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Bioinformatics Advance Access published online on March 2, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp098
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Multivariate Analysis of Variance Test for Gene Set Analysis

Chen-An Tsai 1 and James J. Chen 2,*

1 Department of Public Health & Biostatistics Center, China Medical University, Taichung, Taiwan, 2 Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA

*To whom correspondence should be addressed. Dr. James Chen, E-mail: jchen{at}nctr.fda.gov


  Abstract

Motivation: Gene class testing (GCT) or gene set analysis (GSA) is a statistical approach to determine whether some functionally predefined sets of genes express differently under different experimental conditions. Shortcomings of the Fisher's exact test for the over-representation analysis are illustrated by an example. Most alternative GSA methods are developed for data collected from two experimental conditions, and most is based on a univariate gene-by-gene test statistic or assume independence among genes in the gene set. A multivariate analysis of variance (MANOVA) approach is proposed for studies with two or more experimental conditions.

Results: When the number of genes in the gene set is greater than the number of samples, the sample covariance matrix is singular and ill-condition. The use of standard multivariate methods can result in biases in the analysis. The proposed MANOVA test uses a shrinkage covariance matrix estimator for the sample covariance matrix. The MANOVA test and six other GSA published methods, PCA, SAM-GS, ANCOVA, Global, GSEA, and MaxMean, are evalu-ated using simulation. The MANOVA test appears to perform the best in terms of control of Type I error and power under the models considered in the simulation. Several publicly available microarray datasets under two and three experimental conditions are analyzed for illustrations of GSA. Most methods, except for GSEA and Max-Mean, generally are comparable in terms of power of identification of significant gene sets.

Availability: A free R-code to perform MANOVA test is available at http://mail.cmu.edu.tw/~catsai/research.htm

Contact: JamesJ.Chen{at}fda.hhs.gov; catsai{at}mail.cmu.edu.tw

Associate Editor: Prof. John Quackenbush

Received on October 7, 2008; revised on February 13, 2009; accepted on February 16, 2009

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