renamed file and commented out image intensity calculation

Author: cornhundred

src/celldega/qc/__init__.py

@@ -5,10 +5,10 @@
 import tifffile as tiff
 from skimage.exposure import equalize_adapthist
 
-def processing(transcript_metadata_file, transcript_data_file, cell_polygon_metadata_file, cell_polygon_data_file, image_files, thickness, subset_interval_y_x, pixel_size, tech_name):
+def segmentation_qc(transcript_metadata_file, transcript_data_file, cell_polygon_metadata_file, cell_polygon_data_file, image_files, thickness, subset_interval_y_x, pixel_size, tech_name):
 
-    metrics = {}    
-    trx_meta = pd.read_parquet(transcript_metadata_file)  
+    metrics = {}
+    trx_meta = pd.read_parquet(transcript_metadata_file)
 
     if transcript_data_file.endswith(".csv"):
         trx = pd.read_csv(transcript_data_file)
@@ -19,24 +19,24 @@ def processing(transcript_metadata_file, transcript_data_file, cell_polygon_meta
 
     cell_gdf = gpd.read_parquet(cell_polygon_data_file)
     cell_meta_gdf = gpd.read_parquet(cell_polygon_metadata_file)
-    
+
     percentage_of_assigned_transcripts = (len(trx_meta) / len(trx)) * 100
-    
-    for image_index, image_path in enumerate(image_files):
-        with tiff.TiffFile(image_path, is_ome=False) as image_file:
 
-            series = image_file.series[0]
-            plane = series.pages[0]
+    # for image_index, image_path in enumerate(image_files):
+    #     with tiff.TiffFile(image_path, is_ome=False) as image_file:
+
+    #         series = image_file.series[0]
+    #         plane = series.pages[0]
 
-            subset_channel_image = equalize_adapthist(plane.asarray()[subset_interval_y_x[0]:subset_interval_y_x[1], subset_interval_y_x[2]:subset_interval_y_x[3]], kernel_size=[100, 100], clip_limit=0.01, nbins=256)
+    #         subset_channel_image = equalize_adapthist(plane.asarray()[subset_interval_y_x[0]:subset_interval_y_x[1], subset_interval_y_x[2]:subset_interval_y_x[3]], kernel_size=[100, 100], clip_limit=0.01, nbins=256)
 
-            metrics[f"{image_index}_indexed_image_channel_intensity"] = np.mean(subset_channel_image)
+    #         metrics[f"{image_index}_indexed_image_channel_intensity"] = np.mean(subset_channel_image)
 
     metrics['proportion_transcripts_assigned_to_cells'] = percentage_of_assigned_transcripts
     metrics['total_number_of_cells'] = len(cell_gdf)
     metrics['average_cell_area'] = cell_gdf['geometry'].area.mean()
     metrics['average_cell_volume'] = (cell_gdf['geometry'].area * thickness).mean()
-    
+
     metrics['average_transcripts_per_cell'] = trx_meta.groupby('cell_index').size().mean()
     metrics['median_transcripts_per_cell'] = trx_meta.groupby("cell_index")["transcript_index"].count().median()
 
@@ -57,7 +57,7 @@ def processing(transcript_metadata_file, transcript_data_file, cell_polygon_meta
     metrics_df = metrics_df.T
     metrics_df.columns = [tech_name]
     metrics_df = metrics_df.T
-    
+
     gene_specific_metrics_df = pd.DataFrame({
         "proportion_of_cells_expressing_gene": (trx_meta.groupby('gene')['cell_index'].nunique()) / len(cell_gdf),
         "average_expression_of_gene": trx_meta.groupby('gene')['cell_index'].mean(),
@@ -69,7 +69,7 @@ def processing(transcript_metadata_file, transcript_data_file, cell_polygon_meta
     return metrics_df, gene_specific_metrics_df
 
 def ist_segmentation_metrics(transcript_metadata_file, transcript_data_file, cell_polygon_metadata_file, cell_polygon_data_file, image_files, subset_interval_y_x, pixel_size, tech_name, thickness=1):
-    
+
     """
     A function to calculate segmentation quality control
     metrics for imaging spatial transcriptomics data.