DMRcaller {DMRcaller} | R Documentation

## 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.

`computeMethylationDataSpatialCorrelation`

  Computes the correlation between methylation levels as a function of the distances between the Cytosines.

`plotMethylationDataSpatialCorrelation`

  Plots the correlation of methylation levels for Cytosines located at a certain distance apart.

`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.

`joinReplicates`

  Merges two GRanges objects with single reads columns. It is necessary to start the analysis of DMRs with biological replicates.

`computeDMRsReplicates`

  Computes the differentially methylated regions between two conditions with multiple biological replicates.

### Author(s)

Nicolae Radu Zabet n.r.zabet@gen.cam.ac.uk, Jonathan Michael Foonlan Tsang jmft2@cam.ac.uk Alessandro Pio Greco apgrec@essex.ac.uk

Maintainer: Nicolae Radu Zabet n.r.zabet@gen.cam.ac.uk

### 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)

    # plot the correlation of methylation levels as a function of distance
    plotMethylationDataSpatialCorrelation(methylationDataList[["WT"]],
                               distances = c(1,5,10,15), regions = NULL,
                               conditionsNames = c("WT","met1-3"),
                               context = c("CG"),
                               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")
    # loading synthetic data
    data("syntheticDataReplicates")

    # creating condition vector
    condition <- c("a", "a", "b", "b")

    # computing DMRs using the neighbourhood method
    DMRsReplicatesNeighbourhood <- computeDMRsReplicates(methylationData = methylationData,
                                                         condition = condition,
                                                         regions = NULL,
                                                         context = "CHH",
                                                         method = "neighbourhood",
                                                         test = "betareg",
                                                         pseudocountM = 1,
                                                         pseudocountN = 2,
                                                         pValueThreshold = 0.01,
                                                         minCytosinesCount = 4,
                                                         minProportionDifference = 0.4,
                                                         minGap = 200,
                                                         minSize = 50,
                                                         minReadsPerCytosine = 4,
                                                         cores = 1)

    ## End(Not run)

[Package DMRcaller version 1.19.0 Index]