metQTL: Creates a track from a file for metQTL data

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

Description

Creates a track from a BED file for metQTL data using the Gviz bioconductor package. A complete list of features and their associated colours can be found in the user guide.

Usage

metQTL(gen, chr, start, end, bedFilePath, featureDisplay, showId=FALSE,
type_stacking="squish",just_group="above", title="metQTL")

Arguments

gen	the name of the genome. Default value=hg19
chr	The chromosome of interest
start	The starting position in the region of interest (the smallest value)
end	The end position in the region of interest (the largest value)
bedFilePath	The file path to the .BED file containing the data to be visualised
featureDisplay	A vector of metQTL features to be displayed, such as SNP. Spelling and capitalisation of features must be identical to those in the user guide. There are three possibilities. First, the visualisation of only one feature (e.g. featureDisplay <- "CpG"), only the name of the specific feature is required. Second, visualisation of a set of features, for this a vector of features is required (e.g. featureDisplay <- c("SNP","CpG")). Finally, visualison all features in the genomic region, achived by using the word "all" (e.g. featureDisplay <- "all"), "all" is set by default. You can find the complete list of features and their associated colours in the user guide.
showId	Allows the visualization of the Id of metQTL group.
type_stacking	Sets the type of stacking used by Gviz for plots. By default this is set to 'squish'. For more information see Gviz user guide.
just_group	position. say where we write the name of group (choice in c("above","righ","left"))
title	The name of the annotation track

Value

An AnnotationTrack object of Gviz

Author(s)

Tiphaine Martin

Tom Hardiman

References

http://bioconductor.org/packages/release/bioc/html/Gviz.html

Got to ENSEMBLregulation binding motif biomart

Examples

library("Gviz")

gen <- 'hg19'
chr <- "chr15"
start <- 74889136
end <-   75018200
featureDisplay <- "trans_local_metQTL"
type_stacking <- "squish"

extdata <- system.file("extdata", package="coMET",mustWork=TRUE)
mqtlbedFilePath <- file.path(extdata, "metQTL.bed")

if(interactive()){
  metQTLTrackSingle <- metQTL(gen,chr,start, end,mqtlbedFilePath,
  featureDisplay = featureDisplay )
  plotTracks(metQTLTrackSingle, from = start, to = end)
} else {
  data(metQTLTrackSingle)
  plotTracks(metQTLTrackSingle, from = start, to = end)
}

###

library("Gviz")

gen <- 'hg19'
chr <- "chr15"
start <- 74889136
end <-   75018200

featureDisplay <- c("trans_local_metQTL","CpG")

extdata <- system.file("extdata", package="coMET",mustWork=TRUE)
bedFilePath <- file.path(extdata, "metQTL.bed")

if(interactive()){
  metQTLTrackMultiple <- metQTL(gen,chr,start, end, bedFilePath,
  featureDisplay = featureDisplay )
  plotTracks(metQTLTrackMultiple, from = start, to = end)
} else {
  data(metQTLTrackMultiple)
  plotTracks(metQTLTrackMultiple, from = start, to = end)
}

#####

library("Gviz")

gen <- 'hg19'
chr <- "chr15"
start <- 74889136
end <-   75018200

featureDisplay <- "all"

extdata <- system.file("extdata", package="coMET",mustWork=TRUE)
bedFilePath <- file.path(extdata, "metQTL.bed")

if(interactive()){
  metQTLTrackAll <- metQTL(gen,chr,start, end, bedFilePath,
  featureDisplay = featureDisplay )
  plotTracks(metQTLTrackAll, from = start, to = end)
} else {
  data(metQTLTrackAll)
  plotTracks(metQTLTrackAll, from = start, to = end)
}

coMET documentation built on Nov. 8, 2020, 5 p.m.