add_transform_test

dance/transforms/graph/resept_graph.py

@@ -19,10 +19,12 @@ class RESEPTGraph(BaseTransform):
 
     """
 
-    def __init__(self, fiducial_diameter_fullres=144.56835055243283, tissue_hires_scalef=0.150015, **kwargs):
+    def __init__(self, fiducial_diameter_fullres=144.56835055243283, tissue_hires_scalef=0.150015,
+                 n_neighbors: int = 15, **kwargs):
         super().__init__(**kwargs)
         self.fiducial_diameter_fullres = fiducial_diameter_fullres
         self.tissue_hires_scalef = tissue_hires_scalef
+        self.n_neighbors = n_neighbors
 
     def scale_to_RGB(self, channel, truncated_percent):
         truncated_down = np.percentile(channel, truncated_percent)
@@ -34,6 +36,7 @@ def scale_to_RGB(self, channel, truncated_percent):
 
     def __call__(self, data: Data) -> Data:
         xy_pixel = data.get_feature(return_type="numpy", channel="spatial_pixel", channel_type="obsm")
+        sc.pp.neighbors(data.data, n_neighbors=self.n_neighbors)
         sc.tl.umap(data.data, n_components=3)
         X_transform = data.get_feature(return_type="numpy", channel="X_umap", channel_type="obsm")
         X_transform[:, 0] = self.scale_to_RGB(X_transform[:, 0], 100)

dance/transforms/sc3_feature.py

@@ -86,5 +86,5 @@ def __call__(self, data):
         sim_matrix_all = np.array(sim_matrix_all)
         sim_matrix_mean = np.mean(sim_matrix_all, axis=0)
 
-        data.data.uns[self.out] = sim_matrix_mean
+        data.data.obsm[self.out] = sim_matrix_mean
         return data