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: |
1.4.4 |
| Depends: |
R (≥ 2.10) |
| Imports: |
siggenes, affy, multtest, survival, xtable, gcrma, heatmap.plus, biomaRt, GSA, MASS, FactoMineR, cluster, AnnotationDbi |
| Suggests: |
hgu133plus2.db, lumi, vsn, GOstats, GO.db |
| Published: |
2014-03-28 |
| 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 |