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Variance stabilization and calibration for microarray data

Bioconductor version: Release (3.1)

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.

Author: 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.

Maintainer: Wolfgang Huber <huber at>

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PDF R Script Introduction to vsn
PDF R Script Likelihood Calculations for vsn
PDF R Script Verifying and assessing the performance with simulated data
PDF   Reference Manual


biocViews Microarray, OneChannel, Preprocessing, Software, TwoChannel
Version 3.36.0
In Bioconductor since BioC 1.6 (R-2.1) or earlier (> 10.5 years)
License Artistic-2.0
Depends R (>= 2.10), Biobase(>= 2.5.5)
Imports methods, affy(>= 1.23.4), limma, lattice
Suggests affydata, hgu95av2cdf
Depends On Me affyPara, cellHTS2, MmPalateMiRNA, webbioc
Imports Me arrayQualityMetrics, imageHTS, LVSmiRNA, metaseqR, MSnbase, pvca, Ringo, tilingArray
Suggests Me adSplit, beadarray, BiocCaseStudies, cellHTS, DESeq, DESeq2, estrogen, flowVS, ggbio, GlobalAncova, globaltest, limma, lumi, PAA, twilight
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