Rochester, Minnesota




Jun Chen, Ph.D., is a quantitative health sciences researcher whose work focuses on 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 and single-cell sequencing data. Much of this effort is motivated by ongoing collaborations in projects that study the role of the human microbiome in disease using metagenomic sequencing and the genomic architecture of vaccine response using single-cell sequencing.

Focus areas

  • Quantitative methods for microbiome and single-cell 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 and computational problems, including testing the association between the microbiome and disease; microbiome subtype discovery; microbiome biomarker discovery; microbiome-based predictive models; and integrating microbiome data with other omics data. Dr. Chen is interested in developing statistical methods for single-cell sequencing data, including cell clustering, differential abundance analysis, differential expression analysis, and multimodal data integration.
  • Statistical methods for high-dimensional and compositional data analysis. Motivated by specific problems in genomic and microbiome data analysis, Dr. Chen is active in developing general statistical methodologies for high-dimensional and compositional data analysis. His research focuses on integrating auxiliary data, including prior biological knowledge and structural information, and on addressing compositional effects in various statistical tasks of high-dimensional data analysis, including multiple testing, regression analysis, canonical correlation analysis, and confounder adjustment.
  • Quantitative methods for epigenetic and epigenomic data. Dr. Chen is also interested in developing statistical methods for epigenetic and epigenomic studies, including epigenome-wide association studies, multiple-trait expression quantitative trait loci (eQTL) studies, and integrative genomics. He is particularly interested in methods that integrate microbiome data with other omics data to better understand the biology of disease.
  • Collaborative biomedical research. Dr. Chen's collaborations span numerous diseases. He seeks to understand the microbiome etiology of many 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 advance tools to aid in clinical decision-making based on each patient's microbiome profile. Microbiome-based disease subtype can be used to stratify 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.

Professional highlights

  • Associate editor for Frontiers in Genetics, 2021-present
  • Associate editor for Statistics in Biosciences, 2021-present
  • Bioinformatics section editor for PeerJ, 2019-present
  • Gerstner Family Career Development Award, Mayo Clinic Center for Individualized Medicine, 2014
  • Saul Winegrad Award for Outstanding Dissertation, University of Pennsylvania, 2013


Primary Appointment

  1. Consultant, Division of Computational Biology, Department of Quantitative Health Sciences

Academic Rank

  1. Professor of Biostatistics


  1. Postdoctoral Fellowship - Biostatistics Harvard School of Public Health
  2. Ph.D. - Genomics and Computational Biology with emphasis in Biostatistics University of Pennsylvania
  3. MA - Statistics University of Pennsylvania
  4. ME - Computer Science (Pattern Recognition and Machine Intelligence) Shanghai Jiao Tong University
  5. MD Fudan University

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