Ligrec Reproducibility and Bug Fix in Sparse Data.

src/squidpy/gr/_ligrec.py

@@ -735,11 +735,11 @@ def extractor(res: Sequence[TempResult]) -> TempResult:
     clustering = np.array(data["clusters"].values, dtype=np.int32)
 
     mean = groups.mean().values.T  # (n_genes, n_clusters)
-    mask = groups.apply(lambda c: ((c > 0).sum() / len(c)) >= threshold).values.T  # (n_genes, n_clusters)
+    mask = groups.apply(lambda c: ((c > 0).astype(int).sum() / len(c)) >= threshold).values.T  # (n_genes, n_clusters)
+
     # (n_cells, n_genes)
     data = np.array(data[data.columns.difference(["clusters"])].values, dtype=np.float64, order="C")
     # all 3 should be C contiguous
-
     return parallelize(  # type: ignore[no-any-return]
         _analysis_helper,
         np.arange(n_perms, dtype=np.int32).tolist(),

tests/graph/test_ligrec.py

@@ -14,6 +14,7 @@
 from pandas.testing import assert_frame_equal
 from scanpy import settings as s
 from scanpy.datasets import blobs
+from scipy.sparse import csc_matrix
 
 from squidpy._constants._pkg_constants import Key
 from squidpy.gr import ligrec
@@ -461,3 +462,104 @@ def test_none_source_target(self, adata: AnnData):
         )
         assert isinstance(pt.interactions, pd.DataFrame)
         assert len(pt.interactions) == 1
+
+    def test_ligrec_nan_counts(self):
+        """
+        For the test case with 2 clusters (A, B) and 3 gene pairs (Gene1→Gene2, Gene2→Gene3, Gene3→Gene1):
+
+        The mask is computed for each gene in each cluster as:
+        mask[gene, cluster] = (number of cells with value > 0) / (total cells in cluster) >= threshold
+
+        Number of cells with value > 0 in each cluster:
+        Cluster A: [1, 3, 0]
+        Cluster B: [1, 0, 3]
+
+        Number of cells with value > 0 in each cluster divided by total number of cells in the cluster:
+        Cluster A: [1/3, 3/3, 0/3] = [0.33, 1.0, 0.0]
+        Cluster B: [1/3, 0/3, 3/3] = [0.33, 0.0, 1.0]
+
+        Using threshold=0.8 on this data, the mask is:
+        Cluster A: [False, True, False]
+        Cluster B: [False, False, True]
+
+        A value in the result becomes NaN if either:
+        - The ligand's mask is False in the source cluster, OR
+        - The receptor's mask is False in the target cluster
+
+        Only in one combination, the mask is both True in the source and target cluster.
+        This is the case for Gene2→Gene3 in A→B.
+
+        This means from all the possible cluster pairs (A→A, A→B, B→A, B→B) and gene pairs (Gene1→Gene2, Gene2→Gene3, Gene3→Gene1),
+        (4 cluster pairs * 3 gene pairs = 12 combinations) only one combination is non-NaN.
+
+        Therefore, the total number of NaNs is 11.
+
+        The expected p-values are:
+        cluster_1        A         B
+        cluster_2        A    B    A    B
+        source target
+        GENE1  GENE2   NaN  NaN  NaN  NaN
+        GENE2  GENE3   NaN  0.0  NaN  NaN
+        GENE3  GENE1   NaN  NaN  NaN  NaN
+
+        """
+        # only Gene2→Gene3 is non-NaN
+        #
+
+        expected_pvalues = np.array(
+            [
+                [
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    np.nan,
+                    0.0,
+                    np.nan,
+                    np.nan,
+                ],
+                [
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                ],
+            ]
+        )
+
+        expected_nans = 11
+        # Setup test data
+        threshold = 0.8
+        interactions = pd.DataFrame({"source": ["Gene1", "Gene2", "Gene3"], "target": ["Gene2", "Gene3", "Gene1"]})
+
+        # Create sparse matrix with test data
+        X = csc_matrix(
+            [
+                [1.0, 0.1, 0.0],  # A1
+                [0.0, 1.0, 0.0],  # A2
+                [0.0, 1.0, 0.0],  # A3
+                [0.1, 0.0, 1.0],  # B1
+                [0.0, 0.0, 1.0],  # B2
+                [0.0, 0.0, 1.0],  # B3
+            ]
+        )
+
+        # Create AnnData object
+        adata = AnnData(
+            X=X,
+            obs=pd.DataFrame({"cluster": ["A"] * 3 + ["B"] * 3}, index=[f"cell{i}" for i in range(1, 7)]),
+            var=pd.DataFrame(index=["Gene1", "Gene2", "Gene3"]),
+        )
+        adata.obs["cluster"] = adata.obs["cluster"].astype("category")
+
+        # Run ligrec and compare NaN counts
+        res = ligrec(
+            adata, cluster_key="cluster", interactions=interactions, threshold=threshold, use_raw=False, copy=True
+        )
+
+        actual_nans = np.sum(np.isnan(res["pvalues"].values))
+
+        assert actual_nans == expected_nans, f"NaN count mismatch: expected {expected_nans}, got {actual_nans}"
+        np.testing.assert_array_equal(res["pvalues"].values, expected_pvalues)