diff --git a/src/metrics/quality/quality_metrics.py b/src/metrics/quality/quality_metrics.py
index 0ec9d5a2..b354bf00 100644
--- a/src/metrics/quality/quality_metrics.py
+++ b/src/metrics/quality/quality_metrics.py
@@ -1,8 +1,8 @@
+import numpy as np
 import pandas as pd
 import spatialdata as sd
 
 
-
 def proportion_of_assigned_reads(
     sdata: sd.SpatialData,
 ) -> [float, pd.Series]:
@@ -22,10 +22,8 @@ def proportion_of_assigned_reads(
         
     """
     
-    sdata['transcripts']['assigned'] = sdata['transcripts']['cell_id'] != 0
-    
     # Proportion of assigned reads
-    prop_of_assigned_reads = float(((sdata['transcripts']['assigned']).sum() / len(sdata['transcripts'])).compute())
+    prop_of_assigned_reads = float(sdata["counts"].layers["counts"].sum() / len(sdata['transcripts']))
     
     # Proportion of assigned reads per gene
     if prop_of_assigned_reads == 1.0:
@@ -39,10 +37,56 @@ def proportion_of_assigned_reads(
             data=0.0
         )
     else:
-        df = pd.crosstab(sdata['transcripts']['feature_name'], sdata['transcripts']['assigned'])
-        prop_of_assigned_reads_per_gene = df[True] / (df[False] + df[True])
+        genes, counts = np.unique(sdata['transcripts']['feature_name'], return_counts=True)
+        df = pd.DataFrame(index=genes, data = {"fraction":0, "count":counts, "count_assigned":0})
+        df.loc[sdata["counts"].var_names, "count_assigned"] = np.array(sdata["counts"].layers["counts"].sum(axis=0))[0,:]
+        df["fraction"] = df["count_assigned"] / df["count"]
+        prop_of_assigned_reads_per_gene = df["fraction"]
     
     return prop_of_assigned_reads, prop_of_assigned_reads_per_gene
+
+# Previous version only based on transcripts table. 
+#
+#def proportion_of_assigned_reads(
+#    sdata: sd.SpatialData,
+#) -> [float, pd.Series]:
+#    """ Calculate the proportion of assigned reads
+#
+#    Parameters
+#    ----------
+#    sdata : sd.SpatialData
+#        SpatialData object with sdata['transcripts'] including the column 'cell_id'
+#        
+#    Returns
+#    -------
+#    float
+#        Proportion of assigned reads
+#    pd.Series
+#        Proportion of assigned reads per gene
+#        
+#    """
+#    
+#    sdata['transcripts']['assigned'] = sdata['transcripts']['cell_id'] != 0
+#    
+#    # Proportion of assigned reads
+#    prop_of_assigned_reads = float(((sdata['transcripts']['assigned']).sum() / len(sdata['transcripts'])).compute())
+#    
+#    # Proportion of assigned reads per gene
+#    if prop_of_assigned_reads == 1.0:
+#        prop_of_assigned_reads_per_gene = pd.Series(
+#            index=sdata['transcripts']['feature_name'].unique().compute().values,
+#            data=1.0
+#        )
+#    elif prop_of_assigned_reads == 0.0:
+#        prop_of_assigned_reads_per_gene = pd.Series(
+#            index=sdata['transcripts']['feature_name'].unique().compute().values,
+#            data=0.0
+#        )
+#    else:
+#        df = pd.crosstab(sdata['transcripts']['feature_name'], sdata['transcripts']['assigned'])
+#        prop_of_assigned_reads_per_gene = df[True] / (df[False] + df[True])
+#    
+#    return prop_of_assigned_reads, prop_of_assigned_reads_per_gene