Bayesian integrative model for multi-omics data with missingness.

ABSTRACT:

Motivation

Integrative analysis of multi-omics data from different high-throughput experimental platforms provides valuable insight into regulatory mechanisms associated with complex diseases, and gains statistical power to detect markers that are otherwise overlooked by single-platform omics analysis. In practice, a significant portion of samples may not be measured completely due to insufficient tissues or restricted budget (e.g. gene expression profile are measured but not methylation). Current multi-omics integrative methods require complete data. A common practice is to ignore samples with any missing platform and perform complete case analysis, which leads to substantial loss of statistical power.

Methods

In this article, inspired by the popular Integrative Bayesian An

SUBMITTER: Fang Z 

PROVIDER: S-EPMC6223369 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature