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Time-course high-throughput gene expression data are emerging in genomic andtranslational medicine. Extracting interesting time-course patterns from apatient cohort can provide biological insights for further clinical researchand patient treatment. We propose principal trend analysis (PTA) to extractprincipal 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. Wedemonstrate the usefulness of PTA by applying it to longitudinal geneexpression data of a circadian regulation system and burn patients. Theseapplications show that PTA can extract interesting time-course trends withbiological significance, which helps the understanding of biological mechanismsof 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).

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