Translator Disclaimer
June 2007 Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis
Michael A. Newton, Fernando A. Quintana, Johan A. den Boon, Srikumar Sengupta, Paul Ahlquist
Ann. Appl. Stat. 1(1): 85-106 (June 2007). DOI: 10.1214/07-AOAS104


A prespecified set of genes may be enriched, to varying degrees, for genes that have altered expression levels relative to two or more states of a cell. Knowing the enrichment of gene sets defined by functional categories, such as gene ontology (GO) annotations, is valuable for analyzing the biological signals in microarray expression data. A common approach to measuring enrichment is by cross-classifying genes according to membership in a functional category and membership on a selected list of significantly altered genes. A small Fisher’s exact test p-value, for example, in this 2×2 table is indicative of enrichment. Other category analysis methods retain the quantitative gene-level scores and measure significance by referring a category-level statistic to a permutation distribution associated with the original differential expression problem. We describe a class of random-set scoring methods that measure distinct components of the enrichment signal. The class includes Fisher’s test based on selected genes and also tests that average gene-level evidence across the category. Averaging and selection methods are compared empirically using Affymetrix data on expression in nasopharyngeal cancer tissue, and theoretically using a location model of differential expression. We find that each method has a domain of superiority in the state space of enrichment problems, and that both methods have benefits in practice. Our analysis also addresses two problems related to multiple-category inference, namely, that equally enriched categories are not detected with equal probability if they are of different sizes, and also that there is dependence among category statistics owing to shared genes. Random-set enrichment calculations do not require Monte Carlo for implementation. They are made available in the R package allez.


Download Citation

Michael A. Newton. Fernando A. Quintana. Johan A. den Boon. Srikumar Sengupta. Paul Ahlquist. "Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis." Ann. Appl. Stat. 1 (1) 85 - 106, June 2007.


Published: June 2007
First available in Project Euclid: 29 June 2007

zbMATH: 1129.62103
MathSciNet: MR2393842
Digital Object Identifier: 10.1214/07-AOAS104

Rights: Copyright © 2007 Institute of Mathematical Statistics


Vol.1 • No. 1 • June 2007
Back to Top