TransST: transfer learning embedded spatial factor modeling of spatial transcriptomics data
In summary, the proposed method TransST is both effective and robust in identifying cell subclusters and detecting corresponding driving biomarkers in spatial transcriptomics data.
Authors
- Anil K. Rustgi (ORCID: https://orcid.org/0000-0002-6439-6919)
- Shuo Shuo Liu
- Yuxuan Chen (ORCID: https://orcid.org/0000-0003-0727-1834)
- Jianhua Hu (ORCID: https://orcid.org/0000-0002-3306-6722)
- Shikun Wang (ORCID: https://orcid.org/0009-0003-9628-2785)
- Ming Yuan (ORCID: https://orcid.org/0009-0004-6598-9718)
Institutions
- City University of New York (US)
- Columbia University Irving Medical Center (US)
- Columbia University (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-11-06
- DOI
- https://doi.org/10.1186/s12859-025-06099-z
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Cancer Institute
- National Institute of Allergy and Infectious Diseases