coMET-package: visualisation of regional epigenome-wide association scan...

In TiphaineCMartin/coMET: coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns

visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns (and also for other omic-WAS)

Description

coMET is an R package for visualising EWAS results in a genomic region. Along with phenotype-association plots, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. The software is designed for epigenetic data, but can also be applied to genomic and functional genomic datasets (other omic-WAS results) in any species.

Details

Package:	coMET
Type:	Package
Version:	1.11.5
Date:	2018-04-16
License:	GPL (>=2)

coMET is an R package that can generate regional plots of EWAS results, DNA co-methylation patterns, and genomic information. A coMET figure includes 3 panels with a plot of P-values from EWAS, customized annotation tracks, and a triangle heatmap plot which demonstrates the correlation structure of DNA methylation at the CpG sites in the genomic region. Plots are created as PDF or EPS files.

Author(s)

Tiphaine C. Martin, Thomas Hardiman, Idil Yet, Pei-Chien Tsai, Jordana T. Bell

Maintainer: Tiphaine Martin <[e-mail]>

Website: http://www.epigen.kcl.ac.uk/comet

References

Martin, T.C, Yet, I, Tsai, P-C, Bell, J.T., coMET: visualisation of regional epigenome-wide association scan results and DNA co-methylation patterns, BMC bioinformatics, 2015.

Examples

extdata <- system.file("extdata", package="coMET",mustWork=TRUE)
configfile <- file.path(extdata, "config_cyp1b1_zoom_4comet.txt")
myinfofile <- file.path(extdata, "cyp1b1_infofile.txt")
myexpressfile <- file.path(extdata, "cyp1b1_infofile_exprGene_region.txt")
mycorrelation <- file.path(extdata, "cyp1b1_res37_rawMatrix.txt")

chrom <- "chr2"
start <- 38290160
end <- 38303219
gen <- "hg38"

if(interactive()){
    genetrack <-genes_ENSEMBL(gen,chrom,start,end,showId=TRUE)
    snptrack <- snpBiomart_ENSEMBL(gen, chrom, start, end,
                dataset="hsapiens_snp_som",showId=FALSE)
    strutrack <- structureBiomart_ENSEMBL(gen, chrom, start, end,
                strand, dataset="hsapiens_structvar_som")
    clinVariant<-ClinVarMain_UCSC(gen,chrom,start,end)
    clinCNV<-ClinVarCnv_UCSC(gen,chrom,start,end)
    gwastrack <-GWAScatalog_UCSC(gen,chrom,start,end)
    geneRtrack <-GeneReviews_UCSC(gen,chrom,start,end)
    listgviz <- list(genetrack,snptrack,strutrack,clinVariant,
                 clinCNV,gwastrack,geneRtrack)
    comet(config.file=configfile, mydata.file=myinfofile, mydata.type="file",
      cormatrix.file=mycorrelation, cormatrix.type="listfile",
      mydata.large.file=myexpressfile, mydata.large.type="listfile",
      tracks.gviz=listgviz, verbose=FALSE, print.image=FALSE,disp.pvalueplot=FALSE)
} else {
    data(geneENSEMBLtrack)
    data(snpBiomarttrack)
    data(ISCAtrack)
    data(strucBiomarttrack)
    data(ClinVarCnvTrack)
    data(clinVarMaintrack)
    data(GWASTrack)
    data(GeneReviewTrack)

    listgviz <- list(genetrack,snptrack,strutrack,clinVariant,
                    clinCNV,gwastrack,geneRtrack)
    comet(config.file=configfile, mydata.file=myinfofile, mydata.type="listfile",
        cormatrix.file=mycorrelation, cormatrix.type="listfile",
        mydata.large.file=myexpressfile, mydata.large.type="listfile",
        tracks.gviz=listgviz,
        verbose=FALSE, print.image=FALSE,disp.pvalueplot=TRUE)
}

TiphaineCMartin/coMET documentation built on April 27, 2022, 6:45 a.m.