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Bioinformatics Advance Access published online on November 17, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm531
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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.

GlobalANCOVA: Exploration and Assessment of Gene Group Effects

Manuela Hummel a,*, Reinhard Meister b and Ulrich Mansmann a,c

aIBE, University of Munich, Germany, bTechnische Fachhochschule Berlin, Germany, cDepartment of Statistics, University of Munich, Germany

*To whom correspondence should be addressed. Dr. Manuela Hummel, E-mail: hummel{at}ibe.med.uni-muenchen.de


  Abstract

Motivation:

Several authors have studied expression in gene sets with specific goals: overrepresentation of interesting genes in functional groups, predictive power for class membership, and searches for groups where the constituent genes show coordinated changes in expression under the experimental conditions. The purpose of this paper is to follow the third direction. One important aspect is that the gene sets under analysis are known a priori and are not determined from the experimental data at hand. Our goal is to provide a methodology that helps to identify the relevant structural constituents (phenotypical, experimental design, biological component) that determine gene expression in a group.

Results:

Gene-wise linear models are used to formalize the structural aspects of a study. The full model is contrasted with a reduced model that lacks the relevant design component. A comparison with respect to goodness of fit is made and quantified. An asymptotic test and a permutation test are derived to test the null hypothesis that the reduced model sufficiently explains the observed expression within the gene group of interest. Graphical tools are available to illustrate and interpret the results of the analysis. Examples demonstrate the wide range of application.

Availability:

The R-package GlobalAncova (http://www.bioconductor.org) offers data and functions as well as a vignette to guide the user through specific analysis steps.

Contact: hummel{at}ibe.med.uni-muenchen.de

Associate Editor: Prof. John Quackenbush

Received on July 9, 2007; revised on September 24, 2007; accepted on October 16, 2007

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