Principal trend analysis for time-course data with applications in genomic medicine

Abstract

Time-course high-throughput gene expression data are emerging in genomic and translational medicine. Extracting interesting time-course patterns from a patient cohort can provide biological insights for further clinical research and patient treatment. We propose principal trend analysis (PTA) to extract principal trends of time-course gene expression data from a group of patients, and identify genes that make dominant contributions to the principal trends. Through simulations, we demonstrate the utility of PTA for dimension reduction, time-course signal recovery and feature selection with high-dimensional data. Moreover, PTA derives new insights in real biological and clinical research. We demonstrate the usefulness of PTA by applying it to longitudinal gene expression data of a circadian regulation system and burn patients. These applications show that PTA can extract interesting time-course trends with biological significance, which helps the understanding of biological mechanisms of circadian regulation systems as well as the recovery of burn patients. Overall, the proposed PTA approach will benefit the genomic medicine research. Our method is implemented into an R-package: PTA (Principal Trend Analysis).