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Logo of bmcgenoBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Genomics
BMC Genomics. 2009; 10: 63.
Published online Feb 5, 2009. doi:  10.1186/1471-2164-10-63
PMCID: PMC2652496
Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes
Steven P Wilder,1 Pamela J Kaisaki,1 Karène Argoud,1 Anita Salhan,1 Jiannis Ragoussis,1 Marie-Thérèse Bihoreau,1 and Dominique Gauguiercorresponding author1
1The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7BN, UK
corresponding authorCorresponding author.
Steven P Wilder: wilder/at/; Pamela J Kaisaki: pkaisaki/at/; Karène Argoud: karene/at/; Anita Salhan: anita/at/; Jiannis Ragoussis: ioannis.ragoussis/at/; Marie-Thérèse Bihoreau: bihoreau/at/; Dominique Gauguier: gdomi/at/
Received May 15, 2008; Accepted February 5, 2009.
Microarray technologies are widely used to quantify the abundance of transcripts corresponding to thousands of genes. To maximise the robustness of transcriptome results, we have tested the performance and reproducibility of rat and mouse gene expression data obtained with Affymetrix, Illumina and Operon platforms.
We present a thorough analysis of the degree of reproducibility provided by analysing the transcriptomic profile of the same animals of several experimental groups under different popular microarray technologies in different tissues. Concordant results from inter- and intra-platform comparisons were maximised by testing many popular computational methods for generating fold changes and significances and by only considering oligonucleotides giving high expression levels. The choice of Affymetrix signal extraction technique was shown to have the greatest effect on the concordance across platforms. In both species, when choosing optimal methods, the agreement between data generated on the Affymetrix and Illumina was excellent; this was verified using qRT-PCR on a selection of genes present on all platforms.
This study provides an extensive assessment of analytical methods best suited for processing data from different microarray technologies and can assist integration of technologically different gene expression datasets in biological systems.
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