Model-based clustering of multi-tissue gene expression data

TL;DR

This paper introduces revamp, a Bayesian model-based clustering method for multi-tissue gene expression data that preserves tissue-specific information and reveals biologically meaningful gene clusters related to disease processes.

Contribution

The paper presents a novel multi-tissue clustering algorithm, revamp, that incorporates tissue similarity and identifies conserved and tissue-specific gene clusters.

Findings

Revamp produces clusters enriched for tissue-dependent protein interactions.

Clusters are associated with coronary artery disease phenotypes.

Method outperforms traditional clustering approaches.

Abstract

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. 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 of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Using data from seven vascular and metabolic tissues from over 100 individuals in the STockholm Atherosclerosis Gene Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by revamp are more enriched for tissue-dependent protein-protein interactions compared to alternative approaches. We further demonstrate that revamp results in easily interpretable multi-tissue gene expression associations to key coronary artery disease processes and clinical phenotypes in the STAGE individuals. Revamp is implemented in the Lemon-Tree software, available at https://github.com/eb00/lemon-tree