Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/BSmooth.tstat.R
Compute tstatistics based on smoothed wholegenome bisulfite sequencing data.
1 2 3 
BSseq 
An object of class 
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

verbose 
Should the function be verbose? 
Tstatistics 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
tstatistics are corrected for lowfrequency 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 tstatistics more symmetric.
Additional details in the reference.
An object of class BSseqTstat
.
Kasper Daniel Hansen [email protected]
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/gb20121310r83.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  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 HDF5Arraybacked 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")
}

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