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Article Abstract
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/.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987873 | PMC |
| http://dx.doi.org/10.1186/1756-0500-3-277 | DOI Listing |
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