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Update pages/DLC_corg_week16.py
Browse files- pages/DLC_corg_week16.py +33 -33
pages/DLC_corg_week16.py
CHANGED
@@ -56,7 +56,7 @@ storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STO
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# Load in multiple dataframes
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df = pl.scan_parquet(f"az://{dataset}.parquet", storage_options=storage_options).collect()
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# Create the second tab content with scatter-
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tab2_content = html.Div([
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html.Div([
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html.Label("S-cycle genes"),
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@@ -74,20 +74,20 @@ tab2_content = html.Div([
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"GAS2L3","CBX5","CENPA"]),
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]),
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html.Div([
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dcc.Graph(id='scatter-
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]),
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html.Div([
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dcc.Graph(id='scatter-
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]),
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html.Div([
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dcc.Graph(id='scatter-
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]),
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html.Div([
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dcc.Graph(id='scatter-
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]),
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])
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# Create the second tab content with scatter-
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tab3_content = html.Div([
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html.Div([
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html.Label("UMAP condition 1"),
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@@ -97,16 +97,16 @@ tab3_content = html.Div([
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dcc.Dropdown(id='dpdn6', value="PAX6", multi=False,
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options=df.columns),
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html.Div([
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dcc.Graph(id='scatter-
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]),
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html.Div([
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dcc.Graph(id='scatter-
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]),
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html.Div([
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dcc.Graph(id='scatter-
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]),
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html.Div([
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dcc.Graph(id='my-
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className='four columns',config=config_fig
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)
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]),
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@@ -123,7 +123,7 @@ tab4_content = html.Div([
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options=df.columns),
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]),
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html.Div([
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dcc.Graph(id='scatter-
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]),
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])
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@@ -142,15 +142,15 @@ layout = html.Div([
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])
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@callback(
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Output(component_id='scatter-
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Output(component_id='scatter-
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Output(component_id='scatter-
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Output(component_id='scatter-
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Output(component_id='scatter-
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Output(component_id='scatter-
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Output(component_id='scatter-
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Output(component_id='scatter-
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Output(component_id='my-
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Input(component_id='dpdn2', component_property='value'),
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Input(component_id='dpdn3', component_property='value'),
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Input(component_id='dpdn4', component_property='value'),
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@@ -204,53 +204,53 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
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# Final part to join the percentage expressed and mean expression levels
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expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")
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-
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name=None, title="S-cycle gene:",template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample', title="G2M-cycle gene:",template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample', title="S score:",template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample', title="G2M score:",template="seaborn")
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# Sort values of custom in-between
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dff = dff.sort(condition1_chosen)
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name=None,hover_data = None, template="seaborn",category_orders=cat_ord)
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-
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-
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-
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample',template="seaborn")
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample',template="seaborn",category_orders=cat_ord)
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# Reorder categories on natural sorting or on the integrated cell state order of the paper
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if col_chosen == "integrated_cell_states":
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size="percentage", size_max = 20,
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name=col_chosen,template="seaborn",category_orders={col_chosen: natsorted(expression_means[col_chosen].unique()),"Gene": condition3_chosen})
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else:
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-
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size="percentage", size_max = 20,
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name=col_chosen,template="seaborn",category_orders={col_chosen: natsorted(expression_means[col_chosen].unique()),"Gene": condition3_chosen})
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color=condition1_chosen, hover_name=condition1_chosen,template="seaborn",category_orders=cat_ord)
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return
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# Load in multiple dataframes
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df = pl.scan_parquet(f"az://{dataset}.parquet", storage_options=storage_options).collect()
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# Create the second tab content with scatter-plot_dbw16-5 and scatter-plot_dbw16-6
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tab2_content = html.Div([
