Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -392,17 +392,11 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
     pie_title = f'Percentage of Total Cells: {total_cells}'  # Include total cells in the title
 
     # Calculate the mean expression
-    round_precision = 2
-
-    values_mean = []
-    for i in ["Cdc45","Mcm2","Mcm5"]:
-        category_means = dff.group_by(i).agg(pl.col(i).count().alias("count"))
-        category_sums = dff.group_by(i).agg(pl.col(i).sum().alias("sum"))
-        total_count = len(dff)
-        category_means = category_sums.select((pl.col("sum") / total_count).round(round_precision).alias(i))
-        values_mean += category_means[i].to_list()
-    print(values_mean)
-
+    
+    # Melt wide format DataFrame into long format
+    dff_pre = dff["batch","Cdc45","Uhrf1","Mcm2","Slbp"]
+    dff_long = dff_pre.melt(id_vars="batch", variable_name="Gene", value_name="Expression")
+    
     fig_pie = px.pie(names=labels, values=values, title=pie_title,template="seaborn")
 
     # Create the scatter plots
@@ -451,7 +445,7 @@ 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.scatter(data_frame=dff, x=condition1_chosen, y=condition3_chosen, color=condition3_chosen,
+    fig_scatter_12 = px.scatter(data_frame=dff_long, x="batch", y=condition3_chosen, color=condition3_chosen,
                             #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                             hover_name='batch',template="seaborn")