How To Draw Volcano Plots Of Some Genes
Volcano plot in Python
What is Volcano plot?
- Volcano plot is a ii-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 (eastward.chiliad. normal vs. treated) in terms of log fold change (X-axis) and negative log10 of p value (Y-axis). The higher the indicate 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 ii weather.
Applications
- Easy to visualize the expression of thousands of genes obtained from omics enquiry (eg. Transcriptomics, genomics, proteomics, etc.) and pinpoint genes with significant changes
How to create Volcano plot in Python?
- We will use
bioinfokit v2.0.viiior later - Cheque 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. 2022 to place statistically significantly induced or downregulated genes in response to salt stress in Spartina alterniflora (Read paper). Here's yous can download cistron expression dataset used for generating volcano plot: dataset
Note: If y'all have your ain dataset, you should import it as pandas dataframe. Learn how to import information using pandas
from bioinfokit import analys , visuz # load dataset every bit 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 ane.050000e-55 visuz . GeneExpression . volcano ( df = df , lfc = 'log2FC' , pv = 'p-value' ) # plot will be saved in same directory (volcano.png) # set parameter show=Truthful, if you want view the prototype instead of saving Generated volcano plot by above lawmaking (green: upregulated and red: downregulated genes),
Change background theme to night,
visuz . GeneExpression . volcano ( df = df , lfc = 'log2FC' , pv = 'p-value' , theme = 'dark' ) 
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 , ane )) 
Alter color of volcano plot
# alter colormap visuz . GeneExpression . volcano ( df = df , lfc = 'log2FC' , pv = 'p-value' , color = ( "#00239CFF" , "grey" , "#E10600FF" )) 
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" , "gray" , "#E10600FF" ), plotlegend = True , legendpos = 'upper right' , legendanchor = ( 1.46 , 1 )) 
Alter transparency of volcano plot
visuz . GeneExpression . volcano ( df = df , lfc = 'log2FC' , pv = 'p-value' , color = ( "#00239CFF" , "greyness" , "#E10600FF" ), valpha = 0.5 ) 
Change the shape of the points
# add together star shape # check more shapes at https://matplotlib.org/3.1.ane/api/markers_api.html visuz . GeneExpression . volcano ( df = df , lfc = 'log2FC' , pv = 'p-value' , color = ( "#00239CFF" , "grey" , "#E10600FF" ), markerdot = '*' ) 
Change the shape and size of the points
visuz . GeneExpression . volcano ( df = df , lfc = 'log2FC' , pv = 'p-value' , colour = ( "#00239CFF" , "gray" , "#E10600FF" ), markerdot = '*' , dotsize = 30 ) 
Add gene labels (text mode) to the points,
# add cistron customized labels # note: here you need to provide column proper noun of cistron Ids (geneid parameter) # default simple text will exist 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, prepare genenames='deg' 
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.i" ), gstyle = ii ) 
Add gene names instead of gene Ids,
# add cistron customized labels # annotation: here you need to provide column name of gene Ids (geneid parameter) # equally the dataset only accept 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.i" : "EP" , "LOC_Os01g50030.i" : "CPuORF25" , "LOC_Os06g40940.iii" : "GDH" , "LOC_Os03g03720.ane" : "G3PD" }), gstyle = 2 ) 
Add threshold lines,
visuz . GeneExpression . volcano ( df = df , lfc = "log2FC" , pv = "p-value" , geneid = "GeneNames" , genenames = ({ "LOC_Os09g01000.1" : "EP" , "LOC_Os01g50030.ane" : "CPuORF25" , "LOC_Os06g40940.iii" : "GDH" , "LOC_Os03g03720.1" : "G3PD" }), gstyle = two , sign_line = Truthful ) 
Alter 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 = two , sign_line = True , xlm = ( - half-dozen , 6 , ane ), ylm = ( 0 , 61 , five ), figtype = 'svg' , axtickfontsize = x , axtickfontname = 'Verdana' ) 
In addition to these parameters, the parameters for figure type (figtype), X and Y centrality ticks range (xlm, ylm), centrality labels (axxlabel, axylabel),
axis labels font size (axlabelfontsize), and axis tick labels font size and name (axtickfontsize, axtickfontname) tin can be provided.
To create a inverted volcano plot,
# you can utilise interactive python panel, jupyter or python code # I am using interactive python console from bioinfokit import visuz # here you can modify 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,
Change color inverted volcano plot
# modify colormap visuz . GeneExpression . involcano ( df = df , lfc = "log2FC" , pv = "p-value" , color = ( "#00239CFF" , "greyness" , "#E10600FF" )) 
Add together factor names instead of gene Ids,
# add gene customized labels # annotation: here you demand to provide cavalcade name of factor Ids (geneid parameter) # as the dataset simply have geneids, y'all need to provide tuple of gene Id and respective gene names visuz . GeneExpression . involcano ( df = df , lfc = "log2FC" , pv = "p-value" , geneid = "GeneNames" , genenames = ({ "LOC_Os09g01000.1" : "EP" , "LOC_Os01g50030.one" : "CPuORF25" , "LOC_Os06g40940.3" : "GDH" , "LOC_Os03g03720.1" : "G3PD" }), gstyle = 2 ) 
In addition to these parameters, the parameters for figure type (figtype), axis labels (axxlabel, axylabel), centrality labels font size (axlabelfontsize), and centrality tick labels font size and name (axtickfontsize, axtickfontname) can be provided.
If you lot take any questions, comments or recommendations, please email me at reneshbe@gmail.com
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