A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis.

ABSTRACT: For pathway analysis of genomic data, the most common methods involve combining p-values from individual statistical tests. However, there are several multivariate statistical methods that can be used to test whether a pathway has changed. Because of the large number of variables and pathway sizes in genomics data, some of these statistics cannot be computed. However, in metabolomics data, the number of variables and pathway sizes are typically much smaller, making such computations feasible. Of particular interest is being able to detect changes in pathways that may not be detected for the individual variables. We compare the performance of both the p-value methods and multivariate statistics for self-contained tests with an extensive simulation study and a human metabolomics study. Permu

SUBMITTER: Mitchell MW 

PROVIDER: S-EPMC4415974 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature