Academic Journal
A variational graph autoencoder guided by residual multi-head attention reveals heterogeneity in spatial transcriptomics.
| Τίτλος: | A variational graph autoencoder guided by residual multi-head attention reveals heterogeneity in spatial transcriptomics. |
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| Συγγραφείς: | Li J; School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China., Lv H; School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China., Jiang F; Editorial Office of Journal of Qingdao University of Science and Technology (Natural Science Edition), Qingdao University of Science and Technology, Qingdao 266061, China. Electronic address: jfhict@qust.edu.cn., Yu B; School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China. Electronic address: yubin@qust.edu.cn. |
| Πηγή: | Computational biology and chemistry [Comput Biol Chem] 2026 Oct; Vol. 124 (Pt 1), pp. 109112. Date of Electronic Publication: 2026 May 06. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: England NLM ID: 101157394 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-928X (Electronic) Linking ISSN: 14769271 NLM ISO Abbreviation: Comput Biol Chem Subsets: MEDLINE |
| Imprint Name(s): | Publication: Oxford : Elsevier Original Publication: Oxford : Pergamon, c2003- |
| Ιατρικοί όροι (MeSH): | Spatial Transcriptomics*, Autoencoder ; Graph Neural Networks ; Animals ; Humans |
| Περίληψη: | Spatial transcriptomics has recently emerged as a crucial technical tool for uncovering tissue structural heterogeneity and cellular spatial relationships, as it simultaneously captures gene expression data and spatial location information. However, current methods still have limitations in multimodal information integration, local boundary delineation, and the modeling of complex spatial dependencies, making it difficult to adequately capture the spatial structural features of tissues where local and global characteristics coexist. To this end, this paper proposes RMVGAE, a spatial transcriptomics analysis model that combines residual multi-head attention mechanisms with variational graph autoencoders. The model takes histological images, gene expression matrices, and spatial location information as inputs. It constructs morphological similarity matrices, gene similarity matrices, and spatial adjacency matrices, achieving enhanced multimodal representations through neighborhood feature fusion. Critical features are captured by introducing a residual multi-head attention module, and latent representations of spatial spots are learned using a variational graph autoencoder. The experimental results indicate that RMVGAE is capable of identifying spatial domains more accurately across multiple spatial transcriptomic datasets. Differential expression analysis and enrichment analysis further demonstrate that this model has significant biological relevance and application potential. Ablation experiments also validated the effectiveness of the designed residual multi-head attention module in spatial domain recognition. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Residual multi-head attention; Spatial domain identification; Spatial transcriptomics; Variational graph autoencoder |
| Entry Date(s): | Date Created: 20260514 Date Completed: 20260618 Latest Revision: 20260618 |
| Update Code: | 20260619 |
| DOI: | 10.1016/j.compbiolchem.2026.109112 |
| PMID: | 42134147 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1476-928X |
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| DOI: | 10.1016/j.compbiolchem.2026.109112 |