inmoose.deseq2.DESeqDataSet.DESeqDataSet.normalizationFactors

property DESeqDataSet.normalizationFactors

accessor for the normalization factors

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.

Notes

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 \(j\):

\[\begin{split}K_{ij} \sim \mathcal{NB}(\mu_{ij}, \\alpha_i) \mu_{ij} = NF_{ij} q_{ij}\end{split}\]

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 library size normalization. They should have gene-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.