def test_dataset(self):
test_resources = 'src/test/resources'
vds = hc.import_vcf(test_resources + '/sample.vcf')
vds2 = hc.import_vcf(test_resources + '/sample2.vcf')
for (dataset, dataset2) in [(vds, vds2)]:
gt = 'g.GT'
dataset = dataset.cache()
dataset2 = dataset2.persist()
dataset.write('/tmp/sample.vds', overwrite=True)
dataset.count()
self.assertEqual(dataset.head(3).count_variants(), 3)
dataset.query_variants(['variants.count()'])
dataset.query_samples(['samples.count()'])
(dataset.annotate_samples_expr(
'sa.nCalled = gs.filter(g => isDefined({0})).count()'.format(
gt)).samples_table().select(['s', 'nCalled = sa.nCalled'
]).export('/tmp/sa.tsv'))
dataset.annotate_global_expr('global.foo = 5')
dataset.annotate_global_expr(['global.foo = 5', 'global.bar = 6'])
dataset = dataset.annotate_samples_table(
hc.import_table(test_resources +
'/sampleAnnotations.tsv').key_by('Sample'),
expr=
'sa.isCase = table.Status == "CASE", sa.qPhen = table.qPhen')
(dataset.annotate_variants_expr(
'va.nCalled = gs.filter(g => isDefined({0})).count()'.format(
gt)).count())
loci_tb = (
hc.import_table(test_resources + '/sample2_loci.tsv').annotate(
'locus = Locus(chr, pos.toInt32())').key_by('locus'))
(dataset.annotate_variants_table(loci_tb,
root='va.locus_annot').count())
variants_tb = (hc.import_table(
test_resources + '/variantAnnotations.tsv'
).annotate(
'variant = Variant(Chromosome, Position.toInt32(), Ref, Alt)').
key_by('variant'))
(dataset.annotate_variants_table(variants_tb,
root='va.table').count())
(dataset.annotate_variants_vds(
dataset, expr='va.good = va.info.AF == vds.info.AF').count())
downsampled = dataset.sample_variants(0.10)
downsampled.variants_table().select(
['chr = v.contig',
'pos = v.start']).export('/tmp/sample2_loci.tsv')
downsampled.variants_table().select('v').export(
'/tmp/sample2_variants.tsv')
with open(test_resources + '/sample2.sample_list') as f:
samples = [s.strip() for s in f]
(dataset.filter_samples_list(samples).count()[0] == 56)
locus_tb = (
hc.import_table(test_resources + '/sample2_loci.tsv').annotate(
'locus = Locus(chr, pos.toInt32())').key_by('locus'))
(dataset.annotate_variants_table(locus_tb,
root='va.locus_annot').count())
tb = (hc.import_table(
test_resources + '/variantAnnotations.tsv'
).annotate(
'variant = Variant(Chromosome, Position.toInt32(), Ref, Alt)').
key_by('variant'))
(dataset.annotate_variants_table(tb, root='va.table').count())
(dataset.annotate_variants_vds(
dataset, expr='va.good = va.info.AF == vds.info.AF').count())
dataset.export_vcf('/tmp/sample2.vcf.bgz')
self.assertEqual(dataset.drop_samples().count()[0], 0)
self.assertEqual(dataset.drop_variants().count()[1], 0)
dataset_dedup = (hc.import_vcf([
test_resources + '/sample2.vcf',
test_resources + '/sample2.vcf'
]).deduplicate())
self.assertEqual(dataset_dedup.count()[1], 735)
(dataset.filter_samples_expr('pcoin(0.5)').samples_table().select(
's').export('/tmp/sample2.sample_list'))
(dataset.filter_variants_expr('pcoin(0.5)').variants_table().
select('v').export('/tmp/sample2.variant_list'))
(dataset.filter_variants_table(
KeyTable.import_interval_list(
test_resources + '/annotinterall.interval_list')).count())
dataset.filter_intervals(Interval.parse('1:100-end')).count()
dataset.filter_intervals(map(Interval.parse,
['1:100-end', '3-22'])).count()
(dataset.filter_variants_table(
KeyTable.import_interval_list(
test_resources + '/annotinterall.interval_list')).count())
self.assertEqual(
dataset2.filter_variants_table(
hc.import_table(test_resources + '/sample2_variants.tsv',
key='f0',
impute=True,
no_header=True)).count()[1], 21)
m2 = {
r.f0: r.f1
for r in hc.import_table(test_resources +
'/sample2_rename.tsv',
no_header=True).collect()
}
self.assertEqual(
dataset2.join(dataset2.rename_samples(m2)).count()[0], 200)
dataset._typecheck()
dataset.variants_table().export('/tmp/variants.tsv')
self.assertTrue(
(dataset.variants_table().annotate('va = json(va)')).same(
hc.import_table('/tmp/variants.tsv',
impute=True).key_by('v')))
dataset.samples_table().export('/tmp/samples.tsv')
self.assertTrue((
dataset.samples_table().annotate('s = s, sa = json(sa)')).same(
hc.import_table('/tmp/samples.tsv',
impute=True).key_by('s')))
gt_string = 'gt = g.GT, gq = g.GQ'
gt_string2 = 'gt: g.GT, gq: g.GQ'
cols = ['v = v', 'info = va.info']
for s in dataset.sample_ids:
cols.append('`{s}`.gt = va.G["{s}"].gt'.format(s=s))
cols.append('`{s}`.gq = va.G["{s}"].gq'.format(s=s))
dataset_table = (dataset.annotate_variants_expr(
'va.G = index(gs.map(g => { s: s, %s }).collect(), s)' %
gt_string2).variants_table().select(cols))
dataset_table_typs = {
fd.name: fd.typ
for fd in dataset_table.schema.fields
}
dataset_table.export('/tmp/sample_kt.tsv')
self.assertTrue((dataset.make_table(
'v = v, info = va.info', gt_string, ['v'])).same(
hc.import_table('/tmp/sample_kt.tsv',
types=dataset_table_typs).key_by('v')))
dataset.annotate_variants_expr(
"va.nHet = gs.filter(g => {0}.isHet()).count()".format(gt))
dataset.make_table('v = v, info = va.info', 'gt = {0}'.format(gt),
['v'])
dataset.num_partitions()
dataset.file_version()
dataset.sample_ids[:5]
dataset.variant_schema
dataset.sample_schema
self.assertEqual(dataset2.num_samples, 100)
self.assertEqual(dataset2.count_variants(), 735)
dataset.annotate_variants_table(dataset.variants_table(),
root="va")
kt = (dataset.variants_table().annotate("v2 = v").key_by(
["v", "v2"]))
dataset.annotate_variants_table(kt,
root='va.foo',
vds_key=["v", "v"])
self.assertEqual(kt.query('v.fraction(x => x == v2)'), 1.0)
dataset.genotypes_table()
## This is very slow!!!
variants_py = (dataset.annotate_variants_expr(
'va.hets = gs.filter(g => {0}.isHet()).collect()'.format(
gt)).variants_table().filter('pcoin(0.1)').collect())
expr = 'g.GT.isHet() && g.GQ > 20'
(dataset.filter_genotypes(expr).genotypes_table().select([
'v', 's', 'nNonRefAlleles = {0}.nNonRefAlleles()'.format(gt)
]).export('/tmp/sample2_genotypes.tsv'))
self.assertTrue((dataset.repartition(16,
shuffle=False).same(dataset)))
self.assertTrue(dataset.naive_coalesce(2).same(dataset))
print(dataset.storage_level())
dataset = dataset.unpersist()
dataset2 = dataset2.unpersist()
new_sample_order = dataset.sample_ids[:]
random.shuffle(new_sample_order)
self.assertEqual(
vds.reorder_samples(new_sample_order).sample_ids,
new_sample_order)
sample = hc.import_vcf(test_resources + '/sample.vcf').cache()
sample.summarize().report()
sample.drop_samples().summarize().report()
sample_split = sample.split_multi_hts()
sample2 = hc.import_vcf(test_resources + '/sample2.vcf')
sample2 = sample2.persist()
sample2_split = sample2.split_multi_hts()
sample.annotate_alleles_expr_hts(
'va.gs = gs.map(g => g.GT).callStats(g => v)').count()
sample.annotate_alleles_expr_hts(
['va.gs = gs.map(g => g.GT).callStats(g => v)',
'va.foo = 5']).count()
glob, concordance1, concordance2 = (
sample2_split.concordance(sample2_split))
print(glob[1][4])
print(glob[4][0])
print(glob[:][3])
concordance1.write('/tmp/foo.kt', overwrite=True)
concordance2.write('/tmp/foo.kt', overwrite=True)
sample2_split.export_gen('/tmp/sample2.gen', 5)
sample2_split.export_plink('/tmp/sample2')
sample2.filter_variants_expr('v.isBiallelic').count()
sample2.split_multi_hts().grm().export_gcta_grm_bin('/tmp/sample2.grm')
sample2.hardcalls().count()
sample2_split.ibd(min=0.2, max=0.6)
sample2.split_multi_hts().impute_sex().variant_schema
self.assertEqual(sample2.genotype_schema, Type.hts_schema())
m2 = {
r.f0: r.f1
for r in hc.import_table(test_resources + '/sample2_rename.tsv',
no_header=True,
impute=True).collect()
}
self.assertEqual(
sample2.join(sample2.rename_samples(m2)).count()[0], 200)
cov = hc.import_table(test_resources + '/regressionLinear.cov',
types={
'Cov1': TFloat64(),
'Cov2': TFloat64()
}).key_by('Sample')
phen1 = hc.import_table(test_resources + '/regressionLinear.pheno',
missing='0',
types={
'Pheno': TFloat64()
}).key_by('Sample')
phen2 = hc.import_table(test_resources +
'/regressionLogisticBoolean.pheno',
missing='0',
types={
'isCase': TBoolean()
}).key_by('Sample')
regression = (hc.import_vcf(
test_resources +
'/regressionLinear.vcf').split_multi_hts().annotate_samples_table(
cov, root='sa.cov').annotate_samples_table(
phen1, root='sa.pheno.Pheno').annotate_samples_table(
phen2, root='sa.pheno.isCase'))
(regression.linreg(['sa.pheno.Pheno'],
'g.GT.nNonRefAlleles()',
covariates=['sa.cov.Cov1',
'sa.cov.Cov2 + 1 - 1']).count())
(regression.logreg('wald',
'sa.pheno.isCase',
'g.GT.nNonRefAlleles()',
covariates=['sa.cov.Cov1',
'sa.cov.Cov2 + 1 - 1']).count())
vds_assoc = (regression.annotate_samples_expr(
'sa.culprit = gs.filter(g => v == Variant("1", 1, "C", "T")).map(g => g.GT.gt).collect()[0]'
).annotate_samples_expr(
'sa.pheno.PhenoLMM = (1 + 0.1 * sa.cov.Cov1 * sa.cov.Cov2) * sa.culprit'
))
covariatesSkat = hc.import_table(test_resources + "/skat.cov",
impute=True).key_by("Sample")
phenotypesSkat = (hc.import_table(test_resources + "/skat.pheno",
types={
"Pheno": TFloat64()
},
missing="0").key_by("Sample"))
intervalsSkat = KeyTable.import_interval_list(test_resources +
"/skat.interval_list")
weightsSkat = (hc.import_table(test_resources + "/skat.weights",
types={
"locus": TLocus(),
"weight": TFloat64()
}).key_by("locus"))
skatVds = (vds2.split_multi_hts().annotate_variants_table(
intervalsSkat, root="va.gene").annotate_variants_table(
weightsSkat, root="va.weight").annotate_samples_table(
phenotypesSkat, root="sa.pheno").annotate_samples_table(
covariatesSkat, root="sa.cov").annotate_samples_expr(
"sa.pheno = if (sa.pheno == 1.0) false else " +
"if (sa.pheno == 2.0) true else NA: Boolean"))
(skatVds.skat(key_expr='va.gene',
weight_expr='va.weight',
y='sa.pheno',
x='g.GT.nNonRefAlleles()',
covariates=['sa.cov.Cov1', 'sa.cov.Cov2'],
logistic=False).count())
(skatVds.skat(key_expr='va.gene',
weight_expr='va.weight',
y='sa.pheno',
x='plDosage(g.PL)',
covariates=['sa.cov.Cov1', 'sa.cov.Cov2'],
logistic=True).count())
vds_kinship = vds_assoc.filter_variants_expr('v.start < 4')
km = vds_kinship.rrm(False, False)
ld_matrix_path = '/tmp/ldmatrix'
ldMatrix = vds_kinship.ld_matrix()
if os.path.isdir(ld_matrix_path):
shutil.rmtree(ld_matrix_path)
ldMatrix.write(ld_matrix_path)
LDMatrix.read(ld_matrix_path).to_local_matrix()
vds_assoc = vds_assoc.lmmreg(km, 'sa.pheno.PhenoLMM',
'g.GT.nNonRefAlleles()',
['sa.cov.Cov1', 'sa.cov.Cov2'])
vds_assoc.variants_table().select(['Variant = v', 'va.lmmreg.*'
]).export('/tmp/lmmreg.tsv')
men, fam, ind, var = sample_split.mendel_errors(
Pedigree.read(test_resources + '/sample.fam'))
men.select(['fid', 's', 'code'])
fam.select(['father', 'nChildren'])
self.assertEqual(ind.key, ['s'])
self.assertEqual(var.key, ['v'])
sample_split.annotate_variants_table(var, root='va.mendel').count()
sample_split.pca_of_normalized_genotypes()
sample_split.tdt(Pedigree.read(test_resources + '/sample.fam'))
sample2_split.variant_qc().variant_schema
sample2.variants_table().export('/tmp/variants.tsv')
self.assertTrue(
(sample2.variants_table().annotate('va = json(va)')).same(
hc.import_table('/tmp/variants.tsv', impute=True).key_by('v')))
sample2.samples_table().export('/tmp/samples.tsv')
self.assertTrue(
(sample2.samples_table().annotate('s = s, sa = json(sa)')).same(
hc.import_table('/tmp/samples.tsv', impute=True).key_by('s')))
cols = ['v = v', 'info = va.info']
for s in sample2.sample_ids:
cols.append('{s}.gt = va.G["{s}"].gt'.format(s=s))
cols.append('{s}.gq = va.G["{s}"].gq'.format(s=s))
sample2_table = (sample2.annotate_variants_expr(
'va.G = index(gs.map(g => { s: s, gt: g.GT, gq: g.GQ }).collect(), s)'
).variants_table().select(cols))
sample2_table.export('/tmp/sample_kt.tsv')
sample2_typs = {fd.name: fd.typ for fd in sample2_table.schema.fields}
self.assertTrue((sample2.make_table(
'v = v, info = va.info', 'gt = g.GT, gq = g.GQ', ['v'])).same(
hc.import_table('/tmp/sample_kt.tsv',
types=sample2_typs).key_by('v')))
sample_split.annotate_variants_expr(
"va.nHet = gs.filter(g => g.GT.isHet()).count()")
sample2.make_table('v = v, info = va.info', 'gt = g.GT', ['v'])
sample.num_partitions()
sample.file_version()
sample.sample_ids[:5]
sample2.filter_alleles_hts('pcoin(0.5)')
sample_split.ld_prune(8).variants_table().select('v').export(
"/tmp/testLDPrune.tsv")
kt = (sample2.variants_table().annotate("v2 = v").key_by(["v", "v2"]))
sample2.annotate_variants_table(kt, root="va.foo", vds_key=["v", "v"])
self.assertEqual(kt.query('v.fraction(x => x == v2)'), 1.0)
variants_py = (sample.annotate_variants_expr(
'va.hets = gs.filter(g => g.GT.isHet).collect()').variants_table().
take(5))
VariantDataset.from_table(sample.variants_table())
def test_dataset(self):
test_resources = 'src/test/resources'
vds = hc.import_vcf(test_resources + '/sample.vcf')
vds2 = hc.import_vcf(test_resources + '/sample2.vcf')
gds = hc.import_vcf(test_resources + '/sample.vcf', generic=True)
gds2 = hc.import_vcf(test_resources + '/sample2.vcf', generic=True)
for (dataset, dataset2) in [(vds, vds2), (gds, gds2)]:
if dataset._is_generic_genotype:
gt = 'g.GT'
else:
gt = 'g'
dataset.cache()
dataset2.persist()
dataset.write('/tmp/sample.vds', overwrite=True)
dataset.count()
dataset.query_variants(['variants.count()'])
dataset.query_samples(['samples.count()'])
(dataset.annotate_samples_expr(
'sa.nCalled = gs.filter(g => {0}.isCalled()).count()'.format(
gt)).export_samples('/tmp/sa.tsv',
's = s, nCalled = sa.nCalled'))
dataset.annotate_global_expr('global.foo = 5')
dataset.annotate_global_expr(['global.foo = 5', 'global.bar = 6'])
dataset = dataset.annotate_samples_table(
hc.import_table(test_resources +
'/sampleAnnotations.tsv').key_by('Sample'),
expr=
'sa.isCase = table.Status == "CASE", sa.qPhen = table.qPhen')
(dataset.annotate_variants_expr(
'va.nCalled = gs.filter(g => {0}.isCalled()).count()'.format(
gt)).count())
loci_tb = (
hc.import_table(test_resources + '/sample2_loci.tsv').annotate(
'locus = Locus(chr, pos.toInt())').key_by('locus'))
(dataset.annotate_variants_table(loci_tb,
root='va.locus_annot').count())
variants_tb = (hc.import_table(
test_resources + '/variantAnnotations.tsv').annotate(
'variant = Variant(Chromosome, Position.toInt(), Ref, Alt)'
).key_by('variant'))
(dataset.annotate_variants_table(variants_tb,
root='va.table').count())
(dataset.annotate_variants_vds(
dataset, expr='va.good = va.info.AF == vds.info.AF').count())
downsampled = dataset.sample_variants(0.10)
downsampled.export_variants('/tmp/sample2_loci.tsv',
'chr = v.contig, pos = v.start')
downsampled.export_variants('/tmp/sample2_variants.tsv', 'v')
with open(test_resources + '/sample2.sample_list') as f:
samples = [s.strip() for s in f]
(dataset.filter_samples_list(samples).count()[0] == 56)
locus_tb = (
hc.import_table(test_resources + '/sample2_loci.tsv').annotate(
'locus = Locus(chr, pos.toInt())').key_by('locus'))
(dataset.annotate_variants_table(locus_tb,
root='va.locus_annot').count())
tb = (hc.import_table(
test_resources + '/variantAnnotations.tsv').annotate(
'variant = Variant(Chromosome, Position.toInt(), Ref, Alt)'
).key_by('variant'))
(dataset.annotate_variants_table(tb, root='va.table').count())
(dataset.annotate_variants_vds(
dataset, expr='va.good = va.info.AF == vds.info.AF').count())
dataset.export_vcf('/tmp/sample2.vcf.bgz')
self.assertEqual(dataset.drop_samples().count()[0], 0)
self.assertEqual(dataset.drop_variants().count()[1], 0)
dataset_dedup = (hc.import_vcf([
test_resources + '/sample2.vcf',
test_resources + '/sample2.vcf'
]).deduplicate())
self.assertEqual(dataset_dedup.count()[1], 735)
(dataset.filter_samples_expr('pcoin(0.5)').export_samples(
'/tmp/sample2.sample_list', 's'))
(dataset.filter_variants_expr('pcoin(0.5)').export_variants(
'/tmp/sample2.variant_list', 'v'))
(dataset.filter_variants_table(
KeyTable.import_interval_list(
test_resources + '/annotinterall.interval_list')).count())
dataset.filter_intervals(Interval.parse('1:100-end')).count()
dataset.filter_intervals(map(Interval.parse,
['1:100-end', '3-22'])).count()
(dataset.filter_variants_table(
KeyTable.import_interval_list(
test_resources + '/annotinterall.interval_list')).count())
self.assertEqual(
dataset2.filter_variants_table(
hc.import_table(test_resources + '/sample2_variants.tsv',
key='f0',
impute=True,
no_header=True)).count()[1], 21)
m2 = {
r.f0: r.f1
for r in hc.import_table(test_resources +
'/sample2_rename.tsv',
no_header=True).collect()
}
self.assertEqual(
dataset2.join(dataset2.rename_samples(m2)).count()[0], 200)
dataset._typecheck()
dataset.export_variants('/tmp/variants.tsv', 'v = v, va = va')
self.assertTrue(
(dataset.variants_table().annotate('va = json(va)')).same(
hc.import_table('/tmp/variants.tsv',
impute=True).key_by('v')))
dataset.export_samples('/tmp/samples.tsv', 's = s, sa = sa')
self.assertTrue((
dataset.samples_table().annotate('s = s, sa = json(sa)')).same(
hc.import_table('/tmp/samples.tsv',
impute=True).key_by('s')))
if dataset._is_generic_genotype:
gt_string = 'gt = g.GT, gq = g.GQ'
gt_string2 = 'gt: g.GT, gq: g.GQ'
else:
gt_string = 'gt = g.gt, gq = g.gq'
gt_string2 = 'gt: g.gt, gq: g.gq'
cols = ['v = v, info = va.info']
for s in dataset.sample_ids:
cols.append(
'{s}.gt = va.G["{s}"].gt, {s}.gq = va.G["{s}"].gq'.format(
s=s))
(dataset.annotate_variants_expr(
'va.G = index(gs.map(g => { s: s, %s }).collect(), s)' %
gt_string2).export_variants('/tmp/sample_kt.tsv',
','.join(cols)))
((dataset.make_table(
'v = v, info = va.info', gt_string, ['v'])).same(
hc.import_table('/tmp/sample_kt.tsv').key_by('v')))
dataset.annotate_variants_expr(
"va.nHet = gs.filter(g => {0}.isHet()).count()".format(gt))
dataset.aggregate_by_key(
"Variant = v",
"nHet = g.map(g => {0}.isHet().toInt()).sum().toLong()".format(
gt))
dataset.aggregate_by_key(["Variant = v"], [
"nHet = g.map(g => {0}.isHet().toInt()).sum().toLong()".format(
gt)
])
dataset.make_table('v = v, info = va.info', 'gt = {0}'.format(gt),
['v'])
dataset.num_partitions()
dataset.file_version()
dataset.sample_ids[:5]
dataset.variant_schema
dataset.sample_schema
self.assertEqual(dataset2.num_samples, 100)
self.assertEqual(dataset2.count_variants(), 735)
dataset.annotate_variants_table(dataset.variants_table(),
root="va")
kt = (dataset.variants_table().annotate("v2 = v").key_by(
["v", "v2"]))
dataset.annotate_variants_table(kt,
root='va.foo',
vds_key=["v", "v"])
self.assertEqual(kt.query('v.fraction(x => x == v2)'), 1.0)
dataset.genotypes_table()
## This is very slow!!!
variants_py = (dataset.annotate_variants_expr(
'va.hets = gs.filter(g => {0}.isHet()).collect()'.format(
gt)).variants_table().filter('pcoin(0.1)').collect())
if dataset._is_generic_genotype:
expr = 'g.GT.isHet() && g.GQ > 20'
else:
expr = 'g.isHet() && g.gq > 20'
(dataset.filter_genotypes(expr).export_genotypes(
'/tmp/sample2_genotypes.tsv',
'v, s, {0}.nNonRefAlleles()'.format(gt)))
self.assertTrue((dataset.repartition(16,
shuffle=False).same(dataset)))
print(dataset.storage_level())
dataset.unpersist()
dataset2.unpersist()
sample = hc.import_vcf(test_resources + '/sample.vcf')
sample.cache()
sample.summarize().report()
sample_split = sample.split_multi()
sample2 = hc.import_vcf(test_resources + '/sample2.vcf')
sample2.persist()
sample2_split = sample2.split_multi()
sample.annotate_alleles_expr('va.gs = gs.callStats(g => v)').count()
sample.annotate_alleles_expr(
['va.gs = gs.callStats(g => v)', 'va.foo = 5']).count()
glob, concordance1, concordance2 = (
sample2_split.concordance(sample2_split))
print(glob[1][4])
print(glob[4][0])
print(glob[:][3])
concordance1.write('/tmp/foo.kt', overwrite=True)
concordance2.write('/tmp/foo.kt', overwrite=True)
sample2_split.export_gen('/tmp/sample2.gen', 5)
sample2_split.export_plink('/tmp/sample2')
sample2.filter_multi().count()
sample2.split_multi().grm().export_gcta_grm_bin('/tmp/sample2.grm')
sample2.hardcalls().count()
sample2_split.ibd(min=0.2, max=0.6)
sample2.split_multi().impute_sex().variant_schema
self.assertTrue(isinstance(sample2.genotype_schema, TGenotype))
m2 = {
r.f0: r.f1
for r in hc.import_table(test_resources + '/sample2_rename.tsv',
no_header=True,
impute=True).collect()
}
self.assertEqual(
sample2.join(sample2.rename_samples(m2)).count()[0], 200)
cov = hc.import_table(test_resources + '/regressionLinear.cov',
types={
'Cov1': TDouble(),
'Cov2': TDouble()
}).key_by('Sample')
phen1 = hc.import_table(test_resources + '/regressionLinear.pheno',
missing='0',
types={
'Pheno': TDouble()
}).key_by('Sample')
phen2 = hc.import_table(test_resources +
'/regressionLogisticBoolean.pheno',
missing='0',
types={
'isCase': TBoolean()
}).key_by('Sample')
regression = (hc.import_vcf(
test_resources +
'/regressionLinear.vcf').split_multi().annotate_samples_table(
cov, root='sa.cov').annotate_samples_table(
phen1, root='sa.pheno.Pheno').annotate_samples_table(
phen2, root='sa.pheno.isCase'))
(regression.linreg('sa.pheno.Pheno',
covariates=['sa.cov.Cov1',
'sa.cov.Cov2 + 1 - 1']).count())
(regression.logreg('wald',
'sa.pheno.isCase',
covariates=['sa.cov.Cov1',
'sa.cov.Cov2 + 1 - 1']).count())
vds_assoc = (regression.annotate_samples_expr(
'sa.culprit = gs.filter(g => v == Variant("1", 1, "C", "T")).map(g => g.gt).collect()[0]'
).annotate_samples_expr(
'sa.pheno.PhenoLMM = (1 + 0.1 * sa.cov.Cov1 * sa.cov.Cov2) * sa.culprit'
))
vds_kinship = vds_assoc.filter_variants_expr('v.start < 4')
km = vds_kinship.rrm(False, False)
ldMatrix = vds_kinship.ld_matrix()
vds_assoc = vds_assoc.lmmreg(km, 'sa.pheno.PhenoLMM',
['sa.cov.Cov1', 'sa.cov.Cov2'])
vds_assoc.export_variants('/tmp/lmmreg.tsv',
'Variant = v, va.lmmreg.*')
men, fam, ind, var = sample_split.mendel_errors(
Pedigree.read(test_resources + '/sample.fam'))
men.select(['fid', 's', 'code'])
fam.select(['father', 'nChildren'])
self.assertEqual(ind.key, ['s'])
self.assertEqual(var.key, ['v'])
sample_split.annotate_variants_table(var, root='va.mendel').count()
sample_split.pca('sa.scores')
sample_split.tdt(Pedigree.read(test_resources + '/sample.fam'))
sample2_split.variant_qc().variant_schema
sample2.export_variants('/tmp/variants.tsv', 'v = v, va = va')
self.assertTrue(
(sample2.variants_table().annotate('va = json(va)')).same(
hc.import_table('/tmp/variants.tsv', impute=True).key_by('v')))
sample2.export_samples('/tmp/samples.tsv', 's = s, sa = sa')
self.assertTrue(
(sample2.samples_table().annotate('s = s, sa = json(sa)')).same(
hc.import_table('/tmp/samples.tsv', impute=True).key_by('s')))
cols = ['v = v, info = va.info']
for s in sample2.sample_ids:
cols.append(
'{s}.gt = va.G["{s}"].gt, {s}.gq = va.G["{s}"].gq'.format(s=s))
(sample2.annotate_variants_expr(
'va.G = index(gs.map(g => { s: s, gt: g.gt, gq: g.gq }).collect(), s)'
).export_variants('/tmp/sample_kt.tsv', ','.join(cols)))
((sample2.make_table(
'v = v, info = va.info', 'gt = g.gt, gq = g.gq',
['v'])).same(hc.import_table('/tmp/sample_kt.tsv').key_by('v')))
sample_split.annotate_variants_expr(
"va.nHet = gs.filter(g => g.isHet()).count()")
sample_split.aggregate_by_key(
"Variant = v",
"nHet = g.map(g => g.isHet().toInt()).sum().toLong()")
sample_split.aggregate_by_key(
["Variant = v"],
["nHet = g.map(g => g.isHet().toInt()).sum().toLong()"])
sample2.make_table('v = v, info = va.info', 'gt = g.gt', ['v'])
sample.num_partitions()
sample.file_version()
sample.sample_ids[:5]
self.assertFalse(sample2.was_split())
self.assertTrue(sample_split.was_split())
sample2.filter_alleles('pcoin(0.5)')
gds.annotate_genotypes_expr('g = g.GT.toGenotype()').split_multi()
sample_split.ld_prune().export_variants("/tmp/testLDPrune.tsv", "v")
kt = (sample2.variants_table().annotate("v2 = v").key_by(["v", "v2"]))
sample2.annotate_variants_table(kt, root="va.foo", vds_key=["v", "v"])
self.assertEqual(kt.query('v.fraction(x => x == v2)'), 1.0)
variants_py = (sample.annotate_variants_expr(
'va.hets = gs.filter(g => g.isHet).collect()').variants_table().
collect())
VariantDataset.from_table(sample.variants_table())
