Open Access Highly Accessed Research article

Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells

Seraya Maouche1, Odette Poirier1, Tiphaine Godefroy1, Robert Olaso2, Ivo Gut2, Jean-Phillipe Collet3, Gilles Montalescot3 and François Cambien1*

Author Affiliations

1 INSERM UMR S525, Faculté de Médecine Pierre et Marie Curie, Université Paris VI, 91 Boulevard de l'Hôpital, Paris 75634 Cedex 13, France

2 Centre National de Génotypage, Evry, France

3 INSERM U856, Institut de Cardiologie, Groupe Hospitalier Pitié-Salpêtrière, Paris, France

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BMC Genomics 2008, 9:302  doi:10.1186/1471-2164-9-302

Published: 25 June 2008

Abstract

Background

In this study we assessed the respective ability of Affymetrix and Illumina microarray methodologies to answer a relevant biological question, namely the change in gene expression between resting monocytes and macrophages derived from these monocytes. Five RNA samples for each type of cell were hybridized to the two platforms in parallel. In addition, a reference list of differentially expressed genes (DEG) was generated from a larger number of hybridizations (mRNA from 86 individuals) using the RNG/MRC two-color platform.

Results

Our results show an important overlap of the Illumina and Affymetrix DEG lists. In addition, more than 70% of the genes in these lists were also present in the reference list. Overall the two platforms had very similar performance in terms of biological significance, evaluated by the presence in the DEG lists of an excess of genes belonging to Gene Ontology (GO) categories relevant for the biology of monocytes and macrophages. Our results support the conclusion of the MicroArray Quality Control (MAQC) project that the criteria used to constitute the DEG lists strongly influence the degree of concordance among platforms. However the importance of prioritizing genes by magnitude of effect (fold change) rather than statistical significance (p-value) to enhance cross-platform reproducibility recommended by the MAQC authors was not supported by our data.

Conclusion

Functional analysis based on GO enrichment demonstrates that the 2 compared technologies delivered very similar results and identified most of the relevant GO categories enriched in the reference list.