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Open Access

Peer-reviewed

Research Article

SpaVGN: A hybrid deep learning framework for high-resolution spatial transcriptomics data reconstruction and spatial domain identification

Haiyan Wang ,

Contributed equally to this work with: Haiyan Wang, Yanping Zhang

Roles Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Mathematics and Physics, Hebei University of Engineering, Handan, China
⨯
Yanping Zhang ,

Contributed equally to this work with: Haiyan Wang, Yanping Zhang

Roles Conceptualization, Investigation, Project administration, Resources, Supervision

Affiliation School of Mathematics and Physics, Hebei University of Engineering, Handan, China
⨯
Yangyang Zhang,

Roles Writing – review & editing

Affiliation School of Mathematics and Physics, Hebei University of Engineering, Handan, China
⨯
Xuening Zhao,

Roles Writing – review & editing

Affiliation School of Mathematics and Physics, Hebei University of Engineering, Handan, China
⨯
Zijia Bai,

Roles Writing – review & editing

Affiliation School of Mathematics and Physics, Hebei University of Engineering, Handan, China
⨯
Xuejing Ma,

Roles Writing – review & editing

Affiliation School of Mathematics and Physics, Hebei University of Engineering, Handan, China
⨯
Chunguang Zhao

Roles Conceptualization, Funding acquisition, Supervision

* E-mail: zhaochunguang888@126.com

Affiliation School of Mathematics and Physics, Handan University, Handan, China

https://orcid.org/0009-0002-5723-138X

⨯

SpaVGN: A hybrid deep learning framework for high-resolution spatial transcriptomics data reconstruction and spatial domain identification

Haiyan Wang,
Yanping Zhang,
Yangyang Zhang,
Xuening Zhao,
Zijia Bai,
Xuejing Ma,
Chunguang Zhao

x

Published: August 14, 2025
https://doi.org/10.1371/journal.pone.0329122

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