Rename pages/DLC_corg_week16.py to pages/DLC_wt2an2.py

pages/DLC_corg_week16.py

@@ -1,493 +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
-pl.enable_string_cache(False)
-
-dash.register_page(__name__, location="sidebar")
-
-dataset = "data10xflex/corg/DLC_corg_w16"
-
-# 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.read_parquet(f"az://{dataset}.parquet", storage_options=storage_options)
-
-# Setup the app
-#external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
-#app = dash.Dash(__name__, use_pages=True) #, requests_pathname_prefix='/dashboard1/'
-
-#df = pl.read_parquet(filepath,storage_options=storage_options)
-#df = pl.DataFrame()
-#abfs = AzureBlobFileSystem(account_name=accountname,account_key=accountkey)
-#df = df.rename({"__index_level_0__": "Unnamed: 0"})
-
-#df1 = pl.read_parquet(filepath, storage_options=storage_options)
-
-#df2 = pl.read_parquet(f"az://data10xflex/{dataset_chosen}.parquet", storage_options=storage_options)
-
-#tab0_content = html.Div([
-#    html.Label("Dataset chosen"),    
-#    dcc.Dropdown(id='dpdn1', value="corg/10xflexcorg_umap_clusres", multi=False,
-#                 options=["corg/10xflexcorg_umap_clusres","d1011/10xflexd1011_umap_clusres"])
-#])
-
-#@app.callback(
-#    Input(component_id='dpdn1', component_property='value')
-#)
-
-#def update_filepath(dpdn1):
-#    global df
-#    if str(f"az://data10xflex/{dpdn1}.parquet") != str(filepath):
-#        print("not identical filepath, chosing other")
-#        df2 = pl.read_parquet(f"az://data10xflex/{dpdn1}.parquet", storage_options=storage_options)
-#        df = df2
-#    return
-
-#df = pl.read_parquet(filepath, storage_options=storage_options)
-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
-#conditions = df[col_batch].unique().to_list()
-# 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([
-    html.Label("Column chosen"),    
-    dcc.Dropdown(id='dpdn2', value="batch", multi=False,
-                 options=df.columns),
-    html.Label("N Genes by Counts"),
-    dcc.RangeSlider(
-        id='range-slider_db4-1',
-        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_db4-1', type='number', value=min_value, debounce=True),
-    dcc.Input(id='max-slider_db4-1', type='number', value=max_value, debounce=True),
-    html.Label("Total Counts"),
-    dcc.RangeSlider(
-        id='range-slider_db4-2',
-        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_db4-2', type='number', value=min_value_2, debounce=True),
-    dcc.Input(id='max-slider_db4-2', type='number', value=max_value_2, debounce=True),
-    html.Label("Percent Mitochondrial Genes"),
-    dcc.RangeSlider(
-        id='range-slider_db4-3',
-        step=5,
-        min=0,
-        max=100,
-        value=[min_value_3, max_value_3],
-    ),
-    dcc.Input(id='min-slider_db4-3', type='number', value=min_value_3, debounce=True),
-    dcc.Input(id='max-slider_db4-3', type='number', value=max_value_3, debounce=True),
-    html.Div([
-        dcc.Graph(id='pie-graph_db4', figure={}, className='four columns',config=config_fig),
-        dcc.Graph(id='my-graph_db4', figure={}, clickData=None, hoverData=None,
-                  className='four columns',config=config_fig
-                  ),
-        dcc.Graph(id='scatter-plot_db4', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db4-2', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db4-3', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db4-4', figure={}, className='four columns',config=config_fig)
-    ]),
-])
-
-# Create the second tab content with scatter-plot_db4-5 and scatter-plot_db4-6
-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",
-    "MLF1IP",
-    "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', 'FAM64A', 'SMC4', 'CCNB2', 'CKAP2L', 'CKAP2', 'AURKB', 'BUB1', 'KIF11', 'ANP32E', 'TUBB4B', 'GTSE1', 'KIF20B', 'HJURP', 'CDCA3', 'HN1', '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_db4-5', figure={}, className='three columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db4-6', figure={}, className='three columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db4-7', figure={}, className='three columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db4-8', figure={}, className='three columns',config=config_fig)
-    ]),
-])
-
-# Create the second tab content with scatter-plot_db4-5 and scatter-plot_db4-6
-tab3_content = html.Div([
-    html.Div([
-            html.Label("UMAP condition 1"),
-            dcc.Dropdown(id='dpdn5', value="batch", 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_db4-9', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db4-10', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-        dcc.Graph(id='scatter-plot_db4-11', figure={}, className='four columns',config=config_fig)
-    ]),
-    html.Div([
-            dcc.Graph(id='my-graph_db42', figure={}, clickData=None, hoverData=None,
-                  className='four columns',config=config_fig
-                  )
-    ]),
-    ]),
-])
-#    html.Div([
-#        dcc.Graph(id='scatter-plot_db4-12', figure={}, className='four columns',config=config_fig)
-#    ]),
-
-
-tab4_content = html.Div([
-    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_db4-12', 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='Cell cycle', value='tab2', children=tab2_content),
-        dcc.Tab(label='Custom', value='tab3', children=tab3_content),
-        dcc.Tab(label='Multi dot', value='tab4', children=tab4_content),
-    ]),
-])
-
-# Define the circular callback
-@callback(
-    Output("min-slider_db4-1", "value"),
-    Output("max-slider_db4-1", "value"),
