gaga

DOI: 10.18129/B9.bioc.gaga    

GaGa hierarchical model for high-throughput data analysis

Bioconductor version: Release (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>

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("gaga")

Documentation

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

browseVignettes("gaga")

 

PDF	R Script	Manual for the gaga library
PDF		Reference Manual

Details

biocViews	Classification, DifferentialExpression, MassSpectrometry, MultipleComparison, OneChannel, Software
Version	2.24.0
In Bioconductor since	BioC 2.2 (R-2.7) (10 years)
License	GPL (>= 2)
Depends	R (>= 2.8.0), Biobase, coda, EBarrays, mgcv
Imports
LinkingTo
Suggests
SystemRequirements
Enhances	parallel
URL
Depends On Me
Imports Me	casper
Suggests Me
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
Package Short Url	http://bioconductor.org/packages/gaga/
Package Downloads Report	Download Stats