pages/keratinocytes_scVI_integration.py

# 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, callback
import plotly.express as px
import dash_callback_chain
import yaml
import polars as pl
import os
from natsort import natsorted
#pl.enable_string_cache(False)

dash.register_page(__name__, location="sidebar")

dataset = "datasingleron/keratinocytes/singleron_keratinocytes_clusres_scVI"

# Set custom resolution for plots:
config_fig = {
  'toImageButtonOptions': {
    'format': 'svg',
    'filename': 'custom_image',
    'height': 600,
    'width': 700,
    'scale': 1,
  }
}
from adlfs import AzureBlobFileSystem
mountpount=os.environ['AZURE_MOUNT_POINT'],
AZURE_STORAGE_ACCESS_KEY=os.getenv('AZURE_STORAGE_ACCESS_KEY')
AZURE_STORAGE_ACCOUNT=os.getenv('AZURE_STORAGE_ACCOUNT')

# Load in config file
config_path = "./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")
col_batch = "batch_renamed"
col_features = config.get("col_features")
col_counts = config.get("col_counts")
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

# Load in multiple dataframes
df = pl.scan_parquet(f"az://{dataset}.parquet", storage_options=storage_options).collect()

# Create the second tab content with scatter-plot_db2-5 and scatter-plot_db2-6
tab2_content = html.Div([
    html.Div([
            html.Label("S-cycle genes"),
            dcc.Dropdown(id='dpdn3', value="MCM5", multi=False,
                 options=["MCM5","PCNA","TYMS","FEN1","MCM2","MCM4","RRM1","UNG","GINS2","MCM6","CDCA7","DTL",
                          "PRIM1","UHRF1","HELLS","RFC2","RPA2","NASP","RAD51AP1","GMNN","WDR76","SLBP","CCNE2","UBR7",
                          "POLD3","MSH2","ATAD2","RAD51","RRM2","CDC45","CDC6","EXO1","TIPIN","DSCC1","BLM","CASP8AP2",
                          "USP1","CLSPN","POLA1","CHAF1B","BRIP1","E2F8"]),
            html.Label("G2M-cycle genes"),
            dcc.Dropdown(id='dpdn4', value="TOP2A", multi=False,
                 options=["HMGB2","CDK1","NUSAP1","UBE2C","BIRC5","TPX2","TOP2A","NDC80","CKS2","NUF2","CKS1B","MKI67",
                          "TMPO","CENPF","TACC3","SMC4","CCNB2","CKAP2L","CKAP2","AURKB","BUB1","KIF11","ANP32E","TUBB4B",
                          "GTSE1","KIF20B","HJURP","CDCA3","CDC20","TTK","CDC25C","KIF2C","RANGAP1","NCAPD2","DLGAP5","CDCA2",
                          "CDCA8","ECT2","KIF23","HMMR","AURKA","PSRC1","ANLN","LBR","CKAP5","CENPE","CTCF","NEK2","G2E3",
                          "GAS2L3","CBX5","CENPA"]),
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot_db2-5', figure={}, className='three columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot_db2-6', figure={}, className='three columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot_db2-7', figure={}, className='three columns',config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot_db2-8', figure={}, className='three columns',config=config_fig)
    ]),
])

# Create the second tab content with scatter-plot_db2-5 and scatter-plot_db2-6
tab3_content = html.Div([
    html.Div([
            html.Label("UMAP condition 1"),
            dcc.Dropdown(id='dpdn5', value="batch_renamed", multi=False,
                 options=df.columns),
            html.Label("UMAP condition 2"),
            dcc.Dropdown(id='dpdn6', value="AREG", multi=False,
                 options=df.columns),
    html.Div([
        dcc.Graph(id='scatter-plot_db2-9', figure={}, className='four columns', hoverData=None ,config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot_db2-10', figure={}, className='four columns', hoverData=None, config=config_fig)
    ]),
    html.Div([
        dcc.Graph(id='scatter-plot_db2-11', figure={}, className='four columns',config=config_fig)
    ]),
    html.Div([
            dcc.Graph(id='my-graph_db22', figure={}, clickData=None, hoverData=None,
                  className='four columns',config=config_fig
                  )
    ]),
    ]),
])

tab4_content = html.Div([
     html.Label("Column chosen"),    
     dcc.Dropdown(id='dpdn2', value="leiden_0.45", multi=False,
                  options=df.columns),
    html.Div([
            html.Label("Multi gene"),
            dcc.Dropdown(id='dpdn7', value=["KRT4","VIM","KRT14","KRT15","AREG"], multi=True,
                 options=df.columns),
]),
    html.Div([
        dcc.Graph(id='scatter-plot_db2-12', figure={}, className='row',style={'width': '100vh', 'height': '90vh'})
    ]),
])

