def test_multibatches_features():
data = [
np.random.randint(1, 5, size=(20, 10)),
np.random.randint(1, 10, size=(20, 10)),
np.random.randint(1, 10, size=(20, 10)),
np.random.randint(1, 10, size=(30, 10)),
]
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
vae = VAE(dataset.nb_genes, dataset.n_batches)
trainer = UnsupervisedTrainer(vae, dataset, train_size=0.5, use_cuda=use_cuda)
trainer.train(n_epochs=2)
trainer.test_set.imputation(n_samples=2, transform_batch=0)
trainer.train_set.imputation(n_samples=2, transform_batch=[0, 1, 2])
def test_populate_from_per_batch_list(self):
data = [
np.random.randint(1, 5, size=(7, 10)),
np.random.randint(1, 5, size=(5, 10)),
np.random.randint(1, 5, size=(3, 10)),
]
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
self.assertEqual(dataset.nb_cells, 15)
self.assertEqual(dataset.nb_genes, 10)
true_batch_indices = np.concatenate([
np.zeros((7, 1), dtype=int),
np.ones((5, 1), dtype=int),
2 * np.ones((3, 1), dtype=int),
])
self.assertListEqual(true_batch_indices.tolist(),
dataset.batch_indices.tolist())
def test_batch_correction(self):
data = [
np.random.randint(1, 5, size=(50, 25)),
np.random.randint(1, 5, size=(50, 25)),
np.random.randint(1, 5, size=(50, 25)),
]
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
n_genes = dataset.nb_genes
n_top = n_genes // 2
dataset.highly_variable_genes(n_bins=3, flavor="seurat")
dataset.highly_variable_genes(n_bins=3, flavor="seurat")
df = dataset.highly_variable_genes(n_bins=3,
n_top_genes=n_top,
flavor="seurat")
assert df["highly_variable"].sum() >= n_top
pass
def test_dense_subsample_genes(self):
data = [
np.random.randint(1, 5, size=(50, 26)),
np.random.randint(1, 5, size=(50, 26)),
np.random.randint(1, 5, size=(50, 26)),
]
# With default
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
n_genes = dataset.nb_genes
n_top = n_genes // 2
dataset.subsample_genes(new_n_genes=n_top)
assert dataset.nb_genes < n_genes
# For some reason the new number of genes can be slightly different than n_top
# With Seurat
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
dataset.subsample_genes(new_n_genes=n_top, mode="seurat")
assert dataset.nb_genes < n_genes
def test_batch_correction(self):
data = [
np.random.randint(1, 5, size=(50, 25)),
np.random.randint(1, 5, size=(50, 25)),
np.random.randint(1, 5, size=(50, 25)),
]
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
n_genes = dataset.nb_genes
n_top = n_genes // 2
dataset._highly_variable_genes(n_bins=3, flavor="seurat_v2")
df = dataset._highly_variable_genes(
n_bins=3, n_top_genes=n_top, flavor="seurat_v2"
)
assert df["highly_variable"].sum() >= n_top
dataset.subsample_genes(new_n_genes=n_top)
new_genes = dataset.nb_genes
assert n_genes > new_genes, "subsample_genes did not filter out genes"
pass
def test_dense_subsample_genes(self):
data = [
np.random.randint(1, 5, size=(50, 26)),
np.random.randint(1, 5, size=(50, 26)),
np.random.randint(1, 5, size=(50, 26)),
]
# With default
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
n_genes = dataset.nb_genes
n_top = n_genes // 2
dataset.subsample_genes(new_n_genes=n_top, mode="cell_ranger")
assert dataset.nb_genes == n_top
# With Seurat v2
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
dataset.subsample_genes(new_n_genes=n_top, mode="seurat_v2")
assert dataset.nb_genes == n_top
# With Seurat v3
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
dataset.subsample_genes(new_n_genes=n_top, mode="seurat_v3")
assert dataset.nb_genes == n_top