def test_get_results(self):
""" Test our ability to collect the appropriate samples. """
sample = Sample()
sample.accession_code = 'GSM45588'
sample.save()
result = ComputationalResult()
result.save()
computed_file1 = ComputedFile()
computed_file1.filename = "oh_boy.txt"
computed_file1.result = result
computed_file1.size_in_bytes = 123
computed_file1.is_smashable = True
computed_file1.save()
computed_file2 = ComputedFile()
computed_file2.filename = "gee_whiz.bmp"
computed_file2.result = result
computed_file2.size_in_bytes = 123
computed_file2.is_smashable = False
computed_file2.save()
assoc = SampleResultAssociation()
assoc.sample = sample
assoc.result = result
assoc.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file1
assoc.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file2
assoc.save()
computed_files = sample.get_result_files()
self.assertEqual(computed_files.count(), 2)
def prepare_job():
# Create 10 job directories
for i in range(JOBS):
os.makedirs(LOCAL_ROOT_DIR + "/processor_job_" + str(i), exist_ok=True)
# These live on prod volumes at locations such as:
# /var/ebs/SRP057116/SRR1972985/SRR1972985.sra
os.makedirs(LOCAL_ROOT_DIR + "/SRP" + str(i), exist_ok=True)
os.makedirs(LOCAL_ROOT_DIR + "/SRP" + str(i) + "/SRR" + str(i),
exist_ok=True)
sample = Sample()
sample.accession_code = "SRR" + str(i)
sample.save()
cr = ComputationalResult()
cr.save()
cf = ComputedFile()
cf.result = cr
cf.size_in_bytes = 666
cf.save()
scfa = SampleComputedFileAssociation()
scfa.sample = sample
scfa.computed_file = cf
scfa.save()
# Create a job out of the range with index in it to make sure we
# don't delete index directories since that's where transcriptome
# indices get downloaded to.
os.makedirs(LOCAL_ROOT_DIR + "/processor_job_" + str(JOBS + 1) + "_index",
exist_ok=True)
os.makedirs(LOCAL_ROOT_DIR + "/SRP" + str(JOBS + 1) + "/SRR" +
str(JOBS + 1),
exist_ok=True)
sample = Sample()
sample.accession_code = "SRR" + str(JOBS + 1)
sample.save()
# Save two jobs so that we trigger two special circumstances, one
# where the job is still running and the other where the job isn't
# in Batch anymore.
pj = ProcessorJob()
pj.pipeline_applied = "SALMON"
pj.batch_job_id = "running_job"
pj.save()
pj = ProcessorJob()
pj.pipeline_applied = "SALMON"
pj.batch_job_id = "missing_job"
pj.save()
pj = ProcessorJob()
pj.pipeline_applied = "JANITOR"
pj.save()
return pj
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 test_fail(self):
""" Test our ability to fail """
result = ComputationalResult()
result.save()
sample = Sample()
sample.accession_code = 'XXX'
sample.title = 'XXX'
sample.organism = Organism.get_object_for_name("HOMO_SAPIENS")
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.filename = "NOT_REAL.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()
ds = Dataset()
ds.data = {'GSE51081': ['XXX']}
ds.aggregate_by = 'EXPERIMENT'
ds.scale_by = 'MINMAX'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
dsid = ds.id
job = ProcessorJob()
job.pipeline_applied = "SMASHER"
job.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(job.pk, upload=False)
ds = Dataset.objects.get(id=dsid)
print(ds.failure_reason)
print(final_context['dataset'].failure_reason)
self.assertNotEqual(final_context['unsmashable_files'], [])
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 create_sample_for_experiment(sample_info: Dict,
experiment: Experiment) -> Sample:
result = ComputationalResult()
result.save()
sample = Sample()
sample.accession_code = sample_info["accession_code"]
sample.title = sample_info.get("title",
None) or sample_info["accession_code"]
sample.organism = sample_info["organism"]
sample.technology = sample_info["technology"]
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
if sample_info.get("filename") is not None:
computed_file = ComputedFile()
computed_file.filename = sample_info["filename"]
computed_file.absolute_file_path = sample_info[
"data_dir"] + sample_info["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()
return sample
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 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 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_create_quantpendia(self):
job = ProcessorJob()
