def test_geo_experiment_missing_metadata(self):
"""Tests that a GEO experiment has its missing metadata added."""
# 1. Create an experiment with a bad title
BAD_TITLE = "GEO accession GSE1337 is currently private\
and is scheduled to be released on Jan 01, 1970."
experiment = Experiment()
experiment.accession_code = "GSE11915"
experiment.source_database = "GEO"
experiment.title = BAD_TITLE
experiment.save()
# 2. Setup is done, actually run the command.
command = Command()
command.handle()
# Test that the title was fixed
self.assertNotEqual(
Experiment.objects.get_or_create(
accession_code=experiment.accession_code)[0].title,
BAD_TITLE,
)
# Run the command again to make sure that it does not fail if there are no changes
command = Command()
command.handle()
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 test_sra_experiment_missing_alternate_accession(self):
"""Tests that an SRA experiment has its missing alternate_accession_code added."""
# 1. Create an experiment without an alternate_accession_code
experiment = Experiment()
experiment.accession_code = "SRP094947"
experiment.source_database = "SRA"
experiment.title = "Not important"
experiment.save()
# 2. We need to add a sample because the way that the SRA surveyor finds metadata is
# through run accessions
sample = Sample()
sample.accession_code = "SRR5099111"
sample.technology = "RNA-SEQ"
sample.source_database = "SRA"
sample.title = "Not important"
sample.save()
ExperimentSampleAssociation.objects.get_or_create(
experiment=experiment, sample=sample)
# 3. Setup is done, actually run the command.
command = Command()
command.handle()
# 4. Refresh the experiment
experiment.refresh_from_db()
# Test that the correct alternate_accession_code was added
self.assertEquals(experiment.alternate_accession_code, "GSE92260")
def test_get_sample_keywords(self):
experiment = Experiment()
experiment.save()
sample = Sample()
sample.title = "123"
sample.accession_code = "123"
sample.age = 23
sample.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
length = OntologyTerm()
length.ontology_term = "EFO:0002939"
length.human_readable_name = "medulloblastoma"
length.save()
sk = SampleKeyword()
sk.name = length
sk.source, _ = Contribution.objects.get_or_create(
source_name="Refinebio Tests", methods_url="ccdatalab.org")
sk.sample = sample
sk.save()
self.assertEqual(set(experiment.get_sample_keywords()),
set(["medulloblastoma"]))
def test_qn_management_command(self):
"""Test that the management command fires off and then does not create
a job for an organism that does not have enough samples on the same
platform."""
homo_sapiens = Organism(name="HOMO_SAPIENS", taxonomy_id=9606)
homo_sapiens.save()
experiment = Experiment()
experiment.accession_code = "12345"
experiment.save()
codes = ["1", "2", "3", "4", "5", "6"]
# We don't have a 0.tsv
for code in codes:
sample = Sample()
sample.accession_code = code
sample.title = code
sample.platform_accession_code = "A-MEXP-1171"
sample.manufacturer = "SLIPPERY DICK'S DISCOUNT MICROARRAYS"
sample.organism = homo_sapiens
sample.technology = "MICROARRAY"
sample.is_processed = True
sample.save()
cr = ComputationalResult()
cr.save()
computed_file = ComputedFile()
computed_file.filename = code + ".tsv"
computed_file.absolute_file_path = "/home/user/data_store/QN/" + code + ".tsv"
computed_file.size_in_bytes = int(code)
computed_file.result = cr
computed_file.is_smashable = True
computed_file.save()
scfa = SampleComputedFileAssociation()
scfa.sample = sample
scfa.computed_file = computed_file
scfa.save()
exsa = ExperimentSampleAssociation()
exsa.experiment = experiment
exsa.sample = sample
exsa.save()
out = StringIO()
try:
call_command("create_qn_target", organism="homo_sapiens", min=1, stdout=out)
except SystemExit as e: # this is okay!
pass
stdout = out.getvalue()
self.assertFalse("Target file" in stdout)
# There's not enough samples available in this scenario so we
# shouldn't have even made a processor job.
self.assertEqual(ProcessorJob.objects.count(), 0)
def make_test_data(organism):
experiment = Experiment()
experiment.accession_code = "GSE51088"
experiment.technology = "RNA-SEQ"
experiment.save()
xoa = ExperimentOrganismAssociation()
xoa.experiment = experiment
xoa.organism = organism
xoa.save()
result = ComputationalResult()
result.save()
sample = Sample()
sample.accession_code = "GSM1237818"
sample.title = "GSM1237818"
sample.organism = organism
sample.technology = "RNA-SEQ"
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.s3_key = "smasher-test-quant.sf"
computed_file.s3_bucket = "data-refinery-test-assets"
computed_file.filename = "quant.sf"
computed_file.absolute_file_path = "/home/user/data_store/QUANT/smasher-test-quant.sf"
computed_file.result = result
computed_file.is_smashable = True
computed_file.size_in_bytes = 123123
computed_file.sha1 = (
"08c7ea90b66b52f7cd9d9a569717a1f5f3874967" # this matches with the downloaded file
)
computed_file.save()
computed_file = ComputedFile()
computed_file.filename = "logquant.tsv"
computed_file.is_smashable = True
computed_file.size_in_bytes = 123123
computed_file.result = result
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
def test_qn_reference(self, mock_send_job):
organism = Organism(name="HOMO_SAPIENS", taxonomy_id=9606)
organism.save()
experiment = Experiment()
experiment.accession_code = "12345"
experiment.save()
for code in [str(i) for i in range(1, 401)]:
sample = Sample()
sample.accession_code = code
sample.title = code
sample.platform_name = f"Affymetrix {organism.name}"
sample.platform_accession_code = f"A-MEXP-{organism.name}"
sample.manufacturer = "AFFYMETRIX"
sample.organism = organism
sample.technology = "MICROARRAY"
sample.is_processed = True
sample.has_raw = True
sample.save()
cr = ComputationalResult()
cr.save()
computed_file = ComputedFile()
computed_file.filename = code + ".tsv"
computed_file.absolute_file_path = "/home/user/data_store/QN/" + code + ".tsv"
computed_file.size_in_bytes = int(code)
computed_file.result = cr
computed_file.is_smashable = True
computed_file.save()
scfa = SampleComputedFileAssociation()
scfa.sample = sample
scfa.computed_file = computed_file
scfa.save()
exsa = ExperimentSampleAssociation()
exsa.experiment = experiment
exsa.sample = sample
exsa.save()
# We need more than one organism for the tests, but can't
# repeat accesion codes, so halfway through just change the organism.
if int(code) == 200:
organism = Organism(name="MUS_MUSCULUS", taxonomy_id=111)
organism.save()
# Setup is done, actually run the command.
command = Command()
command.handle(organisms="HOMO_SAPIENS,MUS_MUSCULUS")
self.assertEqual(len(mock_send_job.mock_calls), 2)
self.assertEqual(ProcessorJob.objects.count(), 2)
def setUp(self):
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()
self.experiment = experiment
# Create some samples to attach keywords to
sample = Sample()
sample.accession_code = "SRR123"
sample.technology = "RNA-SEQ"
sample.source_database = "SRA"
sample.title = "Not important"
sample.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
sample2 = Sample()
sample2.accession_code = "SRR456"
sample2.technology = "RNA-SEQ"
sample2.source_database = "SRA"
sample2.title = "Not important"
sample2.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample2
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
# Create the ontology terms I'm using in the tests
name = OntologyTerm()
name.ontology_term = "PATO:0000122"
name.human_readable_name = "length"
name.save()
unit = OntologyTerm()
unit.ontology_term = "UO:0010012"
unit.human_readable_name = "thou"
unit.save()
contribution = Contribution()
contribution.source_name = "refinebio_tests"
contribution.methods_url = "ccdatalab.org"
contribution.save()
self.contribution = contribution
def test_get_sample_metadata_fields_none(self):
experiment = Experiment()
experiment.save()
sample = Sample()
sample.title = "123"
sample.accession_code = "123"
sample.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
self.assertEqual(experiment.get_sample_metadata_fields(), [])
def test_get_sample_metadata_fields(self):
experiment = Experiment()
experiment.save()
sample = Sample()
sample.title = "123"
sample.accession_code = "123"
sample.specimen_part = "Lung"
sample.sex = "Male"
sample.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
self.assertEqual(set(experiment.get_sample_metadata_fields()), set(['specimen_part', 'sex']))
def prepare_experiment(ids: List[int]) -> Experiment:
(homo_sapiens, _) = Organism.objects.get_or_create(name="HOMO_SAPIENS",
taxonomy_id=9606)
experiment = Experiment()
experiment.accession_code = "12345"
experiment.save()
codes = [str(i) for i in ids]
for code in codes:
sample = Sample()
sample.accession_code = code
sample.title = code
sample.platform_accession_code = "A-MEXP-1171"
sample.manufacturer = "SLIPPERY DICK'S DISCOUNT MICROARRAYS"
sample.organism = homo_sapiens
sample.technology = "MICROARRAY"
sample.is_processed = True
sample.save()
cr = ComputationalResult()
cr.save()
computed_file = ComputedFile()
computed_file.filename = code + ".tsv"
computed_file.absolute_file_path = "/home/user/data_store/QN/" + code + ".tsv"
computed_file.size_in_bytes = int(code)
computed_file.result = cr
computed_file.is_smashable = True
computed_file.save()
scfa = SampleComputedFileAssociation()
scfa.sample = sample
scfa.computed_file = computed_file
scfa.save()
exsa = ExperimentSampleAssociation()
exsa.experiment = experiment
exsa.sample = sample
exsa.save()
def test_sra_experiment_missing_metadata(self):
"""Tests that an SRA experiment has its missing metadata added."""
# 1. Create an experiment with a bad title
BAD_TITLE = "GEO accession GSE1337 is currently private\
and is scheduled to be released on Jan 01, 1970."
experiment = Experiment()
experiment.accession_code = "DRP003977"
experiment.source_database = "SRA"
experiment.title = BAD_TITLE
experiment.save()
# 2. We need to add a sample because the way that the SRA surveyor finds metadata is
# through run accessions
sample = Sample()
sample.accession_code = "DRR002116"
sample.technology = "RNA-SEQ"
sample.source_database = "SRA"
sample.title = "Not important"
sample.save()
ExperimentSampleAssociation.objects.get_or_create(
experiment=experiment, sample=sample)
# 3. Setup is done, actually run the command.
command = Command()
command.handle()
# Test that the title was fixed
self.assertNotEqual(
Experiment.objects.get_or_create(
accession_code=experiment.accession_code)[0].title,
BAD_TITLE,
)
# Run the command again to make sure that it does not fail if there are no changes
command = Command()
command.handle()
def create_experiment_and_samples_from_api(
self, experiment_accession_code) -> (Experiment, List[Sample]):
""" The main surveyor - find the Experiment and Samples from NCBI GEO.
Uses the GEOParse library, for which docs can be found here: https://geoparse.readthedocs.io/en/latest/usage.html#working-with-geo-objects
"""
# Cleaning up is tracked here: https://github.com/guma44/GEOparse/issues/41
gse = GEOparse.get_GEO(experiment_accession_code,
destdir=self.get_temp_path(),
how="brief",
silent=True)
preprocessed_samples = harmony.preprocess_geo(gse.gsms.items())
harmonized_samples = harmony.harmonize(preprocessed_samples)
# Create the experiment object
try:
experiment_object = Experiment.objects.get(
accession_code=experiment_accession_code)
logger.debug(
"Experiment %s already exists, skipping object creation.",
experiment_accession_code,
survey_job=self.survey_job.id,
)
except Experiment.DoesNotExist:
experiment_object = Experiment()
experiment_object.accession_code = experiment_accession_code
GeoSurveyor._apply_metadata_to_experiment(experiment_object, gse)
experiment_object.save()
experiment_annotation = ExperimentAnnotation()
experiment_annotation.data = gse.metadata
experiment_annotation.experiment = experiment_object
experiment_annotation.is_ccdl = False
experiment_annotation.save()
# Okay, here's the situation!
# Sometimes, samples have a direct single representation for themselves.
# Othertimes, there is a single file with references to every sample in it.
created_samples = []
for sample_accession_code, sample in gse.gsms.items():
try:
sample_object = Sample.objects.get(
accession_code=sample_accession_code)
logger.debug(
"Sample %s from experiment %s already exists, skipping object creation.",
sample_accession_code,
experiment_object.accession_code,
survey_job=self.survey_job.id,
)
# Associate it with the experiment, but since it
# already exists it already has original files
# associated with it and it's already been downloaded,
# so don't add it to created_samples.
ExperimentSampleAssociation.objects.get_or_create(
experiment=experiment_object, sample=sample_object)
ExperimentOrganismAssociation.objects.get_or_create(
experiment=experiment_object,
organism=sample_object.organism)
except Sample.DoesNotExist:
organism = Organism.get_object_for_name(
sample.metadata["organism_ch1"][0].upper())
sample_object = Sample()
sample_object.source_database = "GEO"
sample_object.accession_code = sample_accession_code
sample_object.organism = organism
# If data processing step, it isn't raw.
sample_object.has_raw = not sample.metadata.get(
"data_processing", None)
ExperimentOrganismAssociation.objects.get_or_create(
experiment=experiment_object, organism=organism)
sample_object.title = sample.metadata["title"][0]
self.set_platform_properties(sample_object, sample.metadata,
gse)
GeoSurveyor._apply_harmonized_metadata_to_sample(
sample_object, harmonized_samples[sample_object.title])
# Sample-level protocol_info
sample_object.protocol_info = self.get_sample_protocol_info(
sample.metadata, sample_accession_code)
sample_object.save()
logger.debug("Created Sample: " + str(sample_object))
sample_annotation = SampleAnnotation()
sample_annotation.sample = sample_object
sample_annotation.data = sample.metadata
sample_annotation.is_ccdl = False
sample_annotation.save()
sample_supplements = sample.metadata.get(
"supplementary_file", [])
for supplementary_file_url in sample_supplements:
# Why do they give us this?
if supplementary_file_url == "NONE":
break
# We never want these!
if "idat.gz" in supplementary_file_url.lower():
continue
if "chp.gz" in supplementary_file_url.lower():
continue
if "ndf.gz" in supplementary_file_url.lower():
continue
if "pos.gz" in supplementary_file_url.lower():
continue
if "pair.gz" in supplementary_file_url.lower():
continue
if "gff.gz" in supplementary_file_url.lower():
continue
# Sometimes, we are lied to about the data processing step.
lower_file_url = supplementary_file_url.lower()
if (".cel" in lower_file_url
or ("_non_normalized.txt" in lower_file_url)
or ("_non-normalized.txt" in lower_file_url)
or ("-non-normalized.txt" in lower_file_url)
or ("-non_normalized.txt" in lower_file_url)):
sample_object.has_raw = True
sample_object.save()
# filename and source_filename are the same for these
filename = FileUtils.get_filename(supplementary_file_url)
original_file = OriginalFile.objects.get_or_create(
source_url=supplementary_file_url,
filename=filename,
source_filename=filename,
has_raw=sample_object.has_raw,
is_archive=FileUtils.is_archive(filename),
)[0]
logger.debug("Created OriginalFile: " + str(original_file))
original_file_sample_association = OriginalFileSampleAssociation.objects.get_or_create(
original_file=original_file, sample=sample_object)
if original_file.is_affy_data():
# Only Affymetrix Microarrays produce .CEL files
sample_object.technology = "MICROARRAY"
sample_object.manufacturer = "AFFYMETRIX"
sample_object.save()
# It's okay to survey RNA-Seq samples from GEO, but we
# don't actually want to download/process any RNA-Seq
# data unless it comes from SRA.
if sample_object.technology != "RNA-SEQ":
created_samples.append(sample_object)
# Now that we've determined the technology at the
# sample level, we can set it at the experiment level,
# just gotta make sure to only do it once. There can
# be more than one technology, this should be changed
# as part of:
# https://github.com/AlexsLemonade/refinebio/issues/1099
if not experiment_object.technology:
experiment_object.technology = sample_object.technology
experiment_object.save()
ExperimentSampleAssociation.objects.get_or_create(
experiment=experiment_object, sample=sample_object)
# These supplementary files _may-or-may-not_ contain the type of raw data we can process.
for experiment_supplement_url in gse.metadata.get(
"supplementary_file", []):
# filename and source_filename are the same for these
filename = experiment_supplement_url.split("/")[-1]
original_file = OriginalFile.objects.get_or_create(
source_url=experiment_supplement_url,
filename=filename,
source_filename=filename,
has_raw=sample_object.has_raw,
is_archive=True,
)[0]
logger.debug("Created OriginalFile: " + str(original_file))
lower_supplement_url = experiment_supplement_url.lower()
if (("_non_normalized.txt" in lower_supplement_url)
or ("_non-normalized.txt" in lower_supplement_url)
or ("-non-normalized.txt" in lower_supplement_url)
or ("-non_normalized.txt" in lower_supplement_url)):
for sample_object in created_samples:
sample_object.has_raw = True
sample_object.save()
OriginalFileSampleAssociation.objects.get_or_create(
sample=sample_object, original_file=original_file)
# Delete this Original file if it isn't being used.
if (OriginalFileSampleAssociation.objects.filter(
original_file=original_file).count() == 0):
original_file.delete()
# These are the Miniml/Soft/Matrix URLs that are always(?) provided.
# GEO describes different types of data formatting as "families"
family_url = self.get_miniml_url(experiment_accession_code)
miniml_original_file = OriginalFile.objects.get_or_create(
source_url=family_url,
source_filename=family_url.split("/")[-1],
has_raw=sample_object.has_raw,
is_archive=True,
)[0]
for sample_object in created_samples:
# We don't need a .txt if we have a .CEL
if sample_object.has_raw:
continue
OriginalFileSampleAssociation.objects.get_or_create(
sample=sample_object, original_file=miniml_original_file)
# Delete this Original file if it isn't being used.
if (OriginalFileSampleAssociation.objects.filter(
original_file=miniml_original_file).count() == 0):
miniml_original_file.delete()
# Trash the temp path
try:
shutil.rmtree(self.get_temp_path())
except Exception:
# There was a problem during surveying so this didn't get created.
# It's not a big deal.
pass
return experiment_object, created_samples
def test_organism_shepherd_command(self, mock_nomad, mock_send_job,
mock_get_active_volumes):
"""Tests that the organism shepherd requeues jobs in the right order.
The situation we're setting up is basically this:
* There are two experiments.
* One of them has 1/2 samples processed, the other 0/1
* One of them needs a DownloaderJob requeued and the other
needs a ProcessorJob requued.
And what we're going to test for is:
* Both of the jobs that need to be requeued are requeued.
* The experiment with a processed sample is requeued first
because it has a higher completion percentage.
"""
# First, set up our mocks to prevent network calls.
mock_send_job.return_value = True
active_volumes = {"1", "2", "3"}
mock_get_active_volumes.return_value = active_volumes
def mock_init_nomad(host, port=0, timeout=0):
ret_value = MagicMock()
ret_value.jobs = MagicMock()
ret_value.jobs.get_jobs = MagicMock()
ret_value.jobs.get_jobs.side_effect = lambda: []
return ret_value
mock_nomad.side_effect = mock_init_nomad
zebrafish = Organism(name="DANIO_RERIO",
taxonomy_id=1337,
is_scientific_name=True)
zebrafish.save()
# Experiment that is 0% complete.
zero_percent_experiment = Experiment(accession_code='ERP037000')
zero_percent_experiment.technology = 'RNA-SEQ'
zero_percent_experiment.save()
organism_assoc = ExperimentOrganismAssociation.objects.create(
organism=zebrafish, experiment=zero_percent_experiment)
zero_percent = OriginalFile()
zero_percent.filename = "ERR037001.fastq.gz"
zero_percent.source_filename = "ERR037001.fastq.gz"
zero_percent.source_url = "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR037/ERR037001/ERR037001_1.fastq.gz"
zero_percent.is_archive = True
zero_percent.save()
zero_percent_sample = Sample()
zero_percent_sample.accession_code = 'ERR037001'
zero_percent_sample.organism = zebrafish
zero_percent_sample.save()
assoc = OriginalFileSampleAssociation()
assoc.sample = zero_percent_sample
assoc.original_file = zero_percent
assoc.save()
assoc = ExperimentSampleAssociation()
assoc.sample = zero_percent_sample
assoc.experiment = zero_percent_experiment
assoc.save()
# TODO: fix names of all the variables to be appropriate for this test case.
zero_percent_dl_job = DownloaderJob()
zero_percent_dl_job.accession_code = zero_percent_sample.accession_code
zero_percent_dl_job.downloader_task = "SRA"
zero_percent_dl_job.start_time = timezone.now()
zero_percent_dl_job.end_time = timezone.now()
zero_percent_dl_job.success = False
zero_percent_dl_job.save()
assoc = DownloaderJobOriginalFileAssociation()
assoc.downloader_job = zero_percent_dl_job
assoc.original_file = zero_percent
assoc.save()
# Experiment that is 50% complete.
fify_percent_experiment = Experiment(accession_code='ERP036000')
fify_percent_experiment.technology = 'RNA-SEQ'
fify_percent_experiment.save()
organism_assoc = ExperimentOrganismAssociation.objects.create(
organism=zebrafish, experiment=fify_percent_experiment)
## First sample, this one has been processed.
successful_pj = ProcessorJob()
successful_pj.accession_code = "ERR036000"
successful_pj.pipeline_applied = "SALMON"
successful_pj.ram_amount = 12288
successful_pj.start_time = timezone.now()
successful_pj.end_time = timezone.now()
successful_pj.success = True
successful_pj.save()
successful_og = OriginalFile()
successful_og.filename = "ERR036000.fastq.gz"
successful_og.source_filename = "ERR036000.fastq.gz"
successful_og.source_url = "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR036/ERR036000/ERR036000_1.fastq.gz"
successful_og.is_archive = True
successful_og.save()
successful_sample = Sample()
successful_sample.accession_code = 'ERR036000'
successful_sample.organism = zebrafish
successful_sample.save()
assoc = OriginalFileSampleAssociation()
assoc.sample = successful_sample
assoc.original_file = successful_og
assoc.save()
assoc = ProcessorJobOriginalFileAssociation()
assoc.processor_job = successful_pj
assoc.original_file = successful_og
assoc.save()
assoc = ExperimentSampleAssociation()
assoc.sample = successful_sample
assoc.experiment = fify_percent_experiment
assoc.save()
