def test_populate_from_datasets_dummy_data(self):
data1 = np.random.randint(1, 5, size=(5, 10))
gene_names1 = np.array(["gene_%d" % i for i in range(10)])
dataset1 = GeneExpressionDataset()
dataset1.populate_from_data(data1, gene_names=gene_names1)
data2 = np.random.randint(1, 5, size=(7, 3))
gene_names2 = np.array(["gene_%d" % i for i in range(3)])
dataset2 = GeneExpressionDataset()
dataset2.populate_from_data(data2, gene_names=gene_names2)
data3 = np.random.randint(1, 5, size=(2, 5))
gene_names3 = np.array(["gene_%d" % i for i in range(5)])
dataset3 = GeneExpressionDataset()
dataset3.populate_from_data(data3, gene_names=gene_names3)
dataset = GeneExpressionDataset()
dataset.populate_from_datasets([dataset1, dataset2, dataset3])
self.assertEqual(14, dataset.nb_cells)
self.assertEqual(3, dataset.nb_genes)
self.assertListEqual(["GENE_0", "GENE_1", "GENE_2"],
dataset.gene_names.tolist())
# test for labels sharing
dataset2.labels = [0, 0, 0, 1, 1, 1, 1]
dataset2.initialize_mapped_attribute("labels", "cell_types",
["0", "1"])
dataset3.labels = [0, 1]
dataset3.initialize_mapped_attribute("labels", "cell_types",
["0", "2"])
dataset = GeneExpressionDataset()
dataset.populate_from_datasets([dataset2, dataset3],
shared_labels=True)
self.assertListEqual(
np.squeeze(dataset.labels).tolist(), [0, 0, 0, 1, 1, 1, 1, 0, 2])
self.assertListEqual(dataset.cell_types, ["0", "1", "2"])
dataset_unshared = GeneExpressionDataset()
dataset_unshared.populate_from_datasets([dataset2, dataset3],
shared_labels=False)
self.assertListEqual(
np.squeeze(dataset_unshared.labels).tolist(),
[0, 0, 0, 1, 1, 1, 1, 2, 3])
self.assertListEqual(dataset_unshared.cell_types, ["0", "1", "0", "2"])
# test for batch_indices offsetting
dataset2.batch_indices = [0, 0, 0, 1, 1, 1, 1]
dataset2.initialize_mapped_attribute("batch_indices", "experiment",
["fish_2", "scrna_2"])
dataset3.batch_indices = [0, 1]
dataset3.initialize_mapped_attribute("batch_indices", "experiment",
["fish_3", "scrna_3"])
dataset = GeneExpressionDataset()
dataset.populate_from_datasets([dataset2, dataset3])
self.assertListEqual(
np.squeeze(dataset.batch_indices).tolist(),
[0, 0, 0, 1, 1, 1, 1, 2, 3])
self.assertListEqual(getattr(dataset, "experiment"),
["fish_2", "scrna_2", "fish_3", "scrna_3"])
meta.index = meta.loc[:,"cell_name"].values.astype(str)
var = feather.read_dataframe(input_var)
var.index = var.loc[:,"symbol"].values.astype(str)
annobj = anndata.AnnData(X=rawcounts)
annobj.obs = meta
annobj.var = var
X, local_mean, local_var, batch_indices, labels = GeneExpressionDataset.get_attributes_from_matrix(annobj.X)
geneExp = GeneExpressionDataset(X, local_mean, local_var, batch_indices, labels, gene_names=annobj.var.index)
if bool(batch_id) is not False:
use_batches=True
plates, plates_ids = pd.factorize(annobj.obs[batch_id])
geneExp.batch_indices = plates.reshape(-1, 1)
geneExp.n_batches = np.unique(plates.reshape(-1, 1)).size
else:
use_batches = False
ldvae = LDVAE(geneExp.nb_genes,
n_batch=geneExp.n_batches * use_batches,
n_latent=latent,
n_layers=layer,
n_hidden=hidden,
dispersion=dispersion,
reconstruction_loss=reconstruction_loss)
trainer = UnsupervisedTrainer(ldvae,
geneExp,
train_size=size,
