singleronkeratinocytes / dash_plotly_QC_scRNA.py
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# Dash app to visualize scRNA-seq data quality control metrics from scanpy objects
# Shoutout to Coding-with-Adam for the initial template of the project:
# https://github.com/Coding-with-Adam/Dash-by-Plotly/blob/master/Dash%20Components/Graph/dash-graph.py
import dash
from dash import dcc, html, Output, Input
import plotly.express as px
import dash_callback_chain
import yaml
import polars as pl
import os
pl.enable_string_cache(False)
# 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(filepath, storage_options=storage_options)
# Setup the app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets) #, 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-1',
step=250,
value=[min_value, max_value],
marks={i: str(i) for i in range(min_value, max_value + 1, 250)},
),
dcc.Input(id='min-slider-1', type='number', value=min_value, debounce=True),
dcc.Input(id='max-slider-1', type='number', value=max_value, debounce=True),
html.Label("Total Counts"),
dcc.RangeSlider(
id='range-slider-2',
step=7500,
value=[min_value_2, max_value_2],
marks={i: str(i) for i in range(min_value_2, max_value_2 + 1, 7500)},
),
dcc.Input(id='min-slider-2', type='number', value=min_value_2, debounce=True),
dcc.Input(id='max-slider-2', type='number', value=max_value_2, debounce=True),
html.Label("Percent Mitochondrial Genes"),
dcc.RangeSlider(
id='range-slider-3',
step=5,
min=0,
max=100,
value=[min_value_3, max_value_3],
),
dcc.Input(id='min-slider-3', type='number', value=min_value_3, debounce=True),
dcc.Input(id='max-slider-3', type='number', value=max_value_3, debounce=True),
html.Div([
dcc.Graph(id='pie-graph', figure={}, className='four columns',config=config_fig),
dcc.Graph(id='my-graph', figure={}, clickData=None, hoverData=None,
className='four columns',config=config_fig
),
dcc.Graph(id='scatter-plot', figure={}, className='four columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='scatter-plot-2', figure={}, className='four columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='scatter-plot-3', figure={}, className='four columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='scatter-plot-4', figure={}, className='four columns',config=config_fig)
]),
])
# Create the second tab content with scatter-plot-5 and scatter-plot-6
tab2_content = html.Div([
html.Div([
html.Label("S-cycle genes"),
dcc.Dropdown(id='dpdn3', value="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-5', figure={}, className='three columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='scatter-plot-6', figure={}, className='three columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='scatter-plot-7', figure={}, className='three columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='scatter-plot-8', figure={}, className='three columns',config=config_fig)
]),
])
# Create the second tab content with scatter-plot-5 and scatter-plot-6
tab3_content = html.Div([
html.Div([
html.Label("UMAP condition 1"),
dcc.Dropdown(id='dpdn5', value="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-9', figure={}, className='four columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='scatter-plot-10', figure={}, className='four columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='scatter-plot-11', figure={}, className='four columns',config=config_fig)
]),
html.Div([
dcc.Graph(id='my-graph2', figure={}, clickData=None, hoverData=None,
className='four columns',config=config_fig
)
]),
]),
])
# html.Div([
# dcc.Graph(id='scatter-plot-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","S100A9"], multi=True,
options=df.columns),
]),
html.Div([
dcc.Graph(id='scatter-plot-12', figure={}, className='four columns',config=config_fig)
]),
])
# Define the tabs layout
app.layout = html.Div([
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
@app.callback(
Output("min-slider-1", "value"),
Output("max-slider-1", "value"),
Output("min-slider-2", "value"),
Output("max-slider-2", "value"),
Output("min-slider-3", "value"),
Output("max-slider-3", "value"),
Input("min-slider-1", "value"),
Input("max-slider-1", "value"),
Input("min-slider-2", "value"),
Input("max-slider-2", "value"),
Input("min-slider-3", "value"),
Input("max-slider-3", "value"),
)
def circular_callback(min_1, max_1, min_2, max_2, min_3, max_3):
return min_1, max_1, min_2, max_2, min_3, max_3
@app.callback(
Output('range-slider-1', 'value'),
Output('range-slider-2', 'value'),
Output('range-slider-3', 'value'),
Input('min-slider-1', 'value'),
Input('max-slider-1', 'value'),
Input('min-slider-2', 'value'),
Input('max-slider-2', 'value'),
Input('min-slider-3', 'value'),
Input('max-slider-3', 'value'),
)
def update_slider_values(min_1, max_1, min_2, max_2, min_3, max_3):
return [min_1, max_1], [min_2, max_2], [min_3, max_3]
@app.callback(
Output(component_id='my-graph', component_property='figure'),
Output(component_id='pie-graph', component_property='figure'),
Output(component_id='scatter-plot', component_property='figure'),
Output(component_id='scatter-plot-2', component_property='figure'),
Output(component_id='scatter-plot-3', component_property='figure'),
Output(component_id='scatter-plot-4', component_property='figure'), # Add this new scatter plot
Output(component_id='scatter-plot-5', component_property='figure'),
Output(component_id='scatter-plot-6', component_property='figure'),
Output(component_id='scatter-plot-7', component_property='figure'),
Output(component_id='scatter-plot-8', component_property='figure'),
Output(component_id='scatter-plot-9', component_property='figure'),
Output(component_id='scatter-plot-10', component_property='figure'),
Output(component_id='scatter-plot-11', component_property='figure'),
Output(component_id='scatter-plot-12', component_property='figure'),
Output(component_id='my-graph2', component_property='figure'),
Input(component_id='dpdn2', component_property='value'),
Input(component_id='dpdn3', component_property='value'),
Input(component_id='dpdn4', component_property='value'),
Input(component_id='dpdn5', component_property='value'),
Input(component_id='dpdn6', component_property='value'),
Input(component_id='dpdn7', component_property='value'),
Input(component_id='range-slider-1', component_property='value'),
Input(component_id='range-slider-2', component_property='value'),
Input(component_id='range-slider-3', component_property='value')
)
def update_graph_and_pie_chart(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 = 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(100))
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 = 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 = 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_2 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_mt,
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch',template="seaborn")
fig_scatter_3 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_features,
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch',template="seaborn")
fig_scatter_4 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=col_counts,
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch',template="seaborn")
fig_scatter_5 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=s_chosen,
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch', title="S-cycle gene:",template="seaborn")
fig_scatter_6 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=g2m_chosen,
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch', title="G2M-cycle gene:",template="seaborn")
fig_scatter_7 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="S_score",
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch', title="S score:",template="seaborn")
fig_scatter_8 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color="G2M_score",
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch', title="G2M score:",template="seaborn")
# Sort values of custom in-between
dff = dff.sort(condition1_chosen)
fig_scatter_9 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition1_chosen,
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch',template="seaborn")
fig_scatter_10 = px.scatter(data_frame=dff, x='X_umap-0', y='X_umap-1', color=condition2_chosen,
labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch',template="seaborn")
fig_scatter_11 = px.scatter(data_frame=dff, x=condition1_chosen, y=condition2_chosen, color=condition1_chosen,
#labels={'X_umap-0': 'umap1' , 'X_umap-1': 'umap2'},
hover_name='batch',template="seaborn")
fig_scatter_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_violin2 = px.violin(data_frame=dff, x=condition1_chosen, y=condition2_chosen, box=True, points="all",
color=condition1_chosen, hover_name=condition1_chosen,template="seaborn")
return fig_violin, fig_pie, fig_scatter, fig_scatter_2, fig_scatter_3, fig_scatter_4, fig_scatter_5, fig_scatter_6, fig_scatter_7, fig_scatter_8, fig_scatter_9, fig_scatter_10, fig_scatter_11, fig_scatter_12, fig_violin2
# 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) #