成人VR视频

Xiang Zhou, PhD

Professor of Statistics and Data Science| Yale

WHEN: Wednesday, March 11, 2026, from 3:30 to 4:30 p.m.
WHERE: Hybrid | 2001 成人VR视频 College Avenue, Rm 1140;
NOTE:聽Xiang Zhou will be presenting in-person at SPGH聽

Abstract

Spatial transcriptomics technologies enable the measurement of gene expression with spatial context. Detecting spatially variable genes (SVGs) is a central task in the analysis of such data.In this talk, I will present several computational methods developed by our group for the statistical detection of SVGs at multiple biological resolutions. I will first discuss SPARK, a statistical framework for rigorous identification of spatially expressed genes, and SPARK-X, a nonparametric extension designed for rapid and scalable SVG detection in large spatial transcriptomic studies. I will then introduce CELINA, which focuses on detecting cell type鈥搒pecific spatially variable genes, and ELLA, which models subcellular mRNA localization to identify genes exhibiting within-cell spatial variation in high-resolution spatial transcriptomics data. Together, these methods provide a comprehensive toolkit for detecting spatial gene expression patterns at the tissue, cell-type, and subcellular levels.

Speaker Bio

Xiang Zhou is a Professor in the Department of Statistics and Data Science at Yale University. He received a BS in Biology from Peking University and earned both an MS in Statistics and a PhD in Neurobiology from Duke University. After postdoctoral training and instructorship at the University of Chicago, he joined the University of Michigan in 2014, where he rose to full Professor and held leadership roles in Precision Health and AI & Digital Health Innovation before moving to Yale in 2025. Dr. Zhou is a Fellow of the American Statistical Association and a recipient of the 2024 MBioFAR Award and the 2025 ICIBM Eminent Scholar Award. He serves on the NIH MRAA Study Section and as an Associate Editor for PLOS Genetics and Journal of the American Statistical Association. His research focuses on genomic data science, developing statistical and machine learning methods, including deep learning and AI, for large-scale genetic and genomic data, with applications in GWAS, single-cell sequencing, and spatial multi-omics.