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 |
Please use the canonical form https://CRAN.R-project.org/package=EMA to link to this page.