学术报告

18-04-2022

Learning and Control Lecture 2022系列学术报告—— Deciphering spatial domains from spatially resolved transcript-omics

报告人:张世华 中国科学院数学与系统科学研究院

报告题目:Deciphering spatial domains from spatially resolved transcriptomics

报告时间:2022年4月20日周三 14:00-16:00

报告地点:腾讯会议 ID: 328 757 3757

报告摘要:Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining spatial context of tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we developed a graph attention auto- encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validated STAGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STAGATE could be extended to multiple consecutive sections for reducing batch effects between sections and extracting 3D expression domains from the reconstructed 3D tissue effectively.

报告人简介:张世华,中国科学院数学与系统科学研究院研究员、中国科学院随机复杂结构与数据科学重点实验室副主任、中国科学院大学岗位教授。主要从事生物信息计算、机器智能与优化交叉研究,主要成果发表在Cell、Nature Communications、Advanced Science、National Science Review、Nucleic Acids Research、IEEE TPAMI、IEEE TKDE、IEEE TNNLS等杂志。曾荣获全国百篇优秀博士论文奖(2010)、中国青年科技奖(2013)、中国科学院卢嘉锡青年人才奖(2013)、国家自然科学基金优秀青年基金(2014)、国家万人计划青年拔尖人才(2018)等。成果入选2021年度中国生物信息学十大进展、2019年度中国生物信息学十大算法和工具。

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