Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data.

ABSTRACT: Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no available methods provide a comprehensive framework for statistical inference and uncertainty quantification for these tasks. A novel framework, Bayesian Simultaneous Factorization (BSF), is proposed to decompose multi-omics variation into joint and individual structures simultaneously within a probabilistic framework. BSF uses conjugate normal priors and the posterior mode of this model can be estimated by solving a structured nuclear norm-penalized objective that also achieves rank selection and motivates the choice of hyperparameters. BSF is then extended

SUBMITTER: Samorodnitsky S 

PROVIDER: S-EPMC11210674 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature