gcpca - Generalized Contrastive Principal Component Analysis
Implements dense and sparse generalized contrastive
principal component analysis (gcPCA) with S3 fit objects and
methods for prediction, summaries, and plotting. The gcPCA is a
hyperparameter-free method for comparing high-dimensional
datasets collected under different experimental conditions to
reveal low-dimensional patterns enriched in one condition
compared to the other. Method details are described in de
Oliveira, Garg, Hjerling-Leffler, Batista-Brito, and Sjulson
(2025) <doi:10.1371/journal.pcbi.1012747>.
