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.
methods, affy, limma, lattice, ggplot2, hexbin
The package implements a method for normalising microarray intensities,
        both between colours within array, and between arrays. The method uses a
	robust variant of the maximum-likelihood estimator for the stochastic model of
        microarray data described in the references (see vignette).
	The model incorporates data calibration (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 vsnPlotPar.R vsn.R vsnh.R getIntensityMatrix.R
normalize.AffyBatch.vsn.R sagmbSimulateData.R zzz.R