Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations
GPCCA offers a useful framework for multi-modal data integration, effectively handling missing data and providing informative low-dimensional embeddings. Its performance across cancer genomics and multi-view image data highlights its robustness and potential for broad application. To make the method accessible to the wider research community, we have released an R package, GPCCA, which is available at https://github.com/Kaversoniano/GPCCA.
Authors
- Tianjian Yang (ORCID: https://orcid.org/0000-0001-5853-2681)
- Wei Vivian Li (ORCID: https://orcid.org/0000-0002-2087-2709)
Institutions
- University of California, Riverside (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-10-16
- DOI
- https://doi.org/10.1186/s12859-025-06227-9
- Citations
- 1
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Science Foundation
- National Institutes of Health