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Bioinformatics Advance Access originally published online on November 27, 2007
Bioinformatics 2008 24(2):192-201; doi:10.1093/bioinformatics/btm583
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis

Dan Nettleton 1,*, Justin Recknor 2 and James M. Reecy 3

1Department of Statistics, Iowa State University, Ames, Iowa 50011-1210, USA, 2Eli Lilly and Company, Lilly Research Laboratories, P.O. Box 708, Greenfield, Indiana 46140 and 3Department of Animal Science, Iowa State University, Ames, Iowa 50011-3150, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The field of microarray data analysis is shifting emphasis from methods for identifying differentially expressed genes to methods for identifying differentially expressed gene categories. The latter approaches utilize a priori information about genes to group genes into categories and enhance the interpretation of experiments aimed at identifying expression differences across treatments. While almost all of the existing approaches for identifying differentially expressed gene categories are practically useful, they suffer from a variety of drawbacks. Perhaps most notably, many popular tools are based exclusively on gene-specific statistics that cannot detect many types of multivariate expression change.

Results: We have developed a nonparametric multivariate method for identifying gene categories whose multivariate expression distribution differs across two or more conditions. We illustrate our approach and compare its performance to several existing procedures via the analysis of a real data set and a unique data-based simulation study designed to capture the challenges and complexities of practical data analysis. We show that our method has good power for differentiating between differentially expressed and non-differentially expressed gene categories, and we utilize a resampling based strategy for controling the false discovery rate when testing multiple categories.

Availability: R code (www.r-project.org) for implementing our approach is available from the first author by request.

Contact: dnett{at}iastate.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: John Quackenbush

Received on June 27, 2007; revised on October 11, 2007; accepted on November 19, 2007

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