Update pages/Cornea_v1_integrated_scVI.py

pages/Cornea_v1_integrated_scVI.py

@@ -318,27 +318,11 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
         #(pl.col(col_mt) >= range_value_3[0]) &
         #(pl.col(col_mt) <= range_value_3[1])
 )
-    
-    # #Drop categories that are not in the filtered data
-    # dff = dff.with_columns(dff[col_chosen].cast(pl.Categorical))
-
-    # dff = dff.sort(col_chosen)
-    
-    # # Plot figures
-    # fig_violin_db2 = px.violin(data_frame=dff, x=col_chosen, y=col_features, box=True, points="all",
-    #                         color=col_chosen, hover_name=col_chosen,template="seaborn")
-
-    # # Cache commonly used subexpressions
-    # total_count = pl.lit(len(dff))
-    # category_counts = dff.group_by(col_chosen).agg(pl.col(col_chosen).count().alias("count"))
-    # category_counts = category_counts.with_columns(((pl.col("count") / total_count * 100).round(decimals=2)).alias("normalized_count"))
-
-    # # Sort the dataframe
-    # #category_counts = category_counts.sort(col_chosen) does not work check if the names are different ...
-    
-    # # Display the result
-    # total_cells = total_count  # Calculate total number of cells
-    # pie_title = f'Percentage of Total Cells: {total_cells}'  # Include total cells in the title
+    # Select ordering of plots
+    if col_chosen == "integrated_cell_states":
+        cat_ord= {condition1_chosen: ["LSC-1","LSC-2","LE","CE","Cj","qSK","SK","TSK","CF","EC","Ves","Mel","IC","nm-cSC","MC"]}
+    else:
+        cat_ord= {condition1_chosen: natsorted(dff[condition1_chosen].unique())}
 
     # Calculate the mean expression
     
@@ -373,35 +357,6 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
     # Final part to join the percentage expressed and mean expression levels
     # TO DO
     expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")
-
-    # Order the dataframe on ascending categories
-    #expression_means = expression_means.sort(col_chosen, descending=False)
-    
-    #expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
-    # category_counts = category_counts.sort(col_chosen)
-    
-    # fig_pie_db2 = px.pie(category_counts, values="normalized_count", names=col_chosen, labels=col_chosen, hole=.3, title=pie_title, template="seaborn")
-
-    # #labels = category_counts[col_chosen].to_list()
-    # #values = category_counts["normalized_count"].to_list()
-
-    # # Create the scatter plots
-    # fig_scatter_db2 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_chosen,
-    #                          labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-    #                          hover_name='batch',template="seaborn")
-
-    # fig_scatter_db2_2 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_mt,
-    #                          labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-    #                          hover_name='batch',template="seaborn")
-
-    # fig_scatter_db2_3 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_features,
-    #                          labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-    #                          hover_name='batch',template="seaborn")
-
-
-    # fig_scatter_db2_4 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_counts,
-    #                          labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-    #                          hover_name='batch',template="seaborn")
     
     fig_scatter_db2_5 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=s_chosen,
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
@@ -424,7 +379,7 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
     
     fig_scatter_db2_9 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition1_chosen,
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-                            hover_name='studies',template="seaborn")
+                            hover_name='studies',template="seaborn",category_orders=cat_ord)
     
     fig_scatter_db2_10 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition2_chosen,
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
@@ -432,7 +387,7 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
     
     fig_scatter_db2_11 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, color=condition1_chosen,
                             #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-                            hover_name='studies',template="seaborn")
+                            hover_name='studies',template="seaborn",category_orders=cat_ord)
 
     if col_chosen == "integrated_cell_states":
         fig_scatter_db2_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",