Nature Biotechnology 24, 832 - 840 (2006)
Published online: 2 July 2006; | doi:10.1038/nbt1217
A sequence-oriented comparison of gene expression measurements across different hybridization-based technologiesWinston Patrick Kuo1, 2, 3, 18, Fang Liu4, 18, Jeff Trimarchi2, Claudio Punzo2, Michael Lombardi5, Jasjit Sarang5, Mark E Whipple6, Malini Maysuria7, Kyle Serikawa7, Sun Young Lee8, Donald McCrann9, Jason Kang10, Jeffrey R Shearstone11, Jocelyn Burke2, 12, Daniel J Park2, 12, Xiaowei Wang1, 12, Trent L Rector2, Paola Ricciardi-Castagnoli13, Steven Perrin11, Sangdun Choi14, Roger Bumgarner7, Ju Han Kim15, Glenn F Short III2, 12, Mason W Freeman2, 12, Brian Seed2, 12, Roderick Jensen5, George M Church2, Eivind Hovig4, Connie L Cepko2, Peter Park16, Lucila Ohno-Machado3
& Tor-Kristian Jenssen171
Department of Developmental Biology, Harvard School of Dental Medicine, 188 Longwood Ave., Boston, Massachusetts
02115, USA. 2
Department of Genetics, Harvard Medical School, Howard Hughes Medical Institute, Boston, Massachusetts, USA. 3
Decision Systems Group, Brigham and Women's Hospital, Boston, Massachusetts, USA. 4
Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, Oslo, Norway. 5
Department of Physics, University of Massachusetts Boston, Boston, Massachusetts, USA. 6
Department of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, Washington, USA. 7
Department of Microbiology, University of Washington, Seattle, Washington, USA. 8
Division of Biology, California Institute of Technology, Pasadena, California, USA. 9
Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts, USA. 10
Macrogen Inc., Seoul, Korea. 11
Research Molecular Discovery, Biogen Idec, Cambridge, Massachusetts, USA. 12
Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA. 13
Department of Biotechnology and Bioscience, University of Milano-Bicooca, Milano, Italy. 14
Department of Molecular Science and Technology, Ajou University, Suwon, Korea. 15
Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea. 16
Childrens' Hospital Informatics Program, Harvard Medical School, Boston, Massachusetts, USA. 17
PubGene AS, Vinderen, Oslo, Norway. 18
These authors contributed equally to this work.
Correspondence should be addressed to Winston Patrick Kuo wkuo@genetics.med.harvard.edu 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.
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