報告題目：Deciphering spatial domains from spatially resolved         transcriptomics

報告人：張世華（中國科學院數學與系統科學研究院）

報告時間：2022年5月17日下午4:00-5:00

報告形式：騰訊會議ID：468-751-569

內容簡介：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.

報告人簡介： 張世華，中國科學院數學與系統科學研究院研究員、中國科學院隨機復雜結構與數據科學重點實驗室副主任、中國科學院大學崗位教授。主要從事生物信息計算、機器智能與優化交叉研究，主要成果發表在Cell、Nature Communications、Advanced Science、Cell Reports、National Science Review、Science Bulletin、Nucleic Acids Research、IEEE TPAMI、IEEE TKDE、IEEE TNNLS等雜志。曾榮獲全國百篇優秀博士論文獎（2010）、中國青年科技獎（2013）、中國科學院盧嘉錫青年人才獎（2013）、國家自然科學基金優秀青年基金（2014）等。成果入選2021年度中國生物信息學十大進展、2019年度中國生物信息學十大算法和工具。

（撰稿人：雷錦志；審稿人：郭永峰）

數學科學學院

2022年5月12日