Volcano plot in Python

Renesh Bedre    4 minute read

What is Volcano plot?

  • Volcano plot is a 2-dimensional (2D) scatter plot having a shape like a volcano.
  • Volcano plot used for visualization and identification of statistically significant gene expression changes from two different experimental conditions (e.g. normal vs. treated) in terms of log fold change (X-axis) and negative log10 of p value (Y-axis). The negative log 10 of p value used for scaling purposes which makes genes with lower p values appear at top of volcano plot. The higher the point on Y-axis, the lower is the p value.
  • The wider dispersion of data points in the volcano plot indicates significant changes in gene expression between the two conditions.

Applications

  • Easy to visualize the expression of thousands of genes obtained from omics research (eg. Transcriptomics, genomics, proteomics, etc.) and pinpoint genes with significant changes
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How to create Volcano plot in Python?

  • We will use bioinfokit v2.0.8 or later
  • Check bioinfokit documentation for installation and documentation (check how to install Python packages)
  • For generating a volcano plot, I have used gene expression data published in Bedre et al. 2016 to identify statistically significantly induced or downregulated genes in response to salt stress in Spartina alterniflora (Read paper). Here’s you can download gene expression dataset used for generating volcano plot: dataset

Note: If you have your own dataset, you should import it as pandas dataframe. Learn how to import data using pandas

from bioinfokit import analys, visuz
# load dataset as pandas dataframe
df = analys.get_data('volcano').data
df.head(2)
          GeneNames  value1  value2    log2FC       p-value
0  LOC_Os09g01000.1    8862   32767 -1.886539  1.250000e-55
1  LOC_Os12g42876.1    1099     117  3.231611  1.050000e-55

visuz.GeneExpression.volcano(df=df, lfc='log2FC', pv='p-value')
# plot will be saved in same directory (volcano.png)
# set parameter show=True, if you want view the image instead of saving

Generated volcano plot by above code (green: upregulated and red: downregulated genes),

Basic volcano plot

Change background theme to dark,

visuz.GeneExpression.volcano(df=df, lfc='log2FC', pv='p-value', theme='dark')

Basic volcano plot with dark background

Add legend to the plot and adjust the legend position,

visuz.GeneExpression.volcano(df=df, lfc='log2FC', pv='p-value', plotlegend=True, legendpos='upper right', 
    legendanchor=(1.46,1))

Volcano plot with legend

Change color of volcano plot

# change colormap
visuz.GeneExpression.volcano(df=df, lfc='log2FC', pv='p-value', color=("#00239CFF", "grey", "#E10600FF"))

Volcano plot with red and blue color

Change log fold change and p value threshold,

visuz.GeneExpression.volcano(df=df, lfc='log2FC', pv='p-value', lfc_thr=(1, 2), pv_thr=(0.05, 0.01), 
    color=("#00239CFF", "grey", "#E10600FF"), plotlegend=True, legendpos='upper right', 
    legendanchor=(1.46,1))

Volcano plot with different p value threshold

Change transparency of volcano plot

visuz.GeneExpression.volcano(df=df, lfc='log2FC', pv='p-value', color=("#00239CFF", "grey", "#E10600FF"), 
    valpha=0.5)

Volcano plot with low transparency

Change the shape of the points

# add star shape
# check more shapes at https://matplotlib.org/3.1.1/api/markers_api.html
visuz.GeneExpression.volcano(df=df, lfc='log2FC', pv='p-value', color=("#00239CFF", "grey", "#E10600FF"), 
    markerdot='*')

Volcano plot with star shapes

Change the shape and size of the points

visuz.GeneExpression.volcano(df=df, lfc='log2FC', pv='p-value', color=("#00239CFF", "grey", "#E10600FF"), 
    markerdot='*', dotsize=30)

Volcano plot with star shapes and increased point size

Add gene labels (text style) to the points,

# add gene customized labels
# note: here you need to provide column name of gene Ids (geneid parameter)
# default simple text will be added
visuz.GeneExpression.volcano(df=df, lfc="log2FC", pv="p-value", geneid="GeneNames",
    genenames=("LOC_Os09g01000.1", "LOC_Os01g50030.1", "LOC_Os06g40940.3", "LOC_Os03g03720.1") )
# if you want to label all DEGs defined lfc_thr and pv_thr, set genenames='deg'

