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Biostatistics Advance Access published online on September 14, 2007
Biostatistics, doi:10.1093/biostatistics/kxm033
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© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Cross-study validation and combined analysis of gene expression microarray data

Elizabeth Garrett-Mayer*

Division of Biostatistics, The Hollings Cancer Center, Medical University of South Carolina, Charleston, SC 29425, USA garrettm{at}musc.edu

Giovanni Parmigiani

Division of Biostatistics, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA

Xiaogang Zhong

Department of Mathematics and Applied Statistics, Johns Hopkins University, Baltimore, MD 21218, USA

Leslie Cope

Division of Biostatistics, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA

Edward Gabrielson

Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA

* To whom correspondence should be addressed.

Investigations of transcript levels on a genomic scale using hybridization-based arrays have led to formidable advances in our understanding of the biology of many human illnesses. At the same time, these investigations have generated controversy because of the probabilistic nature of the conclusions and the surfacing of noticeable discrepancies between the results of studies addressing the same biological question. In this article, we present simple and effective data analysis and visualization tools for gauging the degree to which the findings of one study are reproduced by others and for integrating multiple studies in a single analysis. We describe these approaches in the context of studies of breast cancer and illustrate that it is possible to identify a substantial biologically relevant subset of the human genome within which hybridization results are reliable. The subset generally varies with the platforms used, the tissues studied, and the populations being sampled. Despite important differences, it is also possible to develop simple expression measures that allow comparison across platforms, studies, laboratories and populations. Important biological signals are often preserved or enhanced. Cross-study validation and combination of microarray results requires careful, but not overly complex, statistical thinking and can become a routine component of genomic analysis.

Keywords: Breast cancer; Intraclass correlation; Meta-analysis; Prinicipal components; Reliability

Received December 22, 2007; revised May 22, 2007; revised July 16, 2007; accepted for publication August 7, 2007.


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