Description Usage Arguments Value Note Author(s) References See Also Examples
Obtain methylation estimates for BSseq objects, both smoothed and raw.
1 2 3 
BSseq 
An object of class 
regions 
An optional 
type 
This returns either smoothed or raw estimates of the methylation level. 
what 
The type of return object, see details. 
confint 
Should a confidence interval be return for the
methylation estimates (see below). This is only supported if

alpha 
alpha value for the confidence interval. 
withDimnames 
A 
NOTE: The return type of getMeth
varies depending on its
arguments.
If region = NULL
the what
argument is ignored. This is
also the only situation in which confint = TRUE
is supported.
The return value is either a DelayedMatrix
(confint = FALSE
or a list with three DelayedMatrix
components confint = TRUE
(meth
, upper
and
lower
), giving the methylation estimates and (optionally) confidence
intervals.
Confidence intervals for type = "smooth"
is based on standard
errors from the smoothing algorithm (if present). Otherwise it is
based on pointwise confidence intervals for binomial distributions
described in Agresti (see below), specifically the score confidence
interval.
If regions
are specified, what = "perBase"
will make the
function return a list, each element of the list being a
DelayedMatrix corresponding to a genomic region (and each row
of the DelayedMatrix being a loci inside the region). If
what = "perRegion"
the function returns a DelayedMatrix,
with each row corresponding to a region and containing the average
methylation level in that region.
A BSseq
object needs to be smoothed by the function
BSmooth
in order to support type = "smooth"
.
Kasper Daniel Hansen [email protected].
A Agresti and B Coull. Approximate Is Better than "Exact" for Interval Estimation of Binomial Proportions. The American Statistician (1998) 52:119126.
BSseq
for the BSseq
class and
BSmooth
for smoothing such an object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  data(BS.chr22)
head(getMeth(BS.chr22, type = "raw"))
reg < GRanges(seqnames = c("chr22", "chr22"),
ranges = IRanges(start = c(1, 2*10^7), end = c(2*10^7 +1, 4*10^7)))
head(getMeth(BS.chr22, regions = reg, type = "raw", what = "perBase"))
#
# An example using a HDF5Arraybacked BSseq object
#
library(HDF5Array)
# See ?SummarizedExperiment::saveHDF5SummarizedExperiment for details
hdf5_BS.chr22 < saveHDF5SummarizedExperiment(x = BS.chr22,
dir = tempfile())
head(getMeth(hdf5_BS.chr22, type = "raw"))
head(getMeth(hdf5_BS.chr22, regions = reg, type = "raw", what = "perBase"))

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