normalizationFactor: Accessor functions for the 'normalizationFactor' slot in a...

In DSS: Dispersion shrinkage for sequencing data

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 <[e-mail]>

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

DSS documentation built on Nov. 8, 2020, 7:44 p.m.