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gaga

This is the released version of gaga; for the devel version, see gaga.

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3.24 (devel), 3.23 (release), 3.22, 3.21, 3.20, 3.19, 3.18, 3.17, 3.16, 3.15, 3.14, 3.13, 3.12, 3.11, 3.10, 3.9, 3.8, 3.7, 3.6, 3.5, 3.4, 3.3, 3.2, 3.1, 3.0, 2.14, 2.13, 2.12, 2.11, 2.10, 2.9, 2.8, 2.7, 2.6, 2.5

GaGa hierarchical model for high-throughput data analysis

DOI: 10.18129/B9.bioc.gaga

Bioconductor version: Release (3.23)

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")):

David Rossell. gaga: GaGa hierarchical model for high-throughput data analysis. doi:10.18129/B9.bioc.gaga, R package version 2.58.0, https://bioconductor.org/packages/gaga.

Generated from the package metadata; it may differ from the package's own citation.

Installation

To install this package, start R (version "4.6") 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")

Details

Package Archives

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