DMRcaller (version 1.4.2)

DMRcaller: Call Differentially Methylated Regions (DMRs) between two samples

Description

Uses bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects.

Details

The most important functions in the DMRcaller are:

readBismark

reads the Bismark CX report files in a GRanges object.

readBismarkPool

Reads multiple CX report files and pools them together.

saveBismark

saves the methylation data stored in a GRanges object into a Bismark CX report file.

poolMethylationDatasets

pools together multiple methylation datasets.

poolTwoMethylationDatasets

pools together two methylation datasets.

computeMethylationDataCoverage

Computes the coverage for the bisulfite sequencing data.

plotMethylationDataCoverage

Plots the coverage for the bisulfite sequencing data.

computeMethylationProfile

Computes the low resolution profiles for the bisulfite sequencing data at certain locations.

plotMethylationProfile

Plots the low resolution profiles for the bisulfite sequencing data at certain locations.

plotMethylationProfileFromData

Plots the low resolution profiles for the loaded bisulfite sequencing data.

computeDMRs

Computes the differentially methylated regions between two conditions.

filterDMRs

Filters a list of (potential) differentially methylated regions.

mergeDMRsIteratively

Merge DMRs iteratively.

analyseReadsInsideRegionsForCondition

Analyse reads inside regions for condition.

plotLocalMethylationProfile

Plots the methylation profile at one locus for the bisulfite sequencing data.

computeOverlapProfile

Computes the distribution of a set of subregions on a large region.

plotOverlapProfile

Plots the distribution of a set of subregions on a large region.

getWholeChromosomes

Computes the GRanges objects with each chromosome as an element from the methylationData.

See Also

See vignette("rd", package = "DMRcaller") for an overview of the package.

