smoothSds: Smooth the standard deviations using a thresholded running...
In bsseq: Analyze, manage and store bisulfite sequencing data

Description Usage Arguments Details Value Author(s) See Also Examples

Smooth the standard deviations using a thresholded running mean based on smoothed whole-genome bisulfite sequencing data.

1 2	smoothSds(BSseqStat, k = 101, qSd = 0.75, mc.cores = 1, maxGap = 10^8, verbose = TRUE)

`BSseqStat`	An object of class `BSseqStat`, typically an object returned by `BSmooth.fstat(...)` and not constructed by the user.
`k`	A positive scalar, see details.
`qSd`	A scalar between 0 and 1, see details.
`mc.cores`	The number of cores used. Note that setting `mc.cores` to a value greater than 1 is not supported on MS Windows, see the help page for `mclapply`.
`maxGap`	A scalar greater than 0, see details.
`verbose`	Should the function be verbose?

The standard deviation estimates are smoothed using a running mean with a width of k and thresholded using qSd which sets the minimum standard deviation to be the qSd-quantile.

An object of class BSseqStat. More speciically, the input BSseqStat object with the computed statistics added to the stats slot (accessible with getStats).

Kasper Daniel Hansen khansen@jhsph.edu

BSmooth.fstat for the function to create the appropriate BSseqStat input object. BSseqStat also describes the return class. This function is likely to be followed by the use of computeStat.

  
    if(require(bsseqData)) {
        # library(limma) required for makeContrasts()
        library(limma)
        data(keepLoci.ex)
        data(BS.cancer.ex.fit)
        BS.cancer.ex.fit <- updateObject(BS.cancer.ex.fit)
        ## Remember to subset the BSseq object, see vignette for explanation
        ## TODO: Kind of a forced example
        design <- model.matrix(~0 + BS.cancer.ex.fit$Type)
        colnames(design) <- gsub("BS\\.cancer\\.ex\\.fit\\$Type", "",
                                 colnames(design))
        contrasts <- makeContrasts(
            cancer_vs_normal = cancer - normal,
            levels = design
        )
        BS.stat <- BSmooth.fstat(BS.cancer.ex.fit[keepLoci.ex,],
                                 design,
                                 contrasts)
        BS.stat <- smoothSds(BS.stat)
        ## Comparing the raw standard deviations to the smoothed standard
        ## deviations
        summary(getStats(BS.stat, what = "rawSds"))
        summary(getStats(BS.stat, what = "smoothSds"))
    }