Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -394,13 +394,11 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
     # Calculate the mean expression
     
     # Melt wide format DataFrame into long format
-    dff_pre = dff.with_columns(pl.lit(None).cast(pl.Utf8), pl.col("*"))
+    # Specify batch column as string type and gene columns as float type
+    dff_pre = dff.with_columns(pl.lit(None).cast(pl.Utf8).alias("batch"), *[pl.col(col).cast(pl.Float64) for col in ["Cdc45", "Uhrf1", "Mcm2", "Slbp", "Mcm5", "Pola1", "Gmnn", "Cdc6", "Rrm2", "Atad2"]])
 
-    # Cast Cdc45, Uhrf1, Mcm2, Slbp, Mcm5, Pola1, Gmnn, Cdc6, Rrm2, Atad2 columns to float type
-    for gene in ["Cdc45", "Uhrf1", "Mcm2", "Slbp", "Mcm5", "Pola1", "Gmnn", "Cdc6", "Rrm2", "Atad2"]:
-        dff_pre = dff_pre.with_columns(pl.col(gene).cast(pl.Float64))
+    dff_long = dff_pre.melt(id_vars={"batch"}, variable_name="Gene", value_name="Expression").with_columns(pl.col("batch").cast(pl.Categorical))
 
-    dff_long = dff_pre.melt(id_vars="batch", variable_name="Gene", value_name="Expression")
     expression_means = dff_long.group_by(["Region", "Gene"]).agg(pl.mean("Expression"))
     
     fig_pie = px.pie(names=labels, values=values, title=pie_title,template="seaborn")