Update pages/Cornea_v1_integrated_scVI.py

pages/Cornea_v1_integrated_scVI.py

@@ -49,10 +49,10 @@ col_mt = config.get("col_mt")
 
 #filepath = f"az://{path_parquet}"
 
-storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STORAGE_ACCESS_KEY} #,'anon': False
+#storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STORAGE_ACCESS_KEY} #,'anon': False
 
 # Load in multiple dataframes
-df = pl.scan_parquet(f"./data/{dataset}.parquet", storage_options=storage_options).collect()
+df = pl.scan_parquet(f"./data/{dataset}.parquet").collect() #, storage_options=storage_options
 
 # Create the second tab content with scatter-plot_db2-5 and scatter-plot_db2-6
 tab2_content = html.Div([
@@ -165,7 +165,7 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
 )
     # Select ordering of plots
     if condition1_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"]}
+        cat_ord= {condition1_chosen: ["LSC-1","LSC-2","LSE","CE","Cj","qSK","SK","TSK","CF","CEC","B/L EC","Mel","IC","nm-cSC","MC"]}
     else:
         cat_ord= {condition1_chosen: natsorted(dff[condition1_chosen].unique())}
 
@@ -240,7 +240,7 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
         fig_scatter_db2_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
                                     size="percentage", size_max = 20,
                                 #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-                                hover_name=col_chosen,template="seaborn",category_orders={col_chosen: ["LSC-1","LSC-2","LE","CE","Cj","qSK","SK","TSK","CF","EC","Ves","Mel","IC","nm-cSC","MC"],"Gene": condition3_chosen})
+                                hover_name=col_chosen,template="seaborn",category_orders={col_chosen: ["LSC-1","LSC-2","LSE","CE","Cj","qSK","SK","TSK","CF","CEC","B/L EC","Mel","IC","nm-cSC","MC"],"Gene": condition3_chosen})
     else:                                                                                
         fig_scatter_db2_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
                                     size="percentage", size_max = 20,