Update pages/integratedsuture.py

pages/integratedsuture.py

@@ -9,11 +9,12 @@ import dash_callback_chain
 import yaml
 import polars as pl
 import os
-pl.enable_string_cache(False)
+from natsort import natsorted
+#pl.enable_string_cache(False)
 
 dash.register_page(__name__, location="sidebar")
 
-dataset = "datasuture/integrated/sc_liu_suture_integrated_processed"
+dataset = "cornea_v1_umap_clusres_scVI_polars"
 
 # Set custom resolution for plots:
 config_fig = {
@@ -54,114 +55,11 @@ storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STO
 # Load in multiple dataframes
 df = pl.read_parquet(f"az://{dataset}.parquet", storage_options=storage_options)
 
-# Setup the app
-#external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
-#app = dash.Dash(__name__, use_pages=True) #, requests_pathname_prefix='/dashboard1/'
-
-#df = pl.read_parquet(filepath,storage_options=storage_options)
-#df = pl.DataFrame()
-#abfs = AzureBlobFileSystem(account_name=accountname,account_key=accountkey)
-#df = df.rename({"__index_level_0__": "Unnamed: 0"})
-
-#df1 = pl.read_parquet(filepath, storage_options=storage_options)
-
-#df2 = pl.read_parquet(f"az://data10xflex/{dataset_chosen}.parquet", storage_options=storage_options)
-
-#tab0_content = html.Div([
-#    html.Label("Dataset chosen"),    
-#    dcc.Dropdown(id='dpdn1', value="corg/10xflexcorg_umap_clusres", multi=False,
-#                 options=["corg/10xflexcorg_umap_clusres","d1011/10xflexd1011_umap_clusres"])
-#])
-
-#@app.callback(
-#    Input(component_id='dpdn1', component_property='value')
-#)
-
-#def update_filepath(dpdn1):
-#    global df
-#    if str(f"az://data10xflex/{dpdn1}.parquet") != str(filepath):
-#        print("not identical filepath, chosing other")
-#        df2 = pl.read_parquet(f"az://data10xflex/{dpdn1}.parquet", storage_options=storage_options)
-#        df = df2
-#    return
-
-#df = pl.read_parquet(filepath, storage_options=storage_options)
-min_value = df[col_features].min()
-max_value = df[col_features].max()
-
-min_value_2 = df[col_counts].min()
-min_value_2 = round(min_value_2)
-max_value_2 = df[col_counts].max()
-max_value_2 = round(max_value_2)
-
-min_value_3 = df[col_mt].min()
-min_value_3 = round(min_value_3, 1)
-max_value_3 = df[col_mt].max()
-max_value_3 = round(max_value_3, 1)
-
-# Loads in the conditions specified in the yaml file
-
-# Note: Future version perhaps all values from a column in the dataframe of the parquet file
-# Note 2: This could also be a tsv of the categories and own specified colors
-#conditions = df[col_batch].unique().to_list()
-# Create the first tab content
-# Add Sliders for three QC params: N genes by counts, total amount of reads and pct MT reads
-
-tab1_content = html.Div([
-    html.Label("Column chosen"),    
-    dcc.Dropdown(id='dpdn2', value="batch", multi=False,
-                 options=df.columns),
-    html.Label("N Genes by Counts"),
-    dcc.RangeSlider(
-        id='range-slider-1',
-        step=250,
-        value=[min_value, max_value],
-        marks={i: str(i) for i in range(min_value, max_value + 1, 250)},
-    ),
-    dcc.Input(id='min-slider-1', type='number', value=min_value, debounce=True),
-    dcc.Input(id='max-slider-1', type='number', value=max_value, debounce=True),
-    html.Label("Total Counts"),
-    dcc.RangeSlider(
-        id='range-slider-2',
-        step=7500,
-        value=[min_value_2, max_value_2],
-        marks={i: str(i) for i in range(min_value_2, max_value_2 + 1, 7500)},
-    ),
-    dcc.Input(id='min-slider-2', type='number', value=min_value_2, debounce=True),
-    dcc.Input(id='max-slider-2', type='number', value=max_value_2, debounce=True),
-    html.Label("Percent Mitochondrial Genes"),
-    dcc.RangeSlider(
-        id='range-slider-3',
-        step=5,
-        min=0,
-        max=100,
-        value=[min_value_3, max_value_3],
-    ),
-    dcc.Input(id='min-slider-3', type='number', value=min_value_3, debounce=True),
-    dcc.Input(id='max-slider-3', type='number', value=max_value_3, debounce=True),
-    html.Div([
-        dcc.Graph(id='pie-graph', figure={}, className='four columns',config=config_fig),
-        dcc.Graph(id='my-graph', figure={}, clickData=None, hoverData=None,
-                  className='four columns',config=config_fig
-                  ),
-        dcc.Graph(id='scatter-plot', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot-2', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot-3', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot-4', figure={}, className='four columns',config=config_fig)
-    ]),
-])
-
-# Create the second tab content with scatter-plot-5 and scatter-plot-6
+# Create the second tab content with scatter-plot_db4-5 and scatter-plot_db4-6
 tab2_content = html.Div([
     html.Div([
             html.Label("S-cycle genes"),
-            dcc.Dropdown(id='dpdn3', value="Mcm5", multi=False,
+            dcc.Dropdown(id='dpdn3', value="MCM5", multi=False,
                  options=[
     "Cdc45",
     "Uhrf1",
@@ -208,7 +106,7 @@ tab2_content = html.Div([
     "Cdca7"
 ]),
             html.Label("G2M-cycle genes"),
-            dcc.Dropdown(id='dpdn4', value="Top2a", multi=False,
+            dcc.Dropdown(id='dpdn4', value="TOP2A", multi=False,
                  options=[
     "Ube2c",
     "Lbr",
@@ -266,57 +164,56 @@ tab2_content = html.Div([
 ]),
     ]),
     html.Div([
-        dcc.Graph(id='scatter-plot-5', figure={}, className='three columns',config=config_fig)
+        dcc.Graph(id='scatter-plot_db4-5', figure={}, className='three columns',config=config_fig)
     ]),
     html.Div([
-        dcc.Graph(id='scatter-plot-6', figure={}, className='three columns',config=config_fig)
+        dcc.Graph(id='scatter-plot_db4-6', figure={}, className='three columns',config=config_fig)
     ]),
     html.Div([
-        dcc.Graph(id='scatter-plot-7', figure={}, className='three columns',config=config_fig)
+        dcc.Graph(id='scatter-plot_db4-7', figure={}, className='three columns',config=config_fig)
     ]),
     html.Div([
-        dcc.Graph(id='scatter-plot-8', figure={}, className='three columns',config=config_fig)
+        dcc.Graph(id='scatter-plot_db4-8', figure={}, className='three columns',config=config_fig)
     ]),
 ])
 
-# Create the second tab content with scatter-plot-5 and scatter-plot-6
+# Create the second tab content with scatter-plot_db4-5 and scatter-plot_db4-6
 tab3_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-9', figure={}, className='four columns',config=config_fig)
+        dcc.Graph(id='scatter-plot_db4-9', figure={}, className='four columns', hoverData=None ,config=config_fig)
     ]),
     html.Div([
-        dcc.Graph(id='scatter-plot-10', figure={}, className='four columns',config=config_fig)
+        dcc.Graph(id='scatter-plot_db4-10', figure={}, className='four columns', hoverData=None, config=config_fig)
     ]),
     html.Div([
-        dcc.Graph(id='scatter-plot-11', figure={}, className='four columns',config=config_fig)
+        dcc.Graph(id='scatter-plot_db4-11', figure={}, className='four columns',config=config_fig)
     ]),
     html.Div([
-            dcc.Graph(id='my-graph2', figure={}, clickData=None, hoverData=None,
+            dcc.Graph(id='my-graph_db42', figure={}, clickData=None, hoverData=None,
                   className='four columns',config=config_fig
                   )
     ]),
     ]),
 ])
-#    html.Div([
-#        dcc.Graph(id='scatter-plot-12', figure={}, className='four columns',config=config_fig)
-#    ]),
-
 
