Benchmarking deep learning methods for biologically conserved single-cell integration
This benchmark highlights the potential of deep learning-based approaches for single-cell data integration, emphasizing the importance of biologically informed metrics and improved benchmarking strategies.
Authors
- Yixue Li (ORCID: https://orcid.org/0000-0002-1198-7176)
- Chenxin Yi (ORCID: https://orcid.org/0009-0008-6650-9498)
- Jinyu Cheng (ORCID: https://orcid.org/0000-0001-8948-1004)
- Wanquan Liu (ORCID: https://orcid.org/0000-0003-4910-353X)
- Junwei Liu (ORCID: https://orcid.org/0000-0001-8446-4221)
- Jiajun Chen (ORCID: https://orcid.org/0000-0003-1065-9952)
Institutions
- Sun Yat-sen University (CN)
- Chinese University of Hong Kong (HK)
- Shanghai Jiao Tong University (CN)
- Guangzhou Experimental Station (CN)
- Shanghai Institute of Nutrition and Health (CN)
- Guangzhou Regenerative Medicine and Health Guangdong Laboratory (CN)
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality (CN)
- ZheJiang Academy of Agricultural Sciences (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Genome biology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1186/s13059-025-03869-z
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Guangdong Science and Technology Department
- Basic and Applied Basic Research Foundation of Guangdong Province