Empirical pathway analysis, without permutation.

TitleEmpirical pathway analysis, without permutation.
Publication TypeJournal Article
Year of Publication2013
AuthorsZhou, Yi-Hui, William T. Barry, and Fred A. Wright
Date Published2013 Jul
KeywordsBiostatistics, Breast Neoplasms, Computer Simulation, Databases, Genetic, Disease-Free Survival, Female, Gene Expression Profiling, Gene Regulatory Networks, Genes, p53, Humans, Models, Genetic, Models, Statistical, Salivary Glands, Software, Stochastic Processes

Resampling-based expression pathway analysis techniques have been shown to preserve type I error rates, in contrast to simple gene-list approaches that implicitly assume the independence of genes in ranked lists. However, resampling is intensive in computation time and memory requirements. We describe accurate analytic approximations to permutations of score statistics, including novel approaches for Pearson's correlation, and summed score statistics, that have good performance for even relatively small sample sizes. Our approach preserves the essence of permutation pathway analysis, but with greatly reduced computation. Extensions for inclusion of covariates and censored data are described, and we test the performance of our procedures using simulations based on real datasets. These approaches have been implemented in the new R package safeExpress.

Alternate JournalBiostatistics
Original PublicationEmpirical pathway analysis, without permutation.
PubMed ID23428933
PubMed Central IDPMC3677738
Grant ListP30 ES010126 / ES / NIEHS NIH HHS / United States
P42ES005948 / ES / NIEHS NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P30ES010126 / ES / NIEHS NIH HHS / United States