normalizationFactor {DSS} | R Documentation

## Accessor functions for the 'normalizationFactor' slot in a SeqCountData object.

### Description

The normalization factors are used to adjust for technical or biological biases in the sequencing experiments. The factors can either be (1) a vector with length equals to the number of columns of the count data; or (2) a matrix with the same dimension of the count data.

### Usage

    ## S4 method for signature 'SeqCountSet'
    normalizationFactor(object)
    ## S4 replacement method for signature 'SeqCountSet,numeric'
    normalizationFactor(object) <- value
    ## S4 replacement method for signature 'SeqCountSet,matrix'
    normalizationFactor(object) <- value

### Arguments

`object` | A SeqCountData object.
`value` | A numeric vector or matrix. If it is a vector it must have length equals to the number of columns of the count data. For matrix it must have the same dimension of the count data.

### Details

The vector normalization factors are used mostly to correct for sequencing depth from different datasets. The matrix factor applies a different normalizing constant for each gene at each sample to adjust for a broader range of artifacts such as GC content.

### Author(s)

Hao Wu <hao.wu@emory.edu>

### See Also

dispersion

### Examples

    data(seqData)
    ## obtain nomalization factor
    seqData=estNormFactors(seqData, "quantile")
    normalizationFactor(seqData)

    ## assign as vector
    normalizationFactor(seqData)=rep(1, ncol(exprs(seqData))) ## getan error here

    ## or assign as a matrix
    f=matrix(1, nrow=nrow(exprs(seqData)), ncol=ncol(exprs(seqData)))
    normalizationFactor(seqData)=f

[Package DSS version 2.36.0 Index]