# Variance stabilization and calibration for microarray data

## Documentation for package ‘vsn’ version 3.2.1

## User Guides and Package Vignettes

Read overview or browse directory.

## Help Pages

vsn-package | vsn
class:vsn | Class to contain result of a vsn fit
class:vsnInput | Class to contain input data and parameters for vsn functions
coef,vsn-method | Class to contain result of a vsn fit
coefficients,vsn-method | Class to contain result of a vsn fit
coerce,RGList,NChannelSet-method | Fit the vsn model
dim,vsn-method | Class to contain result of a vsn fit
dim,vsnInput-method | Class to contain input data and parameters for vsn functions
exprs,vsn-method | Class to contain result of a vsn fit
getIntensityMatrix | Extract a matrix of microarray intensities from various kinds of objects
justvsn | Wrapper functions for vsn
kidney | Intensity data for 1 cDNA slide with two adjacent tissue samples from a nephrectomy (kidney)
logLik,vsnInput-method | Calculate the log likelihood and its gradient for the vsn model
logLik-methods | Calculate the log likelihood and its gradient for the vsn model
lymphoma | Intensity data for 8 cDNA slides with CLL and DLBL samples from the Alizadeh et al. paper in Nature 2000
meanSdPlot | Plot row standard deviations versus row means
meanSdPlot,ExpressionSet-method | Plot row standard deviations versus row means
meanSdPlot,exprSet-method | Plot row standard deviations versus row means
meanSdPlot,matrix-method | Plot row standard deviations versus row means
meanSdPlot,vsn-method | Plot row standard deviations versus row means
meanSdPlot-methods | Plot row standard deviations versus row means
ncol,vsn-method | Class to contain result of a vsn fit
ncol,vsnInput-method | Class to contain input data and parameters for vsn functions
normalize.AffyBatch.vsn | Wrapper for vsn to be used as a normalization method with expresso
nrow,vsn-method | Class to contain result of a vsn fit
nrow,vsnInput-method | Class to contain input data and parameters for vsn functions
plotVsnLogLik | Calculate the log likelihood and its gradient for the vsn model
predict,vsn-method | Apply the vsn transformation to data
sagmbAssess | Simulate data and assess vsn's parameter estimation
sagmbSimulateData | Simulate data and assess vsn's parameter estimation
scalingFactorTransformation | The transformation that is applied to the scaling parameter of the vsn model
show,vsn-method | Class to contain result of a vsn fit
show,vsnInput-method | Class to contain input data and parameters for vsn functions
vsn | Variance stabilization and calibration for microarray data.
vsn-class | Class to contain result of a vsn fit
vsn2 | Fit the vsn model
vsn2,AffyBatch-method | Fit the vsn model
vsn2,ExpressionSet-method | Fit the vsn model
vsn2,matrix-method | Fit the vsn model
vsn2,NChannelSet-method | Fit the vsn model
vsn2,numeric-method | Fit the vsn model
vsn2,RGList-method | Fit the vsn model
vsn2-methods | Fit the vsn model
vsnh | A function that transforms a matrix of microarray intensities
vsnInput | Class to contain input data and parameters for vsn functions
vsnInput-class | Class to contain input data and parameters for vsn functions
vsnMatrix | Fit the vsn model
vsnPlotPar | Plot trajectories of calibration and transformation parameters for a vsn fit
vsnrma | Wrapper functions for vsn
[,vsn-method | Class to contain result of a vsn fit
[,vsnInput-method | Class to contain input data and parameters for vsn functions