title: Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods.
abstract: Gene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained. Benefits of self-contained methods include that they can be used for genome-wide, candidate gene, or pathway studies, and have been reported to be more powerful than competitive methods. We therefore investigated ten self-contained methods that can be used for continuous, discrete and time-to-event phenotypes. To assess the power and type I error rate for the various previously proposed and novel approaches, an extensive simulation study was completed in which the scenarios varied according to: number of genes in a gene set, number of genes associated with the phenotype, effect sizes, correlation between expression of genes within a gene set, and the sample size. In addition to the simulated data, the various methods were applied to a pharmacogenomic study of the drug gemcitabine. Simulation results demonstrated that overall Fisher's method and the global model with random effects have the highest power for a wide range of scenarios, while the analysis based on the first principal component and Kolmogorov-Smirnov test tended to have lowest power. The methods investigated here are likely to play an important role in identifying pathways that contribute to complex traits.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA. fridley.brooke@mayo.edu
authors.forename: Brooke L
authors.initials: BL
authors.lastname: Fridley
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Jenkins
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Biernacka
mesh_terms: D000818:Animals; D003198:Computer Simulation; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards*; D006801:Humans; D010641:Phenotype