## Second sample, this one hasn't been processed.
fifty_percent_unprocessed_og = OriginalFile()
fifty_percent_unprocessed_og.filename = "ERR036001.fastq.gz"
fifty_percent_unprocessed_og.source_filename = "ERR036001.fastq.gz"
fifty_percent_unprocessed_og.source_url = "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR036/ERR036001/ERR036001_1.fastq.gz"
fifty_percent_unprocessed_og.is_archive = True
fifty_percent_unprocessed_og.save()
fifty_percent_unprocessed_sample = Sample()
fifty_percent_unprocessed_sample.accession_code = 'ERR036001'
fifty_percent_unprocessed_sample.organism = zebrafish
fifty_percent_unprocessed_sample.save()
assoc = OriginalFileSampleAssociation()
assoc.sample = fifty_percent_unprocessed_sample
assoc.original_file = fifty_percent_unprocessed_og
assoc.save()
assoc = ExperimentSampleAssociation()
assoc.sample = fifty_percent_unprocessed_sample
assoc.experiment = fify_percent_experiment
assoc.save()
fifty_percent_processor_job = ProcessorJob()
fifty_percent_processor_job.pipeline_applied = "SALMON"
fifty_percent_processor_job.accession_code = fifty_percent_unprocessed_sample.accession_code
fifty_percent_processor_job.ram_amount = 12288
fifty_percent_processor_job.start_time = timezone.now()
fifty_percent_processor_job.end_time = timezone.now()
fifty_percent_processor_job.success = False
fifty_percent_processor_job.save()
assoc = ProcessorJobOriginalFileAssociation()
assoc.processor_job = fifty_percent_processor_job
assoc.original_file = fifty_percent_unprocessed_og
assoc.save()
# Setup is done, actually run the command.
args = []
options = {"organism_name": "DANIO_RERIO"}
call_command("organism_shepherd", *args, **options)
# Verify that the jobs were called in the correct order.
mock_calls = mock_send_job.mock_calls
first_call_job_type = mock_calls[0][1][0]
first_call_job_object = mock_calls[0][2]["job"]
self.assertEqual(first_call_job_type, ProcessorPipeline.SALMON)
self.assertEqual(first_call_job_object.pipeline_applied,
fifty_percent_processor_job.pipeline_applied)
self.assertEqual(first_call_job_object.ram_amount,
fifty_percent_processor_job.ram_amount)
self.assertIn(first_call_job_object.volume_index, active_volumes)
fifty_percent_processor_job.refresh_from_db()
self.assertEqual(first_call_job_object,
fifty_percent_processor_job.retried_job)
second_call_job_type = mock_calls[1][1][0]
second_call_job_object = mock_calls[1][2]["job"]
self.assertEqual(second_call_job_type, Downloaders.SRA)
self.assertEqual(second_call_job_object.accession_code,
zero_percent_dl_job.accession_code)
self.assertEqual(second_call_job_object.downloader_task,
zero_percent_dl_job.downloader_task)
zero_percent_dl_job.refresh_from_db()
self.assertEqual(second_call_job_object,
zero_percent_dl_job.retried_job)
def test_dualtech_smash(self):
""" """
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1487313"
experiment.save()
result = ComputationalResult()
result.save()
gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS")
sample = Sample()
sample.accession_code = 'GSM1487313'
sample.title = 'GSM1487313'
sample.organism = gallus_gallus
sample.technology = "MICROARRAY"
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.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# RNASEQ TECH
experiment2 = Experiment()
experiment2.accession_code = "SRS332914"
experiment2.save()
result2 = ComputationalResult()
result2.save()
sample2 = Sample()
sample2.accession_code = 'SRS332914'
sample2.title = 'SRS332914'
sample2.organism = gallus_gallus
sample2.technology = "RNA-SEQ"
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.absolute_file_path = "/home/user/data_store/PCL/" + computed_file2.filename
computed_file2.result = result2
computed_file2.size_in_bytes = 234
computed_file2.is_smashable = True
computed_file2.save()
assoc2 = SampleComputedFileAssociation()
assoc2.sample = sample2
assoc2.computed_file = computed_file2
assoc2.save()
# CROSS-SMASH BY SPECIES
ds = Dataset()
ds.data = {'GSE1487313': ['GSM1487313'], 'SRX332914': ['SRS332914']}
ds.aggregate_by = 'SPECIES'
ds.scale_by = 'STANDARD'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
self.assertTrue(ds.is_cross_technology())
final_context = smasher.smash(pj.pk, upload=False)
self.assertTrue(os.path.exists(final_context['output_file']))
os.remove(final_context['output_file'])
self.assertEqual(len(final_context['final_frame'].columns), 2)
# THEN BY EXPERIMENT
ds.aggregate_by = 'EXPERIMENT'
ds.save()
dsid = ds.id
ds = Dataset.objects.get(id=dsid)
pj.start_time = None
pj.end_time = None
pj.save()
final_context = smasher.smash(pj.pk, upload=False)
self.assertTrue(os.path.exists(final_context['output_file']))
os.remove(final_context['output_file'])
self.assertEqual(len(final_context['final_frame'].columns), 1)
# THEN BY ALL
ds.aggregate_by = 'ALL'
ds.save()
dsid = ds.id
ds = Dataset.objects.get(id=dsid)
pj.start_time = None
pj.end_time = None
pj.save()
final_context = smasher.smash(pj.pk, upload=False)
self.assertTrue(os.path.exists(final_context['output_file']))
self.assertEqual(len(final_context['final_frame'].columns), 2)
def test_log2(self):
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
# Has non-log2 data:
# https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44421
# ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE44nnn/GSE44421/miniml/GSE44421_family.xml.tgz
experiment = Experiment()
experiment.accession_code = "GSE44421"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1084806'
sample.title = 'GSM1084806'
sample.organism = homo_sapiens
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 = "GSM1084806-tbl-1.txt"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'GSM1084807'
sample.title = 'GSM1084807'
sample.organism = homo_sapiens
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 = "GSM1084807-tbl-1.txt"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE44421': ['GSM1084806', 'GSM1084807']}
ds.aggregate_by = 'EXPERIMENT'
ds.scale_by = 'MINMAX'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(pj.pk, upload=False)
ds = Dataset.objects.get(id=ds.id)
self.assertTrue(final_context['success'])
def test_no_smash_dupe_two(self):
""" Tests the SRP051449 case, where the titles collide. Also uses a real QN target file."""
