Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning

doi:10.1038/s41467-022-33619-9

	Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning
	Zuo, Chunman 1,2; Zhang, Yijian 3; Cao, Chen 4; Feng, Jinwang 5; Jiao, Mingqi 6; Chen, Luonan2,6,7,8
	2022-12-01
发表期刊	NATURE COMMUNICATIONS (IF:14.7[JCR-2023],16.1[5-Year])
EISSN	2041-1723
卷号	13 期号:1
发表状态	已发表
DOI	10.1038/s41467-022-33619-9
摘要	Spatially resolved transcriptomics (SRT) technology enables us to gain novel insights into tissue architecture and cell development, especially in tumors. However, lacking computational exploitation of biological contexts and multi-view features severely hinders the elucidation of tissue heterogeneity. Here, we propose stMVC, a multi-view graph collaborative-learning model that integrates histology, gene expression, spatial location, and biological contexts in analyzing SRT data by attention. Specifically, stMVC adopting semi-supervised graph attention autoencoder separately learns view-specific representations of histological-similarity-graph or spatial-location-graph, and then simultaneously integrates two-view graphs for robust representations through attention under semi-supervision of biological contexts. stMVC outperforms other tools in detecting tissue structure, inferring trajectory relationships, and denoising on benchmark slices of human cortex. Particularly, stMVC identifies disease-related cell-states and their transition cell-states in breast cancer study, which are further validated by the functional and survival analysis of independent clinical data. Those results demonstrate clinical and prognostic applications from SRT data.
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收录类别	SCIE
语种	英语
Scopus 记录号	2-s2.0-85139513751
来源库	Scopus
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/241044
专题	生物医学工程学院生命科学与技术学院_特聘教授组_陈洛南组
作者单位	1.Institute of Artificial Intelligence,Donghua University,Shanghai,201620,China 2.Key Laboratory of Systems Biology,Shanghai Institute of Biochemistry and Cell Biology,Center for Excellence in Molecular Cell Science,Chinese Academy of Sciences,Shanghai,200031,China 3.Department of General Surgery,Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine,Shanghai,200092,China 4.School of Biomedical Engineering and Informatics,Nanjing Medical University,Nanjing,211166,China 5.Key Laboratory of Information Fusion Technology of Ministry of Education,School of Automation,Northwestern Polytechnical University,Xi’an,710072,China 6.Key Laboratory of Systems Health Science of Zhejiang Province,School of Life Science,Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Hangzhou,310024,China 7.Guangdong Institute of Intelligence Science and Technology,Hengqin,Zhuhai,Guangdong,519031,China 8.School of Life Science and Technology,ShanghaiTech University,Shanghai,201210,China
推荐引用方式 GB/T 7714	Zuo, Chunman,Zhang, Yijian,Cao, Chen,et al. Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning[J]. NATURE COMMUNICATIONS,2022,13(1).
APA	Zuo, Chunman,Zhang, Yijian,Cao, Chen,Feng, Jinwang,Jiao, Mingqi,&Chen, Luonan.(2022).Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning.NATURE COMMUNICATIONS,13(1).
MLA	Zuo, Chunman,et al."Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning".NATURE COMMUNICATIONS 13.1(2022).