mergeDMRsIteratively {DMRcaller} | R Documentation

## Merge DMRs iteratively

### Description

This function takes a list of DMRs and attempts to merge DMRs while keeping the new DMRs statistically significant.

### Usage

    mergeDMRsIteratively(DMRs, minGap, respectSigns = TRUE, methylationData1,
      methylationData2, context = "CG", minProportionDifference = 0.4,
      minReadsPerCytosine = 4, pValueThreshold = 0.01, test = "fisher",
      alternative = "two.sided", cores = 1)

### Arguments

`DMRs` | the list of DMRs as a `GRanges` object; e.g. see `computeDMRs`
`minGap` | DMRs separated by a gap of at least `minGap` are not merged.
`respectSigns` | logical value indicating whether to respect the sign when joining DMRs.
`methylationData1` | the methylation data in condition 1 (see `methylationDataList`).
`methylationData2` | the methylation data in condition 2 (see `methylationDataList`).
`context` | the context in which the DMRs are computed (`"CG"`, `"CHG"` or `"CHH"`).
`minProportionDifference` | two adjacent DMRs are merged only if the difference in methylation proportion of the new DMR is higher than `minProportionDifference`.
`minReadsPerCytosine` | two adjacent DMRs are merged only if the number of reads per cytosine of the new DMR is higher than `minReadsPerCytosine`.
`pValueThreshold` | two adjacent DMRs are merged only if the p-value of the new DMR (see `test` below) is lower than `pValueThreshold`. Note that we adjust the p-values using the Benjamini and Hochberg's method to control the false discovery rate.
`test` | the statistical test used to call DMRs (`"fisher"` for Fisher's exact test or `"score"` for Score test).
`alternative` | indicates the alternative hypothesis and must be one of `"two.sided"`, `"greater"` or `"less"`.
`cores` | the number of cores used to compute the DMRs.

### Value

the reduced list of DMRs as a `GRanges` object; e.g. see `computeDMRs`

### Author(s)

Nicolae Radu Zabet

### See Also

`filterDMRs`, `computeDMRs`, `analyseReadsInsideRegionsForCondition` and `DMRsNoiseFilterCG`

### Examples

    # load the methylation data
    data(methylationDataList)

    #load the DMRs in CG context they were computed with minGap = 200
    data(DMRsNoiseFilterCG)

    #merge the DMRs
    DMRsNoiseFilterCGLarger <- mergeDMRsIteratively(DMRsNoiseFilterCG[1:100],
                               minGap = 500, respectSigns = TRUE,
                               methylationDataList[["WT"]],
                               methylationDataList[["met1-3"]],
                               context = "CG", minProportionDifference=0.4,
                               minReadsPerCytosine = 1, pValueThreshold=0.01,
                               test="score",alternative = "two.sided")

    ## Not run:
    #set genomic coordinates where to compute DMRs
    regions <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(1,1E5))

    # compute DMRs and remove gaps smaller than 200 bp
    DMRsNoiseFilterCG200 <- computeDMRs(methylationDataList[["WT"]],
                           methylationDataList[["met1-3"]], regions = regions,
                           context = "CG", method = "noise_filter",
                           windowSize = 100, kernelFunction = "triangular",
                           test = "score", pValueThreshold = 0.01,
                           minCytosinesCount = 1, minProportionDifference = 0.4,
                           minGap = 200, minSize = 0, minReadsPerCytosine = 1,
                           cores = 1)

    DMRsNoiseFilterCG0 <- computeDMRs(methylationDataList[["WT"]],
                           methylationDataList[["met1-3"]], regions = regions,
                           context = "CG", method = "noise_filter",
                           windowSize = 100, kernelFunction = "triangular",
                           test = "score", pValueThreshold = 0.01,
                           minCytosinesCount = 1, minProportionDifference = 0.4,
                           minGap = 0, minSize = 0, minReadsPerCytosine = 1,
                           cores = 1)
    DMRsNoiseFilterCG0Merged200 <- mergeDMRsIteratively(DMRsNoiseFilterCG0,
                                 minGap = 200, respectSigns = TRUE,
                                 methylationDataList[["WT"]],
                                 methylationDataList[["met1-3"]],
                                 context = "CG", minProportionDifference=0.4,
                                 minReadsPerCytosine = 1, pValueThreshold=0.01,
                                 test="score",alternative = "two.sided")

    #check that all newley computed DMRs are identical
    print(all(DMRsNoiseFilterCG200 == DMRsNoiseFilterCG0Merged200))

    ## End(Not run)

[Package DMRcaller version 1.19.0 Index]