To install this package, start R and enter:
## try http if https is not available source("https://bioconductor.org/biocLite.R") biocLite("gaga")
In most cases, you don't need to download the package archive at all.
Bioconductor version: Release (3.1)
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")
):
To install this package, start R and enter:
## try http if https is not available source("https://bioconductor.org/biocLite.R") biocLite("gaga")
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("gaga")
R Script | Manual for the gaga library | |
Reference Manual |
biocViews | Classification, DifferentialExpression, MassSpectrometry, MultipleComparison, OneChannel, Software |
Version | 2.14.0 |
In Bioconductor since | BioC 2.2 (R-2.7) (7.5 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 |
Follow Installation instructions to use this package in your R session.
Package Source | gaga_2.14.0.tar.gz |
Windows Binary | gaga_2.14.0.zip (32- & 64-bit) |
Mac OS X 10.6 (Snow Leopard) | gaga_2.14.0.tgz |
Mac OS X 10.9 (Mavericks) | gaga_2.14.0.tgz |
Subversion source | (username/password: readonly) |
Git source | https://github.com/Bioconductor-mirror/gaga/tree/release-3.1 |
Package Short Url | http://bioconductor.org/packages/gaga/ |
Package Downloads Report | Download Stats |
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