Statistical Modeling and Inference for Gene Networks from Single Cell Data
Abstract: Advances in single-cell RNA sequencing and multimodal technologies have opened new opportunities for inferring gene networks and regulatory relationships in specific cell types, enriching our understanding of cell-type-specific biological functions. However, unique data characteristics such as sequencing depth variation, high data sparsity, and measurement error present significant challenges. In this talk, I will present two statistical methods that address these challenges. CS-CORE infers cell-type-specific gene co-expressions from scRNA-seq data and scMultiMap maps enhancer-gene pairs from paired scRNA-seq and scATAC-seq data. Both methods achieve accurate type-I error control, high reproducibility, scalability, and provide new insights into Alzheimer’s disease mechanisms.
Bio: Dr. Zhang’s research focuses on the development of statistical methods and theory for high-dimensional networks, graphs, tensors, and point processes, with applications in business, social sciences, biology, and medicine. Her work has appeared in leading journals in statistics, machine learning, economics, and biology, and has been supported by grants from the National Science Foundation. She is an elected Fellow of the American Statistical Association (ASA) and serves as an Associate Editor for the Annals of Applied Statistics, Statistica Sinica, and the Journal of the American Statistical Association. She is currently the Area Coordinator for the ISOM area at Goizueta Business School. She received her Ph.D. in Statistics from the University of Illinois at Urbana-Champaign. Prior to joining Emory, she was an Associate Professor of Management Science at the University of Miami, where she also held a secondary appointment in Public Health Sciences.
This event is open to the NYU community for in-person attendance. The general public may RSVP for virtual attendance.