 tab4_content = html.Div([
+     html.Label("Column chosen"),    
+     dcc.Dropdown(id='dpdn2', value="integrated_clusters", multi=False,
+                  options=df.columns),
     html.Div([
             html.Label("Multi gene"),
             dcc.Dropdown(id='dpdn7', value=["Pax6","Krt15","Trp63","Krt14","Krt5","Sox9","Cdk8","Il31ra","Gpha2","Abl1","Areg","Lars2","Calml3","Krt13","Krt19","Psca","Muc20","Muc4","Aqp5","S100a8","S100a9","Lama3","Itgb4","Itga6","Lamc2","Cd44","Cdh1","Thy1","Dcn","Scn7a","Cdh19","Mpz","Ptprc","Cd52","Cd69","Cd86","Rgs5","Des","Myh11","Cd93","Pecam1","Abcg2","Lyve1","Mki67","Top2a","Ube2c","Birc5"], multi=True,
                  options=df.columns),
 ]),
     html.Div([
-        dcc.Graph(id='scatter-plot-12', figure={}, className='row',style={'width': '100vh', 'height': '90vh'}),  # px)
+        dcc.Graph(id='scatter-plot_db4-12', figure={}, className='row',style={'width': '100vh', 'height': '90vh'})
     ]),
 ])
 
@@ -327,105 +224,42 @@ layout = html.Div([
         'font-size': '100%',
         '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='Cell cycle', value='tab2', children=tab2_content),
-        dcc.Tab(label='Custom', value='tab3', children=tab3_content),
+        #dcc.Tab(label='QC', value='tab1', children=tab1_content),
+        dcc.Tab(label='UMAP visualisation', value='tab3', children=tab3_content),
         dcc.Tab(label='Multi dot', value='tab4', children=tab4_content),
+        dcc.Tab(label='Cell cycle', value='tab2', children=tab2_content),
     ]),
 ])
 
-# Define the circular callback
 @callback(
-    Output("min-slider-1", "value"),
-    Output("max-slider-1", "value"),
-    Output("min-slider-2", "value"),
-    Output("max-slider-2", "value"),
-    Output("min-slider-3", "value"),
-    Output("max-slider-3", "value"),
-    Input("min-slider-1", "value"),
-    Input("max-slider-1", "value"),
-    Input("min-slider-2", "value"),
-    Input("max-slider-2", "value"),
-    Input("min-slider-3", "value"),
-    Input("max-slider-3", "value"),
-)
-def circular_callback(min_1, max_1, min_2, max_2, min_3, max_3):
-    return min_1, max_1, min_2, max_2, min_3, max_3
-
-@callback(
-    Output('range-slider-1', 'value'),
-    Output('range-slider-2', 'value'),
-    Output('range-slider-3', 'value'),
-    Input('min-slider-1', 'value'),
-    Input('max-slider-1', 'value'),
-    Input('min-slider-2', 'value'),
-    Input('max-slider-2', 'value'),
-    Input('min-slider-3', 'value'),
-    Input('max-slider-3', 'value'),    
-)
-def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
-    return [min_1, max_1], [min_2, max_2], [min_3, max_3]
-
-@callback(
-    Output(component_id='my-graph', component_property='figure'),
-    Output(component_id='pie-graph', component_property='figure'),
-    Output(component_id='scatter-plot', component_property='figure'),
-    Output(component_id='scatter-plot-2', component_property='figure'),
-    Output(component_id='scatter-plot-3', component_property='figure'),
-    Output(component_id='scatter-plot-4', component_property='figure'),  # Add this new scatter plot
-    Output(component_id='scatter-plot-5', component_property='figure'),
-    Output(component_id='scatter-plot-6', component_property='figure'),
-    Output(component_id='scatter-plot-7', component_property='figure'),
-    Output(component_id='scatter-plot-8', component_property='figure'),
-    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'),
+    Output(component_id='scatter-plot_db4-5', component_property='figure'),
+    Output(component_id='scatter-plot_db4-6', component_property='figure'),
+    Output(component_id='scatter-plot_db4-7', component_property='figure'),
+    Output(component_id='scatter-plot_db4-8', component_property='figure'),
+    Output(component_id='scatter-plot_db4-9', component_property='figure'),
+    Output(component_id='scatter-plot_db4-10', component_property='figure'),
+    Output(component_id='scatter-plot_db4-11', component_property='figure'),
+    Output(component_id='scatter-plot_db4-12', component_property='figure'),
+    Output(component_id='my-graph_db42', component_property='figure'),
     Input(component_id='dpdn2', component_property='value'),
     Input(component_id='dpdn3', component_property='value'),
     Input(component_id='dpdn4', component_property='value'),
     Input(component_id='dpdn5', component_property='value'),
     Input(component_id='dpdn6', component_property='value'),
     Input(component_id='dpdn7', component_property='value'),
-    Input(component_id='range-slider-1', component_property='value'),
-    Input(component_id='range-slider-2', component_property='value'),
-    Input(component_id='range-slider-3', component_property='value'),
      
