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Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder
Dong, Kangning1,2; Zhang, Shihua1,2,3,4
2022-04-01
Source PublicationNATURE COMMUNICATIONS
Volume13Issue:1Pages:12
AbstractBreakthrough technologies for spatially resolved transcriptomics have enabled genome-wide profiling of gene expressions in captured locations. Here the authors integrate gene expressions and spatial locations to identify spatial domains using an adaptive graph attention auto-encoder. Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we develop 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 validate 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 to reduce batch effects between sections and extracting three-dimensional (3D) expression domains from the reconstructed 3D tissue effectively.
DOI10.1038/s41467-022-29439-6
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2019YFA0709501] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA16021400] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDPB17] ; KeyArea Research and Development of Guangdong Province[2020B1111190001] ; National Natural Science Foundation of China[12126605] ; National Natural Science Foundation of China[61621003] ; National Ten Thousand Talent Program for Young Top-notch Talents ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSWSYS008]
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000777408600032
PublisherNATURE PORTFOLIO
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/60282
Collection应用数学研究所
Corresponding AuthorZhang, Shihua
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, CEMS,NCMIS, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
4.Chinese Acad Sci, Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Key Lab Syst Biol, Hangzhou 310024, Peoples R China
Recommended Citation
GB/T 7714
Dong, Kangning,Zhang, Shihua. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder[J]. NATURE COMMUNICATIONS,2022,13(1):12.
APA Dong, Kangning,&Zhang, Shihua.(2022).Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder.NATURE COMMUNICATIONS,13(1),12.
MLA Dong, Kangning,et al."Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder".NATURE COMMUNICATIONS 13.1(2022):12.
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