mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-24 11:48:12 +08:00
Matt Weiden
c7f2032dd7
* Add user-generated annotations tests to the server Partially completes https://github.com/chanzuckerberg/cellxgene/issues/969 * Auto-format python code * @skip_if: passing lambdas > than property strings * Respond to feedback from @bkmartinjr
188 lines
7.9 KiB
Python
188 lines
7.9 KiB
Python
import json
|
|
from os import path
|
|
import pytest
|
|
import time
|
|
import unittest
|
|
import server.test.decode_fbs as decode_fbs
|
|
from parameterized import parameterized_class
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from server.data_anndata.anndata_adaptor import AnndataAdaptor
|
|
from server.common.errors import FilterError
|
|
from server.common.data_locator import DataLocator
|
|
|
|
"""
|
|
Test the anndata adaptor using the pbmc3k data set.
|
|
"""
|
|
|
|
|
|
@parameterized_class(
|
|
("data_locator", "backed"),
|
|
[
|
|
("../example-dataset/pbmc3k.h5ad", False),
|
|
("test/test_datasets/pbmc3k-CSC-gz.h5ad", False),
|
|
("test/test_datasets/pbmc3k-CSR-gz.h5ad", False),
|
|
("../example-dataset/pbmc3k.h5ad", True),
|
|
("test/test_datasets/pbmc3k-CSC-gz.h5ad", True),
|
|
("test/test_datasets/pbmc3k-CSR-gz.h5ad", True),
|
|
],
|
|
)
|
|
class AdaptorTest(unittest.TestCase):
|
|
def setUp(self):
|
|
args = {
|
|
"layout": ["umap"],
|
|
"max_category_items": 100,
|
|
"obs_names": None,
|
|
"var_names": None,
|
|
"diffexp_lfc_cutoff": 0.01,
|
|
"anndata_backed": self.backed,
|
|
}
|
|
self.data = AnndataAdaptor(DataLocator(self.data_locator), args)
|
|
|
|
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):
|
|
obs_index_col_name = self.data.get_schema()["annotations"]["obs"]["index"]
|
|
self.assertIn(obs_index_col_name, self.data.data.obs)
|
|
self.assertEqual(list(self.data.data.obs.index), list(range(2638)))
|
|
var_index_col_name = self.data.get_schema()["annotations"]["var"]["index"]
|
|
self.assertIn(var_index_col_name, self.data.data.var)
|
|
self.assertEqual(list(self.data.data.var.index), list(range(1838)))
|
|
|
|
@pytest.mark.filterwarnings("ignore:Anndata data matrix")
|
|
def test_data_type(self):
|
|
# don't run the test on the more exotic data types, as they don't
|
|
# support the astype() interface (used by this test, but not underlying app)
|
|
if isinstance(self.data.data.X, np.ndarray):
|
|
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]]}}}
|
|
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
|
|
data = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(data["n_rows"], 2638)
|
|
self.assertEqual(data["n_cols"], 102)
|
|
|
|
def test_filter_complex(self):
|
|
filter_ = {
|
|
"filter": {"var": {"annotation_value": [{"name": "n_cells", "min": 10}], "index": [1, 99, [200, 300]]}}
|
|
}
|
|
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
|
|
data = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(data["n_rows"], 2638)
|
|
self.assertEqual(data["n_cols"], 91)
|
|
|
|
def test_obs_and_var_names(self):
|
|
self.assertEqual(np.sum(self.data.data.var[self.data.get_schema()["annotations"]["var"]["index"]].isna()), 0)
|
|
self.assertEqual(np.sum(self.data.data.obs[self.data.get_schema()["annotations"]["obs"]["index"]].isna()), 0)
|
|
|
|
def test_get_schema(self):
|
|
with open(path.join(path.dirname(__file__), "schema.json")) as fh:
|
|
schema = json.load(fh)
|
|
self.assertEqual(self.data.get_schema(), schema)
|
|
|
|
def test_schema_produces_error(self):
|
|
self.data.data.obs["time"] = pd.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.get_features()["layout_obs"].available, True)
|
|
|
|
def test_layout(self):
|
|
fbs = self.data.layout_to_fbs_matrix()
|
|
layout = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(layout["n_cols"], 2)
|
|
self.assertEqual(layout["n_rows"], 2638)
|
|
|
|
X = layout["columns"][0]
|
|
self.assertTrue((X >= 0).all() and (X <= 1).all())
|
|
Y = layout["columns"][1]
|
|
self.assertTrue((Y >= 0).all() and (Y <= 1).all())
|
|
|
|
def test_annotations(self):
|
|
fbs = self.data.annotation_to_fbs_matrix("obs")
|
|
annotations = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(annotations["n_rows"], 2638)
|
|
self.assertEqual(annotations["n_cols"], 5)
|
|
obs_index_col_name = self.data.get_schema()["annotations"]["obs"]["index"]
|
|
self.assertEqual(
|
|
annotations["col_idx"], [obs_index_col_name, "n_genes", "percent_mito", "n_counts", "louvain"],
|
|
)
|
|
|
|
fbs = self.data.annotation_to_fbs_matrix("var")
|
|
annotations = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(annotations["n_rows"], 1838)
|
|
self.assertEqual(annotations["n_cols"], 2)
|
|
var_index_col_name = self.data.get_schema()["annotations"]["var"]["index"]
|
|
self.assertEqual(annotations["col_idx"], [var_index_col_name, "n_cells"])
|
|
|
|
def test_annotation_fields(self):
|
|
fbs = self.data.annotation_to_fbs_matrix("obs", ["n_genes", "n_counts"])
|
|
annotations = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(annotations["n_rows"], 2638)
|
|
self.assertEqual(annotations["n_cols"], 2)
|
|
|
|
var_index_col_name = self.data.get_schema()["annotations"]["var"]["index"]
|
|
fbs = self.data.annotation_to_fbs_matrix("var", [var_index_col_name])
|
|
annotations = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(annotations["n_rows"], 1838)
|
|
self.assertEqual(annotations["n_cols"], 1)
|
|
|
|
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):
|
|
f1 = {"var": {"index": [[0, 10]]}}
|
|
fbs = self.data.data_frame_to_fbs_matrix(f1, "var")
|
|
data = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(data["n_rows"], 2638)
|
|
self.assertEqual(data["n_cols"], 10)
|
|
|
|
with self.assertRaises(ValueError):
|
|
self.data.data_frame_to_fbs_matrix(None, "obs")
|
|
|
|
def test_filtered_data_frame(self):
|
|
filter_ = {"filter": {"var": {"annotation_value": [{"name": "n_cells", "min": 100}]}}}
|
|
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
|
|
data = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(data["n_rows"], 2638)
|
|
self.assertEqual(data["n_cols"], 1040)
|
|
|
|
filter_ = {"filter": {"obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]}}}
|
|
with self.assertRaises(FilterError):
|
|
self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
|
|
|
|
def test_data_named_gene(self):
|
|
var_index_col_name = self.data.get_schema()["annotations"]["var"]["index"]
|
|
filter_ = {"filter": {"var": {"annotation_value": [{"name": var_index_col_name, "values": ["RER1"]}]}}}
|
|
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
|
|
data = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(data["n_rows"], 2638)
|
|
self.assertEqual(data["n_cols"], 1)
|
|
self.assertEqual(data["col_idx"], [4])
|
|
|
|
filter_ = {
|
|
"filter": {"var": {"annotation_value": [{"name": var_index_col_name, "values": ["SPEN", "TYMP", "PRMT2"]}]}}
|
|
}
|
|
fbs = self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
|
|
data = decode_fbs.decode_matrix_FBS(fbs)
|
|
self.assertEqual(data["n_rows"], 2638)
|
|
self.assertEqual(data["n_cols"], 3)
|
|
self.assertTrue((data["col_idx"] == [15, 1818, 1837]).all())
|