def test_pbmc(): pbmc_dataset = PbmcDataset(save_path='tests/data/') purified_pbmc_dataset = PurifiedPBMCDataset(save_path='tests/data/') # all cells purified_t_cells = PurifiedPBMCDataset(save_path='tests/data/', filter_cell_types=range(6)) # only t-cells base_benchmark(pbmc_dataset) assert len(purified_t_cells.cell_types) == 6 assert len(purified_pbmc_dataset.cell_types) == 10
def test_populate(self): dataset = PbmcDataset( save_path="tests/data/", save_path_10X="tests/data/10X", remove_extracted_data=True, ) unsupervised_training_one_epoch(dataset)
def load_datasets(dataset_name, save_path='data/', url=None): if dataset_name == 'synthetic': gene_dataset = SyntheticDataset() elif dataset_name == 'cortex': gene_dataset = CortexDataset() elif dataset_name == 'brain_large': gene_dataset = BrainLargeDataset(save_path=save_path) elif dataset_name == 'retina': gene_dataset = RetinaDataset(save_path=save_path) elif dataset_name == 'cbmc': gene_dataset = CbmcDataset(save_path=save_path) elif dataset_name == 'brain_small': gene_dataset = BrainSmallDataset(save_path=save_path) elif dataset_name == 'hemato': gene_dataset = HematoDataset(save_path='data/HEMATO/') elif dataset_name == 'pbmc': gene_dataset = PbmcDataset(save_path=save_path) elif dataset_name[-5:] == ".loom": gene_dataset = LoomDataset(filename=dataset_name, save_path=save_path, url=url) elif dataset_name[-5:] == ".h5ad": gene_dataset = AnnDataset(dataset_name, save_path=save_path, url=url) elif ".csv" in dataset_name: gene_dataset = CsvDataset(dataset_name, save_path=save_path) else: raise "No such dataset available" return gene_dataset
def load_datasets(dataset_name, save_path="data/", url=None): if dataset_name == "synthetic": gene_dataset = SyntheticDataset() elif dataset_name == "cortex": gene_dataset = CortexDataset() elif dataset_name == "brain_large": gene_dataset = BrainLargeDataset(save_path=save_path) elif dataset_name == "retina": gene_dataset = RetinaDataset(save_path=save_path) elif dataset_name == "cbmc": gene_dataset = CbmcDataset(save_path=save_path) elif dataset_name == "brain_small": gene_dataset = BrainSmallDataset(save_path=save_path) elif dataset_name == "hemato": gene_dataset = HematoDataset(save_path="data/HEMATO/") elif dataset_name == "pbmc": gene_dataset = PbmcDataset(save_path=save_path) elif dataset_name[-5:] == ".loom": gene_dataset = LoomDataset(filename=dataset_name, save_path=save_path, url=url) elif dataset_name[-5:] == ".h5ad": gene_dataset = AnnDataset(dataset_name, save_path=save_path, url=url) elif ".csv" in dataset_name: gene_dataset = CsvDataset(dataset_name, save_path=save_path) else: raise Exception("No such dataset available") return gene_dataset
benchmark_stvae, benchmark_scvi, benchmark_scgen, benchmark_trvae ) FRAMEWORKS = ( 'stvae', 'scvi', 'scgen', 'trvae' ) #EPOCHS = (600, 100, 100, 300) EPOCHS = (1, 1, 1, 1) datasets = { 'scvi_pbmc': PbmcDataset(), 'bermuda_pbmc': CsvDataset( str(DIRPATH / './pbmc/expression.csv'), labels_file = str(DIRPATH / './pbmc/labels.csv'), batch_ids_file = str(DIRPATH / './pbmc/batches.csv'), gene_by_cell = False ), 'mouse': CsvDataset( str(DIRPATH / './mouse_genes/ST1 - original_expression.csv'), labels_file = str(DIRPATH / './mouse_genes/labels.csv'), batch_ids_file = str(DIRPATH / './mouse_genes/batches.csv'), gene_by_cell = False ), #'pancreas': BermudaDataset('./pancreas/muraro_seurat.csv'), 'retina': RetinaDataset(), 'starmap': PreFrontalCortexStarmapDataset(),
def test_pbmc(): pbmc_dataset = PbmcDataset(save_path='tests/data/') base_benchmark(pbmc_dataset)