GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data
GNODEVAE establishes a robust computational framework that synergistically combines neighborhood-awareness, dynamic modeling, and probabilistic expressiveness for single-cell multi-omics analysis. The consistent superior performance across diverse datasets demonstrates its effectiveness as a versatile tool for cell clustering, dimensionality reduction, and pseudotime trajectory analysis in both scRNA-seq and scATAC-seq data mining.
Authors
- Shilei Chen (ORCID: https://orcid.org/0000-0002-5281-8918)
- Zeyu Fu (ORCID: https://orcid.org/0009-0001-8329-0108)
- Chunlin Chen
- Junping Wang (ORCID: https://orcid.org/0000-0003-1190-1448)
- Song Wang (ORCID: https://orcid.org/0000-0003-1252-8091)
Institutions
- Army Medical University (CN)
- Sun Yat-sen University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2025-08-21
- DOI
- https://doi.org/10.1186/s12864-025-11946-7
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China