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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 (3.20)

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 "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


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:

Manual for the gaga library PDF R Script
Reference Manual PDF


biocViews Classification, DifferentialExpression, ImmunoOncology, MassSpectrometry, MultipleComparison, OneChannel, Software
Version 2.51.0
In Bioconductor since BioC 2.2 (R-2.7) (16 years)
License GPL (>= 2)
Depends R (>= 2.8.0), Biobase, coda, EBarrays, mgcv
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Enhances parallel
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Source Package gaga_2.51.0.tar.gz
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macOS Binary (x86_64) gaga_2.51.0.tgz
macOS Binary (arm64) gaga_2.51.0.tgz
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Source Repository (Developer Access) git clone
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