renaming of coloc_ml_processing, testing, and moving colocs to varp

docs/examples.rst

@@ -134,13 +134,13 @@ can be executed with our package:
    adata = metaspace_to_anndata(dataset_id="2023-11-14_21h58m39s", fdr=0.1)
 
    # Perform median filtering and quantile thresholding
-   # The processed data is saved as a layer `adata.layers["colocml_preprocessing"]`
+   # The processed data is saved as a layer `adata.layers["coloc_ml_preprocessing"]`
    # It has the same dimensions as `adata.X`
-   colocalization.colocML_preprocessing(adata, layer="colocml_preprocessing")
+   colocalization.coloc_ml_preprocessing(adata, layer="colocml_preprocessing")
 
-   # Compute the pairwise colocalization metrix between all ion images
-   # As an input, the processed data from `adata.layers["colocml_preprocessing"]` is used
-   # The colocalization matrix is saved in `adata.uns["colocalization"]`
+   # Compute the pairwise colocalization matrix between all ion images
+   # As an input, the processed data from `adata.layers["coloc_ml_preprocessing"]` is used
+   # The colocalization matrix is saved in `adata.varp["colocalization"]`
    colocalization.colocalization(adata, layer="colocml_preprocessing")
 
 

metaspace_converter/colocalization.py

@@ -8,9 +8,9 @@
 from metaspace_converter.constants import COLOCALIZATION
 
 
-def colocML_preprocessing(
+def coloc_ml_preprocessing(
     adata: AnnData,
-    layer: Optional[str] = "colocml_preprocessing",
+    layer: Optional[str] = "coloc_ml_preprocessing",
     median_filter_size: Tuple[int, int] = (3, 3),
     quantile_threshold: float = 0.5,
 ):
@@ -69,36 +69,41 @@ def colocML_preprocessing(
         adata.layers[layer] = imarray.transpose()
 
 
-def colocalization(adata: AnnData, layer: Optional[str] = "colocml_preprocessing"):
+def colocalization(adata: AnnData, layer: Optional[str] = "coloc_ml_preprocessing"):
     """
     Colocalization of ion images using the cosine similarity metric.
 
     In combination with the ``colocML_preprocessing`` function, this metric performed best in the
     colocML publication (https://doi.org/10.1093/bioinformatics/btaa085).
 
-    It is recommended to call the the ``colocML_preprocessing`` function beforehand.
+    It is recommended to call the the ``coloc_ml_preprocessing`` function beforehand.
 
     Args:
         adata: An AnnData object.
         layer: Key for ``adata.layer`` from which the ionimage_data for preprocessing taken.
-            If ``None``, ``adata.X`` is used. ``colocML_preprocessing`` will save the preprocessed
-            data per default in ``adata.layer['colocml_preprocessing']``.
+            If ``None``, ``adata.X`` is used. ``coloc_ml_preprocessing`` will save the preprocessed
+            data per default in ``adata.layer['coloc_ml_preprocessing']``.
 
     Returns:
-        None. The processed data is saved in ``adata.uns['colocalization']``.
+        None. The processed data is saved in ``adata.varp['colocalization']``.
+
+    Raises:
+        ValueError: If layer is not found in adata.layers.
     """
 
     # Select data
-    if layer == None or layer not in adata.layers.keys():
+    if layer is None:
         data = np.array(adata.X).transpose()
-    else:
+    elif layer in adata.layers.keys():
         data = np.array(adata.layers[layer]).transpose()
+    else:
+        raise ValueError(f"Layer `{layer}` not found in adata.layers.")
 
     # Compute colocalization
     coloc = _pairwise_cosine_similarity(data)
 
     # Save colocalization
-    adata.uns[COLOCALIZATION] = coloc
+    adata.varp[COLOCALIZATION] = coloc
 
 
 def _pairwise_cosine_similarity(data: np.ndarray) -> np.ndarray:

metaspace_converter/tests/colocalization_test.py

@@ -9,7 +9,7 @@
     import _pytest.fixtures
 
 from metaspace_converter import anndata_to_image_array
-from metaspace_converter.colocalization import colocalization, colocML_preprocessing
+from metaspace_converter.colocalization import colocalization, coloc_ml_preprocessing
 from metaspace_converter.constants import COL, COLOCALIZATION, METASPACE_KEY, X, Y
 from metaspace_converter.to_anndata import all_image_pixel_coordinates
 
@@ -46,26 +46,32 @@ def adata_dummy(request: "_pytest.fixtures.SubRequest") -> AnnData:
 )
 def test_colocml_preprocessing(adata_dummy):
 
-    COLOCML_LAYER = "colocml_preprocessing"
+    COLOCML_LAYER = "coloc_ml_preprocessing"
 
     adata = adata_dummy
 
     if adata.X.shape[1] == 0:
         with pytest.raises(ValueError) as e_info:
-            colocML_preprocessing(
+            coloc_ml_preprocessing(
                 adata, median_filter_size=(3, 3), quantile_threshold=0, layer=COLOCML_LAYER
             )
     else:
+
 
         # Test median filtering
-        colocML_preprocessing(
+        coloc_ml_preprocessing(
             adata, median_filter_size=(3, 3), quantile_threshold=0, layer=COLOCML_LAYER
         )
 
         actual = anndata_to_image_array(adata)
 
-        # median filter
+        # Layer exists
+        assert COLOCML_LAYER in adata.layers.keys()
+
+        # Layer sizes match
+        assert adata.X.shape == adata.layers[COLOCML_LAYER].shape
 
+        # median filter
         expected_preprocessing = np.median(actual[0][:3, :3])
         observed = adata.layers[COLOCML_LAYER][adata.uns[METASPACE_KEY]["image_size"][X] + 1, 0]
         assert observed == expected_preprocessing
@@ -77,17 +83,13 @@ def test_colocml_preprocessing(adata_dummy):
         # Quantile thresholding
         adata.X[0].reshape(-1)
 
-        colocML_preprocessing(
+        coloc_ml_preprocessing(
             adata, median_filter_size=(3, 3), quantile_threshold=0.5, layer=COLOCML_LAYER
         )
 
         assert all(np.sum(adata.layers[COLOCML_LAYER] == 0, axis=0) <= 0.5 * adata.X.shape[0])
 
-        # Layer exists
-        assert COLOCML_LAYER in adata.layers.keys()
-
-        # Layer sizes match
-        assert adata.X.shape == adata.layers[COLOCML_LAYER].shape
+
 
 
 def test_colocalization():
@@ -100,8 +102,8 @@ def test_colocalization():
 
     colocalization(adata, layer=None)
 
-    assert COLOCALIZATION in adata.uns.keys()
+    assert COLOCALIZATION in adata.varp.keys()
 
-    coloc = adata.uns[COLOCALIZATION]
+    coloc = adata.varp[COLOCALIZATION]
 
     assert np.all(coloc == expected)