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ALD_app.py
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ALD_app.py
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import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table
from dash.dependencies import Input, Output
import pandas as pd
import numpy as np
import base64
import ast
import seaborn as sns
import plotly.graph_objs as go
from scipy import stats
import plotly.express as px
from config import FNAME_IMAGE1
from config import DATA_PL_MEDIAN, DATA_PLCORR, DATA_PLASMA_LONG, DATA_LIVER_LONG
# Prepare images
def png_encode(image_png):
png_base64 = base64.b64encode(open(image_png, 'rb').read()).decode('ascii')
return (png_base64)
image1_base64 = png_encode(FNAME_IMAGE1)
data_pl_median = pd.read_csv(DATA_PL_MEDIAN, index_col='Protein ID')
options_ploverlap = [{'value': i, 'label': i}
for i in list(data_pl_median['Genename_ProteinID'].unique())]
data_plcorr = pd.read_csv(DATA_PLCORR, index_col='Protein ID')
data_plasma_long = pd.read_csv(DATA_PLASMA_LONG, index_col='Genename_ProteinID')
data_liver_long = pd.read_csv(DATA_LIVER_LONG, index_col='Genename_ProteinID')
options_box_plasma_proteinID = [{'value': i, 'label': i}
for i in list(set(data_plasma_long.index))]
options_box_liver_proteinID = [{'value': i, 'label': i}
for i in list(set(data_liver_long.index))]
options_histology_score = [{'value': i, 'label': i}
for i in ['kleiner', 'nas_inflam', 'nas_steatosis_ordinal']]
# Figures
template='simple_white'
figure_colorful = px.scatter(data_plcorr, x='plasma',y='liver', color='Genename_ProteinID', template=template, opacity=0.8)
figure_colorful.update_traces(marker={'size':7, 'line':dict(width=0.5, color='lightyellow')})
figure_colorful.update_layout(xaxis={'title': 'MS signal in plasma [Log10]'},
yaxis={'title': 'MS signal in liver [Log10]'},
title='The liver-plasma proteome space',
showlegend=False)
# Styles
style_headerh1 = {'backgroundColor': '#243E58', 'color': 'snow', 'textAlign': 'center',
'height': '120px', 'line-height': '120px', 'border': '2px solid white',
'font-style': 'normal', 'font-family': 'Copperplate', 'first-letter': {'color': 'red'}
}
style_graph_2panel = {'display': 'inline-block', 'width': '50%', 'textAlign': 'center'}
style_userinput = {'font-size': '120%', 'color': 'darkblue'}
tab_style = {'height': '80px', 'border': '1px solid black', 'backgroundColor': 'whitesmoke',
'line-height': '40px', 'textAlign': 'center', 'font-size': '120%', 'color': '#243E58'}
selected_tab_style = {'height': '80px', 'border': 'none', 'backgroundColor': 'white',
'line-height': '40px', 'textAlign': 'center', 'font-size': '120%', 'color': '#243E58'}
################################################################################
# building Dash app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
server = app.server
app.layout = html.Div([
html.H1('Proteomics biomarker discovery in liver disease', style=style_headerh1),
html.Div([
dcc.Tabs(id='tabs', value='3', children=[
dcc.Tab(id='tab1', label='Proteome-Histology Integration', value='1',
children=[
html.Div([
html.Div([
html.P('Select a histology score here:', style=style_userinput),
dcc.Dropdown(id='boxplot_histology_score_plasma',
options=options_histology_score, value='kleiner'),
html.P('Select a protein here: (GeneName_ProteinID)',
style=style_userinput),
dcc.Dropdown(
id='boxplot_proteinID',
options=options_box_plasma_proteinID,
value='LGALS3BP__Q08380'),
dcc.Graph(id='box_plasma'),
], style=style_graph_2panel),
html.Div([
html.P('Select a histology score here:', style=style_userinput),
dcc.Dropdown(id='boxplot_histology_score_liver',
options=options_histology_score, value='kleiner'),
html.P('Select a protein here: (GeneName_ProteinID)',
style=style_userinput),
dcc.Dropdown(
id='boxplot_proteinID_liver',
options=options_box_liver_proteinID,
value='LGALS3BP__Q08380'),
dcc.Graph(id='box_liver'),
], style=style_graph_2panel)
], )
],
