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.
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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
and unlike offsets, which are typically on the scale of the predictors (in this case, log counts).
Normalization factors should include library size normalization. They should have
row-wise geometric mean near 1, as is the case with size factors, such that the mean of normalized
counts is close to the mean of unnormalized counts. See example code below.
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dds <- makeExampleDESeqDataSet(n=100, m=4) normFactors <- matrix(runif(nrow(dds)*ncol(dds),0.5,1.5), ncol=ncol(dds),nrow=nrow(dds), dimnames=list(1:nrow(dds),1:ncol(dds))) # the normalization factors matrix should not have 0's in it # it should have geometric mean near 1 for each row normFactors <- normFactors / exp(rowMeans(log(normFactors))) normalizationFactors(dds) <- normFactors dds <- DESeq(dds)
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