Title: Modeling and Predicting Subcellular Transcript Organization in Situ
Speaker: Xiang Zhou, Ph.D.
Professor, Department of Statistics and Data Science
Yale University
Time: Friday, September 11, 1:00 PM – 2:00 PM, E.T.
Free Registration link: https://events.teams.microsoft.com/event/311aa92c-9a6d-4573-973e-f98d5bc64beb@e51cdec9-811d-471d-bbe6-dd3d8d54c28b
Abstract: Subcellular spatial transcriptomics provides an unprecedented view of how RNA molecules are organized within individual cells, offering new opportunities to understand cellular function in their native tissue context. In this talk, I will present two complementary approaches for modeling and interpreting subcellular transcript organization. First, I will introduce ELLA, a statistical framework for detecting and characterizing spatial variation in RNA localization within cells, and briefly discuss its extension to two-dimensional cellular representations. I will then present SVC, a Vision Transformer-based framework that integrates subcellular transcript localization with gene function, cell morphology, cell type, and tissue microenvironment to build spatially grounded representations of genes and cells. SVC enables prediction of subcellular expression patterns for unmeasured genes, spatial imputation, characterization of gene localization similarity, and in silico modeling of cellular perturbations. Together, these approaches illustrate how statistical and AI models can move us from describing where RNA is within cells to predicting and modeling cellular organization in situ.
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 in Human Genetics and Statistics at the University of Chicago, he joined the Department of Biostatistics at 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.
Host and Contact: Yang Shi (Email: yangsh AT karmanos.org)
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Yang Shi
Wayne State University
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