Technical Note
EMA - A R package for Easy Microarray data analysis
-
* Corresponding author: Nicolas Servant Nicolas.Servant@curie.fr
- † Equal contributors
1 Institut Curie, Paris F-75248, France
2 INSERM, U900, Paris F-75248, France
3 Ecole des Mines ParisTech, Fontainebleau, F-77300 France
4 Institut Curie, Departement de Transfert, Paris F-75248, France
5 CNRS, UMR144, Paris F-75248, France
6 CNRS, UMR3347, Orsay F-91405, France
7 INSERM, U1021, Orsay F-91405, France
8 Université Paris-Sud 11, Orsay F-91405, France
BMC Research Notes 2010, 3:277 doi:10.1186/1756-0500-3-277
Published: 3 November 2010Abstract
Background
The increasing number of methodologies and tools currently available to analyse gene expression microarray data can be confusing for non specialist users.
Findings
Based on the experience of biostatisticians of Institut Curie, 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.
Conclusions
Strategy and tools proposed in the EMA R package could provide a useful starting point for many microarrays users. EMA is part of Comprehensive R Archive Network and is freely available at http://bioinfo.curie.fr/projects/ema/ webcite.