normalizationFactors {DESeq2} | R Documentation |
Gene-specific normalization factors for each sample can be
provided as a matrix, which will preempt
sizeFactors
. In some experiments, counts for
each sample have varying dependence on covariates, e.g. on
GC-content for sequencing data run on different days, and
in this case it makes sense to provide gene-specific
factors for each sample rather than a single size factor.
normalizationFactors(object) normalizationFactors(object) <- value ## S4 method for signature 'DESeqDataSet' normalizationFactors(object) ## S4 replacement method for signature 'DESeqDataSet,matrix' normalizationFactors(object)<-value ## S4 method for signature 'DESeqDataSet' normalizationFactors(object)
object |
a |
value |
the matrix of normalization factors |
Normalization factors alter the model of
DESeq
in the following way, for counts
K_ij and normalization factors
NF_ij for gene i and sample j:
K_ij ~ NB(mu_ij, alpha_i)
mu_ij = NF_ij * q_ij
Normalization factors are on the scale of the counts
(similar to sizeFactors
) and unlike offsets,
which are typically on the scale of the predictors (in this
case, log counts). Normalization factors should include
size factor normalization and should have a mean around 1,
as is the case with size factors.
dds <- makeExampleDESeqDataSet() normFactors <- matrix(runif(nrow(dds)*ncol(dds),0.5,1.5), ncol=ncol(dds),nrow=nrow(dds)) normalizationFactors(dds) <- normFactors dds <- estimateDispersions(dds) dds <- nbinomWaldTest(dds)