scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data
scAMZI outperforms competing methods and can facilitate downstream analyses such as cell annotation, marker gene discovery, and cell trajectory inference. The package of scAMZI is made freely available at https://doi.org/10.5281/zenodo.13131559 .
Authors
- Lin Yuan (ORCID: https://orcid.org/0000-0003-1038-9980)
- Lan Ye (ORCID: https://orcid.org/0000-0003-3735-5844)
- Zhijie Xu (ORCID: https://orcid.org/0000-0003-2047-883X)
- Boyuan Meng
Institutions
- Qilu University of Technology (CN)
- Shandong University (CN)
- Shandong Academy of Sciences (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1186/s12864-025-11511-2
- Citations
- 43
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 34.66
Funders
- Texas Space Grant Consortium
- Shandong University
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province