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Article
Nature Methods - 2, 345 - 350 (2005)
Published online: 21 April 2005; | doi:10.1038/nmeth756


There is a Corrigendum (June 2005) associated with this Article.

There is a Corrigendum (June 2005) associated with this Article.

There is a Corrigendum (June 2005) associated with this Article.

There is a Corrigendum (June 2005) associated with this Article.

There is a Corrigendum (June 2005) associated with this Article.

There is a Corrigendum (June 2005) associated with this Article.

There is a Corrigendum (June 2005) associated with this Article.

There is a Corrigendum (June 2005) associated with this Article.

There is a Corrigendum (June 2005) associated with this Article.

Multiple-laboratory comparison of microarray platforms

Rafael A Irizarry1, Daniel Warren2, Forrest Spencer3, Irene F Kim4, Shyam Biswal5, Bryan C Frank6, Edward Gabrielson7, Joe G N Garcia8, Joel Geoghegan9, Gregory Germino4, Constance Griffin10, Sara C Hilmer11, Eric Hoffman11, Anne E Jedlicka12, Ernest Kawasaki9, Francisco Martínez-Murillo13, Laura Morsberger10, Hannah Lee5, David Petersen9, John Quackenbush6, 14, Alan Scott12, Michael Wilson15, 17, Yanqin Yang2, Shui Qing Ye8 & Wayne Yu16

1  Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.

2  Department of Surgery, Johns Hopkins University, Baltimore, Maryland 21205, USA.

3  McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.

4  JHU NIDDK Gene Profiling Center, Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21205, USA.

5  Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.

6  The Institute for Genomic Research, 9712 Medical Center Dr., Rockville, Maryland 20878, USA.

7  Department of Pathology, Johns Hopkins University, Baltimore, Maryland 21231, USA.

8  Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Mason F. Lord Bldg., Center Tower #665, Baltimore, Maryland 21224, USA.

9  NCI's Microarray Core Facility, Advanced Technology Center, Gaithersburg, Maryland 20877, USA.

10  Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21287, USA.

11  Research Center for Genetic Medicine, Children's National Medical Center, George Washington University, Washington, DC 20052, USA.

12  W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.

13  Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, Maryland 21205, USA.

14  Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts 02115-6084, USA.

15  Microarray Research Facility, Research Technologies Branch, DIR, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland 20892, USA.

16  Oncology Microarray Facility, Johns Hopkins University, Baltimore, Maryland 21231, USA.

17  Present address: Ambion, Inc., Austin, Texas 78744, USA.

Correspondence should be addressed to Rafael A Irizarry rafa@jhu.edu

Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC–Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

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Nature Methods
ISSN: 1548-7091
EISSN: 1548-7105
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