smoothSds {bsseq} | R Documentation

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

### Description

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

### Usage

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

### Arguments

`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?

### Details

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.

### Value

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

### Author(s)

Kasper Daniel Hansen khansen@jhsph.edu

### See Also

`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`.

### Examples

        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"))
        }

[Package bsseq version 1.18.0 Index]