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Update pages/dul_suture.py
Browse files- pages/dul_suture.py +82 -82
pages/dul_suture.py
CHANGED
@@ -113,51 +113,51 @@ tab1_content = html.Div([
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options=df.columns),
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html.Label("N Genes by Counts"),
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dcc.RangeSlider(
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id='range-
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step=250,
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value=[min_value, max_value],
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marks={i: str(i) for i in range(min_value, max_value + 1, 250)},
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),
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dcc.Input(id='min-
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dcc.Input(id='max-
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html.Label("Total Counts"),
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dcc.RangeSlider(
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id='range-
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step=7500,
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value=[min_value_2, max_value_2],
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marks={i: str(i) for i in range(min_value_2, max_value_2 + 1, 7500)},
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),
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dcc.Input(id='min-
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dcc.Input(id='max-
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html.Label("Percent Mitochondrial Genes"),
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dcc.RangeSlider(
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id='range-
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step=5,
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min=0,
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max=100,
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value=[min_value_3, max_value_3],
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),
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dcc.Input(id='min-
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dcc.Input(id='max-
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html.Div([
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dcc.Graph(id='pie-
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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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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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tab2_content = html.Div([
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html.Div([
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html.Label("S-cycle genes"),
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@@ -267,20 +267,20 @@ tab2_content = html.Div([
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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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@@ -290,23 +290,23 @@ tab3_content = html.Div([
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dcc.Dropdown(id='dpdn6', value="n_genes_by_counts", 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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]),
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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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@@ -317,7 +317,7 @@ 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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@@ -337,63 +337,63 @@ layout = html.Div([
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# Define the circular callback
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@callback(
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Output("min-
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Output("max-
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Output("min-
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Output("max-
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Output("min-
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Output("max-
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Input("min-
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Input("max-
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Input("min-
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Input("max-
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Input("min-
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Input("max-
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)
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def circular_callback(min_1, max_1, min_2, max_2, min_3, max_3):
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return min_1, max_1, min_2, max_2, min_3, max_3
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@callback(
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Output('range-
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Output('range-
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Output('range-
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Input('min-
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Input('max-
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Input('min-
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Input('max-
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Input('min-
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Input('max-
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)
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def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
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return [min_1, max_1], [min_2, max_2], [min_3, max_3]
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@callback(
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Output(component_id='my-
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Output(component_id='pie-
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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='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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Input(component_id='dpdn5', component_property='value'),
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Input(component_id='dpdn6', component_property='value'),
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Input(component_id='dpdn7', component_property='value'),
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Input(component_id='range-
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Input(component_id='range-
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Input(component_id='range-
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)
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@@ -415,7 +415,7 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
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dff = dff.sort(col_chosen)
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# Plot figures
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color=col_chosen, hover_name=col_chosen,template="seaborn")
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# Cache commonly used subexpressions
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@@ -470,70 +470,70 @@ def update_graph_and_pie_chart(col_chosen, s_chosen, g2m_chosen, condition1_chos
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#expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
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category_counts = category_counts.sort(col_chosen)
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-
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#labels = category_counts[col_chosen].to_list()
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#values = category_counts["normalized_count"].to_list()
