class UtilTest(unittest.TestCase):
def setUp(self):
args = {
"layout": "umap",
"diffexp": "ttest",
"max_category_items": 100,
"obs_names": None,
"var_names": None,
"diffexp_lfc_cutoff": 0.01,
}
self.data = ScanpyEngine("example-dataset/pbmc3k.h5ad", args)
self.data._create_schema()
def test_init(self):
self.assertEqual(self.data.cell_count, 2638)
self.assertEqual(self.data.gene_count, 1838)
epsilon = 0.000_005
self.assertTrue(self.data.data.X[0, 0] - -0.171_469_51 < epsilon)
def test_mandatory_annotations(self):
self.assertIn("name", self.data.data.obs)
self.assertEqual(list(self.data.data.obs.index), list(range(2638)))
self.assertIn("name", self.data.data.var)
self.assertEqual(list(self.data.data.var.index), list(range(1838)))
@pytest.mark.filterwarnings("ignore:Scanpy data matrix")
def test_data_type(self):
self.data.data.X = self.data.data.X.astype("float64")
with self.assertWarns(UserWarning):
self.data._validate_data_types()
def test_filter_idx(self):
filter_ = {
"filter": {
"var": {
"index": [1, 99, [200, 300]]
},
"obs": {
"index": [1, 99, [1000, 2000]]
},
}
}
data = self.data.filter_dataframe(filter_["filter"])
self.assertEqual(data.shape, (1002, 102))
def test_filter_annotation(self):
filter_ = {
"filter": {
"obs": {
"annotation_value": [{
"name": "louvain",
"values": ["NK cells", "CD8 T cells"]
}]
}
}
}
data = self.data.filter_dataframe(filter_["filter"])
self.assertEqual(data.shape, (470, 1838))
filter_ = {
"filter": {
"obs": {
"annotation_value": [{
"name": "n_counts",
"min": 3000
}]
}
}
}
data = self.data.filter_dataframe(filter_["filter"])
self.assertEqual(data.shape, (497, 1838))
def test_filter_annotation_no_uns(self):
filter_ = {
"filter": {
"var": {
"annotation_value": [{
"name": "name",
"values": ["RER1"]
}]
}
}
}
data = self.data.filter_dataframe(filter_["filter"])
self.assertEqual(data.shape[1], 1)
def test_filter_complex(self):
filter_ = {
"filter": {
"var": {
"index": [1, 99, [200, 300]]
},
"obs": {
"annotation_value": [
{
"name": "louvain",
"values": ["NK cells", "CD8 T cells"]
},
{
"name": "n_counts",
"min": 3000
},
],
"index": [1, 99, [1000, 2000]],
},
}
}
data = self.data.filter_dataframe(filter_["filter"])
self.assertEqual(data.shape, (15, 102))
def test_obs_and_var_names(self):
self.assertEqual(np.sum(self.data.data.var["name"].isna()), 0)
self.assertEqual(np.sum(self.data.data.obs["name"].isna()), 0)
def test_schema(self):
with open(path.join(path.dirname(__file__), "schema.json")) as fh:
schema = json.load(fh)
self.assertEqual(self.data.schema, schema)
def test_schema_produces_error(self):
self.data.data.obs["time"] = Series(
list([time.time() for i in range(self.data.cell_count)]),
dtype="datetime64[ns]",
)
with pytest.raises(TypeError):
self.data._create_schema()
def test_config(self):
self.assertEqual(
self.data.features["layout"]["obs"],
{
"available": True,
"interactiveLimit": 50000
},
)
def test_layout(self):
layout = json.loads(self.data.layout(None))
self.assertEqual(layout["layout"]["ndims"], 2)
self.assertEqual(len(layout["layout"]["coordinates"]), 2638)
self.assertEqual(layout["layout"]["coordinates"][0][0], 0)
for idx, val in enumerate(layout["layout"]["coordinates"]):
self.assertLessEqual(val[1], 1)
self.assertLessEqual(val[2], 1)
def test_annotations(self):
annotations = json.loads(self.data.annotation(None, "obs"))
self.assertEqual(
annotations["names"],
["name", "n_genes", "percent_mito", "n_counts", "louvain"],
)
self.assertEqual(len(annotations["data"]), 2638)
annotations = json.loads(self.data.annotation(None, "var"))
self.assertEqual(annotations["names"], ["name", "n_cells"])
self.assertEqual(len(annotations["data"]), 1838)
def test_annotation_fields(self):
annotations = json.loads(
self.data.annotation(None, "obs", ["n_genes", "n_counts"]))
self.assertEqual(annotations["names"], ["n_genes", "n_counts"])
self.assertEqual(len(annotations["data"]), 2638)
annotations = json.loads(self.data.annotation(None, "var", ["name"]))
self.assertEqual(annotations["names"], ["name"])
self.assertEqual(len(annotations["data"]), 1838)
def test_filtered_annotation(self):
filter_ = {
"filter": {
"obs": {
"annotation_value": [{
"name": "n_counts",
"min": 3000
}]
},
"var": {
"annotation_value": [{
"name": "name",
"values": ["ATAD3C", "RER1"]
}]
},
}
}
annotations = json.loads(self.data.annotation(filter_["filter"],
"obs"))
self.assertEqual(
annotations["names"],
["name", "n_genes", "percent_mito", "n_counts", "louvain"],
)
self.assertEqual(len(annotations["data"]), 497)
annotations = json.loads(self.data.annotation(filter_["filter"],
"var"))
self.assertEqual(annotations["names"], ["name", "n_cells"])
self.assertEqual(len(annotations["data"]), 2)
def test_filtered_layout(self):
filter_ = {
"filter": {
"obs": {
"annotation_value": [{
"name": "n_counts",
"min": 3000
}]
}
}
}
layout = json.loads(self.data.layout(filter_["filter"]))
self.assertEqual(len(layout["layout"]["coordinates"]), 497)
def test_diffexp_topN(self):
f1 = {"filter": {"obs": {"index": [[0, 500]]}}}
f2 = {"filter": {"obs": {"index": [[500, 1000]]}}}
result = json.loads(self.data.diffexp_topN(f1["filter"], f2["filter"]))
self.assertEqual(len(result), 10)
result = json.loads(
self.data.diffexp_topN(f1["filter"], f2["filter"], 20))
self.assertEqual(len(result), 20)
def test_data_frame(self):
data_frame_obs = json.loads(self.data.data_frame(None, "obs"))
self.assertEqual(len(data_frame_obs["var"]), 1838)
self.assertEqual(len(data_frame_obs["obs"]), 2638)
data_frame_var = json.loads(self.data.data_frame(None, "var"))
self.assertEqual(len(data_frame_var["var"]), 1838)
self.assertEqual(len(data_frame_var["obs"]), 2638)
def test_filtered_data_frame(self):
filter_ = {
"filter": {
"obs": {
"annotation_value": [{
"name": "n_counts",
"min": 3000
}]
}
}
}
data_frame_obs = json.loads(
self.data.data_frame(filter_["filter"], "obs"))
self.assertEqual(len(data_frame_obs["var"]), 1838)
self.assertEqual(len(data_frame_obs["obs"]), 497)
self.assertIsInstance(data_frame_obs["obs"][0], (list, tuple))
self.assertEqual(type(data_frame_obs["var"][0]), int)
data_frame_var = json.loads(
self.data.data_frame(filter_["filter"], "var"))
self.assertEqual(len(data_frame_var["var"]), 1838)
self.assertEqual(len(data_frame_var["obs"]), 497)
self.assertIsInstance(data_frame_var["var"][0], (list, tuple))
self.assertEqual(type(data_frame_var["obs"][0]), int)
def test_data_single_gene(self):
for axis in ["obs", "var"]:
filter_ = {
"filter": {
"var": {
"annotation_value": [{
"name": "name",
"values": ["RER1"]
}]
}
}
}
data_frame_var = json.loads(
self.data.data_frame(filter_["filter"], axis))
if axis == "obs":
self.assertEqual(type(data_frame_var["var"][0]), int)
self.assertIsInstance(data_frame_var["obs"][0], (list, tuple))
elif axis == "var":
self.assertEqual(type(data_frame_var["obs"][0]), int)
self.assertIsInstance(data_frame_var["var"][0], (list, tuple))
if __name__ == "__main__":
unittest.main()
class NaNTest(unittest.TestCase):
def setUp(self):
self.args = {
"layout": "umap",
"diffexp": "ttest",
"max_category_items": 100,
"obs_names": None,
"var_names": None,
"diffexp_lfc_cutoff": 0.01,
}
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
self.data = ScanpyEngine("server/test/test_datasets/nan.h5ad",
self.args)
self.data._create_schema()
def test_load(self):
with self.assertWarns(UserWarning):
ScanpyEngine("server/test/test_datasets/nan.h5ad", self.args)
def test_init(self):
self.assertEqual(self.data.cell_count, 100)
self.assertEqual(self.data.gene_count, 100)
epsilon = 0.000_005
self.assertTrue(self.data.data.X[0, 0] - -0.171_469_51 < epsilon)
def test_dataframe(self):
data_frame_var = decode_fbs.decode_matrix_FBS(
self.data.data_frame_to_fbs_matrix(None, "var"))
self.assertIsNotNone(data_frame_var)
self.assertEqual(data_frame_var["n_rows"], 100)
self.assertEqual(data_frame_var["n_cols"], 100)
self.assertTrue(math.isnan(data_frame_var["columns"][3][3]))
with pytest.raises(JSONEncodingValueError):
json.loads(self.data.data_frame(None, "obs"))
with pytest.raises(JSONEncodingValueError):
json.loads(self.data.data_frame(None, "var"))
def test_dataframe_obs_not_implemented(self):
with self.assertRaises(ValueError) as cm:
decode_fbs.decode_matrix_FBS(
self.data.data_frame_to_fbs_matrix(None, "obs"))
self.assertIsNotNone(cm.exception)
def test_annotation(self):
annotations = decode_fbs.decode_matrix_FBS(
self.data.annotation_to_fbs_matrix("obs"))
self.assertEqual(
annotations["col_idx"],
["name", "n_genes", "percent_mito", "n_counts", "louvain"])
self.assertEqual(annotations["n_rows"], 100)
self.assertTrue(math.isnan(annotations["columns"][2][0]))
annotations = decode_fbs.decode_matrix_FBS(
self.data.annotation_to_fbs_matrix("var"))
self.assertEqual(annotations["col_idx"],
["name", "n_cells", "var_with_nans"])
self.assertEqual(annotations["n_rows"], 100)
self.assertTrue(math.isnan(annotations["columns"][2][0]))
with pytest.raises(JSONEncodingValueError):
json.loads(self.data.annotation(None, "obs"))
with pytest.raises(JSONEncodingValueError):
json.loads(self.data.annotation(None, "var"))