Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -383,8 +383,8 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
 
     # Calculate the mean expression
     round_precision = 2
-    category_means = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).count().alias("count"))
-    category_sums = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).sum().alias("sum"))
+    category_means = dff.group_by("batch").agg(pl.col("batch").count().alias("count"))
+    category_sums = dff.group_by("batch").agg(pl.col("batch").sum().alias("sum"))
     total_count = len(dff)
     category_means = category_sums.select((pl.col("sum") / total_count).round(round_precision).alias("mean"))
     values_mean = category_means["mean"].to_list()
@@ -437,7 +437,7 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
                             #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                             hover_name='batch',template="seaborn")
     
-    fig_scatter_12 = px.pie(names=labels, values=values_mean,template="seaborn")
+    fig_scatter_12 = px.pie(names=labels, values=values_mean, title=pie_title,template="seaborn")
     
     fig_violin2 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all",
                             color=condition1_chosen, hover_name=condition1_chosen,template="seaborn")