A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies

Winston Patrick Kuo, Fang Liu, Jeff Trimarchi, Claudio Punzo, Michael Lombardi, Jasjit Sarang, Mark E. Whipple, Malini Maysuria, Kyle Serikawa, Sun Young Lee, Donald McCrann, Jason Kang, Jeffrey R. Shearstone, Jocelyn Burke, Daniel J. Park, Xiaowei Wang, Trent L. Rector, Paola Ricciardi-Castagnoli, Steven Perrin, Sangdun ChoiRoger Bumgarner, Ju Han Kim, Glenn F. Short, Mason W. Freeman, Brian Seed, Roderick Jensen, George M. Church, Eivind Hovig, Connie L. Cepko, Peter Park, Lucila Ohno-Machado, Tor Kristian Jenssen

Research output: Contribution to journalArticlepeer-review

129 Scopus citations


Over the last decade, gene expression microarrays have had a profound impact on biomedical research. The diversity of platforms and analytical methods available to researchers have made the comparison of data from multiple platforms challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and 'in-house' platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by quantitative real-time (QRT)-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent preprocessing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.

Original languageEnglish
Pages (from-to)832-840
Number of pages9
JournalNature Biotechnology
Issue number7
StatePublished - Jul 2006
Externally publishedYes


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