EMA: Easy Microarray Data Analysis

We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.

Version: 1.4.7
Depends: R (≥ 2.10)
Imports: siggenes, affy, multtest, survival, xtable, gcrma, heatmap.plus, biomaRt, GSA, MASS, FactoMineR, cluster, AnnotationDbi, Biobase
Suggests: hgu133plus2.db, lumi, GOstats, Category, vsn, GO.db, BiocGenerics, GSEABase
Published: 2020-02-14
Author: Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe
Maintainer: Pierre Gestraud <pierre.gestraud at curie.fr>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: EMA results


Reference manual: EMA.pdf
Package source: EMA_1.4.7.tar.gz
Windows binaries: r-devel: EMA_1.4.7.zip, r-release: EMA_1.4.7.zip, r-oldrel: EMA_1.4.7.zip
macOS binaries: r-release: EMA_1.4.7.tgz, r-oldrel: EMA_1.4.7.tgz
Old sources: EMA archive


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