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Sun
Wei Sun, PhD
- Professor, Biostatistics Program, Public Health Sciences Division, Fred Hutch
- Member, Immunotherapy Integrated Research Center (IIRC), Fred Hutch
- Member, Translational Data Science Integrated Research Center (TDS IRC), Fred Hutch
- Affiliated Professor, Biostatistics, University of Washington
Background
Dr. Wei Sun is an expert in biostatistics who develops statistical and computational methods and software packages to analyze data for different types of “omics” studies — such as genomics (the study of genes) and proteomics (the study of proteins). His work supports the integration of multiple types of “omics” data from tumor samples to study intra-tumor heterogeneity and tumor microenvironments.
Affiliated Professor, Biostatistics, University of North Carolina Chapel Hill
Education
PhD, Statistics, University of California at Los Angeles, 2007
MS, Statistics, University of California at Los Angeles, 2004
BS, Statistics, Peking University, Beijing, 2002
Research Interests
Develop statistical/computational methods and software packages for different types of omic data
Immunomics, including T cell receptor, HLA, and neoantigen data analysis
Deep learning and AI, foundation model for omic, medical imaging or molecular imaging data
Awards & Honors
2017 - Elected Fellow, American Statistical Association
2010 - Junior Faculty Development Awards, UNC-Chapel Hill
Current Projects
Single cell RNA-seq data analysis, scRNA-seq foundation models
Spatial transcriptomic/proteomic data, tumor immune microenvironment
Analysis of omic data from gene knock out studies, e.g., perturb-seq or CIRSPR-Cas9 KO cell lines
Liquid Biopsy, cancer early detection by multi-modality data
Statistical Methods for RNA-seq Data Analysis
Principal Investigator: Wei Sun
This project aims to develop interpretable and robust deep learning methods for supervised analysis of single cell RNA-seq data or spatial transcriptomic data. Our methods and data analysis results will help translate the rich information in single cell RNA-seq or spatial transcriptomics data into knowledge of biology and potentially actionable conclusions to improve human health.
Statistical Methods for Inferring Gene-Phenotype Associations Using Omic Data from Gene Knockout and Human Phenotype Studies
Multiple Principal Investigator: Wei Sun (contact), Li Hsu, Ali Shojaie (UW)
This project develops statistical methods to combine MorPhiC resources and omic data in human phenotype studies to infer gene functions in diverse settings and identify potential causal genes of human phenotypes. These methods will pave the way for utilizing MorPhiC resources to study the impact of gene loss on complex phenotypes.
Precompetitive Collaboration on Liquid Biopsy for Early Cancer Assessment: Data Management and Coordinating Unit
Multiple Principal Investigator submission: Yingye Zheng (contact)/Wei Sun MPI
This project supports the liquid biopsy consortium for consortium coordination, data management, protocol development, as well as innovative and state-of-the-art statistical and computational analysis.
Selected Scientific Publications
Liu S, Bradley P, Sun W. Neural network models for sequence-based TCR and HLA association prediction. PLOS Computational Biology. 2023 Nov 20;19(11):e1011664.
Little P, Liu S, Zhabotynsky V, Li Y, Lin DY, Sun W. A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data. Nature Communications. 2023 May 25;14(1):3030.
Zhang M, Liu S, Miao Z, Han F, Gottardo R, Sun W. IDEAS: individual level differential expression analysis for single-cell RNA-seq data. Genome biology. 2022 Jan 24;23(1):33.
Jin C, Chen M, Lin DY, Sun W. Cell-type-aware analysis of RNA-seq data. Nature Computational Science. 2021 Apr;1(4):253-61.
Sun W. A statistical framework for eQTL mapping using RNA-seq data. Biometrics. 2012 Mar;68(1):1-1.