bsseq (version 1.8.2)

BSmooth.tstat: Compute t-statistics based on smoothed whole-genome bisulfite sequencing data.

Description

Compute t-statistics based on smoothed whole-genome bisulfite sequencing data.

Usage

BSmooth.tstat(BSseq, group1, group2, estimate.var = c("same", "paired", "group2"), local.correct = TRUE, maxGap = NULL, qSd = 0.75, k = 101, mc.cores = 1, verbose = TRUE)

Arguments

BSseq

An object of class BSseq.

group1

A vector of sample names or indexes for the ‘treatment’ group.

group2

A vector of sample names or indexes for the ‘control’ group.

estimate.var

How is the variance estimated, see details.

local.correct

A logical; should local correction be used, see details.

maxGap

A scalar greater than 0, see details.

qSd

A scalar between 0 and 1, see details.

k

A positive scalar, 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.

verbose

Should the function be verbose?

Value

An object of class BSseqTstat.

Details

T-statistics are formed as the difference in means between group 1 and group 2 divided by an estimate of the standard deviation, assuming that the variance in the two groups are the same (same), that we have paired samples (paired) or only estimate the variance based on group 2 (group2). The standard deviation estimates are then 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). Optionally, the t-statistics are corrected for low-frequency patterns.

It is sometimes useful to use local.correct even if no large scale changes in methylation have been found; it makes the marginal distribution of the t-statistics more symmetric.

Additional details in the reference.

References

KD Hansen, B Langmead, and RA Irizarry. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biology (2012) 13:R83. doi:10.1186/gb-2012-13-10-r83.

See Also

BSmooth for the input object and BSseq for its class. BSseqTstat describes the return class. This function is likely to be followed by the use of dmrFinder. And finally, see the package vignette(s) for more information on how to use it.

Examples

if(require(bsseqData)) {
  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
  BS.tstat <- BSmooth.tstat(BS.cancer.ex.fit[keepLoci.ex,],
                            group1 = c("C1", "C2", "C3"),
                            group2 = c("N1", "N2", "N3"),
                            estimate.var = "group2")
  BS.tstat
  ## This object is also stored as BS.cancer.ex.tstat in the
  ## bsseqData package
}