dash_bio.VolcanoPlot Examples and Reference

VolcanoPlot

An example of a default VolcanoPlot component without any extra properties.

Effect sizes

import pandas as pd
from dash import Dash, dcc, html, Input, Output, callback
import dash_bio as dashbio

app = Dash(__name__)

df = pd.read_csv('https://git.io/volcano_data1.csv')

app.layout = html.Div([
    'Effect sizes',
    dcc.RangeSlider(
        id='default-volcanoplot-input',
        min=-3,
        max=3,
        step=0.05,
        marks={i: {'label': str(i)} for i in range(-3, 3)},
        value=[-0.5, 1]
    ),
    html.Br(),
    html.Div(
        dcc.Graph(
            id='dashbio-default-volcanoplot',
            figure=dashbio.VolcanoPlot(
                dataframe=df
            )
        )
    )
])

@callback(
    Output('dashbio-default-volcanoplot', 'figure'),
    Input('default-volcanoplot-input', 'value')
)
def update_volcanoplot(effects):
    return dashbio.VolcanoPlot(
        dataframe=df,
        genomewideline_value=2.5,
        effect_size_line=effects
    )

if __name__ == '__main__':
    app.run(debug=True)

Customization

Colors

Choose the colors of the scatter plot points, the highlighted points, the genome-wide line, and the effect size lines.

import pandas as pd
from dash import dcc
import dash_bio as dashbio

df = pd.read_csv('https://git.io/volcano_data1.csv')

volcanoplot = dashbio.VolcanoPlot(
    dataframe=df,
    effect_size_line_color='#AB63FA',
    genomewideline_color='#EF553B',
    highlight_color='#119DFF',
    col='#2A3F5F'
)

dcc.Graph(figure=volcanoplot)

Point Sizes And Line Widths

Change the size of the points on the scatter plot, and the widths of the effect lines and genome-wide line.

import pandas as pd
from dash import dcc
import dash_bio as dashbio

df = pd.read_csv('https://git.io/volcano_data1.csv')

volcanoplot = dashbio.VolcanoPlot(
    dataframe=df,
    point_size=10,
    effect_size_line_width=4,
    genomewideline_width=2
)

dcc.Graph(figure=volcanoplot)

VolcanoPlot Properties

Access this documentation in your Python terminal with:
```python

help(dash_bio.VolcanoPlot)
```

Our recommended IDE for writing Dash apps is Dash Enterprise’s
Data Science Workspaces,
which has typeahead support for Dash Component Properties.
Find out if your company is using
Dash Enterprise.

dataframe (dataframe; required): A pandas dataframe which must contain at
least the following two columns:
- a numeric quantity to plot such as a p-value or zscore
- a numeric quantity measuring the strength of association,
typically an odds ratio, regression coefficient, or log fold
change. Here, it is referred to as effect_size.

effect_size (string; default 'EFFECTSIZE'): A string denoting the
column name for the effect size. This column must be numeric and must
not contain missing nor NaN values.

p (string; default 'P'): A string denoting the column name for the
float quantity to be plotted on the y-axis. This column must be
numeric. It does not have to be a p-value. It can be any
numeric quantity such as peak heights, Bayes factors, test
statistics. If it is not a p-value, make sure to set logp = False.

snp (string; default 'SNP'): A string denoting the column name for
the SNP names (e.g., rs number). More generally, this column could
be anything that identifies each point being plotted. For example,
in an Epigenomewide association study (EWAS), this could be the
probe name or cg number. This column should be a character. This
argument is optional, however it is necessary to specify it if you
want to highlight points on the plot using the highlight argument
in the figure method.

gene (string; default 'GENE'): A string denoting the column name for
the GENE names. More generally, this could be any annotation
information that should be included in the plot.

annotation (string; optional): A string denoting the column to use
as annotations. This could be any annotation information that you
want to include in the plot (e.g., zscore, effect size, minor
allele frequency).

logp (bool; default True): If True, the -log10 of the p-value is
plotted. It isn’t very useful to plot raw p-values; however,
plotting the raw value could be useful for other genome-wide plots
(e.g., peak heights, Bayes factors, test statistics, and other
“scores”).

xlabel (string; optional): Label of the x axis.

ylabel (string; default '-log10(p)'): Label of the y axis.

point_size (number; default 5): Size of the points of the Scatter
plot.

col (string; optional): Color of the points of the Scatter plot. Can
be in any color format accepted by plotly.graph_objects.

effect_size_line (bool | list; default [-1, 1]): A boolean which
must be either False to deactivate the option, or a list/array containing
the upper and lower bounds of the effect size values. Significant
data points will have lower values than the lower bound, or higher
values than the higher bound. Keeping the default value will
result in assigning the list [-1, 1] to the argument.

effect_size_line_color (string; default 'grey'): Color of the effect
size lines.

effect_size_line_width (number; default 2): Width of the effect size
lines.

genomewideline_value (bool | number; default -log10(5e-8)): A
boolean which must be either False to deactivate the option, or a
numerical value corresponding to the p-value above which the data
points are considered significant.

genomewideline_color (string; default 'red'): Color of the
genome-wide line. Can be in any color format accepted by
plotly.graph_objects.

genomewideline_width (number; default 1): Width of the genome-wide
line.

highlight (bool; default True): Whether the data points considered
significant should be highlighted or not.

highlight_color (string; default 'red'): Color of the data points
highlighted because considered significant. Can be in any color
format accepted by plotly.graph_objects.

# ...
Example 1: Random Volcano Plot
'''
dataframe = pd.DataFrame(
    np.random.randint(0,100,size=(100, 2)),
    columns=['P', 'EFFECTSIZE'])
fig = create_volcano(dataframe, title=dict(text='XYZ Volcano plot'))

plotly.offline.plot(fig, image='png')
'''

Additional keys (misc.): Arbitrary arguments can be passed to modify the
Layout and styling of the graph. A full reference of acceptable args is
available here.

Some commonly used layout keys are:
- title (dict: optional): Dict with compatible properties for the title of
the figure layout.
- xaxis (dict: optional): Dict with compatible properties for the x-axis of
the figure layout.
- yaxis (dict: optional): Dict with compatible properties for the y-axis of
the figure layout.
- height (number; optional): Sets the plot’s height (in px).
- width (number; optional): Sets the plot’s width (in px).
- margin (dict | plotly.graph_objects.layout.Margin instance): A dict or Margin
instance that sets the separation between the main plotting space and
the outside of the figure.
- legend (dict | plotly.graph_objects.layout.Legend instance): A dict or Legend
instance with compatible properties.