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Li, Y.* ; Hu, Q.* ; Han, S.* ; Wang-Sattler, R. ; Du, W.*

Multi-manifolds fusing hyperbolic graph network balanced by pareto optimization for identifying spatial domains of spatial transcriptomics.

Brief. Bioinform. 26:bbaf162 (2025)
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Identifying spatial domains for spatial transcriptomics is crucial for achieving comprehensive insights into the pathogenesis of gene expression. Increasingly, computational methods based on graph neural networks are being developed for spatial transcriptomics. However, previous methods have solely focused on the Euclidean manifold. To effectively exploit and explore the informative and deeper topological structures of inherent manifolds, we presented a Multi-Manifolds fusing hyperbolic graph network, balanced by Pareto optimization, for identifying spatial domains in Spatial Transcriptomics (MManiST). First, we developed multi-manifolds encoders for distinct manifolds using the hyperbolic neural network. Features from different manifolds were then combined using an attention mechanism, with multiple reconstruction losses balanced by Pareto optimization. Extensive experiments on commonly used benchmark datasets show that our method consistently outperforms seven state-of-the-art methods. Additionally, we investigated the validity of each component and the impact of fusion methods in ablation experiments.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Graph Neural Network ; Hyperbolic Space ; Spatial Domain Identification ; Spatial Transcriptomics
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Journal Briefings in Bioinformatics
Quellenangaben Volume: 26, Issue: 2, Pages: , Article Number: bbaf162 Supplement: ,
Publisher Oxford University Press
Publishing Place Great Clarendon St, Oxford Ox2 6dp, England
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Translational Genomics (ITG)
Grants Natural Science Foundation of Jilin Province
National Natural Science Foundation of China