SJIVE: Supervised Joint and Individual Variation Explained.

ABSTRACT: Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all sources. However, existing methods that do both are presently limited because they either (1) only consider data structure shared by all datasets while ignoring structures unique to each source, or (2) they extract underlying structures first without consideration to the outcome. The proposed method, supervised joint and individual variation explained (sJIVE), can simultaneously (1) identify shared (joint) and source-specific (individual) underlyi

SUBMITTER: Palzer EF 

PROVIDER: S-EPMC9481062 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature