A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.

ABSTRACT: Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for ident

SUBMITTER: Mo Q 

PROVIDER: S-EPMC6455926 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature