Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -399,10 +399,10 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
     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="Expression")
+    dff_long = dff_pre.melt(id_vars="batch", variable_name="Gene", value_name="Mean expression")
 
     # Calculate the mean expression levels for each gene in each region
-    expression_means = dff_long.lazy().groupby(["batch", "Gene"]).agg(pl.mean("Expression")).collect()
+    expression_means = dff_long.lazy().groupby(["batch", "Gene"]).agg(pl.mean("Mean expression")).collect()
 
     #expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
     
@@ -454,7 +454,7 @@ 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=expression_means, x="batch", y="Gene", color="Mean expression",
+    fig_scatter_12 = px.scatter(data_frame=expression_means, x="Gene", y="batch", color="Mean expression",
                             #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
                             hover_name='batch',template="seaborn")