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演讲摘要：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.
讲者简介：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、Advanced Science、National Science Review、Nature Communications、Nucleic Acids Research、Science Bulletin、Bioinformatics、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。
第六届中国计算机学会生物信息学会议
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