Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA.

ABSTRACT: High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of gen

SUBMITTER: de Oliveira EF 

PROVIDER: S-EPMC11841894 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature