getMeth: Obtain methylation estimates for BSseq objects.

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

View source: R/BSseq_utils.R

Description

Obtain methylation estimates for BSseq objects, both smoothed and raw.

Usage

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getMeth(BSseq, regions = NULL, type = c("smooth", "raw"),
  what = c("perBase", "perRegion"), confint = FALSE, alpha = 0.95)

Arguments

BSseq

An object of class BSseq.

regions

An optional data.frame or GenomicRanges object specifying a number of genomic regions.

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 what is equal to perBase.

alpha

alpha value for the confidence interval.

Value

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.

Note

A BSseq object needs to be smoothed by the function BSmooth in order to support type = "smooth".

Author(s)

Kasper Daniel Hansen [email protected].

References

A Agresti and B Coull. Approximate Is Better than "Exact" for Interval Estimation of Binomial Proportions. The American Statistician (1998) 52:119-126.

See Also

BSseq for the BSseq class and BSmooth for smoothing such an object.

Examples

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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 HDF5Array-backed 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"))

Bioconductor-mirror/bsseq documentation built on July 28, 2017, 5:20 a.m.