首页|Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks

Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Spatially resolved transcriptomics(SRT)is capable of comprehensively characterizing gene expression patterns and providing an unbiased image of spatial composition.To fully understand the organizational complexity and tumor immune escape mechanism,we propose stMGATF,a multiview graph attention fusion model that integrates gene expression,histological images,spatial location,and gene association.To better extract information,stMGATF exploits SimCLRv2 for visual feature exaction and employs edge feature enhanced graph attention networks for the learning potential embedding of each view.A global attention mechanism is used to adaptively integrate 3 views to obtain low-dimensional representation.Applied to diverse SRT datasets,stMGATF is robust and outperforms other methods in detecting spatial domains and denoising data even with different resolutions and platforms.In particular,stMGATF contributes to the elucidation of tissue heterogeneity and extraction of 3-dimensional expression domains.Importantly,considering the associations between genes in tumors,stMGATF can identify the spatial dark genes ignored by traditional methods,which can be used to predict tumor-driving transcription factors and reveal tumor immune escape mechanisms,providing theoretical evidence for the development of new immunotherapeutic strategies.

Ying Li、Yuejing Lu、Chen Kang、Peiluan Li、Luonan Chen

展开 >

School of Mathematics and Statistics,Henan University of Science and Technology,Luoyang,471023,China

Longmen Laboratory,Luoyang,Henan,471003,China

Key Laboratory of Systems Biology,Institute of Biochemistry and Cell Biology,Center for Excellence in Molecular Cell Science,Chinese Academy of Sciences,Shanghai,201100,China

Key Laboratory of Systems Health Science of Zhejiang Province,Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Hangzhou,310000,China

School of Life Science and Technology,ShanghaiTech University,Shanghai,201100,China

展开 >

National Key R&D Program of ChinaStrategic Priority Research Program of the Chinese Academy of SciencesNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaSpecial Fund for Science and Technology Innovation Strategy of Guangdong ProvinceSpecial Fund for Science and Technology Innovation Strategy of Guangdong ProvinceKey-Area Research and Development Program of Guangdong ProvinceJST Moonshot R&DMajor projects of Henan Province

2022YFA1004800XDB380404001213102031930022T2350003T23410072021B09090500042021B09090600022021B0909060002JPMJMS2021231100220100

2024

10.34133/research.0228

研究(英文)

研究(英文)

CSTPCD
ISSN：
年,卷(期)：2024.2024(2)
  • 58