BSseq-class {bsseq} | R Documentation

## Class BSseq

### Description

A class for representing whole-genome or capture bisulfite sequencing data.

### Objects from the Class

An object from the class links together several pieces of information. (1) genomic locations stored as a `GRanges` object, a location by samples matrix of M values, a location by samples matrix of Cov (coverage) values and phenodata information. In addition, there are slots for representing smoothed data. This class is an extension of RangedSummarizedExperiment.

### Slots

`trans`:

  Object of class `function`. This function transforms the `coef` slot from the scale the smoothing was done to the 0-1 methylation scale.

`parameters`:

  Object of class `list`. A list of parameters representing for example how the data was smoothed.

### Methods

[

  `signature(x = "BSseq")`: Subsetting by location (using integer indices) or sample (using integers or sample names).

length

  Unlike for `RangedSummarizedExperiment`, `length()` is the number of methylation loci (equal to `length(granges(x))`).

sampleNames,sampleNames<-

  Sample names and its replacement function for the object. This is an alias for `colnames`.

pData,pData<-

  Obtain and replace the `pData` slot of the `phenoData` slot. This is an alias for `colData`.

show

  The show method.

combine

  This function combines two `BSSeq` objects. The genomic locations of the new object is the union of the genomic locations of the individual objects. In addition, the methylation data matrices are placed next to each other (as appropriate wrt. the new genomic locations) and zeros are entered into the matrices as needed.

### Utilities

This class extends RangedSummarizedExperiment and therefore inherits a number of useful `GRanges` methods that operate on the `rowRanges` slot, used for accessing and setting the genomic locations and also do `subsetByOverlaps`.

There are a number of almost methods-like functions for operating on objects of class `BSseq`, including `getBSseq`, `collapseBSseq`, and `orderBSseq`. They are detailed below.

`collapseBSseq(BSseq, columns)`

  is used to collapse an object of class `BSseq`. By collapsing we simply mean that certain columns (samples) are merge together by summing up the methylation evidence and coverage. This is a useful function if you start by reading in a dataset based on say flowcells and you (after QC) want to simply add a number of flowcells into one sample. The argument `columns` specify which samples are to be merged, in the following way: it is a character vector of new sample names, and the names of the column vector indicates which samples in the `BSseq` object are to be collapsed. If `columns` have the same length as the number of rows of `BSseq` (and has no names) it is assumed that the ordering corresponds to the sample ordering in `BSseq`.

`orderBSseq(BSseq, seqOrder = NULL)`

  simply orders an object of class `BSseq` according to (increasing) genomic locations. The `seqOrder` vector is a character vector of `seqnames(BSseq)` describing the order of the chromosomes. This is useful for ordering `chr1` before `chr10`.

`chrSelectBSseq(BSseq, seqnames = NULL, order = FALSE)`

  subsets and optionally reorders an object of class `BSseq`. The `seqnames` vector is a character vector of `seqnames(BSseq)` describing which chromosomes should be retained. If `order` is `TRUE`, the chromosomes are also re-ordered using `orderBSseq`.

`getBSseq(BSseq, type = c("Cov", "M", "gr", "coef", "se.coef", "trans", "parameters"))`

  is a general accessor: is used to obtain a specific slot of an object of class `BSseq`. It is primarily intended for internal use in the package, for users we recommend `granges` to get the genomic locations, `getCoverage` to get the coverage slots and `getMeth` to get the smoothed values (if they exist).

`hasBeenSmoothed(BSseq)`

  This function returns a logical depending on whether or not the `BSseq` object has been smoothed using `BSmooth`.

`combineList(list, BACKEND = NULL)`

  This function function is a faster way of using `combine` on multiple BSseq objects. The input is a list, with each component an object of class BSseq. The (slower) alternative is to use `Reduce(combine, list)`.

  The `BACKEND` argument determines which backend should be used for the 'M' and 'Cov' matrices and, if present, the 'coef' and 'se.coef' matrices (the latter two can only be combined if all objects have the same rowRanges). The default, `BACKEND = NULL`, corresponds to using matrix objects. See `?DelayedArray::setRealizationBackend` for alternative backends.

`strandCollapse(BSseq, shift = TRUE)`

  This function operates on a `BSseq` objects which has stranded loci (i.e. loci where the strand is one of ‘+’ or ‘-’). It will collapse the methylation and coverage information across the two strands, unstranding the loci in the process and potentially re-ordering them.

  The argument `shift` indicates whether the positions for the loci on the reverse strand should be shifted one (i.e. the positions for these loci are the positions of the ‘G’ in the ‘CpG’; this is the case for Bismark output for example).

### Coercion

Package versions 1.5.2 and 1.11.1 introduced a new version of representing ‘BSseq’ objects. You can update old serialized (saved) objects by invoking `x <- updateObject(x)`.

### Assays

This class overrides the default implementation of `assays` to make it faster. Per default, no names are added to the returned data matrices.

Assay names can conveniently be obtained by the function `assayNames(x)`

### Author(s)

Kasper Daniel Hansen khansen@jhsph.edu

### See Also

The package vignette. `BSseq` for the constructor function. `RangedSummarizedExperiment` for the underlying class. `getBSseq`, `getCoverage`, and `getMeth` for accessing the data stored in the object and finally `BSmooth` for smoothing the bisulfite sequence data.

### Examples

    M <- matrix(1:9, 3,3)
    colnames(M) <- c("A1", "A2", "A3")
    BStest <- BSseq(pos = 1:3, chr = c("chr1", "chr2", "chr1"), M = M, Cov = M + 2)
    chrSelectBSseq(BStest, seqnames = "chr1", order = TRUE)
    collapseBSseq(BStest, group = c("A", "A", "B"))

    #-------------------------------------------------------------------------------
    # An example using a HDF5-backed BSseq object
    #
    hdf5_BStest <- realize(BStest, "HDF5Array")
    chrSelectBSseq(hdf5_BStest, seqnames = "chr1", order = TRUE)
    collapseBSseq(
        BSseq = hdf5_BStest,
        group = c("A", "A", "B"),
        BACKEND = "HDF5Array",
        type = "integer")

[Package bsseq version 1.24.4 Index]