Update dash_plotly_QC_scRNA.py

dash_plotly_QC_scRNA.py

@@ -272,6 +272,9 @@ tab3_content = html.Div([
                   className='four columns',config=config_fig
                   )
     ]),
+    html.Div([
+        dcc.Graph(id='scatter-plot-12', figure={}, className='four columns',config=config_fig)
+    ]),
 ])
 
 # Define the tabs layout
@@ -331,6 +334,7 @@ def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
     Output(component_id='scatter-plot-9', component_property='figure'),
     Output(component_id='scatter-plot-10', component_property='figure'),
     Output(component_id='scatter-plot-11', component_property='figure'),
+    Output(component_id='scatter-plot-12', component_property='figure'),
     Output(component_id='my-graph2', component_property='figure'),
     Input(component_id='dpdn2', component_property='value'),
     Input(component_id='dpdn3', component_property='value'),
@@ -366,6 +370,11 @@ def update_graph_and_pie_chart(batch_chosen, s_chosen, g2m_chosen, condition1_ch
     total_count = len(dff)
     category_counts = category_counts.with_columns((pl.col("count") / total_count * 100).alias("normalized_count"))
 
+    # Calculate the mean expression
+    round_precision = 2
+    category_sums = dff.group_by(condition1_chosen).agg(pl.col(condition1_chosen).sum().alias("sum"))
+    category_means = category_sums.select((pl.col("sum") / total_count).round(round_precision).alias("mean"))
+
 # Display the result
     labels = category_counts["batch"].to_list()
     values = category_counts["normalized_count"].to_list()
@@ -421,11 +430,15 @@ 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=dff, x=condition1_chosen, y=condition2_chosen, size=category_means, color='batch',
+                            #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
+                            hover_name='batch',template="seaborn")
+    
     fig_violin2 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all",
                             color=condition1_chosen, hover_name=condition1_chosen,template="seaborn")
 
 
-    return fig_violin, fig_pie, fig_scatter, fig_scatter_2, fig_scatter_3, fig_scatter_4, fig_scatter_5, fig_scatter_6, fig_scatter_7, fig_scatter_8, fig_scatter_9, fig_scatter_10, fig_scatter_11, fig_violin2
+    return fig_violin, fig_pie, fig_scatter, fig_scatter_2, fig_scatter_3, fig_scatter_4, fig_scatter_5, fig_scatter_6, fig_scatter_7, fig_scatter_8, fig_scatter_9, fig_scatter_10, fig_scatter_11, fig_scatter_12, fig_violin2
 
 # Set http://localhost:5000/ in web browser
 # Now create your regular FASTAPI application