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Bioinformatics Advance Access published online on January 12, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti260
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Bioinformatics © Oxford University Press 2005; all rights reserved.
Received October 30, 2004
Revised December 31, 2004
Accepted December 31, 2004

Article

Significance analysis of functional categories in gene expression studies: a structured permutation approach

William T. Barry 1, Andrew B. Nobel 2, and Fred A. Wright 1*

1 Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina 27599-7420, USA
2 Department of Statistics, University of North Carolina at Chapel Hill, North Carolina 27599-3260, USA

* To whom correspondence should be addressed.
Fred A. Wright, E-mail: fwright{at}bios.unc.edu


  Abstract

Motivation: In high-throughput genomic and proteomic experiments, investigators monitor expression across a set of experimental conditions. To gain an understanding of broader biological phenomena, researchers have until recently been limited to post hoc analyses of significant gene lists.

Method: We describe a general framework, Significance Analysis of Function and Expression (SAFE), for conducting valid tests of gene categories ab initio. SAFE is a 2-stage, permutation-based method that can be applied to various experimental designs, accounts for the unknown correlation among genes, and enables permutation-based estimation of error rates.

Results: The utility and flexibility of SAFE is illustrated with a microarray dataset of human lung carcinomas and gene categories based on Gene Ontology and the Protein Family database. Significant gene categories were observed in comparisons of (1) tumor versus normal tissue, (2) multiple tumor subtypes, and (3) survival times.

Availability: Code to implement SAFE in the statistical package R is available from the authors.


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