报告时间：2026年4月28日（周二）上午 09:00-10:00
报告地点：腾讯会议 584-268-692（密码：260428）
报告人：席瑞斌 教授，北京大学
报告摘要：
Spatial transcriptomics (ST) technologies revolutionized tissue architecture studies by capturing gene expression with spatial context. However, high-dimensional ST data often have limited spatial resolution and exhibit considerable noise and sparsity, posing substantial challenges in deciphering subtle spatial structures and underlying biological activities. Here we introduce 'spatial high-definition embedding mapping' (SpaHDmap), an interpretable dimension reduction framework that enhances spatial resolution by integrating ST gene expression with high-resolution histology images. SpaHDmap incorporates non-negative matrix factorization into a deep learning framework, enabling the identification of high-resolution spatial metagenes (embeddings). Furthermore, SpaHDmap can simultaneously analyse multiple samples and is compatible with various types of histology images. Extensive evaluations on synthetic, public and newly sequenced ST datasets from various technologies and tissue types demonstrate that SpaHDmap can effectively produce high-resolution spatial metagenes, and detect refined spatial structures. SpaHDmap represents a powerful approach for integrating ST data and histology images, offering deeper insights into complex tissue structures and functions.
报告人简介：
席瑞斌，北京大学博雅特聘教授，主持国家杰出青年基金，入选国家海外高层次人才引进计划青年项目，主要研究方向为生物统计、生物信息、大数据以及生命科学中的人工智能。在Nature, Nature Genetics, Nature Cell Biology, Nature Communications, Science Translational Medicine, PNAS, Biometrika, AOAS，NeurIPS, ICML等顶级期刊、会议发表文章70余篇。在现场统计研究会、医学数学专业委员会等学术组织担任秘书长、副会长、常务理事、理事等职务。
邀请人：马欢飞