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import dash |
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from dash import dcc, html, Output, Input, callback |
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import plotly.express as px |
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import dash_callback_chain |
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import yaml |
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import polars as pl |
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import os |
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pl.enable_string_cache(False) |
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dash.register_page(__name__, location="sidebar") |
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dataset = "data10xflex/d1011/10xflexd1011_umap_clusres" |
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config_fig = { |
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'toImageButtonOptions': { |
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'format': 'svg', |
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'filename': 'custom_image', |
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'height': 600, |
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'width': 700, |
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'scale': 1, |
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} |
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} |
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from adlfs import AzureBlobFileSystem |
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mountpount=os.environ['AZURE_MOUNT_POINT'], |
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AZURE_STORAGE_ACCESS_KEY=os.getenv('AZURE_STORAGE_ACCESS_KEY') |
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AZURE_STORAGE_ACCOUNT=os.getenv('AZURE_STORAGE_ACCOUNT') |
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config_path = "./data/config.yaml" |
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def read_config(filename): |
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with open(filename, 'r') as yaml_file: |
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config = yaml.safe_load(yaml_file) |
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return config |
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config = read_config(config_path) |
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path_parquet = config.get("path_parquet") |
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col_batch = config.get("col_batch") |
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col_features = config.get("col_features") |
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col_counts = config.get("col_counts") |
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col_mt = config.get("col_mt") |
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storage_options={'account_name': AZURE_STORAGE_ACCOUNT, 'account_key': AZURE_STORAGE_ACCESS_KEY,'anon': False} |
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df = pl.read_parquet(f"az://{dataset}.parquet", storage_options=storage_options) |
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min_value = df[col_features].min() |
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max_value = df[col_features].max() |
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min_value_2 = df[col_counts].min() |
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min_value_2 = round(min_value_2) |
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max_value_2 = df[col_counts].max() |
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max_value_2 = round(max_value_2) |
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min_value_3 = df[col_mt].min() |
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min_value_3 = round(min_value_3, 1) |
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max_value_3 = df[col_mt].max() |
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max_value_3 = round(max_value_3, 1) |
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tab1_content = html.Div([ |
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html.Label("Column chosen"), |
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dcc.Dropdown(id='dpdn2', value="batch", multi=False, |
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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-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-1', type='number', value=min_value, debounce=True), |
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dcc.Input(id='max-slider-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-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-2', type='number', value=min_value_2, debounce=True), |
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dcc.Input(id='max-slider-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-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-3', type='number', value=min_value_3, debounce=True), |
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dcc.Input(id='max-slider-3', type='number', value=max_value_3, debounce=True), |
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html.Div([ |
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dcc.Graph(id='pie-graph', figure={}, className='four columns',config=config_fig), |
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dcc.Graph(id='my-graph', 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', 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-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-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-4', figure={}, className='four columns',config=config_fig) |
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]), |
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]) |
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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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dcc.Dropdown(id='dpdn3', value="MCM5", multi=False, |
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options=[ |
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"MCM5", |
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"PCNA", |
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"TYMS", |
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"FEN1", |
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"MCM2", |
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"MCM4", |
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"RRM1", |
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"UNG", |
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"GINS2", |
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"MCM6", |
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"CDCA7", |
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"DTL", |
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"PRIM1", |
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"UHRF1", |
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"MLF1IP", |
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"HELLS", |
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"RFC2", |
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"RPA2", |
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"NASP", |
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"RAD51AP1", |
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"GMNN", |
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"WDR76", |
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"SLBP", |
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"CCNE2", |
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"UBR7", |
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"POLD3", |
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"MSH2", |
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"ATAD2", |
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"RAD51", |
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"RRM2", |
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"CDC45", |
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"CDC6", |
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"EXO1", |
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"TIPIN", |
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"DSCC1", |
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"BLM", |
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"CASP8AP2", |
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"USP1", |
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"CLSPN", |
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"POLA1", |
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"CHAF1B", |
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"BRIP1", |
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"E2F8" |
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]), |
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html.Label("G2M-cycle genes"), |
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dcc.Dropdown(id='dpdn4', value="TOP2A", multi=False, |
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options=[ |
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'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', |
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'CENPE', 'CTCF', 'NEK2', 'G2E3', 'GAS2L3', 'CBX5', 'CENPA' |
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]), |
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]), |
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html.Div([ |
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dcc.Graph(id='scatter-plot-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-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-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-8', figure={}, className='three columns',config=config_fig) |
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]), |
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]) |
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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='dpdn5', value="batch", multi=False, |
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options=df.columns), |
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html.Label("UMAP condition 2"), |
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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-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-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-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-graph2', 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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tab4_content = html.Div([ |
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html.Div([ |
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html.Label("Multi gene"), |
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dcc.Dropdown(id='dpdn7', value=["PAX6","TP63","S100A9"], multi=True, |
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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-12', figure={}, className='four columns',config=config_fig) |
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]), |
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]) |
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layout = html.Div([ |
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dcc.Tabs(id='tabs', style= {'width': 600, |
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'font-size': '100%', |
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'height': 50}, value='tab1',children=[ |
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dcc.Tab(label='QC', value='tab1', children=tab1_content), |
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dcc.Tab(label='Cell cycle', value='tab2', children=tab2_content), |
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dcc.Tab(label='Custom', value='tab3', children=tab3_content), |
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dcc.Tab(label='Multi dot', value='tab4', children=tab4_content), |
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]), |
