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gaga

GaGa hierarchical model for high-throughput data analysis


Bioconductor version: Release (3.18)

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 "4.3") 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

Details

biocViews Classification, DifferentialExpression, ImmunoOncology, MassSpectrometry, MultipleComparison, OneChannel, Software
Version 2.48.0
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
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Enhances parallel
Depends On Me
Imports Me casper
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Build Report Build Report

Package Archives

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

Source Package gaga_2.48.0.tar.gz
Windows Binary gaga_2.48.0.zip (64-bit only)
macOS Binary (x86_64) gaga_2.48.0.tgz
macOS Binary (arm64) gaga_2.48.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/gaga
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/gaga
Bioc Package Browser https://code.bioconductor.org/browse/gaga/
Package Short Url https://bioconductor.org/packages/gaga/
Package Downloads Report Download Stats