karyoploteR: Plot Karyotype and Visualize Genomic Data
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R,
R packages,
Bioconductor,
Genomic data visualization
karyoploteR is an R/Bioconductor package to to plot virtually anything along the genome.
Installation:
Basic workflow to create a karyoplot:
1) start with an empty plot with no data apart from the ideograms themselves using the function plotKaryotype() .
2) add the data plots as required. To add the data there are functions based on the R base graphics: kpPoints(), kpLines(), kpSegments(), kpRects(), … - that can be used to plot virtually anything along the genome and other functions at a higher level useful to plot more specific genomic data types -e.g. kpPlotRegions(), kpPlotCoverage(), …-.
A simple example is to plot a set of regions representing copy-number gains and losses using the kpPlotRegions() function:
Read more: karyoploteR
Installation:
Code R :
source("https://bioconductor.org/biocLite.R") biocLite("karyoploteR")
Basic workflow to create a karyoplot:
1) start with an empty plot with no data apart from the ideograms themselves using the function plotKaryotype() .
2) add the data plots as required. To add the data there are functions based on the R base graphics: kpPoints(), kpLines(), kpSegments(), kpRects(), … - that can be used to plot virtually anything along the genome and other functions at a higher level useful to plot more specific genomic data types -e.g. kpPlotRegions(), kpPlotCoverage(), …-.
A simple example is to plot a set of regions representing copy-number gains and losses using the kpPlotRegions() function:
Code R :
gains <- makeGRangesFromDataFrame(data.frame(chr=c("chr1", "chr5", "chr17", "chr22"), start=c(1, 1000000, 4000000, 1), end=c(5000000, 3200000, 80000000, 1200000))) losses <- makeGRangesFromDataFrame(data.frame(chr=c("chr3", "chr9", "chr17"), start=c(80000000, 20000000, 1), end=c(170000000, 30000000, 25000000))) kp <- plotKaryotype(genome="hg19") kpPlotRegions(kp, gains, col="#FFAACC") kpPlotRegions(kp, losses, col="#CCFFAA")
Read more: karyoploteR