def test_sample_ids_combiner_pipeline_preserve_sample_order(self):
sample_ids = [
hash_name('sample2'),
hash_name('sample1'),
hash_name('sample3')
]
variant_calls = [
vcfio.VariantCall(sample_id=sample_ids[0]),
vcfio.VariantCall(sample_id=sample_ids[1]),
vcfio.VariantCall(sample_id=sample_ids[2])
]
variants = [
vcfio.Variant(
calls=[variant_calls[0], variant_calls[1], variant_calls[2]]),
vcfio.Variant(
calls=[variant_calls[0], variant_calls[1], variant_calls[2]])
]
pipeline = TestPipeline()
combined_sample_ids = (
pipeline
| transforms.Create(variants)
| 'CombineSampleIds' >>
combine_sample_ids.SampleIdsCombiner(preserve_sample_order=True)
| combiners.ToList())
assert_that(combined_sample_ids, equal_to([sample_ids]))
pipeline.run()
def test_sample_ids_combiner_pipeline_duplicate_sample_ids(self):
variant_call = vcfio.VariantCall(sample_id=hash_name('sample1'))
variants = [vcfio.Variant(calls=[variant_call, variant_call])]
pipeline = TestPipeline()
_ = (pipeline
| transforms.Create(variants)
| 'CombineSampleIds' >> combine_sample_ids.SampleIdsCombiner()
| combiners.ToList())
with self.assertRaises(ValueError):
pipeline.run()
def _shard_variants(known_args, pipeline_args, pipeline_mode):
# type: (argparse.Namespace, List[str], int) -> List[str]
"""Reads the variants and writes them to VCF shards.
Returns:
The VCF shards directory.
"""
options = pipeline_options.PipelineOptions(pipeline_args)
google_cloud_options = options.view_as(pipeline_options.GoogleCloudOptions)
shard_files_job_name = pipeline_common.generate_unique_name(
_SHARD_VCF_FILES_JOB_NAME)
_update_google_cloud_job_name(google_cloud_options, shard_files_job_name)
vcf_shards_output_dir = filesystems.FileSystems.join(
known_args.annotation_output_dir, _SHARDS_FOLDER)
with beam.Pipeline(options=options) as p:
variants = _read_variants(known_args.all_patterns,
p,
known_args,
pipeline_mode,
pre_infer_headers=False,
keep_raw_sample_names=True,
use_1_based_coordinate=False)
sample_ids = (
variants
| 'CombineSampleIds' >> combine_sample_ids.SampleIdsCombiner()
| 'CombineToList' >> beam.combiners.ToList())
# TODO(tneymanov): Annotation pipeline currently stores sample IDs instead
# of sample names in the the sharded VCF files, which would lead to double
# hashing of samples. Needs to be fixed ASAP.
_ = (variants
| 'DensifyVariants' >> densify_variants.DensifyVariants(
beam.pvalue.AsSingleton(sample_ids))
| 'WriteToShards' >> write_variants_to_shards.WriteToShards(
vcf_shards_output_dir, beam.pvalue.AsSingleton(sample_ids),
known_args.number_of_variants_per_shard))
return [
vep_runner_util.format_dir_path(vcf_shards_output_dir) +
_GCS_RECURSIVE_WILDCARD
]
def test_sample_ids_combiner_pipeline_preserve_sample_order_error(self):
sample_ids = [
hash_name('sample1'),
hash_name('sample2'),
hash_name('sample3')
]
variant_calls = [
vcfio.VariantCall(sample_id=sample_ids[0]),
vcfio.VariantCall(sample_id=sample_ids[1]),
vcfio.VariantCall(sample_id=sample_ids[2])
]
variants = [
vcfio.Variant(calls=[variant_calls[0], variant_calls[1]]),
vcfio.Variant(calls=[variant_calls[1], variant_calls[2]])
]
pipeline = TestPipeline()
_ = (pipeline
| transforms.Create(variants)
| 'CombineSampleIds' >>
combine_sample_ids.SampleIdsCombiner(preserve_sample_order=True)
| combiners.ToList())
with self.assertRaises(ValueError):
pipeline.run()
def _bigquery_to_vcf_shards(
known_args, # type: argparse.Namespace
beam_pipeline_options, # type: pipeline_options.PipelineOptions
vcf_data_temp_folder, # type: str
header_file_path, # type: str
):
# type: (...) -> None
"""Runs BigQuery to VCF shards pipelines.
