def test_processed_samples_only(self):
""" Don't return unprocessed samples """
experiment = Experiment()
experiment.accession_code = "GSX12345"
experiment.is_public = True
experiment.save()
sample = Sample()
sample.title = "I am unprocessed"
sample.accession_code = "GSXUnprocessed"
sample.is_processed = False
sample.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
# we return all experiments
response = self.client.get(
reverse("search", kwargs={"version": API_VERSION}),
{"search": "GSX12345"})
self.assertEqual(response.json()["count"], 1)
# check requesting only experiments with processed samples
response = self.client.get(
reverse("search", kwargs={"version": API_VERSION}),
{
"search": "GSX12345",
"num_processed_samples__gt": 0
},
)
self.assertEqual(response.json()["count"], 0)
sample2 = Sample()
sample2.title = "I am processed"
sample2.accession_code = "GSXProcessed"
sample2.is_processed = True
sample2.save()
experiment_sample2_association = ExperimentSampleAssociation()
experiment_sample2_association.sample = sample2
experiment_sample2_association.experiment = experiment
experiment_sample2_association.save()
# update cached values
experiment.num_total_samples = 2
experiment.num_processed_samples = 1
experiment.save()
response = self.client.get(
reverse("search", kwargs={"version": API_VERSION}),
{"search": "GSX12345"})
self.assertEqual(response.json()["count"], 1)
self.assertEqual(len(experiment.processed_samples), 1)
experiment.delete()
sample.delete()
sample2.delete()
def prepare_computed_files():
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1487313"
experiment.num_processed_samples = 1
experiment.save()
result = ComputationalResult()
result.save()
gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS",
taxonomy_id=1001)
sample = Sample()
sample.accession_code = "GSM1487313"
sample.title = "GSM1487313"
sample.organism = gallus_gallus
sample.technology = "MICROARRAY"
sample.is_processed = True
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1487313_liver.PCL"
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.s3_key = "GSM1487313_liver.PCL"
computed_file.s3_bucket = TEST_DATA_BUCKET
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# RNASEQ TECH
experiment2 = Experiment()
experiment2.accession_code = "SRP332914"
experiment2.num_processed_samples = 1
experiment2.save()
result2 = ComputationalResult()
result2.save()
sample2 = Sample()
sample2.accession_code = "SRR332914"
sample2.title = "SRR332914"
sample2.organism = gallus_gallus
sample2.technology = "RNA-SEQ"
sample2.is_processed = True
sample2.save()
sra2 = SampleResultAssociation()
sra2.sample = sample2
sra2.result = result2
sra2.save()
esa2 = ExperimentSampleAssociation()
esa2.experiment = experiment2
esa2.sample = sample2
esa2.save()
computed_file2 = ComputedFile()
computed_file2.filename = "SRP149598_gene_lengthScaledTPM.tsv"
computed_file2.result = result2
computed_file2.size_in_bytes = 234
computed_file2.is_smashable = True
computed_file2.s3_key = "SRP149598_gene_lengthScaledTPM.tsv"
computed_file2.s3_bucket = TEST_DATA_BUCKET
computed_file2.save()
assoc2 = SampleComputedFileAssociation()
assoc2.sample = sample2
assoc2.computed_file = computed_file2
assoc2.save()
def setUp(self):
# Saving this for if we have protected endpoints
# self.superuser = User.objects.create_superuser('john', '*****@*****.**', 'johnpassword')
# self.client.login(username='******', password='******')
# self.user = User.objects.create(username="******")
experiment = Experiment()
experiment.accession_code = "GSE000"
experiment.alternate_accession_code = "E-GEOD-000"
experiment.title = "NONONONO"
experiment.description = "Boooooourns. Wasabi."
experiment.technology = "RNA-SEQ"
experiment.save()
experiment = Experiment()
experiment.accession_code = "GSE123"
experiment.title = "Hey Ho Let's Go"
experiment.description = (
"This is a very exciting test experiment. Faygo soda. Blah blah blah."
