Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -361,22 +361,18 @@ def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
 )
 
 def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen, range_value_1, range_value_2, range_value_3):
-    batch_col = dff['batch']
-    batch_cat = batch_col.cast(pl.Categorical)
-    feature_cols = pl.col(cols)
-    count_cols = pl.col(col_counts)
-    mt_cols = pl.col(col_mt)
-    
-    # Perform filtering on all features simultaneously
-    filter_expr = (batch_cat.is_in(batch_chosen)) & \
-                  (feature_cols >= range_value_1[0]) & \
-                  (feature_cols <= range_value_1[1]) & \
-                  (count_cols >= range_value_2[0]) & \
-                  (count_cols <= range_value_2[1]) & \
-                  (mt_cols >= range_value_3[0]) & \
-                  (mt_cols <= range_value_3[1])
+    dff = df.filter(
+        (pl.col('batch').cast(str).is_in(batch_chosen)) &
+        (pl.col(col_features) >= range_value_1[0]) &
+        (pl.col(col_features) <= range_value_1[1]) &
+        (pl.col(col_counts) >= range_value_2[0]) &
+        (pl.col(col_counts) <= range_value_2[1]) &
+        (pl.col(col_mt) >= range_value_3[0]) &
+        (pl.col(col_mt) <= range_value_3[1])
+)
     
-    dff = dff.filter(filter_expr)
+    #Drop categories that are not in the filtered data
+    dff = dff.with_columns(dff['batch'].cast(pl.Categorical))
 
     # Plot figures
     fig_violin = px.violin(data_frame=dff, x='batch', y=col_features, box=True, points="all",