gaga

GaGa hierarchical model for microarray data analysis

Bioconductor version: Release (2.7)

This package fits Rossell's generalizations of the Gamma-Gamma hierarchical model for microarray data analysis, which substantially improve the quality of the fit at a low computational cost. The model can be fit via empirical Bayes (Expectation-Maximization and Simulated Annealing) and fully Bayesian techniques (Gibbs and Metropolis-Hastings posterior sampling). Routines are provided to perform differential expression analysis and class prediction.

Author: David Rossell .

Maintainer: David Rossell

To install this package, start R and enter:

source("http:///biocLite.R")
biocLite("gaga")    

Documentation

PDF

R Script

Manual for the gaga library

Reference Manual

Details

biocViews	Bioinformatics, DifferentialExpression, Classification
Depends	R, Biobase, coda
Imports
Suggests
System Requirements
License	GPL (>= 2)
URL
Depends On Me
Imports Me
Suggests Me
Version	1.10.0

Package Downloads

Package Source	gaga_1.10.0.tar.gz
Windows Binary	gaga_1.10.0.zip (32- & 64-bit)
MacOS 10.5 (Leopard) binary	gaga_1.10.0.tgz
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