BiAEImpute: a robust bidirectional autoencoder framework for High-fidelity dropout imputation in single-cell transcriptomics
BiAEImpute proves to be efficacious and resilient in the imputation of missing data in scRNA-seq, contributing to enhanced accuracy in downstream analyses. The source code of BiAEImpute is available at https://github.com/LiuXinyuan6/BiAEImpute .
Authors
- Yu Wang (ORCID: https://orcid.org/0000-0003-4874-3658)
- Xinyuan Liu (ORCID: https://orcid.org/0000-0001-6195-6223)
- Yi Zhang (ORCID: https://orcid.org/0000-0001-6167-7945)
- Yin Wang
Institutions
- Guilin University of Aerospace Technology (CN)
- Guilin University of Technology (CN)
- Guilin University of Electronic Technology (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1186/s12864-025-11988-x
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Guangxi Zhuang Autonomous Region
- National Natural Science Foundation of China