Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder
Chinese Academy of Sciences · National Center for Mathematics and Interdisciplinary Sciences · +3 more institutions
Abstract
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…
Citation impact
- FWCI
- 49.20
- Percentile
- 100%
- References
- 45
Authors
2- KDKangning DongCorresponding
Chinese Academy of Sciences, National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences
- SZShihua Zhang
Kunming Institute of Zoology, Chinese Academy of Sciences, National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences
Topics & keywords
- Computer science
- Spatial analysis
- Spatial contextual awareness
- Context (archaeology)
- Graph
- Similarity (geometry)
- Cluster analysis
- Pattern recognition (psychology)
Funding
- NNNational Natural Science Foundation of ChinaAwards: 61621003, 12126605, 61621003, 12126605, 2019YFA0709501, XDA16021400
- CAChinese Academy of SciencesAwards: QYZDB-SSW-SYS008, XDA16021400, XDPB17
- NKNational Key Research and Development Program of ChinaAward: 2019YFA0709501
- NTNational Ten Thousand Talent Program