A Pipeline for High-Dimensional Time Course Gene Expression Data to Study Dynamic Network Responses to Viral Infections
Hulin Wu, Ph.D., Dean’s Professor
Department of Biostatistics and Computational Biology
Director, Center for Integrative Bioinformatics and Experimental Mathematics
University of Rochester School of Medicine and Dentistry
A new pipeline for high-dimensional time course gene expression data is developed based on the concept of function data analysis (FDA) with a purpose to study dynamic network responses at gene level. The pipeline includes significant testing for dynamic response genes (DRGs), clustering gene response curves, constructing dynamic gene response networks using differential equation models, network feature analysis, dynamic system analysis, and biological annotations. Novel statistical methods and modeling approaches are developed for the pipeline, which include high-dimensional ODE model selection, parameter estimation, and dynamic system characteristic analysis. We illustrate the pipeline and the proposed methods using genome-wide time course gene expression data from mice and human subjects challenged by influenza viruses. Some interesting biological findings will be discussed.