EMA - A R package for Easy Microarray data analysis
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Published on: May 21, 2019
Nicolas Servant1, Eleonore Gravier, Pierre Gestraud
1Institut Curie, Paris F-75248, France. Nicolas.Servant@curie.fr.
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/.
