BSmooth.tstat {bsseq} | R Documentation

## 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?

### 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.

### Value

An object of class `BSseqTstat`.

### Author(s)

Kasper Daniel Hansen khansen@jhsph.edu

### 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

      #---------------------------------------------------------------------------
      # 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")

    }

[Package bsseq version 1.18.0 Index]