SeqCountSet-class {DSS} | R Documentation

## Class `"SeqCountSet"` - container for count data from sequencing experiment

### Description

This class is the main container for storing *RNA-seq* data. It is directly inherited fro 'ExpressionSet' class, with two more fields 'normalizationFactor' for normalization factors and 'dispersion' for gene-wise dispersions.

The class for BS-seq data is *BSseq*, which is imported from bsseq package.

### Slots

`normalizationFactor`:

  Normalization factor for counts.

`dispersion`:

  Gene-wise dispersions.

`experimentData`:

  See 'ExpressionSet'.

`assayData`:

  See 'ExpressionSet'.

`phenoData`:

  See 'ExpressionSet'.

`featureData`:

  See 'ExpressionSet'.

`annotation`:

  See 'ExpressionSet'.

`protocolData`:

  See 'ExpressionSet'.

### Extends

Class `"ExpressionSet"`, directly. Class `"eSet"`, by class "ExpressionSet", distance 2. Class `"VersionedBiobase"`, by class "ExpressionSet", distance 3. Class `"Versioned"`, by class "ExpressionSet", distance 4.

### Constructor

  `newSeqCountSet(counts, designs,normalizationFactor,featureData)`: Creates a 'SeqCountSet' object.

  `counts`

    A matrix of integers with rows corresponding to genes and columns for samples.

  `designs`

    A vector or data frame representing experimental design. The length of the vector or number of rows of the data frame must match the number of columns of input counts. This field can be accessed using 'pData' function.

  `normalizationFactor`

    A vector or matrix of normalization factors for the counts.

  `featureData`

    Additional information for genes as an 'AnnotatedDataFrame' object. This field can be access by using 'featureData' function.

### Methods

dispersion, dispersion<- :

  Access and set gene-wise dispersions.

normalizationFactor, normalizationFactor<- :

  Access and set normalization factors.

### Note

This is similar to 'CountDataSet' in DESeq or 'DGEList' in edgeR.

### Author(s)

Hao Wu <hao.wu@emory.edu>

### See Also

dispersion, normalizationFactor

### Examples

    ## simulate data from RNA-seq
    counts=matrix(rpois(600, 10), ncol=6)
    designs=c(0,0,0,1,1,1)
    seqData=newSeqCountSet(counts, designs)
    seqData
    pData(seqData)
    head(exprs(seqData))

    ## multiple factor designs
    design=data.frame(gender=c(rep("M",4), rep("F",4)), strain=rep(c("WT", "Mutant"),4))
    X=model.matrix(~gender+strain, data=design)
    counts=matrix(rpois(800, 10), ncol=8)
    seqData=newSeqCountSet(counts, as.data.frame(X))
    seqData
    pData(seqData)

[Package DSS version 2.34.0 Index]