-    Output("min-slider_db4-2", "value"),
-    Output("max-slider_db4-2", "value"),
-    Output("min-slider_db4-3", "value"),
-    Output("max-slider_db4-3", "value"),
-    Input("min-slider_db4-1", "value"),
-    Input("max-slider_db4-1", "value"),
-    Input("min-slider_db4-2", "value"),
-    Input("max-slider_db4-2", "value"),
-    Input("min-slider_db4-3", "value"),
-    Input("max-slider_db4-3", "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
-
-@callback(
-    Output('range-slider_db4-1', 'value'),
-    Output('range-slider_db4-2', 'value'),
-    Output('range-slider_db4-3', 'value'),
-    Input('min-slider_db4-1', 'value'),
-    Input('max-slider_db4-1', 'value'),
-    Input('min-slider_db4-2', 'value'),
-    Input('max-slider_db4-2', 'value'),
-    Input('min-slider_db4-3', 'value'),
-    Input('max-slider_db4-3', '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]
-
-@callback(
-    Output(component_id='my-graph_db4', component_property='figure'),
-    Output(component_id='pie-graph_db4', component_property='figure'),
-    Output(component_id='scatter-plot_db4', component_property='figure'),
-    Output(component_id='scatter-plot_db4-2', component_property='figure'),
-    Output(component_id='scatter-plot_db4-3', component_property='figure'),
-    Output(component_id='scatter-plot_db4-4', component_property='figure'),  # Add this new scatter plot
-    Output(component_id='scatter-plot_db4-5', component_property='figure'),
-    Output(component_id='scatter-plot_db4-6', component_property='figure'),
-    Output(component_id='scatter-plot_db4-7', component_property='figure'),
-    Output(component_id='scatter-plot_db4-8', component_property='figure'),
-    Output(component_id='scatter-plot_db4-9', component_property='figure'),
-    Output(component_id='scatter-plot_db4-10', component_property='figure'),
-    Output(component_id='scatter-plot_db4-11', component_property='figure'),
-    Output(component_id='scatter-plot_db4-12', component_property='figure'),
-    Output(component_id='my-graph_db42', 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'),
-    Input(component_id='range-slider_db4-1', component_property='value'),
-    Input(component_id='range-slider_db4-2', component_property='value'),
-    Input(component_id='range-slider_db4-3', 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)) &
-        (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[col_chosen].cast(pl.Categorical))
-
-    dff = dff.sort(col_chosen)
-    
-    # Plot figures
-    fig_violin_db4 = px.violin(data_frame=dff, x=col_chosen, y=col_features, box=True, points="all",
-                            color=col_chosen, hover_name=col_chosen,template="seaborn")
-
-    # Cache commonly used subexpressions
-    total_count = pl.lit(len(dff))
-    category_counts = dff.group_by(col_chosen).agg(pl.col(col_chosen).count().alias("count"))
-    category_counts = category_counts.with_columns(((pl.col("count") / total_count * 100).round(decimals=2)).alias("normalized_count"))
-
-    # Sort the dataframe
-    #category_counts = category_counts.sort(col_chosen) does not work check if the names are different ...
-    
-    # Display the result
-    total_cells = total_count  # Calculate total number of cells
-    pie_title = f'Percentage of Total Cells: {total_cells}'  # Include total cells in the title
-
-    # 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
-    # TO DO
-    expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner")
-
-    # Order the dataframe on ascending categories
-    expression_means = expression_means.sort(col_chosen, descending=True)
-    
-    #expression_means = expression_means.select(["batch", "Gene", "Expression"] + condition3_chosen)
-    category_counts = category_counts.sort(col_chosen)
-    
-    fig_pie_db4 = px.pie(category_counts, values="normalized_count", names=col_chosen, labels=col_chosen, hole=.3, title=pie_title, template="seaborn")
-
-    #labels = category_counts[col_chosen].to_list()
-    #values = category_counts["normalized_count"].to_list()
-
-    # Create the scatter plots
-    fig_scatter_db4 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_chosen,
-                             labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
-                             hover_name='batch',template="seaborn")
-
-    fig_scatter_db4_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_db4_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_db4_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_db4_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_db4_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_db4_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_db4_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")
-
-    # Sort values of custom in-between
-    dff = dff.sort(condition1_chosen)
-    
-    fig_scatter_db4_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_db4_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_db4_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',template="seaborn")
-    
-    fig_scatter_db4_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")
-    
-    fig_violin_db42 = 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_db4, fig_pie_db4, fig_scatter_db4, fig_scatter_db4_2, fig_scatter_db4_3, fig_scatter_db4_4, fig_scatter_db4_5, fig_scatter_db4_6, fig_scatter_db4_7, fig_scatter_db4_8, fig_scatter_db4_9, fig_scatter_db4_10, fig_scatter_db4_11, fig_scatter_db4_12, fig_violin_db42
-
-# 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) #

pages/DLC_wt2an2.py

@@ -0,0 +1,260 @@
+# 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