gaga

This package is for version 3.6 of Bioconductor; for the stable, up-to-date release version, see gaga.

GaGa hierarchical model for high-throughput data analysis

Bioconductor version: 3.6

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 gmail.com>.

Maintainer: David Rossell <rosselldavid at gmail.com>

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

Installation

To install this package, start R (version "3.4") and enter:


if (!require("BiocManager", quietly = TRUE))
 install.packages("BiocManager")
BiocManager::install("gaga")

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

Documentation

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

browseVignettes("gaga")

Manual for the gaga library PDF R Script

Reference Manual PDF

Need some help? Ask on the Bioconductor Support site!

Details

biocViews Classification, DifferentialExpression, MassSpectrometry, MultipleComparison, OneChannel, Software

Version 2.24.0

In Bioconductor since BioC 2.2 (R-2.7) (16 years)

License GPL (>= 2)

Depends R (>= 2.8.0), Biobase, coda, EBarrays, mgcv

Imports

System Requirements

URL

Suggests

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Enhances parallel

Depends On Me

Imports Me casper

Suggests Me

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Build Report Build Report

Package Archives

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

Source Package gaga_2.24.0.tar.gz

Windows Binary gaga_2.24.0.zip (32- & 64-bit)

Mac OS X 10.11 (El Capitan) gaga_2.24.0.tgz

Source Repository git clone https://git.bioconductor.org/packages/gaga

Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/gaga

Package Short Url https://bioconductor.org/packages/gaga/

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Old Source Packages for BioC 3.6 Source Archive