Update pages/Cornea_v1_integrated_scVI.py

pages/Cornea_v1_integrated_scVI.py

@@ -158,7 +158,7 @@ df = pl.scan_parquet(f"./data/{dataset}.parquet", storage_options=storage_option
 # ])
 
 # Create the second tab content with scatter-plot_db2-5 and scatter-plot_db2-6
-tab2_content = html.Div([
+tab4_content = html.Div([
     html.Div([
             html.Label("S-cycle genes"),
             dcc.Dropdown(id='dpdn3', value="MCM5", multi=False,
@@ -229,13 +229,13 @@ tab2_content = html.Div([
 ])
 
 # Create the second tab content with scatter-plot_db2-5 and scatter-plot_db2-6
-tab3_content = html.Div([
+tab2_content = html.Div([
     html.Div([
             html.Label("UMAP condition 1"),
-            dcc.Dropdown(id='dpdn5', value="batch", multi=False,
+            dcc.Dropdown(id='dpdn5', value="studies", multi=False,
                  options=df.columns),
             html.Label("UMAP condition 2"),
-            dcc.Dropdown(id='dpdn6', value="n_genes_by_counts", multi=False,
+            dcc.Dropdown(id='dpdn6', value="PAX6", multi=False,
                  options=df.columns),
     html.Div([
         dcc.Graph(id='scatter-plot_db2-9', figure={}, className='four columns',config=config_fig)
@@ -258,7 +258,7 @@ tab3_content = html.Div([
 #    ]),
 
 
-tab4_content = html.Div([
+tab3_content = html.Div([
      html.Label("Column chosen"),    
      dcc.Dropdown(id='dpdn2', value="latest", multi=False,
                   options=df.columns),
@@ -280,9 +280,9 @@ layout = html.Div([
         'height': 50}, value='tab1',children=[
         #dcc.Tab(label='Dataset', value='tab0', children=tab0_content),
         #dcc.Tab(label='QC', value='tab1', children=tab1_content),
-        dcc.Tab(label='Multi dot', value='tab4', children=tab4_content),
-        dcc.Tab(label='Cell cycle', value='tab2', children=tab2_content),
-        dcc.Tab(label='Custom', value='tab3', children=tab3_content),
+        dcc.Tab(label='Multi dot', value='tab3', children=tab3_content),
+        dcc.Tab(label='Cell cycle', value='tab4', children=tab4_content),
+        dcc.Tab(label='UMAP visualisation', value='tab2', children=tab2_content),
     ]),
 ])
 
@@ -446,34 +446,34 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
     
     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'},
-                            hover_name='batch', title="S-cycle gene:",template="seaborn")
+                            hover_name='studies', title="S-cycle gene:",template="seaborn")
 
     fig_scatter_db2_6 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=g2m_chosen,
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-                            hover_name='batch', title="G2M-cycle gene:",template="seaborn")
+                            hover_name='studies', title="G2M-cycle gene:",template="seaborn")
     
     fig_scatter_db2_7 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="S_score",
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-                            hover_name='batch', title="S score:",template="seaborn")
+                            hover_name='studies', title="S score:",template="seaborn")
     
     fig_scatter_db2_8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="G2M_score",
                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-                            hover_name='batch', title="G2M score:",template="seaborn")
+                            hover_name='studies', title="G2M score:",template="seaborn")
 
     # Sort values of custom in-between
     dff = dff.sort(condition1_chosen)
     
     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='batch',template="seaborn")
+                            hover_name='studies',template="seaborn")
     
     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'},
-                            hover_name='batch',template="seaborn")
+                            hover_name='studies',template="seaborn")
     
     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='batch',template="seaborn")
+                            hover_name='studies',template="seaborn")
     
     fig_scatter_db2_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
                                 size="percentage", size_max = 20,