DEGA-290-custom-segmentation-landscape-files-fix

src/celldega/pre/__init__.py

@@ -842,13 +842,6 @@ def add_custom_segmentation(
         / f"meta_gene_{segmentation_parameters['segmentation_approach']}.parquet",
     )
 
-    save_cbg_gene_parquets(
-        base_path=path_landscape_files,
-        cbg=cbg_custom,
-        verbose=True,
-        segmentation_approach=segmentation_parameters["segmentation_approach"],
-    )
-
     make_meta_cell_image_coord(
         technology=segmentation_parameters["technology"],
         path_transformation_matrix=str(
@@ -864,6 +857,13 @@ def add_custom_segmentation(
         image_scale=image_scale,
     )
 
+    save_cbg_gene_parquets(
+        base_path=path_landscape_files,
+        cbg=cbg_custom,
+        verbose=True,
+        segmentation_approach=segmentation_parameters["segmentation_approach"],
+    )
+
     create_cluster_and_meta_cluster(
         technology=segmentation_parameters["technology"],
         path_landscape_files=path_landscape_files,

src/celldega/pre/boundary_tile.py

@@ -259,14 +259,18 @@ def get_cell_polygons(
         )
         cells_orig = gpd.GeoDataFrame(grouped, geometry="geometry")[["geometry"]]
 
+        cells_orig.rename(columns={"geometry": "geometry_micron"}, inplace=True)
+
     # Transform geometries
     cells_orig["geometry"] = batch_transform_geometries(
-        cells_orig["geometry"], transformation_matrix, 1
+        cells_orig["geometry_micron"], transformation_matrix, 1
     )
     cells_orig["GEOMETRY"] = cells_orig["geometry"].apply(
         lambda geom: [[list(coord) for coord in geom.exterior.coords]]
     )
 
+    cells_orig.set_geometry("geometry", inplace=True)
+
     return cells_orig
 
 

src/celldega/qc/__init__.py

@@ -68,7 +68,7 @@ def _process_custom_technology(base_path):
     trx = pd.read_parquet(Path(base_path) / "transcripts.parquet")
     trx_meta = trx[trx[cell_index] != -1][[transcript_index, cell_index, gene]]
     cell_gdf = gpd.read_parquet(Path(base_path) / "cell_polygons.parquet")
-    cell_meta_gdf = gpd.read_parquet(Path(base_path) / "cell_metadata.parquet")
+    cell_meta_gdf = gpd.read_parquet(Path(base_path) / "cell_metadata_micron_space.parquet")
 
     return cell_index, gene, transcript_index, trx_meta, cell_gdf, cell_meta_gdf
 
@@ -92,12 +92,15 @@ def _process_xenium_technology(base_path, segmentation_parameters):
     trx = trx.rename(columns={"name": gene})
     trx_meta = trx[trx[cell_index] != "UNASSIGNED"][[transcript_index, cell_index, gene]]
 
+    transformation_matrix = pd.read_csv(Path(base_path) / "micron_to_image_transform.csv", header=None, sep=" ").values
     cell_gdf = get_cell_polygons(
         technology=segmentation_parameters["technology"],
         path_cell_boundaries=Path(base_path) / "cell_boundaries.parquet",
+        transformation_matrix = transformation_matrix
     )
 
-    cell_gdf = gpd.GeoDataFrame(geometry=cell_gdf["geometry"])
+    cell_gdf = gpd.GeoDataFrame(geometry=cell_gdf["geometry_micron"])
+
     cell_gdf["geometry"] = cell_gdf["geometry"].apply(get_largest_polygon)
     cell_gdf.reset_index(inplace=True)
     cell_gdf["area"] = cell_gdf["geometry"].area
@@ -461,14 +464,15 @@ def _create_barplot_visualization(
     - cell_a_name: Name of cell type A
     - cell_b_name: Name of cell type B
     """
+
     orthogonal_data = pd.DataFrame(
         {
             "Technology": [i for i in cell_a_with_b_cell_specific_genes for _ in range(2)],
             "Category": [
                 f"{cell_a_name} with {cell_b_name} genes",
                 f"{cell_b_name} with {cell_a_name} genes",
             ]
-            * 4,
+            * len(cell_a_with_b_cell_specific_genes.keys()),
             "Count": [
                 gene
                 for pair in zip(