This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2006 .

Research

Cross platform microarray analysis for robust identification of differentially expressed genes

Roberta Bosotti¹ , Giuseppe Locatelli¹ , Sandra Healy¹ , Emanuela Scacheri¹ , Luca Sartori^2,4 , Ciro Mercurio^1,4 , Raffaele Calogero³ and Antonella Isacchi¹

¹  Genomics Unit, Biotechnology Department, Nerviano Medical Sciences S.r.l., Viale Pasteur 10, 20014 Nerviano (MI), Italy

²  Research Informatics Group, Chemistry Department, Nerviano Medical Sciences S.r.l., Viale Pasteur 10, 20014 Nerviano (MI), Italy

³  Genomics and Bioinformatics Unit, Dipartimento di Scienze Cliniche e Biologiche c/o Az. Ospedaliera S. Luigi Regione Gonzole 10, 10043 Orbassano (TO), Italy

⁴  Current address: Genextra S.p.A. c/o IFOM-IEO Campus, Via Adamello 16, 20134 Milan, Italy

author email corresponding author email

BMC Bioinformatics 2007, 8(Suppl 1):S5doi:10.1186/1471-2105-8-S1-S5

Published:	8 March 2007

Abstract

Background

Microarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles.

In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes.

Results

In this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms.

We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with ~80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR.

Conclusion

In this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes.