Update dash_plotly_QC_scRNA.py

Parameterizing the dotplot condition

dash_plotly_QC_scRNA.py

@@ -334,22 +334,22 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
     # Melt wide format DataFrame into long format
     # Specify batch column as string type and gene columns as float type
     list_conds = condition3_chosen
-    list_conds += ["batch"]
+    list_conds += [condition1_chosen]
     dff_pre = dff.select(list_conds)
     
     # Melt wide format DataFrame into long format
-    dff_long = dff_pre.melt(id_vars="batch", variable_name="Gene", value_name="Mean expression")
+    dff_long = dff_pre.melt(id_vars=condition1_chosen, variable_name="Gene", value_name="Mean expression")
 
     # Calculate the mean expression levels for each gene in each region
-    expression_means = dff_long.lazy().group_by(["batch", "Gene"]).agg(pl.mean("Mean expression")).collect()
+    expression_means = dff_long.lazy().group_by([condition1_chosen, "Gene"]).agg(pl.mean("Mean expression")).collect()
     
     # Calculate the percentage total expressed
-    dff_long1 = dff_pre.melt(id_vars="batch", variable_name="Gene")#.group_by(pl.all()).agg(pl.len())
+    dff_long1 = dff_pre.melt(id_vars=condition1_chosen, variable_name="Gene")#.group_by(pl.all()).agg(pl.len())
     count = 1
     dff_long2 = dff_long1.with_columns(pl.lit(count).alias("len"))
     dff_long3 = dff_long2.filter(pl.col("value") > 0).group_by(["batch", "Gene"]).agg(pl.sum("len").alias("len"))
-    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")
+    dff_long4 = dff_long2.group_by([condition1_chosen, "Gene"]).agg(pl.sum("len").alias("total"))
+    dff_5 = dff_long4.join(dff_long3, on=[condition1_chosen,"Gene"], how="outer")
     result = dff_5.select([
         pl.when((pl.col('len').is_not_null()) & (pl.col('total').is_not_null()))
               .then(pl.col('len') / pl.col('total')*100)
@@ -357,11 +357,11 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
     ])
     result = result.with_columns(pl.col("%").fill_null(100))
     dff_5[["percentage"]] = result[["%"]]
-    dff_5 = dff_5.select(pl.col("batch","Gene","percentage"))
+    dff_5 = dff_5.select(pl.col(condition1_chosen,"Gene","percentage"))
 
     # Final part to join the percentage expressed and mean expression levels
     # TO DO
-    expression_means = expression_means.join(dff_5, on=["batch","Gene"], how="inner")
+    expression_means = expression_means.join(dff_5, on=[condition1_chosen,"Gene"], how="inner")
     
     #expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)