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# Create the scatter plots
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch', 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='batch', title="G2M-cycle gene:",template="seaborn")
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-
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch', title="S score:",template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch', 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='batch',template="seaborn")
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labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
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hover_name='batch',template="seaborn")
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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")
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color=condition1_chosen, hover_name=condition1_chosen,template="seaborn")
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-
return
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# Set http://localhost:5000/ in web browser
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# Now create your regular FASTAPI application
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options=df.columns),
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html.Label("N Genes by Counts"),
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dcc.RangeSlider(
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id='range-slider_db8-1',
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step=250,
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value=[min_value, max_value],
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marks={i: str(i) for i in range(min_value, max_value + 1, 250)},
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),
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dcc.Input(id='min-slider_db8-1', type='number', value=min_value, debounce=True),
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dcc.Input(id='max-slider_db8-1', type='number', value=max_value, debounce=True),
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html.Label("Total Counts"),
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dcc.RangeSlider(
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id='range-slider_db8-2',
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step=7500,
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value=[min_value_2, max_value_2],
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marks={i: str(i) for i in range(min_value_2, max_value_2 + 1, 7500)},
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),
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dcc.Input(id='min-slider_db8-2', type='number', value=min_value_2, debounce=True),
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dcc.Input(id='max-slider_db8-2', type='number', value=max_value_2, debounce=True),
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html.Label("Percent Mitochondrial Genes"),
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dcc.RangeSlider(
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id='range-slider_db8-3',
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step=5,
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min=0,
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max=100,
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value=[min_value_3, max_value_3],
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),
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dcc.Input(id='min-slider_db8-3', type='number', value=min_value_3, debounce=True),
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dcc.Input(id='max-slider_db8-3', type='number', value=max_value_3, debounce=True),
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html.Div([
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dcc.Graph(id='pie-graph_db8', figure={}, className='four columns',config=config_fig),
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dcc.Graph(id='my-graph_db8', figure={}, clickData=None, hoverData=None,
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className='four columns',config=config_fig
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),
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dcc.Graph(id='scatter-plot_db8', figure={}, className='four columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_db8-2', figure={}, className='four columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_db8-3', figure={}, className='four columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_db8-4', figure={}, className='four columns',config=config_fig)
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]),
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])
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+
# Create the second tab content with scatter-plot_db8-5 and scatter-plot_db8-6
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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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]),
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html.Div([
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dcc.Graph(id='scatter-plot_db8-5', 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_db8-6', 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_db8-7', 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_db8-8', 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_db8-5 and scatter-plot_db8-6
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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="n_genes_by_counts", multi=False,
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options=df.columns),
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html.Div([
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dcc.Graph(id='scatter-plot_db8-9', figure={}, className='four columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_db8-10', figure={}, className='four columns',config=config_fig)
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]),
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html.Div([
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dcc.Graph(id='scatter-plot_db8-11', 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_db82', 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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]),
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])
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# html.Div([
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# dcc.Graph(id='scatter-plot_db8-12', figure={}, className='four columns',config=config_fig)
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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_db8-12', figure={}, className='row',style={'width': '100vh', 'height': '90vh'}), # px)
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]),
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])
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# Define the circular callback
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@callback(
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Output("min-slider_db8-1", "value"),
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Output("max-slider_db8-1", "value"),
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Output("min-slider_db8-2", "value"),
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+
Output("max-slider_db8-2", "value"),
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+
Output("min-slider_db8-3", "value"),
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+
Output("max-slider_db8-3", "value"),
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+
Input("min-slider_db8-1", "value"),
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+
Input("max-slider_db8-1", "value"),
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348 |
+
Input("min-slider_db8-2", "value"),
