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Bioinformatics Advance Access originally published online on May 19, 2005
Bioinformatics 2005 21(14):3105-3113; doi:10.1093/bioinformatics/bti496
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Hotelling's T2 multivariate profiling for detecting differential expression in microarrays

Yan Lu 1, Peng-Yuan Liu 1, Peng Xiao 1 and Hong-Wen Deng 2,1,3,*

1Osteoporosis Research Center, Creighton University 601 N. 30th Street, Suite 6787, Omaha, NE 68131, USA
2The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University Xi'an 710049, PR China
3Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University Changsha, Hunan 410081, PR China

*To whom correspondence should be addressed.

Summary: The most widely used statistical methods for finding differentially expressed genes (DEGs) are essentially univariate. In this study, we present a new T2 statistic for analyzing microarray data. We implemented our method using a multiple forward search (MFS) algorithm that is designed for selecting a subset of feature vectors in high-dimensional microarray datasets. The proposed T2 statistic is a corollary to that originally developed for multivariate analyses and possesses two prominent statistical properties. First, our method takes into account multidimensional structure of microarray data. The utilization of the information hidden in gene interactions allows for finding genes whose differential expressions are not marginally detectable in univariate testing methods. Second, the statistic has a close relationship to discriminant analyses for classification of gene expression patterns. Our search algorithm sequentially maximizes gene expression difference/distance between two groups of genes. Including such a set of DEGs into initial feature variables may increase the power of classification rules. We validated our method by using a spike-in HGU95 dataset from Affymetrix. The utility of the new method was demonstrated by application to the analyses of gene expression patterns in human liver cancers and breast cancers. Extensive bioinformatics analyses and cross-validation of DEGs identified in the application datasets showed the significant advantages of our new algorithm.

Availability: The program for the method proposed in this article and supplementary material are available at http://orclinux.creighton.edu/hotelling/index.htm

Contact: deng{at}creighton.edu

Supplementary information: http://orclinux.creighton.edu/hotelling/index.htm

Received on October 13, 2004; revised on April 15, 2005; accepted on April 15, 2005

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