It reads the variants from BigQuery table, groups a collection of variants
within a contiguous region of the genome (the size of the collection is
adjustable through flag `--number_of_bases_per_shard`), sorts them, and then
writes to one VCF file. All VCF data files are saved in
`vcf_data_temp_folder`.
Also, it writes the meta info and data header with the sample names to
`vcf_header_file_path`.
"""
schema = _get_schema(known_args.input_table)
variant_query = _get_variant_query(known_args, schema)
logging.info('Processing BigQuery query %s:', variant_query)
project_id, dataset_id, table_id = bigquery_util.parse_table_reference(
known_args.input_table)
bq_variant_source = bigquery.BigQuerySource(query=variant_query,
validate=True,
use_standard_sql=True)
annotation_names = _extract_annotation_names(schema)
base_table_id = bigquery_util.get_table_base_name(table_id)
sample_query = _SAMPLE_INFO_QUERY_TEMPLATE.format(
PROJECT_ID=project_id,
DATASET_ID=dataset_id,
TABLE_NAME=bigquery_util.compose_table_name(base_table_id,
SAMPLE_INFO_TABLE_SUFFIX))
bq_sample_source = bigquery.BigQuerySource(query=sample_query,
validate=True,
use_standard_sql=True)
with beam.Pipeline(options=beam_pipeline_options) as p:
variants = (p
| 'ReadFromBigQuery ' >> beam.io.Read(bq_variant_source)
| bigquery_to_variant.BigQueryToVariant(annotation_names))
sample_table_rows = (
p
| 'ReadFromSampleTable' >> beam.io.Read(bq_sample_source))
if known_args.sample_names:
temp_sample_names = (p
| transforms.Create(known_args.sample_names,
reshuffle=False))
else:
# Get sample names from sample IDs in the variants and sort.
id_to_name_hash_table = (sample_table_rows
| 'SampleIdToNameDict' >>
sample_mapping_table.SampleIdToNameDict())
temp_sample_ids = (
variants
| 'CombineSampleIds' >> combine_sample_ids.SampleIdsCombiner(
known_args.preserve_sample_order))
temp_sample_names = (
temp_sample_ids
| 'GetSampleNames' >> sample_mapping_table.GetSampleNames(
beam.pvalue.AsSingleton(id_to_name_hash_table))
| 'CombineToList' >> beam.combiners.ToList()
| 'SortSampleNames' >> beam.ParDo(sorted))
name_to_id_hash_table = (
sample_table_rows
|
'SampleNameToIdDict' >> sample_mapping_table.SampleNameToIdDict())
sample_ids = (temp_sample_names
| 'GetSampleIds' >> sample_mapping_table.GetSampleIds(
beam.pvalue.AsSingleton(name_to_id_hash_table))
| 'CombineSortedSampleIds' >> beam.combiners.ToList())
sample_names = temp_sample_names | beam.combiners.ToList()
_ = (sample_names
| 'GenerateVcfDataHeader' >> beam.ParDo(
_write_vcf_header_with_sample_names, _VCF_FIXED_COLUMNS,
known_args.representative_header_file, header_file_path))
_ = (variants
| densify_variants.DensifyVariants(
beam.pvalue.AsSingleton(sample_ids))
| 'PairVariantWithKey' >> beam.Map(
_pair_variant_with_key, known_args.number_of_bases_per_shard)
| 'GroupVariantsByKey' >> beam.GroupByKey()
| beam.ParDo(_get_file_path_and_sorted_variants,
vcf_data_temp_folder)
| vcfio.WriteVcfDataLines(known_args.bq_uses_1_based_coordinate))