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+
Input("max-slider_db8-2", "value"),
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+
Input("min-slider_db8-3", "value"),
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+
Input("max-slider_db8-3", "value"),
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)
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def circular_callback(min_1, max_1, min_2, max_2, min_3, max_3):
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return min_1, max_1, min_2, max_2, min_3, max_3
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@callback(
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Output('range-slider_db8-1', 'value'),
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Output('range-slider_db8-2', 'value'),
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Output('range-slider_db8-3', 'value'),
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+
Input('min-slider_db8-1', 'value'),
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+
Input('max-slider_db8-1', 'value'),
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+
Input('min-slider_db8-2', 'value'),
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+
Input('max-slider_db8-2', 'value'),
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+
Input('min-slider_db8-3', 'value'),
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+
Input('max-slider_db8-3', 'value'),
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)
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def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
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return [min_1, max_1], [min_2, max_2], [min_3, max_3]
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@callback(
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Output(component_id='my-graph_db8', component_property='figure'),
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+
Output(component_id='pie-graph_db8', component_property='figure'),
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+
Output(component_id='scatter-plot_db8', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-2', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-3', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-4', component_property='figure'), # Add this new scatter plot
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+
Output(component_id='scatter-plot_db8-5', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-6', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-7', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-8', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-9', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-10', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-11', component_property='figure'),
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+
Output(component_id='scatter-plot_db8-12', component_property='figure'),
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+
Output(component_id='my-graph_db82', 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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Input(component_id='dpdn5', component_property='value'),
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Input(component_id='dpdn6', component_property='value'),
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Input(component_id='dpdn7', component_property='value'),
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+
Input(component_id='range-slider_db8-1', component_property='value'),
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+
Input(component_id='range-slider_db8-2', component_property='value'),
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+
Input(component_id='range-slider_db8-3', component_property='value'),
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)
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dff = dff.sort(col_chosen)
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# Plot figures
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418 |
+
fig_violin_db8 = px.violin(data_frame=dff, x=col_chosen, y=col_features, box=True, points="all",
|
419 |
color=col_chosen, hover_name=col_chosen,template="seaborn")
|
420 |
|
421 |
# Cache commonly used subexpressions
|
|
|
470 |
#expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
|
471 |
category_counts = category_counts.sort(col_chosen)
|
472 |
|
473 |
+
fig_pie_db8 = px.pie(category_counts, values="normalized_count", names=col_chosen, labels=col_chosen, hole=.3, title=pie_title, template="seaborn")
|
474 |
|
475 |
#labels = category_counts[col_chosen].to_list()
|
476 |
#values = category_counts["normalized_count"].to_list()
|
477 |
|
478 |
# Create the scatter plots
|
479 |
+
fig_scatter_db8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_chosen,
|
480 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
481 |
hover_name='batch',template="seaborn")
|
482 |
|
483 |
+
fig_scatter_db8_2 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_mt,
|
484 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
485 |
hover_name='batch',template="seaborn")
|
486 |
|
487 |
+
fig_scatter_db8_3 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_features,
|
488 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
489 |
hover_name='batch',template="seaborn")
|
490 |
|
491 |
|
492 |
+
fig_scatter_db8_4 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_counts,
|
493 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
494 |
hover_name='batch',template="seaborn")
|
495 |
|
496 |
+
fig_scatter_db8_5 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=s_chosen,
|
497 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
498 |
hover_name='batch', title="S-cycle gene:",template="seaborn")
|
499 |
|
500 |
+
fig_scatter_db8_6 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=g2m_chosen,
|
501 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
502 |
hover_name='batch', title="G2M-cycle gene:",template="seaborn")
|
503 |
|
504 |
+
fig_scatter_db8_7 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="S_score",
|
505 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
506 |
hover_name='batch', title="S score:",template="seaborn")
|
507 |
|
508 |
+
fig_scatter_db8_8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="G2M_score",
|
509 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
510 |
hover_name='batch', title="G2M score:",template="seaborn")
|
511 |
|
512 |
# Sort values of custom in-between
|
513 |
dff = dff.sort(condition1_chosen)
|
514 |
|
515 |
+
fig_scatter_db8_9 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition1_chosen,
|
516 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
517 |
hover_name='batch',template="seaborn")
|
518 |
|
519 |
+
fig_scatter_db8_10 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition2_chosen,
|
520 |
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
521 |
hover_name='batch',template="seaborn")
|
522 |
|
523 |
+
fig_scatter_db8_11 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, color=condition1_chosen,
|
524 |
#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
525 |
hover_name='batch',template="seaborn")
|
526 |
|
527 |
+
fig_scatter_db8_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression",
|
528 |
size="percentage", size_max = 20,
|
529 |
#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
|
530 |
hover_name=col_chosen,template="seaborn")
|
531 |
|
532 |
+
fig_violin_db82 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all",
|
533 |
color=condition1_chosen, hover_name=condition1_chosen,template="seaborn")
|
534 |
|
535 |
|
536 |
+
return fig_violin_db8, fig_pie_db8, fig_scatter_db8, fig_scatter_db8_2, fig_scatter_db8_3, fig_scatter_db8_4, fig_scatter_db8_5, fig_scatter_db8_6, fig_scatter_db8_7, fig_scatter_db8_8, fig_scatter_db8_9, fig_scatter_db8_10, fig_scatter_db8_11, fig_scatter_db8_12, fig_violin_db82
|
537 |
|
538 |
# Set http://localhost:5000/ in web browser
|
539 |
# Now create your regular FASTAPI application
|