mergeDMRsIteratively: Merge DMRs iteratively

In nrzabet/DMRcaller: Differentially Methylated Regions caller

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)

nrzabet/DMRcaller documentation built on May 23, 2019, 2:50 p.m.