Added unittests for annotator

scanpy/tests/test_annotator.py

@@ -0,0 +1,158 @@
+import unittest
+from importlib.util import find_spec
+
+import pandas as pd
+import numpy as np
+from scanpy import AnnData
+from scanpy.external.tl import annotator
+
+import pytest
+
+
+@pytest.mark.skipif(
+    find_spec('pointannotator') is None, reason="point-annotator not installed"
+)
+class AnnotatorTests(unittest.TestCase):
+    def setUp(self):
+        self.markers = pd.DataFrame(
+            [
+                ["Type 1", "111"],
+                ["Type 1", "112"],
+                ["Type 1", "113"],
+                ["Type 1", "114"],
+                ["Type 2", "211"],
+                ["Type 2", "212"],
+                ["Type 2", "213"],
+                ["Type 2", "214"],
+            ],
+            columns=["Cell Type", "Gene"],
+        )
+
+        genes = ["111", "112", "113", "114", "211", "212", "213", "214"]
+        self.data = pd.DataFrame(
+            np.array(
+                [
+                    [1, 1, 1, 1.1, 0, 0, 0, 0],
+                    [1, 0.8, 0.9, 1, 0, 0, 0, 0],
+                    [0.7, 1.1, 1, 1.2, 0, 0, 0, 0],
+                    [0.8, 0.7, 1.1, 1, 0, 0.1, 0, 0],
+                    [0, 0, 0, 0, 1.05, 1.05, 1.1, 1],
+                    [0, 0, 0, 0, 1.1, 1.0, 1.05, 1.1],
+                    [0, 0, 0, 0, 1.05, 0.9, 1.1, 1.1],
+                    [0, 0, 0, 0, 0.9, 0.9, 1.2, 1],
+                ]
+            ),
+            columns=genes,
+        )
+
+        # transform data to AnnData
+        self.anndata = AnnData(self.data.values, var=self.data.columns.values)
+
+    def basic_check(self, annotations):
+        self.assertEqual(type(annotations), AnnData)
+        self.assertEqual(len(annotations), len(self.anndata))
+        self.assertTupleEqual(
+            annotations.shape, (8, 2)
+        )  # two types in the data
+        self.assertGreater(np.nansum(annotations.X), 0)
+        self.assertLessEqual(np.nanmax(annotations.X), 1)
+        self.assertGreaterEqual(np.nanmin(annotations.X), 0)
+
+    def test_annotator(self):
+        annotations = annotator(
+            self.anndata, self.markers, normalize=False, num_genes=15
+        )
+
+        self.basic_check(annotations)
+
+    def test_remove_empty_column(self):
+        """
+        Type 3 column must be removed here, since this cell type does not
+        belong to any cell.
+        """
+        additinal_markers = pd.DataFrame(
+            [["Type 3", "311"], ["Type 3", "312"], ["Type 3", "313"]],
+            columns=["Cell Type", "Gene"],
+        )
+        markers = self.markers.append(additinal_markers)
+
+        annotations = annotator(self.anndata, markers, num_genes=20)
+
+        self.basic_check(annotations)
+
+        annotations = annotator(
+            self.anndata,
+            markers,
+            num_genes=20,
+            return_nonzero_annotations=False,
+        )
+        self.assertEqual(len(annotations), len(self.anndata))
+        self.assertTupleEqual(
+            annotations.shape, (8, 3)
+        )  # two types in the data
+        self.assertGreater(np.nansum(annotations.X), 0)
+        self.assertLessEqual(np.nanmax(annotations.X), 1)
+        self.assertGreaterEqual(np.nanmin(annotations.X), 0)
+
+    def test_sf(self):
+        """
+        Test annotations with hypergeom.sf
+        """
+        annotations = annotator(
+            self.anndata, self.markers, num_genes=15, p_value_fun="hypergeom"
+        )
+
+        self.basic_check(annotations)
+
+    def test_scoring(self):
+        # scoring SCORING_EXP_RATIO
+        annotations = annotator(
+            self.anndata, self.markers, num_genes=15, scoring="exp_ratio"
+        )
+
+        self.basic_check(annotations)
+
+        # scoring SCORING_MARKERS_SUM
+        annotations = annotator(
+            self.anndata,
+            self.markers,
+            num_genes=15,
+            scoring="sum_of_expressed_markers",
+        )
+
+        self.assertEqual(type(annotations), AnnData)
+        self.assertEqual(len(annotations), len(self.anndata))
+        self.assertTupleEqual(
+            annotations.shape, (8, 2)
+        )  # two types in the data
+
+        # based on provided data it should match
+        # the third row is skipped, since it is special
+        self.assertAlmostEqual(
+            annotations.X[0, 0], self.data.iloc[0].sum(), places=6
+        )
+        self.assertAlmostEqual(
+            annotations.X[5, 1], self.data.iloc[5].sum(), places=6
+        )
+
+        # scoring SCORING_LOG_FDR
+        annotations = annotator(
+            self.anndata, self.markers, num_genes=15, scoring="log_fdr"
+        )
+
+        self.assertEqual(type(annotations), AnnData)
+        self.assertEqual(len(annotations), len(self.anndata))
+        self.assertTupleEqual(
+            annotations.shape, (8, 2)
+        )  # two types in the data
+
+        # scoring SCORING_LOG_PVALUE
+        annotations = annotator(
+            self.anndata, self.markers, num_genes=15, scoring="log_p_value"
+        )
+
+        self.assertEqual(type(annotations), AnnData)
+        self.assertEqual(len(annotations), len(self.anndata))
+        self.assertTupleEqual(
+            annotations.shape, (8, 2)
+        )  # two types in the data

setup.py

@@ -38,7 +38,15 @@
             'sphinx_autodoc_typehints',
             'scanpydoc',
         ],
-        test=['pytest>=4.4', 'dask[array]', 'fsspec', 'zappy', 'zarr', 'black'],
+        test=[
+            'pytest>=4.4',
+            'dask[array]',
+            'fsspec',
+            'zappy',
+            'zarr',
+            'black',
+            'point-annotator',
+        ],
     ),
     packages=find_packages(),
     entry_points=dict(console_scripts=['scanpy=scanpy.cli:console_main']),