title: Rotation gene set testing for longitudinal expression data.
abstract: Gene set analysis methods are popular tools for identifying differentially expressed gene sets in microarray data. Most existing methods use a permutation test to assess significance for each gene set. The permutation test's assumption of exchangeable samples is often not satisfied for time-series data and complex experimental designs, and in addition it requires a certain number of samples to compute p-values accurately. The method presented here uses a rotation test rather than a permutation test to assess significance. The rotation test can compute accurate p-values also for very small sample sizes. The method can handle complex designs and is particularly suited for longitudinal microarray data where the samples may have complex correlation structures. Dependencies between genes, modeled with the use of gene networks, are incorporated in the estimation of correlations between samples. In addition, the method can test for both gene sets that are differentially expressed and gene sets that show strong time trends. We show on simulated longitudinal data that the ability to identify important gene sets may be improved by taking the correlation structure between samples into account. Applied to real data, the method identifies both gene sets with constant expression and gene sets with strong time trends.
authors.affiliation: Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, N-1432 Aas, Norway.
authors.forename: Guro
authors.initials: G
authors.lastname: Dørum
authors.forename: Lars
authors.initials: L
authors.lastname: Snipen
authors.forename: Margrete
authors.initials: M
authors.lastname: Solheim
authors.forename: Solve
authors.initials: S
authors.lastname: Saebø
mesh_terms: D000704:Analysis of Variance; D001699:Biometry / Q000379:methods*; D013293:Enterococcus faecalis / Q000235:genetics / Q000502:physiology; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D016014:Linear Models; D008137:Longitudinal Studies; D013312:Stress, Physiological / Q000235:genetics
keywords: Gene expression; Gene set analysis; Longitudinal microarray data; Rotation test; Time series