Merge pull request #99 from lilab-bcb/spaceranger-2-0

Fix issue on spatial coordinates
lilab-bcb · Sep 9, 2022 · 7deb6ba · 7deb6ba
2 parents db791c2 + 410e3ed
commit 7deb6ba
Showing 1 changed file with 32 additions and 21 deletions.
diff --git a/pegasusio/spatial_utils.py b/pegasusio/spatial_utils.py
@@ -23,7 +23,9 @@ def load_visium_folder(input_path) -> MultimodalData:
     sample_id = input_path.split("/")[-1]
     # Load count matrix.
     hdf5_filename = "filtered_feature_bc_matrix.h5"
-    assert hdf5_filename in file_list, "Filtered feature-barcode matrix in HDF5 format is missing!"
+    assert (
+        hdf5_filename in file_list
+    ), "Filtered feature-barcode matrix in HDF5 format is missing!"
     rna_data = load_10x_h5_file(f"{input_path}/{hdf5_filename}")
 
     # Load spatial metadata.
@@ -34,7 +36,7 @@ def load_visium_folder(input_path) -> MultimodalData:
     tissue_pos_csv = f"{input_path}/spatial/tissue_positions.csv"
     if os.path.exists(tissue_pos_csv):
         spatial_metadata = pd.read_csv(tissue_pos_csv)
-        spatial_metadata.rename(columns={'barcode': 'barcodekey'}, inplace=True)
+        spatial_metadata.rename(columns={"barcode": "barcodekey"}, inplace=True)
     else:
         tissue_pos_csv = f"{input_path}/spatial/tissue_positions_list.csv"
         assert os.path.exists(tissue_pos_csv), f"Cannot locate file {tissue_pos_csv}!"
@@ -45,41 +47,50 @@ def load_visium_folder(input_path) -> MultimodalData:
                 "in_tissue",
                 "array_row",
                 "array_col",
-                "pxl_col_in_fullres",
                 "pxl_row_in_fullres",
+                "pxl_col_in_fullres",
             ],
         )
     process_spatial_metadata(spatial_metadata)
 
-    barcode_metadata = rna_data.obs.join(spatial_metadata, how='left')
+    barcode_metadata = rna_data.obs.join(spatial_metadata, how="left")
     feature_metadata = rna_data.var
 
     matrices = {"X": rna_data.X}
     metadata = {"genome": rna_data.get_genome(), "modality": "visium"}
 
-    #  Store “pxl_col_in_fullres” and ”pxl_row_in_fullres” as a 2D array,
-    # which is the spatial location info of each cell in the dataset.
-    spatial_coords = barcode_metadata[['pxl_row_in_fullres', 'pxl_col_in_fullres']]
-    barcode_multiarrays = {"X_spatial": spatial_coords.to_numpy()}
-    barcode_metadata.drop(columns=['pxl_row_in_fullres', 'pxl_col_in_fullres'], inplace=True)
+    #  Store “pxl_col_in_fullres” and ”pxl_row_in_fullres” as a 2D array, which is the spatial location info of each cell in the dataset.
+    barcode_multiarrays = {
+        "X_spatial": barcode_metadata[
+            ["pxl_col_in_fullres", "pxl_row_in_fullres"]
+        ].to_numpy()
+    }
+    barcode_metadata.drop(
+        columns=["pxl_row_in_fullres", "pxl_col_in_fullres"], inplace=True
+    )
 
     # Store image metadata as a Pandas DataFrame, with the following structure:
-    image_metadata = pd.DataFrame()
+    image_metadata = pd.DataFrame(
+        columns=["sample_id", "image_id", "data", "scale_factor", "spot_diameter"]
+    )
     spatial_path = f"{input_path}/spatial"
 
     with open(f"{spatial_path}/scalefactors_json.json") as fp:
         scale_factors = json.load(fp)
 
-    def get_image_data(filepath, sample_id, image_id, scaleFactor, spot_diameter_fullres):
+    def get_image_data(
+        filepath, sample_id, image_id, scaleFactor, spot_diameter_fullres
+    ):
         data = Image.open(filepath)
-        dict = {
-            "sample_id": sample_id,
-            "image_id": image_id,
-            "data": data,
-            "scale_factor": scaleFactor,
-            "spot_diameter": spot_diameter_fullres * scaleFactor,
-        }
-        return dict
+        return pd.DataFrame(
+            {
+                "sample_id": [sample_id],
+                "image_id": [image_id],
+                "data": [data],
+                "scale_factor": [scaleFactor],
+                "spot_diameter": [spot_diameter_fullres * scaleFactor],
+            }
+        )
 
     for png in [f for f in os.listdir(spatial_path) if re.match(".*\.png", f)]:
         if ("_hires_" in png) or ("_lowres_" in png):
@@ -90,9 +101,9 @@ def get_image_data(filepath, sample_id, image_id, scaleFactor, spot_diameter_ful
                 sample_id,
                 res_tag,
                 scale_factors[f"tissue_{res_tag}_scalef"],
-                scale_factors["spot_diameter_fullres"]
+                scale_factors["spot_diameter_fullres"],
             )
-            image_metadata = image_metadata.append(image_item, ignore_index=True)
+            image_metadata = pd.concat([image_metadata, image_item], ignore_index=True)
 
     assert not image_metadata.empty, "the image data frame is empty"
     spdata = SpatialData(