BSmooth.tstat: Compute t-statistics based on smoothed whole-genome bisulfite...
In bsseq: Analyze, manage and store bisulfite sequencing data

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

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

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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)

`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?

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.

An object of class BSseqTstat.

Kasper Daniel Hansen khansen@jhsph.edu

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.

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.

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

  #---------------------------------------------------------------------------
  # An example using a HDF5Array-backed BSseq object
  #
  
    library(HDF5Array)
    # See ?SummarizedExperiment::saveHDF5SummarizedExperiment for details
    hdf5_BS.cancer.ex.fit <- saveHDF5SummarizedExperiment(
        x = BS.cancer.ex.fit[keepLoci.ex, ],
        dir = tempfile())
    hdf5_BS.tstat <- BSmooth.tstat(hdf5_BS.cancer.ex.fit,
                                   group1 = c("C1", "C2", "C3"),
                                   group2 = c("N1", "N2", "N3"),
                                   estimate.var = "group2")
  
}