job = ProcessorJob()
job.pipeline_applied = "SMASHER"
job.save()
experiment = Experiment()
experiment.accession_code = "SRP051449"
experiment.save()
result = ComputationalResult()
result.save()
danio_rerio = Organism.get_object_for_name("DANIO_RERIO")
sample = Sample()
sample.accession_code = 'SRR1731761'
sample.title = 'Danio rerio'
sample.organism = danio_rerio
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 = "SRR1731761_output_gene_lengthScaledTPM.tsv"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'SRR1731762'
sample.title = 'Danio rerio'
sample.organism = danio_rerio
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 = "SRR1731762_output_gene_lengthScaledTPM.tsv"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'SRP051449': ['SRR1731761', 'SRR1731762']}
ds.aggregate_by = 'SPECIES'
ds.scale_by = 'NONE'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = True
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
cr = ComputationalResult()
cr.save()
computed_file = ComputedFile()
computed_file.filename = "danio_target.tsv"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = cr
computed_file.size_in_bytes = 123
computed_file.is_smashable = False
computed_file.save()
cra = ComputationalResultAnnotation()
cra.data = {'organism_id': danio_rerio.id, 'is_qn': True}
cra.result = cr
cra.save()
final_context = smasher.smash(job.pk, upload=False)
self.assertTrue(final_context['success'])
def test_no_smash_dupe(self):
""" """
job = ProcessorJob()
job.pipeline_applied = "SMASHER"
job.save()
experiment = Experiment()
experiment.accession_code = "GSE51081"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1237810'
sample.title = 'GSM1237810'
sample.organism = homo_sapiens
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 = "GSM1237810_T09-1084.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'GSM1237811'
sample.title = 'GSM1237811'
sample.organism = homo_sapiens
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810', 'GSM1237811']}
ds.aggregate_by = 'ALL'
ds.scale_by = 'STANDARD'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(job.pk, upload=False)
dsid = ds.id
ds = Dataset.objects.get(id=dsid)
self.assertTrue(ds.success)
for column in final_context['original_merged'].columns:
self.assertTrue('_x' not in column)
def test_create_compendia_danio(self):
job = ProcessorJob()
job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value
job.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1234"
experiment.save()
result = ComputationalResult()
result.save()
qn_target = ComputedFile()
qn_target.filename = "danio_target.tsv"
qn_target.absolute_file_path = "/home/user/data_store/QN/danio_target.tsv"
qn_target.is_qn_target = True
qn_target.size_in_bytes = "12345"
qn_target.sha1 = "aabbccddeeff"
qn_target.result = result
qn_target.save()
danio_rerio = Organism(name="DANIO_RERIO",
taxonomy_id=1,
qn_target=result)
danio_rerio.save()
cra = ComputationalResultAnnotation()
cra.data = {}
cra.data["organism_id"] = danio_rerio.id
cra.data["is_qn"] = True
cra.result = result
cra.save()
result = ComputationalResult()
result.save()
micros = []
for file in os.listdir("/home/user/data_store/raw/TEST/MICROARRAY/"):
if "microarray.txt" in file:
continue
sample = Sample()
sample.accession_code = file
sample.title = file
sample.organism = danio_rerio
sample.technology = "MICROARRAY"
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 = file
computed_file.absolute_file_path = "/home/user/data_store/raw/TEST/MICROARRAY/" + file
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
micros.append(file)
experiment = Experiment()
experiment.accession_code = "GSE5678"
experiment.save()
result = ComputationalResult()
result.save()
rnas = []
for file in os.listdir("/home/user/data_store/raw/TEST/RNASEQ/"):
if "rnaseq.txt" in file:
continue
sample = Sample()
sample.accession_code = file
sample.title = file
sample.organism = danio_rerio
sample.technology = "RNASEQ"
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 = file
computed_file.absolute_file_path = "/home/user/data_store/raw/TEST/RNASEQ/" + file
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
rnas.append(file)
# Missing sample that will be filtered
sample = Sample()
sample.accession_code = "GSM1487222"
sample.title = "this sample will be filtered"
sample.organism = danio_rerio
sample.technology = "RNASEQ"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
rnas.append(sample.accession_code)
dset = Dataset()
dset.data = {"GSE1234": micros, "GSE5678": rnas}
dset.scale_by = "NONE"
dset.aggregate_by = "SPECIES"
dset.svd_algorithm = "ARPACK"
dset.quantile_normalize = False
dset.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = dset
pjda.save()
final_context = create_compendia.create_compendia(job.id)
# Verify result
self.assertEqual(
final_context["compendium_result"].result.computedfile_set.count(),
1)
for file in final_context[
"compendium_result"].result.computedfile_set.all():
self.assertTrue(os.path.exists(file.absolute_file_path))
# test compendium_result
self.assertEqual(final_context["compendium_result"].svd_algorithm,
"ARPACK")
self.assertEqual(
final_context["compendium_result"].primary_organism.name,
final_context["organism_name"])
self.assertEqual(
final_context["compendium_result"].primary_organism.name,
"DANIO_RERIO")
self.assertEqual(final_context["compendium_result"].organisms.count(),
1)
# check that sample with no computed file was skipped
self.assertTrue("GSM1487222" in final_context["filtered_samples"])
self.assertEqual(
final_context["filtered_samples"]["GSM1487222"]
["experiment_accession_code"], "GSE5678")
def test_create_compendia(self):
job = ProcessorJob()
job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value
job.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1487313"
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.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.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# Missing sample that will be filtered
sample = Sample()
sample.accession_code = "GSM1487222"
sample.title = "this sample will be filtered"
sample.organism = gallus_gallus
sample.technology = "MICROARRAY"
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 = "GSM1487222_empty.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/doesnt_exists.PCL"
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# RNASEQ TECH
experiment2 = Experiment()
experiment2.accession_code = "SRS332914"
experiment2.save()
result2 = ComputationalResult()
result2.save()
sample2 = Sample()
sample2.accession_code = "SRS332914"
sample2.title = "SRS332914"
sample2.organism = gallus_gallus
sample2.technology = "RNA-SEQ"
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.absolute_file_path = "/home/user/data_store/PCL/" + computed_file2.filename
computed_file2.result = result2
computed_file2.size_in_bytes = 234
computed_file2.is_smashable = True
computed_file2.save()
assoc2 = SampleComputedFileAssociation()
assoc2.sample = sample2
assoc2.computed_file = computed_file2
assoc2.save()
dset = Dataset()
dset.data = {
"GSE1487313": ["GSM1487313", "GSM1487222"],
"SRX332914": ["SRS332914"]
}
dset.scale_by = "NONE"
dset.aggregate_by = "SPECIES"
dset.svd_algorithm = "ARPACK"
dset.quantile_normalize = False
dset.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = dset
pjda.save()
final_context = create_compendia.create_compendia(job.id)
self.assertFalse(job.success)
# check that sample with no computed file was skipped
self.assertTrue("GSM1487222" in final_context["filtered_samples"])
self.assertEqual(
final_context["filtered_samples"]["GSM1487222"]
["experiment_accession_code"],
"GSE1487313",
)
def test_no_repeat_jobs(self):
"""Make sure that queue_downloader_jobs queues all expected Downloader
jobs for a given experiment.
"""
# First, create an experiment with two samples associated with it
# and create two original files for each of those samples.
experiment_object = Experiment()
experiment_object.accession_code = "Experiment1"
experiment_object.save()
sample_object = Sample()
sample_object.accession_code = "Sample1"
sample_object.platform_accession_code = "Illumina Genome Analyzer"
sample_object.platform_accession_name = "Illumina Genome Analyzer"
sample_object.technology = "RNA-SEQ"
sample_object.manufacturer = "ILLUMINA"
sample_object.source_database = "SRA"
sample_object.save()
original_file_1 = OriginalFile()
original_file_1.source_url = "first_url"
original_file_1.source_filename = "first_filename"
original_file_1.is_downloaded = False
original_file_1.has_raw = True
original_file_1.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file_1
original_file_sample_association.sample = sample_object
original_file_sample_association.save()
original_file_2 = OriginalFile()
original_file_2.source_url = "second_url"
original_file_2.source_filename = "second_filename"
original_file_2.is_downloaded = False
original_file_2.has_raw = True
original_file_2.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file_2
original_file_sample_association.sample = sample_object
original_file_sample_association.save()
dlj = DownloaderJob()
dlj.save()
DownloaderJobOriginalFileAssociation(
downloader_job=dlj, original_file=original_file_1
).save()
DownloaderJobOriginalFileAssociation(
downloader_job=dlj, original_file=original_file_2
).save()
survey_job = SurveyJob(source_type="SRA")
survey_job.save()
surveyor = SraSurveyor(survey_job)
surveyor.queue_downloader_job_for_original_files(
[original_file_1, original_file_2], experiment_object.accession_code
)
# We made one DownloaderJob in this test, so
# queue_downloader_job_for_original_files didn't have anything
# to do, so there should still be only one:
self.assertEqual(1, DownloaderJob.objects.all().count())
def test_make_experiment_result_associations(self):
"""Tests that the correct associations are made.
The situation we're setting up is basically this:
* tximport has been run for an experiment.
* It made associations between the samples in
the experiment and the ComputationalResult.
* It didn't make associations between the
experiment itself and the ComputationalResult.
* There is a second experiment that hasn't had
tximport run but shares a sample with the
other experiment.
* This second experiment has a sample which has
not yet had tximport run on it.
And what we're going to test for is:
* An association is created between the tximport
result and the first experiment.
* An association is NOT created between the
tximport result and the second experiment.