 )
 
-def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen, range_value_1, range_value_2, range_value_3): #batch_chosen, 
+def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, condition3_chosen): #, range_value_1, range_value_2, range_value_3 batch_chosen, 
     batch_chosen = df[col_chosen].unique().to_list()
     dff = df.filter(
-        (pl.col(col_chosen).cast(str).is_in(batch_chosen)) &
-        (pl.col(col_features) >= range_value_1[0]) &
-        (pl.col(col_features) <= range_value_1[1]) &
-        (pl.col(col_counts) >= range_value_2[0]) &
-        (pl.col(col_counts) <= range_value_2[1]) &
-        (pl.col(col_mt) >= range_value_3[0]) &
-        (pl.col(col_mt) <= range_value_3[1])
+        (pl.col(col_chosen).cast(str).is_in(batch_chosen)) #&
 )
-    
-    #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 = 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 condition1_chosen == "integrated_cell_states":
+        cat_ord= {condition1_chosen: natsorted(dff[condition1_chosen].unique())}
+    else:
+        cat_ord= {condition1_chosen: natsorted(dff[condition1_chosen].unique())}
 
     # Calculate the mean expression
     
@@ -437,9 +271,9 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
     
     # Melt wide format DataFrame into long format
     dff_long = dff_pre.melt(id_vars=col_chosen, variable_name="Gene", value_name="Mean expression")
-
+    
     # Calculate the mean expression levels for each gene in each region
-    expression_means = dff_long.lazy().group_by([col_chosen, "Gene"]).agg(pl.mean("Mean expression")).collect()
+    expression_means = dff_long.lazy().group_by([col_chosen, "Gene"]).agg(pl.mean("Mean expression")).collect() #
     
     # Calculate the percentage total expressed
     dff_long1 = dff_pre.melt(id_vars=col_chosen, variable_name="Gene")#.group_by(pl.all()).agg(pl.len())
@@ -458,82 +292,55 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
     dff_5 = dff_5.select(pl.col(col_chosen,"Gene","percentage"))
 
     # 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=True)
-    
-    #expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
-    category_counts = category_counts.sort(col_chosen)
     
-    fig_pie = 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 = 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_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_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_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_5 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=s_chosen,
+    fig_scatter_db4_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=None, title="S-cycle gene:",template="seaborn")
 
-    fig_scatter_6 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=g2m_chosen,
+    fig_scatter_db4_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_7 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="S_score",
+    fig_scatter_db4_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_8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="G2M_score",
+    fig_scatter_db4_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_9 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition1_chosen,
+    fig_scatter_db4_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=None,hover_data = None, template="seaborn",category_orders=cat_ord)
+    fig_scatter_db4_9.update_traces(hoverinfo='none', hovertemplate=None)
+    fig_scatter_db4_9.update_layout(hovermode=False)
     
-    fig_scatter_10 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition2_chosen,
+    fig_scatter_db4_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")
-    
-    fig_scatter_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_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
-                                size="percentage", size_max = 20,
+    fig_scatter_db4_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=col_chosen,template="seaborn")
+                            hover_name='studies',template="seaborn",category_orders=cat_ord)
+
+    # Reorder categories on natural sorting or on the integrated cell state order of the paper
+    if col_chosen == "integrated_cell_states":
+        fig_scatter_db4_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: natsorted(expression_means[col_chosen].unique())})
+    else:                                                                                
+        fig_scatter_db4_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: natsorted(expression_means[col_chosen].unique()),"Gene": condition3_chosen})
     
-    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_scatter_12, fig_violin2
+    fig_violin_db42 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all",
+                            color=condition1_chosen, hover_name=condition1_chosen,template="seaborn",category_orders=cat_ord)
 
-# Set http://localhost:5000/ in web browser
-# Now create your regular FASTAPI application
 
-#if __name__ == '__main__':
-#    app.run_server(debug=False, use_reloader=False, host='0.0.0.0', port=5000) #
+    return fig_scatter_db4_5, fig_scatter_db4_6, fig_scatter_db4_7, fig_scatter_db4_8, fig_scatter_db4_9, fig_scatter_db4_10, fig_scatter_db4_11, fig_scatter_db4_12, fig_violin_db42