# Define the tabs layout
layout = html.Div([
    html.H1(f'Dataset analysis dashboard: {dataset}'),
    dcc.Tabs(id='tabs', style= {'width': 600,
        '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='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),
    ]),
])

@callback(
    Output(component_id='scatter-plot_db2-5', component_property='figure'),
    Output(component_id='scatter-plot_db2-6', component_property='figure'),
    Output(component_id='scatter-plot_db2-7', component_property='figure'),
    Output(component_id='scatter-plot_db2-8', component_property='figure'),
    Output(component_id='scatter-plot_db2-9', component_property='figure'),
    Output(component_id='scatter-plot_db2-10', component_property='figure'),
    Output(component_id='scatter-plot_db2-11', component_property='figure'),
    Output(component_id='scatter-plot_db2-12', component_property='figure'),
    Output(component_id='my-graph_db22', 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'),
     
)

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)) #&
)
    # Select ordering of plots
    if condition1_chosen == "leiden_0.45":
        cat_ord= {condition1_chosen: ["1","2","3","4"]}
    else:
        cat_ord= {condition1_chosen: natsorted(dff[condition1_chosen].unique())}

    # Calculate the mean expression
    
    # Melt wide format DataFrame into long format
    # Specify batch column as string type and gene columns as float type
    list_conds = condition3_chosen
    list_conds += [col_chosen]
    dff_pre = dff.select(list_conds)
    
    # 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() #
    
    # Calculate the percentage total expressed
    dff_long1 = dff_pre.melt(id_vars=col_chosen, variable_name="Gene")#.group_by(pl.all()).agg(pl.len())
    count = 1
    dff_long2 = dff_long1.with_columns(pl.lit(count).alias("len"))
    dff_long3 = dff_long2.filter(pl.col("value") > 0).group_by([col_chosen, "Gene"]).agg(pl.sum("len").alias("len"))
    dff_long4 = dff_long2.group_by([col_chosen, "Gene"]).agg(pl.sum("len").alias("total"))
    dff_5 = dff_long4.join(dff_long3, on=[col_chosen,"Gene"], how="outer")
    result = dff_5.select([
        pl.when((pl.col('len').is_not_null()) & (pl.col('total').is_not_null()))
              .then(pl.col('len') / pl.col('total')*100)
              .otherwise(None).alias("%"),
    ])
    result = result.with_columns(pl.col("%").fill_null(0))
    dff_5[["percentage"]] = result[["%"]]
    dff_5 = dff_5.select(pl.col(col_chosen,"Gene","percentage"))

    # Final part to join the percentage expressed and mean expression levels
    expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")
    
    fig_scatter_db2_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=None, title="S-cycle gene:",template="seaborn")

    fig_scatter_db2_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_renamed', title="G2M-cycle gene:",template="seaborn")
    
    fig_scatter_db2_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_renamed', title="S score:",template="seaborn")
    
    fig_scatter_db2_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_renamed', title="G2M score:",template="seaborn")

    # Sort values of custom in-between
    dff = dff.sort(condition1_chosen)
    
    fig_scatter_db2_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=None,hover_data = None, template="seaborn",category_orders=cat_ord)
    fig_scatter_db2_9.update_traces(hoverinfo='none', hovertemplate=None)
    fig_scatter_db2_9.update_layout(hovermode=False)
    
    fig_scatter_db2_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_renamed',template="seaborn")
    
    fig_scatter_db2_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_renamed',template="seaborn",category_orders=cat_ord)

    # Reorder categories on natural sorting or on the integrated cell state order of the paper
    if col_chosen == "leiden_0.45":
        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: ["1","2","3","4"],"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,
                                #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_violin_db22 = 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)


    return fig_scatter_db2_5, fig_scatter_db2_6, fig_scatter_db2_7, fig_scatter_db2_8, fig_scatter_db2_9, fig_scatter_db2_10, fig_scatter_db2_11, fig_scatter_db2_12, fig_violin_db22 #fig_violin_db2, fig_pie_db2, fig_scatter_db2, fig_scatter_db2_2, fig_scatter_db2_3, fig_scatter_db2_4, 

# 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) #