"""
# Get an organism to set on samples:
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS",
taxonomy_id=9606)
# Create the tximport processor and result:
processor = Processor()
processor.name = "Tximport"
processor.version = "v9.9.9"
processor.docker_image = "dr_salmon"
processor.environment = '{"some": "environment"}'
processor.save()
result = ComputationalResult()
result.commands.append("tximport invocation")
result.is_ccdl = True
result.processor = processor
result.save()
# Create the first experiment and it's samples:
processed_experiment = Experiment()
processed_experiment.accession_code = "SRP12345"
processed_experiment.save()
processed_sample_one = Sample()
processed_sample_one.accession_code = "SRX12345"
processed_sample_one.title = "SRX12345"
processed_sample_one.organism = homo_sapiens
processed_sample_one.save()
sra = SampleResultAssociation()
sra.sample = processed_sample_one
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = processed_experiment
esa.sample = processed_sample_one
esa.save()
processed_sample_two = Sample()
processed_sample_two.accession_code = "SRX12346"
processed_sample_two.title = "SRX12346"
processed_sample_two.organism = homo_sapiens
processed_sample_two.save()
sra = SampleResultAssociation()
sra.sample = processed_sample_two
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = processed_experiment
esa.sample = processed_sample_two
esa.save()
# Create the second experiment and it's additional sample.
unprocessed_experiment = Experiment()
unprocessed_experiment.accession_code = "SRP6789"
unprocessed_experiment.save()
unprocessed_sample = Sample()
unprocessed_sample.accession_code = "SRX6789"
unprocessed_sample.title = "SRX6789"
unprocessed_sample.organism = homo_sapiens
unprocessed_sample.save()
sra = SampleResultAssociation()
sra.sample = unprocessed_sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = unprocessed_experiment
esa.sample = unprocessed_sample
esa.save()
esa = ExperimentSampleAssociation()
esa.experiment = unprocessed_experiment
esa.sample = processed_sample_two
esa.save()
# Run the function we're testing:
make_experiment_result_associations()
# Test that only one association was created and that it was
# to the processed experiment:
eras = ExperimentResultAssociation.objects.all()
self.assertEqual(len(eras), 1)
self.assertEqual(eras.first().experiment, processed_experiment)
def test_queue_downloader_jobs_for_original_files(self, mock_send_task):
"""Make sure that queue_downloader_jobs queues all expected Downloader
jobs for a given experiment.
"""
# First, create an experiment with two samples associated with it
# and create two original files for each of those samples.
experiment_object = Experiment()
experiment_object.accession_code = "Experiment1"
experiment_object.save()
sample_object_1 = Sample()
sample_object_1.accession_code = "Sample1"
sample_object_1.platform_accession_code = "Illumina Genome Analyzer"
sample_object_1.platform_accession_name = "Illumina Genome Analyzer"
sample_object_1.technology = "RNA-SEQ"
sample_object_1.manufacturer = "ILLUMINA"
sample_object_1.source_database = "SRA"
sample_object_1.save()
sample_object_2 = Sample()
sample_object_2.accession_code = "Sample2"
sample_object_2.platform_accession_code = "Illumina Genome Analyzer"
sample_object_2.platform_accession_name = "Illumina Genome Analyzer"
sample_object_2.technology = "RNA-SEQ"
sample_object_2.manufacturer = "ILLUMINA"
sample_object_2.source_database = "SRA"
sample_object_2.save()
association = ExperimentSampleAssociation()
association.experiment = experiment_object
association.sample = sample_object_1
association.save()
association = ExperimentSampleAssociation()
association.experiment = experiment_object
association.sample = sample_object_2
association.save()
sample_1_original_files = []
sample_2_original_files = []
original_file = OriginalFile()
original_file.source_url = "first_url"
original_file.source_filename = "first_filename"
original_file.is_downloaded = False
original_file.has_raw = True
original_file.save()
sample_1_original_files.append(original_file)
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object_1
original_file_sample_association.save()
original_file = OriginalFile()
original_file.source_url = "second_url"
original_file.source_filename = "second_filename"
original_file.is_downloaded = False
original_file.has_raw = True
original_file.save()
sample_2_original_files.append(original_file)
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object_1
original_file_sample_association.save()
original_file = OriginalFile()
original_file.source_url = "third_url"
original_file.source_filename = "third_filename"
original_file.is_downloaded = False
original_file.has_raw = True
original_file.save()
sample_2_original_files.append(original_file)
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object_2
original_file_sample_association.save()
original_file = OriginalFile()
original_file.source_url = "fourth_url"
original_file.source_filename = "fourth_filename"
original_file.is_downloaded = False
original_file.has_raw = True
original_file.save()
sample_2_original_files.append(original_file)
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.original_file = original_file
original_file_sample_association.sample = sample_object_2
original_file_sample_association.save()
survey_job = SurveyJob(source_type="SRA")
survey_job.save()
surveyor = SraSurveyor(survey_job)
surveyor.queue_downloader_job_for_original_files(
sample_1_original_files, experiment_object.accession_code
)
surveyor.queue_downloader_job_for_original_files(
sample_2_original_files, experiment_object.accession_code
)
self.assertEqual(DownloaderJob.objects.all().count(), 2)
def create_experiment_from_api(
self, experiment_accession_code: str) -> (Experiment, Dict):
"""Given an experiment accession code, create an Experiment object.
Also returns a dictionary of additional information about the
platform discovered for the experiment.
Will raise an UnsupportedPlatformException if this experiment was
conducted using a platform which we don't support.
See an example at: https://www.ebi.ac.uk/arrayexpress/json/v3/experiments/E-MTAB-3050/sample
"""
request_url = EXPERIMENTS_URL + experiment_accession_code
experiment_request = utils.requests_retry_session().get(request_url,
timeout=60)
try:
parsed_json = experiment_request.json(
)["experiments"]["experiment"][0]
except KeyError:
logger.error("Remote experiment has no Experiment data!",
experiment_accession_code=experiment_accession_code,
survey_job=self.survey_job.id)
raise
experiment = {}
experiment["name"] = parsed_json["name"]
experiment["experiment_accession_code"] = experiment_accession_code
# This experiment has no platform at all, and is therefore useless.
if 'arraydesign' not in parsed_json or len(
parsed_json["arraydesign"]) == 0:
logger.warn("Remote experiment has no arraydesign listed.",
experiment_accession_code=experiment_accession_code,
survey_job=self.survey_job.id)
raise UnsupportedPlatformException
# If there is more than one arraydesign listed in the experiment
# then there is no other way to determine which array was used
# for which sample other than looking at the header of the CEL
# file. That obviously cannot happen until the CEL file has been
# downloaded so we can just mark it as UNKNOWN and let the
# downloader inspect the downloaded file to determine the
# array then.
elif len(parsed_json["arraydesign"]
) != 1 or "accession" not in parsed_json["arraydesign"][0]:
experiment["platform_accession_code"] = UNKNOWN
experiment["platform_accession_name"] = UNKNOWN
experiment["manufacturer"] = UNKNOWN
else:
external_accession = parsed_json["arraydesign"][0]["accession"]
for platform in get_supported_microarray_platforms():
if platform["external_accession"] == external_accession:
experiment[
"platform_accession_code"] = get_normalized_platform(
platform["platform_accession"])
# Illumina appears in the accession codes for
# platforms manufactured by Illumina
if "ILLUMINA" in experiment[
"platform_accession_code"].upper():
experiment["manufacturer"] = "ILLUMINA"
experiment["platform_accession_name"] = platform[
"platform_accession"]
else:
# It's not Illumina, the only other supported Microarray platform is
# Affy. As our list of supported platforms grows this logic will
# need to get more sophisticated.
experiment["manufacturer"] = "AFFYMETRIX"
platform_mapping = get_readable_affymetrix_names()
experiment[
"platform_accession_name"] = platform_mapping[
platform["platform_accession"]]
if "platform_accession_code" not in experiment:
# We don't know what platform this accession corresponds to.
experiment["platform_accession_code"] = external_accession
experiment["platform_accession_name"] = UNKNOWN
experiment["manufacturer"] = UNKNOWN
experiment["release_date"] = parsed_json["releasedate"]
if "lastupdatedate" in parsed_json:
experiment["last_update_date"] = parsed_json["lastupdatedate"]
else:
experiment["last_update_date"] = parsed_json["releasedate"]
# Create the experiment object
try:
experiment_object = Experiment.objects.get(
accession_code=experiment_accession_code)
logger.debug(
"Experiment already exists, skipping object creation.",
experiment_accession_code=experiment_accession_code,
survey_job=self.survey_job.id)
except Experiment.DoesNotExist:
