vst: Quickly estimate dispersion trend and apply a variance...

In DESeq2: Differential gene expression analysis based on the negative binomial distribution

Description

This is a wrapper for the varianceStabilizingTransformation (VST) that provides much faster estimation of the dispersion trend used to determine the formula for the VST. The speed-up is accomplished by subsetting to a smaller number of genes in order to estimate this dispersion trend. The subset of genes is chosen deterministically, to span the range of genes' mean normalized count. This wrapper for the VST is not blind to the experimental design: the sample covariate information is used to estimate the global trend of genes' dispersion values over the genes' mean normalized count. It can be made strictly blind to experimental design by first assigning a design of ~1 before running this function, or by avoiding subsetting and using varianceStabilizingTransformation.

Usage

vst(object, blind = TRUE, nsub = 1000, fitType = "parametric")

Arguments

object	a DESeqDataSet or a matrix of counts
blind	logical, whether to blind the transformation to the experimental design (see varianceStabilizingTransformation)
nsub	the number of genes to subset to (default 1000)
fitType	for estimation of dispersions: this parameter is passed on to estimateDispersions (options described there)

Value

a DESeqTranform object or a matrix of transformed, normalized counts

Examples

dds <- makeExampleDESeqDataSet(n=2000, m=20)
vsd <- vst(dds)

DESeq2 documentation built on Feb. 22, 2021, 10 a.m.