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 <email@example.com>|
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...
vsnPlotPar: Plot trajectories of calibration and transformation...