def METHOD_NAME(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()
    # Add a second non-downloadable sample. This one should not be included
    # in the count of samples available in the metadata
    sample2 = Sample()
    sample2.accession_code = "GSM1237819"
    sample2.title = "GSM1237819"
    sample2.organism = homo_sapiens
    sample2.technology = "RNA-SEQ"
    sample2.save()
    esa2 = ExperimentSampleAssociation()
    esa2.experiment = experiment
    esa2.sample = sample2
    esa2.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", "GSM1237819"]}
    ds.aggregate_by = "EXPERIMENT"
    ds.scale_by = "STANDARD"
    ds.email_address = "null@derp.com"
    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(final_context["success"])
    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"))
    # 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))
    zf = zipfile.ZipFile(quantpendia_file.absolute_file_path)
    with zf.open("aggregated_metadata.json") as f:
        metadata = json.load(f)
        self.assertTrue(metadata.get("quant_sf_only"))
        self.assertEqual(metadata.get("compendium_version"), 1)
        self.assertEqual(metadata.get("num_samples"), 1)
        self.assertEqual(metadata.get("num_experiments"), 1)
        # Make sure the data were not quantile normalized
        self.assertFalse(metadata.get("quantile_normalized"))