mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-15 20:57:56 +08:00
141f802824
* add cutoff for low expression genes in topN selection * remove debugging printfs * change cli param name for diffexp cutoff * change CLI param name * second try at diffexp - using lfc sort with pval cutoff * use lfc cutoff * update comments to match code; cap p-value adjustment to max of 1 * lint * explain diffexp in readme * add link * add diffexp-lfc-cutoff to test config * update test to match revised diffexp spec * fix latent bug in GET arg handling that was breaking tests * lint * comment cleanup * fix variance overestimation so it is symmetric * lint
257 lines
9.2 KiB
Python
257 lines
9.2 KiB
Python
import json
|
|
from os import path
|
|
import pytest
|
|
import time
|
|
import unittest
|
|
import argparse
|
|
|
|
import numpy as np
|
|
from pandas import Series
|
|
|
|
from server.app.scanpy_engine.scanpy_engine import ScanpyEngine
|
|
|
|
|
|
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 = 0.000005
|
|
self.assertTrue(self.data.data.X[0, 0] - -0.17146951 < 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 = self.data.layout(None)
|
|
self.assertEqual(layout["ndims"], 2)
|
|
self.assertEqual(len(layout["coordinates"]), 2638)
|
|
self.assertEqual(layout["coordinates"][0][0], 0)
|
|
for idx, val in enumerate(layout["coordinates"]):
|
|
self.assertLessEqual(val[1], 1)
|
|
self.assertLessEqual(val[2], 1)
|
|
|
|
def test_annotations(self):
|
|
annotations = self.data.annotation(None, "obs")
|
|
self.assertEqual(annotations["names"], ["name", "n_genes", "percent_mito", "n_counts", "louvain"])
|
|
self.assertEqual(len(annotations["data"]), 2638)
|
|
annotations = self.data.annotation(None, "var")
|
|
self.assertEqual(annotations["names"], ["name", "n_cells"])
|
|
self.assertEqual(len(annotations["data"]), 1838)
|
|
|
|
def test_annotation_fields(self):
|
|
annotations = self.data.annotation(None, "obs", ["n_genes", "n_counts"])
|
|
self.assertEqual(annotations["names"], ["n_genes", "n_counts"])
|
|
self.assertEqual(len(annotations["data"]), 2638)
|
|
annotations = 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 = 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 = 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 = self.data.layout(filter_["filter"])
|
|
self.assertEqual(len(layout["coordinates"]), 497)
|
|
|
|
def test_diffexp_topN(self):
|
|
f1 = {
|
|
"filter": {
|
|
"obs": {
|
|
"index": [[0, 500]]
|
|
}
|
|
}
|
|
}
|
|
f2 = {
|
|
"filter": {
|
|
"obs": {
|
|
"index": [[500, 1000]]
|
|
}
|
|
}
|
|
}
|
|
result = self.data.diffexp_topN(f1["filter"], f2["filter"])
|
|
self.assertEqual(len(result), 10)
|
|
result = self.data.diffexp_topN(f1["filter"], f2["filter"], 20)
|
|
self.assertEqual(len(result), 20)
|
|
|
|
def test_data_frame(self):
|
|
data_frame_obs = 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 = 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 = 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 = 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 = 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()
|