Volcano plot with point labels

Add gene labels (box style) to the points,

visuz.GeneExpression.volcano(df=df, lfc="log2FC", pv="p-value", geneid="GeneNames",
    genenames=("LOC_Os09g01000.1", "LOC_Os01g50030.1", "LOC_Os06g40940.3", "LOC_Os03g03720.1"), gstyle=2 )

Volcano plot with box style point labels

Add gene names instead of gene Ids,

# add gene customized labels
# note: here you need to provide column name of gene Ids (geneid parameter)
# as the dataset only have geneids, you need to provide tuple of gene Id and corresponding gene names
visuz.GeneExpression.volcano(df=df, lfc="log2FC", pv="p-value", geneid="GeneNames", 
    genenames=({"LOC_Os09g01000.1":"EP", "LOC_Os01g50030.1":"CPuORF25", "LOC_Os06g40940.3":"GDH", "LOC_Os03g03720.1":"G3PD"}),
    gstyle=2)

Volcano plot with different IDs

Add threshold lines,

visuz.GeneExpression.volcano(df=df, lfc="log2FC", pv="p-value", geneid="GeneNames", 
    genenames=({"LOC_Os09g01000.1":"EP", "LOC_Os01g50030.1":"CPuORF25", "LOC_Os06g40940.3":"GDH", "LOC_Os03g03720.1":"G3PD"}),
    gstyle=2, sign_line=True)

Volcano plot with threshold lines

Change X and Y range ticks, font size and name for tick labels

visuz.GeneExpression.volcano(df=df, lfc="log2FC", pv="p-value", geneid="GeneNames", 
    genenames=({"LOC_Os09g01000.1":"EP", "LOC_Os01g50030.1":"CPuORF25", "LOC_Os06g40940.3":"GDH", "LOC_Os03g03720.1":"G3PD"}),
    gstyle=2, sign_line=True, xlm=(-6,6,1), ylm=(0,61,5), figtype='svg', axtickfontsize=10,
    axtickfontname='Verdana')

Volcano plot with change in ticks range for x and y axis

In addition to these parameters, the parameters for figure type (figtype), X and Y axis ticks range (xlm, ylm), axis labels (axxlabel, axylabel),
axis labels font size (axlabelfontsize), and axis tick labels font size and name (axtickfontsize, axtickfontname) can be provided.

To create a inverted volcano plot,

# you can use interactive python console, jupyter or python code
# I am using interactive python console
from bioinfokit import visuz
# here you can change default parameters. 
# Read documentation at https://github.com/reneshbedre/bioinfokit
visuz.GeneExpression.involcano(df=df, lfc="log2FC", pv="p-value")

Generated inverted volcano plot by adding above code,

Basic inverted volcano plot

Change color inverted volcano plot

# change colormap
visuz.GeneExpression.involcano(df=df, lfc="log2FC", pv="p-value", color=("#00239CFF", "grey", "#E10600FF"))

Inverted volcano plot with red and blue colors

Add gene names instead of gene Ids,

# add gene customized labels
# note: here you need to provide column name of gene Ids (geneid parameter)
# as the dataset only have geneids, you need to provide tuple of gene Id and corresponding gene names
visuz.GeneExpression.involcano(df=df, lfc="log2FC", pv="p-value", geneid="GeneNames", 
    genenames=({"LOC_Os09g01000.1":"EP", "LOC_Os01g50030.1":"CPuORF25", "LOC_Os06g40940.3":"GDH", "LOC_Os03g03720.1":"G3PD"}), 
    gstyle=2)

inverted volcano plot with labels

In addition to these parameters, the parameters for figure type (figtype), axis labels (axxlabel, axylabel), axis labels font size (axlabelfontsize), and axis tick labels font size and name (axtickfontsize, axtickfontname) can be provided.

If you have any questions, comments or recommendations, please email me at reneshbe@gmail.com


This work is licensed under a Creative Commons Attribution 4.0 International License

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