Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -301,7 +301,7 @@ 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):
     dff = df.filter(
-        (pl.col('batch').cast(str).is_in(batch_chosen)) &
+        (pl.col(condition1_chosen).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]) &
@@ -311,19 +311,19 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
 )
     
     #Drop categories that are not in the filtered data
-    dff = dff.with_columns(dff['batch'].cast(pl.Categorical))
+    dff = dff.with_columns(dff[condition1_chosen].cast(pl.Categorical))
 
     # Plot figures
-    fig_violin = px.violin(data_frame=dff, x='batch', y=col_features, box=True, points="all",
-                            color='batch', hover_name='batch',template="seaborn")
+    fig_violin = px.violin(data_frame=dff, x=condition1_chosen, y=col_features, box=True, points="all",
+                            color=condition1_chosen, hover_name=condition1_chosen,template="seaborn")
 
     # Cache commonly used subexpressions
     total_count = pl.lit(len(dff))
-    category_counts = dff.group_by("batch").agg(pl.col("batch").count().alias("count"))
+    category_counts = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).count().alias("count"))
     category_counts = category_counts.with_columns(((pl.col("count") / total_count * 100).round(decimals=2)).alias("normalized_count"))
 
     # Display the result
-    labels = category_counts["batch"].to_list()
+    labels = category_counts[condition1_chosen].to_list()
     values = category_counts["normalized_count"].to_list()
 
     total_cells = total_count  # Calculate total number of cells