def test_dbmp002_suggestiveline_value(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(dash_bio.ManhattanPlot(dataframe=_data)))
nested_component_app_callback(app,
dash_duo,
component=dash_bio.ManhattanPlot,
component_data=_data,
data_prop_name='dataframe',
test_prop_name='suggestiveline_value',
test_prop_value=5,
prop_value_type='int',
take_snapshot=True)
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(dash_bio.ManhattanPlot(dataframe=_data)))
nested_component_app_callback(app,
dash_duo,
component=dash_bio.ManhattanPlot,
component_data=_data,
data_prop_name='dataframe',
test_prop_name='suggestiveline_value',
test_prop_value=9,
prop_value_type='int',
take_snapshot=True)
def update_graph(slider_genome, slider_indic):
"""update the data sets upon change the genomewideline value"""
return dash_bio.ManhattanPlot(
df,
genomewideline_value=float(slider_genome),
suggestiveline_value=float(slider_indic),
)
def update_manhattanplot(threshold):
return dashbio.ManhattanPlot(
dataframe=df,
genomewideline_value=threshold,
suggestiveline_color='#AA00AA',
genomewideline_color='#AA5500',
)
def test_dbmp005_highlight_color(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(dash_bio.ManhattanPlot(dataframe=_data)))
nested_component_app_callback(app,
dash_duo,
component=dash_bio.ManhattanPlot,
component_data=_data,
data_prop_name='dataframe',
test_prop_name='highlight_color',
test_prop_value='rgb(76, 184, 254)',
prop_value_type='string',
take_snapshot=True)
def test_dbmp003_genomewide_color(dash_duo):
app = dash.Dash(__name__)
app.layout = html.Div(
nested_component_layout(dash_bio.ManhattanPlot(dataframe=_data)))
nested_component_app_callback(app,
dash_duo,
component=dash_bio.ManhattanPlot,
component_data=_data,
data_prop_name='dataframe',
test_prop_name='genomewideline_color',
test_prop_value='rgb(209, 171, 251)',
prop_value_type='string')
dash_duo.wait_for_style_to_equal(
'#test-graph svg:nth-child(3) g.layer-above g.shapelayer > path:nth-child(2)',
'stroke', 'rgb(209, 171, 251)')
# Import required libraries
import os
import pandas as pd
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Input, Output
import dash_bio
DATAPATH = os.path.join(".", "tests", "dashbio_demos", "sample_data", "manhattan_")
# Load the data
df = pd.read_csv("{}data.csv".format(DATAPATH))
# Feed the data to a function which creates a Manhattan Plot figure
fig = dash_bio.ManhattanPlot(df)
text_style = {
'color': "#506784",
'font-family': 'Open Sans'
}
def description():
return 'Display genomic studies results sorted out by chromosome with ' \
'this Manhattan plot.\
Perfect to visualize genome wide association studies (GWAS).'
def header_colors():
return {
DATAPATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
# Load dataset into a DataFrame
df = pd.read_csv(os.path.join(DATAPATH, 'data.csv'))
n_chr = 23 # number of chromosome pairs in humans
assert 'CHR' in df.columns
assert df['CHR'].max() == n_chr
# Trim down the data
DATASET = df.groupby('CHR').apply(lambda u: u.head(50))
DATASET = DATASET.droplevel('CHR').reset_index(drop=True)
# Feed the data to a function which creates a Manhattan Plot figure
fig = dash_bio.ManhattanPlot(DATASET)
def description():
return 'Display genomic studies results sorted out by chromosome with ' \
'this Manhattan plot.\
Perfect to visualize genome-wide association studies (GWAS).'
def header_colors():
return {'bg_color': '#0D76BF', 'font_color': '#fff', 'light_logo': True}
def layout():
return html.Div(
id='mhp-page-content',
html.H1("Manhattan Plot", className="title"),
html.P("Manhattan Plot is a type of scatter plot and commonly used in genome-wide association studies (GWAS) to visualize display significant SNPs efficiently.",
className="subtitle is-5"),
html.A("Go to Docs for 'Dash' library", href="https://dash.plot.ly/", target="_blank", className="button is-outlined is-info is-rounded"),
html.Hr(style={"background": "#CCC"}),
html.P("*The genome-wide significance threshold was set as 5e-8 and plotted with green line, and the most significant SNPs are colored in red.", className="subtitle is-5"),
]),
html.Div(className="column is-half is-centered", children=[
# Here is Manhattan Plot
dcc.Graph(figure=dashbio.ManhattanPlot(
dataframe = gwas_data,
highlight_color = "#ff3860", # The color of the significant SNPs
genomewideline_value = -(m.log10(5e-8)), # The genome-wide significance threshold
genomewideline_width = 2, # Boldness of the genome-wide line
genomewideline_color = "#00d1b2", # The color of genome-wide significance line
suggestiveline_value = False, # If you would like to add a suggestive threshold, turn it into a float value.
annotation = "ADD_INFO", # If you would like to add extra info to the annotation part, here put the column name.
showgrid = False, # If you would like to see grid lines in the background, turn it into True.
title = None, # If you would like to add a title to the graph, just add "My Manhattan Plot"
xlabel = "chromosome"
)), # ends of Manhattan Plot
]),
]),
# Footer Part
html.Div(className="columns is-centered is-vcentered has-background-light", style={"padding": "100px 0px"},
children=[
html.Div(className="column is-half is-centered is-vcentered", children=[
html.H1("Thanks for your effort to open and come here!", className="title"),
html.P("I will keep continue to append new interactive graphs and figures as much as I can.", className="subtitle is-5"),
import dash_bio as dashbio
import dash_html_components as html
import dash_core_components as dcc
import base64
import pandas as pd
df = pd.read_csv(
"https://raw.githubusercontent.com/plotly/dash-bio/master/tests/dashbio_demos/dash-manhattan-plot/data/data.csv"
)
component = dcc.Graph(figure=dashbio.ManhattanPlot(dataframe=df, ),
style={'width': '700px'})
component_image = html.Img(src='data:image/png;base64,{}'.format(
base64.b64encode(open('./images/pic_manhattan.png',
'rb').read()).decode()),
style={'margin-left': '1px'})
def callbacks(app):
return
app.layout = html.Div([
html.H1('Welcome to the app'),
html.H3('You are successfully authorized'),
dcc.Markdown(children=markdown_text),
dcc.Slider(id='manhattanplot-input',
min=1,
max=6,
marks={i: {
'label': str(i)
}
for i in range(10)},
value=6),
html.Br(),
html.Div(
dcc.Graph(id='my-dashbio-manhattanplot',
figure=dashbio.ManhattanPlot(dataframe=df)))
])
@app.callback(dash.dependencies.Output('my-dashbio-manhattanplot', 'figure'),
[dash.dependencies.Input('manhattanplot-input', 'value')])
def update_manhattanplot(threshold):
return dashbio.ManhattanPlot(
dataframe=df,
genomewideline_value=threshold,
suggestiveline_color='#AA00AA',
genomewideline_color='#AA5500',
)
def update_manhattanplot(threshold):
return dashbio.ManhattanPlot(dataframe=df, genomewideline_value=threshold)