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html.Div([
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html.Label("S-cycle genes"),
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"GAS2L3","CBX5","CENPA"]),
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_dbw16-5', figure={}, className='three columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_dbw16-6', figure={}, className='three columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_dbw16-7', figure={}, className='three columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_dbw16-8', figure={}, className='three columns',config=config_fig)
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]),
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])
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# Create the second tab content with scatter-plot_dbw16-5 and scatter-plot_dbw16-6
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tab3_content = html.Div([
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html.Div([
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html.Label("UMAP condition 1"),
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dcc.Dropdown(id='dpdn6', value="PAX6", multi=False,
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options=df.columns),
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html.Div([
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dcc.Graph(id='scatter-plot_dbw16-9', figure={}, className='four columns', hoverData=None ,config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_dbw16-10', figure={}, className='four columns', hoverData=None, config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_dbw16-11', figure={}, className='four columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='my-graph_dbw162', figure={}, clickData=None, hoverData=None,
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className='four columns',config=config_fig
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)
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]),
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options=df.columns),
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_dbw16-12', figure={}, className='row',style={'width': '100vh', 'height': '90vh'})
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]),
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])
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])
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@callback(
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Output(component_id='scatter-plot_dbw16-5', component_property='figure'),
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Output(component_id='scatter-plot_dbw16-6', component_property='figure'),
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Output(component_id='scatter-plot_dbw16-7', component_property='figure'),
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Output(component_id='scatter-plot_dbw16-8', component_property='figure'),
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Output(component_id='scatter-plot_dbw16-9', component_property='figure'),
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Output(component_id='scatter-plot_dbw16-10', component_property='figure'),
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Output(component_id='scatter-plot_dbw16-11', component_property='figure'),
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Output(component_id='scatter-plot_dbw16-12', component_property='figure'),
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Output(component_id='my-graph_dbw162', component_property='figure'),
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Input(component_id='dpdn2', component_property='value'),
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Input(component_id='dpdn3', component_property='value'),
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Input(component_id='dpdn4', component_property='value'),
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# Final part to join the percentage expressed and mean expression levels
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expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")
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fig_scatter_dbw16_5 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=s_chosen,
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name=None, title="S-cycle gene:",template="seaborn")
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fig_scatter_dbw16_6 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=g2m_chosen,
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample', title="G2M-cycle gene:",template="seaborn")
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fig_scatter_dbw16_7 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="S_score",
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample', title="S score:",template="seaborn")
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fig_scatter_dbw16_8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="G2M_score",
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample', title="G2M score:",template="seaborn")
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# Sort values of custom in-between
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dff = dff.sort(condition1_chosen)
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fig_scatter_dbw16_9 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition1_chosen,
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name=None,hover_data = None, template="seaborn",category_orders=cat_ord)
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fig_scatter_dbw16_9.update_traces(hoverinfo='none', hovertemplate=None)
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fig_scatter_dbw16_9.update_layout(hovermode=False)
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fig_scatter_dbw16_10 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition2_chosen,
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample',template="seaborn")
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fig_scatter_dbw16_11 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, color=condition1_chosen,
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='sample',template="seaborn",category_orders=cat_ord)
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# Reorder categories on natural sorting or on the integrated cell state order of the paper
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if col_chosen == "integrated_cell_states":
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fig_scatter_dbw16_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
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size="percentage", size_max = 20,
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name=col_chosen,template="seaborn",category_orders={col_chosen: natsorted(expression_means[col_chosen].unique()),"Gene": condition3_chosen})
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else:
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fig_scatter_dbw16_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
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size="percentage", size_max = 20,
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name=col_chosen,template="seaborn",category_orders={col_chosen: natsorted(expression_means[col_chosen].unique()),"Gene": condition3_chosen})
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fig_violin_dbw162 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all",
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color=condition1_chosen, hover_name=condition1_chosen,template="seaborn",category_orders=cat_ord)
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return fig_scatter_dbw16_5, fig_scatter_dbw16_6, fig_scatter_dbw16_7, fig_scatter_dbw16_8, fig_scatter_dbw16_9, fig_scatter_dbw16_10, fig_scatter_dbw16_11, fig_scatter_dbw16_12, fig_violin_dbw162 #fig_violin_dbw16, fig_pie_dbw16, fig_scatter_dbw16, fig_scatter_dbw16_2, fig_scatter_dbw16_3, fig_scatter_dbw16_4,
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