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.

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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

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article

Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations

Tianjian Yang, Wei Vivian Li
1 citations
BMC Bioinformatics
Gene expression and cancer classification
0.81
article

Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations

Tianjian Yang, Wei Vivian Li
article en
1 citations

Abstract

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.

BMC BioinformaticsVol. 26(1)
University of California, Riverside (US)
National Science Foundation, National Institutes of Health
Openalex Percentile: Top 28%
Gene expression and cancer classification
0.81
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