# Dash app to visualize scRNA-seq data quality control metrics from scanpy objects # Shoutout to Coding-with-Adam for the initial template of the project: # https://github.com/Coding-with-Adam/Dash-by-Plotly/blob/master/Dash%20Components/Graph/dash-graph.py import dash from dash import dcc, html, Output, Input import plotly.express as px import dash_callback_chain import yaml import polars as pl pl.enable_string_cache(False) # Set custom resolution for plots: config_fig = { 'toImageButtonOptions': { 'format': 'svg', 'filename': 'custom_image', 'height': 600, 'width': 700, 'scale': 1, } } config_path = "./azure/data/config.yaml" # Add the read-in data from the yaml file def read_config(filename): with open(filename, 'r') as yaml_file: config = yaml.safe_load(yaml_file) return config config = read_config(config_path) path_parquet = config.get("path_parquet") conditions = config.get("conditions") col_features = config.get("col_features") col_counts = config.get("col_counts") col_mt = config.get("col_mt") # Import the data from one .parquet file df = pl.read_parquet(path_parquet) #df = df.rename({"__index_level_0__": "Unnamed: 0"}) # Setup the app external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets, requests_pathname_prefix='/dashboard1/') 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 # 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([ dcc.Dropdown(id='dpdn2', value=conditions, multi=True, options=conditions), 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=0.1, min=0, max=1, 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 tab2_content = html.Div([ html.Div([ html.Label("S-cycle genes"), dcc.Dropdown(id='dpdn3', value="Cdc45", multi=False, options=[ "Cdc45", "Uhrf1", "Mcm2", "Slbp", "Mcm5", "Pola1", "Gmnn", "Cdc6", "Rrm2", "Atad2", "Dscc1", "Mcm4", "Chaf1b", "Rfc2", "Msh2", "Fen1", "Hells", "Prim1", "Tyms", "Mcm6", "Wdr76", "Rad51", "Pcna", "Ccne2", "Casp8ap2", "Usp1", "Nasp", "Rpa2", "Ung", "Rad51ap1", "Blm", "Pold3", "Rrm1", "Cenpu", "Gins2", "Tipin", "Brip1", "Dtl", "Exo1", "Ubr7", "Clspn", "E2f8", "Cdca7" ]), html.Label("G2M-cycle genes"), dcc.Dropdown(id='dpdn4', value="Top2a", multi=False, options=[ "Ube2c", "Lbr", "Ctcf", "Cdc20", "Cbx5", "Kif11", "Anp32e", "Birc5", "Cdk1", "Tmpo", "Hmmr", "Pimreg", "Aurkb", "Top2a", "Gtse1", "Rangap1", "Cdca3", "Ndc80", "Kif20b", "Cenpf", "Nek2", "Nuf2", "Nusap1", "Bub1", "Tpx2", "Aurka", "Ect2", "Cks1b", "Kif2c", "Cdca8", "Cenpa", "Mki67", "Ccnb2", "Kif23", "Smc4", "G2e3", "Tubb4b", "Anln", "Tacc3", "Dlgap5", "Ckap2", "Ncapd2", "Ttk", "Ckap5", "Cdc25c", "Hjurp", "Cenpe", "Ckap2l", "Cdca2", "Hmgb2", "Cks2", "Psrc1", "Gas2l3" ]), ]), html.Div([ dcc.Graph(id='scatter-plot-5', figure={}, className='three columns',config=config_fig) ]), html.Div([ dcc.Graph(id='scatter-plot-6', figure={}, className='three columns',config=config_fig) ]), html.Div([ dcc.Graph(id='scatter-plot-7', figure={}, className='three columns',config=config_fig) ]), html.Div([ dcc.Graph(id='scatter-plot-8', figure={}, className='three columns',config=config_fig) ]), ]) # Create the second tab content with scatter-plot-5 and scatter-plot-6 tab3_content = html.Div([ html.Div([ html.Label("UMAP condition 1"), dcc.Dropdown(id='dpdn5', value="total_counts", multi=False, options=df.columns), html.Label("UMAP condition 2"), dcc.Dropdown(id='dpdn6', value="n_genes_by_counts", multi=False, options=df.columns), ]), html.Div([ dcc.Graph(id='scatter-plot-9', figure={}, className='four columns',config=config_fig) ]), html.Div([ dcc.Graph(id='scatter-plot-10', figure={}, className='four columns',config=config_fig) ]), html.Div([ dcc.Graph(id='scatter-plot-11', figure={}, className='four columns',config=config_fig) ]), html.Div([ dcc.Graph(id='my-graph2', figure={}, clickData=None, hoverData=None, className='four columns',config=config_fig ) ]), ]) # Define the tabs layout app.layout = html.Div([ dcc.Tabs(id='tabs', style= {'width': 400, 'font-size': '100%', 'height': 50}, value='tab1',children=[ 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), ]), ]) # Define the circular callback @app.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 @app.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] @app.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='my-graph2', 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='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(batch_chosen, s_chosen, g2m_chosen, condition1_chosen, condition2_chosen, range_value_1, range_value_2, range_value_3): dff = df.filter( (pl.col('batch').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]) ) #Drop categories that are not in the filtered data dff = dff.with_columns(dff['batch'].cast(str)) dff = dff.with_columns(dff['batch'].cast(pl.Categorical)) # Plot figures fig_violin = px.violin(data_frame=dff, x='batch', y=col_features, box=True, points="all", color='batch', hover_name='batch',template="seaborn") # Calculate the percentage of each category (normalized_count) for pie chart category_counts = dff.group_by("batch").agg(pl.col("batch").count().alias("count")) total_count = len(dff) category_counts = category_counts.with_columns((pl.col("count") / total_count * 100).alias("normalized_count")) # Display the result labels = category_counts["batch"].to_list() values = category_counts["normalized_count"].to_list() total_cells = total_count # Calculate total number of cells pie_title = f'Percentage of Total Cells: {total_cells}' # Include total cells in the title fig_pie = px.pie(names=labels, values=values, title=pie_title,template="seaborn") # Create the scatter plots fig_scatter = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color='batch', 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, labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'}, hover_name='batch', 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, labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'}, hover_name='batch', 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", labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'}, hover_name='batch', title="S score:",template="seaborn") fig_scatter_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") fig_scatter_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") fig_scatter_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='batch', #labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'}, hover_name='batch',template="seaborn") 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_violin2 # Set http://localhost:5000/ in web browser # Now create your regular FASTAPI application if __name__ == '__main__': app.run_server(debug=True, use_reloader=False) #host='0.0.0.0', #, port=5000