job.pipeline_applied = ProcessorPipeline.CREATE_QUANTPENDIA.value
job.save()
experiment = Experiment()
experiment.accession_code = "GSE51088"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS",
taxonomy_id=9606)
sample = Sample()
sample.accession_code = "GSM1237818"
sample.title = "GSM1237818"
sample.organism = homo_sapiens
sample.technology = "RNA-SEQ"
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()
ds = Dataset()
ds.data = {"GSE51088": ["GSM1237818"]}
ds.aggregate_by = "EXPERIMENT"
ds.scale_by = "STANDARD"
ds.email_address = "*****@*****.**"
ds.quant_sf_only = True # Make the dataset include quant.sf files only
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
final_context = create_quantpendia(job.id)
self.assertTrue(
os.path.exists(final_context["output_dir"] +
"/GSE51088/GSM1237818_quant.sf"))
self.assertTrue(
os.path.exists(final_context["output_dir"] + "/README.md"))
self.assertTrue(
os.path.exists(final_context["output_dir"] + "/LICENSE.TXT"))
self.assertTrue(
os.path.exists(final_context["output_dir"] +
"/aggregated_metadata.json"))
self.assertTrue(final_context["metadata"]["quant_sf_only"])
self.assertEqual(final_context["metadata"]["num_samples"], 1)
self.assertEqual(final_context["metadata"]["num_experiments"], 1)
# test that archive exists
quantpendia_file = ComputedFile.objects.filter(
is_compendia=True, quant_sf_only=True).latest()
self.assertTrue(os.path.exists(quantpendia_file.absolute_file_path))
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(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 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 test_qn_reference(self):
job = ProcessorJob()
job.pipeline_applied = "QN_REFERENCE"
job.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
experiment = Experiment()
experiment.accession_code = "12345"
experiment.save()
for code in ['1', '2', '3', '4', '5', '6']:
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()
file = ComputedFile()
file.filename = code + ".tsv"
file.absolute_file_path = "/home/user/data_store/QN/" + code + ".tsv"
file.size_in_bytes = int(code)
file.result = cr
file.is_smashable = True
file.save()
scfa = SampleComputedFileAssociation()
scfa.sample = sample
scfa.computed_file = 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']), 556)
target = utils.get_most_recent_qn_target_for_organism(homo_sapiens)
self.assertEqual(target.sha1, '636d72d5cbf4b9785b0bd271a1430b615feaa7ea')
###
# Smasher with QN
###
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'])
self.assertEqual(final_context['merged_qn']['1'][0], -0.4379488528812934)
self.assertEqual(final_context['original_merged']['1'][0], -0.576210936113982)
##
# Test via management command
##
from django.core.management import call_command
from django.test import TestCase
from django.utils.six import StringIO
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.assertTrue('Target file' in stdout)
path = stdout.split('\n')[0].split(':')[1].strip()
self.assertTrue(os.path.exists(path))
self.assertEqual(path, utils.get_most_recent_qn_target_for_organism(homo_sapiens).absolute_file_path)
def test_cleandb(self):
sample = Sample()
sample.save()
result = ComputationalResult()
result.save()
good_file = ComputedFile()
good_file.s3_bucket = "my_cool_bucket"
good_file.s3_key = "my_sweet_key"
good_file.size_in_bytes = 1337
good_file.result = result
good_file.is_public = True
good_file.is_smashable = True
good_file.save()
sca = SampleComputedFileAssociation()
sca.sample = sample
sca.computed_file = good_file
sca.save()
bad_file = ComputedFile()
bad_file.s3_bucket = None
bad_file.s3_key = None
bad_file.result = result
bad_file.size_in_bytes = 7331
bad_file.is_public = True
bad_file.is_smashable = True
bad_file.save()
sca = SampleComputedFileAssociation()
sca.sample = sample
sca.computed_file = bad_file
sca.save()
self.assertEqual(sample.computed_files.count(), 2)
self.assertEqual(sample.get_most_recent_smashable_result_file().id,
bad_file.id)
job_control.clean_database()
self.assertEqual(sample.get_most_recent_smashable_result_file().id,
good_file.id)
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'])
def _run_salmontools(job_context: Dict) -> Dict:
""" Run Salmontools to extract unmapped genes. """
logger.debug("Running SalmonTools ...")
