Hello,

I have employed several DMR finding methods and I'm now interested in visualizing the region.

I would like to visualize the individual methylation level of each CpG for every observation in the DMR (y-axis) across the genomic location (x-axis) with a loess line by levels of a factor variable.

This type of plot appears in the original minfi paper (Fig. 5-6A) and also the origianl bump hunting manuscript (Fig. 1).

Are there any functions within minfi to achieve this or other resources? Any help could be greatly appreciated.

Thank you so much,

Andres

Hi,

Following code has been extracted from Daniel Hansen Lab Github course on 450K analysis (https://github.com/hansenlab/tutorial.450k/blob/master/vignettes/methylation450k.Rmd). I hope it helps.

Plotting DMRs

As with DMPs, it's always a good idea to plot DMRs. r Biocpkg("minfi") does not currently include any functionality for doing this directly but will soon[^soon]. In the mean time, we can (with a little bit of a work) make a publication-quality figure using the r Biocpkg("Gviz").

[^soon]: Famous last words ...

NOTE: This figure takes a while to generate because r Biocpkg("Gviz") needs to first download some data.

library(Gviz)
genome <- "hg19"
# NOTE: Using the non-permuted 
dmr <- bh_dmrs$table[1, ]
chrom <- dmr$chr
start <- dmr$start
end <- dmr$end
minbase <- start - 0.25 * (end - start)
maxbase <- end + 0.25 * (end - start)
pal <- c("#E41A1C", "#377EB8")

# Start building the tracks
iTrack <- IdeogramTrack(genome = genome, 
                        chromosome = dmr$chr, 
                        name = "")
gTrack <- GenomeAxisTrack(col = "black", 
                          cex = 1, 
                          name = "", 
                          fontcolor = "black")
# NOTE: This track takes a little while to create
rTrack <- UcscTrack(genome = genome, 
                    chromosome = chrom, 
                    track = "refGene",
                    from = minbase,
                    to = maxbase, 
                    trackType = "GeneRegionTrack",
                    rstarts = "exonStarts", 
                    rends = "exonEnds", 
                    gene = "name",
                    symbol = "name2", 
                    transcript = "name",
                    strand = "strand",
                    fill = "darkblue",
                    stacking = "squish", 
                    name = "RefSeq",
                    showId = TRUE, 
                    geneSymbol = TRUE)
# methylation data track
gr <- granges(GRset.funnorm)
gr$beta <- getBeta(GRset.funnorm)
methTrack <- DataTrack(range = gr,
                       groups = targets$status,
                       genome = genome,
                       chromosome = chrom, 
                       ylim = c(-0.05, 1.05), 
                       col = pal,
                       type = c("a","p"), 
                       name = "DNA Meth.\n(beta value)",
                       background.panel = "white", 
                       legend = TRUE, 
                       cex.title = 0.8,
                       cex.axis = 0.8, 
                       cex.legend = 0.8)
# DMR position data track
dmrTrack <- AnnotationTrack(start = start, 
                            end = end, 
                            genome = genome, 
                            name = "DMR",
                            chromosom = chrom)
# Finally, plot the tracks
tracks <- list(iTrack, gTrack, methTrack, dmrTrack, rTrack)
sizes <- c(2, 2, 5, 2, 3) # set up the relative sizes of the tracks
plotTracks(tracks, 
           from = minbase, 
           to = maxbase, 
           showTitle = TRUE, 
           add53 = TRUE,
           add35 = TRUE, 
           grid = TRUE, 
           lty.grid = 3, 
           sizes = sizes, 
           length(tracks))

You could also try the gtrellis bioconductor package; see example form DMR