DOI: 10.18129/B9.bioc.gaga    

This is the development version of gaga; for the stable release version, see gaga.

GaGa hierarchical model for high-throughput data analysis

Bioconductor version: Development (3.9)

Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).

Author: David Rossell <rosselldavid at>.

Maintainer: David Rossell <rosselldavid at>

Citation (from within R, enter citation("gaga")):


To install this package, start R (version "3.6") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
BiocManager::install("gaga", version = "3.9")

For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:



PDF R Script Manual for the gaga library
PDF   Reference Manual


biocViews Classification, DifferentialExpression, ImmunoOncology, MassSpectrometry, MultipleComparison, OneChannel, Software
Version 2.29.1
In Bioconductor since BioC 2.2 (R-2.7) (11 years)
License GPL (>= 2)
Depends R (>= 2.8.0), Biobase, coda, EBarrays, mgcv
Enhances parallel
Depends On Me
Imports Me casper
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package gaga_2.29.1.tar.gz
Windows Binary
Mac OS X 10.11 (El Capitan) gaga_2.29.1.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
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