Variance stabilization and calibration for microarray data

Wolfgang Huber, with contributions from Anja von
        Heydebreck. Many comments and suggestions by users are
        acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich
        Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth

affydata, hgu95av2cdf, BiocStyle, knitr, dplyr, testthat

The package implements a method for normalising microarray intensities, and works
        for single- and multiple-color arrays. It can also be used
	for data from other technologies, as long as they have similar format. The method uses a
	robust variant of the maximum-likelihood estimator for an additive-multiplicative error
	model and affine calibration. The model incorporates data calibration step (a.k.a.
	normalization), a model for the dependence of the variance on the mean intensity and a
        variance stabilizing data transformation. Differences between
        transformed intensities are analogous to "normalized
        log-ratios". However, in contrast to the latter, their
        variance is independent of the mean, and they are usually more
        sensitive and specific in detecting differential
        transcription.

[1] Variance stabilization applied to microarray data
calibration and to the quantification of differential
expression, Wolfgang Huber, Anja von Heydebreck, Holger
Sueltmann, Annemarie Poustka, Martin Vingron; Bioinformatics
(2002) 18 Suppl1 S96-S104. [2] Parameter estimation for the
calibration and variance stabilization of microarray data,
Wolfgang Huber, Anja von Heydebreck, Holger Sueltmann,
Annemarie Poustka, and Martin Vingron; Statistical Applications
in Genetics and Molecular Biology (2003) Vol. 2 No. 1, Article
3; http://www.bepress.com/sagmb/vol2/iss1/art3.

AllClasses.R AllGenerics.R vsn2.R vsnLogLik.R justvsn.R
methods-vsnInput.R methods-vsn.R methods-vsn2.R
methods-predict.R RGList_to_NChannelSet.R meanSdPlot-methods.R
plotLikelihood.R normalize.AffyBatch.vsn.R sagmbSimulateData.R
zzz.R