Bayesian inference of networks across multiple sample groups and data types.

ABSTRACT: In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sa

SUBMITTER: Shaddox E 

PROVIDER: S-EPMC7672535 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature