Fix layers parameter in score_genes with .raw (scverse#3155)

docs/release-notes/1.11.0.md

@@ -4,6 +4,7 @@
 ```
 
 * Add layer argument to {func}`scanpy.tl.score_genes` and {func}`scanpy.tl.score_genes_cell_cycle` {pr}`2921` {smaller}`L Zappia`
+* Prevent `raw` conflict with `layer` in {func}`~scanpy.tl.score_genes` {pr}`3155` {smaller}`S Dicks`
 
 ```{rubric} Docs
 ```

src/scanpy/_utils/__init__.py

@@ -247,14 +247,18 @@ def _check_array_function_arguments(**kwargs):
         )
 
 
-def _check_use_raw(adata: AnnData, use_raw: None | bool) -> bool:
+def _check_use_raw(
+    adata: AnnData, use_raw: None | bool, *, layer: str | None = None
+) -> bool:
     """
     Normalize checking `use_raw`.
 
     My intentention here is to also provide a single place to throw a deprecation warning from in future.
     """
     if use_raw is not None:
         return use_raw
+    if layer is not None:
+        return False
     return adata.raw is not None
 
 

src/scanpy/plotting/_anndata.py

@@ -2020,9 +2020,7 @@ def _prepare_dataframe(
     """
 
     sanitize_anndata(adata)
-    use_raw = _check_use_raw(adata, use_raw)
-    if layer is not None:
-        use_raw = False
+    use_raw = _check_use_raw(adata, use_raw, layer=layer)
     if isinstance(var_names, str):
         var_names = [var_names]
 

src/scanpy/tools/_score_genes.py

@@ -122,7 +122,7 @@ def score_genes(
     """
     start = logg.info(f"computing score {score_name!r}")
     adata = adata.copy() if copy else adata
-    use_raw = _check_use_raw(adata, use_raw)
+    use_raw = _check_use_raw(adata, use_raw, layer=layer)
     if is_backed_type(adata.X) and not use_raw:
         raise NotImplementedError(
             f"score_genes is not implemented for matrices of type {type(adata.X)}"

tests/test_score_genes.py

@@ -251,6 +251,7 @@ def test_layer():
     sc.tl.score_genes(adata, gene_set, score_name="X_score")
     # score layer (`del` makes sure it actually uses the layer)
     adata.layers["test"] = adata.X.copy()
+    adata.raw = adata
     del adata.X
     sc.tl.score_genes(adata, gene_set, score_name="test_score", layer="test")