The research of Jun Chen, Ph.D., concerns the development and application of powerful and robust statistical methods for high-dimensional "omics" data, arising from modern high-throughput technologies such as microarray and next-generation sequencing. Dr. Chen is particularly interested in methods for microbiome sequencing data. Much of this effort is motivated by ongoing collaborations in projects that study the role of the human microbiome in disease pathogenesis using metagenomic sequencing.
Dr. Chen's methodology development focuses on efficiently modeling microbiome sequencing data, taking into account its inherent structure and large variability. The ultimate goal of his research is to provide tools to integrate the microbiome data into practice of individualized medicine.
- Quantitative methods for microbiome sequencing data. Dr. Chen's research focuses on addressing the specific data characteristics of microbiome sequencing data — which are phylogeny-constrained, zero-inflated and overdispersed high-dimensional count data — in various statistical or computational problems, including testing association between the microbiome and disease, microbiome subtype discovery, microbiome biomarker discovery, and predictive models integrating the microbiome data.
- Quantitative methods for (epi)genetic and (epi)genomic data. Dr. Chen is also interested in developing statistical methods for (epi)genetic and (epi)genomic studies including (epi)genome-wide association studies, multiple-trait expression quantitative trait loci (eQTL) studies and integrative genomics. He is particularly interested in methods that integrate the microbiome data with other omics data to better understand the biology of disease.
- Collaborative microbiome data analysis. Dr. Chen's collaborations cover many different diseases. He seeks to understand the microbiome etiology of many human diseases by rigorous statistical procedures, including establishing disease-microbiome association, quantifying the microbiome's contribution in disease pathogenicity, identifying disease subtypes, and discovering particular microbiome features that correlate with various disease phenotypes.
Significance to patient care
Dr. Chen's methodology development aims to develop tools to aid in clinical decision-making based on the patient's microbiome profile. Microbiome-based disease subtype can be used for stratifying patients into groups, based on which different treatment can be administered. Predictive models can be used to predict disease risk for disease prevention and diagnosis, drug response for personalized treatment, and prognosis for better treatment management.
- Gerstner Family Career Development Award, Mayo Clinic Center for Individualized Medicine, 2014
- Saul Winegrad Award for Outstanding Dissertation, University of Pennsylvania, 2012