vsn: Variance stabilization and calibration for microarray data

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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.

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
Date of publication
None
Maintainer
Wolfgang Huber <whuber@embl.de>
License
Artistic-2.0
Version
3.42.3
URLs

View on Bioconductor

Man pages

class.vsn
Class to contain result of a vsn fit
class.vsnInput
Class to contain input data and parameters for vsn functions
justvsn
Wrapper functions for vsn
kidney
Intensity data for 1 cDNA slide with two adjacent tissue...
lymphoma
Intensity data for 8 cDNA slides with CLL and DLBL samples...
meanSdPlot
Plot row standard deviations versus row means
normalize.AffyBatch.vsn
Wrapper for vsn to be used as a normalization method with...
sagmbSimulateData
Simulate data and assess vsn's parameter estimation
scalingFactorTransformation
The transformation that is applied to the scaling parameter...
vsn
Variance stabilization and calibration for microarray data.
vsn2
Fit the vsn model
vsn2trsf
Apply the vsn transformation to data
vsnh
A function that transforms a matrix of microarray...
vsnLikelihood
Calculate the log likelihood and its gradient for the vsn...
vsn-package
vsn
vsnPlotPar
Plot trajectories of calibration and transformation...

Files in this package

vsn/.Rinstignore
vsn/DESCRIPTION
vsn/NAMESPACE
vsn/R
vsn/R/AllClasses.R
vsn/R/AllGenerics.R
vsn/R/RGList_to_NChannelSet.R
vsn/R/getIntensityMatrix.R
vsn/R/justvsn.R
vsn/R/meanSdPlot-methods.R
vsn/R/methods-predict.R
vsn/R/methods-vsn.R
vsn/R/methods-vsn2.R
vsn/R/methods-vsnInput.R
vsn/R/normalize.AffyBatch.vsn.R
vsn/R/plotLikelihood.R
vsn/R/sagmbSimulateData.R
vsn/R/vsn.R
vsn/R/vsn2.R
vsn/R/vsnLogLik.R
vsn/R/vsnPlotPar.R
vsn/R/vsnh.R
vsn/R/zzz.R
vsn/build
vsn/build/vignette.rds
vsn/data
vsn/data/kidney.RData
vsn/data/lymphoma.RData
vsn/inst
vsn/inst/CITATION
vsn/inst/doc
vsn/inst/doc/A-vsn.R
vsn/inst/doc/A-vsn.Rnw
vsn/inst/doc/A-vsn.pdf
vsn/inst/doc/C-likelihoodcomputations.R
vsn/inst/doc/C-likelihoodcomputations.Rnw
vsn/inst/doc/C-likelihoodcomputations.pdf
vsn/inst/doc/D-convergence.Rnw
vsn/inst/doc/D-convergence.pdf
vsn/inst/doc/vsn.R
vsn/inst/doc/vsn.Rmd
vsn/inst/doc/vsn.html
vsn/inst/scripts
vsn/inst/scripts/README
vsn/inst/scripts/convergence.Rnw
vsn/inst/scripts/lymphomasamples.txt
vsn/inst/scripts/makedata.R
vsn/inst/scripts/swirl.R
vsn/inst/scripts/testderiv.R
vsn/inst/scripts/testmlest.R
vsn/inst/scripts/testprofiling.R
vsn/inst/vignettes
vsn/inst/vignettes/4-convergence.Rnw
vsn/inst/vignettes/4-convergence.pdf
vsn/man
vsn/man/class.vsn.Rd
vsn/man/class.vsnInput.Rd
vsn/man/justvsn.Rd
vsn/man/kidney.Rd
vsn/man/lymphoma.Rd
vsn/man/meanSdPlot.Rd
vsn/man/normalize.AffyBatch.vsn.Rd
vsn/man/sagmbSimulateData.Rd
vsn/man/scalingFactorTransformation.Rd
vsn/man/vsn-package.Rd
vsn/man/vsn.Rd
vsn/man/vsn2.Rd
vsn/man/vsn2trsf.Rd
vsn/man/vsnLikelihood.Rd
vsn/man/vsnPlotPar.Rd
vsn/man/vsnh.Rd
vsn/src
vsn/src/init.c
vsn/src/vsn.c
vsn/src/vsn.h
vsn/src/vsn2.c
vsn/vignettes
vsn/vignettes/A-vsn.Rnw
vsn/vignettes/C-likelihoodcomputations.Rnw
vsn/vignettes/D-convergence.Rnw
vsn/vignettes/vsn.Rmd
vsn/vignettes/vsn.bib