)
experiment.technology = "MICROARRAY"
experiment.save()
self.experiment = experiment
experiment_annotation = ExperimentAnnotation()
experiment_annotation.data = {"hello": "world", "123": 456}
experiment_annotation.experiment = experiment
experiment_annotation.save()
# Create 26 test organisms numbered 0-25 for pagination test, so there should be 29 organisms total (with the 3 others below)
for i in range(26):
Organism(name=("TEST_ORGANISM_{}".format(i)),
taxonomy_id=(1234 + i)).save()
ailuropoda = Organism(name="AILUROPODA_MELANOLEUCA",
taxonomy_id=9646,
is_scientific_name=True)
ailuropoda.save()
self.homo_sapiens = Organism(name="HOMO_SAPIENS",
taxonomy_id=9606,
is_scientific_name=True)
self.homo_sapiens.save()
self.danio_rerio = Organism(name="DANIO_RERIO",
taxonomy_id=1337,
is_scientific_name=True)
self.danio_rerio.save()
sample = Sample()
sample.title = "123"
sample.accession_code = "123"
sample.is_processed = True
sample.organism = ailuropoda
sample.save()
sample = Sample()
sample.title = "789"
sample.accession_code = "789"
sample.is_processed = True
sample.organism = ailuropoda
sample.save()
self.sample = sample
# add qn target for sample organism
result = ComputationalResult()
result.commands.append("create_qn_target.py")
result.is_ccdl = True
result.is_public = True
result.processor = None
result.save()
cra = ComputationalResultAnnotation()
cra.result = result
cra.data = {"organism_id": ailuropoda.id, "is_qn": True}
cra.save()
ailuropoda.qn_target = result
ailuropoda.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {"goodbye": "world", "789": 123}
sample_annotation.sample = sample
sample_annotation.save()
original_file = OriginalFile()
original_file.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.sample = sample
original_file_sample_association.original_file = original_file
original_file_sample_association.save()
downloader_job = DownloaderJob()
downloader_job.save()
download_assoc = DownloaderJobOriginalFileAssociation()
download_assoc.original_file = original_file
download_assoc.downloader_job = downloader_job
download_assoc.save()
processor_job = ProcessorJob()
processor_job.save()
processor_assoc = ProcessorJobOriginalFileAssociation()
processor_assoc.original_file = original_file
processor_assoc.processor_job = processor_job
processor_assoc.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
experiment.num_total_samples = 1
experiment.num_processed_samples = 1
experiment.save()
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
processor = Processor()
processor.name = "Salmon Quant"
processor.version = "v9.9.9"
processor.docker_image = "dr_salmon"
processor.environment = '{"some": "environment"}'
processor.save()
computational_result_short = ComputationalResult(processor=processor)
computational_result_short.save()
organism_index = OrganismIndex()
organism_index.index_type = "TRANSCRIPTOME_SHORT"
organism_index.organism = self.danio_rerio
organism_index.result = computational_result_short
organism_index.absolute_directory_path = (
"/home/user/data_store/salmon_tests/TRANSCRIPTOME_INDEX/SHORT")
organism_index.is_public = True
organism_index.s3_url = "not_blank"
organism_index.save()
return
def setUpClass(cls):
super(ESTestCases, cls).setUpClass() # ref https://stackoverflow.com/a/29655301/763705
"""Set up class."""
experiment = Experiment()
experiment.accession_code = "GSE000-X"
experiment.title = "NONONONO"
experiment.description = "Boooooourns. Wasabi."
experiment.technology = "RNA-SEQ"
experiment.save()
experiment = Experiment()
experiment.accession_code = "GSE123-X"
experiment.title = "Hey Ho Let's Go"
experiment.description = (
"This is a very exciting test experiment. Faygo soda. Blah blah blah."
)
experiment.technology = "MICROARRAY"
experiment.num_processed_samples = 1 # added below
experiment.num_total_samples = 1
experiment.num_downloadable_samples = 1
experiment.save()
experiment_annotation = ExperimentAnnotation()
experiment_annotation.data = {"hello": "world", "123": 456}
experiment_annotation.experiment = experiment
experiment_annotation.save()
sample = Sample()
sample.title = "123"
sample.accession_code = "123"
sample.save()
organism = Organism(
name="AILUROPODA_MELANOLEUCA", taxonomy_id=9646, is_scientific_name=True
)
organism.save()
sample = Sample()
sample.title = "789"
sample.accession_code = "789"
sample.is_processed = True
sample.organism = organism
sample.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {"goodbye": "world", "789": 123}
sample_annotation.sample = sample
sample_annotation.save()
original_file = OriginalFile()
original_file.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.sample = sample
original_file_sample_association.original_file = original_file
original_file_sample_association.save()
downloader_job = DownloaderJob()
downloader_job.save()
download_assoc = DownloaderJobOriginalFileAssociation()
download_assoc.original_file = original_file
download_assoc.downloader_job = downloader_job
download_assoc.save()
processor_job = ProcessorJob()
processor_job.save()
processor_assoc = ProcessorJobOriginalFileAssociation()
processor_assoc.original_file = original_file
processor_assoc.processor_job = processor_job
processor_assoc.save()
# associate the experiment with the sample
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
result = ComputationalResult()
result.save()
# and create a qn tarjet for the sample
computational_result = ComputationalResultAnnotation()
computational_result.result = result
computational_result.data = {"is_qn": True, "organism_id": sample.organism.id}
computational_result.save()
# and associate it with the sample organism
sample.organism.qn_target = result
sample.organism.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
# clear default cache and reindex
# otherwise the organisms with qn_targes will be cached.
cache.clear()
call_command("search_index", "--rebuild", "-f")