A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering
scCAGN integrates AAE and cross-attention GCN with dynamic fusion, achieving state-of-the-art scRNA-seq clustering (0.9732 NMI, 13% average gain) across eight datasets. Validated via ablation and hyperparameter tests, it advances label-free cell discovery and enables further multimodal integration to dissect cellular heterogeneity.
Authors
- Xinyu Gao (ORCID: https://orcid.org/0000-0001-5340-4816)
- Yingying Feng (ORCID: https://orcid.org/0000-0002-5901-6846)
- Binhua Tang (ORCID: https://orcid.org/0000-0003-4744-5835)
Institutions
- Hohai University (CN)
- Fudan University (CN)
- Changzhou University (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1186/s12864-025-11941-y
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Education of the People's Republic of China
- Hohai University