scPerb: Predict single-cell perturbation via style transfer-based variational autoencoder
scPerb offers a significant advancement in predicting cellular responses by effectively separating and transferring perturbation-related variances. This framework not only enhances prediction accuracy but also provides a robust tool for computational biology, addressing the limitations of current methodologies.
Authors
- Qianqian Song (ORCID: https://orcid.org/0000-0002-9615-2994)
- Minghao Zhou (ORCID: https://orcid.org/0000-0001-5555-8800)
- Kai Zhang (ORCID: https://orcid.org/0000-0001-6097-7217)
- Zhankui Tang (ORCID: https://orcid.org/0009-0005-1449-5096)
Institutions
- Duke University (US)
- University of Florida (US)
- Trinity College (US)
Publication Details
- Journal
- Journal of Advanced Research
- Published
- 2024-10-31
- DOI
- https://doi.org/10.1016/j.jare.2024.10.035
- Citations
- 2
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- National Institutes of Health
- National Institute of General Medical Sciences