Examples

## Not run: 
# # load the methylation data
# data(methylationDataList)
# 
# #plot the low resolution profile at 5 Kb resolution
# par(mar=c(4, 4, 3, 1)+0.1)
# plotMethylationProfileFromData(methylationDataList[["WT"]],
#                                methylationDataList[["met1-3"]],
#                                conditionsNames=c("WT", "met1-3"),
#                                windowSize = 5000, autoscale = TRUE,
#                                context = c("CG", "CHG", "CHH"),
#                                labels = LETTERS)
# 
# # compute low resolution profile in 10 Kb windows in CG context
# lowResProfileWTCG <- computeMethylationProfile(methylationDataList[["WT"]],
#                      region, windowSize = 10000, context = "CG")
# 
# lowResProfileMet13CG <- computeMethylationProfile(
#                      methylationDataList[["met1-3"]], region,
#                      windowSize = 10000, context = "CG")
# 
# lowResProfileCG <- GRangesList("WT" = lowResProfileWTCG,
#                    "met1-3" = lowResProfileMet13CG)
# 
# # compute low resolution profile in 10 Kb windows in CHG context
# lowResProfileWTCHG <- computeMethylationProfile(methylationDataList[["WT"]],
#                      region, windowSize = 10000, context = "CHG")
# 
# lowResProfileMet13CHG <- computeMethylationProfile(
#                      methylationDataList[["met1-3"]], region,
#                      windowSize = 10000, context = "CHG")
# 
# lowResProfileCHG <- GRangesList("WT" = lowResProfileWTCHG,
#                    "met1-3" = lowResProfileMet13CHG)
# 
# # plot the low resolution profile
# par(mar=c(4, 4, 3, 1)+0.1)
# par(mfrow=c(2,1))
# plotMethylationProfile(lowResProfileCG, autoscale = FALSE,
#                        labels = LETTERS[1],
#                        title="CG methylation on Chromosome 3",
#                        col=c("#D55E00","#E69F00"),  pch = c(1,0),
#                        lty = c(4,1))
# plotMethylationProfile(lowResProfileCHG, autoscale = FALSE,
#                        labels = LETTERS[2],
#                        title="CHG methylation on Chromosome 3",
#                        col=c("#0072B2", "#56B4E9"),  pch = c(16,2),
#                        lty = c(3,2))
# 
# # plot the coverage in all three contexts
# plotMethylationDataCoverage(methylationDataList[["WT"]],
#                            methylationDataList[["met1-3"]],
#                            breaks = 1:15, regions = NULL,
#                            conditionsNames = c("WT","met1-3"),
#                            context = c("CG", "CHG", "CHH"),
#                            proportion = TRUE, labels = LETTERS, col = NULL,
#                            pch = c(1,0,16,2,15,17), lty = c(4,1,3,2,6,5),
#                            contextPerRow = FALSE)
# 
# # the regions where to compute the DMRs
# regions <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(1,1E6))
# 
# # compute the DMRs in CG context with noise_filter method
# DMRsNoiseFilterCG <- computeDMRs(methylationDataList[["WT"]],
#                      methylationDataList[["met1-3"]], regions = regions,
#                      context = "CG", method = "noise_filter",
#                      windowSize = 100, kernelFunction = "triangular",
#                      test = "score", pValueThreshold = 0.01,
#                      minCytosinesCount = 4, minProportionDifference = 0.4,
#                      minGap = 200, minSize = 50, minReadsPerCytosine = 4,
#                      cores = 1)
# 
# # compute the DMRs in CG context with neighbourhood method
# DMRsNeighbourhoodCG <- computeDMRs(methylationDataList[["WT"]],
#                        methylationDataList[["met1-3"]], regions = regions,
#                        context = "CG", method = "neighbourhood",
#                        test = "score", pValueThreshold = 0.01,
#                        minCytosinesCount = 4, minProportionDifference = 0.4,
#                        minGap = 200, minSize = 50, minReadsPerCytosine = 4,
#                        cores = 1)
# 
# # compute the DMRs in CG context with bins method
# DMRsBinsCG <- computeDMRs(methylationDataList[["WT"]],
#                methylationDataList[["met1-3"]], regions = regions,
#                context = "CG", method = "bins", binSize = 100,
#                test = "score", pValueThreshold = 0.01, minCytosinesCount = 4,
#                minProportionDifference = 0.4, minGap = 200, minSize = 50,
#                minReadsPerCytosine = 4, cores = 1)
# 
# # load the gene annotation data
# data(GEs)
# 
# #select the genes
# genes <- GEs[which(GEs$type == "gene")]
# 
# # the regions where to compute the DMRs
# genes <- genes[overlapsAny(genes, regions)]
# 
# # filter genes that are differntially methylated in the two conditions
# DMRsGenesCG <- filterDMRs(methylationDataList[["WT"]],
#                methylationDataList[["met1-3"]], potentialDMRs = genes,
#                context = "CG", test = "score", pValueThreshold = 0.01,
#                minCytosinesCount = 4, minProportionDifference = 0.4,
#                minReadsPerCytosine = 3, cores = 1)
# 
# #merge the DMRs
# DMRsNoiseFilterCGLarger <- mergeDMRsIteratively(DMRsNoiseFilterCG,
#                            minGap = 500, respectSigns = TRUE,
#                            methylationDataList[["WT"]],
#                            methylationDataList[["met1-3"]],
#                            context = "CG", minProportionDifference=0.4,
#                            minReadsPerCytosine = 1, pValueThreshold=0.01,
#                            test="score",alternative = "two.sided")
# 
# #select the genes
# genes <- GEs[which(GEs$type == "gene")]
# 
# # the coordinates of the area to be plotted
# chr3Reg <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(510000,530000))
# 
# # load the DMRs in CG context
# data(DMRsNoiseFilterCG)
# 
# DMRsCGlist <- list("noise filter"=DMRsNoiseFilterCG,
#                    "neighbourhood"=DMRsNeighbourhoodCG,
#                    "bins"=DMRsBinsCG,
#                    "genes"=DMRsGenesCG)
# 
# 
# # plot the CG methylation
# par(mar=c(4, 4, 3, 1)+0.1)
# par(mfrow=c(1,1))
# plotLocalMethylationProfile(methylationDataList[["WT"]],
#                            methylationDataList[["met1-3"]], chr3Reg,
#                            DMRsCGlist, c("WT", "met1-3"), GEs,
#                            windowSize=100, main="CG methylation")
# 
# 
# hotspotsHypo <- computeOverlapProfile(
#                DMRsNoiseFilterCG[(DMRsNoiseFilterCG$regionType == "loss")],
#                region, windowSize=2000, binary=TRUE, cores=1)
# 
# hotspotsHyper <- computeOverlapProfile(
#                DMRsNoiseFilterCG[(DMRsNoiseFilterCG$regionType == "gain")],
#                region, windowSize=2000, binary=TRUE, cores=1)
# 
# plotOverlapProfile(GRangesList("Chr3"=hotspotsHypo),
#                    GRangesList("Chr3"=hotspotsHyper),
#                    names=c("loss", "gain"), title="CG methylation")
# ## End(Not run)