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Bioinformatics Advance Access published online on May 3, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn215
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

An efficient method to identify differentially expressed genes in microarray experiments

Huaizhen Qin 1, Tao Feng 1,3, Scott A. Harding 2, Chung-Jui Tsai 2 and Shuanglin Zhang 1,3,*

1Department of Mathematical Sciences and 2Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, 49931, USA and 3Department of Mathematics, Heilongjiang University, Harbin 150080, China

*To whom correspondence should be addressed. Dr. Shuanglin Zhang, E-mail: shuzhang{at}mtu.edu


  Abstract

Motivation: Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss.

Results: We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes.

Availability: The C++ code to implement the proposed method is available upon request for academic use.

Contact: shuzhang{at}mtu.edu

Associate Editor: Prof. Quackenbush

Received on July 25, 2007; revised on March 25, 2008; accepted on April 29, 2008

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