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Bioinformatics Advance Access published online on July 15, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth421
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received December 14, 2003
Revised June 29, 2004
Accepted July 10, 2004

Article

Limited agreement among three global gene expression methods highlights the requirement for non-global validation

Peter M. Haverty 1, Li-Li Hsiao 2, Steven R. Gullans 2, Ulla Hansen 3, Zhiping Weng 4*

1 Bioinformatics Program, Boston University, Boston, MA 02215, USA
2 Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
3 Bioinformatics Program, Boston University, Boston, MA 02215, USA; Department of Biology, Boston University, MA 02215, USA
4 Bioinformatics Program, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA

* To whom correspondence should be addressed. E-mail: zhiping{at}bu.edu.


  Abstract

Motivation: DNA microarrays have revolutionized biological research, but their reliability and accuracy have not been extensively evaluated. Thorough testing of microarrays through comparison to dissimilar gene expression methods is necessary in order to determine their accuracy.

Results: We have systematically compared three global gene expression methods on all available histologically normal samples from five human organ types. The data included 25 Affymetrix high-density oligonucleotide array (HDA) experiments, 23 EST Based Expression (EBE) experiments and 5 SAGE experiments. The reported gene-by-gene expression patterns showed a wide range of correlations between pairs of methods. This level of agreement was sufficient for accurate clustering of data sets from like tissues and dissimilar methods, but highlights the need for thorough validation of individual gene expression measurements by alternate, non-global methods. Furthermore, analyses of mRNA abundance distributions indicate limitations in the EBE and SAGE methods at both high and low expression levels.


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