binning: Methimpute binning functions

In ataudt/methimpute: Imputation-guided re-construction of complete methylomes from WGBS data

Description

This page provides an overview of all methimpute binning functions.

Usage

binCounts(data, binsize)

binPositions(data, binsize)

binMethylome(data, binsize, contexts = "total", columns.average = NULL)

Arguments

data	A GRanges-class object with metadata columns 'context' and 'counts' (which is a matrix with columns 'methylated' and 'total').
binsize	The window size used for binning.
contexts	A character vector with contexts for which the binning will be done.
columns.average	A character vector with names of columns in data that should be averaged in bins.

Value

A GRanges-class object for binCounts and binPostions. A list() of GRanges-class objects for binMethylome.

Functions

• binCounts: Get the aggregated number of counts in each bin (no context).

• binPositions: Get the number of cytosines in each bin (total and per context).

• binMethylome: Get number of cytosines and aggregated counts for the specified contexts.

Examples

## Get some toy data
file <- system.file("data","arabidopsis_toydata.RData",
                    package="methimpute")
data <- get(load(file))
print(data)
## Bin the data in various ways
binCounts(data, binsize=1000)
binPositions(data, binsize=1000)
binMethylome(data, binsize=1000, contexts=c("total", "CG"),
            columns.average=NULL)

ataudt/methimpute documentation built on May 10, 2019, 2:07 p.m.