# We aren't sure these fields will be populated, or how many there will be.
# Try to join them all together, or set a sensible default.
experiment_descripton = ""
if "description" in parsed_json and len(
parsed_json["description"]) > 0:
for description_item in parsed_json["description"]:
if "text" in description_item:
experiment_descripton = experiment_descripton + description_item[
"text"] + "\n"
if experiment_descripton == "":
experiment_descripton = "Description not available.\n"
experiment_object = Experiment()
experiment_object.accession_code = experiment_accession_code
experiment_object.source_url = request_url
experiment_object.source_database = "ARRAY_EXPRESS"
experiment_object.title = parsed_json["name"]
# This will need to be updated if we ever use Array
# Express to get other kinds of data.
experiment_object.technology = "MICROARRAY"
experiment_object.description = experiment_descripton
experiment_object.source_first_published = parse_datetime(
experiment["release_date"])
experiment_object.source_last_modified = parse_datetime(
experiment["last_update_date"])
experiment_object.save()
json_xa = ExperimentAnnotation()
json_xa.experiment = experiment_object
json_xa.data = parsed_json
json_xa.is_ccdl = False
json_xa.save()
## Fetch and parse the IDF/SDRF file for any other fields
IDF_URL_TEMPLATE = "https://www.ebi.ac.uk/arrayexpress/files/{code}/{code}.idf.txt"
idf_url = IDF_URL_TEMPLATE.format(code=experiment_accession_code)
idf_text = utils.requests_retry_session().get(idf_url,
timeout=60).text
lines = idf_text.split('\n')
idf_dict = {}
for line in lines:
keyval = line.strip().split('\t')
if len(keyval) == 2:
idf_dict[keyval[0]] = keyval[1]
elif len(keyval) > 2:
idf_dict[keyval[0]] = keyval[1:]
idf_xa = ExperimentAnnotation()
idf_xa.data = idf_dict
idf_xa.experiment = experiment_object
idf_xa.is_ccdl = False
idf_xa.save()
if 'Investigation Title' in idf_dict:
experiment_object.title = idf_dict['Investigation Title']
if 'Person Affiliation' in idf_dict:
# This is very rare, ex: E-MEXP-32
if isinstance(idf_dict['Person Affiliation'], list):
unique_people = list(set(idf_dict['Person Affiliation']))
experiment_object.submitter_institution = ", ".join(
unique_people)[:255]
else:
experiment_object.submitter_institution = idf_dict[
'Person Affiliation']
# Get protocol_description from "<experiment_url>/protocols"
# instead of from idf_dict, because the former provides more
# details.
protocol_url = request_url + '/protocols'
protocol_request = utils.requests_retry_session().get(protocol_url,
timeout=60)
try:
experiment_object.protocol_description = protocol_request.json(
)['protocols']
except KeyError:
logger.warning(
"Remote experiment has no protocol data!",
experiment_accession_code=experiment_accession_code,
survey_job=self.survey_job.id)
if 'Publication Title' in idf_dict:
# This will happen for some superseries.
# Ex: E-GEOD-29536
# Assume most recent is "best:, store the rest in experiment annotation.
if isinstance(idf_dict['Publication Title'], list):
experiment_object.publication_title = "; ".join(
idf_dict['Publication Title'])
else:
experiment_object.publication_title = idf_dict[
'Publication Title']
experiment_object.has_publication = True
if 'Publication DOI' in idf_dict:
if isinstance(idf_dict['Publication DOI'], list):
experiment_object.publication_doi = ", ".join(
idf_dict['Publication DOI'])
else:
experiment_object.publication_doi = idf_dict[
'Publication DOI']
experiment_object.has_publication = True
if 'PubMed ID' in idf_dict:
if isinstance(idf_dict['PubMed ID'], list):
experiment_object.pubmed_id = ", ".join(
idf_dict['PubMed ID'])
else:
experiment_object.pubmed_id = idf_dict['PubMed ID']
experiment_object.has_publication = True
# Scrape publication title and authorship from Pubmed
if experiment_object.pubmed_id:
pubmed_metadata = utils.get_title_and_authors_for_pubmed_id(
experiment_object.pubmed_id)
experiment_object.publication_title = pubmed_metadata[0]
experiment_object.publication_authors = pubmed_metadata[1]
experiment_object.save()
platform_dict = {}
for k in ('platform_accession_code', 'platform_accession_name',
'manufacturer'):
platform_dict[k] = experiment[k]
return experiment_object, platform_dict
def test_qn_reference(self):
job = ProcessorJob()
job.pipeline_applied = "QN_REFERENCE"
job.save()
homo_sapiens = Organism(name="HOMO_SAPIENS", taxonomy_id=9606)
homo_sapiens.save()
experiment = Experiment()
experiment.accession_code = "12345"
experiment.save()
# We don't have a 0.tsv
codes = [str(i) for i in range(1, 201)]
for code in codes:
sample = Sample()
sample.accession_code = code
sample.title = code
sample.platform_accession_code = "A-MEXP-1171"
sample.manufacturer = "SLIPPERY DICK'S DISCOUNT MICROARRAYS"
sample.organism = homo_sapiens
sample.technology = "MICROARRAY"
sample.is_processed = True
sample.save()
cr = ComputationalResult()
cr.save()
computed_file = ComputedFile()
computed_file.filename = code + ".tsv"
computed_file.absolute_file_path = "/home/user/data_store/QN/" + code + ".tsv"
computed_file.size_in_bytes = int(code)
computed_file.result = cr
computed_file.is_smashable = True
computed_file.save()
scfa = SampleComputedFileAssociation()
scfa.sample = sample
scfa.computed_file = computed_file
scfa.save()
exsa = ExperimentSampleAssociation()
exsa.experiment = experiment
exsa.sample = sample
exsa.save()
dataset = Dataset()
dataset.data = {"12345": ["1", "2", "3", "4", "5", "6"]}
dataset.aggregate_by = "ALL"
dataset.scale_by = "NONE"
dataset.quantile_normalize = False # We don't QN because we're creating the target now
dataset.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = dataset
pjda.save()
final_context = qn_reference.create_qn_reference(job.pk)
self.assertTrue(final_context["success"])
self.assertTrue(os.path.exists(final_context["target_file"]))
self.assertEqual(os.path.getsize(final_context["target_file"]), 562)
homo_sapiens.refresh_from_db()
target = homo_sapiens.qn_target.computedfile_set.latest()
self.assertEqual(target.sha1, "de69d348f8b239479e2330d596c4013a7b0b2b6a")
