Package: vsn 3.73.0
Wolfgang Huber
vsn: Variance stabilization and calibration for microarray data
The package implements a method for normalising microarray intensities from 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.
Authors:
vsn_3.73.0.tar.gz
vsn_3.73.0.zip(r-4.5)vsn_3.73.0.zip(r-4.4)vsn_3.73.0.zip(r-4.3)
vsn_3.73.0.tgz(r-4.4-arm64)vsn_3.73.0.tgz(r-4.4-x86_64)vsn_3.73.0.tgz(r-4.3-arm64)vsn_3.73.0.tgz(r-4.3-x86_64)
vsn_3.73.0.tar.gz(r-4.5-noble)vsn_3.73.0.tar.gz(r-4.4-noble)
vsn_3.73.0.tgz(r-4.4-emscripten)vsn_3.73.0.tgz(r-4.3-emscripten)
vsn.pdf |vsn.html✨
vsn/json (API)
NEWS
| # Install 'vsn' in R: |
| install.packages('vsn', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
On BioConductor:vsn-3.73.0(bioc 3.20)vsn-3.72.0(bioc 3.19)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:59a091a8c8
Exports:coefcoefficientscoerceexprsjustvsnlogLikmeanSdPlotncolnrowplotVsnLogLikpredictsagmbAssesssagmbSimulateDatascalingFactorTransformationshowvsn2vsnMatrixvsnrma
Dependencies:affyaffyioBiobaseBiocGenericsBiocManagerclicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclelimmamagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpreprocessCoreR6RColorBrewerrlangscalesstatmodtibbleutf8vctrsviridisLitewithrzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| vsn | vsn-package |
| Wrapper functions for vsn | justvsn vsnrma |
| Intensity data for one cDNA slide with two adjacent tissue samples from a nephrectomy (kidney) | kidney |
| Calculate the log likelihood and its gradient for the vsn model | logLik,vsnInput-method logLik-methods plotVsnLogLik |
| Intensity data for 8 cDNA slides with CLL and DLBL samples from the Alizadeh et al. paper in Nature 2000 | lymphoma |
| Plot row standard deviations versus row means | meanSdPlot meanSdPlot,ExpressionSet-method meanSdPlot,MAList-method meanSdPlot,matrix-method meanSdPlot,vsn-method meanSdPlot-methods |
| Wrapper for vsn to be used as a normalization method with expresso | normalize.AffyBatch.vsn |
| Simulate data and assess vsn's parameter estimation | sagmbAssess sagmbSimulateData |
| The transformation that is applied to the scaling parameter of the vsn model | scalingFactorTransformation |
| Class to contain result of a vsn fit | class:vsn coef,vsn-method coefficients,vsn-method dim,vsn-method exprs,vsn-method ncol,vsn-method nrow,vsn-method show,vsn-method vsn-class [,vsn-method |
| Fit the vsn model | coerce,RGList,NChannelSet-method vsn2 vsn2,AffyBatch-method vsn2,ExpressionSet-method vsn2,matrix-method vsn2,NChannelSet-method vsn2,numeric-method vsn2,RGList-method vsn2-methods vsnMatrix |
| Apply the vsn transformation to data | predict,vsn-method |
| Class to contain input data and parameters for vsn functions | class:vsnInput dim,vsnInput-method ncol,vsnInput-method nrow,vsnInput-method show,vsnInput-method vsnInput vsnInput-class [,vsnInput-method |
