gaga
GaGa hierarchical model for microarray data analysis
Bioconductor version: 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 <rosselldavid at gmail.com>.
Maintainer: David Rossell <rosselldavid at gmail.com>
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("gaga")
To cite this package in a publication, start R and enter:
citation("gaga")
Documentation
| R Script | Manual for the gaga library | |
| Reference Manual |
Details
| biocViews | Bioinformatics, DifferentialExpression, Classification |
| Depends | R (>= 2.5.0), Biobase, coda |
| Imports | |
| Suggests | |
| System Requirements | |
| License | GPL (>= 2) |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Version | 1.10.0 |
| Since | Bioconductor 2.2 (R-2.7) |
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 |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Annotation
- Variants
- Flow Cytometry and other assays
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