unmapped_filename = job_context[
"output_directory"] + "aux_info/unmapped_names.txt"
command_str = "salmontools extract-unmapped -u {unmapped_file} -o {output} "
output_prefix = job_context["salmontools_directory"] + "unmapped_by_salmon"
command_str = command_str.format(unmapped_file=unmapped_filename,
output=output_prefix)
if "input_file_path_2" in job_context:
command_str += "-1 {input_1} -2 {input_2}"
command_str = command_str.format(
input_1=job_context["input_file_path"],
input_2=job_context["input_file_path_2"])
else:
command_str += "-r {input_1}"
command_str = command_str.format(
input_1=job_context["input_file_path"])
start_time = timezone.now()
logger.debug(
"Running the following SalmonTools command: %s",
command_str,
processor_job=job_context["job_id"],
)
completed_command = subprocess.run(command_str.split(),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
end_time = timezone.now()
# As of SalmonTools 0.1.0, completed_command.returncode is always 0,
# (even if error happens). completed_command.stderr is not totally
# reliable either, because it will output the following line even
# when the execution succeeds:
# "There were <N> unmapped reads\n"
# in which "<N>" is the number of lines in input unmapped_names.txt.
#
# As a workaround, we are using a regular expression here to test
# the status of SalmonTools execution. Any text in stderr that is
# not in the above format is treated as error message.
status_str = completed_command.stderr.decode().strip()
success_pattern = r"^There were \d+ unmapped reads$"
if re.match(success_pattern, status_str):
# Zip up the output of salmontools
try:
with tarfile.open(job_context["salmontools_archive"],
"w:gz") as tar:
tar.add(job_context["salmontools_directory"], arcname=os.sep)
except Exception:
logger.exception(
"Exception caught while zipping processed directory %s",
job_context["salmontools_directory"],
processor_job=job_context["job_id"],
)
failure_template = "Exception caught while zipping salmontools directory {}"
job_context["job"].failure_reason = failure_template.format(
job_context["salmontools_archive"])
job_context["success"] = False
return job_context
result = ComputationalResult()
result.commands.append(command_str)
result.time_start = start_time
result.time_end = end_time
result.is_ccdl = True
try:
processor_key = "SALMONTOOLS"
result.processor = utils.find_processor(processor_key)
except Exception as e:
return utils.handle_processor_exception(job_context, processor_key,
e)
result.save()
job_context["pipeline"].steps.append(result.id)
assoc = SampleResultAssociation()
assoc.sample = job_context["sample"]
assoc.result = result
assoc.save()
computed_file = ComputedFile()
computed_file.filename = job_context["salmontools_archive"].split(
"/")[-1]
computed_file.absolute_file_path = job_context["salmontools_archive"]
computed_file.calculate_sha1()
computed_file.calculate_size()
computed_file.is_public = True
computed_file.is_smashable = False
computed_file.is_qc = True
computed_file.result = result
computed_file.save()
job_context["computed_files"].append(computed_file)
assoc = SampleComputedFileAssociation()
assoc.sample = job_context["sample"]
assoc.computed_file = computed_file
assoc.save()
job_context["result"] = result
job_context["success"] = True
else: # error in salmontools
logger.error(
"Shell call to salmontools failed with error message: %s",
status_str,
processor_job=job_context["job_id"],
)
job_context["job"].failure_reason = (
"Shell call to salmontools failed because: " + status_str)
job_context["success"] = False
return job_context
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_create_compendia_danio(self):
job = ProcessorJob()
job.pipeline_applied = "COMPENDIA"
job.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1234"
experiment.save()
result = ComputationalResult()
result.save()
danio_rerio = Organism.get_object_for_name("DANIO_RERIO")
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)
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()
cra = ComputationalResultAnnotation()
cra.data = {}
cra.data['organism_id'] = danio_rerio.id
cra.data['is_qn'] = True
cra.result = result
cra.save()
dset = Dataset()
dset.data = {'GSE1234': micros, 'GSE5678': rnas}
dset.scale_by = 'NONE'
dset.aggregate_by = 'SPECIES'
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(len(final_context['computed_files']), 3)
for file in final_context['computed_files']:
self.assertTrue(os.path.exists(file.absolute_file_path))
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_create_compendia(self):
job = ProcessorJob()
job.pipeline_applied = "COMPENDIA"
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")
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()
dset = Dataset()
dset.data = {'GSE1487313': ['GSM1487313'], 'SRX332914': ['SRS332914']}
dset.scale_by = 'NONE'
dset.aggregate_by = 'SPECIES'
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)
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_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")