def generate_unique_name(cls, tantalus_api, jira, version, args,
input_datasets, input_results):
# Get hash of lane data based on bams from the same ticket
bam_datasets = tantalus_api.list(
'sequence_dataset',
analysis__jira_ticket=jira,
library__library_id=args['library_id'],
dataset_type='BAM',
aligner__name__startswith=args['aligner'],
reference_genome__name=args['ref_genome'],
)
# TODO: check aligner and reference genome against bam dataset
lanes_hashed = get_datasets_lanes_hash(tantalus_api,
[d['id'] for d in bam_datasets])
# TODO: control aligner vocabulary elsewhere
assert args['aligner'] in ('BWA_ALN', 'BWA_MEM')
name = templates.SC_QC_ANALYSIS_NAME_TEMPLATE.format(
analysis_type=cls.analysis_type_,
aligner=args['aligner'],
ref_genome=args['ref_genome'],
library_id=args['library_id'],
lanes_hashed=lanes_hashed,
)
return name
def generate_unique_name(self, jira, version, args, input_datasets,
input_results):
lanes_hashed = get_datasets_lanes_hash(tantalus_api, input_datasets)
name = templates.TENX_ANALYSIS_NAME_TEMPLATE.format(
analysis_type="tenx",
ref_genome=args['ref_genome'],
library_id=args['library_id'],
lanes_hashed=lanes_hashed,
)
return name
def generate_unique_name(cls, tantalus_api, jira, version, args, input_datasets, input_results):
lanes_hashed = get_datasets_lanes_hash(tantalus_api, input_datasets)
name = templates.SC_QC_ANALYSIS_NAME_TEMPLATE.format(
analysis_type=cls.analysis_type_,
aligner=args['aligner'],
ref_genome=args['ref_genome'],
library_id=args['library_id'],
lanes_hashed=lanes_hashed,
)
return name
def generate_unique_name(cls, tantalus_api, jira, version, args,
input_datasets, input_results):
lanes_hashed = get_datasets_lanes_hash(tantalus_api, input_datasets)
# TODO: control aligner vocabulary elsewhere
assert args['aligner'] in ('BWA_ALN', 'BWA_MEM')
name = templates.SC_QC_ANALYSIS_NAME_TEMPLATE.format(
analysis_type=cls.analysis_type_,
aligner=args['aligner'],
ref_genome=args['ref_genome'],
library_id=args['library_id'],
lanes_hashed=lanes_hashed,
)
return name
def create_output_datasets(self, tag_name=None, update=False):
library_id = self.args["library_id"]
ref_genome = self.args["ref_genome"]
dna_library = tantalus_api.get("dna_library", library_id=library_id)
tenx_library = colossus_api.get("tenxlibrary", name=library_id)
sample_id = tenx_library["sample"]["sample_id"]
sample = tantalus_api.get("sample", sample_id=sample_id)
storage_name = "scrna_bams"
storage_client = tantalus_api.get_storage(storage_name)
sequence_lanes = self.get_lane_ids()
lanes_hashed = get_datasets_lanes_hash(tantalus_api,
self.analysis["input_datasets"])
bam_filepath = os.path.join(storage_client["prefix"], library_id,
"bams.tar.gz")
file_resource, file_instance = tantalus_api.add_file(storage_name,
bam_filepath,
update=True)
name = "BAM-{}-SC_RNASEQ-lanes_{}-{}".format(library_id, lanes_hashed,
ref_genome)
sequence_dataset = tantalus_api.get_or_create(
"sequence_dataset",
name=name,
dataset_type="BAM",
sample=sample["id"],
library=dna_library["id"],
sequence_lanes=sequence_lanes,
file_resources=[file_resource["id"]],
reference_genome=self.args["ref_genome"],
aligner=None,
analysis=self.analysis['id'],
)
log.info("Created sequence dataset {}".format(name))
