scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling
In this paper, we validate the proposed scE2EGAE method through its application to the denoising task of scRNA-Seq data. This method demonstrates its capability to learn inter-cellular relationships and construct cell-to-cell graphs, thereby enhancing the downstream analysis of scRNA-Seq data. Our approach can serve as an inspiration for future research on scRNA-Seq analysis methods based on GNNs, holding broad application prospects.
Authors
- Yuanning Liu (ORCID: https://orcid.org/0000-0001-7926-7936)
- Hao Zhang (ORCID: https://orcid.org/0000-0002-2058-7123)
- Shuo Wang (ORCID: https://orcid.org/0000-0003-4810-8785)
- Zhen Liu (ORCID: https://orcid.org/0009-0006-0168-2711)
Institutions
- Nagasaki Institute of Applied Science (JP)
- Jilin University (CN)
Publication Details
- Journal
- Biology Direct
- Published
- 2025-05-27
- DOI
- https://doi.org/10.1186/s13062-025-00616-z
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Jilin Province
- National Natural Science Foundation of China
- People's Government of Jilin Province
- Industrial Innovation Funds of Jilin Province of China