Model-based clustering of multi-tissue gene expression data

Author:

Erola Pau12ORCID,Björkegren Johan L M34,Michoel Tom15ORCID

Affiliation:

1. Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Midlothian EH25 9RG, UK

2. MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK

3. Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

4. Integrated Cardio Metabolic Centre (ICMC), Karolinska Institutet, Huddinge 141 57, Sweden

5. Computational Biology Unit, Department of Informatics, University of Bergen, Bergen N-5020, Norway

Abstract

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 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. Availability and implementation Revamp is implemented in the Lemon-Tree software, available at https://github.com/eb00/lemon-tree Supplementary information Supplementary data are available at Bioinformatics online.

Funder

BBSRC

Roslin Institute Strategic Programme

NIH

NHLBI

CRUK

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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