| Literature DB >> 16823376 |
Winston Patrick Kuo1, 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 Choi, Roger 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.
Abstract
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.Mesh:
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Year: 2006 PMID: 16823376 DOI: 10.1038/nbt1217
Source DB: PubMed Journal: Nat Biotechnol ISSN: 1087-0156 Impact factor: 54.908