A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits.

ABSTRACT:

Motivation

Epidemiologic, clinical and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups.

Results

We propose an integrative model to estimate latent unknown clusters (LUCID) aiming to both distinguish unique genomic, exposure and informative biomarkers/omic effects while jointly estimating subgroups relevant to the outcome of interest. Simulation studies indicate that we can obtain consistent estimates reflective of the true simulated values, accurately estimate subgroups and recapitulate subgroup-specific effects. We also demonstrate the use of the integrat

SUBMITTER: Peng C 

PROVIDER: S-EPMC7986585 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature