write(
get_ccdg_results_path(data_type=data_type, result="relatedness"),
overwrite=args.overwrite,
)
if args.compute_related_samples_to_drop:
relatedness_ht = hl.read_table(
get_ccdg_results_path(data_type=data_type, result="relatedness")
)
related_samples_to_remove = hl.maximal_independent_set(
relartedness_ht.i, pairs.j, False
).checkpoint(
get_ccdg_results_path(data_type=data_type, result="related_samples"),
overwrite=args.overwrite,
)
if args.update_variant_filtered_pca_mt:
pca_var_ht = hl.read_table(get_pca_variants_path())
mt = hl.vds.to_dense_mt(get_qc_vds(data_type, split=True))
mt = mt.filter_rows(hl.is_defined(pca_var_ht[mt.row_key])).checkpoint(
get_pca_variants_path(ld_pruned=True, data=f"ccdg_{data_type}", mt=True),
overwrite=args.overwrite,
_read_if_exists=(not args.overwrite),
)
if args.run_pc_project:
## TODO: Rank samples and hard filter samples
mt = hl.read_matrix_table(
get_pca_variants_path(ld_pruned=True, data=f"ccdg_{data_type}", mt=True)
)
pca_loadings = hl.read_table(path_to_gnomad_loadings)
pca_ht = hl.experimental.pc_project(
mt.GT,
pca_loadings.loadings,
pca_loadings.pca_af,
)
pca_ht.checkpoint(
get_ccdg_results_path(
data_type=data_type, result="gnomad_pc_project_scores"
),
overwrite=args.overwrite,
)
# related_ht = hl.read_table(
# get_ccdg_results_path(data_type=data_type, result="related_samples")
# )
#
# related_mt = mt.filter_cols(hl.is_defined(related_mt[mt.col_key]), keep=True)
# pca_mt = mt.filter_cols(hl.is_defined(related_mt[mt.col_key]), keep=False)
# pca_ht = hl.experimental.pc_project(
# pca_mt.GT, pca_loadings.loadings, pca_loadings.pca_af
# )
# pca_mt = pca_mt.annotate_cols(scores=pca_ht[pca_mt.col_key].scores)
#
# related_ht = hl.experimental.pc_project(
# related_mt.GT, pca_loadings.loadings, pca_loadings.pca_af
# )
# related_mt = related_mt.annotate_cols(
# scores=related_ht[related_mt.col_key].scores
# )
if args.assign_pops:
with hl.hadoop_open(
path_to_gnomad_rf,
"rb",
) as f:
fit = pickle.load(f)
# Reduce the scores to only those used in the RF model, this was 6 for v2 and 16 for v3.1
n_pcs = fit.n_features_
pca_ht = hl.read_table(
get_ccdg_results_path(
data_type=data_type, result="gnomad_pc_project_scores"
)
)
pca_ht = pca_ht.annotate(scores=pca_ht.scores[:n_pcs])
pop_ht, rf_model = assign_population_pcs(
pca_ht,
pc_cols=pca_ht.scores,
fit=fit,
)
pop_ht = pop_ht.checkpoint(
get_ccdg_results_path(data_type=data_type, result="pop_assignment"),
overwrite=args.overwrite,
_read_if_exists=not args.overwrite,
)
pop_ht.transmute(
**{f"PC{i + 1}": pop_ht.pca_scores[i] for i in range(n_pcs)}
).export(
get_ccdg_results_path(data_type=data_type, result="pop_assignment")[:-2]
+ "tsv"
)
with hl.hadoop_open(
get_ccdg_results_path(data_type=data_type, result="pop_RF_fit")[:-2]
+ "pickle",
"wb",
) as out:
pickle.dump(rf_model, out)
if args.calculate_inbreeding:
qc_mt = hl.read_matrix_table(
get_pca_variants_path(ld_pruned=True, data=f"ccdg_{data_type}", mt=True)
)
pop_ht = hl.read_table(
get_ccdg_results_path(data_type=data_type, result="pop_assignment"),
)
qc_mt = qc_mt.annotate_cols(pop=pop_ht[qc_mt.col_key].pop)
qc_mt = qc_mt.annotate_rows(
call_stats_by_pop=hl.agg.group_by(
qc_mt.pop, hl.agg.call_stats(qc_mt.GT, qc_mt.alleles)
)
)
inbreeding_ht = (
qc_mt.annotate_cols(
inbreeding=hl.agg.inbreeding(
qc_mt.GT, qc_mt.call_stats_by_pop[qc_mt.pop].AF[1]
)
)
.cols()
.select("inbreeding")
)
inbreeding_ht.write(
get_ccdg_results_path(data_type=data_type, result="inbreeding"),
overwrite=args.overwrite,
)
if args.apply_stratified_filters or args.apply_regressed_filters:
filtering_qc_metrics = args.filtering_qc_metrics.split(",")
sample_qc_ht = hl.read_table(
get_ccdg_results_path(data_type=data_type, result="sample_qc_bi_allelic")
)
pc_scores = hl.read_table(
get_ccdg_results_path(data_type=data_type, result="pc_scores")
)
sample_qc_ht = sample_qc_ht.select(
scores=pc_scores[sample_qc_ht.key]["scores"],
)
pop_ht = hl.read_table(
get_ccdg_results_path(data_type=data_type, result="pop_assignment"),
)
if "inbreeding" in filtering_qc_metrics:
inbreeding_ht = hl.read_table(
get_ccdg_results_path(data_type=data_type, result="inbreeding")
)[sample_qc_ht.key]
