|Title||Empirical pathway analysis, without permutation.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Zhou, Yi-Hui, William T. Barry, and Fred A. Wright|
|Date Published||2013 Jul|
|Keywords||Biostatistics, 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.
|Original Publication||Empirical pathway analysis, without permutation.|
|PubMed Central ID||PMC3677738|
|Grant List||P30 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
Empirical pathway analysis, without permutation.