vsn: Variance stabilization and calibration for microarray data
Version 3.44.0

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.

Package details

AuthorWolfgang 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
Bioconductor views Microarray OneChannel Preprocessing TwoChannel
MaintainerWolfgang Huber <wolfgang.huber@embl.de>
URL http://www.r-project.org http://www.ebi.ac.uk/huber
Package repositoryView on Bioconductor
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vsn documentation built on May 31, 2017, 11:06 a.m.