Delete pages/DLC_wt2an2.py

pages/DLC_wt2an2.py

@@ -1,260 +0,0 @@
-# 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 = "data10xflex/corg/all_polars"
-
-# 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 = config.get("col_batch")
-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
-#azfs = AzureBlobFileSystem(**storage_options )
-
-# 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_db10-5 and scatter-plot_db10-6
-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_db10-5', figure={}, className='three columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db10-6', figure={}, className='three columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db10-7', figure={}, className='three columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db10-8', figure={}, className='three columns',config=config_fig)
-    ]),
-])
-
-# Create the second tab content with scatter-plot_db10-5 and scatter-plot_db10-6
-tab3_content = html.Div([
-    html.Div([
-            html.Label("UMAP condition 1"),
-            dcc.Dropdown(id='dpdn5', value="sample", multi=False,
-                 options=df.columns),
-            html.Label("UMAP condition 2"),
-            dcc.Dropdown(id='dpdn6', value="PAX6", multi=False,
-                 options=df.columns),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db10-9', figure={}, className='four columns', hoverData=None ,config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db10-10', figure={}, className='four columns', hoverData=None, config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db10-11', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-            dcc.Graph(id='my-graph_db102', 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_res_1.35", multi=False,
-                  options=df.columns),
-    html.Div([
-            html.Label("Multi gene"),
-            dcc.Dropdown(id='dpdn7', value=['PAX6', 'TP63', 'OTX2', 'SIX3', 'LHX2', 'SIX6', 'SOX2', 'PMEL', 
-                                            'RAX', 'LIN28A', 'ABCG2', 'KRT8', 'KRT7', 
-                                            'KRT19', 'COL1A2', 'AQP1', 'LUM', 'TFAP2A', 'HAND1', 'S100A9', 
-                                            'SPP1', 'TEK', 'FOXC2', 'PECAM1', 'SOX9'], multi=True,
-                 options=df.columns),
-]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db10-12', 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_db10-5', component_property='figure'),
-    Output(component_id='scatter-plot_db10-6', component_property='figure'),
-    Output(component_id='scatter-plot_db10-7', component_property='figure'),
-    Output(component_id='scatter-plot_db10-8', component_property='figure'),
-    Output(component_id='scatter-plot_db10-9', component_property='figure'),
-    Output(component_id='scatter-plot_db10-10', component_property='figure'),
-    Output(component_id='scatter-plot_db10-11', component_property='figure'),
-    Output(component_id='scatter-plot_db10-12', component_property='figure'),
-    Output(component_id='my-graph_db102', 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 == "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
-    
-    # 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_db10_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_db10_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='sample', title="G2M-cycle gene:",template="seaborn")
-    
-    fig_scatter_db10_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='sample', title="S score:",template="seaborn")
-    
-    fig_scatter_db10_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='sample', title="G2M score:",template="seaborn")
-
-    # Sort values of custom in-between
-    dff = dff.sort(condition1_chosen)
-    
-    fig_scatter_db10_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_db10_9.update_traces(hoverinfo='none', hovertemplate=None)
-    fig_scatter_db10_9.update_layout(hovermode=False)
-    
-    fig_scatter_db10_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='sample',template="seaborn")
-    
-    fig_scatter_db10_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='sample',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_db10_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})
-    else:                                                                                
-        fig_scatter_db10_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_db102 = 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_db10_5, fig_scatter_db10_6, fig_scatter_db10_7, fig_scatter_db10_8, fig_scatter_db10_9, fig_scatter_db10_10, fig_scatter_db10_11, fig_scatter_db10_12, fig_violin_db102 #fig_violin_db10, fig_pie_db10, fig_scatter_db10, fig_scatter_db10_2, fig_scatter_db10_3, fig_scatter_db10_4, 
-
-# Set http://localhost:5000/ in web browser