style=tab_style, selected_style=selected_tab_style),
dcc.Tab(id='tab2', label='Study Overview', value='2',
children=[
html.Div([
html.Br(),
html.Br(),
html.Img(src='data:image/png;base64,{}'.format(image1_base64),
style={
'height': '50%',
'width': '50%'})
], style={'width': '90%', 'textAlign': 'center', 'padding-left': '5%'})
], style=tab_style, selected_style=selected_tab_style),
dcc.Tab(id='tab3', label='Liver-Plasma Proteme Integration', value='3',
children=[
html.Div([
html.Div([
html.Br(),
html.P('Search here: (GeneName_ProteinID)', style=style_userinput),
dcc.Dropdown(options=options_ploverlap, value='ALB__P02768'),
dcc.Graph(figure=figure_colorful)
], style={'width': '60%', 'textAlign': 'center', 'padding-left': '20%'}),
html.Div([
html.Br(),
html.P('Search here: (GeneName_ProteinID)', style=style_userinput),
dcc.Dropdown(id='pl_median_scatter_input',
options=options_ploverlap, value='CRP__P02741'),
], style={'width': '60%', 'textAlign': 'center', 'padding-left': '20%'}),
html.Div([
html.Br(),
dcc.Graph(id='pl_median_scatter')
], style=style_graph_2panel),
html.Div([
html.Br(),
dcc.Graph(id='pl_corr_scatter')
], style=style_graph_2panel)
])
], style=tab_style, selected_style=selected_tab_style)
])
])
])
@app.callback(
Output('pl_median_scatter', 'figure'),
[Input('pl_median_scatter_input', 'value')]
)
def update_figure_plcorr_scatter(input_value):
df = data_pl_median.copy()
df = df.reset_index().set_index('Genename_ProteinID')
df['color'] = 'other proteins'
df.loc[input_value, 'color'] = 'selected protein'
figure = px.scatter(df, x='plasma', y='liver', template=template,
color='color',
color_discrete_sequence=['deepskyblue', 'darkred'],
opacity=0.8)
figure.update_traces(marker={'size':12, 'line':dict(color='black', width=1.5)})
figure.update_layout(title='Liver-Plasma proteome correlation', xaxis={'title': 'Protein intensity in plasma [Log10]'},
yaxis={'title': 'Protein intensity in liver [Log10]'},
width=500,
showlegend=False)
return figure
@app.callback(
Output('pl_corr_scatter', 'figure'),
[Input('pl_median_scatter_input', 'value')]
)
def update_figure_plcorr_scatter(input_value):
# df = data_plcorr.copy()
df = data_plcorr[data_plcorr['Genename_ProteinID'] == input_value]
fig = px.scatter(df, x='plasma', y='liver', template=template,
color_discrete_sequence=['deepskyblue'],
opacity=0.8)
fig.update_traces(marker={'size':12, 'line':dict(color='black', width=1.5)})
fig.update_layout(xaxis={'title': 'MS signal in plasma [Log10]'},
yaxis={'title': 'MS signal in liver [Log10]'},
width=500,
title=f'Pair-wise correlation: {input_value}')
return fig
@app.callback(
dash.dependencies.Output('box_plasma', 'figure'),
[dash.dependencies.Input('boxplot_histology_score_plasma', 'value'),
dash.dependencies.Input('boxplot_proteinID', 'value')]
)
def figure_box_plot_plasma(histologyscore, proteinID):
df = data_plasma_long.loc[proteinID]
df = df[df['score_type'] == histologyscore]
df['mcolor']='deepskyblue'
figure = px.box(df, x='score', y="Intensity", notched=True, points='all', template=template,
color='mcolor', color_discrete_sequence=['steelblue'])
figure.update_layout(yaxis={'title': 'Protein intensity [Log2]'},
title='Protein levels in plasma',
showlegend=False)
return figure
@app.callback(
dash.dependencies.Output('box_liver', 'figure'),
[dash.dependencies.Input('boxplot_histology_score_liver', 'value'),
dash.dependencies.Input('boxplot_proteinID_liver', 'value')]
)
def figure_box_plot_liver(histologyscore, proteinID):
df = data_liver_long.loc[proteinID]
df = df[df['score_type'] == histologyscore]
df['mcolor']='deepskyblue'
figure = px.box(df, x='score', y="Intensity", notched=False, points='all', template=template,
color='mcolor', color_discrete_sequence=['steelblue'])
figure.update_layout(yaxis={'title': 'Protein intensity [Log2]'},
title='Protein levels in the liver',
showlegend=False)
return figure
if __name__ == '__main__':
DEBUG = False
app.run_server(debug=DEBUG)