sample_qc_ht = sample_qc_ht.annotate(
inbreeding=inbreeding_ht.inbreeding.f_stat
)
if args.apply_regressed_filters:
n_pcs = args.regress_n_pcs
residuals_ht = compute_qc_metrics_residuals(
ht=sample_qc_ht,
pc_scores=sample_qc_ht.scores[:n_pcs],
qc_metrics={
metric: sample_qc_ht[metric] for metric in filtering_qc_metrics
},
)
residuals_ht = residuals_ht.filter(
hl.is_missing(hard_filtered_samples.ht()[residuals_ht.key])
)
stratified_metrics_ht = compute_stratified_metrics_filter(
ht=residuals_ht,
qc_metrics=dict(residuals_ht.row_value),
metric_threshold={
"n_singleton_residual": (math.inf, 8.0),
"r_het_hom_var_residual": (math.inf, 4.0),
},
)
residuals_ht = residuals_ht.annotate(
**stratified_metrics_ht[residuals_ht.key]
)
residuals_ht = residuals_ht.annotate_globals(
**stratified_metrics_ht.index_globals(),
n_pcs=n_pcs,
)
else:
logger.info(
"Computing stratified QC metrics filters using metrics: "
+ ", ".join(filtering_qc_metrics)
)
sample_qc_ht = sample_qc_ht.annotate(qc_pop=pop_ht[sample_qc_ht.key].pop)
# TODO: compute hard-filtered samples
sample_qc_ht = sample_qc_ht.filter(
hl.is_missing(hard_filtered_samples.ht()[sample_qc_ht.key])
)
stratified_metrics_ht = compute_stratified_metrics_filter(
sample_qc_ht,
qc_metrics={
metric: sample_qc_ht[metric] for metric in filtering_qc_metrics
},
strata={"qc_pop": sample_qc_ht.qc_pop},
metric_threshold={"n_singleton": (4.0, 8.0)},
)
def query():
"""Query script entry point."""
hl.init(default_reference='GRCh38')
# save relatedness estimates for pc_relate global populations
ht = hl.read_table(PC_RELATE_ESTIMATE_GLOBAL)
related_samples = ht.filter(ht.kin > 0.1)
pc_relate_global = pd.DataFrame({
'i_s': related_samples.i.s.collect(),
'j_s': related_samples.j.s.collect(),
'kin': related_samples.kin.collect(),
})
filename = output_path(f'pc_relate_global_matrix.csv', 'analysis')
pc_relate_global.to_csv(filename, index=False)
# get maximal independent set
pairs = ht.filter(ht['kin'] >= 0.125)
related_samples_to_remove = hl.maximal_independent_set(
pairs.i, pairs.j, False)
related_samples = pd.DataFrame(
{'removed_individual': related_samples_to_remove.node.s.collect()})
filename = output_path(f'pc_relate_global_maximal_independent_set.csv',
'analysis')
related_samples.to_csv(filename, index=False)
# save relatedness estimates for pc_relate NFE samples
ht = hl.read_table(PC_RELATE_ESTIMATE_NFE)
related_samples = ht.filter(ht.kin > 0.1)
pc_relate_nfe = pd.DataFrame({
'i_s': related_samples.i.s.collect(),
'j_s': related_samples.j.s.collect(),
'kin': related_samples.kin.collect(),
})
filename = output_path(f'pc_relate_nfe_matrix.csv', 'analysis')
pc_relate_nfe.to_csv(filename, index=False)
# get maximal independent set
pairs = ht.filter(ht['kin'] >= 0.125)
related_samples_to_remove = hl.maximal_independent_set(
pairs.i, pairs.j, False)
related_samples = pd.DataFrame(
{'removed_individual': related_samples_to_remove.node.s.collect()})
filename = output_path(f'pc_relate_nfe_maximal_independent_set.csv',
'analysis')
related_samples.to_csv(filename, index=False)
# save relatedness estimates for KING NFE samples
mt = hl.read_matrix_table(KING_ESTIMATE_NFE)
ht = mt.entries()
# remove entries where samples are identical
related_samples = ht.filter(ht.s_1 != ht.s)
related_samples = ht.filter(ht.phi > 0.1)
king_nfe = pd.DataFrame({
'i_s': related_samples.s_1.collect(),
'j_s': related_samples.s.collect(),
'kin': related_samples.phi.collect(),
})
filename = output_path(f'king_nfe_matrix_90k.csv', 'analysis')
king_nfe.to_csv(filename, index=False)
# save KING NFE maximal independent set
second_degree_related_samples = ht.filter(
(ht.s_1 != ht.s) & (ht.phi > 0.125), keep=True)
struct = hl.struct(i=second_degree_related_samples.s_1,
j=second_degree_related_samples.s)
struct = struct.annotate(phi=second_degree_related_samples.phi)
related_samples_to_remove = hl.maximal_independent_set(
struct.i,
struct.j,
False # pylint: disable=E1101
)
related_samples = pd.DataFrame(
{'related_individual': related_samples_to_remove.node.collect()})
filename = output_path(
f'king_90k_related_samples_maximal_independent_set.csv', 'analysis')
related_samples.to_csv(filename, index=False)