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]) |
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@callback( |
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Output("min-slider-1", "value"), |
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Output("max-slider-1", "value"), |
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Output("min-slider-2", "value"), |
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Output("max-slider-2", "value"), |
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Output("min-slider-3", "value"), |
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Output("max-slider-3", "value"), |
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Input("min-slider-1", "value"), |
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Input("max-slider-1", "value"), |
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Input("min-slider-2", "value"), |
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Input("max-slider-2", "value"), |
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Input("min-slider-3", "value"), |
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Input("max-slider-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-1', 'value'), |
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Output('range-slider-2', 'value'), |
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Output('range-slider-3', 'value'), |
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Input('min-slider-1', 'value'), |
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Input('max-slider-1', 'value'), |
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Input('min-slider-2', 'value'), |
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Input('max-slider-2', 'value'), |
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Input('min-slider-3', 'value'), |
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Input('max-slider-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', component_property='figure'), |
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Output(component_id='pie-graph', component_property='figure'), |
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Output(component_id='scatter-plot', component_property='figure'), |
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Output(component_id='scatter-plot-2', component_property='figure'), |
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Output(component_id='scatter-plot-3', component_property='figure'), |
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Output(component_id='scatter-plot-4', component_property='figure'), |
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Output(component_id='scatter-plot-5', component_property='figure'), |
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Output(component_id='scatter-plot-6', component_property='figure'), |
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Output(component_id='scatter-plot-7', component_property='figure'), |
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Output(component_id='scatter-plot-8', component_property='figure'), |
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Output(component_id='scatter-plot-9', component_property='figure'), |
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Output(component_id='scatter-plot-10', component_property='figure'), |
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Output(component_id='scatter-plot-11', component_property='figure'), |
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Output(component_id='scatter-plot-12', component_property='figure'), |
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Output(component_id='my-graph2', 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-1', component_property='value'), |
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Input(component_id='range-slider-2', component_property='value'), |
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Input(component_id='range-slider-3', component_property='value') |
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) |
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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): |
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batch_chosen = df[col_chosen].unique().to_list() |
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dff = df.filter( |
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(pl.col(col_chosen).cast(str).is_in(batch_chosen)) & |
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(pl.col(col_features) >= range_value_1[0]) & |
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(pl.col(col_features) <= range_value_1[1]) & |
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(pl.col(col_counts) >= range_value_2[0]) & |
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(pl.col(col_counts) <= range_value_2[1]) & |
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(pl.col(col_mt) >= range_value_3[0]) & |
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(pl.col(col_mt) <= range_value_3[1]) |
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) |
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dff = dff.with_columns(dff[col_chosen].cast(pl.Categorical)) |
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dff = dff.sort(col_chosen) |
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fig_violin = px.violin(data_frame=dff, x=col_chosen, y=col_features, box=True, points="all", |
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color=col_chosen, hover_name=col_chosen,template="seaborn") |
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total_count = pl.lit(len(dff)) |
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category_counts = dff.group_by(col_chosen).agg(pl.col(col_chosen).count().alias("count")) |
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category_counts = category_counts.with_columns(((pl.col("count") / total_count * 100).round(decimals=2)).alias("normalized_count")) |
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total_cells = total_count |
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pie_title = f'Percentage of Total Cells: {total_cells}' |
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list_conds = condition3_chosen |
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list_conds += [col_chosen] |
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dff_pre = dff.select(list_conds) |
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dff_long = dff_pre.melt(id_vars=col_chosen, variable_name="Gene", value_name="Mean expression") |
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expression_means = dff_long.lazy().group_by([col_chosen, "Gene"]).agg(pl.mean("Mean expression")).collect() |
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dff_long1 = dff_pre.melt(id_vars=col_chosen, variable_name="Gene") |
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count = 1 |
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dff_long2 = dff_long1.with_columns(pl.lit(count).alias("len")) |
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dff_long3 = dff_long2.filter(pl.col("value") > 0).group_by([col_chosen, "Gene"]).agg(pl.sum("len").alias("len")) |
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dff_long4 = dff_long2.group_by([col_chosen, "Gene"]).agg(pl.sum("len").alias("total")) |
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dff_5 = dff_long4.join(dff_long3, on=[col_chosen,"Gene"], how="outer") |
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result = dff_5.select([ |
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pl.when((pl.col('len').is_not_null()) & (pl.col('total').is_not_null())) |
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.then(pl.col('len') / pl.col('total')*100) |
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.otherwise(None).alias("%"), |
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]) |
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result = result.with_columns(pl.col("%").fill_null(100)) |
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dff_5[["percentage"]] = result[["%"]] |
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dff_5 = dff_5.select(pl.col(col_chosen,"Gene","percentage")) |
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expression_means = expression_means.join(dff_5, on=[col_chosen,"Gene"], how="inner") |
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expression_means = expression_means.sort(col_chosen, descending=True) |
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category_counts = category_counts.sort(col_chosen) |
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fig_pie = px.pie(category_counts, values="normalized_count", names=col_chosen, labels=col_chosen, hole=.3, title=pie_title, template="seaborn") |
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fig_scatter = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_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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fig_scatter_2 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_mt, |
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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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fig_scatter_3 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_features, |
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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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fig_scatter_4 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_counts, |
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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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fig_scatter_5 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=s_chosen, |
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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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fig_scatter_6 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=g2m_chosen, |
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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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fig_scatter_7 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="S_score", |
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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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fig_scatter_8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="G2M_score", |
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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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dff = dff.sort(condition1_chosen) |
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fig_scatter_9 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=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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fig_scatter_10 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition2_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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fig_scatter_11 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, color=condition1_chosen, |
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hover_name='batch',template="seaborn") |
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fig_scatter_12 = px.scatter(data_frame=expression_means, x="Gene", y=col_chosen, color="Mean expression", |
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size="percentage", size_max = 20, |
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hover_name=col_chosen,template="seaborn") |
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fig_violin2 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all", |
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color=condition1_chosen, hover_name=condition1_chosen,template="seaborn") |
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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_scatter_12, fig_violin2 |
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