filterDMRs: Filter DMRs
In DMRcaller: Differentially Methylated Regions caller

Description Usage Arguments Value Author(s) See Also Examples

This function verifies whether a set of pottential DMRs (e.g. genes, transposons, CpG islands) are differentially methylated or not.

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filterDMRs(methylationData1, methylationData2, potentialDMRs, context = "CG",
  test = "fisher", pValueThreshold = 0.01, minCytosinesCount = 4,
  minProportionDifference = 0.4, minReadsPerCytosine = 3, cores = 1)

`methylationData1`	the methylation data in condition 1 (see `methylationDataList`).
`methylationData2`	the methylation data in condition 2 (see `methylationDataList`).
`potentialDMRs`	a `GRanges` object with potential DMRs where to compute the DMRs. This can be a a list of gene and/or transposable elements coordinates.
`context`	the context in which the DMRs are computed (`"CG"`, `"CHG"` or `"CHH"`).
`test`	the statistical test used to call DMRs (`"fisher"` for Fisher's exact test or `"score"` for Score test).
`pValueThreshold`	DMRs with p-values (when performing the statistical test; see `test`) higher or equal than `pValueThreshold` are discarded. Note that we adjust the p-values using the Benjamini and Hochberg's method to control the false discovery rate.
`minCytosinesCount`	DMRs with less cytosines in the specified context than `minCytosinesCount` will be discarded.
`minProportionDifference`	DMRs where the difference in methylation proportion between the two conditions is lower than `minProportionDifference` are discarded.
`minReadsPerCytosine`	DMRs with the average number of reads lower than `minReadsPerCytosine` are discarded.
`cores`	the number of cores used to compute the DMRs.

a GRanges object with 11 metadata columns that contain the DMRs; see computeDMRs.

Nicolae Radu Zabet

DMRsNoiseFilterCG, computeDMRs, analyseReadsInsideRegionsForCondition and mergeDMRsIteratively

# load the methylation data
data(methylationDataList)
# load the gene annotation data
data(GEs)

#select the genes
genes <- GEs[which(GEs$type == "gene")]

# the regions where to compute the DMRs
regions <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(1,1E5))
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)

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: ‘S4Vectors’

The following object is masked from ‘package:base’:

    expand.grid

Loading required package: IRanges
Loading required package: GenomeInfoDb
Parameters checking ...
Extract methylation in the corresponding context 
Computing DMRs at  Chr3:101..999999 
Selecting data...
Identifying DMRs...