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PMID: 15846361 Published · ppublish English Comparative Study Journal Article Research Support, U.S. Gov't, P.H.S.

Multiple-laboratory comparison of microarray platforms.

Nature methods ·Vol. 2 ·No. 5 ·2005-05-00 ·Pages 345-50

Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson E, Garcia JG, Geoghegan J, Germino G, Griffin C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki E, Martínez-Murillo F, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang Y, Ye SQ, Yu W

Abstract

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.

MeSH Terms
Baltimore District of Columbia Gene Expression Profiling/standards Humans Laboratories/standards Oligonucleotide Array Sequence Analysis/standards Reproducibility of Results
Authors & Affiliations
25 authors, click to expand affiliations / ORCID
Irizarry Rafael A
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA. [email protected]
Warren Daniel
Spencer Forrest
Kim Irene F
Biswal Shyam
Frank Bryan C
Gabrielson Edward
Garcia Joe G N
Geoghegan Joel
Germino Gregory
Griffin Constance
Hilmer Sara C
Hoffman Eric
Jedlicka Anne E
Kawasaki Ernest
Martínez-Murillo Francisco
Morsberger Laura
Lee Hannah
Petersen David
Quackenbush John
Scott Alan
Wilson Michael
Yang Yanqin
Ye Shui Qing
Yu Wayne
Article Info
Journal
Nature methods
Abbr.
Nat Methods
ISSN
1548-7091
Published
2005-05-00
Epub
2005-00-21
Pages
345-50
Language
English
Region
United States
NLM ID
101215604
Subset
IM
Grants
NIDDK NIH HHS · U24DK58757 · United States
Corrections
ErratumIn
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