Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -403,7 +403,24 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
 
     # Calculate the mean expression levels for each gene in each region
     expression_means = dff_long.lazy().groupby(["batch", "Gene"]).agg(pl.mean("Mean expression")).collect()
-
+    
+    # Calculate the percentage total expressed
+    dff_long2 = dff_pre.melt(id_vars="batch", variable_name="Gene").group_by(pl.all()).len()
+    dff_long3 = dff_long2.filter(value = 0).group_by(["batch", "Gene"]).agg(pl.sum("len").alias("total"))
+    dff_long4 = dff_long2.group_by(["batch", "Gene"]).agg(pl.sum("len").alias("total"))
+    dff_5 = dff_long4.join(dff_long3, on=["batch","Gene"], how="outer")
+    result = dff_5.select([
+        pl.when((pl.col('total_right').is_not_null()) & (pl.col('total').is_not_null()))
+              .then(pl.col('total_right') / pl.col('total'))
+              .otherwise(None).alias("%"),
+    ])
+    result = result.with_columns(pl.col("%").fill_null(1.0))
+    dff_5[["percentage"]] = result[["%"]]
+    dff_5 = dff_5.select(pl.col("batch","Gene","percentage"))
+
+    # Final part to join the percentage expressed and mean expression levels
+    # TO DO
+    
     #expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
     
     fig_pie = px.pie(names=labels, values=values, title=pie_title,template="seaborn")