# Create and run a smasher job that will use the QN target we just made.
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
ds = Dataset()
ds.data = {"12345": ["1", "2", "3", "4", "5"]}
ds.aggregate_by = "SPECIES"
ds.scale_by = "STANDARD"
ds.email_address = "*****@*****.**"
ds.quantile_normalize = True
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(pj.pk, upload=False)
self.assertTrue(final_context["success"])
np.testing.assert_almost_equal(final_context["merged_qn"]["1"][0], -0.4379488527774811)
np.testing.assert_almost_equal(final_context["original_merged"]["1"][0], -0.5762109)
def test_bad_overlap(self):
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
experiment = Experiment()
experiment.accession_code = "GSE51081"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1237810'
sample.title = 'GSM1237810'
sample.organism = homo_sapiens
sample.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {'hi': 'friend'}
sample_annotation.sample = sample
sample_annotation.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 = "big.PCL"
computed_file.absolute_file_path = "/home/user/data_store/BADSMASH/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'GSM1237812'
sample.title = 'GSM1237812'
sample.organism = homo_sapiens
sample.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.filename = "small.PCL"
computed_file.absolute_file_path = "/home/user/data_store/BADSMASH/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810', 'GSM1237812']}
ds.aggregate_by = 'ALL' # [ALL or SPECIES or EXPERIMENT]
ds.scale_by = 'NONE' # [NONE or MINMAX or STANDARD or ROBUST]
ds.email_address = "*****@*****.**"
#ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(pj.pk, upload=False)
ds = Dataset.objects.get(id=ds.id)
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
# Now, make sure the bad can't zero this out.
sample = Sample()
sample.accession_code = 'GSM999'
sample.title = 'GSM999'
sample.organism = homo_sapiens
sample.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.filename = "bad.PCL"
computed_file.absolute_file_path = "/home/user/data_store/BADSMASH/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810', 'GSM1237812', 'GSM999']}
ds.aggregate_by = 'ALL' # [ALL or SPECIES or EXPERIMENT]
ds.scale_by = 'NONE' # [NONE or MINMAX or STANDARD or ROBUST]
ds.email_address = "*****@*****.**"
#ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(pj.pk, upload=False)
ds = Dataset.objects.get(id=ds.id)
self.assertEqual(len(final_context['final_frame']), 4)
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 prepare_job():
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
experiment = Experiment()
experiment.accession_code = "GSE51081"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1237810'
sample.title = 'GSM1237810'
sample.organism = homo_sapiens
sample.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {'hi': 'friend'}
sample_annotation.sample = sample
sample_annotation.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 = "GSM1237810_T09-1084.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'GSM1237812'
sample.title = 'GSM1237812'
sample.organism = homo_sapiens
sample.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1237812_S97-PURE.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1237812_S97-PURE.DAT"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = False
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810', 'GSM1237812']}
ds.aggregate_by = 'EXPERIMENT' # [ALL or SPECIES or EXPERIMENT]
ds.scale_by = 'STANDARD' # [NONE or MINMAX or STANDARD or ROBUST]
ds.email_address = "*****@*****.**"
#ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
return pj
def _generate_experiment_and_samples(
self,
run_accession: str,
study_accession: str = None) -> (Experiment, List[Sample]):
"""Generates Experiments and Samples for the provided run_accession."""
metadata = SraSurveyor.gather_all_metadata(run_accession)
if metadata == {}:
if study_accession:
logger.error(
"Could not discover any metadata for run.",
accession=run_accession,
study_accession=study_accession,
)
else:
logger.error("Could not discover any metadata for run.",
accession=run_accession)
return (None, None) # This will cascade properly
if DOWNLOAD_SOURCE == "ENA":
if metadata["library_layout"] == "PAIRED":
files_urls = [
_build_ena_file_url(run_accession, "_1"),
_build_ena_file_url(run_accession, "_2"),
]
else:
files_urls = [_build_ena_file_url(run_accession)]
else:
files_urls = [SraSurveyor._build_ncbi_file_url(run_accession)]
# Figure out the Organism for this sample
organism_name = metadata.pop("organism_name", None)
if not organism_name:
logger.error("Could not discover organism type for run.",
accession=run_accession)
return (None, None) # This will cascade properly
organism_name = organism_name.upper()
organism = Organism.get_object_for_name(organism_name)
##
# Experiment
##
experiment_accession_code = metadata.get("study_accession")
try:
experiment_object = Experiment.objects.get(
accession_code=experiment_accession_code)
logger.debug(
"Experiment already exists, skipping object creation.",
experiment_accession_code=experiment_accession_code,
survey_job=self.survey_job.id,
)
except Experiment.DoesNotExist:
experiment_object = Experiment()
experiment_object.accession_code = experiment_accession_code
SraSurveyor._apply_metadata_to_experiment(experiment_object,
metadata)
experiment_object.save()
##
# Experiment Metadata
##
json_xa = ExperimentAnnotation()
json_xa.experiment = experiment_object
json_xa.data = metadata
json_xa.is_ccdl = False
json_xa.save()
##
# Samples
##
sample_accession_code = metadata.pop("run_accession")
# Create the sample object
try:
sample_object = Sample.objects.get(
accession_code=sample_accession_code)
# If current experiment includes new protocol information,
# merge it into the sample's existing protocol_info.
protocol_info, is_updated = self.update_sample_protocol_info(
sample_object.protocol_info,
experiment_object.protocol_description,
experiment_object.source_url,
)
if is_updated:
sample_object.protocol_info = protocol_info
sample_object.save()
logger.debug(
"Sample %s already exists, skipping object creation.",
sample_accession_code,
experiment_accession_code=experiment_object.accession_code,
survey_job=self.survey_job.id,
)
except Sample.DoesNotExist:
sample_object = Sample()
sample_object.source_database = "SRA"
sample_object.accession_code = sample_accession_code
sample_object.organism = organism
sample_object.platform_name = metadata.get(
"platform_instrument_model", "UNKNOWN")
# The platform_name is human readable and contains spaces,
# accession codes shouldn't have spaces though:
sample_object.platform_accession_code = sample_object.platform_name.replace(
" ", "")
sample_object.technology = "RNA-SEQ"
if ("ILLUMINA" in sample_object.platform_name.upper()
or "NEXTSEQ" in sample_object.platform_name.upper()):
sample_object.manufacturer = "ILLUMINA"
elif "ION TORRENT" in sample_object.platform_name.upper():
sample_object.manufacturer = "ION_TORRENT"
else:
sample_object.manufacturer = "UNKNOWN"
SraSurveyor._apply_harmonized_metadata_to_sample(
sample_object, metadata)
protocol_info, is_updated = self.update_sample_protocol_info(
existing_protocols=[],
experiment_protocol=experiment_object.protocol_description,
experiment_url=experiment_object.source_url,
)
# Do not check is_updated the first time because we must
# save a list so we can append to it later.
sample_object.protocol_info = protocol_info
sample_object.save()
for file_url in files_urls:
original_file = OriginalFile.objects.get_or_create(
source_url=file_url,
source_filename=file_url.split("/")[-1],
has_raw=True)[0]
OriginalFileSampleAssociation.objects.get_or_create(
original_file=original_file, sample=sample_object)
# Create associations if they don't already exist
ExperimentSampleAssociation.objects.get_or_create(
experiment=experiment_object, sample=sample_object)
ExperimentOrganismAssociation.objects.get_or_create(
experiment=experiment_object, organism=organism)
return experiment_object, [sample_object]
def test_no_smash_all_diff_species(self):
""" Smashing together with 'ALL' with different species is a really weird behavior.
This test isn't really testing a normal case, just make sure that it's marking the
unsmashable files.
"""
job = ProcessorJob()
job.pipeline_applied = "SMASHER"
job.save()
experiment = Experiment()
experiment.accession_code = "GSE51081"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1237810'
sample.title = 'GSM1237810'
sample.organism = homo_sapiens
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 = "GSM1237810_T09-1084.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
experiment = Experiment()
experiment.accession_code = "GSE51084"
experiment.save()
mus_mus = Organism.get_object_for_name("MUS_MUSCULUS")
sample = Sample()
sample.accession_code = 'GSM1238108'
sample.title = 'GSM1238108'
sample.organism = homo_sapiens
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 = "GSM1238108-tbl-1.txt"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810'], 'GSE51084': ['GSM1238108']}
ds.aggregate_by = 'ALL'
ds.scale_by = 'STANDARD'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(job.pk, upload=False)
dsid = ds.id
ds = Dataset.objects.get(id=dsid)
print(ds.failure_reason)
print(final_context['dataset'].failure_reason)
self.assertEqual(final_context['unsmashable_files'], ['GSM1238108'])