Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.

ABSTRACT: Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indi

SUBMITTER: Hejblum BP 

PROVIDER: S-EPMC4482329 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature