normalizeVSN: Variance Stabilizing Normalization (vsn)

In hdeberg/limma: Linear Models for Microarray Data

Description

Apply variance stabilizing normalization (vsn) to limma data objects.

Usage

normalizeVSN(x, ...)

Arguments

x	a numeric matrix, EListRaw or RGList object.
...	other arguments are passed to vsn

Details

This is an interface to the vsnMatrix function from the vsn package. The input x should contain raw intensities. If x contains background and well as foreground intensities, these will be subtracted from the foreground intensities before vsnMatrix is called.

Note that the vsn algorithm performs background correction and normalization simultaneously. If the data are from two-color microarrays, then the red and green intensities are treated as if they were single channel data, i.e., red and green channels from the same array are treated as unpaired. This algorithm is therefore separate from the backgroundCorrection, normalizeWithinArrays, then normalizeBetweenArrays paradigm used elsewhere in the limma package.

Value

The class of the output depends on the input. If x is a matrix, then the result is a matrix of the same size. If x is an EListRaw object, then an EList object with expression values on the log2 scale is produced. For x is an RGList, then an MAList object with M and A-values on the log2 scale is produced.

Author(s)

Gordon Smyth

References

Huber, W, von Heydebreck, A, Sueltmann, H, Poustka, A, Vingron, M (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18 Supplement 1, S96-S104.

See Also

An overview of LIMMA functions for normalization is given in 05.Normalization.

See also vsnMatrix in the vsn package.

Examples

ngenes <- 100
narrays <- 4
x <- matrix(rnorm(ngenes*narrays),100,4)
y <- normalizeVSN(x)

hdeberg/limma documentation built on Dec. 20, 2021, 3:43 p.m.