Change niche flavor to cellcharter_simple and default distance = 3

src/squidpy/gr/_niche.py

@@ -45,6 +45,7 @@ def calculate_niche(
     n_components: int | None = None,
     random_state: int = 42,
     spatial_connectivities_key: str = "spatial_connectivities",
+    use_rep: str | None = None,
     inplace: bool = True,
 ) -> AnnData:
     """
@@ -58,7 +59,7 @@ def calculate_niche(
         Method to use for niche calculation. Available options are:
             - `{fla.NEIGHBORHOOD.s!r}` - cluster the neighborhood profile.
             - `{fla.UTAG.s!r}` - use utag algorithm (matrix multiplication).
-            - `{fla.CELLCHARTER.s!r}` - cluster adjacency matrix with Gaussian Mixture Model (GMM) using CellCharter's approach.
+            - `{fla.CELLCHARTER.s!r}` - a simplified version of CellCharter's approach, using PCA for dimensionality reduction. An arbitrary embedding can be used instead of PCA by setting the `use_rep` parameter which will try to find the embedding in `adata.obsm`.
     %(library_key)s
         If provided, niches will be calculated separately for each unique value in this column.
         Each niche will be prefixed with the library identifier.
@@ -103,6 +104,9 @@ def calculate_niche(
         Optional if flavor == `{fla.CELLCHARTER.s!r}`.
     spatial_connectivities_key
         Key in `adata.obsp` where spatial connectivities are stored.
+    use_rep
+        Key in `adata.obsm` where the embedding is stored. If provided, this embedding will be used instead of PCA for dimensionality reduction.
+        Optional if flavor == `{fla.CELLCHARTER.s!r}`.
     inplace
         If 'True', perform the operation in place.
         If 'False', return a new AnnData object with the niche labels.
@@ -111,6 +115,12 @@ def calculate_niche(
     if flavor == "cellcharter" and aggregation is None:
         aggregation = "mean"
 
+    if distance is None:
+        distance = 3 if flavor == "cellcharter" else 1
+
+    if flavor == "cellcharter" and n_components is None:
+        n_components = 10
+
     _validate_niche_args(
         data,
         flavor,
@@ -127,15 +137,13 @@ def calculate_niche(
         aggregation,
         n_components,
         random_state,
+        use_rep,
         inplace,
     )
 
     if resolutions is None:
         resolutions = [0.5]
 
-    if distance is None:
-        distance = 1
-
     if isinstance(data, SpatialData):
         orig_adata = data.tables[table_key]
         adata = orig_adata.copy()
@@ -225,6 +233,7 @@ def calculate_niche(
             n_components,
             random_state,
             spatial_connectivities_key,
+            use_rep,
         )
 
     if not inplace:
@@ -293,6 +302,7 @@ def _calculate_niches(
     n_components: int | None,
     random_state: int,
     spatial_connectivities_key: str,
+    use_rep: str | None,
 ) -> None:
     """Calculate niches using the specified flavor and parameters."""
     if flavor == "neighborhood":
@@ -321,6 +331,7 @@ def _calculate_niches(
             n_components,
             random_state,
             spatial_connectivities_key,
+            use_rep,
         )
 
 
@@ -470,6 +481,7 @@ def _get_cellcharter_niches(
     n_components: int,
     random_state: int,
     spatial_connectivities_key: str,
+    use_rep: str | None = None,
 ) -> None:
     """adapted from https://github.com/CSOgroup/cellcharter/blob/main/src/cellcharter/gr/_aggr.py
     and https://github.com/CSOgroup/cellcharter/blob/main/src/cellcharter/tl/_gmm.py"""
@@ -494,11 +506,32 @@ def _get_cellcharter_niches(
 
     concatenated_matrix = hstack(aggregated_matrices)  # Stack all matrices horizontally
     arr = concatenated_matrix.toarray()  # Densify
-    arr_ad = ad.AnnData(X=arr)
-    sc.tl.pca(arr_ad)
+
+    if use_rep is not None:
+        # Use provided embedding from adata.obsm
+        if use_rep not in adata.obsm:
+            raise KeyError(
+                f"Embedding key '{use_rep}' not found in adata.obsm. Available keys: {list(adata.obsm.keys())}"
+            )
+        embedding = adata.obsm[use_rep]
+        # Ensure embedding has the right number of components
+        if embedding.shape[1] < n_components:
+            raise ValueError(
+                f"Embedding has {embedding.shape[1]} components, but n_components={n_components}. Please provide an embedding with at least {n_components} components."
+            )
+        # Use only the first n_components
+        embedding = embedding[:, :n_components]
+    else:
+        logg.warning(
+            "CellCharter recommends to use a dimensionality reduced embedding of the data, e.g. a scVI embedding. Since 'use_rep' is not provided, PCA will be used as proxy - performance may be suboptimal."
+        )
+
+        arr_ad = ad.AnnData(X=arr)
+        sc.tl.pca(arr_ad)
+        embedding = arr_ad.obsm["X_pca"]
 
     # cluster concatenated matrix with GMM, each cluster label equals to a niche label
-    niches = _get_GMM_clusters(arr_ad.obsm["X_pca"], n_components, random_state)
+    niches = _get_GMM_clusters(embedding, n_components, random_state)
 
     adata.obs["cellcharter_niche"] = pd.Categorical(niches)
     return
@@ -681,6 +714,7 @@ def _validate_niche_args(
     aggregation: str | None,
     n_components: int | None,
     random_state: int,
+    use_rep: str | None,
     inplace: bool,
 ) -> None:
     """
@@ -761,8 +795,8 @@ def _validate_niche_args(
             ],
         },
         "cellcharter": {
-            "required": ["distance", "aggregation", "n_components", "random_state"],
-            "optional": [],
+            "required": ["distance", "aggregation", "random_state"],
+            "optional": ["n_components", "use_rep"],
             "unused": [
                 "groups",
                 "min_niche_size",
@@ -794,6 +828,7 @@ def _validate_niche_args(
             "aggregation": aggregation,
             "n_components": n_components,
             "random_state": random_state,
+            "use_rep": use_rep,
         },
         flavor_param_specs[flavor],
     )
@@ -828,6 +863,9 @@ def _validate_niche_args(
         if not isinstance(random_state, int):
             raise TypeError(f"'random_state' must be an integer, got {type(random_state).__name__}")
 
+        if use_rep is not None and not isinstance(use_rep, str):
+            raise TypeError(f"'use_rep' must be a string, got {type(use_rep).__name__}")
+
         # for mypy
         if resolutions is None:
             resolutions = [0.0]