Model-based clustering of multi-tissue gene expression data.

ABSTRACT:

Motivation

Recently, it has become feasible to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. When traditional clustering methods are applied to this type of data, important information is lost, because they either require all tissues to be analyzed independently, ignoring dependencies and similarities between tissues, or to merge tissues in a single, monolithic dataset, ignoring individual characteristics of tissues.

Results

We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prior information on physiological tissue similarity, and which results in a set of clusters, each consisting of a core set o

SUBMITTER: Erola P 

PROVIDER: S-EPMC7162352 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature