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"""Tests for deepvariant .variant_labeler."""
import collections
from absl.testing import absltest
from absl.testing import parameterized
from third_party.nucleus.io import vcf
from third_party.nucleus.testing import test_utils
from third_party.nucleus.util import ranges
from third_party.nucleus.util import variant_utils
from deepvariant import testdata
from deepvariant.labeler import customized_classes_labeler
from third_party.nucleus.protos import variants_pb2
from third_party.nucleus.util import vcf_constants
def setUpModule():
testdata.init()
FakeVCFObject = collections.namedtuple('FakeVCFObject', ['field_access_cache'])
CUSTOMIZED_INFO_FIELD_NAME = 'type'
CUSTOMIZED_CLASSES_LIST = 'ref,class1,class2'
def _add_class_to_variant(variant, class_status):
if class_status is None:
return variant
header = variants_pb2.VcfHeader(
infos=[
variants_pb2.VcfInfo(
id=CUSTOMIZED_INFO_FIELD_NAME,
number='A',
type=vcf_constants.STRING_TYPE,
description='Customized class label for the variant.',
)
]
)
my_cache = vcf.VcfHeaderCache(header)
vcf_object = FakeVCFObject(field_access_cache=my_cache)
variant_utils.set_info(
variant, CUSTOMIZED_INFO_FIELD_NAME, class_status, vcf_object=vcf_object
)
return variant
class CustomizedClassesVariantLabelerTest(parameterized.TestCase):
# Confident variants: SNP, deletion, and multi-allelic.
snp_class1 = _add_class_to_variant(
test_utils.make_variant(start=10, alleles=['A', 'C'], gt=[0, 1]),
class_status='class1',
)
snp_class2 = _add_class_to_variant(
test_utils.make_variant(start=20, alleles=['ACG', 'A'], gt=[1, 1]),
class_status='class2',
)
multiallelic = _add_class_to_variant(
test_utils.make_variant(
start=30, alleles=['ACT', 'ACTGT', 'A'], gt=[1, 2]
),
class_status='class2',
)
# Outside our confident regions.
non_confident = _add_class_to_variant(
test_utils.make_variant(start=200, alleles=['A', 'C'], gt=[0, 1]),
class_status='class1',
)
filtered = _add_class_to_variant(
test_utils.make_variant(start=40, filters='FAILED', gt=[0, 1]),
class_status='class1',
)
# TODO: check the following cases:
# no_class_status
# invalid_class_status
# (Value error should be produced in both cases)
variants = [snp_class1, snp_class2, multiallelic, non_confident, filtered]
def _make_labeler(self, variants, confident_regions):
return customized_classes_labeler.CustomizedClassesVariantLabeler(
truth_vcf_reader=vcf.InMemoryVcfReader(variants),
confident_regions=confident_regions,
classes_list=CUSTOMIZED_CLASSES_LIST,
info_field_name=CUSTOMIZED_INFO_FIELD_NAME,
)
@parameterized.parameters(
# Simple tests: we get back our matching variants in the confident regions
dict(
candidate=snp_class1,
expected_confident=True,
expected_truth=snp_class1,
expected_label=1,
),
dict(
candidate=snp_class2,
expected_confident=True,
expected_truth=snp_class2,
expected_label=2,
),
# For multiallelic variants, we default to class 0.
dict(
candidate=multiallelic,
expected_confident=True,
expected_truth=multiallelic,
expected_label=2,
),
# Test the behavior outside of our confident regions.
# If we provide a variant outside the confident regions (non_confident) we
# don't get back any expected_truth variants.
dict(
candidate=non_confident,
expected_confident=False,
expected_truth=None,
expected_label=0,
),
# No matching variant, so we get a None as well as False.
dict(
candidate=test_utils.make_variant(start=300, alleles=['A', 'C']),
expected_confident=False,
expected_truth=None,
expected_label=0,
),
# This variant doesn't have any match but we're confident in it.
dict(
candidate=test_utils.make_variant(start=15, alleles=['C', 'A']),
expected_confident=True,
expected_label=0,
expected_truth=test_utils.make_variant(
start=15, alleles=['C', 'A'], gt=[0, 0]
),
),
# These variant start at our SNP but has a different allele. We are
# confident and we get back the true snp variant.
# However, we are on a different allele, so its status is unknown.
# TODO: Confirm this case.
dict(
candidate=test_utils.make_variant(
start=snp_class1.start, alleles=['A', 'G']
),
expected_confident=True,
expected_label=0,
expected_truth=snp_class1,
),
# TODO: checking this assumption is correct:
# If the alleles don't match, return class 0?
dict(
candidate=test_utils.make_variant(
start=snp_class1.start, alleles=['AC', 'C']
),
expected_confident=True,
expected_label=0,
expected_truth=snp_class1,
),
dict(
candidate=test_utils.make_variant(
start=snp_class1.start, alleles=['A', 'CA']
),
expected_confident=True,
expected_label=0,
expected_truth=snp_class1,
),
# Checks that we don't match against the filtered truth variant in our
# database. This means that we return not the filtered variant but one
# with a (0, 0) genotype.
dict(
candidate=test_utils.make_variant(start=filtered.start),
expected_confident=True,
expected_label=0,
expected_truth=test_utils.make_variant(
start=filtered.start, gt=(0, 0)
),
),
# These variant start at our SNP but has a different first alt allele 'G'.
# The second alt ('C') matches snp_class1, so we still got back the
# expected_label.
dict(
candidate=test_utils.make_variant(
start=snp_class1.start, alleles=['A', 'G', 'C']
),
expected_confident=True,
expected_label=1,
expected_truth=snp_class1,
variant_alt_alleles_indices=[1],
),
# And, even if the variant_alt_alleles_indices is a composite one ([0,1]),
# We still label it as long as one of them matches the truth_alt.
dict(
candidate=test_utils.make_variant(
start=snp_class1.start, alleles=['A', 'G', 'C']
),
expected_confident=True,
expected_label=1,
expected_truth=snp_class1,
variant_alt_alleles_indices=[0, 1],
),
# ... But we won't label it if the alt_allele_indices does not cover the
# truth alt.
dict(
candidate=test_utils.make_variant(
start=snp_class1.start, alleles=['A', 'G', 'C']
),
expected_confident=True,
expected_label=0,
expected_truth=snp_class1,
variant_alt_alleles_indices=[0],
),
)
def test_label_variants(
self,
candidate,
expected_confident,
expected_truth,
expected_label=None,
variant_alt_alleles_indices=None,
):
if variant_alt_alleles_indices is None:
variant_alt_alleles_indices = [0]
labeler = self._make_labeler(
self.variants,
ranges.RangeSet(
[ranges.make_range(self.snp_class1.reference_name, 10, 100)]
),
)
# Call _match so we can compare our expected truth with the actual one.
is_confident, truth_variant = labeler._match(candidate)
self.assertEqual(expected_truth, truth_variant)
self.assertEqual(is_confident, expected_confident)
# Now call label_variants to exercise the higher-level API.
classes_dict = (
customized_classes_labeler.CustomizedClassesVariantLabel.classes_dict
)
if expected_label is None and expected_truth is not None:
expected_class_str = (
expected_truth.info[
customized_classes_labeler.CustomizedClassesVariantLabel.info_field_name
]
.values[0]
.string_value
)
expected_label = classes_dict[expected_class_str]
labels = list(labeler.label_variants([candidate]))
self.assertLen(labels, 1)
self.assertEqual(candidate, labels[0].variant)
self.assertEqual(expected_confident, labels[0].is_confident)
self.assertEqual(
expected_label,
labels[0].label_for_alt_alleles(variant_alt_alleles_indices),
)
def test_match_selects_variant_by_start(self):
# Tests that match() selects the variant at the same start even if that
# variant doesn't have the same alleles at candidate and there's an
# overlapping with the same alleles.
overlapping = [
test_utils.make_variant(start=20, alleles=['CC', 'A'], gt=[1, 1]),
test_utils.make_variant(start=21, alleles=['AAA', 'A'], gt=[0, 1]),
test_utils.make_variant(start=22, alleles=['AA', 'A'], gt=[1, 1]),
]
candidate = test_utils.make_variant(start=21, alleles=['CC', 'A'])
labeler = self._make_labeler(
overlapping,
ranges.RangeSet(
[ranges.make_range(overlapping[0].reference_name, 0, 100)]
),
)
is_confident, truth_variant = labeler._match(candidate)
self.assertEqual(is_confident, True)
self.assertEqual(truth_variant, overlapping[1])
if __name__ == '__main__':
absltest.main()
