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EMA - A R package for Easy Microarray data analysis

Nicolas Servant123*, Eleonore Gravier1234, Pierre Gestraud123, Cecile Laurent123678, Caroline Paccard123, Anne Biton1235, Isabel Brito123, Jonas Mandel123, Bernard Asselain123, Emmanuel Barillot123 and Philippe Hupé1235

Author Affiliations

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

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BMC Research Notes 2010, 3:277  doi:10.1186/1756-0500-3-277

Published: 3 November 2010



The increasing number of methodologies and tools currently available to analyse gene expression microarray data can be confusing for non specialist users.


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.


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 webcite.