Abstract
MotivationData visualization is a crucial tool for data exploration, analysis and interpretation. For the visualization of genomic data there lacks a tool to create customizable non-circular plots of whole genomes from any species.ResultsWe have developed karyoploteR, an R/Bioconductor package to create linear chromosomal representations of any genome with genomic annotations and experimental data plotted along them. Plot creation process is inspired in R base graphics, with a main function creating karyoplots with no data and multiple additional functions, including custom functions written by the end-user, adding data and other graphical elements. This approach allows the creation of highly customizable plots from arbitrary data with complete freedom on data positioning and representation.Availability and implementationkaryoploteR is released under Artistic-2.0 License. Source code and documentation are freely available through Bioconductor (http://www.bioconductor.org/packages/karyoploteR) and at the examples and tutorial page at https://bernatgel.github.io/karyoploter_tutorial.
Highlights
Data visualization is an important part of data analysis
There are other R packages capable of plotting whole genome diagrams such as: ggbio (Yin et al, 2012), based on the grammar of graphics that can produce different plot types including ideogram and karyogram plots; IdeoViz (Pai and Ren, 2014), to plot binned data along the genome either as lines or bars; or chromPlot (Orostica and Verdugo, 2016), to plot up to four datasets given in a predefined format
It is possible to create different data plotting regions either above or below the ideograms as well as customizing all sizings and margins by changing the values stored in plot.params
Summary
Data visualization is an important part of data analysis. It efficiently summarizes complex data, facilitates exploration and can reveal nonobvious patterns in the data. There are other R packages capable of plotting whole genome diagrams such as: ggbio (Yin et al, 2012), based on the grammar of graphics that can produce different plot types including ideogram and karyogram plots; IdeoViz (Pai and Ren, 2014), to plot binned data along the genome either as lines or bars; or chromPlot (Orostica and Verdugo, 2016), to plot up to four datasets given in a predefined format These packages are either limited in the amount or type of data they can plot (IdeoViz and chromPlot) or have limited customization options (ggbio). It’s inspired on the R base graphics, building plots with multiple successive calls to simple plotting functions
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