PROBABILISTIC CONTRASTIVE DIMENSION REDUCTION FOR CASE-CONTROL STUDY DATA.

ABSTRACT: Case-control experiments are essential to the scientific method, as they allow researchers to test biological hypotheses by looking for differences in outcome between cases and controls. It is then of interest to characterize variation that is enriched in a "foreground" (case) dataset relative to a "background" (control) dataset. For example, in a genomics context, the goal is to identify low-dimensional transcriptional structure unique to patients with certain disease (cases) vs. those without that disease (controls). In this work we propose probabilistic contrastive principal component analysis (PCPCA), a probabilistic dimension reduction method designed for case-control data. We describe inference in PCPCA through a contrastive likelihood and show that our model generalizes PCA, probabi

SUBMITTER: Li D 

PROVIDER: S-EPMC12700624 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature