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Bioinformatics Advance Access published online on August 30, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti647
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received May 19, 2005
Revised June 21, 2005
Accepted August 25, 2005

Article

Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data

Lei Xu 1*, Aik Choon Tan 1, Daniel Q. Naiman 2, Donald Geman 2, and Raimond L. Winslow 1

1 The Whitaker Biomedical Engineering Institute and Center for Cardiovascular Bioinformatics and Modeling, The Johns Hopkins University, Baltimore MD 21218 USA
2 The Whitaker Biomedical Engineering Institute and Center for Cardiovascular Bioinformatics and Modeling, The Johns Hopkins University, Baltimore MD 21218 USA; Department of Applied Mathematics and Statistics and Center for Imaging Sciences, The Johns Hopkins University, Baltimore MD 21218 USA

* To whom correspondence should be addressed.
Lei Xu, E-mail: leixu{at}jhu.edu


  Abstract

Motivation: DNA Microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers.

Results: We present a novel, simple method of integrating different microarray data sets to identify marker genes and apply the method to prostate cancer data sets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray data sets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity, and specificity on independent data sets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis.


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