gcpca
Generalized Contrastive Principal Component Analysis
v0.0.1
·
Apr 1, 2026
·
MIT + file LICENSE
Description
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>.
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OK 6 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Apr 2, 2026
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0.0.1
Apr 1, 2026