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Nucleic Acids Research 2005 33(18):e157; doi:10.1093/nar/gni159
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Published online 13 October 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

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Transformation of expression intensities across generations of Affymetrix microarrays using sequence matching and regression modeling

Soumyaroop Bhattacharya1,2,* and Thomas J. Mariani1,2

1Division of Pulmonary Medicine, Pulmonary Medicine Thorn 908, Brigham and Women's Hospital 2Pulmonary Bioinformatics, The Lung Biology Center, Harvard Medical School Boston, MA 02115, USA

*To whom correspondence should be addressed. Tel: +1 617 732 6265; Fax: +1 617 232 4623; Email: sbhattacharya{at}rics.bwh.harvard.edu

Received April 20, 2005. Revised July 11, 2005. Accepted September 25, 2005.

The utility of previously generated microarray data is severely limited owing to small study size, leading to under-powered analysis, and failure of replication. Multiplicity of platforms and various sources of systematic noise limit the ability to compile existing data from similar studies. We present a model for transformation of data across different generations of Affymetrix arrays, developed using previously published datasets describing technical replicates performed with two generations of arrays. The transformation is based upon a probe set-specific regression model, generated from replicate measurements across platforms, performed using correlation coefficients. The model, when applied to the expression intensities of 5069 shared, sequence-matched probe sets in three different generations of Affymetrix Human oligonucleotide arrays, showed significant improvement in inter generation correlations between sample-wide means and individual probe set pairs. The approach was further validated by an observed reduction in Euclidean distance between signal intensities across generations for the predicted values. Finally, application of the model to independent, but related datasets resulted in improved clustering of samples based upon their biological, as opposed to technical, attributes. Our results suggest that this transformation method is a valuable tool for integrating microarray datasets from different generations of arrays.


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